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hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/weight_init.py
|
import torch
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2)
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
# type: (Tensor, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are
applied while sampling the normal with mean/std applied, therefore a, b args
should be adjusted to match the range of mean, std args.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
with torch.no_grad():
return _trunc_normal_(tensor, mean, std, a, b)
def trunc_normal_tf_(tensor, mean=0., std=1., a=-2., b=2.):
# type: (Tensor, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
and the result is subsquently scaled and shifted by the mean and std args.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
with torch.no_grad():
_trunc_normal_(tensor, 0, 1.0, a, b)
tensor.mul_(std).add_(mean)
return tensor
def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
if mode == 'fan_in':
denom = fan_in
elif mode == 'fan_out':
denom = fan_out
elif mode == 'fan_avg':
denom = (fan_in + fan_out) / 2
variance = scale / denom
if distribution == "truncated_normal":
# constant is stddev of standard normal truncated to (-2, 2)
trunc_normal_tf_(tensor, std=math.sqrt(variance) / .87962566103423978)
elif distribution == "normal":
with torch.no_grad():
tensor.normal_(std=math.sqrt(variance))
elif distribution == "uniform":
bound = math.sqrt(3 * variance)
with torch.no_grad():
tensor.uniform_(-bound, bound)
else:
raise ValueError(f"invalid distribution {distribution}")
def lecun_normal_(tensor):
variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/create_act.py
|
""" Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Union, Callable, Type
from .activations import *
from .activations_jit import *
from .activations_me import *
from .config import is_exportable, is_scriptable, is_no_jit
# PyTorch has an optimized, native 'silu' (aka 'swish') operator as of PyTorch 1.7.
# Also hardsigmoid, hardswish, and soon mish. This code will use native version if present.
# Eventually, the custom SiLU, Mish, Hard*, layers will be removed and only native variants will be used.
_has_silu = 'silu' in dir(torch.nn.functional)
_has_hardswish = 'hardswish' in dir(torch.nn.functional)
_has_hardsigmoid = 'hardsigmoid' in dir(torch.nn.functional)
_has_mish = 'mish' in dir(torch.nn.functional)
_ACT_FN_DEFAULT = dict(
silu=F.silu if _has_silu else swish,
swish=F.silu if _has_silu else swish,
mish=F.mish if _has_mish else mish,
relu=F.relu,
relu6=F.relu6,
leaky_relu=F.leaky_relu,
elu=F.elu,
celu=F.celu,
selu=F.selu,
gelu=gelu,
gelu_tanh=gelu_tanh,
quick_gelu=quick_gelu,
sigmoid=sigmoid,
tanh=tanh,
hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid,
hard_swish=F.hardswish if _has_hardswish else hard_swish,
hard_mish=hard_mish,
)
_ACT_FN_JIT = dict(
silu=F.silu if _has_silu else swish_jit,
swish=F.silu if _has_silu else swish_jit,
mish=F.mish if _has_mish else mish_jit,
hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid_jit,
hard_swish=F.hardswish if _has_hardswish else hard_swish_jit,
hard_mish=hard_mish_jit,
)
_ACT_FN_ME = dict(
silu=F.silu if _has_silu else swish_me,
swish=F.silu if _has_silu else swish_me,
mish=F.mish if _has_mish else mish_me,
hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid_me,
hard_swish=F.hardswish if _has_hardswish else hard_swish_me,
hard_mish=hard_mish_me,
)
_ACT_FNS = (_ACT_FN_ME, _ACT_FN_JIT, _ACT_FN_DEFAULT)
for a in _ACT_FNS:
a.setdefault('hardsigmoid', a.get('hard_sigmoid'))
a.setdefault('hardswish', a.get('hard_swish'))
_ACT_LAYER_DEFAULT = dict(
silu=nn.SiLU if _has_silu else Swish,
swish=nn.SiLU if _has_silu else Swish,
mish=nn.Mish if _has_mish else Mish,
relu=nn.ReLU,
relu6=nn.ReLU6,
leaky_relu=nn.LeakyReLU,
elu=nn.ELU,
prelu=PReLU,
celu=nn.CELU,
selu=nn.SELU,
gelu=GELU,
gelu_tanh=GELUTanh,
quick_gelu=QuickGELU,
sigmoid=Sigmoid,
tanh=Tanh,
hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoid,
hard_swish=nn.Hardswish if _has_hardswish else HardSwish,
hard_mish=HardMish,
identity=nn.Identity,
)
_ACT_LAYER_JIT = dict(
silu=nn.SiLU if _has_silu else SwishJit,
swish=nn.SiLU if _has_silu else SwishJit,
mish=nn.Mish if _has_mish else MishJit,
hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoidJit,
hard_swish=nn.Hardswish if _has_hardswish else HardSwishJit,
hard_mish=HardMishJit,
)
_ACT_LAYER_ME = dict(
silu=nn.SiLU if _has_silu else SwishMe,
swish=nn.SiLU if _has_silu else SwishMe,
mish=nn.Mish if _has_mish else MishMe,
hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoidMe,
hard_swish=nn.Hardswish if _has_hardswish else HardSwishMe,
hard_mish=HardMishMe,
)
_ACT_LAYERS = (_ACT_LAYER_ME, _ACT_LAYER_JIT, _ACT_LAYER_DEFAULT)
for a in _ACT_LAYERS:
a.setdefault('hardsigmoid', a.get('hard_sigmoid'))
a.setdefault('hardswish', a.get('hard_swish'))
def get_act_fn(name: Union[Callable, str] = 'relu'):
""" Activation Function Factory
Fetching activation fns by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if not name:
return None
if isinstance(name, Callable):
return name
if not (is_no_jit() or is_exportable() or is_scriptable()):
# If not exporting or scripting the model, first look for a memory-efficient version with
# custom autograd, then fallback
if name in _ACT_FN_ME:
return _ACT_FN_ME[name]
if not (is_no_jit() or is_exportable()):
if name in _ACT_FN_JIT:
return _ACT_FN_JIT[name]
return _ACT_FN_DEFAULT[name]
def get_act_layer(name: Union[Type[nn.Module], str] = 'relu'):
""" Activation Layer Factory
Fetching activation layers by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if name is None:
return None
if not isinstance(name, str):
# callable, module, etc
return name
if not name:
return None
if not (is_no_jit() or is_exportable() or is_scriptable()):
if name in _ACT_LAYER_ME:
return _ACT_LAYER_ME[name]
if not (is_no_jit() or is_exportable()):
if name in _ACT_LAYER_JIT:
return _ACT_LAYER_JIT[name]
return _ACT_LAYER_DEFAULT[name]
def create_act_layer(name: Union[nn.Module, str], inplace=None, **kwargs):
act_layer = get_act_layer(name)
if act_layer is None:
return None
if inplace is None:
return act_layer(**kwargs)
try:
return act_layer(inplace=inplace, **kwargs)
except TypeError:
# recover if act layer doesn't have inplace arg
return act_layer(**kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/fast_norm.py
|
""" 'Fast' Normalization Functions
For GroupNorm and LayerNorm these functions bypass typical AMP upcast to float32.
Additionally, for LayerNorm, the APEX fused LN is used if available (which also does not upcast)
Hacked together by / Copyright 2022 Ross Wightman
"""
from typing import List, Optional
import torch
from torch.nn import functional as F
try:
from apex.normalization.fused_layer_norm import fused_layer_norm_affine
has_apex = True
except ImportError:
has_apex = False
try:
from apex.normalization.fused_layer_norm import fused_rms_norm_affine, fused_rms_norm
has_apex_rmsnorm = True
except ImportError:
has_apex_rmsnorm = False
# fast (ie lower precision LN) can be disabled with this flag if issues crop up
_USE_FAST_NORM = False # defaulting to False for now
def is_fast_norm():
return _USE_FAST_NORM
def set_fast_norm(enable=True):
global _USE_FAST_NORM
_USE_FAST_NORM = enable
def fast_group_norm(
x: torch.Tensor,
num_groups: int,
weight: Optional[torch.Tensor] = None,
bias: Optional[torch.Tensor] = None,
eps: float = 1e-5
) -> torch.Tensor:
if torch.jit.is_scripting():
# currently cannot use is_autocast_enabled within torchscript
return F.group_norm(x, num_groups, weight, bias, eps)
if torch.is_autocast_enabled():
# normally native AMP casts GN inputs to float32
# here we use the low precision autocast dtype
# FIXME what to do re CPU autocast?
dt = torch.get_autocast_gpu_dtype()
x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt) if bias is not None else None
with torch.cuda.amp.autocast(enabled=False):
return F.group_norm(x, num_groups, weight, bias, eps)
def fast_layer_norm(
x: torch.Tensor,
normalized_shape: List[int],
weight: Optional[torch.Tensor] = None,
bias: Optional[torch.Tensor] = None,
eps: float = 1e-5
) -> torch.Tensor:
if torch.jit.is_scripting():
# currently cannot use is_autocast_enabled within torchscript
return F.layer_norm(x, normalized_shape, weight, bias, eps)
if has_apex:
return fused_layer_norm_affine(x, weight, bias, normalized_shape, eps)
if torch.is_autocast_enabled():
# normally native AMP casts LN inputs to float32
# apex LN does not, this is behaving like Apex
dt = torch.get_autocast_gpu_dtype()
# FIXME what to do re CPU autocast?
x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt) if bias is not None else None
with torch.cuda.amp.autocast(enabled=False):
return F.layer_norm(x, normalized_shape, weight, bias, eps)
def rms_norm(
x: torch.Tensor,
normalized_shape: List[int],
weight: Optional[torch.Tensor] = None,
eps: float = 1e-5,
):
norm_ndim = len(normalized_shape)
if torch.jit.is_scripting():
# ndim = len(x.shape)
# dims = list(range(ndim - norm_ndim, ndim)) # this doesn't work on pytorch <= 1.13.x
# NOTE -ve dims cause torchscript to crash in some cases, out of options to work around
assert norm_ndim == 1
v = torch.var(x, dim=-1).unsqueeze(-1) # ts crashes with -ve dim + keepdim=True
else:
dims = tuple(range(-1, -norm_ndim - 1, -1))
v = torch.var(x, dim=dims, keepdim=True)
x = x * torch.rsqrt(v + eps)
if weight is not None:
x = x * weight
return x
def fast_rms_norm(
x: torch.Tensor,
normalized_shape: List[int],
weight: Optional[torch.Tensor] = None,
eps: float = 1e-5,
) -> torch.Tensor:
if torch.jit.is_scripting():
# this must be by itself, cannot merge with has_apex_rmsnorm
return rms_norm(x, normalized_shape, weight, eps)
if has_apex_rmsnorm:
if weight is None:
return fused_rms_norm(x, normalized_shape, eps)
else:
return fused_rms_norm_affine(x, weight, normalized_shape, eps)
# fallback
return rms_norm(x, normalized_shape, weight, eps)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/evo_norm.py
|
""" EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {13539--13550},
publisher = {Curran Associates, Inc.},
title = {Evolving Normalization-Activation Layers},
url = {https://proceedings.neurips.cc/paper/2020/file/9d4c03631b8b0c85ae08bf05eda37d0f-Paper.pdf},
volume = {33},
year = {2020}
}
An attempt at getting decent performing EvoNorms running in PyTorch.
While faster than other PyTorch impl, still quite a ways off the built-in BatchNorm
in terms of memory usage and throughput on GPUs.
I'm testing these modules on TPU w/ PyTorch XLA. Promising start but
currently working around some issues with builtin torch/tensor.var/std. Unlike
GPU, similar train speeds for EvoNormS variants and BatchNorm.
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Sequence, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from .create_act import create_act_layer
from .trace_utils import _assert
def instance_std(x, eps: float = 1e-5):
std = x.float().var(dim=(2, 3), unbiased=False, keepdim=True).add(eps).sqrt().to(x.dtype)
return std.expand(x.shape)
def instance_std_tpu(x, eps: float = 1e-5):
std = manual_var(x, dim=(2, 3)).add(eps).sqrt()
return std.expand(x.shape)
# instance_std = instance_std_tpu
def instance_rms(x, eps: float = 1e-5):
rms = x.float().square().mean(dim=(2, 3), keepdim=True).add(eps).sqrt().to(x.dtype)
return rms.expand(x.shape)
def manual_var(x, dim: Union[int, Sequence[int]], diff_sqm: bool = False):
xm = x.mean(dim=dim, keepdim=True)
if diff_sqm:
# difference of squared mean and mean squared, faster on TPU can be less stable
var = ((x * x).mean(dim=dim, keepdim=True) - (xm * xm)).clamp(0)
else:
var = ((x - xm) * (x - xm)).mean(dim=dim, keepdim=True)
return var
def group_std(x, groups: int = 32, eps: float = 1e-5, flatten: bool = False):
B, C, H, W = x.shape
x_dtype = x.dtype
_assert(C % groups == 0, '')
if flatten:
x = x.reshape(B, groups, -1) # FIXME simpler shape causing TPU / XLA issues
std = x.float().var(dim=2, unbiased=False, keepdim=True).add(eps).sqrt().to(x_dtype)
else:
x = x.reshape(B, groups, C // groups, H, W)
std = x.float().var(dim=(2, 3, 4), unbiased=False, keepdim=True).add(eps).sqrt().to(x_dtype)
return std.expand(x.shape).reshape(B, C, H, W)
def group_std_tpu(x, groups: int = 32, eps: float = 1e-5, diff_sqm: bool = False, flatten: bool = False):
# This is a workaround for some stability / odd behaviour of .var and .std
# running on PyTorch XLA w/ TPUs. These manual var impl are producing much better results
B, C, H, W = x.shape
_assert(C % groups == 0, '')
if flatten:
x = x.reshape(B, groups, -1) # FIXME simpler shape causing TPU / XLA issues
var = manual_var(x, dim=-1, diff_sqm=diff_sqm)
else:
x = x.reshape(B, groups, C // groups, H, W)
var = manual_var(x, dim=(2, 3, 4), diff_sqm=diff_sqm)
return var.add(eps).sqrt().expand(x.shape).reshape(B, C, H, W)
#group_std = group_std_tpu # FIXME TPU temporary
def group_rms(x, groups: int = 32, eps: float = 1e-5):
B, C, H, W = x.shape
_assert(C % groups == 0, '')
x_dtype = x.dtype
x = x.reshape(B, groups, C // groups, H, W)
rms = x.float().square().mean(dim=(2, 3, 4), keepdim=True).add(eps).sqrt_().to(x_dtype)
return rms.expand(x.shape).reshape(B, C, H, W)
class EvoNorm2dB0(nn.Module):
def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-3, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.momentum = momentum
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.v = nn.Parameter(torch.ones(num_features)) if apply_act else None
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
if self.v is not None:
nn.init.ones_(self.v)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.v is not None:
if self.training:
var = x.float().var(dim=(0, 2, 3), unbiased=False)
# var = manual_var(x, dim=(0, 2, 3)).squeeze()
n = x.numel() / x.shape[1]
self.running_var.copy_(
self.running_var * (1 - self.momentum) +
var.detach() * self.momentum * (n / (n - 1)))
else:
var = self.running_var
left = var.add(self.eps).sqrt_().to(x_dtype).view(v_shape).expand_as(x)
v = self.v.to(x_dtype).view(v_shape)
right = x * v + instance_std(x, self.eps)
x = x / left.max(right)
return x * self.weight.to(x_dtype).view(v_shape) + self.bias.to(x_dtype).view(v_shape)
class EvoNorm2dB1(nn.Module):
def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-5, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.momentum = momentum
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
if self.training:
var = x.float().var(dim=(0, 2, 3), unbiased=False)
n = x.numel() / x.shape[1]
self.running_var.copy_(
self.running_var * (1 - self.momentum) +
var.detach().to(self.running_var.dtype) * self.momentum * (n / (n - 1)))
else:
var = self.running_var
var = var.to(x_dtype).view(v_shape)
left = var.add(self.eps).sqrt_()
right = (x + 1) * instance_rms(x, self.eps)
x = x / left.max(right)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dB2(nn.Module):
def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-5, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.momentum = momentum
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
if self.training:
var = x.float().var(dim=(0, 2, 3), unbiased=False)
n = x.numel() / x.shape[1]
self.running_var.copy_(
self.running_var * (1 - self.momentum) +
var.detach().to(self.running_var.dtype) * self.momentum * (n / (n - 1)))
else:
var = self.running_var
var = var.to(x_dtype).view(v_shape)
left = var.add(self.eps).sqrt_()
right = instance_rms(x, self.eps) - x
x = x / left.max(right)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS0(nn.Module):
def __init__(self, num_features, groups=32, group_size=None, apply_act=True, eps=1e-5, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
if group_size:
assert num_features % group_size == 0
self.groups = num_features // group_size
else:
self.groups = groups
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.v = nn.Parameter(torch.ones(num_features)) if apply_act else None
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
if self.v is not None:
nn.init.ones_(self.v)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.v is not None:
v = self.v.view(v_shape).to(x_dtype)
x = x * (x * v).sigmoid() / group_std(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS0a(EvoNorm2dS0):
def __init__(self, num_features, groups=32, group_size=None, apply_act=True, eps=1e-3, **_):
super().__init__(
num_features, groups=groups, group_size=group_size, apply_act=apply_act, eps=eps)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
d = group_std(x, self.groups, self.eps)
if self.v is not None:
v = self.v.view(v_shape).to(x_dtype)
x = x * (x * v).sigmoid()
x = x / d
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS1(nn.Module):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-5, **_):
super().__init__()
act_layer = act_layer or nn.SiLU
self.apply_act = apply_act # apply activation (non-linearity)
if act_layer is not None and apply_act:
self.act = create_act_layer(act_layer)
else:
self.act = nn.Identity()
if group_size:
assert num_features % group_size == 0
self.groups = num_features // group_size
else:
self.groups = groups
self.eps = eps
self.pre_act_norm = False
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
x = self.act(x) / group_std(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS1a(EvoNorm2dS1):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-3, **_):
super().__init__(
num_features, groups=groups, group_size=group_size, apply_act=apply_act, act_layer=act_layer, eps=eps)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = self.act(x) / group_std(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS2(nn.Module):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-5, **_):
super().__init__()
act_layer = act_layer or nn.SiLU
self.apply_act = apply_act # apply activation (non-linearity)
if act_layer is not None and apply_act:
self.act = create_act_layer(act_layer)
else:
self.act = nn.Identity()
if group_size:
assert num_features % group_size == 0
self.groups = num_features // group_size
else:
self.groups = groups
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
x = self.act(x) / group_rms(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS2a(EvoNorm2dS2):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-3, **_):
super().__init__(
num_features, groups=groups, group_size=group_size, apply_act=apply_act, act_layer=act_layer, eps=eps)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = self.act(x) / group_rms(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
| 0
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hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/pos_embed.py
|
""" Position Embedding Utilities
Hacked together by / Copyright 2022 Ross Wightman
"""
import logging
import math
from typing import List, Tuple, Optional, Union
import torch
import torch.nn.functional as F
from .helpers import to_2tuple
_logger = logging.getLogger(__name__)
def resample_abs_pos_embed(
posemb,
new_size: List[int],
old_size: Optional[List[int]] = None,
num_prefix_tokens: int = 1,
interpolation: str = 'bicubic',
antialias: bool = True,
verbose: bool = False,
):
# sort out sizes, assume square if old size not provided
num_pos_tokens = posemb.shape[1]
num_new_tokens = new_size[0] * new_size[1] + num_prefix_tokens
if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]:
return posemb
if old_size is None:
hw = int(math.sqrt(num_pos_tokens - num_prefix_tokens))
old_size = hw, hw
if num_prefix_tokens:
posemb_prefix, posemb = posemb[:, :num_prefix_tokens], posemb[:, num_prefix_tokens:]
else:
posemb_prefix, posemb = None, posemb
# do the interpolation
embed_dim = posemb.shape[-1]
orig_dtype = posemb.dtype
posemb = posemb.float() # interpolate needs float32
posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2)
posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim)
posemb = posemb.to(orig_dtype)
# add back extra (class, etc) prefix tokens
if posemb_prefix is not None:
posemb = torch.cat([posemb_prefix, posemb], dim=1)
if not torch.jit.is_scripting() and verbose:
_logger.info(f'Resized position embedding: {old_size} to {new_size}.')
return posemb
def resample_abs_pos_embed_nhwc(
posemb,
new_size: List[int],
interpolation: str = 'bicubic',
antialias: bool = True,
verbose: bool = False,
):
if new_size[0] == posemb.shape[-3] and new_size[1] == posemb.shape[-2]:
return posemb
orig_dtype = posemb.dtype
posemb = posemb.float()
# do the interpolation
posemb = posemb.reshape(1, posemb.shape[-3], posemb.shape[-2], posemb.shape[-1]).permute(0, 3, 1, 2)
posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
posemb = posemb.permute(0, 2, 3, 1).to(orig_dtype)
if not torch.jit.is_scripting() and verbose:
_logger.info(f'Resized position embedding: {posemb.shape[-3:-1]} to {new_size}.')
return posemb
| 0
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hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/eca.py
|
"""
ECA module from ECAnet
paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://arxiv.org/abs/1910.03151
Original ECA model borrowed from https://github.com/BangguWu/ECANet
Modified circular ECA implementation and adaption for use in timm package
by Chris Ha https://github.com/VRandme
Original License:
MIT License
Copyright (c) 2019 BangguWu, Qilong Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import math
from torch import nn
import torch.nn.functional as F
from .create_act import create_act_layer
from .helpers import make_divisible
class EcaModule(nn.Module):
"""Constructs an ECA module.
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual calculations according to channel.
gamma, beta: when channel is given parameters of mapping function
refer to original paper https://arxiv.org/pdf/1910.03151.pdf
(default=None. if channel size not given, use k_size given for kernel size.)
kernel_size: Adaptive selection of kernel size (default=3)
gamm: used in kernel_size calc, see above
beta: used in kernel_size calc, see above
act_layer: optional non-linearity after conv, enables conv bias, this is an experiment
gate_layer: gating non-linearity to use
"""
def __init__(
self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid',
rd_ratio=1/8, rd_channels=None, rd_divisor=8, use_mlp=False):
super(EcaModule, self).__init__()
if channels is not None:
t = int(abs(math.log(channels, 2) + beta) / gamma)
kernel_size = max(t if t % 2 else t + 1, 3)
assert kernel_size % 2 == 1
padding = (kernel_size - 1) // 2
if use_mlp:
# NOTE 'mlp' mode is a timm experiment, not in paper
assert channels is not None
if rd_channels is None:
rd_channels = make_divisible(channels * rd_ratio, divisor=rd_divisor)
act_layer = act_layer or nn.ReLU
self.conv = nn.Conv1d(1, rd_channels, kernel_size=1, padding=0, bias=True)
self.act = create_act_layer(act_layer)
self.conv2 = nn.Conv1d(rd_channels, 1, kernel_size=kernel_size, padding=padding, bias=True)
else:
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=padding, bias=False)
self.act = None
self.conv2 = None
self.gate = create_act_layer(gate_layer)
def forward(self, x):
y = x.mean((2, 3)).view(x.shape[0], 1, -1) # view for 1d conv
y = self.conv(y)
if self.conv2 is not None:
y = self.act(y)
y = self.conv2(y)
y = self.gate(y).view(x.shape[0], -1, 1, 1)
return x * y.expand_as(x)
EfficientChannelAttn = EcaModule # alias
class CecaModule(nn.Module):
"""Constructs a circular ECA module.
ECA module where the conv uses circular padding rather than zero padding.
Unlike the spatial dimension, the channels do not have inherent ordering nor
locality. Although this module in essence, applies such an assumption, it is unnecessary
to limit the channels on either "edge" from being circularly adapted to each other.
This will fundamentally increase connectivity and possibly increase performance metrics
(accuracy, robustness), without significantly impacting resource metrics
(parameter size, throughput,latency, etc)
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual calculations according to channel.
gamma, beta: when channel is given parameters of mapping function
refer to original paper https://arxiv.org/pdf/1910.03151.pdf
(default=None. if channel size not given, use k_size given for kernel size.)
kernel_size: Adaptive selection of kernel size (default=3)
gamm: used in kernel_size calc, see above
beta: used in kernel_size calc, see above
act_layer: optional non-linearity after conv, enables conv bias, this is an experiment
gate_layer: gating non-linearity to use
"""
def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid'):
super(CecaModule, self).__init__()
if channels is not None:
t = int(abs(math.log(channels, 2) + beta) / gamma)
kernel_size = max(t if t % 2 else t + 1, 3)
has_act = act_layer is not None
assert kernel_size % 2 == 1
# PyTorch circular padding mode is buggy as of pytorch 1.4
# see https://github.com/pytorch/pytorch/pull/17240
# implement manual circular padding
self.padding = (kernel_size - 1) // 2
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=0, bias=has_act)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
y = x.mean((2, 3)).view(x.shape[0], 1, -1)
# Manually implement circular padding, F.pad does not seemed to be bugged
y = F.pad(y, (self.padding, self.padding), mode='circular')
y = self.conv(y)
y = self.gate(y).view(x.shape[0], -1, 1, 1)
return x * y.expand_as(x)
CircularEfficientChannelAttn = CecaModule
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/blur_pool.py
|
"""
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .padding import get_padding
class BlurPool2d(nn.Module):
r"""Creates a module that computes blurs and downsample a given feature map.
See :cite:`zhang2019shiftinvar` for more details.
Corresponds to the Downsample class, which does blurring and subsampling
Args:
channels = Number of input channels
filt_size (int): binomial filter size for blurring. currently supports 3 (default) and 5.
stride (int): downsampling filter stride
Returns:
torch.Tensor: the transformed tensor.
"""
def __init__(self, channels, filt_size=3, stride=2) -> None:
super(BlurPool2d, self).__init__()
assert filt_size > 1
self.channels = channels
self.filt_size = filt_size
self.stride = stride
self.padding = [get_padding(filt_size, stride, dilation=1)] * 4
coeffs = torch.tensor((np.poly1d((0.5, 0.5)) ** (self.filt_size - 1)).coeffs.astype(np.float32))
blur_filter = (coeffs[:, None] * coeffs[None, :])[None, None, :, :].repeat(self.channels, 1, 1, 1)
self.register_buffer('filt', blur_filter, persistent=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = F.pad(x, self.padding, 'reflect')
return F.conv2d(x, self.filt, stride=self.stride, groups=self.channels)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/mlp.py
|
""" MLP module w/ dropout and configurable activation layer
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partial
from torch import nn as nn
from .grn import GlobalResponseNorm
from .helpers import to_2tuple
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=None,
bias=True,
drop=0.,
use_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class GluMlp(nn.Module):
""" MLP w/ GLU style gating
See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.Sigmoid,
norm_layer=None,
bias=True,
drop=0.,
use_conv=False,
gate_last=True,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
assert hidden_features % 2 == 0
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.chunk_dim = 1 if use_conv else -1
self.gate_last = gate_last # use second half of width for gate
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = norm_layer(hidden_features // 2) if norm_layer is not None else nn.Identity()
self.fc2 = linear_layer(hidden_features // 2, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def init_weights(self):
# override init of fc1 w/ gate portion set to weight near zero, bias=1
fc1_mid = self.fc1.bias.shape[0] // 2
nn.init.ones_(self.fc1.bias[fc1_mid:])
nn.init.normal_(self.fc1.weight[fc1_mid:], std=1e-6)
def forward(self, x):
x = self.fc1(x)
x1, x2 = x.chunk(2, dim=self.chunk_dim)
x = x1 * self.act(x2) if self.gate_last else self.act(x1) * x2
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
SwiGLUPacked = partial(GluMlp, act_layer=nn.SiLU, gate_last=False)
class SwiGLU(nn.Module):
""" SwiGLU
NOTE: GluMLP above can implement SwiGLU, but this impl has split fc1 and
better matches some other common impl which makes mapping checkpoints simpler.
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.SiLU,
norm_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1_g = nn.Linear(in_features, hidden_features, bias=bias[0])
self.fc1_x = nn.Linear(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def init_weights(self):
# override init of fc1 w/ gate portion set to weight near zero, bias=1
nn.init.ones_(self.fc1_g.bias)
nn.init.normal_(self.fc1_g.weight, std=1e-6)
def forward(self, x):
x_gate = self.fc1_g(x)
x = self.fc1_x(x)
x = self.act(x_gate) * x
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class GatedMlp(nn.Module):
""" MLP as used in gMLP
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=None,
gate_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
if gate_layer is not None:
assert hidden_features % 2 == 0
self.gate = gate_layer(hidden_features)
hidden_features = hidden_features // 2 # FIXME base reduction on gate property?
else:
self.gate = nn.Identity()
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.gate(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.ReLU,
norm_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
self.act = act_layer()
self.drop = nn.Dropout(drop)
self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])
def forward(self, x):
x = self.fc1(x)
x = self.norm(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
return x
class GlobalResponseNormMlp(nn.Module):
""" MLP w/ Global Response Norm (see grn.py), nn.Linear or 1x1 Conv2d
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
bias=True,
drop=0.,
use_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.grn = GlobalResponseNorm(hidden_features, channels_last=not use_conv)
self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.grn(x)
x = self.fc2(x)
x = self.drop2(x)
return x
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/helpers.py
|
""" Layer/Module Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
from itertools import repeat
import collections.abc
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return tuple(x)
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple
def make_divisible(v, divisor=8, min_value=None, round_limit=.9):
min_value = min_value or divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < round_limit * v:
new_v += divisor
return new_v
def extend_tuple(x, n):
# pads a tuple to specified n by padding with last value
if not isinstance(x, (tuple, list)):
x = (x,)
else:
x = tuple(x)
pad_n = n - len(x)
if pad_n <= 0:
return x[:n]
return x + (x[-1],) * pad_n
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hf_public_repos/pytorch-image-models/timm
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hf_public_repos/pytorch-image-models/timm/layers/halo_attn.py
|
""" Halo Self Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
@misc{2103.12731,
Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
Jonathon Shlens},
Title = {Scaling Local Self-Attention for Parameter Efficient Visual Backbones},
Year = {2021},
}
Status:
This impl is a WIP, there is no official ref impl and some details in paper weren't clear to me.
The attention mechanism works but it's slow as implemented.
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import List
import torch
from torch import nn
import torch.nn.functional as F
from .helpers import make_divisible
from .weight_init import trunc_normal_
from .trace_utils import _assert
def rel_logits_1d(q, rel_k, permute_mask: List[int]):
""" Compute relative logits along one dimension
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
Args:
q: (batch, height, width, dim)
rel_k: (2 * window - 1, dim)
permute_mask: permute output dim according to this
"""
B, H, W, dim = q.shape
rel_size = rel_k.shape[0]
win_size = (rel_size + 1) // 2
x = (q @ rel_k.transpose(-1, -2))
x = x.reshape(-1, W, rel_size)
# pad to shift from relative to absolute indexing
x_pad = F.pad(x, [0, 1]).flatten(1)
x_pad = F.pad(x_pad, [0, rel_size - W])
# reshape and slice out the padded elements
x_pad = x_pad.reshape(-1, W + 1, rel_size)
x = x_pad[:, :W, win_size - 1:]
# reshape and tile
x = x.reshape(B, H, 1, W, win_size).expand(-1, -1, win_size, -1, -1)
return x.permute(permute_mask)
class PosEmbedRel(nn.Module):
""" Relative Position Embedding
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
"""
def __init__(self, block_size, win_size, dim_head, scale):
"""
Args:
block_size (int): block size
win_size (int): neighbourhood window size
dim_head (int): attention head dim
scale (float): scale factor (for init)
"""
super().__init__()
self.block_size = block_size
self.dim_head = dim_head
self.height_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
self.width_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
def forward(self, q):
B, BB, HW, _ = q.shape
# relative logits in width dimension.
q = q.reshape(-1, self.block_size, self.block_size, self.dim_head)
rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
# relative logits in height dimension.
q = q.transpose(1, 2)
rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2))
rel_logits = rel_logits_h + rel_logits_w
rel_logits = rel_logits.reshape(B, BB, HW, -1)
return rel_logits
class HaloAttn(nn.Module):
""" Halo Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
The internal dimensions of the attention module are controlled by the interaction of several arguments.
* the output dimension of the module is specified by dim_out, which falls back to input dim if not set
* the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
* the query and key (qk) dimensions are determined by
* num_heads * dim_head if dim_head is not None
* num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None
* as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used
Args:
dim (int): input dimension to the module
dim_out (int): output dimension of the module, same as dim if not set
feat_size (Tuple[int, int]): size of input feature_map (not used, for arg compat with bottle/lambda)
stride: output stride of the module, query downscaled if > 1 (default: 1).
num_heads: parallel attention heads (default: 8).
dim_head: dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
block_size (int): size of blocks. (default: 8)
halo_size (int): size of halo overlap. (default: 3)
qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
qkv_bias (bool) : add bias to q, k, and v projections
avg_down (bool): use average pool downsample instead of strided query blocks
scale_pos_embed (bool): scale the position embedding as well as Q @ K
"""
def __init__(
self, dim, dim_out=None, feat_size=None, stride=1, num_heads=8, dim_head=None, block_size=8, halo_size=3,
qk_ratio=1.0, qkv_bias=False, avg_down=False, scale_pos_embed=False):
super().__init__()
dim_out = dim_out or dim
assert dim_out % num_heads == 0
assert stride in (1, 2)
self.num_heads = num_heads
self.dim_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
self.dim_head_v = dim_out // self.num_heads
self.dim_out_qk = num_heads * self.dim_head_qk
self.dim_out_v = num_heads * self.dim_head_v
self.scale = self.dim_head_qk ** -0.5
self.scale_pos_embed = scale_pos_embed
self.block_size = self.block_size_ds = block_size
self.halo_size = halo_size
self.win_size = block_size + halo_size * 2 # neighbourhood window size
self.block_stride = 1
use_avg_pool = False
if stride > 1:
use_avg_pool = avg_down or block_size % stride != 0
self.block_stride = 1 if use_avg_pool else stride
self.block_size_ds = self.block_size // self.block_stride
# FIXME not clear if this stride behaviour is what the paper intended
# Also, the paper mentions using a 3D conv for dealing with the blocking/gather, and leaving
# data in unfolded block form. I haven't wrapped my head around how that'd look.
self.q = nn.Conv2d(dim, self.dim_out_qk, 1, stride=self.block_stride, bias=qkv_bias)
self.kv = nn.Conv2d(dim, self.dim_out_qk + self.dim_out_v, 1, bias=qkv_bias)
self.pos_embed = PosEmbedRel(
block_size=self.block_size_ds, win_size=self.win_size, dim_head=self.dim_head_qk, scale=self.scale)
self.pool = nn.AvgPool2d(2, 2) if use_avg_pool else nn.Identity()
self.reset_parameters()
def reset_parameters(self):
std = self.q.weight.shape[1] ** -0.5 # fan-in
trunc_normal_(self.q.weight, std=std)
trunc_normal_(self.kv.weight, std=std)
trunc_normal_(self.pos_embed.height_rel, std=self.scale)
trunc_normal_(self.pos_embed.width_rel, std=self.scale)
def forward(self, x):
B, C, H, W = x.shape
_assert(H % self.block_size == 0, '')
_assert(W % self.block_size == 0, '')
num_h_blocks = H // self.block_size
num_w_blocks = W // self.block_size
num_blocks = num_h_blocks * num_w_blocks
q = self.q(x)
# unfold
q = q.reshape(
-1, self.dim_head_qk,
num_h_blocks, self.block_size_ds, num_w_blocks, self.block_size_ds).permute(0, 1, 3, 5, 2, 4)
# B, num_heads * dim_head * block_size ** 2, num_blocks
q = q.reshape(B * self.num_heads, self.dim_head_qk, -1, num_blocks).transpose(1, 3)
# B * num_heads, num_blocks, block_size ** 2, dim_head
kv = self.kv(x)
# Generate overlapping windows for kv. This approach is good for GPU and CPU. However, unfold() is not
# lowered for PyTorch XLA so it will be very slow. See code at bottom of file for XLA friendly approach.
# FIXME figure out how to switch impl between this and conv2d if XLA being used.
kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size])
kv = kv.unfold(2, self.win_size, self.block_size).unfold(3, self.win_size, self.block_size).reshape(
B * self.num_heads, self.dim_head_qk + self.dim_head_v, num_blocks, -1).permute(0, 2, 3, 1)
k, v = torch.split(kv, [self.dim_head_qk, self.dim_head_v], dim=-1)
# B * num_heads, num_blocks, win_size ** 2, dim_head_qk or dim_head_v
if self.scale_pos_embed:
attn = (q @ k.transpose(-1, -2) + self.pos_embed(q)) * self.scale
else:
attn = (q @ k.transpose(-1, -2)) * self.scale + self.pos_embed(q)
# B * num_heads, num_blocks, block_size ** 2, win_size ** 2
attn = attn.softmax(dim=-1)
out = (attn @ v).transpose(1, 3) # B * num_heads, dim_head_v, block_size ** 2, num_blocks
# fold
out = out.reshape(-1, self.block_size_ds, self.block_size_ds, num_h_blocks, num_w_blocks)
out = out.permute(0, 3, 1, 4, 2).contiguous().view(
B, self.dim_out_v, H // self.block_stride, W // self.block_stride)
# B, dim_out, H // block_stride, W // block_stride
out = self.pool(out)
return out
""" Three alternatives for overlapping windows.
`.unfold().unfold()` is same speed as stride tricks with similar clarity as F.unfold()
if is_xla:
# This code achieves haloing on PyTorch XLA with reasonable runtime trade-off, it is
# EXTREMELY slow for backward on a GPU though so I need a way of selecting based on environment.
WW = self.win_size ** 2
pw = torch.eye(WW, dtype=x.dtype, device=x.device).reshape(WW, 1, self.win_size, self.win_size)
kv = F.conv2d(kv.reshape(-1, 1, H, W), pw, stride=self.block_size, padding=self.halo_size)
elif self.stride_tricks:
kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size]).contiguous()
kv = kv.as_strided((
B, self.dim_out_qk + self.dim_out_v, self.win_size, self.win_size, num_h_blocks, num_w_blocks),
stride=(kv.stride(0), kv.stride(1), kv.shape[-1], 1, self.block_size * kv.shape[-1], self.block_size))
else:
kv = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size)
kv = kv.reshape(
B * self.num_heads, self.dim_head_qk + self.dim_head_v, -1, num_blocks).transpose(1, 3)
"""
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hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/format.py
|
from enum import Enum
from typing import Union
import torch
class Format(str, Enum):
NCHW = 'NCHW'
NHWC = 'NHWC'
NCL = 'NCL'
NLC = 'NLC'
FormatT = Union[str, Format]
def get_spatial_dim(fmt: FormatT):
fmt = Format(fmt)
if fmt is Format.NLC:
dim = (1,)
elif fmt is Format.NCL:
dim = (2,)
elif fmt is Format.NHWC:
dim = (1, 2)
else:
dim = (2, 3)
return dim
def get_channel_dim(fmt: FormatT):
fmt = Format(fmt)
if fmt is Format.NHWC:
dim = 3
elif fmt is Format.NLC:
dim = 2
else:
dim = 1
return dim
def nchw_to(x: torch.Tensor, fmt: Format):
if fmt == Format.NHWC:
x = x.permute(0, 2, 3, 1)
elif fmt == Format.NLC:
x = x.flatten(2).transpose(1, 2)
elif fmt == Format.NCL:
x = x.flatten(2)
return x
def nhwc_to(x: torch.Tensor, fmt: Format):
if fmt == Format.NCHW:
x = x.permute(0, 3, 1, 2)
elif fmt == Format.NLC:
x = x.flatten(1, 2)
elif fmt == Format.NCL:
x = x.flatten(1, 2).transpose(1, 2)
return x
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hf_public_repos/pytorch-image-models/timm
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hf_public_repos/pytorch-image-models/timm/layers/create_norm_act.py
|
""" NormAct (Normalizaiton + Activation Layer) Factory
Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
isntances in models. Where these are used it will be possible to swap separate BN + act layers with
combined modules like IABN or EvoNorms.
Hacked together by / Copyright 2020 Ross Wightman
"""
import types
import functools
from .evo_norm import *
from .filter_response_norm import FilterResponseNormAct2d, FilterResponseNormTlu2d
from .norm_act import BatchNormAct2d, GroupNormAct, LayerNormAct, LayerNormAct2d
from .inplace_abn import InplaceAbn
_NORM_ACT_MAP = dict(
batchnorm=BatchNormAct2d,
batchnorm2d=BatchNormAct2d,
groupnorm=GroupNormAct,
groupnorm1=functools.partial(GroupNormAct, num_groups=1),
layernorm=LayerNormAct,
layernorm2d=LayerNormAct2d,
evonormb0=EvoNorm2dB0,
evonormb1=EvoNorm2dB1,
evonormb2=EvoNorm2dB2,
evonorms0=EvoNorm2dS0,
evonorms0a=EvoNorm2dS0a,
evonorms1=EvoNorm2dS1,
evonorms1a=EvoNorm2dS1a,
evonorms2=EvoNorm2dS2,
evonorms2a=EvoNorm2dS2a,
frn=FilterResponseNormAct2d,
frntlu=FilterResponseNormTlu2d,
inplaceabn=InplaceAbn,
iabn=InplaceAbn,
)
_NORM_ACT_TYPES = {m for n, m in _NORM_ACT_MAP.items()}
# has act_layer arg to define act type
_NORM_ACT_REQUIRES_ARG = {
BatchNormAct2d, GroupNormAct, LayerNormAct, LayerNormAct2d, FilterResponseNormAct2d, InplaceAbn}
def create_norm_act_layer(layer_name, num_features, act_layer=None, apply_act=True, jit=False, **kwargs):
layer = get_norm_act_layer(layer_name, act_layer=act_layer)
layer_instance = layer(num_features, apply_act=apply_act, **kwargs)
if jit:
layer_instance = torch.jit.script(layer_instance)
return layer_instance
def get_norm_act_layer(norm_layer, act_layer=None):
if norm_layer is None:
return None
assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
assert act_layer is None or isinstance(act_layer, (type, str, types.FunctionType, functools.partial))
norm_act_kwargs = {}
# unbind partial fn, so args can be rebound later
if isinstance(norm_layer, functools.partial):
norm_act_kwargs.update(norm_layer.keywords)
norm_layer = norm_layer.func
if isinstance(norm_layer, str):
if not norm_layer:
return None
layer_name = norm_layer.replace('_', '').lower().split('-')[0]
norm_act_layer = _NORM_ACT_MAP[layer_name]
elif norm_layer in _NORM_ACT_TYPES:
norm_act_layer = norm_layer
elif isinstance(norm_layer, types.FunctionType):
# if function type, must be a lambda/fn that creates a norm_act layer
norm_act_layer = norm_layer
else:
type_name = norm_layer.__name__.lower()
if type_name.startswith('batchnorm'):
norm_act_layer = BatchNormAct2d
elif type_name.startswith('groupnorm'):
norm_act_layer = GroupNormAct
elif type_name.startswith('groupnorm1'):
norm_act_layer = functools.partial(GroupNormAct, num_groups=1)
elif type_name.startswith('layernorm2d'):
norm_act_layer = LayerNormAct2d
elif type_name.startswith('layernorm'):
norm_act_layer = LayerNormAct
else:
assert False, f"No equivalent norm_act layer for {type_name}"
if norm_act_layer in _NORM_ACT_REQUIRES_ARG:
# pass `act_layer` through for backwards compat where `act_layer=None` implies no activation.
# In the future, may force use of `apply_act` with `act_layer` arg bound to relevant NormAct types
norm_act_kwargs.setdefault('act_layer', act_layer)
if norm_act_kwargs:
norm_act_layer = functools.partial(norm_act_layer, **norm_act_kwargs) # bind/rebind args
return norm_act_layer
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hf_public_repos/pytorch-image-models/timm
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hf_public_repos/pytorch-image-models/timm/layers/test_time_pool.py
|
""" Test Time Pooling (Average-Max Pool)
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from torch import nn
import torch.nn.functional as F
from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
_logger = logging.getLogger(__name__)
class TestTimePoolHead(nn.Module):
def __init__(self, base, original_pool=7):
super(TestTimePoolHead, self).__init__()
self.base = base
self.original_pool = original_pool
base_fc = self.base.get_classifier()
if isinstance(base_fc, nn.Conv2d):
self.fc = base_fc
else:
self.fc = nn.Conv2d(
self.base.num_features, self.base.num_classes, kernel_size=1, bias=True)
self.fc.weight.data.copy_(base_fc.weight.data.view(self.fc.weight.size()))
self.fc.bias.data.copy_(base_fc.bias.data.view(self.fc.bias.size()))
self.base.reset_classifier(0) # delete original fc layer
def forward(self, x):
x = self.base.forward_features(x)
x = F.avg_pool2d(x, kernel_size=self.original_pool, stride=1)
x = self.fc(x)
x = adaptive_avgmax_pool2d(x, 1)
return x.view(x.size(0), -1)
def apply_test_time_pool(model, config, use_test_size=False):
test_time_pool = False
if not hasattr(model, 'default_cfg') or not model.default_cfg:
return model, False
if use_test_size and 'test_input_size' in model.default_cfg:
df_input_size = model.default_cfg['test_input_size']
else:
df_input_size = model.default_cfg['input_size']
if config['input_size'][-1] > df_input_size[-1] and config['input_size'][-2] > df_input_size[-2]:
_logger.info('Target input size %s > pretrained default %s, using test time pooling' %
(str(config['input_size'][-2:]), str(df_input_size[-2:])))
model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size'])
test_time_pool = True
return model, test_time_pool
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hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/global_context.py
|
""" Global Context Attention Block
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
- https://arxiv.org/abs/1904.11492
Official code consulted as reference: https://github.com/xvjiarui/GCNet
Hacked together by / Copyright 2021 Ross Wightman
"""
from torch import nn as nn
import torch.nn.functional as F
from .create_act import create_act_layer, get_act_layer
from .helpers import make_divisible
from .mlp import ConvMlp
from .norm import LayerNorm2d
class GlobalContext(nn.Module):
def __init__(self, channels, use_attn=True, fuse_add=False, fuse_scale=True, init_last_zero=False,
rd_ratio=1./8, rd_channels=None, rd_divisor=1, act_layer=nn.ReLU, gate_layer='sigmoid'):
super(GlobalContext, self).__init__()
act_layer = get_act_layer(act_layer)
self.conv_attn = nn.Conv2d(channels, 1, kernel_size=1, bias=True) if use_attn else None
if rd_channels is None:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
if fuse_add:
self.mlp_add = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
else:
self.mlp_add = None
if fuse_scale:
self.mlp_scale = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
else:
self.mlp_scale = None
self.gate = create_act_layer(gate_layer)
self.init_last_zero = init_last_zero
self.reset_parameters()
def reset_parameters(self):
if self.conv_attn is not None:
nn.init.kaiming_normal_(self.conv_attn.weight, mode='fan_in', nonlinearity='relu')
if self.mlp_add is not None:
nn.init.zeros_(self.mlp_add.fc2.weight)
def forward(self, x):
B, C, H, W = x.shape
if self.conv_attn is not None:
attn = self.conv_attn(x).reshape(B, 1, H * W) # (B, 1, H * W)
attn = F.softmax(attn, dim=-1).unsqueeze(3) # (B, 1, H * W, 1)
context = x.reshape(B, C, H * W).unsqueeze(1) @ attn
context = context.view(B, C, 1, 1)
else:
context = x.mean(dim=(2, 3), keepdim=True)
if self.mlp_scale is not None:
mlp_x = self.mlp_scale(context)
x = x * self.gate(mlp_x)
if self.mlp_add is not None:
mlp_x = self.mlp_add(context)
x = x + mlp_x
return x
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hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/linear.py
|
""" Linear layer (alternate definition)
"""
import torch
import torch.nn.functional as F
from torch import nn as nn
class Linear(nn.Linear):
r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting
weight & bias to input.dtype to work around an issue w/ torch.addmm in this use case.
"""
def forward(self, input: torch.Tensor) -> torch.Tensor:
if torch.jit.is_scripting():
bias = self.bias.to(dtype=input.dtype) if self.bias is not None else None
return F.linear(input, self.weight.to(dtype=input.dtype), bias=bias)
else:
return F.linear(input, self.weight, self.bias)
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hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/pos_embed_sincos.py
|
""" Sin-cos, fourier, rotary position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
from typing import List, Tuple, Optional, Union
import torch
from torch import nn as nn
from .trace_utils import _assert
def pixel_freq_bands(
num_bands: int,
max_freq: float = 224.,
linear_bands: bool = True,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
):
if linear_bands:
bands = torch.linspace(1.0, max_freq / 2, num_bands, dtype=dtype, device=device)
else:
bands = 2 ** torch.linspace(0, math.log(max_freq, 2) - 1, num_bands, dtype=dtype, device=device)
return bands * torch.pi
def freq_bands(
num_bands: int,
temperature: float = 10000.,
step: int = 2,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
) -> torch.Tensor:
bands = 1. / (temperature ** (torch.arange(0, num_bands, step, dtype=dtype, device=device) / num_bands))
return bands
def build_sincos2d_pos_embed(
feat_shape: List[int],
dim: int = 64,
temperature: float = 10000.,
reverse_coord: bool = False,
interleave_sin_cos: bool = False,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None
) -> torch.Tensor:
"""
Args:
feat_shape:
dim:
temperature:
reverse_coord: stack grid order W, H instead of H, W
interleave_sin_cos: sin, cos, sin, cos stack instead of sin, sin, cos, cos
dtype:
device:
Returns:
"""
assert dim % 4 == 0, 'Embed dimension must be divisible by 4 for sin-cos 2D position embedding'
pos_dim = dim // 4
bands = freq_bands(pos_dim, temperature=temperature, step=1, dtype=dtype, device=device)
if reverse_coord:
feat_shape = feat_shape[::-1] # stack W, H instead of H, W
grid = torch.stack(torch.meshgrid(
[torch.arange(s, device=device, dtype=dtype) for s in feat_shape])).flatten(1).transpose(0, 1)
pos2 = grid.unsqueeze(-1) * bands.unsqueeze(0)
# FIXME add support for unflattened spatial dim?
stack_dim = 2 if interleave_sin_cos else 1 # stack sin, cos, sin, cos instead of sin sin cos cos
pos_emb = torch.stack([torch.sin(pos2), torch.cos(pos2)], dim=stack_dim).flatten(1)
return pos_emb
def build_fourier_pos_embed(
feat_shape: List[int],
bands: Optional[torch.Tensor] = None,
num_bands: int = 64,
max_res: int = 224,
temperature: float = 10000.,
linear_bands: bool = False,
include_grid: bool = False,
in_pixels: bool = True,
ref_feat_shape: Optional[List[int]] = None,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
) -> List[torch.Tensor]:
"""
Args:
feat_shape: Feature shape for embedding.
bands: Pre-calculated frequency bands.
num_bands: Number of frequency bands (determines output dim).
max_res: Maximum resolution for pixel based freq.
temperature: Temperature for non-pixel freq.
linear_bands: Linear band spacing for pixel based freq.
include_grid: Include the spatial grid in output.
in_pixels: Output in pixel freq.
ref_feat_shape: Reference feature shape for resize / fine-tune.
dtype: Output dtype.
device: Output device.
Returns:
"""
if bands is None:
if in_pixels:
bands = pixel_freq_bands(
num_bands,
float(max_res),
linear_bands=linear_bands,
dtype=dtype,
device=device,
)
else:
bands = freq_bands(
num_bands,
temperature=temperature,
step=1,
dtype=dtype,
device=device,
)
else:
if device is None:
device = bands.device
if dtype is None:
dtype = bands.dtype
if in_pixels:
t = [torch.linspace(-1., 1., steps=s, device=device, dtype=dtype) for s in feat_shape]
else:
t = [torch.arange(s, device=device, dtype=dtype) for s in feat_shape]
if ref_feat_shape is not None:
# eva's scheme for resizing rope embeddings (ref shape = pretrain)
t = [x / f * r for x, f, r in zip(t, feat_shape, ref_feat_shape)]
grid = torch.stack(torch.meshgrid(t), dim=-1)
grid = grid.unsqueeze(-1)
pos = grid * bands
pos_sin, pos_cos = pos.sin(), pos.cos()
out = [grid, pos_sin, pos_cos] if include_grid else [pos_sin, pos_cos]
return out
class FourierEmbed(nn.Module):
def __init__(
self,
max_res: int = 224,
num_bands: int = 64,
concat_grid=True,
keep_spatial=False,
):
super().__init__()
self.max_res = max_res
self.num_bands = num_bands
self.concat_grid = concat_grid
self.keep_spatial = keep_spatial
self.register_buffer(
'bands',
pixel_freq_bands(max_res, num_bands),
persistent=False,
)
def forward(self, x):
B, C = x.shape[:2]
feat_shape = x.shape[2:]
emb = build_fourier_pos_embed(
feat_shape,
self.bands,
include_grid=self.concat_grid,
dtype=x.dtype,
device=x.device,
)
emb = torch.cat(emb, dim=-1)
emb = emb.transpose(-1, -2).flatten(len(feat_shape))
batch_expand = (B,) + (-1,) * (x.ndim - 1)
# FIXME support nD
if self.keep_spatial:
x = torch.cat([x, emb.unsqueeze(0).expand(batch_expand).permute(0, 3, 1, 2)], dim=1)
else:
x = torch.cat([x.permute(0, 2, 3, 1), emb.unsqueeze(0).expand(batch_expand)], dim=-1)
x = x.reshape(B, feat_shape.numel(), -1)
return x
def rot(x):
return torch.stack([-x[..., 1::2], x[..., ::2]], -1).reshape(x.shape)
def apply_rot_embed(x: torch.Tensor, sin_emb, cos_emb):
if sin_emb.ndim == 3:
return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x)
return x * cos_emb + rot(x) * sin_emb
def apply_rot_embed_list(x: List[torch.Tensor], sin_emb, cos_emb):
if isinstance(x, torch.Tensor):
x = [x]
return [t * cos_emb + rot(t) * sin_emb for t in x]
def apply_rot_embed_cat(x: torch.Tensor, emb):
sin_emb, cos_emb = emb.tensor_split(2, -1)
if sin_emb.ndim == 3:
return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x)
return x * cos_emb + rot(x) * sin_emb
def apply_keep_indices_nlc(x, pos_embed, keep_indices):
pos_embed = pos_embed.unsqueeze(0).expand(x.shape[0], -1, -1)
pos_embed = pos_embed.gather(1, keep_indices.unsqueeze(-1).expand(-1, -1, pos_embed.shape[-1]))
return pos_embed
def build_rotary_pos_embed(
feat_shape: List[int],
bands: Optional[torch.Tensor] = None,
dim: int = 64,
max_res: int = 224,
temperature: float = 10000.,
linear_bands: bool = False,
in_pixels: bool = True,
ref_feat_shape: Optional[List[int]] = None,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
):
"""
Args:
feat_shape: Spatial shape of the target tensor for embedding.
bands: Optional pre-generated frequency bands
dim: Output dimension of embedding tensor.
max_res: Maximum resolution for pixel mode.
temperature: Temperature (inv freq) for non-pixel mode
linear_bands: Linearly (instead of log) spaced bands for pixel mode
in_pixels: Pixel vs language (inv freq) mode.
dtype: Output dtype.
device: Output device.
Returns:
"""
sin_emb, cos_emb = build_fourier_pos_embed(
feat_shape,
bands=bands,
num_bands=dim // 4,
max_res=max_res,
temperature=temperature,
linear_bands=linear_bands,
in_pixels=in_pixels,
ref_feat_shape=ref_feat_shape,
device=device,
dtype=dtype,
)
num_spatial_dim = 1
# this would be much nicer as a .numel() call to torch.Size(), but torchscript sucks
for x in feat_shape:
num_spatial_dim *= x
sin_emb = sin_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
cos_emb = cos_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
return sin_emb, cos_emb
class RotaryEmbedding(nn.Module):
""" Rotary position embedding
NOTE: This is my initial attempt at impl rotary embedding for spatial use, it has not
been well tested, and will likely change. It will be moved to its own file.
The following impl/resources were referenced for this impl:
* https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py
* https://blog.eleuther.ai/rotary-embeddings/
"""
def __init__(
self,
dim,
max_res=224,
temperature=10000,
in_pixels=True,
linear_bands: bool = False,
feat_shape: Optional[List[int]] = None,
ref_feat_shape: Optional[List[int]] = None,
):
super().__init__()
self.dim = dim
self.max_res = max_res
self.temperature = temperature
self.in_pixels = in_pixels
self.feat_shape = feat_shape
self.ref_feat_shape = ref_feat_shape
if feat_shape is None:
# only cache bands
if in_pixels:
bands = pixel_freq_bands(
dim // 4,
float(max_res),
linear_bands=linear_bands,
)
else:
bands = freq_bands(
dim // 4,
temperature=temperature,
step=1,
)
print(bands)
self.register_buffer(
'bands',
bands,
persistent=False,
)
self.pos_embed_sin = None
self.pos_embed_cos = None
else:
# cache full sin/cos embeddings if shape provided up front
emb_sin, emb_cos = build_rotary_pos_embed(
feat_shape=feat_shape,
dim=dim,
max_res=max_res,
linear_bands=linear_bands,
in_pixels=in_pixels,
ref_feat_shape=self.ref_feat_shape,
)
self.bands = None
self.register_buffer(
'pos_embed_sin',
emb_sin,
persistent=False,
)
self.register_buffer(
'pos_embed_cos',
emb_cos,
persistent=False,
)
def get_embed(self, shape: Optional[List[int]] = None):
if self.bands is not None:
# rebuild embeddings every call, use if target shape changes
assert shape is not None
return build_rotary_pos_embed(
shape,
self.bands,
in_pixels=self.in_pixels,
)
else:
return self.pos_embed_sin, self.pos_embed_cos
def forward(self, x):
# assuming channel-first tensor where spatial dim are >= 2
sin_emb, cos_emb = self.get_embed(x.shape[2:])
return apply_rot_embed(x, sin_emb, cos_emb)
class RotaryEmbeddingCat(nn.Module):
""" Rotary position embedding w/ concatenatd sin & cos
The following impl/resources were referenced for this impl:
* https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py
* https://blog.eleuther.ai/rotary-embeddings/
"""
def __init__(
self,
dim,
max_res=224,
temperature=10000,
in_pixels=True,
linear_bands: bool = False,
feat_shape: Optional[List[int]] = None,
ref_feat_shape: Optional[List[int]] = None,
):
super().__init__()
self.dim = dim
self.max_res = max_res
self.temperature = temperature
self.in_pixels = in_pixels
self.feat_shape = feat_shape
self.ref_feat_shape = ref_feat_shape
if feat_shape is None:
# only cache bands
if in_pixels:
bands = pixel_freq_bands(
dim // 4,
float(max_res),
linear_bands=linear_bands,
)
else:
bands = freq_bands(
dim // 4,
temperature=temperature,
step=1,
)
self.register_buffer(
'bands',
bands,
persistent=False,
)
self.pos_embed = None
else:
# cache full sin/cos embeddings if shape provided up front
embeds = build_rotary_pos_embed(
feat_shape=feat_shape,
dim=dim,
max_res=max_res,
linear_bands=linear_bands,
in_pixels=in_pixels,
ref_feat_shape=self.ref_feat_shape,
)
self.bands = None
self.register_buffer(
'pos_embed',
torch.cat(embeds, -1),
persistent=False,
)
def get_embed(self, shape: Optional[List[int]] = None):
if self.bands is not None and shape is not None:
# rebuild embeddings every call, use if target shape changes
embeds = build_rotary_pos_embed(
shape,
self.bands,
in_pixels=self.in_pixels,
ref_feat_shape=self.ref_feat_shape,
)
return torch.cat(embeds, -1)
elif self.pos_embed is not None:
return self.pos_embed
else:
assert False, "get_embed() requires pre-computed pos_embed or valid shape w/ pre-computed bands"
def forward(self, x):
# assuming channel-first tensor where spatial dim are >= 2
pos_embed = self.get_embed(x.shape[2:])
return apply_rot_embed_cat(x, pos_embed)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/ml_decoder.py
|
from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
model.global_pool = nn.Identity()
del model.fc
num_classes = model.num_classes
num_features = model.num_features
model.fc = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
elif hasattr(model, 'global_pool') and hasattr(model, 'classifier'): # EfficientNet
model.global_pool = nn.Identity()
del model.classifier
num_classes = model.num_classes
num_features = model.num_features
model.classifier = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
elif 'RegNet' in model._get_name() or 'TResNet' in model._get_name(): # hasattr(model, 'head')
del model.head
num_classes = model.num_classes
num_features = model.num_features
model.head = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
else:
print("Model code-writing is not aligned currently with ml-decoder")
exit(-1)
if hasattr(model, 'drop_rate'): # Ml-Decoder has inner dropout
model.drop_rate = 0
return model
class TransformerDecoderLayerOptimal(nn.Module):
def __init__(self, d_model, nhead=8, dim_feedforward=2048, dropout=0.1, activation="relu",
layer_norm_eps=1e-5) -> None:
super(TransformerDecoderLayerOptimal, self).__init__()
self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.dropout = nn.Dropout(dropout)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.activation = _get_activation_fn(activation)
def __setstate__(self, state):
if 'activation' not in state:
state['activation'] = torch.nn.functional.relu
super(TransformerDecoderLayerOptimal, self).__setstate__(state)
def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
tgt = tgt + self.dropout1(tgt)
tgt = self.norm1(tgt)
tgt2 = self.multihead_attn(tgt, memory, memory)[0]
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt
# @torch.jit.script
# class ExtrapClasses(object):
# def __init__(self, num_queries: int, group_size: int):
# self.num_queries = num_queries
# self.group_size = group_size
#
# def __call__(self, h: torch.Tensor, class_embed_w: torch.Tensor, class_embed_b: torch.Tensor, out_extrap:
# torch.Tensor):
# # h = h.unsqueeze(-1).expand(-1, -1, -1, self.group_size)
# h = h[..., None].repeat(1, 1, 1, self.group_size) # torch.Size([bs, 5, 768, groups])
# w = class_embed_w.view((self.num_queries, h.shape[2], self.group_size))
# out = (h * w).sum(dim=2) + class_embed_b
# out = out.view((h.shape[0], self.group_size * self.num_queries))
# return out
@torch.jit.script
class GroupFC(object):
def __init__(self, embed_len_decoder: int):
self.embed_len_decoder = embed_len_decoder
def __call__(self, h: torch.Tensor, duplicate_pooling: torch.Tensor, out_extrap: torch.Tensor):
for i in range(self.embed_len_decoder):
h_i = h[:, i, :]
w_i = duplicate_pooling[i, :, :]
out_extrap[:, i, :] = torch.matmul(h_i, w_i)
class MLDecoder(nn.Module):
def __init__(self, num_classes, num_of_groups=-1, decoder_embedding=768, initial_num_features=2048):
super(MLDecoder, self).__init__()
embed_len_decoder = 100 if num_of_groups < 0 else num_of_groups
if embed_len_decoder > num_classes:
embed_len_decoder = num_classes
# switching to 768 initial embeddings
decoder_embedding = 768 if decoder_embedding < 0 else decoder_embedding
self.embed_standart = nn.Linear(initial_num_features, decoder_embedding)
# decoder
decoder_dropout = 0.1
num_layers_decoder = 1
dim_feedforward = 2048
layer_decode = TransformerDecoderLayerOptimal(d_model=decoder_embedding,
dim_feedforward=dim_feedforward, dropout=decoder_dropout)
self.decoder = nn.TransformerDecoder(layer_decode, num_layers=num_layers_decoder)
# non-learnable queries
self.query_embed = nn.Embedding(embed_len_decoder, decoder_embedding)
self.query_embed.requires_grad_(False)
# group fully-connected
self.num_classes = num_classes
self.duplicate_factor = int(num_classes / embed_len_decoder + 0.999)
self.duplicate_pooling = torch.nn.Parameter(
torch.Tensor(embed_len_decoder, decoder_embedding, self.duplicate_factor))
self.duplicate_pooling_bias = torch.nn.Parameter(torch.Tensor(num_classes))
torch.nn.init.xavier_normal_(self.duplicate_pooling)
torch.nn.init.constant_(self.duplicate_pooling_bias, 0)
self.group_fc = GroupFC(embed_len_decoder)
def forward(self, x):
if len(x.shape) == 4: # [bs,2048, 7,7]
embedding_spatial = x.flatten(2).transpose(1, 2)
else: # [bs, 197,468]
embedding_spatial = x
embedding_spatial_786 = self.embed_standart(embedding_spatial)
embedding_spatial_786 = torch.nn.functional.relu(embedding_spatial_786, inplace=True)
bs = embedding_spatial_786.shape[0]
query_embed = self.query_embed.weight
# tgt = query_embed.unsqueeze(1).repeat(1, bs, 1)
tgt = query_embed.unsqueeze(1).expand(-1, bs, -1) # no allocation of memory with expand
h = self.decoder(tgt, embedding_spatial_786.transpose(0, 1)) # [embed_len_decoder, batch, 768]
h = h.transpose(0, 1)
out_extrap = torch.zeros(h.shape[0], h.shape[1], self.duplicate_factor, device=h.device, dtype=h.dtype)
self.group_fc(h, self.duplicate_pooling, out_extrap)
h_out = out_extrap.flatten(1)[:, :self.num_classes]
h_out += self.duplicate_pooling_bias
logits = h_out
return logits
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/filter_response_norm.py
|
""" Filter Response Norm in PyTorch
Based on `Filter Response Normalization Layer` - https://arxiv.org/abs/1911.09737
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
import torch.nn as nn
from .create_act import create_act_layer
from .trace_utils import _assert
def inv_instance_rms(x, eps: float = 1e-5):
rms = x.square().float().mean(dim=(2, 3), keepdim=True).add(eps).rsqrt().to(x.dtype)
return rms.expand(x.shape)
class FilterResponseNormTlu2d(nn.Module):
def __init__(self, num_features, apply_act=True, eps=1e-5, rms=True, **_):
super(FilterResponseNormTlu2d, self).__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.rms = rms
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.tau = nn.Parameter(torch.zeros(num_features)) if apply_act else None
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
if self.tau is not None:
nn.init.zeros_(self.tau)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = x * inv_instance_rms(x, self.eps)
x = x * self.weight.view(v_shape).to(dtype=x_dtype) + self.bias.view(v_shape).to(dtype=x_dtype)
return torch.maximum(x, self.tau.reshape(v_shape).to(dtype=x_dtype)) if self.tau is not None else x
class FilterResponseNormAct2d(nn.Module):
def __init__(self, num_features, apply_act=True, act_layer=nn.ReLU, inplace=None, rms=True, eps=1e-5, **_):
super(FilterResponseNormAct2d, self).__init__()
if act_layer is not None and apply_act:
self.act = create_act_layer(act_layer, inplace=inplace)
else:
self.act = nn.Identity()
self.rms = rms
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = x * inv_instance_rms(x, self.eps)
x = x * self.weight.view(v_shape).to(dtype=x_dtype) + self.bias.view(v_shape).to(dtype=x_dtype)
return self.act(x)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/std_conv.py
|
""" Convolution with Weight Standardization (StdConv and ScaledStdConv)
StdConv:
@article{weightstandardization,
author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
title = {Weight Standardization},
journal = {arXiv preprint arXiv:1903.10520},
year = {2019},
}
Code: https://github.com/joe-siyuan-qiao/WeightStandardization
ScaledStdConv:
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Official Deepmind JAX code: https://github.com/deepmind/deepmind-research/tree/master/nfnets
Hacked together by / copyright Ross Wightman, 2021.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from .padding import get_padding, get_padding_value, pad_same
class StdConv2d(nn.Conv2d):
"""Conv2d with Weight Standardization. Used for BiT ResNet-V2 models.
Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
https://arxiv.org/abs/1903.10520v2
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding=None,
dilation=1, groups=1, bias=False, eps=1e-6):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
in_channel, out_channels, kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
self.eps = eps
def forward(self, x):
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
return x
class StdConv2dSame(nn.Conv2d):
"""Conv2d with Weight Standardization. TF compatible SAME padding. Used for ViT Hybrid model.
Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
https://arxiv.org/abs/1903.10520v2
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding='SAME',
dilation=1, groups=1, bias=False, eps=1e-6):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channel, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.same_pad = is_dynamic
self.eps = eps
def forward(self, x):
if self.same_pad:
x = pad_same(x, self.kernel_size, self.stride, self.dilation)
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
return x
class ScaledStdConv2d(nn.Conv2d):
"""Conv2d layer with Scaled Weight Standardization.
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
https://arxiv.org/abs/2101.08692
NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
"""
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding=None,
dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5 # gamma * 1 / sqrt(fan-in)
self.eps = eps
def forward(self, x):
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
weight=(self.gain * self.scale).view(-1),
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
class ScaledStdConv2dSame(nn.Conv2d):
"""Conv2d layer with Scaled Weight Standardization and Tensorflow-like SAME padding support
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
https://arxiv.org/abs/2101.08692
NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
"""
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding='SAME',
dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5
self.same_pad = is_dynamic
self.eps = eps
def forward(self, x):
if self.same_pad:
x = pad_same(x, self.kernel_size, self.stride, self.dilation)
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
weight=(self.gain * self.scale).view(-1),
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
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hf_public_repos/pytorch-image-models/timm/layers/padding.py
|
""" Padding Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from typing import List, Tuple
import torch
import torch.nn.functional as F
# Calculate symmetric padding for a convolution
def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
# Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution
def get_same_padding(x: int, kernel_size: int, stride: int, dilation: int):
if isinstance(x, torch.Tensor):
return torch.clamp(((x / stride).ceil() - 1) * stride + (kernel_size - 1) * dilation + 1 - x, min=0)
else:
return max((math.ceil(x / stride) - 1) * stride + (kernel_size - 1) * dilation + 1 - x, 0)
# Can SAME padding for given args be done statically?
def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, **_):
return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
def pad_same_arg(
input_size: List[int],
kernel_size: List[int],
stride: List[int],
dilation: List[int] = (1, 1),
) -> List[int]:
ih, iw = input_size
kh, kw = kernel_size
pad_h = get_same_padding(ih, kh, stride[0], dilation[0])
pad_w = get_same_padding(iw, kw, stride[1], dilation[1])
return [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]
# Dynamically pad input x with 'SAME' padding for conv with specified args
def pad_same(
x,
kernel_size: List[int],
stride: List[int],
dilation: List[int] = (1, 1),
value: float = 0,
):
ih, iw = x.size()[-2:]
pad_h = get_same_padding(ih, kernel_size[0], stride[0], dilation[0])
pad_w = get_same_padding(iw, kernel_size[1], stride[1], dilation[1])
x = F.pad(x, (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2), value=value)
return x
def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bool]:
dynamic = False
if isinstance(padding, str):
# for any string padding, the padding will be calculated for you, one of three ways
padding = padding.lower()
if padding == 'same':
# TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
if is_static_pad(kernel_size, **kwargs):
# static case, no extra overhead
padding = get_padding(kernel_size, **kwargs)
else:
# dynamic 'SAME' padding, has runtime/GPU memory overhead
padding = 0
dynamic = True
elif padding == 'valid':
# 'VALID' padding, same as padding=0
padding = 0
else:
# Default to PyTorch style 'same'-ish symmetric padding
padding = get_padding(kernel_size, **kwargs)
return padding, dynamic
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hf_public_repos/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py
|
""" PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles feature dim
Both a functional and a nn.Module version of the pooling is provided.
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from .format import get_spatial_dim, get_channel_dim
_int_tuple_2_t = Union[int, Tuple[int, int]]
def adaptive_pool_feat_mult(pool_type='avg'):
if pool_type.endswith('catavgmax'):
return 2
else:
return 1
def adaptive_avgmax_pool2d(x, output_size: _int_tuple_2_t = 1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size)
return 0.5 * (x_avg + x_max)
def adaptive_catavgmax_pool2d(x, output_size: _int_tuple_2_t = 1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size)
return torch.cat((x_avg, x_max), 1)
def select_adaptive_pool2d(x, pool_type='avg', output_size: _int_tuple_2_t = 1):
"""Selectable global pooling function with dynamic input kernel size
"""
if pool_type == 'avg':
x = F.adaptive_avg_pool2d(x, output_size)
elif pool_type == 'avgmax':
x = adaptive_avgmax_pool2d(x, output_size)
elif pool_type == 'catavgmax':
x = adaptive_catavgmax_pool2d(x, output_size)
elif pool_type == 'max':
x = F.adaptive_max_pool2d(x, output_size)
else:
assert False, 'Invalid pool type: %s' % pool_type
return x
class FastAdaptiveAvgPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: F = 'NCHW'):
super(FastAdaptiveAvgPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
return x.mean(self.dim, keepdim=not self.flatten)
class FastAdaptiveMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
return x.amax(self.dim, keepdim=not self.flatten)
class FastAdaptiveAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
x_avg = x.mean(self.dim, keepdim=not self.flatten)
x_max = x.amax(self.dim, keepdim=not self.flatten)
return 0.5 * x_avg + 0.5 * x_max
class FastAdaptiveCatAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveCatAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim_reduce = get_spatial_dim(input_fmt)
if flatten:
self.dim_cat = 1
else:
self.dim_cat = get_channel_dim(input_fmt)
def forward(self, x):
x_avg = x.mean(self.dim_reduce, keepdim=not self.flatten)
x_max = x.amax(self.dim_reduce, keepdim=not self.flatten)
return torch.cat((x_avg, x_max), self.dim_cat)
class AdaptiveAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
return adaptive_avgmax_pool2d(x, self.output_size)
class AdaptiveCatAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveCatAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
return adaptive_catavgmax_pool2d(x, self.output_size)
class SelectAdaptivePool2d(nn.Module):
"""Selectable global pooling layer with dynamic input kernel size
"""
def __init__(
self,
output_size: _int_tuple_2_t = 1,
pool_type: str = 'fast',
flatten: bool = False,
input_fmt: str = 'NCHW',
):
super(SelectAdaptivePool2d, self).__init__()
assert input_fmt in ('NCHW', 'NHWC')
self.pool_type = pool_type or '' # convert other falsy values to empty string for consistent TS typing
if not pool_type:
self.pool = nn.Identity() # pass through
self.flatten = nn.Flatten(1) if flatten else nn.Identity()
elif pool_type.startswith('fast') or input_fmt != 'NCHW':
assert output_size == 1, 'Fast pooling and non NCHW input formats require output_size == 1.'
if pool_type.endswith('avgmax'):
self.pool = FastAdaptiveAvgMaxPool(flatten, input_fmt=input_fmt)
elif pool_type.endswith('catavgmax'):
self.pool = FastAdaptiveCatAvgMaxPool(flatten, input_fmt=input_fmt)
elif pool_type.endswith('max'):
self.pool = FastAdaptiveMaxPool(flatten, input_fmt=input_fmt)
else:
self.pool = FastAdaptiveAvgPool(flatten, input_fmt=input_fmt)
self.flatten = nn.Identity()
else:
assert input_fmt == 'NCHW'
if pool_type == 'avgmax':
self.pool = AdaptiveAvgMaxPool2d(output_size)
elif pool_type == 'catavgmax':
self.pool = AdaptiveCatAvgMaxPool2d(output_size)
elif pool_type == 'max':
self.pool = nn.AdaptiveMaxPool2d(output_size)
else:
self.pool = nn.AdaptiveAvgPool2d(output_size)
self.flatten = nn.Flatten(1) if flatten else nn.Identity()
def is_identity(self):
return not self.pool_type
def forward(self, x):
x = self.pool(x)
x = self.flatten(x)
return x
def feat_mult(self):
return adaptive_pool_feat_mult(self.pool_type)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'pool_type=' + self.pool_type \
+ ', flatten=' + str(self.flatten) + ')'
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hf_public_repos/pytorch-image-models/timm
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hf_public_repos/pytorch-image-models/timm/layers/grn.py
|
""" Global Response Normalization Module
Based on the GRN layer presented in
`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
This implementation
* works for both NCHW and NHWC tensor layouts
* uses affine param names matching existing torch norm layers
* slightly improves eager mode performance via fused addcmul
Hacked together by / Copyright 2023 Ross Wightman
"""
import torch
from torch import nn as nn
class GlobalResponseNorm(nn.Module):
""" Global Response Normalization layer
"""
def __init__(self, dim, eps=1e-6, channels_last=True):
super().__init__()
self.eps = eps
if channels_last:
self.spatial_dim = (1, 2)
self.channel_dim = -1
self.wb_shape = (1, 1, 1, -1)
else:
self.spatial_dim = (2, 3)
self.channel_dim = 1
self.wb_shape = (1, -1, 1, 1)
self.weight = nn.Parameter(torch.zeros(dim))
self.bias = nn.Parameter(torch.zeros(dim))
def forward(self, x):
x_g = x.norm(p=2, dim=self.spatial_dim, keepdim=True)
x_n = x_g / (x_g.mean(dim=self.channel_dim, keepdim=True) + self.eps)
return x + torch.addcmul(self.bias.view(self.wb_shape), self.weight.view(self.wb_shape), x * x_n)
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hf_public_repos/pytorch-image-models/timm
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hf_public_repos/pytorch-image-models/timm/layers/typing.py
|
from typing import Callable, Tuple, Type, Union
import torch
LayerType = Union[str, Callable, Type[torch.nn.Module]]
PadType = Union[str, int, Tuple[int, int]]
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hf_public_repos/pytorch-image-models/timm
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hf_public_repos/pytorch-image-models/timm/layers/__init__.py
|
from .activations import *
from .adaptive_avgmax_pool import \
adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
from .attention_pool import AttentionPoolLatent
from .attention_pool2d import AttentionPool2d, RotAttentionPool2d, RotaryEmbedding
from .blur_pool import BlurPool2d
from .classifier import ClassifierHead, create_classifier, NormMlpClassifierHead
from .cond_conv2d import CondConv2d, get_condconv_initializer
from .config import is_exportable, is_scriptable, is_no_jit, use_fused_attn, \
set_exportable, set_scriptable, set_no_jit, set_layer_config, set_fused_attn
from .conv2d_same import Conv2dSame, conv2d_same
from .conv_bn_act import ConvNormAct, ConvNormActAa, ConvBnAct
from .create_act import create_act_layer, get_act_layer, get_act_fn
from .create_attn import get_attn, create_attn
from .create_conv2d import create_conv2d
from .create_norm import get_norm_layer, create_norm_layer
from .create_norm_act import get_norm_act_layer, create_norm_act_layer, get_norm_act_layer
from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path
from .eca import EcaModule, CecaModule, EfficientChannelAttn, CircularEfficientChannelAttn
from .evo_norm import EvoNorm2dB0, EvoNorm2dB1, EvoNorm2dB2,\
EvoNorm2dS0, EvoNorm2dS0a, EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a
from .fast_norm import is_fast_norm, set_fast_norm, fast_group_norm, fast_layer_norm
from .filter_response_norm import FilterResponseNormTlu2d, FilterResponseNormAct2d
from .format import Format, get_channel_dim, get_spatial_dim, nchw_to, nhwc_to
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible, extend_tuple
from .inplace_abn import InplaceAbn
from .linear import Linear
from .mixed_conv2d import MixedConv2d
from .mlp import Mlp, GluMlp, GatedMlp, SwiGLU, SwiGLUPacked, ConvMlp, GlobalResponseNormMlp
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from .norm_act import BatchNormAct2d, GroupNormAct, GroupNorm1Act, LayerNormAct, LayerNormAct2d,\
SyncBatchNormAct, convert_sync_batchnorm, FrozenBatchNormAct2d, freeze_batch_norm_2d, unfreeze_batch_norm_2d
from .padding import get_padding, get_same_padding, pad_same
from .patch_dropout import PatchDropout
from .patch_embed import PatchEmbed, PatchEmbedWithSize, resample_patch_embed
from .pool2d_same import AvgPool2dSame, create_pool2d
from .pos_embed import resample_abs_pos_embed, resample_abs_pos_embed_nhwc
from .pos_embed_rel import RelPosMlp, RelPosBias, RelPosBiasTf, gen_relative_position_index, gen_relative_log_coords, \
resize_rel_pos_bias_table, resize_rel_pos_bias_table_simple, resize_rel_pos_bias_table_levit
from .pos_embed_sincos import pixel_freq_bands, freq_bands, build_sincos2d_pos_embed, build_fourier_pos_embed, \
build_rotary_pos_embed, apply_rot_embed, apply_rot_embed_cat, apply_rot_embed_list, apply_keep_indices_nlc, \
FourierEmbed, RotaryEmbedding, RotaryEmbeddingCat
from .squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite
from .selective_kernel import SelectiveKernel
from .separable_conv import SeparableConv2d, SeparableConvNormAct
from .space_to_depth import SpaceToDepthModule, SpaceToDepth, DepthToSpace
from .split_attn import SplitAttn
from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame
from .test_time_pool import TestTimePoolHead, apply_test_time_pool
from .trace_utils import _assert, _float_to_int
from .typing import LayerType, PadType
from .weight_init import trunc_normal_, trunc_normal_tf_, variance_scaling_, lecun_normal_
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hf_public_repos/pytorch-image-models/timm
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hf_public_repos/pytorch-image-models/timm/layers/create_conv2d.py
|
""" Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
""" Select a 2d convolution implementation based on arguments
Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d.
Used extensively by EfficientNet, MobileNetv3 and related networks.
"""
if isinstance(kernel_size, list):
assert 'num_experts' not in kwargs # MixNet + CondConv combo not supported currently
if 'groups' in kwargs:
groups = kwargs.pop('groups')
if groups == in_channels:
kwargs['depthwise'] = True
else:
assert groups == 1
# We're going to use only lists for defining the MixedConv2d kernel groups,
# ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
m = MixedConv2d(in_channels, out_channels, kernel_size, **kwargs)
else:
depthwise = kwargs.pop('depthwise', False)
# for DW out_channels must be multiple of in_channels as must have out_channels % groups == 0
groups = in_channels if depthwise else kwargs.pop('groups', 1)
if 'num_experts' in kwargs and kwargs['num_experts'] > 0:
m = CondConv2d(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
else:
m = create_conv2d_pad(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
return m
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hf_public_repos/pytorch-image-models/timm
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hf_public_repos/pytorch-image-models/timm/layers/separable_conv.py
|
""" Depthwise Separable Conv Modules
Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class SeparableConvNormAct(nn.Module):
""" Separable Conv w/ trailing Norm and Activation
"""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False,
channel_multiplier=1.0, pw_kernel_size=1, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU,
apply_act=True, drop_layer=None):
super(SeparableConvNormAct, self).__init__()
self.conv_dw = create_conv2d(
in_channels, int(in_channels * channel_multiplier), kernel_size,
stride=stride, dilation=dilation, padding=padding, depthwise=True)
self.conv_pw = create_conv2d(
int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias)
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs)
@property
def in_channels(self):
return self.conv_dw.in_channels
@property
def out_channels(self):
return self.conv_pw.out_channels
def forward(self, x):
x = self.conv_dw(x)
x = self.conv_pw(x)
x = self.bn(x)
return x
SeparableConvBnAct = SeparableConvNormAct
class SeparableConv2d(nn.Module):
""" Separable Conv
"""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False,
channel_multiplier=1.0, pw_kernel_size=1):
super(SeparableConv2d, self).__init__()
self.conv_dw = create_conv2d(
in_channels, int(in_channels * channel_multiplier), kernel_size,
stride=stride, dilation=dilation, padding=padding, depthwise=True)
self.conv_pw = create_conv2d(
int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias)
@property
def in_channels(self):
return self.conv_dw.in_channels
@property
def out_channels(self):
return self.conv_pw.out_channels
def forward(self, x):
x = self.conv_dw(x)
x = self.conv_pw(x)
return x
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hf_public_repos/pytorch-image-models/timm
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hf_public_repos/pytorch-image-models/timm/layers/create_attn.py
|
""" Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .halo_attn import HaloAttn
from .lambda_layer import LambdaLayer
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .selective_kernel import SelectiveKernel
from .split_attn import SplitAttn
from .squeeze_excite import SEModule, EffectiveSEModule
def get_attn(attn_type):
if isinstance(attn_type, torch.nn.Module):
return attn_type
module_cls = None
if attn_type:
if isinstance(attn_type, str):
attn_type = attn_type.lower()
# Lightweight attention modules (channel and/or coarse spatial).
# Typically added to existing network architecture blocks in addition to existing convolutions.
if attn_type == 'se':
module_cls = SEModule
elif attn_type == 'ese':
module_cls = EffectiveSEModule
elif attn_type == 'eca':
module_cls = EcaModule
elif attn_type == 'ecam':
module_cls = partial(EcaModule, use_mlp=True)
elif attn_type == 'ceca':
module_cls = CecaModule
elif attn_type == 'ge':
module_cls = GatherExcite
elif attn_type == 'gc':
module_cls = GlobalContext
elif attn_type == 'gca':
module_cls = partial(GlobalContext, fuse_add=True, fuse_scale=False)
elif attn_type == 'cbam':
module_cls = CbamModule
elif attn_type == 'lcbam':
module_cls = LightCbamModule
# Attention / attention-like modules w/ significant params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'sk':
module_cls = SelectiveKernel
elif attn_type == 'splat':
module_cls = SplitAttn
# Self-attention / attention-like modules w/ significant compute and/or params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'lambda':
return LambdaLayer
elif attn_type == 'bottleneck':
return BottleneckAttn
elif attn_type == 'halo':
return HaloAttn
elif attn_type == 'nl':
module_cls = NonLocalAttn
elif attn_type == 'bat':
module_cls = BatNonLocalAttn
# Woops!
else:
assert False, "Invalid attn module (%s)" % attn_type
elif isinstance(attn_type, bool):
if attn_type:
module_cls = SEModule
else:
module_cls = attn_type
return module_cls
def create_attn(attn_type, channels, **kwargs):
module_cls = get_attn(attn_type)
if module_cls is not None:
# NOTE: it's expected the first (positional) argument of all attention layers is the # input channels
return module_cls(channels, **kwargs)
return None
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/inplace_abn.py
|
import torch
from torch import nn as nn
try:
from inplace_abn.functions import inplace_abn, inplace_abn_sync
has_iabn = True
except ImportError:
has_iabn = False
def inplace_abn(x, weight, bias, running_mean, running_var,
training=True, momentum=0.1, eps=1e-05, activation="leaky_relu", activation_param=0.01):
raise ImportError(
"Please install InplaceABN:'pip install git+https://github.com/mapillary/inplace_abn.git@v1.0.12'")
def inplace_abn_sync(**kwargs):
inplace_abn(**kwargs)
class InplaceAbn(nn.Module):
"""Activated Batch Normalization
This gathers a BatchNorm and an activation function in a single module
Parameters
----------
num_features : int
Number of feature channels in the input and output.
eps : float
Small constant to prevent numerical issues.
momentum : float
Momentum factor applied to compute running statistics.
affine : bool
If `True` apply learned scale and shift transformation after normalization.
act_layer : str or nn.Module type
Name or type of the activation functions, one of: `leaky_relu`, `elu`
act_param : float
Negative slope for the `leaky_relu` activation.
"""
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, apply_act=True,
act_layer="leaky_relu", act_param=0.01, drop_layer=None):
super(InplaceAbn, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
self.momentum = momentum
if apply_act:
if isinstance(act_layer, str):
assert act_layer in ('leaky_relu', 'elu', 'identity', '')
self.act_name = act_layer if act_layer else 'identity'
else:
# convert act layer passed as type to string
if act_layer == nn.ELU:
self.act_name = 'elu'
elif act_layer == nn.LeakyReLU:
self.act_name = 'leaky_relu'
elif act_layer is None or act_layer == nn.Identity:
self.act_name = 'identity'
else:
assert False, f'Invalid act layer {act_layer.__name__} for IABN'
else:
self.act_name = 'identity'
self.act_param = act_param
if self.affine:
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.constant_(self.running_mean, 0)
nn.init.constant_(self.running_var, 1)
if self.affine:
nn.init.constant_(self.weight, 1)
nn.init.constant_(self.bias, 0)
def forward(self, x):
output = inplace_abn(
x, self.weight, self.bias, self.running_mean, self.running_var,
self.training, self.momentum, self.eps, self.act_name, self.act_param)
if isinstance(output, tuple):
output = output[0]
return output
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/patch_embed.py
|
""" Image to Patch Embedding using Conv2d
A convolution based approach to patchifying a 2D image w/ embedding projection.
Based on code in:
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision/tree/main/big_vision
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from typing import Callable, List, Optional, Tuple, Union
import torch
from torch import nn as nn
import torch.nn.functional as F
from .format import Format, nchw_to
from .helpers import to_2tuple
from .trace_utils import _assert
_logger = logging.getLogger(__name__)
class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding
"""
output_fmt: Format
dynamic_img_pad: torch.jit.Final[bool]
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten: bool = True,
output_fmt: Optional[str] = None,
bias: bool = True,
strict_img_size: bool = True,
dynamic_img_pad: bool = False,
):
super().__init__()
self.patch_size = to_2tuple(patch_size)
if img_size is not None:
self.img_size = to_2tuple(img_size)
self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
self.num_patches = self.grid_size[0] * self.grid_size[1]
else:
self.img_size = None
self.grid_size = None
self.num_patches = None
if output_fmt is not None:
self.flatten = False
self.output_fmt = Format(output_fmt)
else:
# flatten spatial dim and transpose to channels last, kept for bwd compat
self.flatten = flatten
self.output_fmt = Format.NCHW
self.strict_img_size = strict_img_size
self.dynamic_img_pad = dynamic_img_pad
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
B, C, H, W = x.shape
if self.img_size is not None:
if self.strict_img_size:
_assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
_assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
elif not self.dynamic_img_pad:
_assert(
H % self.patch_size[0] == 0,
f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
)
_assert(
W % self.patch_size[1] == 0,
f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
)
if self.dynamic_img_pad:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
x = F.pad(x, (0, pad_w, 0, pad_h))
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
elif self.output_fmt != Format.NCHW:
x = nchw_to(x, self.output_fmt)
x = self.norm(x)
return x
class PatchEmbedWithSize(PatchEmbed):
""" 2D Image to Patch Embedding
"""
output_fmt: Format
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten: bool = True,
output_fmt: Optional[str] = None,
bias: bool = True,
):
super().__init__(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer,
flatten=flatten,
output_fmt=output_fmt,
bias=bias,
)
def forward(self, x) -> Tuple[torch.Tensor, List[int]]:
B, C, H, W = x.shape
if self.img_size is not None:
_assert(H % self.patch_size[0] == 0, f"Input image height ({H}) must be divisible by patch size ({self.patch_size[0]}).")
_assert(W % self.patch_size[1] == 0, f"Input image width ({W}) must be divisible by patch size ({self.patch_size[1]}).")
x = self.proj(x)
grid_size = x.shape[-2:]
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
elif self.output_fmt != Format.NCHW:
x = nchw_to(x, self.output_fmt)
x = self.norm(x)
return x, grid_size
def resample_patch_embed(
patch_embed,
new_size: List[int],
interpolation: str = 'bicubic',
antialias: bool = True,
verbose: bool = False,
):
"""Resample the weights of the patch embedding kernel to target resolution.
We resample the patch embedding kernel by approximately inverting the effect
of patch resizing.
Code based on:
https://github.com/google-research/big_vision/blob/b00544b81f8694488d5f36295aeb7972f3755ffe/big_vision/models/proj/flexi/vit.py
With this resizing, we can for example load a B/8 filter into a B/16 model
and, on 2x larger input image, the result will match.
Args:
patch_embed: original parameter to be resized.
new_size (tuple(int, int): target shape (height, width)-only.
interpolation (str): interpolation for resize
antialias (bool): use anti-aliasing filter in resize
verbose (bool): log operation
Returns:
Resized patch embedding kernel.
"""
import numpy as np
try:
import functorch
vmap = functorch.vmap
except ImportError:
if hasattr(torch, 'vmap'):
vmap = torch.vmap
else:
assert False, "functorch or a version of torch with vmap is required for FlexiViT resizing."
assert len(patch_embed.shape) == 4, "Four dimensions expected"
assert len(new_size) == 2, "New shape should only be hw"
old_size = patch_embed.shape[-2:]
if tuple(old_size) == tuple(new_size):
return patch_embed
if verbose:
_logger.info(f"Resize patch embedding {patch_embed.shape} to {new_size}, w/ {interpolation} interpolation.")
def resize(x_np, _new_size):
x_tf = torch.Tensor(x_np)[None, None, ...]
x_upsampled = F.interpolate(
x_tf, size=_new_size, mode=interpolation, antialias=antialias)[0, 0, ...].numpy()
return x_upsampled
def get_resize_mat(_old_size, _new_size):
mat = []
for i in range(np.prod(_old_size)):
basis_vec = np.zeros(_old_size)
basis_vec[np.unravel_index(i, _old_size)] = 1.
mat.append(resize(basis_vec, _new_size).reshape(-1))
return np.stack(mat).T
resize_mat = get_resize_mat(old_size, new_size)
resize_mat_pinv = torch.Tensor(np.linalg.pinv(resize_mat.T))
def resample_kernel(kernel):
resampled_kernel = resize_mat_pinv @ kernel.reshape(-1)
return resampled_kernel.reshape(new_size)
v_resample_kernel = vmap(vmap(resample_kernel, 0, 0), 1, 1)
orig_dtype = patch_embed.dtype
patch_embed = patch_embed.float()
patch_embed = v_resample_kernel(patch_embed)
patch_embed = patch_embed.to(orig_dtype)
return patch_embed
# def divs(n, m=None):
# m = m or n // 2
# if m == 1:
# return [1]
# if n % m == 0:
# return [m] + divs(n, m - 1)
# return divs(n, m - 1)
#
#
# class FlexiPatchEmbed(nn.Module):
# """ 2D Image to Patch Embedding w/ Flexible Patch sizes (FlexiViT)
# FIXME WIP
# """
# def __init__(
# self,
# img_size=240,
# patch_size=16,
# in_chans=3,
# embed_dim=768,
# base_img_size=240,
# base_patch_size=32,
# norm_layer=None,
# flatten=True,
# bias=True,
# ):
# super().__init__()
# self.img_size = to_2tuple(img_size)
# self.patch_size = to_2tuple(patch_size)
# self.num_patches = 0
#
# # full range for 240 = (5, 6, 8, 10, 12, 14, 15, 16, 20, 24, 30, 40, 48)
# self.seqhw = (6, 8, 10, 12, 14, 15, 16, 20, 24, 30)
#
# self.base_img_size = to_2tuple(base_img_size)
# self.base_patch_size = to_2tuple(base_patch_size)
# self.base_grid_size = tuple([i // p for i, p in zip(self.base_img_size, self.base_patch_size)])
# self.base_num_patches = self.base_grid_size[0] * self.base_grid_size[1]
#
# self.flatten = flatten
# self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=bias)
# self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
#
# def forward(self, x):
# B, C, H, W = x.shape
#
# if self.patch_size == self.base_patch_size:
# weight = self.proj.weight
# else:
# weight = resample_patch_embed(self.proj.weight, self.patch_size)
# patch_size = self.patch_size
# x = F.conv2d(x, weight, bias=self.proj.bias, stride=patch_size)
# if self.flatten:
# x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
# x = self.norm(x)
# return x
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/create_norm.py
|
""" Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import functools
import types
from typing import Type
import torch.nn as nn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from torchvision.ops.misc import FrozenBatchNorm2d
_NORM_MAP = dict(
batchnorm=nn.BatchNorm2d,
batchnorm2d=nn.BatchNorm2d,
batchnorm1d=nn.BatchNorm1d,
groupnorm=GroupNorm,
groupnorm1=GroupNorm1,
layernorm=LayerNorm,
layernorm2d=LayerNorm2d,
rmsnorm=RmsNorm,
frozenbatchnorm2d=FrozenBatchNorm2d,
)
_NORM_TYPES = {m for n, m in _NORM_MAP.items()}
def create_norm_layer(layer_name, num_features, **kwargs):
layer = get_norm_layer(layer_name)
layer_instance = layer(num_features, **kwargs)
return layer_instance
def get_norm_layer(norm_layer):
if norm_layer is None:
return None
assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
norm_kwargs = {}
# unbind partial fn, so args can be rebound later
if isinstance(norm_layer, functools.partial):
norm_kwargs.update(norm_layer.keywords)
norm_layer = norm_layer.func
if isinstance(norm_layer, str):
if not norm_layer:
return None
layer_name = norm_layer.replace('_', '')
norm_layer = _NORM_MAP[layer_name]
else:
norm_layer = norm_layer
if norm_kwargs:
norm_layer = functools.partial(norm_layer, **norm_kwargs) # bind/rebind args
return norm_layer
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/layers/config.py
|
""" Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to have layers with no jit optimization (includes activations)
_NO_JIT = False
# Set to True if prefer to have activation layers with no jit optimization
# NOTE not currently used as no difference between no_jit and no_activation jit as only layers obeying
# the jit flags so far are activations. This will change as more layers are updated and/or added.
_NO_ACTIVATION_JIT = False
# Set to True if exporting a model with Same padding via ONNX
_EXPORTABLE = False
# Set to True if wanting to use torch.jit.script on a model
_SCRIPTABLE = False
# use torch.scaled_dot_product_attention where possible
_HAS_FUSED_ATTN = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if 'TIMM_FUSED_ATTN' in os.environ:
_USE_FUSED_ATTN = int(os.environ['TIMM_FUSED_ATTN'])
else:
_USE_FUSED_ATTN = 1 # 0 == off, 1 == on (for tested use), 2 == on (for experimental use)
def is_no_jit():
return _NO_JIT
class set_no_jit:
def __init__(self, mode: bool) -> None:
global _NO_JIT
self.prev = _NO_JIT
_NO_JIT = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _NO_JIT
_NO_JIT = self.prev
return False
def is_exportable():
return _EXPORTABLE
class set_exportable:
def __init__(self, mode: bool) -> None:
global _EXPORTABLE
self.prev = _EXPORTABLE
_EXPORTABLE = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _EXPORTABLE
_EXPORTABLE = self.prev
return False
def is_scriptable():
return _SCRIPTABLE
class set_scriptable:
def __init__(self, mode: bool) -> None:
global _SCRIPTABLE
self.prev = _SCRIPTABLE
_SCRIPTABLE = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _SCRIPTABLE
_SCRIPTABLE = self.prev
return False
class set_layer_config:
""" Layer config context manager that allows setting all layer config flags at once.
If a flag arg is None, it will not change the current value.
"""
def __init__(
self,
scriptable: Optional[bool] = None,
exportable: Optional[bool] = None,
no_jit: Optional[bool] = None,
no_activation_jit: Optional[bool] = None):
global _SCRIPTABLE
global _EXPORTABLE
global _NO_JIT
global _NO_ACTIVATION_JIT
self.prev = _SCRIPTABLE, _EXPORTABLE, _NO_JIT, _NO_ACTIVATION_JIT
if scriptable is not None:
_SCRIPTABLE = scriptable
if exportable is not None:
_EXPORTABLE = exportable
if no_jit is not None:
_NO_JIT = no_jit
if no_activation_jit is not None:
_NO_ACTIVATION_JIT = no_activation_jit
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _SCRIPTABLE
global _EXPORTABLE
global _NO_JIT
global _NO_ACTIVATION_JIT
_SCRIPTABLE, _EXPORTABLE, _NO_JIT, _NO_ACTIVATION_JIT = self.prev
return False
def use_fused_attn(experimental: bool = False) -> bool:
# NOTE: ONNX export cannot handle F.scaled_dot_product_attention as of pytorch 2.0
if not _HAS_FUSED_ATTN or _EXPORTABLE:
return False
if experimental:
return _USE_FUSED_ATTN > 1
return _USE_FUSED_ATTN > 0
def set_fused_attn(enable: bool = True, experimental: bool = False):
global _USE_FUSED_ATTN
if not _HAS_FUSED_ATTN:
warnings.warn('This version of pytorch does not have F.scaled_dot_product_attention, fused_attn flag ignored.')
return
if experimental and enable:
_USE_FUSED_ATTN = 2
elif enable:
_USE_FUSED_ATTN = 1
else:
_USE_FUSED_ATTN = 0
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/_registry.py
|
""" Model Registry
Hacked together by / Copyright 2020 Ross Wightman
"""
import fnmatch
import re
import sys
import warnings
from collections import defaultdict, deque
from copy import deepcopy
from dataclasses import replace
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Sequence, Union, Tuple
from ._pretrained import PretrainedCfg, DefaultCfg
__all__ = [
'split_model_name_tag', 'get_arch_name', 'register_model', 'generate_default_cfgs',
'list_models', 'list_pretrained', 'is_model', 'model_entrypoint', 'list_modules', 'is_model_in_modules',
'get_pretrained_cfg_value', 'is_model_pretrained'
]
_module_to_models: Dict[str, Set[str]] = defaultdict(set) # dict of sets to check membership of model in module
_model_to_module: Dict[str, str] = {} # mapping of model names to module names
_model_entrypoints: Dict[str, Callable[..., Any]] = {} # mapping of model names to architecture entrypoint fns
_model_has_pretrained: Set[str] = set() # set of model names that have pretrained weight url present
_model_default_cfgs: Dict[str, PretrainedCfg] = {} # central repo for model arch -> default cfg objects
_model_pretrained_cfgs: Dict[str, PretrainedCfg] = {} # central repo for model arch.tag -> pretrained cfgs
_model_with_tags: Dict[str, List[str]] = defaultdict(list) # shortcut to map each model arch to all model + tag names
_module_to_deprecated_models: Dict[str, Dict[str, Optional[str]]] = defaultdict(dict)
_deprecated_models: Dict[str, Optional[str]] = {}
def split_model_name_tag(model_name: str, no_tag: str = '') -> Tuple[str, str]:
model_name, *tag_list = model_name.split('.', 1)
tag = tag_list[0] if tag_list else no_tag
return model_name, tag
def get_arch_name(model_name: str) -> str:
return split_model_name_tag(model_name)[0]
def generate_default_cfgs(cfgs: Dict[str, Union[Dict[str, Any], PretrainedCfg]]):
out = defaultdict(DefaultCfg)
default_set = set() # no tag and tags ending with * are prioritized as default
for k, v in cfgs.items():
if isinstance(v, dict):
v = PretrainedCfg(**v)
has_weights = v.has_weights
model, tag = split_model_name_tag(k)
is_default_set = model in default_set
priority = (has_weights and not tag) or (tag.endswith('*') and not is_default_set)
tag = tag.strip('*')
default_cfg = out[model]
if priority:
default_cfg.tags.appendleft(tag)
default_set.add(model)
elif has_weights and not default_cfg.is_pretrained:
default_cfg.tags.appendleft(tag)
else:
default_cfg.tags.append(tag)
if has_weights:
default_cfg.is_pretrained = True
default_cfg.cfgs[tag] = v
return out
def register_model(fn: Callable[..., Any]) -> Callable[..., Any]:
# lookup containing module
mod = sys.modules[fn.__module__]
module_name_split = fn.__module__.split('.')
module_name = module_name_split[-1] if len(module_name_split) else ''
# add model to __all__ in module
model_name = fn.__name__
if hasattr(mod, '__all__'):
mod.__all__.append(model_name)
else:
mod.__all__ = [model_name] # type: ignore
# add entries to registry dict/sets
if model_name in _model_entrypoints:
warnings.warn(
f'Overwriting {model_name} in registry with {fn.__module__}.{model_name}. This is because the name being '
'registered conflicts with an existing name. Please check if this is not expected.',
stacklevel=2,
)
_model_entrypoints[model_name] = fn
_model_to_module[model_name] = module_name
_module_to_models[module_name].add(model_name)
if hasattr(mod, 'default_cfgs') and model_name in mod.default_cfgs:
# this will catch all models that have entrypoint matching cfg key, but miss any aliasing
# entrypoints or non-matching combos
default_cfg = mod.default_cfgs[model_name]
if not isinstance(default_cfg, DefaultCfg):
# new style default cfg dataclass w/ multiple entries per model-arch
assert isinstance(default_cfg, dict)
# old style cfg dict per model-arch
pretrained_cfg = PretrainedCfg(**default_cfg)
default_cfg = DefaultCfg(tags=deque(['']), cfgs={'': pretrained_cfg})
for tag_idx, tag in enumerate(default_cfg.tags):
is_default = tag_idx == 0
pretrained_cfg = default_cfg.cfgs[tag]
model_name_tag = '.'.join([model_name, tag]) if tag else model_name
replace_items = dict(architecture=model_name, tag=tag if tag else None)
if pretrained_cfg.hf_hub_id and pretrained_cfg.hf_hub_id == 'timm/':
# auto-complete hub name w/ architecture.tag
replace_items['hf_hub_id'] = pretrained_cfg.hf_hub_id + model_name_tag
pretrained_cfg = replace(pretrained_cfg, **replace_items)
if is_default:
_model_pretrained_cfgs[model_name] = pretrained_cfg
if pretrained_cfg.has_weights:
# add tagless entry if it's default and has weights
_model_has_pretrained.add(model_name)
if tag:
_model_pretrained_cfgs[model_name_tag] = pretrained_cfg
if pretrained_cfg.has_weights:
# add model w/ tag if tag is valid
_model_has_pretrained.add(model_name_tag)
_model_with_tags[model_name].append(model_name_tag)
else:
_model_with_tags[model_name].append(model_name) # has empty tag (to slowly remove these instances)
_model_default_cfgs[model_name] = default_cfg
return fn
def _deprecated_model_shim(deprecated_name: str, current_fn: Callable = None, current_tag: str = ''):
def _fn(pretrained=False, **kwargs):
assert current_fn is not None, f'Model {deprecated_name} has been removed with no replacement.'
current_name = '.'.join([current_fn.__name__, current_tag]) if current_tag else current_fn.__name__
warnings.warn(f'Mapping deprecated model name {deprecated_name} to current {current_name}.', stacklevel=2)
pretrained_cfg = kwargs.pop('pretrained_cfg', None)
return current_fn(pretrained=pretrained, pretrained_cfg=pretrained_cfg or current_tag, **kwargs)
return _fn
def register_model_deprecations(module_name: str, deprecation_map: Dict[str, Optional[str]]):
mod = sys.modules[module_name]
module_name_split = module_name.split('.')
module_name = module_name_split[-1] if len(module_name_split) else ''
for deprecated, current in deprecation_map.items():
if hasattr(mod, '__all__'):
mod.__all__.append(deprecated)
current_fn = None
current_tag = ''
if current:
current_name, current_tag = split_model_name_tag(current)
current_fn = getattr(mod, current_name)
deprecated_entrypoint_fn = _deprecated_model_shim(deprecated, current_fn, current_tag)
setattr(mod, deprecated, deprecated_entrypoint_fn)
_model_entrypoints[deprecated] = deprecated_entrypoint_fn
_model_to_module[deprecated] = module_name
_module_to_models[module_name].add(deprecated)
_deprecated_models[deprecated] = current
_module_to_deprecated_models[module_name][deprecated] = current
def _natural_key(string_: str) -> List[Union[int, str]]:
"""See https://blog.codinghorror.com/sorting-for-humans-natural-sort-order/"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def _expand_filter(filter: str):
""" expand a 'base_filter' to 'base_filter.*' if no tag portion"""
filter_base, filter_tag = split_model_name_tag(filter)
if not filter_tag:
return ['.'.join([filter_base, '*']), filter]
else:
return [filter]
def list_models(
filter: Union[str, List[str]] = '',
module: str = '',
pretrained: bool = False,
exclude_filters: Union[str, List[str]] = '',
name_matches_cfg: bool = False,
include_tags: Optional[bool] = None,
) -> List[str]:
""" Return list of available model names, sorted alphabetically
Args:
filter - Wildcard filter string that works with fnmatch
module - Limit model selection to a specific submodule (ie 'vision_transformer')
pretrained - Include only models with valid pretrained weights if True
exclude_filters - Wildcard filters to exclude models after including them with filter
name_matches_cfg - Include only models w/ model_name matching default_cfg name (excludes some aliases)
include_tags - Include pretrained tags in model names (model.tag). If None, defaults
set to True when pretrained=True else False (default: None)
Returns:
models - The sorted list of models
Example:
model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
"""
if filter:
include_filters = filter if isinstance(filter, (tuple, list)) else [filter]
else:
include_filters = []
if include_tags is None:
# FIXME should this be default behaviour? or default to include_tags=True?
include_tags = pretrained
all_models: Set[str] = _module_to_models[module] if module else set(_model_entrypoints.keys())
all_models = all_models - _deprecated_models.keys() # remove deprecated models from listings
if include_tags:
# expand model names to include names w/ pretrained tags
models_with_tags: Set[str] = set()
for m in all_models:
models_with_tags.update(_model_with_tags[m])
all_models = models_with_tags
# expand include and exclude filters to include a '.*' for proper match if no tags in filter
include_filters = [ef for f in include_filters for ef in _expand_filter(f)]
exclude_filters = [ef for f in exclude_filters for ef in _expand_filter(f)]
if include_filters:
models: Set[str] = set()
for f in include_filters:
include_models = fnmatch.filter(all_models, f) # include these models
if len(include_models):
models = models.union(include_models)
else:
models = all_models
if exclude_filters:
if not isinstance(exclude_filters, (tuple, list)):
exclude_filters = [exclude_filters]
for xf in exclude_filters:
exclude_models = fnmatch.filter(models, xf) # exclude these models
if len(exclude_models):
models = models.difference(exclude_models)
if pretrained:
models = _model_has_pretrained.intersection(models)
if name_matches_cfg:
models = set(_model_pretrained_cfgs).intersection(models)
return sorted(models, key=_natural_key)
def list_pretrained(
filter: Union[str, List[str]] = '',
exclude_filters: str = '',
) -> List[str]:
return list_models(
filter=filter,
pretrained=True,
exclude_filters=exclude_filters,
include_tags=True,
)
def get_deprecated_models(module: str = '') -> Dict[str, str]:
all_deprecated = _module_to_deprecated_models[module] if module else _deprecated_models
return deepcopy(all_deprecated)
def is_model(model_name: str) -> bool:
""" Check if a model name exists
"""
arch_name = get_arch_name(model_name)
return arch_name in _model_entrypoints
def model_entrypoint(model_name: str, module_filter: Optional[str] = None) -> Callable[..., Any]:
"""Fetch a model entrypoint for specified model name
"""
arch_name = get_arch_name(model_name)
if module_filter and arch_name not in _module_to_models.get(module_filter, {}):
raise RuntimeError(f'Model ({model_name} not found in module {module_filter}.')
return _model_entrypoints[arch_name]
def list_modules() -> List[str]:
""" Return list of module names that contain models / model entrypoints
"""
modules = _module_to_models.keys()
return sorted(modules)
def is_model_in_modules(
model_name: str, module_names: Union[Tuple[str, ...], List[str], Set[str]]
) -> bool:
"""Check if a model exists within a subset of modules
Args:
model_name - name of model to check
module_names - names of modules to search in
"""
arch_name = get_arch_name(model_name)
assert isinstance(module_names, (tuple, list, set))
return any(arch_name in _module_to_models[n] for n in module_names)
def is_model_pretrained(model_name: str) -> bool:
return model_name in _model_has_pretrained
def get_pretrained_cfg(model_name: str, allow_unregistered: bool = True) -> Optional[PretrainedCfg]:
if model_name in _model_pretrained_cfgs:
return deepcopy(_model_pretrained_cfgs[model_name])
arch_name, tag = split_model_name_tag(model_name)
if arch_name in _model_default_cfgs:
# if model arch exists, but the tag is wrong, error out
raise RuntimeError(f'Invalid pretrained tag ({tag}) for {arch_name}.')
if allow_unregistered:
# if model arch doesn't exist, it has no pretrained_cfg registered, allow a default to be created
return None
raise RuntimeError(f'Model architecture ({arch_name}) has no pretrained cfg registered.')
def get_pretrained_cfg_value(model_name: str, cfg_key: str) -> Optional[Any]:
""" Get a specific model default_cfg value by key. None if key doesn't exist.
"""
cfg = get_pretrained_cfg(model_name, allow_unregistered=False)
return getattr(cfg, cfg_key, None)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/byobnet.py
|
""" Bring-Your-Own-Blocks Network
A flexible network w/ dataclass based config for stacking those NN blocks.
This model is currently used to implement the following networks:
GPU Efficient (ResNets) - gernet_l/m/s (original versions called genet, but this was already used (by SENet author)).
Paper: `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
Code and weights: https://github.com/idstcv/GPU-Efficient-Networks, licensed Apache 2.0
RepVGG - repvgg_*
Paper: `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
Code and weights: https://github.com/DingXiaoH/RepVGG, licensed MIT
MobileOne - mobileone_*
Paper: `MobileOne: An Improved One millisecond Mobile Backbone` - https://arxiv.org/abs/2206.04040
Code and weights: https://github.com/apple/ml-mobileone, licensed MIT
In all cases the models have been modified to fit within the design of ByobNet. I've remapped
the original weights and verified accuracies.
For GPU Efficient nets, I used the original names for the blocks since they were for the most part
the same as original residual blocks in ResNe(X)t, DarkNet, and other existing models. Note also some
changes introduced in RegNet were also present in the stem and bottleneck blocks for this model.
A significant number of different network archs can be implemented here, including variants of the
above nets that include attention.
Hacked together by / copyright Ross Wightman, 2021.
"""
import math
from dataclasses import dataclass, field, replace
from functools import partial
from typing import Tuple, List, Dict, Optional, Union, Any, Callable, Sequence
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import ClassifierHead, ConvNormAct, BatchNormAct2d, DropPath, AvgPool2dSame, \
create_conv2d, get_act_layer, get_norm_act_layer, get_attn, make_divisible, to_2tuple, EvoNorm2dS0a
from ._builder import build_model_with_cfg
from ._manipulate import named_apply, checkpoint_seq
from ._registry import generate_default_cfgs, register_model
__all__ = ['ByobNet', 'ByoModelCfg', 'ByoBlockCfg', 'create_byob_stem', 'create_block']
@dataclass
class ByoBlockCfg:
type: Union[str, nn.Module]
d: int # block depth (number of block repeats in stage)
c: int # number of output channels for each block in stage
s: int = 2 # stride of stage (first block)
gs: Optional[Union[int, Callable]] = None # group-size of blocks in stage, conv is depthwise if gs == 1
br: float = 1. # bottleneck-ratio of blocks in stage
# NOTE: these config items override the model cfgs that are applied to all blocks by default
attn_layer: Optional[str] = None
attn_kwargs: Optional[Dict[str, Any]] = None
self_attn_layer: Optional[str] = None
self_attn_kwargs: Optional[Dict[str, Any]] = None
block_kwargs: Optional[Dict[str, Any]] = None
@dataclass
class ByoModelCfg:
blocks: Tuple[Union[ByoBlockCfg, Tuple[ByoBlockCfg, ...]], ...]
downsample: str = 'conv1x1'
stem_type: str = '3x3'
stem_pool: Optional[str] = 'maxpool'
stem_chs: int = 32
width_factor: float = 1.0
num_features: int = 0 # num out_channels for final conv, no final 1x1 conv if 0
zero_init_last: bool = True # zero init last weight (usually bn) in residual path
fixed_input_size: bool = False # model constrained to a fixed-input size / img_size must be provided on creation
act_layer: str = 'relu'
norm_layer: str = 'batchnorm'
# NOTE: these config items will be overridden by the block cfg (per-block) if they are set there
attn_layer: Optional[str] = None
attn_kwargs: dict = field(default_factory=lambda: dict())
self_attn_layer: Optional[str] = None
self_attn_kwargs: dict = field(default_factory=lambda: dict())
block_kwargs: Dict[str, Any] = field(default_factory=lambda: dict())
def _rep_vgg_bcfg(d=(4, 6, 16, 1), wf=(1., 1., 1., 1.), groups=0):
c = (64, 128, 256, 512)
group_size = 0
if groups > 0:
group_size = lambda chs, idx: chs // groups if (idx + 1) % 2 == 0 else 0
bcfg = tuple([ByoBlockCfg(type='rep', d=d, c=c * wf, gs=group_size) for d, c, wf in zip(d, c, wf)])
return bcfg
def _mobileone_bcfg(d=(2, 8, 10, 1), wf=(1., 1., 1., 1.), se_blocks=(), num_conv_branches=1):
c = (64, 128, 256, 512)
prev_c = min(64, c[0] * wf[0])
se_blocks = se_blocks or (0,) * len(d)
bcfg = []
for d, c, w, se in zip(d, c, wf, se_blocks):
scfg = []
for i in range(d):
out_c = c * w
bk = dict(num_conv_branches=num_conv_branches)
ak = {}
if i >= d - se:
ak['attn_layer'] = 'se'
scfg += [ByoBlockCfg(type='one', d=1, c=prev_c, gs=1, block_kwargs=bk, **ak)] # depthwise block
scfg += [ByoBlockCfg(
type='one', d=1, c=out_c, gs=0, block_kwargs=dict(kernel_size=1, **bk), **ak)] # pointwise block
prev_c = out_c
bcfg += [scfg]
return bcfg
def interleave_blocks(
types: Tuple[str, str], d,
every: Union[int, List[int]] = 1,
first: bool = False,
**kwargs,
) -> Tuple[ByoBlockCfg]:
""" interleave 2 block types in stack
"""
assert len(types) == 2
if isinstance(every, int):
every = list(range(0 if first else every, d, every + 1))
if not every:
every = [d - 1]
set(every)
blocks = []
for i in range(d):
block_type = types[1] if i in every else types[0]
blocks += [ByoBlockCfg(type=block_type, d=1, **kwargs)]
return tuple(blocks)
def expand_blocks_cfg(stage_blocks_cfg: Union[ByoBlockCfg, Sequence[ByoBlockCfg]]) -> List[ByoBlockCfg]:
if not isinstance(stage_blocks_cfg, Sequence):
stage_blocks_cfg = (stage_blocks_cfg,)
block_cfgs = []
for i, cfg in enumerate(stage_blocks_cfg):
block_cfgs += [replace(cfg, d=1) for _ in range(cfg.d)]
return block_cfgs
def num_groups(group_size, channels):
if not group_size: # 0 or None
return 1 # normal conv with 1 group
else:
# NOTE group_size == 1 -> depthwise conv
assert channels % group_size == 0
return channels // group_size
@dataclass
class LayerFn:
conv_norm_act: Callable = ConvNormAct
norm_act: Callable = BatchNormAct2d
act: Callable = nn.ReLU
attn: Optional[Callable] = None
self_attn: Optional[Callable] = None
class DownsampleAvg(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
stride: int = 1,
dilation: int = 1,
apply_act: bool = False,
layers: LayerFn = None,
):
""" AvgPool Downsampling as in 'D' ResNet variants."""
super(DownsampleAvg, self).__init__()
layers = layers or LayerFn()
avg_stride = stride if dilation == 1 else 1
if stride > 1 or dilation > 1:
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
else:
self.pool = nn.Identity()
self.conv = layers.conv_norm_act(in_chs, out_chs, 1, apply_act=apply_act)
def forward(self, x):
return self.conv(self.pool(x))
def create_shortcut(
downsample_type: str,
in_chs: int,
out_chs: int,
stride: int,
dilation: Tuple[int, int],
layers: LayerFn,
**kwargs,
):
assert downsample_type in ('avg', 'conv1x1', '')
if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
if not downsample_type:
return None # no shortcut
elif downsample_type == 'avg':
return DownsampleAvg(in_chs, out_chs, stride=stride, dilation=dilation[0], **kwargs)
else:
return layers.conv_norm_act(in_chs, out_chs, kernel_size=1, stride=stride, dilation=dilation[0], **kwargs)
else:
return nn.Identity() # identity shortcut
class BasicBlock(nn.Module):
""" ResNet Basic Block - kxk + kxk
"""
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: Tuple[int, int] = (1, 1),
group_size: Optional[int] = None,
bottle_ratio: float = 1.0,
downsample: str = 'avg',
attn_last: bool = True,
linear_out: bool = False,
layers: LayerFn = None,
drop_block: Callable = None,
drop_path_rate: float = 0.,
):
super(BasicBlock, self).__init__()
layers = layers or LayerFn()
mid_chs = make_divisible(out_chs * bottle_ratio)
groups = num_groups(group_size, mid_chs)
self.shortcut = create_shortcut(
downsample, in_chs, out_chs,
stride=stride, dilation=dilation, apply_act=False, layers=layers,
)
self.conv1_kxk = layers.conv_norm_act(in_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0])
self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
self.conv2_kxk = layers.conv_norm_act(
mid_chs, out_chs, kernel_size,
dilation=dilation[1], groups=groups, drop_layer=drop_block, apply_act=False,
)
self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
def init_weights(self, zero_init_last: bool = False):
if zero_init_last and self.shortcut is not None and getattr(self.conv2_kxk.bn, 'weight', None) is not None:
nn.init.zeros_(self.conv2_kxk.bn.weight)
for attn in (self.attn, self.attn_last):
if hasattr(attn, 'reset_parameters'):
attn.reset_parameters()
def forward(self, x):
shortcut = x
x = self.conv1_kxk(x)
x = self.conv2_kxk(x)
x = self.attn(x)
x = self.drop_path(x)
if self.shortcut is not None:
x = x + self.shortcut(shortcut)
return self.act(x)
class BottleneckBlock(nn.Module):
""" ResNet-like Bottleneck Block - 1x1 - kxk - 1x1
"""
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: Tuple[int, int] = (1, 1),
bottle_ratio: float = 1.,
group_size: Optional[int] = None,
downsample: str = 'avg',
attn_last: bool = False,
linear_out: bool = False,
extra_conv: bool = False,
bottle_in: bool = False,
layers: LayerFn = None,
drop_block: Callable = None,
drop_path_rate: float = 0.,
):
super(BottleneckBlock, self).__init__()
layers = layers or LayerFn()
mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
groups = num_groups(group_size, mid_chs)
self.shortcut = create_shortcut(
downsample, in_chs, out_chs,
stride=stride, dilation=dilation, apply_act=False, layers=layers,
)
self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
self.conv2_kxk = layers.conv_norm_act(
mid_chs, mid_chs, kernel_size,
stride=stride, dilation=dilation[0], groups=groups, drop_layer=drop_block,
)
if extra_conv:
self.conv2b_kxk = layers.conv_norm_act(
mid_chs, mid_chs, kernel_size, dilation=dilation[1], groups=groups)
else:
self.conv2b_kxk = nn.Identity()
self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
self.conv3_1x1 = layers.conv_norm_act(mid_chs, out_chs, 1, apply_act=False)
self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
def init_weights(self, zero_init_last: bool = False):
if zero_init_last and self.shortcut is not None and getattr(self.conv3_1x1.bn, 'weight', None) is not None:
nn.init.zeros_(self.conv3_1x1.bn.weight)
for attn in (self.attn, self.attn_last):
if hasattr(attn, 'reset_parameters'):
attn.reset_parameters()
def forward(self, x):
shortcut = x
x = self.conv1_1x1(x)
x = self.conv2_kxk(x)
x = self.conv2b_kxk(x)
x = self.attn(x)
x = self.conv3_1x1(x)
x = self.attn_last(x)
x = self.drop_path(x)
if self.shortcut is not None:
x = x + self.shortcut(shortcut)
return self.act(x)
class DarkBlock(nn.Module):
""" DarkNet-like (1x1 + 3x3 w/ stride) block
The GE-Net impl included a 1x1 + 3x3 block in their search space. It was not used in the feature models.
This block is pretty much a DarkNet block (also DenseNet) hence the name. Neither DarkNet or DenseNet
uses strides within the block (external 3x3 or maxpool downsampling is done in front of the block repeats).
If one does want to use a lot of these blocks w/ stride, I'd recommend using the EdgeBlock (3x3 /w stride + 1x1)
for more optimal compute.
"""
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: Tuple[int, int] = (1, 1),
bottle_ratio: float = 1.0,
group_size: Optional[int] = None,
downsample: str = 'avg',
attn_last: bool = True,
linear_out: bool = False,
layers: LayerFn = None,
drop_block: Callable = None,
drop_path_rate: float = 0.,
):
super(DarkBlock, self).__init__()
layers = layers or LayerFn()
mid_chs = make_divisible(out_chs * bottle_ratio)
groups = num_groups(group_size, mid_chs)
self.shortcut = create_shortcut(
downsample, in_chs, out_chs,
stride=stride, dilation=dilation, apply_act=False, layers=layers,
)
self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
self.conv2_kxk = layers.conv_norm_act(
mid_chs, out_chs, kernel_size,
stride=stride, dilation=dilation[0], groups=groups, drop_layer=drop_block, apply_act=False,
)
self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
def init_weights(self, zero_init_last: bool = False):
if zero_init_last and self.shortcut is not None and getattr(self.conv2_kxk.bn, 'weight', None) is not None:
nn.init.zeros_(self.conv2_kxk.bn.weight)
for attn in (self.attn, self.attn_last):
if hasattr(attn, 'reset_parameters'):
attn.reset_parameters()
def forward(self, x):
shortcut = x
x = self.conv1_1x1(x)
x = self.attn(x)
x = self.conv2_kxk(x)
x = self.attn_last(x)
x = self.drop_path(x)
if self.shortcut is not None:
x = x + self.shortcut(shortcut)
return self.act(x)
class EdgeBlock(nn.Module):
""" EdgeResidual-like (3x3 + 1x1) block
A two layer block like DarkBlock, but with the order of the 3x3 and 1x1 convs reversed.
Very similar to the EfficientNet Edge-Residual block but this block it ends with activations, is
intended to be used with either expansion or bottleneck contraction, and can use DW/group/non-grouped convs.
FIXME is there a more common 3x3 + 1x1 conv block to name this after?
"""
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: Tuple[int, int] = (1, 1),
bottle_ratio: float = 1.0,
group_size: Optional[int] = None,
downsample: str = 'avg',
attn_last: bool = False,
linear_out: bool = False,
layers: LayerFn = None,
drop_block: Callable = None,
drop_path_rate: float = 0.,
):
super(EdgeBlock, self).__init__()
layers = layers or LayerFn()
mid_chs = make_divisible(out_chs * bottle_ratio)
groups = num_groups(group_size, mid_chs)
self.shortcut = create_shortcut(
downsample, in_chs, out_chs,
stride=stride, dilation=dilation, apply_act=False, layers=layers,
)
self.conv1_kxk = layers.conv_norm_act(
in_chs, mid_chs, kernel_size,
stride=stride, dilation=dilation[0], groups=groups, drop_layer=drop_block,
)
self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
self.conv2_1x1 = layers.conv_norm_act(mid_chs, out_chs, 1, apply_act=False)
self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
def init_weights(self, zero_init_last: bool = False):
if zero_init_last and self.shortcut is not None and getattr(self.conv2_1x1.bn, 'weight', None) is not None:
nn.init.zeros_(self.conv2_1x1.bn.weight)
for attn in (self.attn, self.attn_last):
if hasattr(attn, 'reset_parameters'):
attn.reset_parameters()
def forward(self, x):
shortcut = x
x = self.conv1_kxk(x)
x = self.attn(x)
x = self.conv2_1x1(x)
x = self.attn_last(x)
x = self.drop_path(x)
if self.shortcut is not None:
x = x + self.shortcut(shortcut)
return self.act(x)
class RepVggBlock(nn.Module):
""" RepVGG Block.
Adapted from impl at https://github.com/DingXiaoH/RepVGG
"""
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: Tuple[int, int] = (1, 1),
bottle_ratio: float = 1.0,
group_size: Optional[int] = None,
downsample: str = '',
layers: LayerFn = None,
drop_block: Callable = None,
drop_path_rate: float = 0.,
inference_mode: bool = False
):
super(RepVggBlock, self).__init__()
self.groups = groups = num_groups(group_size, in_chs)
layers = layers or LayerFn()
if inference_mode:
self.reparam_conv = nn.Conv2d(
in_channels=in_chs,
out_channels=out_chs,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
groups=groups,
bias=True,
)
else:
self.reparam_conv = None
use_ident = in_chs == out_chs and stride == 1 and dilation[0] == dilation[1]
self.identity = layers.norm_act(out_chs, apply_act=False) if use_ident else None
self.conv_kxk = layers.conv_norm_act(
in_chs, out_chs, kernel_size,
stride=stride, dilation=dilation[0], groups=groups, drop_layer=drop_block, apply_act=False,
)
self.conv_1x1 = layers.conv_norm_act(in_chs, out_chs, 1, stride=stride, groups=groups, apply_act=False)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. and use_ident else nn.Identity()
self.attn = nn.Identity() if layers.attn is None else layers.attn(out_chs)
self.act = layers.act(inplace=True)
def init_weights(self, zero_init_last: bool = False):
# NOTE this init overrides that base model init with specific changes for the block type
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
nn.init.normal_(m.weight, .1, .1)
nn.init.normal_(m.bias, 0, .1)
if hasattr(self.attn, 'reset_parameters'):
self.attn.reset_parameters()
def forward(self, x):
if self.reparam_conv is not None:
return self.act(self.attn(self.reparam_conv(x)))
if self.identity is None:
x = self.conv_1x1(x) + self.conv_kxk(x)
else:
identity = self.identity(x)
x = self.conv_1x1(x) + self.conv_kxk(x)
x = self.drop_path(x) # not in the paper / official impl, experimental
x += identity
x = self.attn(x) # no attn in the paper / official impl, experimental
return self.act(x)
def reparameterize(self):
""" Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
architecture used at training time to obtain a plain CNN-like structure
for inference.
"""
if self.reparam_conv is not None:
return
kernel, bias = self._get_kernel_bias()
self.reparam_conv = nn.Conv2d(
in_channels=self.conv_kxk.conv.in_channels,
out_channels=self.conv_kxk.conv.out_channels,
kernel_size=self.conv_kxk.conv.kernel_size,
stride=self.conv_kxk.conv.stride,
padding=self.conv_kxk.conv.padding,
dilation=self.conv_kxk.conv.dilation,
groups=self.conv_kxk.conv.groups,
bias=True,
)
self.reparam_conv.weight.data = kernel
self.reparam_conv.bias.data = bias
# Delete un-used branches
for name, para in self.named_parameters():
if 'reparam_conv' in name:
continue
para.detach_()
self.__delattr__('conv_kxk')
self.__delattr__('conv_1x1')
self.__delattr__('identity')
self.__delattr__('drop_path')
def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
""" Method to obtain re-parameterized kernel and bias.
Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83
"""
# get weights and bias of scale branch
kernel_1x1 = 0
bias_1x1 = 0
if self.conv_1x1 is not None:
kernel_1x1, bias_1x1 = self._fuse_bn_tensor(self.conv_1x1)
# Pad scale branch kernel to match conv branch kernel size.
pad = self.conv_kxk.conv.kernel_size[0] // 2
kernel_1x1 = torch.nn.functional.pad(kernel_1x1, [pad, pad, pad, pad])
# get weights and bias of skip branch
kernel_identity = 0
bias_identity = 0
if self.identity is not None:
kernel_identity, bias_identity = self._fuse_bn_tensor(self.identity)
# get weights and bias of conv branches
kernel_conv, bias_conv = self._fuse_bn_tensor(self.conv_kxk)
kernel_final = kernel_conv + kernel_1x1 + kernel_identity
bias_final = bias_conv + bias_1x1 + bias_identity
return kernel_final, bias_final
def _fuse_bn_tensor(self, branch) -> Tuple[torch.Tensor, torch.Tensor]:
""" Method to fuse batchnorm layer with preceeding conv layer.
Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
"""
if isinstance(branch, ConvNormAct):
kernel = branch.conv.weight
running_mean = branch.bn.running_mean
running_var = branch.bn.running_var
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn.eps
else:
assert isinstance(branch, nn.BatchNorm2d)
if not hasattr(self, 'id_tensor'):
in_chs = self.conv_kxk.conv.in_channels
input_dim = in_chs // self.groups
kernel_size = self.conv_kxk.conv.kernel_size
kernel_value = torch.zeros_like(self.conv_kxk.conv.weight)
for i in range(in_chs):
kernel_value[i, i % input_dim, kernel_size[0] // 2, kernel_size[1] // 2] = 1
self.id_tensor = kernel_value
kernel = self.id_tensor
running_mean = branch.running_mean
running_var = branch.running_var
gamma = branch.weight
beta = branch.bias
eps = branch.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
class MobileOneBlock(nn.Module):
""" MobileOne building block.
This block has a multi-branched architecture at train-time
and plain-CNN style architecture at inference time
For more details, please refer to our paper:
`An Improved One millisecond Mobile Backbone` -
https://arxiv.org/pdf/2206.04040.pdf
"""
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: Tuple[int, int] = (1, 1),
bottle_ratio: float = 1.0, # unused
group_size: Optional[int] = None,
downsample: str = '', # unused
inference_mode: bool = False,
num_conv_branches: int = 1,
layers: LayerFn = None,
drop_block: Callable = None,
drop_path_rate: float = 0.,
) -> None:
""" Construct a MobileOneBlock module.
"""
super(MobileOneBlock, self).__init__()
self.num_conv_branches = num_conv_branches
self.groups = groups = num_groups(group_size, in_chs)
layers = layers or LayerFn()
if inference_mode:
self.reparam_conv = nn.Conv2d(
in_channels=in_chs,
out_channels=out_chs,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
groups=groups,
bias=True)
else:
self.reparam_conv = None
# Re-parameterizable skip connection
use_ident = in_chs == out_chs and stride == 1 and dilation[0] == dilation[1]
self.identity = layers.norm_act(out_chs, apply_act=False) if use_ident else None
# Re-parameterizable conv branches
convs = []
for _ in range(self.num_conv_branches):
convs.append(layers.conv_norm_act(
in_chs, out_chs, kernel_size=kernel_size,
stride=stride, groups=groups, apply_act=False))
self.conv_kxk = nn.ModuleList(convs)
# Re-parameterizable scale branch
self.conv_scale = None
if kernel_size > 1:
self.conv_scale = layers.conv_norm_act(
in_chs, out_chs, kernel_size=1,
stride=stride, groups=groups, apply_act=False)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. and use_ident else nn.Identity()
self.attn = nn.Identity() if layers.attn is None else layers.attn(out_chs)
self.act = layers.act(inplace=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
""" Apply forward pass. """
# Inference mode forward pass.
if self.reparam_conv is not None:
return self.act(self.attn(self.reparam_conv(x)))
# Multi-branched train-time forward pass.
# Skip branch output
identity_out = 0
if self.identity is not None:
identity_out = self.identity(x)
# Scale branch output
scale_out = 0
if self.conv_scale is not None:
scale_out = self.conv_scale(x)
# Other branches
out = scale_out
for ck in self.conv_kxk:
out += ck(x)
out = self.drop_path(out)
out += identity_out
return self.act(self.attn(out))
def reparameterize(self):
""" Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
architecture used at training time to obtain a plain CNN-like structure
for inference.
"""
if self.reparam_conv is not None:
return
kernel, bias = self._get_kernel_bias()
self.reparam_conv = nn.Conv2d(
in_channels=self.conv_kxk[0].conv.in_channels,
out_channels=self.conv_kxk[0].conv.out_channels,
kernel_size=self.conv_kxk[0].conv.kernel_size,
stride=self.conv_kxk[0].conv.stride,
padding=self.conv_kxk[0].conv.padding,
dilation=self.conv_kxk[0].conv.dilation,
groups=self.conv_kxk[0].conv.groups,
bias=True)
self.reparam_conv.weight.data = kernel
self.reparam_conv.bias.data = bias
# Delete un-used branches
for name, para in self.named_parameters():
if 'reparam_conv' in name:
continue
para.detach_()
self.__delattr__('conv_kxk')
self.__delattr__('conv_scale')
self.__delattr__('identity')
self.__delattr__('drop_path')
def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
""" Method to obtain re-parameterized kernel and bias.
Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83
"""
# get weights and bias of scale branch
kernel_scale = 0
bias_scale = 0
if self.conv_scale is not None:
kernel_scale, bias_scale = self._fuse_bn_tensor(self.conv_scale)
# Pad scale branch kernel to match conv branch kernel size.
pad = self.conv_kxk[0].conv.kernel_size[0] // 2
kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])
# get weights and bias of skip branch
kernel_identity = 0
bias_identity = 0
if self.identity is not None:
kernel_identity, bias_identity = self._fuse_bn_tensor(self.identity)
# get weights and bias of conv branches
kernel_conv = 0
bias_conv = 0
for ix in range(self.num_conv_branches):
_kernel, _bias = self._fuse_bn_tensor(self.conv_kxk[ix])
kernel_conv += _kernel
bias_conv += _bias
kernel_final = kernel_conv + kernel_scale + kernel_identity
bias_final = bias_conv + bias_scale + bias_identity
return kernel_final, bias_final
def _fuse_bn_tensor(self, branch) -> Tuple[torch.Tensor, torch.Tensor]:
""" Method to fuse batchnorm layer with preceeding conv layer.
Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
"""
if isinstance(branch, ConvNormAct):
kernel = branch.conv.weight
running_mean = branch.bn.running_mean
running_var = branch.bn.running_var
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn.eps
else:
assert isinstance(branch, nn.BatchNorm2d)
if not hasattr(self, 'id_tensor'):
in_chs = self.conv_kxk[0].conv.in_channels
input_dim = in_chs // self.groups
kernel_size = self.conv_kxk[0].conv.kernel_size
kernel_value = torch.zeros_like(self.conv_kxk[0].conv.weight)
for i in range(in_chs):
kernel_value[i, i % input_dim, kernel_size[0] // 2, kernel_size[1] // 2] = 1
self.id_tensor = kernel_value
kernel = self.id_tensor
running_mean = branch.running_mean
running_var = branch.running_var
gamma = branch.weight
beta = branch.bias
eps = branch.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
class SelfAttnBlock(nn.Module):
""" ResNet-like Bottleneck Block - 1x1 - optional kxk - self attn - 1x1
"""
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: Tuple[int, int] = (1, 1),
bottle_ratio: float = 1.,
group_size: Optional[int] = None,
downsample: str = 'avg',
extra_conv: bool = False,
linear_out: bool = False,
bottle_in: bool = False,
post_attn_na: bool = True,
feat_size: Optional[Tuple[int, int]] = None,
layers: LayerFn = None,
drop_block: Callable = None,
drop_path_rate: float = 0.,
):
super(SelfAttnBlock, self).__init__()
assert layers is not None
mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
groups = num_groups(group_size, mid_chs)
self.shortcut = create_shortcut(
downsample, in_chs, out_chs,
stride=stride, dilation=dilation, apply_act=False, layers=layers,
)
self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
if extra_conv:
self.conv2_kxk = layers.conv_norm_act(
mid_chs, mid_chs, kernel_size,
stride=stride, dilation=dilation[0], groups=groups, drop_layer=drop_block,
)
stride = 1 # striding done via conv if enabled
else:
self.conv2_kxk = nn.Identity()
opt_kwargs = {} if feat_size is None else dict(feat_size=feat_size)
# FIXME need to dilate self attn to have dilated network support, moop moop
self.self_attn = layers.self_attn(mid_chs, stride=stride, **opt_kwargs)
self.post_attn = layers.norm_act(mid_chs) if post_attn_na else nn.Identity()
self.conv3_1x1 = layers.conv_norm_act(mid_chs, out_chs, 1, apply_act=False)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
def init_weights(self, zero_init_last: bool = False):
if zero_init_last and self.shortcut is not None and getattr(self.conv3_1x1.bn, 'weight', None) is not None:
nn.init.zeros_(self.conv3_1x1.bn.weight)
if hasattr(self.self_attn, 'reset_parameters'):
self.self_attn.reset_parameters()
def forward(self, x):
shortcut = x
x = self.conv1_1x1(x)
x = self.conv2_kxk(x)
x = self.self_attn(x)
x = self.post_attn(x)
x = self.conv3_1x1(x)
x = self.drop_path(x)
if self.shortcut is not None:
x = x + self.shortcut(shortcut)
return self.act(x)
_block_registry = dict(
basic=BasicBlock,
bottle=BottleneckBlock,
dark=DarkBlock,
edge=EdgeBlock,
rep=RepVggBlock,
one=MobileOneBlock,
self_attn=SelfAttnBlock,
)
def register_block(block_type:str, block_fn: nn.Module):
_block_registry[block_type] = block_fn
def create_block(block: Union[str, nn.Module], **kwargs):
if isinstance(block, (nn.Module, partial)):
return block(**kwargs)
assert block in _block_registry, f'Unknown block type ({block}'
return _block_registry[block](**kwargs)
class Stem(nn.Sequential):
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 4,
pool: str = 'maxpool',
num_rep: int = 3,
num_act: Optional[int] = None,
chs_decay: float = 0.5,
layers: LayerFn = None,
):
super().__init__()
assert stride in (2, 4)
layers = layers or LayerFn()
if isinstance(out_chs, (list, tuple)):
num_rep = len(out_chs)
stem_chs = out_chs
else:
stem_chs = [round(out_chs * chs_decay ** i) for i in range(num_rep)][::-1]
self.stride = stride
self.feature_info = [] # track intermediate features
prev_feat = ''
stem_strides = [2] + [1] * (num_rep - 1)
if stride == 4 and not pool:
# set last conv in stack to be strided if stride == 4 and no pooling layer
stem_strides[-1] = 2
num_act = num_rep if num_act is None else num_act
# if num_act < num_rep, first convs in stack won't have bn + act
stem_norm_acts = [False] * (num_rep - num_act) + [True] * num_act
prev_chs = in_chs
curr_stride = 1
for i, (ch, s, na) in enumerate(zip(stem_chs, stem_strides, stem_norm_acts)):
layer_fn = layers.conv_norm_act if na else create_conv2d
conv_name = f'conv{i + 1}'
if i > 0 and s > 1:
self.feature_info.append(dict(num_chs=prev_chs, reduction=curr_stride, module=prev_feat))
self.add_module(conv_name, layer_fn(prev_chs, ch, kernel_size=kernel_size, stride=s))
prev_chs = ch
curr_stride *= s
prev_feat = conv_name
if pool and 'max' in pool.lower():
self.feature_info.append(dict(num_chs=prev_chs, reduction=curr_stride, module=prev_feat))
self.add_module('pool', nn.MaxPool2d(3, 2, 1))
curr_stride *= 2
prev_feat = 'pool'
self.feature_info.append(dict(num_chs=prev_chs, reduction=curr_stride, module=prev_feat))
assert curr_stride == stride
def create_byob_stem(
in_chs: int,
out_chs: int,
stem_type: str = '',
pool_type: str = '',
feat_prefix: str = 'stem',
layers: LayerFn = None,
):
layers = layers or LayerFn()
assert stem_type in ('', 'quad', 'quad2', 'tiered', 'deep', 'rep', 'one', '7x7', '3x3')
if 'quad' in stem_type:
# based on NFNet stem, stack of 4 3x3 convs
num_act = 2 if 'quad2' in stem_type else None
stem = Stem(in_chs, out_chs, num_rep=4, num_act=num_act, pool=pool_type, layers=layers)
elif 'tiered' in stem_type:
# 3x3 stack of 3 convs as in my ResNet-T
stem = Stem(in_chs, (3 * out_chs // 8, out_chs // 2, out_chs), pool=pool_type, layers=layers)
elif 'deep' in stem_type:
# 3x3 stack of 3 convs as in ResNet-D
stem = Stem(in_chs, out_chs, num_rep=3, chs_decay=1.0, pool=pool_type, layers=layers)
elif 'rep' in stem_type:
stem = RepVggBlock(in_chs, out_chs, stride=2, layers=layers)
elif 'one' in stem_type:
stem = MobileOneBlock(in_chs, out_chs, kernel_size=3, stride=2, layers=layers)
elif '7x7' in stem_type:
# 7x7 stem conv as in ResNet
if pool_type:
stem = Stem(in_chs, out_chs, 7, num_rep=1, pool=pool_type, layers=layers)
else:
stem = layers.conv_norm_act(in_chs, out_chs, 7, stride=2)
else:
# 3x3 stem conv as in RegNet is the default
if pool_type:
stem = Stem(in_chs, out_chs, 3, num_rep=1, pool=pool_type, layers=layers)
else:
stem = layers.conv_norm_act(in_chs, out_chs, 3, stride=2)
if isinstance(stem, Stem):
feature_info = [dict(f, module='.'.join([feat_prefix, f['module']])) for f in stem.feature_info]
else:
feature_info = [dict(num_chs=out_chs, reduction=2, module=feat_prefix)]
return stem, feature_info
def reduce_feat_size(feat_size, stride=2):
return None if feat_size is None else tuple([s // stride for s in feat_size])
def override_kwargs(block_kwargs, model_kwargs):
""" Override model level attn/self-attn/block kwargs w/ block level
NOTE: kwargs are NOT merged across levels, block_kwargs will fully replace model_kwargs
for the block if set to anything that isn't None.
i.e. an empty block_kwargs dict will remove kwargs set at model level for that block
"""
out_kwargs = block_kwargs if block_kwargs is not None else model_kwargs
return out_kwargs or {} # make sure None isn't returned
def update_block_kwargs(block_kwargs: Dict[str, Any], block_cfg: ByoBlockCfg, model_cfg: ByoModelCfg, ):
layer_fns = block_kwargs['layers']
# override attn layer / args with block local config
attn_set = block_cfg.attn_layer is not None
if attn_set or block_cfg.attn_kwargs is not None:
# override attn layer config
if attn_set and not block_cfg.attn_layer:
# empty string for attn_layer type will disable attn for this block
attn_layer = None
else:
attn_kwargs = override_kwargs(block_cfg.attn_kwargs, model_cfg.attn_kwargs)
attn_layer = block_cfg.attn_layer or model_cfg.attn_layer
attn_layer = partial(get_attn(attn_layer), **attn_kwargs) if attn_layer is not None else None
layer_fns = replace(layer_fns, attn=attn_layer)
# override self-attn layer / args with block local cfg
self_attn_set = block_cfg.self_attn_layer is not None
if self_attn_set or block_cfg.self_attn_kwargs is not None:
# override attn layer config
if self_attn_set and not block_cfg.self_attn_layer: # attn_layer == ''
# empty string for self_attn_layer type will disable attn for this block
self_attn_layer = None
else:
self_attn_kwargs = override_kwargs(block_cfg.self_attn_kwargs, model_cfg.self_attn_kwargs)
self_attn_layer = block_cfg.self_attn_layer or model_cfg.self_attn_layer
self_attn_layer = partial(get_attn(self_attn_layer), **self_attn_kwargs) \
if self_attn_layer is not None else None
layer_fns = replace(layer_fns, self_attn=self_attn_layer)
block_kwargs['layers'] = layer_fns
# add additional block_kwargs specified in block_cfg or model_cfg, precedence to block if set
block_kwargs.update(override_kwargs(block_cfg.block_kwargs, model_cfg.block_kwargs))
def create_byob_stages(
cfg: ByoModelCfg,
drop_path_rate: float,
output_stride: int,
stem_feat: Dict[str, Any],
feat_size: Optional[int] = None,
layers: Optional[LayerFn] = None,
block_kwargs_fn: Optional[Callable] = update_block_kwargs,
):
layers = layers or LayerFn()
feature_info = []
block_cfgs = [expand_blocks_cfg(s) for s in cfg.blocks]
depths = [sum([bc.d for bc in stage_bcs]) for stage_bcs in block_cfgs]
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
dilation = 1
net_stride = stem_feat['reduction']
prev_chs = stem_feat['num_chs']
prev_feat = stem_feat
stages = []
for stage_idx, stage_block_cfgs in enumerate(block_cfgs):
stride = stage_block_cfgs[0].s
if stride != 1 and prev_feat:
feature_info.append(prev_feat)
if net_stride >= output_stride and stride > 1:
dilation *= stride
stride = 1
net_stride *= stride
first_dilation = 1 if dilation in (1, 2) else 2
blocks = []
for block_idx, block_cfg in enumerate(stage_block_cfgs):
out_chs = make_divisible(block_cfg.c * cfg.width_factor)
group_size = block_cfg.gs
if isinstance(group_size, Callable):
group_size = group_size(out_chs, block_idx)
block_kwargs = dict( # Blocks used in this model must accept these arguments
in_chs=prev_chs,
out_chs=out_chs,
stride=stride if block_idx == 0 else 1,
dilation=(first_dilation, dilation),
group_size=group_size,
bottle_ratio=block_cfg.br,
downsample=cfg.downsample,
drop_path_rate=dpr[stage_idx][block_idx],
layers=layers,
)
if block_cfg.type in ('self_attn',):
# add feat_size arg for blocks that support/need it
block_kwargs['feat_size'] = feat_size
block_kwargs_fn(block_kwargs, block_cfg=block_cfg, model_cfg=cfg)
blocks += [create_block(block_cfg.type, **block_kwargs)]
first_dilation = dilation
prev_chs = out_chs
if stride > 1 and block_idx == 0:
feat_size = reduce_feat_size(feat_size, stride)
stages += [nn.Sequential(*blocks)]
prev_feat = dict(num_chs=prev_chs, reduction=net_stride, module=f'stages.{stage_idx}')
feature_info.append(prev_feat)
return nn.Sequential(*stages), feature_info
def get_layer_fns(cfg: ByoModelCfg):
act = get_act_layer(cfg.act_layer)
norm_act = get_norm_act_layer(norm_layer=cfg.norm_layer, act_layer=act)
conv_norm_act = partial(ConvNormAct, norm_layer=cfg.norm_layer, act_layer=act)
attn = partial(get_attn(cfg.attn_layer), **cfg.attn_kwargs) if cfg.attn_layer else None
self_attn = partial(get_attn(cfg.self_attn_layer), **cfg.self_attn_kwargs) if cfg.self_attn_layer else None
layer_fn = LayerFn(conv_norm_act=conv_norm_act, norm_act=norm_act, act=act, attn=attn, self_attn=self_attn)
return layer_fn
class ByobNet(nn.Module):
""" 'Bring-your-own-blocks' Net
A flexible network backbone that allows building model stem + blocks via
dataclass cfg definition w/ factory functions for module instantiation.
Current assumption is that both stem and blocks are in conv-bn-act order (w/ block ending in act).
"""
def __init__(
self,
cfg: ByoModelCfg,
num_classes: int = 1000,
in_chans: int = 3,
global_pool: str = 'avg',
output_stride: int = 32,
img_size: Optional[Union[int, Tuple[int, int]]] = None,
drop_rate: float = 0.,
drop_path_rate: float =0.,
zero_init_last: bool = True,
**kwargs,
):
"""
Args:
cfg: Model architecture configuration.
num_classes: Number of classifier classes.
in_chans: Number of input channels.
global_pool: Global pooling type.
output_stride: Output stride of network, one of (8, 16, 32).
img_size: Image size for fixed image size models (i.e. self-attn).
drop_rate: Classifier dropout rate.
drop_path_rate: Stochastic depth drop-path rate.
zero_init_last: Zero-init last weight of residual path.
**kwargs: Extra kwargs overlayed onto cfg.
"""
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
cfg = replace(cfg, **kwargs) # overlay kwargs onto cfg
layers = get_layer_fns(cfg)
if cfg.fixed_input_size:
assert img_size is not None, 'img_size argument is required for fixed input size model'
feat_size = to_2tuple(img_size) if img_size is not None else None
self.feature_info = []
stem_chs = int(round((cfg.stem_chs or cfg.blocks[0].c) * cfg.width_factor))
self.stem, stem_feat = create_byob_stem(in_chans, stem_chs, cfg.stem_type, cfg.stem_pool, layers=layers)
self.feature_info.extend(stem_feat[:-1])
feat_size = reduce_feat_size(feat_size, stride=stem_feat[-1]['reduction'])
self.stages, stage_feat = create_byob_stages(
cfg,
drop_path_rate,
output_stride,
stem_feat[-1],
layers=layers,
feat_size=feat_size,
)
self.feature_info.extend(stage_feat[:-1])
prev_chs = stage_feat[-1]['num_chs']
if cfg.num_features:
self.num_features = int(round(cfg.width_factor * cfg.num_features))
self.final_conv = layers.conv_norm_act(prev_chs, self.num_features, 1)
else:
self.num_features = prev_chs
self.final_conv = nn.Identity()
self.feature_info += [
dict(num_chs=self.num_features, reduction=stage_feat[-1]['reduction'], module='final_conv')]
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=self.drop_rate,
)
# init weights
named_apply(partial(_init_weights, zero_init_last=zero_init_last), self)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem',
blocks=[
(r'^stages\.(\d+)' if coarse else r'^stages\.(\d+)\.(\d+)', None),
(r'^final_conv', (99999,))
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.head.reset(num_classes, global_pool)
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
x = self.final_conv(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module, name='', zero_init_last=False):
if isinstance(module, nn.Conv2d):
fan_out = module.kernel_size[0] * module.kernel_size[1] * module.out_channels
fan_out //= module.groups
module.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.01)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.BatchNorm2d):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
module.init_weights(zero_init_last=zero_init_last)
model_cfgs = dict(
gernet_l=ByoModelCfg(
blocks=(
ByoBlockCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
ByoBlockCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
ByoBlockCfg(type='bottle', d=5, c=640, s=2, gs=1, br=3.),
ByoBlockCfg(type='bottle', d=4, c=640, s=1, gs=1, br=3.),
),
stem_chs=32,
stem_pool=None,
num_features=2560,
),
gernet_m=ByoModelCfg(
blocks=(
ByoBlockCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
ByoBlockCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
ByoBlockCfg(type='bottle', d=4, c=640, s=2, gs=1, br=3.),
ByoBlockCfg(type='bottle', d=1, c=640, s=1, gs=1, br=3.),
),
stem_chs=32,
stem_pool=None,
num_features=2560,
),
gernet_s=ByoModelCfg(
blocks=(
ByoBlockCfg(type='basic', d=1, c=48, s=2, gs=0, br=1.),
ByoBlockCfg(type='basic', d=3, c=48, s=2, gs=0, br=1.),
ByoBlockCfg(type='bottle', d=7, c=384, s=2, gs=0, br=1 / 4),
ByoBlockCfg(type='bottle', d=2, c=560, s=2, gs=1, br=3.),
ByoBlockCfg(type='bottle', d=1, c=256, s=1, gs=1, br=3.),
),
stem_chs=13,
stem_pool=None,
num_features=1920,
),
repvgg_a0=ByoModelCfg(
blocks=_rep_vgg_bcfg(d=(2, 4, 14, 1), wf=(0.75, 0.75, 0.75, 2.5)),
stem_type='rep',
stem_chs=48,
),
repvgg_a1=ByoModelCfg(
blocks=_rep_vgg_bcfg(d=(2, 4, 14, 1), wf=(1, 1, 1, 2.5)),
stem_type='rep',
stem_chs=64,
),
repvgg_a2=ByoModelCfg(
blocks=_rep_vgg_bcfg(d=(2, 4, 14, 1), wf=(1.5, 1.5, 1.5, 2.75)),
stem_type='rep',
stem_chs=64,
),
repvgg_b0=ByoModelCfg(
blocks=_rep_vgg_bcfg(wf=(1., 1., 1., 2.5)),
stem_type='rep',
stem_chs=64,
),
repvgg_b1=ByoModelCfg(
blocks=_rep_vgg_bcfg(wf=(2., 2., 2., 4.)),
stem_type='rep',
stem_chs=64,
),
repvgg_b1g4=ByoModelCfg(
blocks=_rep_vgg_bcfg(wf=(2., 2., 2., 4.), groups=4),
stem_type='rep',
stem_chs=64,
),
repvgg_b2=ByoModelCfg(
blocks=_rep_vgg_bcfg(wf=(2.5, 2.5, 2.5, 5.)),
stem_type='rep',
stem_chs=64,
),
repvgg_b2g4=ByoModelCfg(
blocks=_rep_vgg_bcfg(wf=(2.5, 2.5, 2.5, 5.), groups=4),
stem_type='rep',
stem_chs=64,
),
repvgg_b3=ByoModelCfg(
blocks=_rep_vgg_bcfg(wf=(3., 3., 3., 5.)),
stem_type='rep',
stem_chs=64,
),
repvgg_b3g4=ByoModelCfg(
blocks=_rep_vgg_bcfg(wf=(3., 3., 3., 5.), groups=4),
stem_type='rep',
stem_chs=64,
),
repvgg_d2se=ByoModelCfg(
blocks=_rep_vgg_bcfg(d=(8, 14, 24, 1), wf=(2.5, 2.5, 2.5, 5.)),
stem_type='rep',
stem_chs=64,
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.0625, rd_divisor=1),
),
# 4 x conv stem w/ 2 act, no maxpool, 2,4,6,4 repeats, group size 32 in first 3 blocks
# DW convs in last block, 2048 pre-FC, silu act
resnet51q=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=6, c=1536, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=1536, s=2, gs=1, br=1.0),
),
stem_chs=128,
stem_type='quad2',
stem_pool=None,
num_features=2048,
act_layer='silu',
),
# 4 x conv stem w/ 4 act, no maxpool, 1,4,6,4 repeats, edge block first, group size 32 in next 2 blocks
# DW convs in last block, 4 conv for each bottle block, 2048 pre-FC, silu act
resnet61q=ByoModelCfg(
blocks=(
ByoBlockCfg(type='edge', d=1, c=256, s=1, gs=0, br=1.0, block_kwargs=dict()),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=6, c=1536, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=1536, s=2, gs=1, br=1.0),
),
stem_chs=128,
stem_type='quad',
stem_pool=None,
num_features=2048,
act_layer='silu',
block_kwargs=dict(extra_conv=True),
),
# A series of ResNeXt-26 models w/ one of none, GC, SE, ECA, BAT attn, group size 32, SiLU act,
# and a tiered stem w/ maxpool
resnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
),
gcresnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
attn_layer='gca',
),
seresnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
attn_layer='se',
),
eca_resnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
attn_layer='eca',
),
bat_resnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
attn_layer='bat',
attn_kwargs=dict(block_size=8)
),
# ResNet-32 (2, 3, 3, 2) models w/ no attn, no groups, SiLU act, no pre-fc feat layer, tiered stem w/o maxpool
resnet32ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
num_features=0,
act_layer='silu',
),
# ResNet-33 (2, 3, 3, 2) models w/ no attn, no groups, SiLU act, 1280 pre-FC feat, tiered stem w/o maxpool
resnet33ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
num_features=1280,
act_layer='silu',
),
# A series of ResNet-33 (2, 3, 3, 2) models w/ one of GC, SE, ECA attn, no groups, SiLU act, 1280 pre-FC feat
# and a tiered stem w/ no maxpool
gcresnet33ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
num_features=1280,
act_layer='silu',
attn_layer='gca',
),
seresnet33ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
num_features=1280,
act_layer='silu',
attn_layer='se',
),
eca_resnet33ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
num_features=1280,
act_layer='silu',
attn_layer='eca',
),
gcresnet50t=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, br=0.25),
ByoBlockCfg(type='bottle', d=6, c=1024, s=2, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=2048, s=2, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
attn_layer='gca',
),
gcresnext50ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=6, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
attn_layer='gca',
),
# experimental models, closer to a RegNetZ than a ResNet. Similar to EfficientNets but w/ groups instead of DW
regnetz_b16=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=3),
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=3),
ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=3),
ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=3),
),
stem_chs=32,
stem_pool='',
downsample='',
num_features=1536,
act_layer='silu',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
regnetz_c16=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=4),
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=4),
ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=4),
ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=4),
),
stem_chs=32,
stem_pool='',
downsample='',
num_features=1536,
act_layer='silu',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
regnetz_d32=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=32, br=4),
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=32, br=4),
ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=32, br=4),
ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=32, br=4),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
downsample='',
num_features=1792,
act_layer='silu',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
regnetz_d8=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=8, br=4),
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=8, br=4),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
downsample='',
num_features=1792,
act_layer='silu',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
regnetz_e8=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=96, s=1, gs=8, br=4),
ByoBlockCfg(type='bottle', d=8, c=192, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=16, c=384, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=8, br=4),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
downsample='',
num_features=2048,
act_layer='silu',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
# experimental EvoNorm configs
regnetz_b16_evos=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=3),
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=3),
ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=3),
ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=3),
),
stem_chs=32,
stem_pool='',
downsample='',
num_features=1536,
act_layer='silu',
norm_layer=partial(EvoNorm2dS0a, group_size=16),
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
regnetz_c16_evos=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=4),
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=4),
ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=4),
ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=4),
),
stem_chs=32,
stem_pool='',
downsample='',
num_features=1536,
act_layer='silu',
norm_layer=partial(EvoNorm2dS0a, group_size=16),
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
regnetz_d8_evos=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=8, br=4),
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=8, br=4),
ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=8, br=4),
),
stem_chs=64,
stem_type='deep',
stem_pool='',
downsample='',
num_features=1792,
act_layer='silu',
norm_layer=partial(EvoNorm2dS0a, group_size=16),
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
),
mobileone_s0=ByoModelCfg(
blocks=_mobileone_bcfg(wf=(0.75, 1.0, 1.0, 2.), num_conv_branches=4),
stem_type='one',
stem_chs=48,
),
mobileone_s1=ByoModelCfg(
blocks=_mobileone_bcfg(wf=(1.5, 1.5, 2.0, 2.5)),
stem_type='one',
stem_chs=64,
),
mobileone_s2=ByoModelCfg(
blocks=_mobileone_bcfg(wf=(1.5, 2.0, 2.5, 4.0)),
stem_type='one',
stem_chs=64,
),
mobileone_s3=ByoModelCfg(
blocks=_mobileone_bcfg(wf=(2.0, 2.5, 3.0, 4.0)),
stem_type='one',
stem_chs=64,
),
mobileone_s4=ByoModelCfg(
blocks=_mobileone_bcfg(wf=(3.0, 3.5, 3.5, 4.0), se_blocks=(0, 0, 5, 1)),
stem_type='one',
stem_chs=64,
),
)
def _create_byobnet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
ByobNet, variant, pretrained,
model_cfg=model_cfgs[variant],
feature_cfg=dict(flatten_sequential=True),
**kwargs)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
**kwargs
}
def _cfgr(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
'crop_pct': 0.9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
# GPU-Efficient (ResNet) weights
'gernet_s.idstcv_in1k': _cfg(hf_hub_id='timm/'),
'gernet_m.idstcv_in1k': _cfg(hf_hub_id='timm/'),
'gernet_l.idstcv_in1k': _cfg(hf_hub_id='timm/', input_size=(3, 256, 256), pool_size=(8, 8)),
# RepVGG weights
'repvgg_a0.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_a1.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_a2.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_b0.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_b1.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_b1g4.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_b2.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_b2g4.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_b3.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_b3g4.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit'),
'repvgg_d2se.rvgg_in1k': _cfg(
hf_hub_id='timm/',
first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv'), license='mit',
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0,
),
# experimental ResNet configs
'resnet51q.ra2_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet51q_ra2-d47dcc76.pth',
first_conv='stem.conv1', input_size=(3, 256, 256), pool_size=(8, 8),
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'resnet61q.ra2_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet61q_ra2-6afc536c.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
# ResNeXt-26 models with different attention in Bottleneck blocks
'resnext26ts.ra2_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnext26ts_256_ra2-8bbd9106.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'seresnext26ts.ch_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnext26ts_256-6f0d74a3.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'gcresnext26ts.ch_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext26ts_256-e414378b.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'eca_resnext26ts.ch_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnext26ts_256-5a1d030f.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'bat_resnext26ts.ch_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/bat_resnext26ts_256-fa6fd595.pth',
min_input_size=(3, 256, 256)),
# ResNet-32 / 33 models with different attention in Bottleneck blocks
'resnet32ts.ra2_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet32ts_256-aacf5250.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'resnet33ts.ra2_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet33ts_256-e91b09a4.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'gcresnet33ts.ra2_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet33ts_256-0e0cd345.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'seresnet33ts.ra2_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnet33ts_256-f8ad44d9.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'eca_resnet33ts.ra2_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnet33ts_256-8f98face.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'gcresnet50t.ra2_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet50t_256-96374d1c.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'gcresnext50ts.ch_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext50ts_256-3e0f515e.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
# custom `timm` specific RegNetZ inspired models w/ different sizing from paper
'regnetz_b16.ra3_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_b_raa-677d9606.pth',
first_conv='stem.conv', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 224, 224), pool_size=(7, 7), crop_pct=0.94, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'regnetz_c16.ra3_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_c_rab2_256-a54bf36a.pth',
first_conv='stem.conv', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
crop_pct=0.94, test_input_size=(3, 320, 320), test_crop_pct=1.0),
'regnetz_d32.ra3_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_d_rab_256-b8073a89.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.95, test_input_size=(3, 320, 320)),
'regnetz_d8.ra3_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_d8_bh-afc03c55.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.94, test_input_size=(3, 320, 320), test_crop_pct=1.0),
'regnetz_e8.ra3_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_e8_bh-aace8e6e.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.94, test_input_size=(3, 320, 320), test_crop_pct=1.0),
'regnetz_b16_evos.untrained': _cfgr(
first_conv='stem.conv', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 224, 224), pool_size=(7, 7), crop_pct=0.95, test_input_size=(3, 288, 288)),
'regnetz_c16_evos.ch_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetz_c16_evos_ch-d8311942.pth',
first_conv='stem.conv', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
crop_pct=0.95, test_input_size=(3, 320, 320)),
'regnetz_d8_evos.ch_in1k': _cfgr(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetz_d8_evos_ch-2bc12646.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0),
'mobileone_s0.apple_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.875,
first_conv=('stem.conv_kxk.0.conv', 'stem.conv_scale.conv'),
),
'mobileone_s1.apple_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.9,
first_conv=('stem.conv_kxk.0.conv', 'stem.conv_scale.conv'),
),
'mobileone_s2.apple_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.9,
first_conv=('stem.conv_kxk.0.conv', 'stem.conv_scale.conv'),
),
'mobileone_s3.apple_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.9,
first_conv=('stem.conv_kxk.0.conv', 'stem.conv_scale.conv'),
),
'mobileone_s4.apple_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.9,
first_conv=('stem.conv_kxk.0.conv', 'stem.conv_scale.conv'),
),
})
@register_model
def gernet_l(pretrained=False, **kwargs) -> ByobNet:
""" GEResNet-Large (GENet-Large from official impl)
`Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
"""
return _create_byobnet('gernet_l', pretrained=pretrained, **kwargs)
@register_model
def gernet_m(pretrained=False, **kwargs) -> ByobNet:
""" GEResNet-Medium (GENet-Normal from official impl)
`Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
"""
return _create_byobnet('gernet_m', pretrained=pretrained, **kwargs)
@register_model
def gernet_s(pretrained=False, **kwargs) -> ByobNet:
""" EResNet-Small (GENet-Small from official impl)
`Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
"""
return _create_byobnet('gernet_s', pretrained=pretrained, **kwargs)
@register_model
def repvgg_a0(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-A0
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_a0', pretrained=pretrained, **kwargs)
@register_model
def repvgg_a1(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-A1
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_a1', pretrained=pretrained, **kwargs)
@register_model
def repvgg_a2(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-A2
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_a2', pretrained=pretrained, **kwargs)
@register_model
def repvgg_b0(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-B0
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_b0', pretrained=pretrained, **kwargs)
@register_model
def repvgg_b1(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-B1
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_b1', pretrained=pretrained, **kwargs)
@register_model
def repvgg_b1g4(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-B1g4
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_b1g4', pretrained=pretrained, **kwargs)
@register_model
def repvgg_b2(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-B2
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_b2', pretrained=pretrained, **kwargs)
@register_model
def repvgg_b2g4(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-B2g4
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_b2g4', pretrained=pretrained, **kwargs)
@register_model
def repvgg_b3(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-B3
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_b3', pretrained=pretrained, **kwargs)
@register_model
def repvgg_b3g4(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-B3g4
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_b3g4', pretrained=pretrained, **kwargs)
@register_model
def repvgg_d2se(pretrained=False, **kwargs) -> ByobNet:
""" RepVGG-D2se
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
"""
return _create_byobnet('repvgg_d2se', pretrained=pretrained, **kwargs)
@register_model
def resnet51q(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('resnet51q', pretrained=pretrained, **kwargs)
@register_model
def resnet61q(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('resnet61q', pretrained=pretrained, **kwargs)
@register_model
def resnext26ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('resnext26ts', pretrained=pretrained, **kwargs)
@register_model
def gcresnext26ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('gcresnext26ts', pretrained=pretrained, **kwargs)
@register_model
def seresnext26ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('seresnext26ts', pretrained=pretrained, **kwargs)
@register_model
def eca_resnext26ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('eca_resnext26ts', pretrained=pretrained, **kwargs)
@register_model
def bat_resnext26ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('bat_resnext26ts', pretrained=pretrained, **kwargs)
@register_model
def resnet32ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('resnet32ts', pretrained=pretrained, **kwargs)
@register_model
def resnet33ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('resnet33ts', pretrained=pretrained, **kwargs)
@register_model
def gcresnet33ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('gcresnet33ts', pretrained=pretrained, **kwargs)
@register_model
def seresnet33ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('seresnet33ts', pretrained=pretrained, **kwargs)
@register_model
def eca_resnet33ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('eca_resnet33ts', pretrained=pretrained, **kwargs)
@register_model
def gcresnet50t(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('gcresnet50t', pretrained=pretrained, **kwargs)
@register_model
def gcresnext50ts(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('gcresnext50ts', pretrained=pretrained, **kwargs)
@register_model
def regnetz_b16(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('regnetz_b16', pretrained=pretrained, **kwargs)
@register_model
def regnetz_c16(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('regnetz_c16', pretrained=pretrained, **kwargs)
@register_model
def regnetz_d32(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('regnetz_d32', pretrained=pretrained, **kwargs)
@register_model
def regnetz_d8(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('regnetz_d8', pretrained=pretrained, **kwargs)
@register_model
def regnetz_e8(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('regnetz_e8', pretrained=pretrained, **kwargs)
@register_model
def regnetz_b16_evos(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('regnetz_b16_evos', pretrained=pretrained, **kwargs)
@register_model
def regnetz_c16_evos(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('regnetz_c16_evos', pretrained=pretrained, **kwargs)
@register_model
def regnetz_d8_evos(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('regnetz_d8_evos', pretrained=pretrained, **kwargs)
@register_model
def mobileone_s0(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('mobileone_s0', pretrained=pretrained, **kwargs)
@register_model
def mobileone_s1(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('mobileone_s1', pretrained=pretrained, **kwargs)
@register_model
def mobileone_s2(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('mobileone_s2', pretrained=pretrained, **kwargs)
@register_model
def mobileone_s3(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('mobileone_s3', pretrained=pretrained, **kwargs)
@register_model
def mobileone_s4(pretrained=False, **kwargs) -> ByobNet:
"""
"""
return _create_byobnet('mobileone_s4', pretrained=pretrained, **kwargs)
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|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/convmixer.py
|
""" ConvMixer
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import SelectAdaptivePool2d
from ._registry import register_model, generate_default_cfgs
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
__all__ = ['ConvMixer']
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x):
return self.fn(x) + x
class ConvMixer(nn.Module):
def __init__(
self,
dim,
depth,
kernel_size=9,
patch_size=7,
in_chans=3,
num_classes=1000,
global_pool='avg',
drop_rate=0.,
act_layer=nn.GELU,
**kwargs,
):
super().__init__()
self.num_classes = num_classes
self.num_features = dim
self.grad_checkpointing = False
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dim, kernel_size=patch_size, stride=patch_size),
act_layer(),
nn.BatchNorm2d(dim)
)
self.blocks = nn.Sequential(
*[nn.Sequential(
Residual(nn.Sequential(
nn.Conv2d(dim, dim, kernel_size, groups=dim, padding="same"),
act_layer(),
nn.BatchNorm2d(dim)
)),
nn.Conv2d(dim, dim, kernel_size=1),
act_layer(),
nn.BatchNorm2d(dim)
) for i in range(depth)]
)
self.pooling = SelectAdaptivePool2d(pool_type=global_pool, flatten=True)
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(dim, num_classes) if num_classes > 0 else nn.Identity()
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(stem=r'^stem', blocks=r'^blocks\.(\d+)')
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.pooling = SelectAdaptivePool2d(pool_type=global_pool, flatten=True)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.pooling(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_convmixer(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for ConvMixer models.')
return build_model_with_cfg(ConvMixer, variant, pretrained, **kwargs)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .96, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head',
'first_conv': 'stem.0',
**kwargs
}
default_cfgs = generate_default_cfgs({
'convmixer_1536_20.in1k': _cfg(hf_hub_id='timm/'),
'convmixer_768_32.in1k': _cfg(hf_hub_id='timm/'),
'convmixer_1024_20_ks9_p14.in1k': _cfg(hf_hub_id='timm/')
})
@register_model
def convmixer_1536_20(pretrained=False, **kwargs) -> ConvMixer:
model_args = dict(dim=1536, depth=20, kernel_size=9, patch_size=7, **kwargs)
return _create_convmixer('convmixer_1536_20', pretrained, **model_args)
@register_model
def convmixer_768_32(pretrained=False, **kwargs) -> ConvMixer:
model_args = dict(dim=768, depth=32, kernel_size=7, patch_size=7, act_layer=nn.ReLU, **kwargs)
return _create_convmixer('convmixer_768_32', pretrained, **model_args)
@register_model
def convmixer_1024_20_ks9_p14(pretrained=False, **kwargs) -> ConvMixer:
model_args = dict(dim=1024, depth=20, kernel_size=9, patch_size=14, **kwargs)
return _create_convmixer('convmixer_1024_20_ks9_p14', pretrained, **model_args)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/tresnet.py
|
"""
TResNet: High Performance GPU-Dedicated Architecture
https://arxiv.org/pdf/2003.13630.pdf
Original model: https://github.com/mrT23/TResNet
"""
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
from timm.layers import SpaceToDepth, BlurPool2d, ClassifierHead, SEModule,\
ConvNormActAa, ConvNormAct, DropPath
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
__all__ = ['TResNet'] # model_registry will add each entrypoint fn to this
class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
use_se=True,
aa_layer=None,
drop_path_rate=0.
):
super(BasicBlock, self).__init__()
self.downsample = downsample
self.stride = stride
act_layer = partial(nn.LeakyReLU, negative_slope=1e-3)
if stride == 1:
self.conv1 = ConvNormAct(inplanes, planes, kernel_size=3, stride=1, act_layer=act_layer)
else:
self.conv1 = ConvNormActAa(
inplanes, planes, kernel_size=3, stride=2, act_layer=act_layer, aa_layer=aa_layer)
self.conv2 = ConvNormAct(planes, planes, kernel_size=3, stride=1, apply_act=False, act_layer=None)
self.act = nn.ReLU(inplace=True)
rd_chs = max(planes * self.expansion // 4, 64)
self.se = SEModule(planes * self.expansion, rd_channels=rd_chs) if use_se else None
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
def forward(self, x):
if self.downsample is not None:
shortcut = self.downsample(x)
else:
shortcut = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None:
out = self.se(out)
out = self.drop_path(out) + shortcut
out = self.act(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
use_se=True,
act_layer=None,
aa_layer=None,
drop_path_rate=0.,
):
super(Bottleneck, self).__init__()
self.downsample = downsample
self.stride = stride
act_layer = act_layer or partial(nn.LeakyReLU, negative_slope=1e-3)
self.conv1 = ConvNormAct(
inplanes, planes, kernel_size=1, stride=1, act_layer=act_layer)
if stride == 1:
self.conv2 = ConvNormAct(
planes, planes, kernel_size=3, stride=1, act_layer=act_layer)
else:
self.conv2 = ConvNormActAa(
planes, planes, kernel_size=3, stride=2, act_layer=act_layer, aa_layer=aa_layer)
reduction_chs = max(planes * self.expansion // 8, 64)
self.se = SEModule(planes, rd_channels=reduction_chs) if use_se else None
self.conv3 = ConvNormAct(
planes, planes * self.expansion, kernel_size=1, stride=1, apply_act=False, act_layer=None)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
self.act = nn.ReLU(inplace=True)
def forward(self, x):
if self.downsample is not None:
shortcut = self.downsample(x)
else:
shortcut = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None:
out = self.se(out)
out = self.conv3(out)
out = self.drop_path(out) + shortcut
out = self.act(out)
return out
class TResNet(nn.Module):
def __init__(
self,
layers,
in_chans=3,
num_classes=1000,
width_factor=1.0,
v2=False,
global_pool='fast',
drop_rate=0.,
drop_path_rate=0.,
):
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
super(TResNet, self).__init__()
aa_layer = BlurPool2d
act_layer = nn.LeakyReLU
# TResnet stages
self.inplanes = int(64 * width_factor)
self.planes = int(64 * width_factor)
if v2:
self.inplanes = self.inplanes // 8 * 8
self.planes = self.planes // 8 * 8
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(layers)).split(layers)]
conv1 = ConvNormAct(in_chans * 16, self.planes, stride=1, kernel_size=3, act_layer=act_layer)
layer1 = self._make_layer(
Bottleneck if v2 else BasicBlock,
self.planes, layers[0], stride=1, use_se=True, aa_layer=aa_layer, drop_path_rate=dpr[0])
layer2 = self._make_layer(
Bottleneck if v2 else BasicBlock,
self.planes * 2, layers[1], stride=2, use_se=True, aa_layer=aa_layer, drop_path_rate=dpr[1])
layer3 = self._make_layer(
Bottleneck,
self.planes * 4, layers[2], stride=2, use_se=True, aa_layer=aa_layer, drop_path_rate=dpr[2])
layer4 = self._make_layer(
Bottleneck,
self.planes * 8, layers[3], stride=2, use_se=False, aa_layer=aa_layer, drop_path_rate=dpr[3])
# body
self.body = nn.Sequential(OrderedDict([
('s2d', SpaceToDepth()),
('conv1', conv1),
('layer1', layer1),
('layer2', layer2),
('layer3', layer3),
('layer4', layer4),
]))
self.feature_info = [
dict(num_chs=self.planes, reduction=2, module=''), # Not with S2D?
dict(num_chs=self.planes * (Bottleneck.expansion if v2 else 1), reduction=4, module='body.layer1'),
dict(num_chs=self.planes * 2 * (Bottleneck.expansion if v2 else 1), reduction=8, module='body.layer2'),
dict(num_chs=self.planes * 4 * Bottleneck.expansion, reduction=16, module='body.layer3'),
dict(num_chs=self.planes * 8 * Bottleneck.expansion, reduction=32, module='body.layer4'),
]
# head
self.num_features = (self.planes * 8) * Bottleneck.expansion
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
# model initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
if isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
# residual connections special initialization
for m in self.modules():
if isinstance(m, BasicBlock):
nn.init.zeros_(m.conv2.bn.weight)
if isinstance(m, Bottleneck):
nn.init.zeros_(m.conv3.bn.weight)
def _make_layer(self, block, planes, blocks, stride=1, use_se=True, aa_layer=None, drop_path_rate=0.):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
layers = []
if stride == 2:
# avg pooling before 1x1 conv
layers.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True, count_include_pad=False))
layers += [ConvNormAct(
self.inplanes, planes * block.expansion, kernel_size=1, stride=1, apply_act=False, act_layer=None)]
downsample = nn.Sequential(*layers)
layers = []
for i in range(blocks):
layers.append(block(
self.inplanes,
planes,
stride=stride if i == 0 else 1,
downsample=downsample if i == 0 else None,
use_se=use_se,
aa_layer=aa_layer,
drop_path_rate=drop_path_rate[i] if isinstance(drop_path_rate, list) else drop_path_rate,
))
self.inplanes = planes * block.expansion
return nn.Sequential(*layers)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(stem=r'^body\.conv1', blocks=r'^body\.layer(\d+)' if coarse else r'^body\.layer(\d+)\.(\d+)')
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.head.reset(num_classes, pool_type=global_pool)
def forward_features(self, x):
if self.grad_checkpointing and not torch.jit.is_scripting():
x = self.body.s2d(x)
x = self.body.conv1(x)
x = checkpoint_seq([
self.body.layer1,
self.body.layer2,
self.body.layer3,
self.body.layer4],
x, flatten=True)
else:
x = self.body(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
if 'body.conv1.conv.weight' in state_dict:
return state_dict
import re
state_dict = state_dict.get('model', state_dict)
state_dict = state_dict.get('state_dict', state_dict)
out_dict = {}
for k, v in state_dict.items():
k = re.sub(r'conv(\d+)\.0.0', lambda x: f'conv{int(x.group(1))}.conv', k)
k = re.sub(r'conv(\d+)\.0.1', lambda x: f'conv{int(x.group(1))}.bn', k)
k = re.sub(r'conv(\d+)\.0', lambda x: f'conv{int(x.group(1))}.conv', k)
k = re.sub(r'conv(\d+)\.1', lambda x: f'conv{int(x.group(1))}.bn', k)
k = re.sub(r'downsample\.(\d+)\.0', lambda x: f'downsample.{int(x.group(1))}.conv', k)
k = re.sub(r'downsample\.(\d+)\.1', lambda x: f'downsample.{int(x.group(1))}.bn', k)
if k.endswith('bn.weight'):
# convert weight from inplace_abn to batchnorm
v = v.abs().add(1e-5)
out_dict[k] = v
return out_dict
def _create_tresnet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
TResNet,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(out_indices=(1, 2, 3, 4), flatten_sequential=True),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': (0., 0., 0.), 'std': (1., 1., 1.),
'first_conv': 'body.conv1.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'tresnet_m.miil_in21k_ft_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_m.miil_in21k': _cfg(hf_hub_id='timm/', num_classes=11221),
'tresnet_m.miil_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_l.miil_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_xl.miil_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_m.miil_in1k_448': _cfg(
input_size=(3, 448, 448), pool_size=(14, 14),
hf_hub_id='timm/'),
'tresnet_l.miil_in1k_448': _cfg(
input_size=(3, 448, 448), pool_size=(14, 14),
hf_hub_id='timm/'),
'tresnet_xl.miil_in1k_448': _cfg(
input_size=(3, 448, 448), pool_size=(14, 14),
hf_hub_id='timm/'),
'tresnet_v2_l.miil_in21k_ft_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_v2_l.miil_in21k': _cfg(hf_hub_id='timm/', num_classes=11221),
})
@register_model
def tresnet_m(pretrained=False, **kwargs) -> TResNet:
model_args = dict(layers=[3, 4, 11, 3])
return _create_tresnet('tresnet_m', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def tresnet_l(pretrained=False, **kwargs) -> TResNet:
model_args = dict(layers=[4, 5, 18, 3], width_factor=1.2)
return _create_tresnet('tresnet_l', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def tresnet_xl(pretrained=False, **kwargs) -> TResNet:
model_args = dict(layers=[4, 5, 24, 3], width_factor=1.3)
return _create_tresnet('tresnet_xl', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def tresnet_v2_l(pretrained=False, **kwargs) -> TResNet:
model_args = dict(layers=[3, 4, 23, 3], width_factor=1.0, v2=True)
return _create_tresnet('tresnet_v2_l', pretrained=pretrained, **dict(model_args, **kwargs))
register_model_deprecations(__name__, {
'tresnet_m_miil_in21k': 'tresnet_m.miil_in21k',
'tresnet_m_448': 'tresnet_m.miil_in1k_448',
'tresnet_l_448': 'tresnet_l.miil_in1k_448',
'tresnet_xl_448': 'tresnet_xl.miil_in1k_448',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/factory.py
|
from ._factory import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/efficientnet.py
|
""" The EfficientNet Family in PyTorch
An implementation of EfficienNet that covers variety of related models with efficient architectures:
* EfficientNet-V2
- `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
* EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/AdvProp/NoisyStudent weight ports)
- EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.11946
- CondConv: Conditionally Parameterized Convolutions for Efficient Inference - https://arxiv.org/abs/1904.04971
- Adversarial Examples Improve Image Recognition - https://arxiv.org/abs/1911.09665
- Self-training with Noisy Student improves ImageNet classification - https://arxiv.org/abs/1911.04252
* MixNet (Small, Medium, and Large)
- MixConv: Mixed Depthwise Convolutional Kernels - https://arxiv.org/abs/1907.09595
* MNasNet B1, A1 (SE), Small
- MnasNet: Platform-Aware Neural Architecture Search for Mobile - https://arxiv.org/abs/1807.11626
* FBNet-C
- FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable NAS - https://arxiv.org/abs/1812.03443
* Single-Path NAS Pixel1
- Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877
* TinyNet
- Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets - https://arxiv.org/abs/2010.14819
- Definitions & weights borrowed from https://github.com/huawei-noah/CV-Backbones/tree/master/tinynet_pytorch
* And likely more...
The majority of the above models (EfficientNet*, MixNet, MnasNet) and original weights were made available
by Mingxing Tan, Quoc Le, and other members of their Google Brain team. Thanks for consistently releasing
the models and weights open source!
Hacked together by / Copyright 2019, Ross Wightman
"""
from functools import partial
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import create_conv2d, create_classifier, get_norm_act_layer, GroupNormAct
from ._builder import build_model_with_cfg, pretrained_cfg_for_features
from ._efficientnet_blocks import SqueezeExcite
from ._efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights, \
round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
from ._features import FeatureInfo, FeatureHooks
from ._manipulate import checkpoint_seq
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
__all__ = ['EfficientNet', 'EfficientNetFeatures']
class EfficientNet(nn.Module):
""" EfficientNet
A flexible and performant PyTorch implementation of efficient network architectures, including:
* EfficientNet-V2 Small, Medium, Large, XL & B0-B3
* EfficientNet B0-B8, L2
* EfficientNet-EdgeTPU
* EfficientNet-CondConv
* MixNet S, M, L, XL
* MnasNet A1, B1, and small
* MobileNet-V2
* FBNet C
* Single-Path NAS Pixel1
* TinyNet
"""
def __init__(
self,
block_args,
num_classes=1000,
num_features=1280,
in_chans=3,
stem_size=32,
fix_stem=False,
output_stride=32,
pad_type='',
round_chs_fn=round_channels,
act_layer=None,
norm_layer=None,
se_layer=None,
drop_rate=0.,
drop_path_rate=0.,
global_pool='avg'
):
super(EfficientNet, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
se_layer = se_layer or SqueezeExcite
self.num_classes = num_classes
self.num_features = num_features
self.drop_rate = drop_rate
self.grad_checkpointing = False
# Stem
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_act_layer(stem_size, inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
output_stride=output_stride,
pad_type=pad_type,
round_chs_fn=round_chs_fn,
act_layer=act_layer,
norm_layer=norm_layer,
se_layer=se_layer,
drop_path_rate=drop_path_rate,
)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = builder.features
head_chs = builder.in_chs
# Head + Pooling
self.conv_head = create_conv2d(head_chs, self.num_features, 1, padding=pad_type)
self.bn2 = norm_act_layer(self.num_features, inplace=True)
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
efficientnet_init_weights(self)
def as_sequential(self):
layers = [self.conv_stem, self.bn1]
layers.extend(self.blocks)
layers.extend([self.conv_head, self.bn2, self.global_pool])
layers.extend([nn.Dropout(self.drop_rate), self.classifier])
return nn.Sequential(*layers)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^conv_stem|bn1',
blocks=[
(r'^blocks\.(\d+)' if coarse else r'^blocks\.(\d+)\.(\d+)', None),
(r'conv_head|bn2', (99999,))
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.conv_stem(x)
x = self.bn1(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x, flatten=True)
else:
x = self.blocks(x)
x = self.conv_head(x)
x = self.bn2(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return x if pre_logits else self.classifier(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
class EfficientNetFeatures(nn.Module):
""" EfficientNet Feature Extractor
A work-in-progress feature extraction module for EfficientNet, to use as a backbone for segmentation
and object detection models.
"""
def __init__(
self,
block_args,
out_indices=(0, 1, 2, 3, 4),
feature_location='bottleneck',
in_chans=3,
stem_size=32,
fix_stem=False,
output_stride=32,
pad_type='',
round_chs_fn=round_channels,
act_layer=None,
norm_layer=None,
se_layer=None,
drop_rate=0.,
drop_path_rate=0.
):
super(EfficientNetFeatures, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
se_layer = se_layer or SqueezeExcite
self.drop_rate = drop_rate
self.grad_checkpointing = False
# Stem
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_act_layer(stem_size, inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
output_stride=output_stride,
pad_type=pad_type,
round_chs_fn=round_chs_fn,
act_layer=act_layer,
norm_layer=norm_layer,
se_layer=se_layer,
drop_path_rate=drop_path_rate,
feature_location=feature_location,
)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = FeatureInfo(builder.features, out_indices)
self._stage_out_idx = {f['stage']: f['index'] for f in self.feature_info.get_dicts()}
efficientnet_init_weights(self)
# Register feature extraction hooks with FeatureHooks helper
self.feature_hooks = None
if feature_location != 'bottleneck':
hooks = self.feature_info.get_dicts(keys=('module', 'hook_type'))
self.feature_hooks = FeatureHooks(hooks, self.named_modules())
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
def forward(self, x) -> List[torch.Tensor]:
x = self.conv_stem(x)
x = self.bn1(x)
if self.feature_hooks is None:
features = []
if 0 in self._stage_out_idx:
features.append(x) # add stem out
for i, b in enumerate(self.blocks):
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(b, x)
else:
x = b(x)
if i + 1 in self._stage_out_idx:
features.append(x)
return features
else:
self.blocks(x)
out = self.feature_hooks.get_output(x.device)
return list(out.values())
def _create_effnet(variant, pretrained=False, **kwargs):
features_mode = ''
model_cls = EfficientNet
kwargs_filter = None
if kwargs.pop('features_only', False):
if 'feature_cfg' in kwargs:
features_mode = 'cfg'
else:
kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'global_pool')
model_cls = EfficientNetFeatures
features_mode = 'cls'
model = build_model_with_cfg(
model_cls,
variant,
pretrained,
features_only=features_mode == 'cfg',
pretrained_strict=features_mode != 'cls',
kwargs_filter=kwargs_filter,
**kwargs,
)
if features_mode == 'cls':
model.pretrained_cfg = model.default_cfg = pretrained_cfg_for_features(model.pretrained_cfg)
return model
def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a mnasnet-a1 model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Paper: https://arxiv.org/pdf/1807.11626.pdf.
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16_noskip'],
# stage 1, 112x112 in
['ir_r2_k3_s2_e6_c24'],
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40_se0.25'],
# stage 3, 28x28 in
['ir_r4_k3_s2_e6_c80'],
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112_se0.25'],
# stage 5, 14x14in
['ir_r3_k5_s2_e6_c160_se0.25'],
# stage 6, 7x7 in
['ir_r1_k3_s1_e6_c320'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a mnasnet-b1 model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Paper: https://arxiv.org/pdf/1807.11626.pdf.
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_c16_noskip'],
# stage 1, 112x112 in
['ir_r3_k3_s2_e3_c24'],
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40'],
# stage 3, 28x28 in
['ir_r3_k5_s2_e6_c80'],
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c96'],
# stage 5, 14x14in
['ir_r4_k5_s2_e6_c192'],
# stage 6, 7x7 in
['ir_r1_k3_s1_e6_c320_noskip']
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a mnasnet-b1 model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Paper: https://arxiv.org/pdf/1807.11626.pdf.
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
['ds_r1_k3_s1_c8'],
['ir_r1_k3_s2_e3_c16'],
['ir_r2_k3_s2_e6_c16'],
['ir_r4_k5_s2_e6_c32_se0.25'],
['ir_r3_k3_s1_e6_c32_se0.25'],
['ir_r3_k5_s2_e6_c88_se0.25'],
['ir_r1_k3_s1_e6_c144']
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=8,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_mobilenet_v2(
variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs):
""" Generate MobileNet-V2 network
Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
Paper: https://arxiv.org/abs/1801.04381
"""
arch_def = [
['ds_r1_k3_s1_c16'],
['ir_r2_k3_s2_e6_c24'],
['ir_r3_k3_s2_e6_c32'],
['ir_r4_k3_s2_e6_c64'],
['ir_r3_k3_s1_e6_c96'],
['ir_r3_k3_s2_e6_c160'],
['ir_r1_k3_s1_e6_c320'],
]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
num_features=1280 if fix_stem_head else max(1280, round_chs_fn(1280)),
stem_size=32,
fix_stem=fix_stem_head,
round_chs_fn=round_chs_fn,
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'relu6'),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
""" FBNet-C
Paper: https://arxiv.org/abs/1812.03443
Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py
NOTE: the impl above does not relate to the 'C' variant here, that was derived from paper,
it was used to confirm some building block details
"""
arch_def = [
['ir_r1_k3_s1_e1_c16'],
['ir_r1_k3_s2_e6_c24', 'ir_r2_k3_s1_e1_c24'],
['ir_r1_k5_s2_e6_c32', 'ir_r1_k5_s1_e3_c32', 'ir_r1_k5_s1_e6_c32', 'ir_r1_k3_s1_e6_c32'],
['ir_r1_k5_s2_e6_c64', 'ir_r1_k5_s1_e3_c64', 'ir_r2_k5_s1_e6_c64'],
['ir_r3_k5_s1_e6_c112', 'ir_r1_k5_s1_e3_c112'],
['ir_r4_k5_s2_e6_c184'],
['ir_r1_k3_s1_e6_c352'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=16,
num_features=1984, # paper suggests this, but is not 100% clear
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates the Single-Path NAS model from search targeted for Pixel1 phone.
Paper: https://arxiv.org/abs/1904.02877
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_c16_noskip'],
# stage 1, 112x112 in
['ir_r3_k3_s2_e3_c24'],
# stage 2, 56x56 in
['ir_r1_k5_s2_e6_c40', 'ir_r3_k3_s1_e3_c40'],
# stage 3, 28x28 in
['ir_r1_k5_s2_e6_c80', 'ir_r3_k3_s1_e3_c80'],
# stage 4, 14x14in
['ir_r1_k5_s1_e6_c96', 'ir_r3_k5_s1_e3_c96'],
# stage 5, 14x14in
['ir_r4_k5_s2_e6_c192'],
# stage 6, 7x7 in
['ir_r1_k3_s1_e6_c320_noskip']
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnet(
variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8,
group_size=None, pretrained=False, **kwargs):
"""Creates an EfficientNet model.
Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
Paper: https://arxiv.org/abs/1905.11946
EfficientNet params
name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
'efficientnet-b8': (2.2, 3.6, 672, 0.5),
'efficientnet-l2': (4.3, 5.3, 800, 0.5),
Args:
channel_multiplier: multiplier to number of channels per layer
depth_multiplier: multiplier to number of repeats per stage
"""
arch_def = [
['ds_r1_k3_s1_e1_c16_se0.25'],
['ir_r2_k3_s2_e6_c24_se0.25'],
['ir_r2_k5_s2_e6_c40_se0.25'],
['ir_r3_k3_s2_e6_c80_se0.25'],
['ir_r3_k5_s1_e6_c112_se0.25'],
['ir_r4_k5_s2_e6_c192_se0.25'],
['ir_r1_k3_s1_e6_c320_se0.25'],
]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier, divisor=channel_divisor)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
num_features=round_chs_fn(1280),
stem_size=32,
round_chs_fn=round_chs_fn,
act_layer=resolve_act_layer(kwargs, 'swish'),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnet_edge(
variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs):
""" Creates an EfficientNet-EdgeTPU model
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
"""
arch_def = [
# NOTE `fc` is present to override a mismatch between stem channels and in chs not
# present in other models
['er_r1_k3_s1_e4_c24_fc24_noskip'],
['er_r2_k3_s2_e8_c32'],
['er_r4_k3_s2_e8_c48'],
['ir_r5_k5_s2_e8_c96'],
['ir_r4_k5_s1_e8_c144'],
['ir_r2_k5_s2_e8_c192'],
]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
num_features=round_chs_fn(1280),
stem_size=32,
round_chs_fn=round_chs_fn,
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'relu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnet_condconv(
variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs):
"""Creates an EfficientNet-CondConv model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv
"""
arch_def = [
['ds_r1_k3_s1_e1_c16_se0.25'],
['ir_r2_k3_s2_e6_c24_se0.25'],
['ir_r2_k5_s2_e6_c40_se0.25'],
['ir_r3_k3_s2_e6_c80_se0.25'],
['ir_r3_k5_s1_e6_c112_se0.25_cc4'],
['ir_r4_k5_s2_e6_c192_se0.25_cc4'],
['ir_r1_k3_s1_e6_c320_se0.25_cc4'],
]
# NOTE unlike official impl, this one uses `cc<x>` option where x is the base number of experts for each stage and
# the expert_multiplier increases that on a per-model basis as with depth/channel multipliers
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, experts_multiplier=experts_multiplier),
num_features=round_chs_fn(1280),
stem_size=32,
round_chs_fn=round_chs_fn,
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'swish'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
"""Creates an EfficientNet-Lite model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite
Paper: https://arxiv.org/abs/1905.11946
EfficientNet params
name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
'efficientnet-lite0': (1.0, 1.0, 224, 0.2),
'efficientnet-lite1': (1.0, 1.1, 240, 0.2),
'efficientnet-lite2': (1.1, 1.2, 260, 0.3),
'efficientnet-lite3': (1.2, 1.4, 280, 0.3),
'efficientnet-lite4': (1.4, 1.8, 300, 0.3),
Args:
channel_multiplier: multiplier to number of channels per layer
depth_multiplier: multiplier to number of repeats per stage
"""
arch_def = [
['ds_r1_k3_s1_e1_c16'],
['ir_r2_k3_s2_e6_c24'],
['ir_r2_k5_s2_e6_c40'],
['ir_r3_k3_s2_e6_c80'],
['ir_r3_k5_s1_e6_c112'],
['ir_r4_k5_s2_e6_c192'],
['ir_r1_k3_s1_e6_c320'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, fix_first_last=True),
num_features=1280,
stem_size=32,
fix_stem=True,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
act_layer=resolve_act_layer(kwargs, 'relu6'),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnetv2_base(
variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2 base model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
"""
arch_def = [
['cn_r1_k3_s1_e1_c16_skip'],
['er_r2_k3_s2_e4_c32'],
['er_r2_k3_s2_e4_c48'],
['ir_r3_k3_s2_e4_c96_se0.25'],
['ir_r5_k3_s1_e6_c112_se0.25'],
['ir_r8_k3_s2_e6_c192_se0.25'],
]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_chs_fn(1280),
stem_size=32,
round_chs_fn=round_chs_fn,
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnetv2_s(
variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, rw=False, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2 Small model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
NOTE: `rw` flag sets up 'small' variant to behave like my initial v2 small model,
before ref the impl was released.
"""
arch_def = [
['cn_r2_k3_s1_e1_c24_skip'],
['er_r4_k3_s2_e4_c48'],
['er_r4_k3_s2_e4_c64'],
['ir_r6_k3_s2_e4_c128_se0.25'],
['ir_r9_k3_s1_e6_c160_se0.25'],
['ir_r15_k3_s2_e6_c256_se0.25'],
]
num_features = 1280
if rw:
# my original variant, based on paper figure differs from the official release
arch_def[0] = ['er_r2_k3_s1_e1_c24']
arch_def[-1] = ['ir_r15_k3_s2_e6_c272_se0.25']
num_features = 1792
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
num_features=round_chs_fn(num_features),
stem_size=24,
round_chs_fn=round_chs_fn,
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2 Medium model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
"""
arch_def = [
['cn_r3_k3_s1_e1_c24_skip'],
['er_r5_k3_s2_e4_c48'],
['er_r5_k3_s2_e4_c80'],
['ir_r7_k3_s2_e4_c160_se0.25'],
['ir_r14_k3_s1_e6_c176_se0.25'],
['ir_r18_k3_s2_e6_c304_se0.25'],
['ir_r5_k3_s1_e6_c512_se0.25'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=1280,
stem_size=24,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2 Large model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
"""
arch_def = [
['cn_r4_k3_s1_e1_c32_skip'],
['er_r7_k3_s2_e4_c64'],
['er_r7_k3_s2_e4_c96'],
['ir_r10_k3_s2_e4_c192_se0.25'],
['ir_r19_k3_s1_e6_c224_se0.25'],
['ir_r25_k3_s2_e6_c384_se0.25'],
['ir_r7_k3_s1_e6_c640_se0.25'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=1280,
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2 Xtra-Large model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
"""
arch_def = [
['cn_r4_k3_s1_e1_c32_skip'],
['er_r8_k3_s2_e4_c64'],
['er_r8_k3_s2_e4_c96'],
['ir_r16_k3_s2_e4_c192_se0.25'],
['ir_r24_k3_s1_e6_c256_se0.25'],
['ir_r32_k3_s2_e6_c512_se0.25'],
['ir_r8_k3_s1_e6_c640_se0.25'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=1280,
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a MixNet Small model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
Paper: https://arxiv.org/abs/1907.09595
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16'], # relu
# stage 1, 112x112 in
['ir_r1_k3_a1.1_p1.1_s2_e6_c24', 'ir_r1_k3_a1.1_p1.1_s1_e3_c24'], # relu
# stage 2, 56x56 in
['ir_r1_k3.5.7_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'], # swish
# stage 3, 28x28 in
['ir_r1_k3.5.7_p1.1_s2_e6_c80_se0.25_nsw', 'ir_r2_k3.5_p1.1_s1_e6_c80_se0.25_nsw'], # swish
# stage 4, 14x14in
['ir_r1_k3.5.7_a1.1_p1.1_s1_e6_c120_se0.5_nsw', 'ir_r2_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'], # swish
# stage 5, 14x14in
['ir_r1_k3.5.7.9.11_s2_e6_c200_se0.5_nsw', 'ir_r2_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'], # swish
# 7x7
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
num_features=1536,
stem_size=16,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a MixNet Medium-Large model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
Paper: https://arxiv.org/abs/1907.09595
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c24'], # relu
# stage 1, 112x112 in
['ir_r1_k3.5.7_a1.1_p1.1_s2_e6_c32', 'ir_r1_k3_a1.1_p1.1_s1_e3_c32'], # relu
# stage 2, 56x56 in
['ir_r1_k3.5.7.9_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'], # swish
# stage 3, 28x28 in
['ir_r1_k3.5.7_s2_e6_c80_se0.25_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e6_c80_se0.25_nsw'], # swish
# stage 4, 14x14in
['ir_r1_k3_s1_e6_c120_se0.5_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'], # swish
# stage 5, 14x14in
['ir_r1_k3.5.7.9_s2_e6_c200_se0.5_nsw', 'ir_r3_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'], # swish
# 7x7
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'),
num_features=1536,
stem_size=24,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_tinynet(
variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs
):
"""Creates a TinyNet model.
"""
arch_def = [
['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'],
['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'],
['ir_r3_k5_s1_e6_c112_se0.25'], ['ir_r4_k5_s2_e6_c192_se0.25'],
['ir_r1_k3_s1_e6_c320_se0.25'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'),
num_features=max(1280, round_channels(1280, model_width, 8, None)),
stem_size=32,
fix_stem=True,
round_chs_fn=partial(round_channels, multiplier=model_width),
act_layer=resolve_act_layer(kwargs, 'swish'),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv_stem', 'classifier': 'classifier',
**kwargs
}
default_cfgs = generate_default_cfgs({
'mnasnet_050.untrained': _cfg(),
'mnasnet_075.untrained': _cfg(),
'mnasnet_100.rmsp_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth',
hf_hub_id='timm/'),
'mnasnet_140.untrained': _cfg(),
'semnasnet_050.untrained': _cfg(),
'semnasnet_075.rmsp_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/semnasnet_075-18710866.pth',
hf_hub_id='timm/'),
'semnasnet_100.rmsp_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth',
hf_hub_id='timm/'),
'semnasnet_140.untrained': _cfg(),
'mnasnet_small.lamb_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_small_lamb-aff75073.pth',
hf_hub_id='timm/'),
'mobilenetv2_035.untrained': _cfg(),
'mobilenetv2_050.lamb_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_050-3d30d450.pth',
hf_hub_id='timm/',
interpolation='bicubic',
),
'mobilenetv2_075.untrained': _cfg(),
'mobilenetv2_100.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth',
hf_hub_id='timm/'),
'mobilenetv2_110d.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth',
hf_hub_id='timm/'),
'mobilenetv2_120d.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth',
hf_hub_id='timm/'),
'mobilenetv2_140.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth',
hf_hub_id='timm/'),
'fbnetc_100.rmsp_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth',
hf_hub_id='timm/',
interpolation='bilinear'),
'spnasnet_100.rmsp_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth',
hf_hub_id='timm/',
interpolation='bilinear'),
# NOTE experimenting with alternate attention
'efficientnet_b0.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth',
hf_hub_id='timm/'),
'efficientnet_b1.ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth',
hf_hub_id='timm/',
test_input_size=(3, 256, 256), crop_pct=1.0),
'efficientnet_b2.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth',
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), test_input_size=(3, 288, 288), crop_pct=1.0),
'efficientnet_b3.ra2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth',
hf_hub_id='timm/',
input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0),
'efficientnet_b4.ra2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b4_ra2_320-7eb33cd5.pth',
hf_hub_id='timm/',
input_size=(3, 320, 320), pool_size=(10, 10), test_input_size=(3, 384, 384), crop_pct=1.0),
'efficientnet_b5.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, crop_mode='squash'),
'efficientnet_b5.sw_in12k': _cfg(
hf_hub_id='timm/',
input_size=(3, 416, 416), pool_size=(13, 13), crop_pct=0.95, num_classes=11821),
'efficientnet_b6.untrained': _cfg(
url='', input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
'efficientnet_b7.untrained': _cfg(
url='', input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
'efficientnet_b8.untrained': _cfg(
url='', input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
'efficientnet_l2.untrained': _cfg(
url='', input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.961),
# FIXME experimental
'efficientnet_b0_gn.untrained': _cfg(),
'efficientnet_b0_g8_gn.untrained': _cfg(),
'efficientnet_b0_g16_evos.untrained': _cfg(),
'efficientnet_b3_gn.untrained': _cfg(
input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0),
'efficientnet_b3_g8_gn.untrained': _cfg(
input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0),
'efficientnet_es.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth',
hf_hub_id='timm/'),
'efficientnet_em.ra2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth',
hf_hub_id='timm/',
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'efficientnet_el.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el-3b455510.pth',
hf_hub_id='timm/',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'efficientnet_es_pruned.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_pruned75-1b7248cf.pth',
hf_hub_id='timm/'),
'efficientnet_el_pruned.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el_pruned70-ef2a2ccf.pth',
hf_hub_id='timm/',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'efficientnet_cc_b0_4e.untrained': _cfg(),
'efficientnet_cc_b0_8e.untrained': _cfg(),
'efficientnet_cc_b1_8e.untrained': _cfg(input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'efficientnet_lite0.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_lite0_ra-37913777.pth',
hf_hub_id='timm/'),
'efficientnet_lite1.untrained': _cfg(
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'efficientnet_lite2.untrained': _cfg(
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
'efficientnet_lite3.untrained': _cfg(
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'efficientnet_lite4.untrained': _cfg(
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
'efficientnet_b1_pruned.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb1_pruned-bea43a3a.pth',
hf_hub_id='timm/',
input_size=(3, 240, 240), pool_size=(8, 8),
crop_pct=0.882, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'efficientnet_b2_pruned.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb2_pruned-08c1b27c.pth',
hf_hub_id='timm/',
input_size=(3, 260, 260), pool_size=(9, 9),
crop_pct=0.890, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'efficientnet_b3_pruned.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb3_pruned-59ecf72d.pth',
hf_hub_id='timm/',
input_size=(3, 300, 300), pool_size=(10, 10),
crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'efficientnetv2_rw_t.ra2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_t_agc-3620981a.pth',
hf_hub_id='timm/',
input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0),
'gc_efficientnetv2_rw_t.agc_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gc_efficientnetv2_rw_t_agc-927a0bde.pth',
hf_hub_id='timm/',
input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0),
'efficientnetv2_rw_s.ra2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2_288-a6477665.pth',
hf_hub_id='timm/',
input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0),
'efficientnetv2_rw_m.agc_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_rw_m_agc-3d90cb1e.pth',
hf_hub_id='timm/',
input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0),
'efficientnetv2_s.untrained': _cfg(
input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0),
'efficientnetv2_m.untrained': _cfg(
input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0),
'efficientnetv2_l.untrained': _cfg(
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'efficientnetv2_xl.untrained': _cfg(
input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnet_b0.ns_jft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth',
hf_hub_id='timm/',
input_size=(3, 224, 224)),
'tf_efficientnet_b1.ns_jft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth',
hf_hub_id='timm/',
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'tf_efficientnet_b2.ns_jft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth',
hf_hub_id='timm/',
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
'tf_efficientnet_b3.ns_jft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth',
hf_hub_id='timm/',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'tf_efficientnet_b4.ns_jft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth',
hf_hub_id='timm/',
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
'tf_efficientnet_b5.ns_jft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth',
hf_hub_id='timm/',
input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
'tf_efficientnet_b6.ns_jft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth',
hf_hub_id='timm/',
input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
'tf_efficientnet_b7.ns_jft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth',
hf_hub_id='timm/',
input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
'tf_efficientnet_l2.ns_jft_in1k_475': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth',
hf_hub_id='timm/',
input_size=(3, 475, 475), pool_size=(15, 15), crop_pct=0.936),
'tf_efficientnet_l2.ns_jft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth',
hf_hub_id='timm/',
input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.96),
'tf_efficientnet_b0.ap_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, input_size=(3, 224, 224)),
'tf_efficientnet_b1.ap_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'tf_efficientnet_b2.ap_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
'tf_efficientnet_b3.ap_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'tf_efficientnet_b4.ap_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
'tf_efficientnet_b5.ap_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
'tf_efficientnet_b6.ap_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
'tf_efficientnet_b7.ap_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
'tf_efficientnet_b8.ap_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
'tf_efficientnet_b5.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth',
hf_hub_id='timm/',
input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
'tf_efficientnet_b7.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth',
hf_hub_id='timm/',
input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
'tf_efficientnet_b8.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth',
hf_hub_id='timm/',
input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
'tf_efficientnet_b0.aa_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth',
hf_hub_id='timm/',
input_size=(3, 224, 224)),
'tf_efficientnet_b1.aa_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth',
hf_hub_id='timm/',
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'tf_efficientnet_b2.aa_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth',
hf_hub_id='timm/',
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
'tf_efficientnet_b3.aa_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth',
hf_hub_id='timm/',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'tf_efficientnet_b4.aa_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth',
hf_hub_id='timm/',
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
'tf_efficientnet_b5.aa_in1k': _cfg(
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_aa-99018a74.pth',
hf_hub_id='timm/',
input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
'tf_efficientnet_b6.aa_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth',
hf_hub_id='timm/',
input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
'tf_efficientnet_b7.aa_in1k': _cfg(
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_aa-076e3472.pth',
hf_hub_id='timm/',
input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
'tf_efficientnet_b0.in1k': _cfg(
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0-0af12548.pth',
hf_hub_id='timm/',
input_size=(3, 224, 224)),
'tf_efficientnet_b1.in1k': _cfg(
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1-5c1377c4.pth',
hf_hub_id='timm/',
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'tf_efficientnet_b2.in1k': _cfg(
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2-e393ef04.pth',
hf_hub_id='timm/',
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
'tf_efficientnet_b3.in1k': _cfg(
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3-e3bd6955.pth',
hf_hub_id='timm/',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'tf_efficientnet_b4.in1k': _cfg(
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4-74ee3bed.pth',
hf_hub_id='timm/',
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
'tf_efficientnet_b5.in1k': _cfg(
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5-c6949ce9.pth',
hf_hub_id='timm/',
input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
'tf_efficientnet_es.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 224, 224), ),
'tf_efficientnet_em.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'tf_efficientnet_el.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'tf_efficientnet_cc_b0_4e.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_efficientnet_cc_b0_8e.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_efficientnet_cc_b1_8e.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'tf_efficientnet_lite0.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res
),
'tf_efficientnet_lite1.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882,
interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res
),
'tf_efficientnet_lite2.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890,
interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res
),
'tf_efficientnet_lite3.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, interpolation='bilinear'),
'tf_efficientnet_lite4.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.920, interpolation='bilinear'),
'tf_efficientnetv2_s.in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21ft1k-d7dafa41.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0),
'tf_efficientnetv2_m.in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21ft1k-bf41664a.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'tf_efficientnetv2_l.in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21ft1k-60127a9d.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'tf_efficientnetv2_xl.in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21ft1k-06c35c48.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'tf_efficientnetv2_s.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s-eb54923e.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0),
'tf_efficientnetv2_m.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m-cc09e0cd.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'tf_efficientnetv2_l.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l-d664b728.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'tf_efficientnetv2_s.in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21k-6337ad01.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0),
'tf_efficientnetv2_m.in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21k-361418a2.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'tf_efficientnetv2_l.in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21k-91a19ec9.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'tf_efficientnetv2_xl.in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21k-fd7e8abf.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'tf_efficientnetv2_b0.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b0-c7cc451f.pth',
hf_hub_id='timm/',
input_size=(3, 192, 192), test_input_size=(3, 224, 224), pool_size=(6, 6)),
'tf_efficientnetv2_b1.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b1-be6e41b0.pth',
hf_hub_id='timm/',
input_size=(3, 192, 192), test_input_size=(3, 240, 240), pool_size=(6, 6), crop_pct=0.882),
'tf_efficientnetv2_b2.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b2-847de54e.pth',
hf_hub_id='timm/',
input_size=(3, 208, 208), test_input_size=(3, 260, 260), pool_size=(7, 7), crop_pct=0.890),
'tf_efficientnetv2_b3.in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.9, crop_mode='squash'),
'tf_efficientnetv2_b3.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b3-57773f13.pth',
hf_hub_id='timm/',
input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.904),
'tf_efficientnetv2_b3.in21k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, num_classes=21843,
input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.904),
'mixnet_s.ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth',
hf_hub_id='timm/'),
'mixnet_m.ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth',
hf_hub_id='timm/'),
'mixnet_l.ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth',
hf_hub_id='timm/'),
'mixnet_xl.ra_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth',
hf_hub_id='timm/'),
'mixnet_xxl.untrained': _cfg(),
'tf_mixnet_s.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth',
hf_hub_id='timm/'),
'tf_mixnet_m.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth',
hf_hub_id='timm/'),
'tf_mixnet_l.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth',
hf_hub_id='timm/'),
"tinynet_a.in1k": _cfg(
input_size=(3, 192, 192), pool_size=(6, 6), # int(224 * 0.86)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_a.pth',
hf_hub_id='timm/'),
"tinynet_b.in1k": _cfg(
input_size=(3, 188, 188), pool_size=(6, 6), # int(224 * 0.84)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_b.pth',
hf_hub_id='timm/'),
"tinynet_c.in1k": _cfg(
input_size=(3, 184, 184), pool_size=(6, 6), # int(224 * 0.825)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_c.pth',
hf_hub_id='timm/'),
"tinynet_d.in1k": _cfg(
input_size=(3, 152, 152), pool_size=(5, 5), # int(224 * 0.68)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_d.pth',
hf_hub_id='timm/'),
"tinynet_e.in1k": _cfg(
input_size=(3, 106, 106), pool_size=(4, 4), # int(224 * 0.475)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth',
hf_hub_id='timm/'),
})
@register_model
def mnasnet_050(pretrained=False, **kwargs) -> EfficientNet:
""" MNASNet B1, depth multiplier of 0.5. """
model = _gen_mnasnet_b1('mnasnet_050', 0.5, pretrained=pretrained, **kwargs)
return model
@register_model
def mnasnet_075(pretrained=False, **kwargs) -> EfficientNet:
""" MNASNet B1, depth multiplier of 0.75. """
model = _gen_mnasnet_b1('mnasnet_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def mnasnet_100(pretrained=False, **kwargs) -> EfficientNet:
""" MNASNet B1, depth multiplier of 1.0. """
model = _gen_mnasnet_b1('mnasnet_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mnasnet_140(pretrained=False, **kwargs) -> EfficientNet:
""" MNASNet B1, depth multiplier of 1.4 """
model = _gen_mnasnet_b1('mnasnet_140', 1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def semnasnet_050(pretrained=False, **kwargs) -> EfficientNet:
""" MNASNet A1 (w/ SE), depth multiplier of 0.5 """
model = _gen_mnasnet_a1('semnasnet_050', 0.5, pretrained=pretrained, **kwargs)
return model
@register_model
def semnasnet_075(pretrained=False, **kwargs) -> EfficientNet:
""" MNASNet A1 (w/ SE), depth multiplier of 0.75. """
model = _gen_mnasnet_a1('semnasnet_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def semnasnet_100(pretrained=False, **kwargs) -> EfficientNet:
""" MNASNet A1 (w/ SE), depth multiplier of 1.0. """
model = _gen_mnasnet_a1('semnasnet_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def semnasnet_140(pretrained=False, **kwargs) -> EfficientNet:
""" MNASNet A1 (w/ SE), depth multiplier of 1.4. """
model = _gen_mnasnet_a1('semnasnet_140', 1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def mnasnet_small(pretrained=False, **kwargs) -> EfficientNet:
""" MNASNet Small, depth multiplier of 1.0. """
model = _gen_mnasnet_small('mnasnet_small', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv2_035(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V2 w/ 0.35 channel multiplier """
model = _gen_mobilenet_v2('mobilenetv2_035', 0.35, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv2_050(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V2 w/ 0.5 channel multiplier """
model = _gen_mobilenet_v2('mobilenetv2_050', 0.5, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv2_075(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V2 w/ 0.75 channel multiplier """
model = _gen_mobilenet_v2('mobilenetv2_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv2_100(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V2 w/ 1.0 channel multiplier """
model = _gen_mobilenet_v2('mobilenetv2_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv2_140(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V2 w/ 1.4 channel multiplier """
model = _gen_mobilenet_v2('mobilenetv2_140', 1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv2_110d(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V2 w/ 1.1 channel, 1.2 depth multipliers"""
model = _gen_mobilenet_v2(
'mobilenetv2_110d', 1.1, depth_multiplier=1.2, fix_stem_head=True, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv2_120d(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V2 w/ 1.2 channel, 1.4 depth multipliers """
model = _gen_mobilenet_v2(
'mobilenetv2_120d', 1.2, depth_multiplier=1.4, fix_stem_head=True, pretrained=pretrained, **kwargs)
return model
@register_model
def fbnetc_100(pretrained=False, **kwargs) -> EfficientNet:
""" FBNet-C """
if pretrained:
# pretrained model trained with non-default BN epsilon
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
model = _gen_fbnetc('fbnetc_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def spnasnet_100(pretrained=False, **kwargs) -> EfficientNet:
""" Single-Path NAS Pixel1"""
model = _gen_spnasnet('spnasnet_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b0(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B0 """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b1(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B1 """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b2(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B2 """
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b3(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B3 """
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b4(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B4 """
# NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b5(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B5 """
# NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b5', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b6(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B6 """
# NOTE for train, drop_rate should be 0.5, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b6', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b7(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B7 """
# NOTE for train, drop_rate should be 0.5, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b7', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b8(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B8 """
# NOTE for train, drop_rate should be 0.5, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b8', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_l2(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-L2."""
# NOTE for train, drop_rate should be 0.5, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_l2', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs)
return model
# FIXME experimental group cong / GroupNorm / EvoNorm experiments
@register_model
def efficientnet_b0_gn(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B0 + GroupNorm"""
model = _gen_efficientnet(
'efficientnet_b0_gn', norm_layer=partial(GroupNormAct, group_size=8), pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b0_g8_gn(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B0 w/ group conv + GroupNorm"""
model = _gen_efficientnet(
'efficientnet_b0_g8_gn', group_size=8, norm_layer=partial(GroupNormAct, group_size=8),
pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b0_g16_evos(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B0 w/ group 16 conv + EvoNorm"""
model = _gen_efficientnet(
'efficientnet_b0_g16_evos', group_size=16, channel_divisor=16,
pretrained=pretrained, **kwargs) #norm_layer=partial(EvoNorm2dS0, group_size=16),
return model
@register_model
def efficientnet_b3_gn(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B3 w/ GroupNorm """
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b3_gn', channel_multiplier=1.2, depth_multiplier=1.4, channel_divisor=16,
norm_layer=partial(GroupNormAct, group_size=16), pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b3_g8_gn(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B3 w/ grouped conv + BN"""
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b3_g8_gn', channel_multiplier=1.2, depth_multiplier=1.4, group_size=8, channel_divisor=16,
norm_layer=partial(GroupNormAct, group_size=16), pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_es(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Edge Small. """
model = _gen_efficientnet_edge(
'efficientnet_es', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_es_pruned(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Edge Small Pruned. For more info: https://github.com/DeGirum/pruned-models/releases/tag/efficientnet_v1.0"""
model = _gen_efficientnet_edge(
'efficientnet_es_pruned', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_em(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Edge-Medium. """
model = _gen_efficientnet_edge(
'efficientnet_em', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_el(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Edge-Large. """
model = _gen_efficientnet_edge(
'efficientnet_el', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_el_pruned(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Edge-Large pruned. For more info: https://github.com/DeGirum/pruned-models/releases/tag/efficientnet_v1.0"""
model = _gen_efficientnet_edge(
'efficientnet_el_pruned', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_cc_b0_4e(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-CondConv-B0 w/ 8 Experts """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
model = _gen_efficientnet_condconv(
'efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_cc_b0_8e(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-CondConv-B0 w/ 8 Experts """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
model = _gen_efficientnet_condconv(
'efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2,
pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_cc_b1_8e(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-CondConv-B1 w/ 8 Experts """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
model = _gen_efficientnet_condconv(
'efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2,
pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_lite0(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite0 """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
model = _gen_efficientnet_lite(
'efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_lite1(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite1 """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
model = _gen_efficientnet_lite(
'efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_lite2(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite2 """
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
model = _gen_efficientnet_lite(
'efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_lite3(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite3 """
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
model = _gen_efficientnet_lite(
'efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_lite4(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite4 """
# NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
model = _gen_efficientnet_lite(
'efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b1_pruned(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B1 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
variant = 'efficientnet_b1_pruned'
model = _gen_efficientnet(
variant, channel_multiplier=1.0, depth_multiplier=1.1, pruned=True, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b2_pruned(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B2 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'efficientnet_b2_pruned', channel_multiplier=1.1, depth_multiplier=1.2, pruned=True,
pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnet_b3_pruned(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B3 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'efficientnet_b3_pruned', channel_multiplier=1.2, depth_multiplier=1.4, pruned=True,
pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_rw_t(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Tiny (Custom variant, tiny not in paper). """
model = _gen_efficientnetv2_s(
'efficientnetv2_rw_t', channel_multiplier=0.8, depth_multiplier=0.9, rw=False, pretrained=pretrained, **kwargs)
return model
@register_model
def gc_efficientnetv2_rw_t(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Tiny w/ Global Context Attn (Custom variant, tiny not in paper). """
model = _gen_efficientnetv2_s(
'gc_efficientnetv2_rw_t', channel_multiplier=0.8, depth_multiplier=0.9,
rw=False, se_layer='gc', pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_rw_s(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Small (RW variant).
NOTE: This is my initial (pre official code release) w/ some differences.
See efficientnetv2_s and tf_efficientnetv2_s for versions that match the official w/ PyTorch vs TF padding
"""
model = _gen_efficientnetv2_s('efficientnetv2_rw_s', rw=True, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_rw_m(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Medium (RW variant).
"""
model = _gen_efficientnetv2_s(
'efficientnetv2_rw_m', channel_multiplier=1.2, depth_multiplier=(1.2,) * 4 + (1.6,) * 2, rw=True,
pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_s(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Small. """
model = _gen_efficientnetv2_s('efficientnetv2_s', pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_m(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Medium. """
model = _gen_efficientnetv2_m('efficientnetv2_m', pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_l(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Large. """
model = _gen_efficientnetv2_l('efficientnetv2_l', pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_xl(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Xtra-Large. """
model = _gen_efficientnetv2_xl('efficientnetv2_xl', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b0(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B0. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_b0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b1(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B1. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b2(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B2. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b3(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B3. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b4(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B4. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_b4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b5(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B5. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_b5', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b6(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B6. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.5
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_b6', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b7(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B7. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.5
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_b7', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b8(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-B8. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.5
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_b8', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_l2(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-L2 NoisyStudent. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.5
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet(
'tf_efficientnet_l2', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_es(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Edge Small. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_edge(
'tf_efficientnet_es', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_em(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Edge-Medium. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_edge(
'tf_efficientnet_em', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_el(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Edge-Large. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_edge(
'tf_efficientnet_el', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_cc_b0_4e(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-CondConv-B0 w/ 4 Experts. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_condconv(
'tf_efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_cc_b0_8e(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-CondConv-B0 w/ 8 Experts. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_condconv(
'tf_efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2,
pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_cc_b1_8e(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-CondConv-B1 w/ 8 Experts. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_condconv(
'tf_efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2,
pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_lite0(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite0 """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_lite(
'tf_efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_lite1(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite1 """
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_lite(
'tf_efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_lite2(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite2 """
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_lite(
'tf_efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_lite3(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite3 """
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_lite(
'tf_efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_lite4(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-Lite4 """
# NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnet_lite(
'tf_efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_s(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Small. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnetv2_s('tf_efficientnetv2_s', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_m(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Medium. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnetv2_m('tf_efficientnetv2_m', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_l(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Large. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnetv2_l('tf_efficientnetv2_l', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_xl(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2 Xtra-Large. Tensorflow compatible variant
"""
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnetv2_xl('tf_efficientnetv2_xl', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_b0(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2-B0. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnetv2_base('tf_efficientnetv2_b0', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_b1(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2-B1. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnetv2_base(
'tf_efficientnetv2_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_b2(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2-B2. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnetv2_base(
'tf_efficientnetv2_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_b3(pretrained=False, **kwargs) -> EfficientNet:
""" EfficientNet-V2-B3. Tensorflow compatible variant """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_efficientnetv2_base(
'tf_efficientnetv2_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def mixnet_s(pretrained=False, **kwargs) -> EfficientNet:
"""Creates a MixNet Small model.
"""
model = _gen_mixnet_s(
'mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mixnet_m(pretrained=False, **kwargs) -> EfficientNet:
"""Creates a MixNet Medium model.
"""
model = _gen_mixnet_m(
'mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mixnet_l(pretrained=False, **kwargs) -> EfficientNet:
"""Creates a MixNet Large model.
"""
model = _gen_mixnet_m(
'mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
return model
@register_model
def mixnet_xl(pretrained=False, **kwargs) -> EfficientNet:
"""Creates a MixNet Extra-Large model.
Not a paper spec, experimental def by RW w/ depth scaling.
"""
model = _gen_mixnet_m(
'mixnet_xl', channel_multiplier=1.6, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
@register_model
def mixnet_xxl(pretrained=False, **kwargs) -> EfficientNet:
"""Creates a MixNet Double Extra Large model.
Not a paper spec, experimental def by RW w/ depth scaling.
"""
model = _gen_mixnet_m(
'mixnet_xxl', channel_multiplier=2.4, depth_multiplier=1.3, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mixnet_s(pretrained=False, **kwargs) -> EfficientNet:
"""Creates a MixNet Small model. Tensorflow compatible variant
"""
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_mixnet_s(
'tf_mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mixnet_m(pretrained=False, **kwargs) -> EfficientNet:
"""Creates a MixNet Medium model. Tensorflow compatible variant
"""
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_mixnet_m(
'tf_mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mixnet_l(pretrained=False, **kwargs) -> EfficientNet:
"""Creates a MixNet Large model. Tensorflow compatible variant
"""
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_mixnet_m(
'tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
return model
@register_model
def tinynet_a(pretrained=False, **kwargs) -> EfficientNet:
model = _gen_tinynet('tinynet_a', 1.0, 1.2, pretrained=pretrained, **kwargs)
return model
@register_model
def tinynet_b(pretrained=False, **kwargs) -> EfficientNet:
model = _gen_tinynet('tinynet_b', 0.75, 1.1, pretrained=pretrained, **kwargs)
return model
@register_model
def tinynet_c(pretrained=False, **kwargs) -> EfficientNet:
model = _gen_tinynet('tinynet_c', 0.54, 0.85, pretrained=pretrained, **kwargs)
return model
@register_model
def tinynet_d(pretrained=False, **kwargs) -> EfficientNet:
model = _gen_tinynet('tinynet_d', 0.54, 0.695, pretrained=pretrained, **kwargs)
return model
@register_model
def tinynet_e(pretrained=False, **kwargs) -> EfficientNet:
model = _gen_tinynet('tinynet_e', 0.51, 0.6, pretrained=pretrained, **kwargs)
return model
register_model_deprecations(__name__, {
'tf_efficientnet_b0_ap': 'tf_efficientnet_b0.ap_in1k',
'tf_efficientnet_b1_ap': 'tf_efficientnet_b1.ap_in1k',
'tf_efficientnet_b2_ap': 'tf_efficientnet_b2.ap_in1k',
'tf_efficientnet_b3_ap': 'tf_efficientnet_b3.ap_in1k',
'tf_efficientnet_b4_ap': 'tf_efficientnet_b4.ap_in1k',
'tf_efficientnet_b5_ap': 'tf_efficientnet_b5.ap_in1k',
'tf_efficientnet_b6_ap': 'tf_efficientnet_b6.ap_in1k',
'tf_efficientnet_b7_ap': 'tf_efficientnet_b7.ap_in1k',
'tf_efficientnet_b8_ap': 'tf_efficientnet_b8.ap_in1k',
'tf_efficientnet_b0_ns': 'tf_efficientnet_b0.ns_jft_in1k',
'tf_efficientnet_b1_ns': 'tf_efficientnet_b1.ns_jft_in1k',
'tf_efficientnet_b2_ns': 'tf_efficientnet_b2.ns_jft_in1k',
'tf_efficientnet_b3_ns': 'tf_efficientnet_b3.ns_jft_in1k',
'tf_efficientnet_b4_ns': 'tf_efficientnet_b4.ns_jft_in1k',
'tf_efficientnet_b5_ns': 'tf_efficientnet_b5.ns_jft_in1k',
'tf_efficientnet_b6_ns': 'tf_efficientnet_b6.ns_jft_in1k',
'tf_efficientnet_b7_ns': 'tf_efficientnet_b7.ns_jft_in1k',
'tf_efficientnet_l2_ns_475': 'tf_efficientnet_l2.ns_jft_in1k_475',
'tf_efficientnet_l2_ns': 'tf_efficientnet_l2.ns_jft_in1k',
'tf_efficientnetv2_s_in21ft1k': 'tf_efficientnetv2_s.in21k_ft_in1k',
'tf_efficientnetv2_m_in21ft1k': 'tf_efficientnetv2_m.in21k_ft_in1k',
'tf_efficientnetv2_l_in21ft1k': 'tf_efficientnetv2_l.in21k_ft_in1k',
'tf_efficientnetv2_xl_in21ft1k': 'tf_efficientnetv2_xl.in21k_ft_in1k',
'tf_efficientnetv2_s_in21k': 'tf_efficientnetv2_s.in21k',
'tf_efficientnetv2_m_in21k': 'tf_efficientnetv2_m.in21k',
'tf_efficientnetv2_l_in21k': 'tf_efficientnetv2_l.in21k',
'tf_efficientnetv2_xl_in21k': 'tf_efficientnetv2_xl.in21k',
'efficientnet_b2a': 'efficientnet_b2',
'efficientnet_b3a': 'efficientnet_b3',
'mnasnet_a1': 'semnasnet_100',
'mnasnet_b1': 'mnasnet_100',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/inception_v3.py
|
""" Inception-V3
Originally from torchvision Inception3 model
Licensed BSD-Clause 3 https://github.com/pytorch/vision/blob/master/LICENSE
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import trunc_normal_, create_classifier, Linear, ConvNormAct
from ._builder import build_model_with_cfg
from ._builder import resolve_pretrained_cfg
from ._manipulate import flatten_modules
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
__all__ = ['InceptionV3'] # model_registry will add each entrypoint fn to this
class InceptionA(nn.Module):
def __init__(self, in_channels, pool_features, conv_block=None):
super(InceptionA, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch1x1 = conv_block(in_channels, 64, kernel_size=1)
self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)
self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)
self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionB(nn.Module):
def __init__(self, in_channels, conv_block=None):
super(InceptionB, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)
def _forward(self, x):
branch3x3 = self.branch3x3(x)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
outputs = [branch3x3, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionC(nn.Module):
def __init__(self, in_channels, channels_7x7, conv_block=None):
super(InceptionC, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch1x1 = conv_block(in_channels, 192, kernel_size=1)
c7 = channels_7x7
self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)
self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)
self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))
self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionD(nn.Module):
def __init__(self, in_channels, conv_block=None):
super(InceptionD, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)
self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)
self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)
self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))
self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)
def _forward(self, x):
branch3x3 = self.branch3x3_1(x)
branch3x3 = self.branch3x3_2(branch3x3)
branch7x7x3 = self.branch7x7x3_1(x)
branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
outputs = [branch3x3, branch7x7x3, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionE(nn.Module):
def __init__(self, in_channels, conv_block=None):
super(InceptionE, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch1x1 = conv_block(in_channels, 320, kernel_size=1)
self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)
self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionAux(nn.Module):
def __init__(self, in_channels, num_classes, conv_block=None):
super(InceptionAux, self).__init__()
conv_block = conv_block or ConvNormAct
self.conv0 = conv_block(in_channels, 128, kernel_size=1)
self.conv1 = conv_block(128, 768, kernel_size=5)
self.conv1.stddev = 0.01
self.fc = Linear(768, num_classes)
self.fc.stddev = 0.001
def forward(self, x):
# N x 768 x 17 x 17
x = F.avg_pool2d(x, kernel_size=5, stride=3)
# N x 768 x 5 x 5
x = self.conv0(x)
# N x 128 x 5 x 5
x = self.conv1(x)
# N x 768 x 1 x 1
# Adaptive average pooling
x = F.adaptive_avg_pool2d(x, (1, 1))
# N x 768 x 1 x 1
x = torch.flatten(x, 1)
# N x 768
x = self.fc(x)
# N x 1000
return x
class InceptionV3(nn.Module):
"""Inception-V3
"""
aux_logits: torch.jit.Final[bool]
def __init__(
self,
num_classes=1000,
in_chans=3,
drop_rate=0.,
global_pool='avg',
aux_logits=False,
norm_layer='batchnorm2d',
norm_eps=1e-3,
act_layer='relu',
):
super(InceptionV3, self).__init__()
self.num_classes = num_classes
self.aux_logits = aux_logits
conv_block = partial(
ConvNormAct,
padding=0,
norm_layer=norm_layer,
act_layer=act_layer,
norm_kwargs=dict(eps=norm_eps),
act_kwargs=dict(inplace=True),
)
self.Conv2d_1a_3x3 = conv_block(in_chans, 32, kernel_size=3, stride=2)
self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
self.Pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
self.Pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
self.Mixed_5b = InceptionA(192, pool_features=32, conv_block=conv_block)
self.Mixed_5c = InceptionA(256, pool_features=64, conv_block=conv_block)
self.Mixed_5d = InceptionA(288, pool_features=64, conv_block=conv_block)
self.Mixed_6a = InceptionB(288, conv_block=conv_block)
self.Mixed_6b = InceptionC(768, channels_7x7=128, conv_block=conv_block)
self.Mixed_6c = InceptionC(768, channels_7x7=160, conv_block=conv_block)
self.Mixed_6d = InceptionC(768, channels_7x7=160, conv_block=conv_block)
self.Mixed_6e = InceptionC(768, channels_7x7=192, conv_block=conv_block)
if aux_logits:
self.AuxLogits = InceptionAux(768, num_classes, conv_block=conv_block)
else:
self.AuxLogits = None
self.Mixed_7a = InceptionD(768, conv_block=conv_block)
self.Mixed_7b = InceptionE(1280, conv_block=conv_block)
self.Mixed_7c = InceptionE(2048, conv_block=conv_block)
self.feature_info = [
dict(num_chs=64, reduction=2, module='Conv2d_2b_3x3'),
dict(num_chs=192, reduction=4, module='Conv2d_4a_3x3'),
dict(num_chs=288, reduction=8, module='Mixed_5d'),
dict(num_chs=768, reduction=16, module='Mixed_6e'),
dict(num_chs=2048, reduction=32, module='Mixed_7c'),
]
self.num_features = 2048
self.global_pool, self.head_drop, self.fc = create_classifier(
self.num_features,
self.num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
)
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
stddev = m.stddev if hasattr(m, 'stddev') else 0.1
trunc_normal_(m.weight, std=stddev)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def group_matcher(self, coarse=False):
module_map = {k: i for i, (k, _) in enumerate(flatten_modules(self.named_children(), prefix=()))}
module_map.pop(('fc',))
def _matcher(name):
if any([name.startswith(n) for n in ('Conv2d_1', 'Conv2d_2')]):
return 0
elif any([name.startswith(n) for n in ('Conv2d_3', 'Conv2d_4')]):
return 1
else:
for k in module_map.keys():
if k == tuple(name.split('.')[:len(k)]):
return module_map[k]
return float('inf')
return _matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_preaux(self, x):
x = self.Conv2d_1a_3x3(x) # N x 32 x 149 x 149
x = self.Conv2d_2a_3x3(x) # N x 32 x 147 x 147
x = self.Conv2d_2b_3x3(x) # N x 64 x 147 x 147
x = self.Pool1(x) # N x 64 x 73 x 73
x = self.Conv2d_3b_1x1(x) # N x 80 x 73 x 73
x = self.Conv2d_4a_3x3(x) # N x 192 x 71 x 71
x = self.Pool2(x) # N x 192 x 35 x 35
x = self.Mixed_5b(x) # N x 256 x 35 x 35
x = self.Mixed_5c(x) # N x 288 x 35 x 35
x = self.Mixed_5d(x) # N x 288 x 35 x 35
x = self.Mixed_6a(x) # N x 768 x 17 x 17
x = self.Mixed_6b(x) # N x 768 x 17 x 17
x = self.Mixed_6c(x) # N x 768 x 17 x 17
x = self.Mixed_6d(x) # N x 768 x 17 x 17
x = self.Mixed_6e(x) # N x 768 x 17 x 17
return x
def forward_postaux(self, x):
x = self.Mixed_7a(x) # N x 1280 x 8 x 8
x = self.Mixed_7b(x) # N x 2048 x 8 x 8
x = self.Mixed_7c(x) # N x 2048 x 8 x 8
return x
def forward_features(self, x):
x = self.forward_preaux(x)
if self.aux_logits:
aux = self.AuxLogits(x)
x = self.forward_postaux(x)
return x, aux
x = self.forward_postaux(x)
return x
def forward_head(self, x):
x = self.global_pool(x)
x = self.head_drop(x)
x = self.fc(x)
return x
def forward(self, x):
if self.aux_logits:
x, aux = self.forward_features(x)
x = self.forward_head(x)
return x, aux
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_inception_v3(variant, pretrained=False, **kwargs):
pretrained_cfg = resolve_pretrained_cfg(variant, pretrained_cfg=kwargs.pop('pretrained_cfg', None))
aux_logits = kwargs.get('aux_logits', False)
has_aux_logits = False
if pretrained_cfg:
# only torchvision pretrained weights have aux logits
has_aux_logits = pretrained_cfg.tag == 'tv_in1k'
if aux_logits:
assert not kwargs.pop('features_only', False)
load_strict = has_aux_logits
else:
load_strict = not has_aux_logits
return build_model_with_cfg(
InceptionV3,
variant,
pretrained,
pretrained_cfg=pretrained_cfg,
pretrained_strict=load_strict,
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'Conv2d_1a_3x3.conv', 'classifier': 'fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
# original PyTorch weights, ported from Tensorflow but modified
'inception_v3.tv_in1k': _cfg(
# NOTE checkpoint has aux logit layer weights
hf_hub_id='timm/',
url='https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth'),
# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
'inception_v3.tf_in1k': _cfg(hf_hub_id='timm/'),
# my port of Tensorflow adversarially trained Inception V3 from
# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
'inception_v3.tf_adv_in1k': _cfg(hf_hub_id='timm/'),
# from gluon pretrained models, best performing in terms of accuracy/loss metrics
# https://gluon-cv.mxnet.io/model_zoo/classification.html
'inception_v3.gluon_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, # also works well with inception defaults
std=IMAGENET_DEFAULT_STD, # also works well with inception defaults
)
})
@register_model
def inception_v3(pretrained=False, **kwargs) -> InceptionV3:
model = _create_inception_v3('inception_v3', pretrained=pretrained, **kwargs)
return model
register_model_deprecations(__name__, {
'tf_inception_v3': 'inception_v3.tf_in1k',
'adv_inception_v3': 'inception_v3.tf_adv_in1k',
'gluon_inception_v3': 'inception_v3.gluon_in1k',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/cspnet.py
|
"""PyTorch CspNet
A PyTorch implementation of Cross Stage Partial Networks including:
* CSPResNet50
* CSPResNeXt50
* CSPDarkNet53
* and DarkNet53 for good measure
Based on paper `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929
Reference impl via darknet cfg files at https://github.com/WongKinYiu/CrossStagePartialNetworks
Hacked together by / Copyright 2020 Ross Wightman
"""
from dataclasses import dataclass, asdict, replace
from functools import partial
from typing import Any, Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import ClassifierHead, ConvNormAct, ConvNormActAa, DropPath, get_attn, create_act_layer, make_divisible
from ._builder import build_model_with_cfg
from ._manipulate import named_apply, MATCH_PREV_GROUP
from ._registry import register_model, generate_default_cfgs
__all__ = ['CspNet'] # model_registry will add each entrypoint fn to this
@dataclass
class CspStemCfg:
out_chs: Union[int, Tuple[int, ...]] = 32
stride: Union[int, Tuple[int, ...]] = 2
kernel_size: int = 3
padding: Union[int, str] = ''
pool: Optional[str] = ''
def _pad_arg(x, n):
# pads an argument tuple to specified n by padding with last value
if not isinstance(x, (tuple, list)):
x = (x,)
curr_n = len(x)
pad_n = n - curr_n
if pad_n <= 0:
return x[:n]
return tuple(x + (x[-1],) * pad_n)
@dataclass
class CspStagesCfg:
depth: Tuple[int, ...] = (3, 3, 5, 2) # block depth (number of block repeats in stages)
out_chs: Tuple[int, ...] = (128, 256, 512, 1024) # number of output channels for blocks in stage
stride: Union[int, Tuple[int, ...]] = 2 # stride of stage
groups: Union[int, Tuple[int, ...]] = 1 # num kxk conv groups
block_ratio: Union[float, Tuple[float, ...]] = 1.0
bottle_ratio: Union[float, Tuple[float, ...]] = 1. # bottleneck-ratio of blocks in stage
avg_down: Union[bool, Tuple[bool, ...]] = False
attn_layer: Optional[Union[str, Tuple[str, ...]]] = None
attn_kwargs: Optional[Union[Dict, Tuple[Dict]]] = None
stage_type: Union[str, Tuple[str]] = 'csp' # stage type ('csp', 'cs2', 'dark')
block_type: Union[str, Tuple[str]] = 'bottle' # blocks type for stages ('bottle', 'dark')
# cross-stage only
expand_ratio: Union[float, Tuple[float, ...]] = 1.0
cross_linear: Union[bool, Tuple[bool, ...]] = False
down_growth: Union[bool, Tuple[bool, ...]] = False
def __post_init__(self):
n = len(self.depth)
assert len(self.out_chs) == n
self.stride = _pad_arg(self.stride, n)
self.groups = _pad_arg(self.groups, n)
self.block_ratio = _pad_arg(self.block_ratio, n)
self.bottle_ratio = _pad_arg(self.bottle_ratio, n)
self.avg_down = _pad_arg(self.avg_down, n)
self.attn_layer = _pad_arg(self.attn_layer, n)
self.attn_kwargs = _pad_arg(self.attn_kwargs, n)
self.stage_type = _pad_arg(self.stage_type, n)
self.block_type = _pad_arg(self.block_type, n)
self.expand_ratio = _pad_arg(self.expand_ratio, n)
self.cross_linear = _pad_arg(self.cross_linear, n)
self.down_growth = _pad_arg(self.down_growth, n)
@dataclass
class CspModelCfg:
stem: CspStemCfg
stages: CspStagesCfg
zero_init_last: bool = True # zero init last weight (usually bn) in residual path
act_layer: str = 'leaky_relu'
norm_layer: str = 'batchnorm'
aa_layer: Optional[str] = None # FIXME support string factory for this
def _cs3_cfg(
width_multiplier=1.0,
depth_multiplier=1.0,
avg_down=False,
act_layer='silu',
focus=False,
attn_layer=None,
attn_kwargs=None,
bottle_ratio=1.0,
block_type='dark',
):
if focus:
stem_cfg = CspStemCfg(
out_chs=make_divisible(64 * width_multiplier),
kernel_size=6, stride=2, padding=2, pool='')
else:
stem_cfg = CspStemCfg(
out_chs=tuple([make_divisible(c * width_multiplier) for c in (32, 64)]),
kernel_size=3, stride=2, pool='')
return CspModelCfg(
stem=stem_cfg,
stages=CspStagesCfg(
out_chs=tuple([make_divisible(c * width_multiplier) for c in (128, 256, 512, 1024)]),
depth=tuple([int(d * depth_multiplier) for d in (3, 6, 9, 3)]),
stride=2,
bottle_ratio=bottle_ratio,
block_ratio=0.5,
avg_down=avg_down,
attn_layer=attn_layer,
attn_kwargs=attn_kwargs,
stage_type='cs3',
block_type=block_type,
),
act_layer=act_layer,
)
class BottleneckBlock(nn.Module):
""" ResNe(X)t Bottleneck Block
"""
def __init__(
self,
in_chs,
out_chs,
dilation=1,
bottle_ratio=0.25,
groups=1,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
attn_last=False,
attn_layer=None,
drop_block=None,
drop_path=0.
):
super(BottleneckBlock, self).__init__()
mid_chs = int(round(out_chs * bottle_ratio))
ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
attn_last = attn_layer is not None and attn_last
attn_first = attn_layer is not None and not attn_last
self.conv1 = ConvNormAct(in_chs, mid_chs, kernel_size=1, **ckwargs)
self.conv2 = ConvNormAct(
mid_chs, mid_chs, kernel_size=3, dilation=dilation, groups=groups,
drop_layer=drop_block, **ckwargs)
self.attn2 = attn_layer(mid_chs, act_layer=act_layer) if attn_first else nn.Identity()
self.conv3 = ConvNormAct(mid_chs, out_chs, kernel_size=1, apply_act=False, **ckwargs)
self.attn3 = attn_layer(out_chs, act_layer=act_layer) if attn_last else nn.Identity()
self.drop_path = DropPath(drop_path) if drop_path else nn.Identity()
self.act3 = create_act_layer(act_layer)
def zero_init_last(self):
nn.init.zeros_(self.conv3.bn.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.conv2(x)
x = self.attn2(x)
x = self.conv3(x)
x = self.attn3(x)
x = self.drop_path(x) + shortcut
# FIXME partial shortcut needed if first block handled as per original, not used for my current impl
#x[:, :shortcut.size(1)] += shortcut
x = self.act3(x)
return x
class DarkBlock(nn.Module):
""" DarkNet Block
"""
def __init__(
self,
in_chs,
out_chs,
dilation=1,
bottle_ratio=0.5,
groups=1,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
attn_layer=None,
drop_block=None,
drop_path=0.
):
super(DarkBlock, self).__init__()
mid_chs = int(round(out_chs * bottle_ratio))
ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
self.conv1 = ConvNormAct(in_chs, mid_chs, kernel_size=1, **ckwargs)
self.attn = attn_layer(mid_chs, act_layer=act_layer) if attn_layer is not None else nn.Identity()
self.conv2 = ConvNormAct(
mid_chs, out_chs, kernel_size=3, dilation=dilation, groups=groups,
drop_layer=drop_block, **ckwargs)
self.drop_path = DropPath(drop_path) if drop_path else nn.Identity()
def zero_init_last(self):
nn.init.zeros_(self.conv2.bn.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.attn(x)
x = self.conv2(x)
x = self.drop_path(x) + shortcut
return x
class EdgeBlock(nn.Module):
""" EdgeResidual / Fused-MBConv / MobileNetV1-like 3x3 + 1x1 block (w/ activated output)
"""
def __init__(
self,
in_chs,
out_chs,
dilation=1,
bottle_ratio=0.5,
groups=1,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
attn_layer=None,
drop_block=None,
drop_path=0.
):
super(EdgeBlock, self).__init__()
mid_chs = int(round(out_chs * bottle_ratio))
ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
self.conv1 = ConvNormAct(
in_chs, mid_chs, kernel_size=3, dilation=dilation, groups=groups,
drop_layer=drop_block, **ckwargs)
self.attn = attn_layer(mid_chs, act_layer=act_layer) if attn_layer is not None else nn.Identity()
self.conv2 = ConvNormAct(mid_chs, out_chs, kernel_size=1, **ckwargs)
self.drop_path = DropPath(drop_path) if drop_path else nn.Identity()
def zero_init_last(self):
nn.init.zeros_(self.conv2.bn.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.attn(x)
x = self.conv2(x)
x = self.drop_path(x) + shortcut
return x
class CrossStage(nn.Module):
"""Cross Stage."""
def __init__(
self,
in_chs,
out_chs,
stride,
dilation,
depth,
block_ratio=1.,
bottle_ratio=1.,
expand_ratio=1.,
groups=1,
first_dilation=None,
avg_down=False,
down_growth=False,
cross_linear=False,
block_dpr=None,
block_fn=BottleneckBlock,
**block_kwargs,
):
super(CrossStage, self).__init__()
first_dilation = first_dilation or dilation
down_chs = out_chs if down_growth else in_chs # grow downsample channels to output channels
self.expand_chs = exp_chs = int(round(out_chs * expand_ratio))
block_out_chs = int(round(out_chs * block_ratio))
conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
aa_layer = block_kwargs.pop('aa_layer', None)
if stride != 1 or first_dilation != dilation:
if avg_down:
self.conv_down = nn.Sequential(
nn.AvgPool2d(2) if stride == 2 else nn.Identity(), # FIXME dilation handling
ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
)
else:
self.conv_down = ConvNormActAa(
in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
aa_layer=aa_layer, **conv_kwargs)
prev_chs = down_chs
else:
self.conv_down = nn.Identity()
prev_chs = in_chs
# FIXME this 1x1 expansion is pushed down into the cross and block paths in the darknet cfgs. Also,
# there is also special case for the first stage for some of the model that results in uneven split
# across the two paths. I did it this way for simplicity for now.
self.conv_exp = ConvNormAct(prev_chs, exp_chs, kernel_size=1, apply_act=not cross_linear, **conv_kwargs)
prev_chs = exp_chs // 2 # output of conv_exp is always split in two
self.blocks = nn.Sequential()
for i in range(depth):
self.blocks.add_module(str(i), block_fn(
in_chs=prev_chs,
out_chs=block_out_chs,
dilation=dilation,
bottle_ratio=bottle_ratio,
groups=groups,
drop_path=block_dpr[i] if block_dpr is not None else 0.,
**block_kwargs,
))
prev_chs = block_out_chs
# transition convs
self.conv_transition_b = ConvNormAct(prev_chs, exp_chs // 2, kernel_size=1, **conv_kwargs)
self.conv_transition = ConvNormAct(exp_chs, out_chs, kernel_size=1, **conv_kwargs)
def forward(self, x):
x = self.conv_down(x)
x = self.conv_exp(x)
xs, xb = x.split(self.expand_chs // 2, dim=1)
xb = self.blocks(xb)
xb = self.conv_transition_b(xb).contiguous()
out = self.conv_transition(torch.cat([xs, xb], dim=1))
return out
class CrossStage3(nn.Module):
"""Cross Stage 3.
Similar to CrossStage, but with only one transition conv for the output.
"""
def __init__(
self,
in_chs,
out_chs,
stride,
dilation,
depth,
block_ratio=1.,
bottle_ratio=1.,
expand_ratio=1.,
groups=1,
first_dilation=None,
avg_down=False,
down_growth=False,
cross_linear=False,
block_dpr=None,
block_fn=BottleneckBlock,
**block_kwargs,
):
super(CrossStage3, self).__init__()
first_dilation = first_dilation or dilation
down_chs = out_chs if down_growth else in_chs # grow downsample channels to output channels
self.expand_chs = exp_chs = int(round(out_chs * expand_ratio))
block_out_chs = int(round(out_chs * block_ratio))
conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
aa_layer = block_kwargs.pop('aa_layer', None)
if stride != 1 or first_dilation != dilation:
if avg_down:
self.conv_down = nn.Sequential(
nn.AvgPool2d(2) if stride == 2 else nn.Identity(), # FIXME dilation handling
ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
)
else:
self.conv_down = ConvNormActAa(
in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
aa_layer=aa_layer, **conv_kwargs)
prev_chs = down_chs
else:
self.conv_down = None
prev_chs = in_chs
# expansion conv
self.conv_exp = ConvNormAct(prev_chs, exp_chs, kernel_size=1, apply_act=not cross_linear, **conv_kwargs)
prev_chs = exp_chs // 2 # expanded output is split in 2 for blocks and cross stage
self.blocks = nn.Sequential()
for i in range(depth):
self.blocks.add_module(str(i), block_fn(
in_chs=prev_chs,
out_chs=block_out_chs,
dilation=dilation,
bottle_ratio=bottle_ratio,
groups=groups,
drop_path=block_dpr[i] if block_dpr is not None else 0.,
**block_kwargs,
))
prev_chs = block_out_chs
# transition convs
self.conv_transition = ConvNormAct(exp_chs, out_chs, kernel_size=1, **conv_kwargs)
def forward(self, x):
x = self.conv_down(x)
x = self.conv_exp(x)
x1, x2 = x.split(self.expand_chs // 2, dim=1)
x1 = self.blocks(x1)
out = self.conv_transition(torch.cat([x1, x2], dim=1))
return out
class DarkStage(nn.Module):
"""DarkNet stage."""
def __init__(
self,
in_chs,
out_chs,
stride,
dilation,
depth,
block_ratio=1.,
bottle_ratio=1.,
groups=1,
first_dilation=None,
avg_down=False,
block_fn=BottleneckBlock,
block_dpr=None,
**block_kwargs,
):
super(DarkStage, self).__init__()
first_dilation = first_dilation or dilation
conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
aa_layer = block_kwargs.pop('aa_layer', None)
if avg_down:
self.conv_down = nn.Sequential(
nn.AvgPool2d(2) if stride == 2 else nn.Identity(), # FIXME dilation handling
ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
)
else:
self.conv_down = ConvNormActAa(
in_chs, out_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
aa_layer=aa_layer, **conv_kwargs)
prev_chs = out_chs
block_out_chs = int(round(out_chs * block_ratio))
self.blocks = nn.Sequential()
for i in range(depth):
self.blocks.add_module(str(i), block_fn(
in_chs=prev_chs,
out_chs=block_out_chs,
dilation=dilation,
bottle_ratio=bottle_ratio,
groups=groups,
drop_path=block_dpr[i] if block_dpr is not None else 0.,
**block_kwargs
))
prev_chs = block_out_chs
def forward(self, x):
x = self.conv_down(x)
x = self.blocks(x)
return x
def create_csp_stem(
in_chans=3,
out_chs=32,
kernel_size=3,
stride=2,
pool='',
padding='',
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
aa_layer=None,
):
stem = nn.Sequential()
feature_info = []
if not isinstance(out_chs, (tuple, list)):
out_chs = [out_chs]
stem_depth = len(out_chs)
assert stem_depth
assert stride in (1, 2, 4)
prev_feat = None
prev_chs = in_chans
last_idx = stem_depth - 1
stem_stride = 1
for i, chs in enumerate(out_chs):
conv_name = f'conv{i + 1}'
conv_stride = 2 if (i == 0 and stride > 1) or (i == last_idx and stride > 2 and not pool) else 1
if conv_stride > 1 and prev_feat is not None:
feature_info.append(prev_feat)
stem.add_module(conv_name, ConvNormAct(
prev_chs, chs, kernel_size,
stride=conv_stride,
padding=padding if i == 0 else '',
act_layer=act_layer,
norm_layer=norm_layer,
))
stem_stride *= conv_stride
prev_chs = chs
prev_feat = dict(num_chs=prev_chs, reduction=stem_stride, module='.'.join(['stem', conv_name]))
if pool:
assert stride > 2
if prev_feat is not None:
feature_info.append(prev_feat)
if aa_layer is not None:
stem.add_module('pool', nn.MaxPool2d(kernel_size=3, stride=1, padding=1))
stem.add_module('aa', aa_layer(channels=prev_chs, stride=2))
pool_name = 'aa'
else:
stem.add_module('pool', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
pool_name = 'pool'
stem_stride *= 2
prev_feat = dict(num_chs=prev_chs, reduction=stem_stride, module='.'.join(['stem', pool_name]))
feature_info.append(prev_feat)
return stem, feature_info
def _get_stage_fn(stage_args):
stage_type = stage_args.pop('stage_type')
assert stage_type in ('dark', 'csp', 'cs3')
if stage_type == 'dark':
stage_args.pop('expand_ratio', None)
stage_args.pop('cross_linear', None)
stage_args.pop('down_growth', None)
stage_fn = DarkStage
elif stage_type == 'csp':
stage_fn = CrossStage
else:
stage_fn = CrossStage3
return stage_fn, stage_args
def _get_block_fn(stage_args):
block_type = stage_args.pop('block_type')
assert block_type in ('dark', 'edge', 'bottle')
if block_type == 'dark':
return DarkBlock, stage_args
elif block_type == 'edge':
return EdgeBlock, stage_args
else:
return BottleneckBlock, stage_args
def _get_attn_fn(stage_args):
attn_layer = stage_args.pop('attn_layer')
attn_kwargs = stage_args.pop('attn_kwargs', None) or {}
if attn_layer is not None:
attn_layer = get_attn(attn_layer)
if attn_kwargs:
attn_layer = partial(attn_layer, **attn_kwargs)
return attn_layer, stage_args
def create_csp_stages(
cfg: CspModelCfg,
drop_path_rate: float,
output_stride: int,
stem_feat: Dict[str, Any],
):
cfg_dict = asdict(cfg.stages)
num_stages = len(cfg.stages.depth)
cfg_dict['block_dpr'] = [None] * num_stages if not drop_path_rate else \
[x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg.stages.depth)).split(cfg.stages.depth)]
stage_args = [dict(zip(cfg_dict.keys(), values)) for values in zip(*cfg_dict.values())]
block_kwargs = dict(
act_layer=cfg.act_layer,
norm_layer=cfg.norm_layer,
)
dilation = 1
net_stride = stem_feat['reduction']
prev_chs = stem_feat['num_chs']
prev_feat = stem_feat
feature_info = []
stages = []
for stage_idx, stage_args in enumerate(stage_args):
stage_fn, stage_args = _get_stage_fn(stage_args)
block_fn, stage_args = _get_block_fn(stage_args)
attn_fn, stage_args = _get_attn_fn(stage_args)
stride = stage_args.pop('stride')
if stride != 1 and prev_feat:
feature_info.append(prev_feat)
if net_stride >= output_stride and stride > 1:
dilation *= stride
stride = 1
net_stride *= stride
first_dilation = 1 if dilation in (1, 2) else 2
stages += [stage_fn(
prev_chs,
**stage_args,
stride=stride,
first_dilation=first_dilation,
dilation=dilation,
block_fn=block_fn,
aa_layer=cfg.aa_layer,
attn_layer=attn_fn, # will be passed through stage as block_kwargs
**block_kwargs,
)]
prev_chs = stage_args['out_chs']
prev_feat = dict(num_chs=prev_chs, reduction=net_stride, module=f'stages.{stage_idx}')
feature_info.append(prev_feat)
return nn.Sequential(*stages), feature_info
class CspNet(nn.Module):
"""Cross Stage Partial base model.
Paper: `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929
Ref Impl: https://github.com/WongKinYiu/CrossStagePartialNetworks
NOTE: There are differences in the way I handle the 1x1 'expansion' conv in this impl vs the
darknet impl. I did it this way for simplicity and less special cases.
"""
def __init__(
self,
cfg: CspModelCfg,
in_chans=3,
num_classes=1000,
output_stride=32,
global_pool='avg',
drop_rate=0.,
drop_path_rate=0.,
zero_init_last=True,
**kwargs,
):
"""
Args:
cfg (CspModelCfg): Model architecture configuration
in_chans (int): Number of input channels (default: 3)
num_classes (int): Number of classifier classes (default: 1000)
output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32)
global_pool (str): Global pooling type (default: 'avg')
drop_rate (float): Dropout rate (default: 0.)
drop_path_rate (float): Stochastic depth drop-path rate (default: 0.)
zero_init_last (bool): Zero-init last weight of residual path
kwargs (dict): Extra kwargs overlayed onto cfg
"""
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
assert output_stride in (8, 16, 32)
cfg = replace(cfg, **kwargs) # overlay kwargs onto cfg
layer_args = dict(
act_layer=cfg.act_layer,
norm_layer=cfg.norm_layer,
aa_layer=cfg.aa_layer
)
self.feature_info = []
# Construct the stem
self.stem, stem_feat_info = create_csp_stem(in_chans, **asdict(cfg.stem), **layer_args)
self.feature_info.extend(stem_feat_info[:-1])
# Construct the stages
self.stages, stage_feat_info = create_csp_stages(
cfg,
drop_path_rate=drop_path_rate,
output_stride=output_stride,
stem_feat=stem_feat_info[-1],
)
prev_chs = stage_feat_info[-1]['num_chs']
self.feature_info.extend(stage_feat_info)
# Construct the head
self.num_features = prev_chs
self.head = ClassifierHead(
in_features=prev_chs, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate)
named_apply(partial(_init_weights, zero_init_last=zero_init_last), self)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+)\.blocks\.(\d+)', None),
(r'^stages\.(\d+)\..*transition', MATCH_PREV_GROUP), # map to last block in stage
(r'^stages\.(\d+)', (0,)),
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module, name, zero_init_last=False):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.01)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif zero_init_last and hasattr(module, 'zero_init_last'):
module.zero_init_last()
model_cfgs = dict(
cspresnet50=CspModelCfg(
stem=CspStemCfg(out_chs=64, kernel_size=7, stride=4, pool='max'),
stages=CspStagesCfg(
depth=(3, 3, 5, 2),
out_chs=(128, 256, 512, 1024),
stride=(1, 2),
expand_ratio=2.,
bottle_ratio=0.5,
cross_linear=True,
),
),
cspresnet50d=CspModelCfg(
stem=CspStemCfg(out_chs=(32, 32, 64), kernel_size=3, stride=4, pool='max'),
stages=CspStagesCfg(
depth=(3, 3, 5, 2),
out_chs=(128, 256, 512, 1024),
stride=(1,) + (2,),
expand_ratio=2.,
bottle_ratio=0.5,
block_ratio=1.,
cross_linear=True,
),
),
cspresnet50w=CspModelCfg(
stem=CspStemCfg(out_chs=(32, 32, 64), kernel_size=3, stride=4, pool='max'),
stages=CspStagesCfg(
depth=(3, 3, 5, 2),
out_chs=(256, 512, 1024, 2048),
stride=(1,) + (2,),
expand_ratio=1.,
bottle_ratio=0.25,
block_ratio=0.5,
cross_linear=True,
),
),
cspresnext50=CspModelCfg(
stem=CspStemCfg(out_chs=64, kernel_size=7, stride=4, pool='max'),
stages=CspStagesCfg(
depth=(3, 3, 5, 2),
out_chs=(256, 512, 1024, 2048),
stride=(1,) + (2,),
groups=32,
expand_ratio=1.,
bottle_ratio=1.,
block_ratio=0.5,
cross_linear=True,
),
),
cspdarknet53=CspModelCfg(
stem=CspStemCfg(out_chs=32, kernel_size=3, stride=1, pool=''),
stages=CspStagesCfg(
depth=(1, 2, 8, 8, 4),
out_chs=(64, 128, 256, 512, 1024),
stride=2,
expand_ratio=(2.,) + (1.,),
bottle_ratio=(0.5,) + (1.,),
block_ratio=(1.,) + (0.5,),
down_growth=True,
block_type='dark',
),
),
darknet17=CspModelCfg(
stem=CspStemCfg(out_chs=32, kernel_size=3, stride=1, pool=''),
stages=CspStagesCfg(
depth=(1,) * 5,
out_chs=(64, 128, 256, 512, 1024),
stride=(2,),
bottle_ratio=(0.5,),
block_ratio=(1.,),
stage_type='dark',
block_type='dark',
),
),
darknet21=CspModelCfg(
stem=CspStemCfg(out_chs=32, kernel_size=3, stride=1, pool=''),
stages=CspStagesCfg(
depth=(1, 1, 1, 2, 2),
out_chs=(64, 128, 256, 512, 1024),
stride=(2,),
bottle_ratio=(0.5,),
block_ratio=(1.,),
stage_type='dark',
block_type='dark',
),
),
sedarknet21=CspModelCfg(
stem=CspStemCfg(out_chs=32, kernel_size=3, stride=1, pool=''),
stages=CspStagesCfg(
depth=(1, 1, 1, 2, 2),
out_chs=(64, 128, 256, 512, 1024),
stride=2,
bottle_ratio=0.5,
block_ratio=1.,
attn_layer='se',
stage_type='dark',
block_type='dark',
),
),
darknet53=CspModelCfg(
stem=CspStemCfg(out_chs=32, kernel_size=3, stride=1, pool=''),
stages=CspStagesCfg(
depth=(1, 2, 8, 8, 4),
out_chs=(64, 128, 256, 512, 1024),
stride=2,
bottle_ratio=0.5,
block_ratio=1.,
stage_type='dark',
block_type='dark',
),
),
darknetaa53=CspModelCfg(
stem=CspStemCfg(out_chs=32, kernel_size=3, stride=1, pool=''),
stages=CspStagesCfg(
depth=(1, 2, 8, 8, 4),
out_chs=(64, 128, 256, 512, 1024),
stride=2,
bottle_ratio=0.5,
block_ratio=1.,
avg_down=True,
stage_type='dark',
block_type='dark',
),
),
cs3darknet_s=_cs3_cfg(width_multiplier=0.5, depth_multiplier=0.5),
cs3darknet_m=_cs3_cfg(width_multiplier=0.75, depth_multiplier=0.67),
cs3darknet_l=_cs3_cfg(),
cs3darknet_x=_cs3_cfg(width_multiplier=1.25, depth_multiplier=1.33),
cs3darknet_focus_s=_cs3_cfg(width_multiplier=0.5, depth_multiplier=0.5, focus=True),
cs3darknet_focus_m=_cs3_cfg(width_multiplier=0.75, depth_multiplier=0.67, focus=True),
cs3darknet_focus_l=_cs3_cfg(focus=True),
cs3darknet_focus_x=_cs3_cfg(width_multiplier=1.25, depth_multiplier=1.33, focus=True),
cs3sedarknet_l=_cs3_cfg(attn_layer='se', attn_kwargs=dict(rd_ratio=.25)),
cs3sedarknet_x=_cs3_cfg(attn_layer='se', width_multiplier=1.25, depth_multiplier=1.33),
cs3sedarknet_xdw=CspModelCfg(
stem=CspStemCfg(out_chs=(32, 64), kernel_size=3, stride=2, pool=''),
stages=CspStagesCfg(
depth=(3, 6, 12, 4),
out_chs=(256, 512, 1024, 2048),
stride=2,
groups=(1, 1, 256, 512),
bottle_ratio=0.5,
block_ratio=0.5,
attn_layer='se',
),
act_layer='silu',
),
cs3edgenet_x=_cs3_cfg(width_multiplier=1.25, depth_multiplier=1.33, bottle_ratio=1.5, block_type='edge'),
cs3se_edgenet_x=_cs3_cfg(
width_multiplier=1.25, depth_multiplier=1.33, bottle_ratio=1.5, block_type='edge',
attn_layer='se', attn_kwargs=dict(rd_ratio=.25)),
)
def _create_cspnet(variant, pretrained=False, **kwargs):
if variant.startswith('darknet') or variant.startswith('cspdarknet'):
# NOTE: DarkNet is one of few models with stride==1 features w/ 6 out_indices [0..5]
default_out_indices = (0, 1, 2, 3, 4, 5)
else:
default_out_indices = (0, 1, 2, 3, 4)
out_indices = kwargs.pop('out_indices', default_out_indices)
return build_model_with_cfg(
CspNet, variant, pretrained,
model_cfg=model_cfgs[variant],
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
'crop_pct': 0.887, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'cspresnet50.ra_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnet50_ra-d3e8d487.pth'),
'cspresnet50d.untrained': _cfg(),
'cspresnet50w.untrained': _cfg(),
'cspresnext50.ra_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnext50_ra_224-648b4713.pth',
),
'cspdarknet53.ra_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspdarknet53_ra_256-d05c7c21.pth'),
'darknet17.untrained': _cfg(),
'darknet21.untrained': _cfg(),
'sedarknet21.untrained': _cfg(),
'darknet53.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/darknet53_256_c2ns-3aeff817.pth',
interpolation='bicubic', test_input_size=(3, 288, 288), test_crop_pct=1.0),
'darknetaa53.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/darknetaa53_c2ns-5c28ec8a.pth',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'cs3darknet_s.untrained': _cfg(interpolation='bicubic'),
'cs3darknet_m.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/cs3darknet_m_c2ns-43f06604.pth',
interpolation='bicubic', test_input_size=(3, 288, 288), test_crop_pct=0.95,
),
'cs3darknet_l.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/cs3darknet_l_c2ns-16220c5d.pth',
interpolation='bicubic', test_input_size=(3, 288, 288), test_crop_pct=0.95),
'cs3darknet_x.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/cs3darknet_x_c2ns-4e4490aa.pth',
interpolation='bicubic', crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'cs3darknet_focus_s.untrained': _cfg(interpolation='bicubic'),
'cs3darknet_focus_m.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/cs3darknet_focus_m_c2ns-e23bed41.pth',
interpolation='bicubic', test_input_size=(3, 288, 288), test_crop_pct=0.95),
'cs3darknet_focus_l.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/cs3darknet_focus_l_c2ns-65ef8888.pth',
interpolation='bicubic', test_input_size=(3, 288, 288), test_crop_pct=0.95),
'cs3darknet_focus_x.untrained': _cfg(interpolation='bicubic'),
'cs3sedarknet_l.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/cs3sedarknet_l_c2ns-e8d1dc13.pth',
interpolation='bicubic', test_input_size=(3, 288, 288), test_crop_pct=0.95),
'cs3sedarknet_x.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/cs3sedarknet_x_c2ns-b4d0abc0.pth',
interpolation='bicubic', test_input_size=(3, 288, 288), test_crop_pct=1.0),
'cs3sedarknet_xdw.untrained': _cfg(interpolation='bicubic'),
'cs3edgenet_x.c2_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/cs3edgenet_x_c2-2e1610a9.pth',
interpolation='bicubic', test_input_size=(3, 288, 288), test_crop_pct=1.0),
'cs3se_edgenet_x.c2ns_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/cs3se_edgenet_x_c2ns-76f8e3ac.pth',
interpolation='bicubic', crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0),
})
@register_model
def cspresnet50(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cspresnet50', pretrained=pretrained, **kwargs)
@register_model
def cspresnet50d(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cspresnet50d', pretrained=pretrained, **kwargs)
@register_model
def cspresnet50w(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cspresnet50w', pretrained=pretrained, **kwargs)
@register_model
def cspresnext50(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cspresnext50', pretrained=pretrained, **kwargs)
@register_model
def cspdarknet53(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cspdarknet53', pretrained=pretrained, **kwargs)
@register_model
def darknet17(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('darknet17', pretrained=pretrained, **kwargs)
@register_model
def darknet21(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('darknet21', pretrained=pretrained, **kwargs)
@register_model
def sedarknet21(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('sedarknet21', pretrained=pretrained, **kwargs)
@register_model
def darknet53(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('darknet53', pretrained=pretrained, **kwargs)
@register_model
def darknetaa53(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('darknetaa53', pretrained=pretrained, **kwargs)
@register_model
def cs3darknet_s(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3darknet_s', pretrained=pretrained, **kwargs)
@register_model
def cs3darknet_m(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3darknet_m', pretrained=pretrained, **kwargs)
@register_model
def cs3darknet_l(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3darknet_l', pretrained=pretrained, **kwargs)
@register_model
def cs3darknet_x(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3darknet_x', pretrained=pretrained, **kwargs)
@register_model
def cs3darknet_focus_s(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3darknet_focus_s', pretrained=pretrained, **kwargs)
@register_model
def cs3darknet_focus_m(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3darknet_focus_m', pretrained=pretrained, **kwargs)
@register_model
def cs3darknet_focus_l(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3darknet_focus_l', pretrained=pretrained, **kwargs)
@register_model
def cs3darknet_focus_x(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3darknet_focus_x', pretrained=pretrained, **kwargs)
@register_model
def cs3sedarknet_l(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3sedarknet_l', pretrained=pretrained, **kwargs)
@register_model
def cs3sedarknet_x(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3sedarknet_x', pretrained=pretrained, **kwargs)
@register_model
def cs3sedarknet_xdw(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3sedarknet_xdw', pretrained=pretrained, **kwargs)
@register_model
def cs3edgenet_x(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3edgenet_x', pretrained=pretrained, **kwargs)
@register_model
def cs3se_edgenet_x(pretrained=False, **kwargs) -> CspNet:
return _create_cspnet('cs3se_edgenet_x', pretrained=pretrained, **kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/vgg.py
|
"""VGG
Adapted from https://github.com/pytorch/vision 'vgg.py' (BSD-3-Clause) with a few changes for
timm functionality.
Copyright 2021 Ross Wightman
"""
from typing import Union, List, Dict, Any, cast
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import ClassifierHead
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_module
from ._registry import register_model, generate_default_cfgs
__all__ = ['VGG']
cfgs: Dict[str, List[Union[str, int]]] = {
'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
@register_notrace_module # reason: FX can't symbolically trace control flow in forward method
class ConvMlp(nn.Module):
def __init__(
self,
in_features=512,
out_features=4096,
kernel_size=7,
mlp_ratio=1.0,
drop_rate: float = 0.2,
act_layer: nn.Module = None,
conv_layer: nn.Module = None,
):
super(ConvMlp, self).__init__()
self.input_kernel_size = kernel_size
mid_features = int(out_features * mlp_ratio)
self.fc1 = conv_layer(in_features, mid_features, kernel_size, bias=True)
self.act1 = act_layer(True)
self.drop = nn.Dropout(drop_rate)
self.fc2 = conv_layer(mid_features, out_features, 1, bias=True)
self.act2 = act_layer(True)
def forward(self, x):
if x.shape[-2] < self.input_kernel_size or x.shape[-1] < self.input_kernel_size:
# keep the input size >= 7x7
output_size = (max(self.input_kernel_size, x.shape[-2]), max(self.input_kernel_size, x.shape[-1]))
x = F.adaptive_avg_pool2d(x, output_size)
x = self.fc1(x)
x = self.act1(x)
x = self.drop(x)
x = self.fc2(x)
x = self.act2(x)
return x
class VGG(nn.Module):
def __init__(
self,
cfg: List[Any],
num_classes: int = 1000,
in_chans: int = 3,
output_stride: int = 32,
mlp_ratio: float = 1.0,
act_layer: nn.Module = nn.ReLU,
conv_layer: nn.Module = nn.Conv2d,
norm_layer: nn.Module = None,
global_pool: str = 'avg',
drop_rate: float = 0.,
) -> None:
super(VGG, self).__init__()
assert output_stride == 32
self.num_classes = num_classes
self.num_features = 4096
self.drop_rate = drop_rate
self.grad_checkpointing = False
self.use_norm = norm_layer is not None
self.feature_info = []
prev_chs = in_chans
net_stride = 1
pool_layer = nn.MaxPool2d
layers: List[nn.Module] = []
for v in cfg:
last_idx = len(layers) - 1
if v == 'M':
self.feature_info.append(dict(num_chs=prev_chs, reduction=net_stride, module=f'features.{last_idx}'))
layers += [pool_layer(kernel_size=2, stride=2)]
net_stride *= 2
else:
v = cast(int, v)
conv2d = conv_layer(prev_chs, v, kernel_size=3, padding=1)
if norm_layer is not None:
layers += [conv2d, norm_layer(v), act_layer(inplace=True)]
else:
layers += [conv2d, act_layer(inplace=True)]
prev_chs = v
self.features = nn.Sequential(*layers)
self.feature_info.append(dict(num_chs=prev_chs, reduction=net_stride, module=f'features.{len(layers) - 1}'))
self.pre_logits = ConvMlp(
prev_chs,
self.num_features,
7,
mlp_ratio=mlp_ratio,
drop_rate=drop_rate,
act_layer=act_layer,
conv_layer=conv_layer,
)
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
)
self._initialize_weights()
@torch.jit.ignore
def group_matcher(self, coarse=False):
# this treats BN layers as separate groups for bn variants, a lot of effort to fix that
return dict(stem=r'^features\.0', blocks=r'^features\.(\d+)')
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.head = ClassifierHead(
self.num_features,
self.num_classes,
pool_type=global_pool,
drop_rate=self.drop_rate,
)
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
x = self.features(x)
return x
def forward_head(self, x: torch.Tensor, pre_logits: bool = False):
x = self.pre_logits(x)
return x if pre_logits else self.head(x)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _initialize_weights(self) -> None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
def _filter_fn(state_dict):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
out_dict = {}
for k, v in state_dict.items():
k_r = k
k_r = k_r.replace('classifier.0', 'pre_logits.fc1')
k_r = k_r.replace('classifier.3', 'pre_logits.fc2')
k_r = k_r.replace('classifier.6', 'head.fc')
if 'classifier.0.weight' in k:
v = v.reshape(-1, 512, 7, 7)
if 'classifier.3.weight' in k:
v = v.reshape(-1, 4096, 1, 1)
out_dict[k_r] = v
return out_dict
def _create_vgg(variant: str, pretrained: bool, **kwargs: Any) -> VGG:
cfg = variant.split('_')[0]
# NOTE: VGG is one of few models with stride==1 features w/ 6 out_indices [0..5]
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5))
model = build_model_with_cfg(
VGG,
variant,
pretrained,
model_cfg=cfgs[cfg],
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
pretrained_filter_fn=_filter_fn,
**kwargs,
)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'features.0', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'vgg11.tv_in1k': _cfg(hf_hub_id='timm/'),
'vgg13.tv_in1k': _cfg(hf_hub_id='timm/'),
'vgg16.tv_in1k': _cfg(hf_hub_id='timm/'),
'vgg19.tv_in1k': _cfg(hf_hub_id='timm/'),
'vgg11_bn.tv_in1k': _cfg(hf_hub_id='timm/'),
'vgg13_bn.tv_in1k': _cfg(hf_hub_id='timm/'),
'vgg16_bn.tv_in1k': _cfg(hf_hub_id='timm/'),
'vgg19_bn.tv_in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def vgg11(pretrained: bool = False, **kwargs: Any) -> VGG:
r"""VGG 11-layer model (configuration "A") from
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
"""
model_args = dict(**kwargs)
return _create_vgg('vgg11', pretrained=pretrained, **model_args)
@register_model
def vgg11_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
r"""VGG 11-layer model (configuration "A") with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
"""
model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
return _create_vgg('vgg11_bn', pretrained=pretrained, **model_args)
@register_model
def vgg13(pretrained: bool = False, **kwargs: Any) -> VGG:
r"""VGG 13-layer model (configuration "B")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
"""
model_args = dict(**kwargs)
return _create_vgg('vgg13', pretrained=pretrained, **model_args)
@register_model
def vgg13_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
r"""VGG 13-layer model (configuration "B") with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
"""
model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
return _create_vgg('vgg13_bn', pretrained=pretrained, **model_args)
@register_model
def vgg16(pretrained: bool = False, **kwargs: Any) -> VGG:
r"""VGG 16-layer model (configuration "D")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
"""
model_args = dict(**kwargs)
return _create_vgg('vgg16', pretrained=pretrained, **model_args)
@register_model
def vgg16_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
r"""VGG 16-layer model (configuration "D") with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
"""
model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
return _create_vgg('vgg16_bn', pretrained=pretrained, **model_args)
@register_model
def vgg19(pretrained: bool = False, **kwargs: Any) -> VGG:
r"""VGG 19-layer model (configuration "E")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
"""
model_args = dict(**kwargs)
return _create_vgg('vgg19', pretrained=pretrained, **model_args)
@register_model
def vgg19_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
r"""VGG 19-layer model (configuration 'E') with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
"""
model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
return _create_vgg('vgg19_bn', pretrained=pretrained, **model_args)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/resnest.py
|
""" ResNeSt Models
Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
from torch import nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import SplitAttn
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
from .resnet import ResNet
class ResNestBottleneck(nn.Module):
"""ResNet Bottleneck
"""
# pylint: disable=unused-argument
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
radix=1,
cardinality=1,
base_width=64,
avd=False,
avd_first=False,
is_first=False,
reduce_first=1,
dilation=1,
first_dilation=None,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
attn_layer=None,
aa_layer=None,
drop_block=None,
drop_path=None,
):
super(ResNestBottleneck, self).__init__()
assert reduce_first == 1 # not supported
assert attn_layer is None # not supported
assert aa_layer is None # TODO not yet supported
assert drop_path is None # TODO not yet supported
group_width = int(planes * (base_width / 64.)) * cardinality
first_dilation = first_dilation or dilation
if avd and (stride > 1 or is_first):
avd_stride = stride
stride = 1
else:
avd_stride = 0
self.radix = radix
self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
self.bn1 = norm_layer(group_width)
self.act1 = act_layer(inplace=True)
self.avd_first = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and avd_first else None
if self.radix >= 1:
self.conv2 = SplitAttn(
group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
dilation=first_dilation, groups=cardinality, radix=radix, norm_layer=norm_layer, drop_layer=drop_block)
self.bn2 = nn.Identity()
self.drop_block = nn.Identity()
self.act2 = nn.Identity()
else:
self.conv2 = nn.Conv2d(
group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
dilation=first_dilation, groups=cardinality, bias=False)
self.bn2 = norm_layer(group_width)
self.drop_block = drop_block() if drop_block is not None else nn.Identity()
self.act2 = act_layer(inplace=True)
self.avd_last = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and not avd_first else None
self.conv3 = nn.Conv2d(group_width, planes * 4, kernel_size=1, bias=False)
self.bn3 = norm_layer(planes*4)
self.act3 = act_layer(inplace=True)
self.downsample = downsample
def zero_init_last(self):
if getattr(self.bn3, 'weight', None) is not None:
nn.init.zeros_(self.bn3.weight)
def forward(self, x):
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.act1(out)
if self.avd_first is not None:
out = self.avd_first(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.drop_block(out)
out = self.act2(out)
if self.avd_last is not None:
out = self.avd_last(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out += shortcut
out = self.act3(out)
return out
def _create_resnest(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
ResNet,
variant,
pretrained,
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1.0', 'classifier': 'fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'resnest14d.gluon_in1k': _cfg(hf_hub_id='timm/'),
'resnest26d.gluon_in1k': _cfg(hf_hub_id='timm/'),
'resnest50d.in1k': _cfg(hf_hub_id='timm/'),
'resnest101e.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8)),
'resnest200e.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=0.909, interpolation='bicubic'),
'resnest269e.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 416, 416), pool_size=(13, 13), crop_pct=0.928, interpolation='bicubic'),
'resnest50d_4s2x40d.in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic'),
'resnest50d_1s4x24d.in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic')
})
@register_model
def resnest14d(pretrained=False, **kwargs) -> ResNet:
""" ResNeSt-14d model. Weights ported from GluonCV.
"""
model_kwargs = dict(
block=ResNestBottleneck, layers=[1, 1, 1, 1],
stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False))
return _create_resnest('resnest14d', pretrained=pretrained, **dict(model_kwargs, **kwargs))
@register_model
def resnest26d(pretrained=False, **kwargs) -> ResNet:
""" ResNeSt-26d model. Weights ported from GluonCV.
"""
model_kwargs = dict(
block=ResNestBottleneck, layers=[2, 2, 2, 2],
stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False))
return _create_resnest('resnest26d', pretrained=pretrained, **dict(model_kwargs, **kwargs))
@register_model
def resnest50d(pretrained=False, **kwargs) -> ResNet:
""" ResNeSt-50d model. Matches paper ResNeSt-50 model, https://arxiv.org/abs/2004.08955
Since this codebase supports all possible variations, 'd' for deep stem, stem_width 32, avg in downsample.
"""
model_kwargs = dict(
block=ResNestBottleneck, layers=[3, 4, 6, 3],
stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False))
return _create_resnest('resnest50d', pretrained=pretrained, **dict(model_kwargs, **kwargs))
@register_model
def resnest101e(pretrained=False, **kwargs) -> ResNet:
""" ResNeSt-101e model. Matches paper ResNeSt-101 model, https://arxiv.org/abs/2004.08955
Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
"""
model_kwargs = dict(
block=ResNestBottleneck, layers=[3, 4, 23, 3],
stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False))
return _create_resnest('resnest101e', pretrained=pretrained, **dict(model_kwargs, **kwargs))
@register_model
def resnest200e(pretrained=False, **kwargs) -> ResNet:
""" ResNeSt-200e model. Matches paper ResNeSt-200 model, https://arxiv.org/abs/2004.08955
Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
"""
model_kwargs = dict(
block=ResNestBottleneck, layers=[3, 24, 36, 3],
stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False))
return _create_resnest('resnest200e', pretrained=pretrained, **dict(model_kwargs, **kwargs))
@register_model
def resnest269e(pretrained=False, **kwargs) -> ResNet:
""" ResNeSt-269e model. Matches paper ResNeSt-269 model, https://arxiv.org/abs/2004.08955
Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
"""
model_kwargs = dict(
block=ResNestBottleneck, layers=[3, 30, 48, 8],
stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False))
return _create_resnest('resnest269e', pretrained=pretrained, **dict(model_kwargs, **kwargs))
@register_model
def resnest50d_4s2x40d(pretrained=False, **kwargs) -> ResNet:
"""ResNeSt-50 4s2x40d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md
"""
model_kwargs = dict(
block=ResNestBottleneck, layers=[3, 4, 6, 3],
stem_type='deep', stem_width=32, avg_down=True, base_width=40, cardinality=2,
block_args=dict(radix=4, avd=True, avd_first=True))
return _create_resnest('resnest50d_4s2x40d', pretrained=pretrained, **dict(model_kwargs, **kwargs))
@register_model
def resnest50d_1s4x24d(pretrained=False, **kwargs) -> ResNet:
"""ResNeSt-50 1s4x24d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md
"""
model_kwargs = dict(
block=ResNestBottleneck, layers=[3, 4, 6, 3],
stem_type='deep', stem_width=32, avg_down=True, base_width=24, cardinality=4,
block_args=dict(radix=1, avd=True, avd_first=True))
return _create_resnest('resnest50d_1s4x24d', pretrained=pretrained, **dict(model_kwargs, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/edgenext.py
|
""" EdgeNeXt
Paper: `EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications`
- https://arxiv.org/abs/2206.10589
Original code and weights from https://github.com/mmaaz60/EdgeNeXt
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
import math
from collections import OrderedDict
from functools import partial
from typing import Tuple
import torch
import torch.nn.functional as F
from torch import nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d, \
use_fused_attn
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_module
from ._manipulate import named_apply, checkpoint_seq
from ._registry import register_model, generate_default_cfgs
__all__ = ['EdgeNeXt'] # model_registry will add each entrypoint fn to this
@register_notrace_module # reason: FX can't symbolically trace torch.arange in forward method
class PositionalEncodingFourier(nn.Module):
def __init__(self, hidden_dim=32, dim=768, temperature=10000):
super().__init__()
self.token_projection = nn.Conv2d(hidden_dim * 2, dim, kernel_size=1)
self.scale = 2 * math.pi
self.temperature = temperature
self.hidden_dim = hidden_dim
self.dim = dim
def forward(self, shape: Tuple[int, int, int]):
device = self.token_projection.weight.device
dtype = self.token_projection.weight.dtype
inv_mask = ~torch.zeros(shape).to(device=device, dtype=torch.bool)
y_embed = inv_mask.cumsum(1, dtype=dtype)
x_embed = inv_mask.cumsum(2, dtype=dtype)
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_t = torch.arange(self.hidden_dim, dtype=dtype, device=device)
dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode='floor') / self.hidden_dim)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack(
(pos_x[:, :, :, 0::2].sin(),
pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos_y = torch.stack(
(pos_y[:, :, :, 0::2].sin(),
pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
pos = self.token_projection(pos)
return pos
class ConvBlock(nn.Module):
def __init__(
self,
dim,
dim_out=None,
kernel_size=7,
stride=1,
conv_bias=True,
expand_ratio=4,
ls_init_value=1e-6,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU, drop_path=0.,
):
super().__init__()
dim_out = dim_out or dim
self.shortcut_after_dw = stride > 1 or dim != dim_out
self.conv_dw = create_conv2d(
dim, dim_out, kernel_size=kernel_size, stride=stride, depthwise=True, bias=conv_bias)
self.norm = norm_layer(dim_out)
self.mlp = Mlp(dim_out, int(expand_ratio * dim_out), act_layer=act_layer)
self.gamma = nn.Parameter(ls_init_value * torch.ones(dim_out)) if ls_init_value > 0 else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.conv_dw(x)
if self.shortcut_after_dw:
shortcut = x
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.norm(x)
x = self.mlp(x)
if self.gamma is not None:
x = self.gamma * x
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
x = shortcut + self.drop_path(x)
return x
class CrossCovarianceAttn(nn.Module):
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.
):
super().__init__()
self.num_heads = num_heads
self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 4, 1)
q, k, v = qkv.unbind(0)
# NOTE, this is NOT spatial attn, q, k, v are B, num_heads, C, L --> C x C attn map
attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) * self.temperature
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v)
x = x.permute(0, 3, 1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
@torch.jit.ignore
def no_weight_decay(self):
return {'temperature'}
class SplitTransposeBlock(nn.Module):
def __init__(
self,
dim,
num_scales=1,
num_heads=8,
expand_ratio=4,
use_pos_emb=True,
conv_bias=True,
qkv_bias=True,
ls_init_value=1e-6,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU,
drop_path=0.,
attn_drop=0.,
proj_drop=0.
):
super().__init__()
width = max(int(math.ceil(dim / num_scales)), int(math.floor(dim // num_scales)))
self.width = width
self.num_scales = max(1, num_scales - 1)
convs = []
for i in range(self.num_scales):
convs.append(create_conv2d(width, width, kernel_size=3, depthwise=True, bias=conv_bias))
self.convs = nn.ModuleList(convs)
self.pos_embd = None
if use_pos_emb:
self.pos_embd = PositionalEncodingFourier(dim=dim)
self.norm_xca = norm_layer(dim)
self.gamma_xca = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value > 0 else None
self.xca = CrossCovarianceAttn(
dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=proj_drop)
self.norm = norm_layer(dim, eps=1e-6)
self.mlp = Mlp(dim, int(expand_ratio * dim), act_layer=act_layer)
self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value > 0 else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
# scales code re-written for torchscript as per my res2net fixes -rw
# NOTE torch.split(x, self.width, 1) causing issues with ONNX export
spx = x.chunk(len(self.convs) + 1, dim=1)
spo = []
sp = spx[0]
for i, conv in enumerate(self.convs):
if i > 0:
sp = sp + spx[i]
sp = conv(sp)
spo.append(sp)
spo.append(spx[-1])
x = torch.cat(spo, 1)
# XCA
B, C, H, W = x.shape
x = x.reshape(B, C, H * W).permute(0, 2, 1)
if self.pos_embd is not None:
pos_encoding = self.pos_embd((B, H, W)).reshape(B, -1, x.shape[1]).permute(0, 2, 1)
x = x + pos_encoding
x = x + self.drop_path(self.gamma_xca * self.xca(self.norm_xca(x)))
x = x.reshape(B, H, W, C)
# Inverted Bottleneck
x = self.norm(x)
x = self.mlp(x)
if self.gamma is not None:
x = self.gamma * x
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
x = shortcut + self.drop_path(x)
return x
class EdgeNeXtStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=2,
depth=2,
num_global_blocks=1,
num_heads=4,
scales=2,
kernel_size=7,
expand_ratio=4,
use_pos_emb=False,
downsample_block=False,
conv_bias=True,
ls_init_value=1.0,
drop_path_rates=None,
norm_layer=LayerNorm2d,
norm_layer_cl=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU
):
super().__init__()
self.grad_checkpointing = False
if downsample_block or stride == 1:
self.downsample = nn.Identity()
else:
self.downsample = nn.Sequential(
norm_layer(in_chs),
nn.Conv2d(in_chs, out_chs, kernel_size=2, stride=2, bias=conv_bias)
)
in_chs = out_chs
stage_blocks = []
for i in range(depth):
if i < depth - num_global_blocks:
stage_blocks.append(
ConvBlock(
dim=in_chs,
dim_out=out_chs,
stride=stride if downsample_block and i == 0 else 1,
conv_bias=conv_bias,
kernel_size=kernel_size,
expand_ratio=expand_ratio,
ls_init_value=ls_init_value,
drop_path=drop_path_rates[i],
norm_layer=norm_layer_cl,
act_layer=act_layer,
)
)
else:
stage_blocks.append(
SplitTransposeBlock(
dim=in_chs,
num_scales=scales,
num_heads=num_heads,
expand_ratio=expand_ratio,
use_pos_emb=use_pos_emb,
conv_bias=conv_bias,
ls_init_value=ls_init_value,
drop_path=drop_path_rates[i],
norm_layer=norm_layer_cl,
act_layer=act_layer,
)
)
in_chs = out_chs
self.blocks = nn.Sequential(*stage_blocks)
def forward(self, x):
x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class EdgeNeXt(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
dims=(24, 48, 88, 168),
depths=(3, 3, 9, 3),
global_block_counts=(0, 1, 1, 1),
kernel_sizes=(3, 5, 7, 9),
heads=(8, 8, 8, 8),
d2_scales=(2, 2, 3, 4),
use_pos_emb=(False, True, False, False),
ls_init_value=1e-6,
head_init_scale=1.,
expand_ratio=4,
downsample_block=False,
conv_bias=True,
stem_type='patch',
head_norm_first=False,
act_layer=nn.GELU,
drop_path_rate=0.,
drop_rate=0.,
):
super().__init__()
self.num_classes = num_classes
self.global_pool = global_pool
self.drop_rate = drop_rate
norm_layer = partial(LayerNorm2d, eps=1e-6)
norm_layer_cl = partial(nn.LayerNorm, eps=1e-6)
self.feature_info = []
assert stem_type in ('patch', 'overlap')
if stem_type == 'patch':
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4, bias=conv_bias),
norm_layer(dims[0]),
)
else:
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=9, stride=4, padding=9 // 2, bias=conv_bias),
norm_layer(dims[0]),
)
curr_stride = 4
stages = []
dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
in_chs = dims[0]
for i in range(4):
stride = 2 if curr_stride == 2 or i > 0 else 1
# FIXME support dilation / output_stride
curr_stride *= stride
stages.append(EdgeNeXtStage(
in_chs=in_chs,
out_chs=dims[i],
stride=stride,
depth=depths[i],
num_global_blocks=global_block_counts[i],
num_heads=heads[i],
drop_path_rates=dp_rates[i],
scales=d2_scales[i],
expand_ratio=expand_ratio,
kernel_size=kernel_sizes[i],
use_pos_emb=use_pos_emb[i],
ls_init_value=ls_init_value,
downsample_block=downsample_block,
conv_bias=conv_bias,
norm_layer=norm_layer,
norm_layer_cl=norm_layer_cl,
act_layer=act_layer,
))
# NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2
in_chs = dims[i]
self.feature_info += [dict(num_chs=in_chs, reduction=curr_stride, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
self.num_features = dims[-1]
self.norm_pre = norm_layer(self.num_features) if head_norm_first else nn.Identity()
self.head = nn.Sequential(OrderedDict([
('global_pool', SelectAdaptivePool2d(pool_type=global_pool)),
('norm', nn.Identity() if head_norm_first else norm_layer(self.num_features)),
('flatten', nn.Flatten(1) if global_pool else nn.Identity()),
('drop', nn.Dropout(self.drop_rate)),
('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())]))
named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+)\.downsample', (0,)), # blocks
(r'^stages\.(\d+)\.blocks\.(\d+)', None),
(r'^norm_pre', (99999,))
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes=0, global_pool=None):
if global_pool is not None:
self.head.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.head.flatten = nn.Flatten(1) if global_pool else nn.Identity()
self.head.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
x = self.norm_pre(x)
return x
def forward_head(self, x, pre_logits: bool = False):
# NOTE nn.Sequential in head broken down since can't call head[:-1](x) in torchscript :(
x = self.head.global_pool(x)
x = self.head.norm(x)
x = self.head.flatten(x)
x = self.head.drop(x)
return x if pre_logits else self.head.fc(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module, name=None, head_init_scale=1.0):
if isinstance(module, nn.Conv2d):
trunc_normal_tf_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Linear):
trunc_normal_tf_(module.weight, std=.02)
nn.init.zeros_(module.bias)
if name and 'head.' in name:
module.weight.data.mul_(head_init_scale)
module.bias.data.mul_(head_init_scale)
def checkpoint_filter_fn(state_dict, model):
""" Remap FB checkpoints -> timm """
if 'head.norm.weight' in state_dict or 'norm_pre.weight' in state_dict:
return state_dict # non-FB checkpoint
# models were released as train checkpoints... :/
if 'model_ema' in state_dict:
state_dict = state_dict['model_ema']
elif 'model' in state_dict:
state_dict = state_dict['model']
elif 'state_dict' in state_dict:
state_dict = state_dict['state_dict']
out_dict = {}
import re
for k, v in state_dict.items():
k = k.replace('downsample_layers.0.', 'stem.')
k = re.sub(r'stages.([0-9]+).([0-9]+)', r'stages.\1.blocks.\2', k)
k = re.sub(r'downsample_layers.([0-9]+).([0-9]+)', r'stages.\1.downsample.\2', k)
k = k.replace('dwconv', 'conv_dw')
k = k.replace('pwconv', 'mlp.fc')
k = k.replace('head.', 'head.fc.')
if k.startswith('norm.'):
k = k.replace('norm', 'head.norm')
if v.ndim == 2 and 'head' not in k:
model_shape = model.state_dict()[k].shape
v = v.reshape(model_shape)
out_dict[k] = v
return out_dict
def _create_edgenext(variant, pretrained=False, **kwargs):
model = build_model_with_cfg(
EdgeNeXt, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
**kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
'crop_pct': 0.9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'edgenext_xx_small.in1k': _cfg(
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'edgenext_x_small.in1k': _cfg(
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'edgenext_small.usi_in1k': _cfg( # USI weights
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0,
),
'edgenext_base.usi_in1k': _cfg( # USI weights
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0,
),
'edgenext_base.in21k_ft_in1k': _cfg( # USI weights
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0,
),
'edgenext_small_rw.sw_in1k': _cfg(
hf_hub_id='timm/',
test_input_size=(3, 320, 320), test_crop_pct=1.0,
),
})
@register_model
def edgenext_xx_small(pretrained=False, **kwargs) -> EdgeNeXt:
# 1.33M & 260.58M @ 256 resolution
# 71.23% Top-1 accuracy
# No AA, Color Jitter=0.4, No Mixup & Cutmix, DropPath=0.0, BS=4096, lr=0.006, multi-scale-sampler
# Jetson FPS=51.66 versus 47.67 for MobileViT_XXS
# For A100: FPS @ BS=1: 212.13 & @ BS=256: 7042.06 versus FPS @ BS=1: 96.68 & @ BS=256: 4624.71 for MobileViT_XXS
model_args = dict(depths=(2, 2, 6, 2), dims=(24, 48, 88, 168), heads=(4, 4, 4, 4))
return _create_edgenext('edgenext_xx_small', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def edgenext_x_small(pretrained=False, **kwargs) -> EdgeNeXt:
# 2.34M & 538.0M @ 256 resolution
# 75.00% Top-1 accuracy
# No AA, No Mixup & Cutmix, DropPath=0.0, BS=4096, lr=0.006, multi-scale-sampler
# Jetson FPS=31.61 versus 28.49 for MobileViT_XS
# For A100: FPS @ BS=1: 179.55 & @ BS=256: 4404.95 versus FPS @ BS=1: 94.55 & @ BS=256: 2361.53 for MobileViT_XS
model_args = dict(depths=(3, 3, 9, 3), dims=(32, 64, 100, 192), heads=(4, 4, 4, 4))
return _create_edgenext('edgenext_x_small', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def edgenext_small(pretrained=False, **kwargs) -> EdgeNeXt:
# 5.59M & 1260.59M @ 256 resolution
# 79.43% Top-1 accuracy
# AA=True, No Mixup & Cutmix, DropPath=0.1, BS=4096, lr=0.006, multi-scale-sampler
# Jetson FPS=20.47 versus 18.86 for MobileViT_S
# For A100: FPS @ BS=1: 172.33 & @ BS=256: 3010.25 versus FPS @ BS=1: 93.84 & @ BS=256: 1785.92 for MobileViT_S
model_args = dict(depths=(3, 3, 9, 3), dims=(48, 96, 160, 304))
return _create_edgenext('edgenext_small', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def edgenext_base(pretrained=False, **kwargs) -> EdgeNeXt:
# 18.51M & 3840.93M @ 256 resolution
# 82.5% (normal) 83.7% (USI) Top-1 accuracy
# AA=True, Mixup & Cutmix, DropPath=0.1, BS=4096, lr=0.006, multi-scale-sampler
# Jetson FPS=xx.xx versus xx.xx for MobileViT_S
# For A100: FPS @ BS=1: xxx.xx & @ BS=256: xxxx.xx
model_args = dict(depths=[3, 3, 9, 3], dims=[80, 160, 288, 584])
return _create_edgenext('edgenext_base', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def edgenext_small_rw(pretrained=False, **kwargs) -> EdgeNeXt:
model_args = dict(
depths=(3, 3, 9, 3), dims=(48, 96, 192, 384),
downsample_block=True, conv_bias=False, stem_type='overlap')
return _create_edgenext('edgenext_small_rw', pretrained=pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/res2net.py
|
""" Res2Net and Res2NeXt
Adapted from Official Pytorch impl at: https://github.com/gasvn/Res2Net/
Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
"""
import math
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
from .resnet import ResNet
__all__ = []
class Bottle2neck(nn.Module):
""" Res2Net/Res2NeXT Bottleneck
Adapted from https://github.com/gasvn/Res2Net/blob/master/res2net.py
"""
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1,
base_width=26,
scale=4,
dilation=1,
first_dilation=None,
act_layer=nn.ReLU,
norm_layer=None,
attn_layer=None,
**_,
):
super(Bottle2neck, self).__init__()
self.scale = scale
self.is_first = stride > 1 or downsample is not None
self.num_scales = max(1, scale - 1)
width = int(math.floor(planes * (base_width / 64.0))) * cardinality
self.width = width
outplanes = planes * self.expansion
first_dilation = first_dilation or dilation
self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False)
self.bn1 = norm_layer(width * scale)
convs = []
bns = []
for i in range(self.num_scales):
convs.append(nn.Conv2d(
width, width, kernel_size=3, stride=stride, padding=first_dilation,
dilation=first_dilation, groups=cardinality, bias=False))
bns.append(norm_layer(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
if self.is_first:
# FIXME this should probably have count_include_pad=False, but hurts original weights
self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
else:
self.pool = None
self.conv3 = nn.Conv2d(width * scale, outplanes, kernel_size=1, bias=False)
self.bn3 = norm_layer(outplanes)
self.se = attn_layer(outplanes) if attn_layer is not None else None
self.relu = act_layer(inplace=True)
self.downsample = downsample
def zero_init_last(self):
if getattr(self.bn3, 'weight', None) is not None:
nn.init.zeros_(self.bn3.weight)
def forward(self, x):
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out, self.width, 1)
spo = []
sp = spx[0] # redundant, for torchscript
for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
if i == 0 or self.is_first:
sp = spx[i]
else:
sp = sp + spx[i]
sp = conv(sp)
sp = bn(sp)
sp = self.relu(sp)
spo.append(sp)
if self.scale > 1:
if self.pool is not None: # self.is_first == True, None check for torchscript
spo.append(self.pool(spx[-1]))
else:
spo.append(spx[-1])
out = torch.cat(spo, 1)
out = self.conv3(out)
out = self.bn3(out)
if self.se is not None:
out = self.se(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out += shortcut
out = self.relu(out)
return out
def _create_res2net(variant, pretrained=False, **kwargs):
return build_model_with_cfg(ResNet, variant, pretrained, **kwargs)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1', 'classifier': 'fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'res2net50_26w_4s.in1k': _cfg(hf_hub_id='timm/'),
'res2net50_48w_2s.in1k': _cfg(hf_hub_id='timm/'),
'res2net50_14w_8s.in1k': _cfg(hf_hub_id='timm/'),
'res2net50_26w_6s.in1k': _cfg(hf_hub_id='timm/'),
'res2net50_26w_8s.in1k': _cfg(hf_hub_id='timm/'),
'res2net101_26w_4s.in1k': _cfg(hf_hub_id='timm/'),
'res2next50.in1k': _cfg(hf_hub_id='timm/'),
'res2net50d.in1k': _cfg(hf_hub_id='timm/', first_conv='conv1.0'),
'res2net101d.in1k': _cfg(hf_hub_id='timm/', first_conv='conv1.0'),
})
@register_model
def res2net50_26w_4s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 26w4s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=4))
return _create_res2net('res2net50_26w_4s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net101_26w_4s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-101 26w4s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, block_args=dict(scale=4))
return _create_res2net('res2net101_26w_4s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50_26w_6s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 26w6s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=6))
return _create_res2net('res2net50_26w_6s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50_26w_8s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 26w8s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=8))
return _create_res2net('res2net50_26w_8s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50_48w_2s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 48w2s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=48, block_args=dict(scale=2))
return _create_res2net('res2net50_48w_2s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50_14w_8s(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Res2Net-50 14w8s model.
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=14, block_args=dict(scale=8))
return _create_res2net('res2net50_14w_8s', pretrained, **dict(model_args, **kwargs))
@register_model
def res2next50(pretrained=False, **kwargs) -> ResNet:
"""Construct Res2NeXt-50 4s
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=4, cardinality=8, block_args=dict(scale=4))
return _create_res2net('res2next50', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net50d(pretrained=False, **kwargs) -> ResNet:
"""Construct Res2Net-50
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, stem_type='deep',
avg_down=True, stem_width=32, block_args=dict(scale=4))
return _create_res2net('res2net50d', pretrained, **dict(model_args, **kwargs))
@register_model
def res2net101d(pretrained=False, **kwargs) -> ResNet:
"""Construct Res2Net-50
"""
model_args = dict(
block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, stem_type='deep',
avg_down=True, stem_width=32, block_args=dict(scale=4))
return _create_res2net('res2net101d', pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/_features_fx.py
|
""" PyTorch FX Based Feature Extraction Helpers
Using https://pytorch.org/vision/stable/feature_extraction.html
"""
from typing import Callable, List, Dict, Union, Type
import torch
from torch import nn
from ._features import _get_feature_info, _get_return_layers
try:
from torchvision.models.feature_extraction import create_feature_extractor as _create_feature_extractor
has_fx_feature_extraction = True
except ImportError:
has_fx_feature_extraction = False
# Layers we went to treat as leaf modules
from timm.layers import Conv2dSame, ScaledStdConv2dSame, CondConv2d, StdConv2dSame
from timm.layers.non_local_attn import BilinearAttnTransform
from timm.layers.pool2d_same import MaxPool2dSame, AvgPool2dSame
from timm.layers.norm_act import (
BatchNormAct2d,
SyncBatchNormAct,
FrozenBatchNormAct2d,
GroupNormAct,
GroupNorm1Act,
LayerNormAct,
LayerNormAct2d
)
__all__ = ['register_notrace_module', 'is_notrace_module', 'get_notrace_modules',
'register_notrace_function', 'is_notrace_function', 'get_notrace_functions',
'create_feature_extractor', 'FeatureGraphNet', 'GraphExtractNet']
# NOTE: By default, any modules from timm.models.layers that we want to treat as leaf modules go here
# BUT modules from timm.models should use the registration mechanism below
_leaf_modules = {
BilinearAttnTransform, # reason: flow control t <= 1
# Reason: get_same_padding has a max which raises a control flow error
Conv2dSame, MaxPool2dSame, ScaledStdConv2dSame, StdConv2dSame, AvgPool2dSame,
CondConv2d, # reason: TypeError: F.conv2d received Proxy in groups=self.groups * B (because B = x.shape[0]),
BatchNormAct2d,
SyncBatchNormAct,
FrozenBatchNormAct2d,
GroupNormAct,
GroupNorm1Act,
LayerNormAct,
LayerNormAct2d,
}
try:
from timm.layers import InplaceAbn
_leaf_modules.add(InplaceAbn)
except ImportError:
pass
def register_notrace_module(module: Type[nn.Module]):
"""
Any module not under timm.models.layers should get this decorator if we don't want to trace through it.
"""
_leaf_modules.add(module)
return module
def is_notrace_module(module: Type[nn.Module]):
return module in _leaf_modules
def get_notrace_modules():
return list(_leaf_modules)
# Functions we want to autowrap (treat them as leaves)
_autowrap_functions = set()
def register_notrace_function(func: Callable):
"""
Decorator for functions which ought not to be traced through
"""
_autowrap_functions.add(func)
return func
def is_notrace_function(func: Callable):
return func in _autowrap_functions
def get_notrace_functions():
return list(_autowrap_functions)
def create_feature_extractor(model: nn.Module, return_nodes: Union[Dict[str, str], List[str]]):
assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
return _create_feature_extractor(
model, return_nodes,
tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)}
)
class FeatureGraphNet(nn.Module):
""" A FX Graph based feature extractor that works with the model feature_info metadata
"""
def __init__(self, model, out_indices, out_map=None):
super().__init__()
assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
self.feature_info = _get_feature_info(model, out_indices)
if out_map is not None:
assert len(out_map) == len(out_indices)
return_nodes = _get_return_layers(self.feature_info, out_map)
self.graph_module = create_feature_extractor(model, return_nodes)
def forward(self, x):
return list(self.graph_module(x).values())
class GraphExtractNet(nn.Module):
""" A standalone feature extraction wrapper that maps dict -> list or single tensor
NOTE:
* one can use feature_extractor directly if dictionary output is desired
* unlike FeatureGraphNet, this is intended to be used standalone and not with model feature_info
metadata for builtin feature extraction mode
* create_feature_extractor can be used directly if dictionary output is acceptable
Args:
model: model to extract features from
return_nodes: node names to return features from (dict or list)
squeeze_out: if only one output, and output in list format, flatten to single tensor
"""
def __init__(self, model, return_nodes: Union[Dict[str, str], List[str]], squeeze_out: bool = True):
super().__init__()
self.squeeze_out = squeeze_out
self.graph_module = create_feature_extractor(model, return_nodes)
def forward(self, x) -> Union[List[torch.Tensor], torch.Tensor]:
out = list(self.graph_module(x).values())
if self.squeeze_out and len(out) == 1:
return out[0]
return out
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/_manipulate.py
|
import collections.abc
import math
import re
from collections import defaultdict
from itertools import chain
from typing import Any, Callable, Dict, Iterator, Tuple, Type, Union
import torch
from torch import nn as nn
from torch.utils.checkpoint import checkpoint
__all__ = ['model_parameters', 'named_apply', 'named_modules', 'named_modules_with_params', 'adapt_input_conv',
'group_with_matcher', 'group_modules', 'group_parameters', 'flatten_modules', 'checkpoint_seq']
def model_parameters(model: nn.Module, exclude_head: bool = False):
if exclude_head:
# FIXME this a bit of a quick and dirty hack to skip classifier head params based on ordering
return [p for p in model.parameters()][:-2]
else:
return model.parameters()
def named_apply(
fn: Callable,
module: nn.Module, name='',
depth_first: bool = True,
include_root: bool = False,
) -> nn.Module:
if not depth_first and include_root:
fn(module=module, name=name)
for child_name, child_module in module.named_children():
child_name = '.'.join((name, child_name)) if name else child_name
named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
if depth_first and include_root:
fn(module=module, name=name)
return module
def named_modules(
module: nn.Module,
name: str = '',
depth_first: bool = True,
include_root: bool = False,
):
if not depth_first and include_root:
yield name, module
for child_name, child_module in module.named_children():
child_name = '.'.join((name, child_name)) if name else child_name
yield from named_modules(
module=child_module, name=child_name, depth_first=depth_first, include_root=True)
if depth_first and include_root:
yield name, module
def named_modules_with_params(
module: nn.Module,
name: str = '',
depth_first: bool = True,
include_root: bool = False,
):
if module._parameters and not depth_first and include_root:
yield name, module
for child_name, child_module in module.named_children():
child_name = '.'.join((name, child_name)) if name else child_name
yield from named_modules_with_params(
module=child_module, name=child_name, depth_first=depth_first, include_root=True)
if module._parameters and depth_first and include_root:
yield name, module
MATCH_PREV_GROUP = (99999,)
def group_with_matcher(
named_objects: Iterator[Tuple[str, Any]],
group_matcher: Union[Dict, Callable],
return_values: bool = False,
reverse: bool = False
):
if isinstance(group_matcher, dict):
# dictionary matcher contains a dict of raw-string regex expr that must be compiled
compiled = []
for group_ordinal, (group_name, mspec) in enumerate(group_matcher.items()):
if mspec is None:
continue
# map all matching specifications into 3-tuple (compiled re, prefix, suffix)
if isinstance(mspec, (tuple, list)):
# multi-entry match specifications require each sub-spec to be a 2-tuple (re, suffix)
for sspec in mspec:
compiled += [(re.compile(sspec[0]), (group_ordinal,), sspec[1])]
else:
compiled += [(re.compile(mspec), (group_ordinal,), None)]
group_matcher = compiled
def _get_grouping(name):
if isinstance(group_matcher, (list, tuple)):
for match_fn, prefix, suffix in group_matcher:
r = match_fn.match(name)
if r:
parts = (prefix, r.groups(), suffix)
# map all tuple elem to int for numeric sort, filter out None entries
return tuple(map(float, chain.from_iterable(filter(None, parts))))
return float('inf'), # un-matched layers (neck, head) mapped to largest ordinal
else:
ord = group_matcher(name)
if not isinstance(ord, collections.abc.Iterable):
return ord,
return tuple(ord)
# map layers into groups via ordinals (ints or tuples of ints) from matcher
grouping = defaultdict(list)
for k, v in named_objects:
grouping[_get_grouping(k)].append(v if return_values else k)
# remap to integers
layer_id_to_param = defaultdict(list)
lid = -1
for k in sorted(filter(lambda x: x is not None, grouping.keys())):
if lid < 0 or k[-1] != MATCH_PREV_GROUP[0]:
lid += 1
layer_id_to_param[lid].extend(grouping[k])
if reverse:
assert not return_values, "reverse mapping only sensible for name output"
# output reverse mapping
param_to_layer_id = {}
for lid, lm in layer_id_to_param.items():
for n in lm:
param_to_layer_id[n] = lid
return param_to_layer_id
return layer_id_to_param
def group_parameters(
module: nn.Module,
group_matcher,
return_values: bool = False,
reverse: bool = False,
):
return group_with_matcher(
module.named_parameters(), group_matcher, return_values=return_values, reverse=reverse)
def group_modules(
module: nn.Module,
group_matcher,
return_values: bool = False,
reverse: bool = False,
):
return group_with_matcher(
named_modules_with_params(module), group_matcher, return_values=return_values, reverse=reverse)
def flatten_modules(
named_modules: Iterator[Tuple[str, nn.Module]],
depth: int = 1,
prefix: Union[str, Tuple[str, ...]] = '',
module_types: Union[str, Tuple[Type[nn.Module]]] = 'sequential',
):
prefix_is_tuple = isinstance(prefix, tuple)
if isinstance(module_types, str):
if module_types == 'container':
module_types = (nn.Sequential, nn.ModuleList, nn.ModuleDict)
else:
module_types = (nn.Sequential,)
for name, module in named_modules:
if depth and isinstance(module, module_types):
yield from flatten_modules(
module.named_children(),
depth - 1,
prefix=(name,) if prefix_is_tuple else name,
module_types=module_types,
)
else:
if prefix_is_tuple:
name = prefix + (name,)
yield name, module
else:
if prefix:
name = '.'.join([prefix, name])
yield name, module
def checkpoint_seq(
functions,
x,
every=1,
flatten=False,
skip_last=False,
preserve_rng_state=True
):
r"""A helper function for checkpointing sequential models.
Sequential models execute a list of modules/functions in order
(sequentially). Therefore, we can divide such a sequence into segments
and checkpoint each segment. All segments except run in :func:`torch.no_grad`
manner, i.e., not storing the intermediate activations. The inputs of each
checkpointed segment will be saved for re-running the segment in the backward pass.
See :func:`~torch.utils.checkpoint.checkpoint` on how checkpointing works.
.. warning::
Checkpointing currently only supports :func:`torch.autograd.backward`
and only if its `inputs` argument is not passed. :func:`torch.autograd.grad`
is not supported.
.. warning:
At least one of the inputs needs to have :code:`requires_grad=True` if
grads are needed for model inputs, otherwise the checkpointed part of the
model won't have gradients.
Args:
functions: A :class:`torch.nn.Sequential` or the list of modules or functions to run sequentially.
x: A Tensor that is input to :attr:`functions`
every: checkpoint every-n functions (default: 1)
flatten (bool): flatten nn.Sequential of nn.Sequentials
skip_last (bool): skip checkpointing the last function in the sequence if True
preserve_rng_state (bool, optional, default=True): Omit stashing and restoring
the RNG state during each checkpoint.
Returns:
Output of running :attr:`functions` sequentially on :attr:`*inputs`
Example:
>>> model = nn.Sequential(...)
>>> input_var = checkpoint_seq(model, input_var, every=2)
"""
def run_function(start, end, functions):
def forward(_x):
for j in range(start, end + 1):
_x = functions[j](_x)
return _x
return forward
if isinstance(functions, torch.nn.Sequential):
functions = functions.children()
if flatten:
functions = chain.from_iterable(functions)
if not isinstance(functions, (tuple, list)):
functions = tuple(functions)
num_checkpointed = len(functions)
if skip_last:
num_checkpointed -= 1
end = -1
for start in range(0, num_checkpointed, every):
end = min(start + every - 1, num_checkpointed - 1)
x = checkpoint(run_function(start, end, functions), x, preserve_rng_state=preserve_rng_state)
if skip_last:
return run_function(end + 1, len(functions) - 1, functions)(x)
return x
def adapt_input_conv(in_chans, conv_weight):
conv_type = conv_weight.dtype
conv_weight = conv_weight.float() # Some weights are in torch.half, ensure it's float for sum on CPU
O, I, J, K = conv_weight.shape
if in_chans == 1:
if I > 3:
assert conv_weight.shape[1] % 3 == 0
# For models with space2depth stems
conv_weight = conv_weight.reshape(O, I // 3, 3, J, K)
conv_weight = conv_weight.sum(dim=2, keepdim=False)
else:
conv_weight = conv_weight.sum(dim=1, keepdim=True)
elif in_chans != 3:
if I != 3:
raise NotImplementedError('Weight format not supported by conversion.')
else:
# NOTE this strategy should be better than random init, but there could be other combinations of
# the original RGB input layer weights that'd work better for specific cases.
repeat = int(math.ceil(in_chans / 3))
conv_weight = conv_weight.repeat(1, repeat, 1, 1)[:, :in_chans, :, :]
conv_weight *= (3 / float(in_chans))
conv_weight = conv_weight.to(conv_type)
return conv_weight
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/repvit.py
|
""" RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={2307.09283},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Adapted from official impl at https://github.com/jameslahm/RepViT
"""
__all__ = ['RepVit']
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from ._registry import register_model, generate_default_cfgs
from ._builder import build_model_with_cfg
from timm.layers import SqueezeExcite, trunc_normal_, to_ntuple, to_2tuple
from ._manipulate import checkpoint_seq
import torch
class ConvNorm(nn.Sequential):
def __init__(self, in_dim, out_dim, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
super().__init__()
self.add_module('c', nn.Conv2d(in_dim, out_dim, ks, stride, pad, dilation, groups, bias=False))
self.add_module('bn', nn.BatchNorm2d(out_dim))
nn.init.constant_(self.bn.weight, bn_weight_init)
nn.init.constant_(self.bn.bias, 0)
@torch.no_grad()
def fuse(self):
c, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
m = nn.Conv2d(
w.size(1) * self.c.groups,
w.size(0),
w.shape[2:],
stride=self.c.stride,
padding=self.c.padding,
dilation=self.c.dilation,
groups=self.c.groups,
device=c.weight.device,
)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class NormLinear(nn.Sequential):
def __init__(self, in_dim, out_dim, bias=True, std=0.02):
super().__init__()
self.add_module('bn', nn.BatchNorm1d(in_dim))
self.add_module('l', nn.Linear(in_dim, out_dim, bias=bias))
trunc_normal_(self.l.weight, std=std)
if bias:
nn.init.constant_(self.l.bias, 0)
@torch.no_grad()
def fuse(self):
bn, l = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
b = bn.bias - self.bn.running_mean * self.bn.weight / (bn.running_var + bn.eps) ** 0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.l.weight.T
else:
b = (l.weight @ b[:, None]).view(-1) + self.l.bias
m = nn.Linear(w.size(1), w.size(0), device=l.weight.device)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class RepVggDw(nn.Module):
def __init__(self, ed, kernel_size, legacy=False):
super().__init__()
self.conv = ConvNorm(ed, ed, kernel_size, 1, (kernel_size - 1) // 2, groups=ed)
if legacy:
self.conv1 = ConvNorm(ed, ed, 1, 1, 0, groups=ed)
# Make torchscript happy.
self.bn = nn.Identity()
else:
self.conv1 = nn.Conv2d(ed, ed, 1, 1, 0, groups=ed)
self.bn = nn.BatchNorm2d(ed)
self.dim = ed
self.legacy = legacy
def forward(self, x):
return self.bn(self.conv(x) + self.conv1(x) + x)
@torch.no_grad()
def fuse(self):
conv = self.conv.fuse()
if self.legacy:
conv1 = self.conv1.fuse()
else:
conv1 = self.conv1
conv_w = conv.weight
conv_b = conv.bias
conv1_w = conv1.weight
conv1_b = conv1.bias
conv1_w = nn.functional.pad(conv1_w, [1, 1, 1, 1])
identity = nn.functional.pad(
torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device), [1, 1, 1, 1]
)
final_conv_w = conv_w + conv1_w + identity
final_conv_b = conv_b + conv1_b
conv.weight.data.copy_(final_conv_w)
conv.bias.data.copy_(final_conv_b)
if not self.legacy:
bn = self.bn
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = conv.weight * w[:, None, None, None]
b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / (bn.running_var + bn.eps) ** 0.5
conv.weight.data.copy_(w)
conv.bias.data.copy_(b)
return conv
class RepVitMlp(nn.Module):
def __init__(self, in_dim, hidden_dim, act_layer):
super().__init__()
self.conv1 = ConvNorm(in_dim, hidden_dim, 1, 1, 0)
self.act = act_layer()
self.conv2 = ConvNorm(hidden_dim, in_dim, 1, 1, 0, bn_weight_init=0)
def forward(self, x):
return self.conv2(self.act(self.conv1(x)))
class RepViTBlock(nn.Module):
def __init__(self, in_dim, mlp_ratio, kernel_size, use_se, act_layer, legacy=False):
super(RepViTBlock, self).__init__()
self.token_mixer = RepVggDw(in_dim, kernel_size, legacy)
self.se = SqueezeExcite(in_dim, 0.25) if use_se else nn.Identity()
self.channel_mixer = RepVitMlp(in_dim, in_dim * mlp_ratio, act_layer)
def forward(self, x):
x = self.token_mixer(x)
x = self.se(x)
identity = x
x = self.channel_mixer(x)
return identity + x
class RepVitStem(nn.Module):
def __init__(self, in_chs, out_chs, act_layer):
super().__init__()
self.conv1 = ConvNorm(in_chs, out_chs // 2, 3, 2, 1)
self.act1 = act_layer()
self.conv2 = ConvNorm(out_chs // 2, out_chs, 3, 2, 1)
self.stride = 4
def forward(self, x):
return self.conv2(self.act1(self.conv1(x)))
class RepVitDownsample(nn.Module):
def __init__(self, in_dim, mlp_ratio, out_dim, kernel_size, act_layer, legacy=False):
super().__init__()
self.pre_block = RepViTBlock(in_dim, mlp_ratio, kernel_size, use_se=False, act_layer=act_layer, legacy=legacy)
self.spatial_downsample = ConvNorm(in_dim, in_dim, kernel_size, 2, (kernel_size - 1) // 2, groups=in_dim)
self.channel_downsample = ConvNorm(in_dim, out_dim, 1, 1)
self.ffn = RepVitMlp(out_dim, out_dim * mlp_ratio, act_layer)
def forward(self, x):
x = self.pre_block(x)
x = self.spatial_downsample(x)
x = self.channel_downsample(x)
identity = x
x = self.ffn(x)
return x + identity
class RepVitClassifier(nn.Module):
def __init__(self, dim, num_classes, distillation=False, drop=0.0):
super().__init__()
self.head_drop = nn.Dropout(drop)
self.head = NormLinear(dim, num_classes) if num_classes > 0 else nn.Identity()
self.distillation = distillation
self.distilled_training = False
self.num_classes = num_classes
if distillation:
self.head_dist = NormLinear(dim, num_classes) if num_classes > 0 else nn.Identity()
def forward(self, x):
x = self.head_drop(x)
if self.distillation:
x1, x2 = self.head(x), self.head_dist(x)
if self.training and self.distilled_training and not torch.jit.is_scripting():
return x1, x2
else:
return (x1 + x2) / 2
else:
x = self.head(x)
return x
@torch.no_grad()
def fuse(self):
if not self.num_classes > 0:
return nn.Identity()
head = self.head.fuse()
if self.distillation:
head_dist = self.head_dist.fuse()
head.weight += head_dist.weight
head.bias += head_dist.bias
head.weight /= 2
head.bias /= 2
return head
else:
return head
class RepVitStage(nn.Module):
def __init__(self, in_dim, out_dim, depth, mlp_ratio, act_layer, kernel_size=3, downsample=True, legacy=False):
super().__init__()
if downsample:
self.downsample = RepVitDownsample(in_dim, mlp_ratio, out_dim, kernel_size, act_layer, legacy)
else:
assert in_dim == out_dim
self.downsample = nn.Identity()
blocks = []
use_se = True
for _ in range(depth):
blocks.append(RepViTBlock(out_dim, mlp_ratio, kernel_size, use_se, act_layer, legacy))
use_se = not use_se
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
x = self.downsample(x)
x = self.blocks(x)
return x
class RepVit(nn.Module):
def __init__(
self,
in_chans=3,
img_size=224,
embed_dim=(48,),
depth=(2,),
mlp_ratio=2,
global_pool='avg',
kernel_size=3,
num_classes=1000,
act_layer=nn.GELU,
distillation=True,
drop_rate=0.0,
legacy=False,
):
super(RepVit, self).__init__()
self.grad_checkpointing = False
self.global_pool = global_pool
self.embed_dim = embed_dim
self.num_classes = num_classes
in_dim = embed_dim[0]
self.stem = RepVitStem(in_chans, in_dim, act_layer)
stride = self.stem.stride
resolution = tuple([i // p for i, p in zip(to_2tuple(img_size), to_2tuple(stride))])
num_stages = len(embed_dim)
mlp_ratios = to_ntuple(num_stages)(mlp_ratio)
self.feature_info = []
stages = []
for i in range(num_stages):
downsample = True if i != 0 else False
stages.append(
RepVitStage(
in_dim,
embed_dim[i],
depth[i],
mlp_ratio=mlp_ratios[i],
act_layer=act_layer,
kernel_size=kernel_size,
downsample=downsample,
legacy=legacy,
)
)
stage_stride = 2 if downsample else 1
stride *= stage_stride
resolution = tuple([(r - 1) // stage_stride + 1 for r in resolution])
self.feature_info += [dict(num_chs=embed_dim[i], reduction=stride, module=f'stages.{i}')]
in_dim = embed_dim[i]
self.stages = nn.Sequential(*stages)
self.num_features = embed_dim[-1]
self.head_drop = nn.Dropout(drop_rate)
self.head = RepVitClassifier(embed_dim[-1], num_classes, distillation)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(stem=r'^stem', blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]) # stem and embed
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None, distillation=False):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = (
RepVitClassifier(self.embed_dim[-1], num_classes, distillation) if num_classes > 0 else nn.Identity()
)
@torch.jit.ignore
def set_distilled_training(self, enable=True):
self.head.distilled_training = enable
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool == 'avg':
x = x.mean((2, 3), keepdim=False)
x = self.head_drop(x)
return self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
@torch.no_grad()
def fuse(self):
def fuse_children(net):
for child_name, child in net.named_children():
if hasattr(child, 'fuse'):
fused = child.fuse()
setattr(net, child_name, fused)
fuse_children(fused)
else:
fuse_children(child)
fuse_children(self)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'input_size': (3, 224, 224),
'pool_size': (7, 7),
'crop_pct': 0.95,
'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN,
'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1.c',
'classifier': ('head.head.l', 'head.head_dist.l'),
**kwargs,
}
default_cfgs = generate_default_cfgs(
{
'repvit_m1.dist_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m2.dist_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m3.dist_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m0_9.dist_300e_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m0_9.dist_450e_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m1_0.dist_300e_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m1_0.dist_450e_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m1_1.dist_300e_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m1_1.dist_450e_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m1_5.dist_300e_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m1_5.dist_450e_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m2_3.dist_300e_in1k': _cfg(
hf_hub_id='timm/',
),
'repvit_m2_3.dist_450e_in1k': _cfg(
hf_hub_id='timm/',
),
}
)
def _create_repvit(variant, pretrained=False, **kwargs):
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
model = build_model_with_cfg(
RepVit,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs,
)
return model
@register_model
def repvit_m1(pretrained=False, **kwargs):
"""
Constructs a RepViT-M1 model
"""
model_args = dict(embed_dim=(48, 96, 192, 384), depth=(2, 2, 14, 2), legacy=True)
return _create_repvit('repvit_m1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def repvit_m2(pretrained=False, **kwargs):
"""
Constructs a RepViT-M2 model
"""
model_args = dict(embed_dim=(64, 128, 256, 512), depth=(2, 2, 12, 2), legacy=True)
return _create_repvit('repvit_m2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def repvit_m3(pretrained=False, **kwargs):
"""
Constructs a RepViT-M3 model
"""
model_args = dict(embed_dim=(64, 128, 256, 512), depth=(4, 4, 18, 2), legacy=True)
return _create_repvit('repvit_m3', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def repvit_m0_9(pretrained=False, **kwargs):
"""
Constructs a RepViT-M0.9 model
"""
model_args = dict(embed_dim=(48, 96, 192, 384), depth=(2, 2, 14, 2))
return _create_repvit('repvit_m0_9', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def repvit_m1_0(pretrained=False, **kwargs):
"""
Constructs a RepViT-M1.0 model
"""
model_args = dict(embed_dim=(56, 112, 224, 448), depth=(2, 2, 14, 2))
return _create_repvit('repvit_m1_0', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def repvit_m1_1(pretrained=False, **kwargs):
"""
Constructs a RepViT-M1.1 model
"""
model_args = dict(embed_dim=(64, 128, 256, 512), depth=(2, 2, 12, 2))
return _create_repvit('repvit_m1_1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def repvit_m1_5(pretrained=False, **kwargs):
"""
Constructs a RepViT-M1.5 model
"""
model_args = dict(embed_dim=(64, 128, 256, 512), depth=(4, 4, 24, 4))
return _create_repvit('repvit_m1_5', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def repvit_m2_3(pretrained=False, **kwargs):
"""
Constructs a RepViT-M2.3 model
"""
model_args = dict(embed_dim=(80, 160, 320, 640), depth=(6, 6, 34, 2))
return _create_repvit('repvit_m2_3', pretrained=pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/efficientvit_mit.py
|
""" EfficientViT (by MIT Song Han's Lab)
Paper: `Efficientvit: Enhanced linear attention for high-resolution low-computation visual recognition`
- https://arxiv.org/abs/2205.14756
Adapted from official impl at https://github.com/mit-han-lab/efficientvit
"""
__all__ = ['EfficientVit']
from typing import Optional
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import SelectAdaptivePool2d, create_conv2d, GELUTanh
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_module
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
def val2list(x: list or tuple or any, repeat_time=1):
if isinstance(x, (list, tuple)):
return list(x)
return [x for _ in range(repeat_time)]
def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1):
# repeat elements if necessary
x = val2list(x)
if len(x) > 0:
x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]
return tuple(x)
def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]:
if isinstance(kernel_size, tuple):
return tuple([get_same_padding(ks) for ks in kernel_size])
else:
assert kernel_size % 2 > 0, "kernel size should be odd number"
return kernel_size // 2
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
dilation=1,
groups=1,
bias=False,
dropout=0.,
norm_layer=nn.BatchNorm2d,
act_layer=nn.ReLU,
):
super(ConvNormAct, self).__init__()
self.dropout = nn.Dropout(dropout, inplace=False)
self.conv = create_conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
groups=groups,
bias=bias,
)
self.norm = norm_layer(num_features=out_channels) if norm_layer else nn.Identity()
self.act = act_layer(inplace=True) if act_layer is not None else nn.Identity()
def forward(self, x):
x = self.dropout(x)
x = self.conv(x)
x = self.norm(x)
x = self.act(x)
return x
class DSConv(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
use_bias=False,
norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
act_layer=(nn.ReLU6, None),
):
super(DSConv, self).__init__()
use_bias = val2tuple(use_bias, 2)
norm_layer = val2tuple(norm_layer, 2)
act_layer = val2tuple(act_layer, 2)
self.depth_conv = ConvNormAct(
in_channels,
in_channels,
kernel_size,
stride,
groups=in_channels,
norm_layer=norm_layer[0],
act_layer=act_layer[0],
bias=use_bias[0],
)
self.point_conv = ConvNormAct(
in_channels,
out_channels,
1,
norm_layer=norm_layer[1],
act_layer=act_layer[1],
bias=use_bias[1],
)
def forward(self, x):
x = self.depth_conv(x)
x = self.point_conv(x)
return x
class ConvBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
mid_channels=None,
expand_ratio=1,
use_bias=False,
norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
act_layer=(nn.ReLU6, None),
):
super(ConvBlock, self).__init__()
use_bias = val2tuple(use_bias, 2)
norm_layer = val2tuple(norm_layer, 2)
act_layer = val2tuple(act_layer, 2)
mid_channels = mid_channels or round(in_channels * expand_ratio)
self.conv1 = ConvNormAct(
in_channels,
mid_channels,
kernel_size,
stride,
norm_layer=norm_layer[0],
act_layer=act_layer[0],
bias=use_bias[0],
)
self.conv2 = ConvNormAct(
mid_channels,
out_channels,
kernel_size,
1,
norm_layer=norm_layer[1],
act_layer=act_layer[1],
bias=use_bias[1],
)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
return x
class MBConv(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
mid_channels=None,
expand_ratio=6,
use_bias=False,
norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d, nn.BatchNorm2d),
act_layer=(nn.ReLU6, nn.ReLU6, None),
):
super(MBConv, self).__init__()
use_bias = val2tuple(use_bias, 3)
norm_layer = val2tuple(norm_layer, 3)
act_layer = val2tuple(act_layer, 3)
mid_channels = mid_channels or round(in_channels * expand_ratio)
self.inverted_conv = ConvNormAct(
in_channels,
mid_channels,
1,
stride=1,
norm_layer=norm_layer[0],
act_layer=act_layer[0],
bias=use_bias[0],
)
self.depth_conv = ConvNormAct(
mid_channels,
mid_channels,
kernel_size,
stride=stride,
groups=mid_channels,
norm_layer=norm_layer[1],
act_layer=act_layer[1],
bias=use_bias[1],
)
self.point_conv = ConvNormAct(
mid_channels,
out_channels,
1,
norm_layer=norm_layer[2],
act_layer=act_layer[2],
bias=use_bias[2],
)
def forward(self, x):
x = self.inverted_conv(x)
x = self.depth_conv(x)
x = self.point_conv(x)
return x
class FusedMBConv(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
mid_channels=None,
expand_ratio=6,
groups=1,
use_bias=False,
norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
act_layer=(nn.ReLU6, None),
):
super(FusedMBConv, self).__init__()
use_bias = val2tuple(use_bias, 2)
norm_layer = val2tuple(norm_layer, 2)
act_layer = val2tuple(act_layer, 2)
mid_channels = mid_channels or round(in_channels * expand_ratio)
self.spatial_conv = ConvNormAct(
in_channels,
mid_channels,
kernel_size,
stride=stride,
groups=groups,
norm_layer=norm_layer[0],
act_layer=act_layer[0],
bias=use_bias[0],
)
self.point_conv = ConvNormAct(
mid_channels,
out_channels,
1,
norm_layer=norm_layer[1],
act_layer=act_layer[1],
bias=use_bias[1],
)
def forward(self, x):
x = self.spatial_conv(x)
x = self.point_conv(x)
return x
class LiteMLA(nn.Module):
"""Lightweight multi-scale linear attention"""
def __init__(
self,
in_channels: int,
out_channels: int,
heads: int or None = None,
heads_ratio: float = 1.0,
dim=8,
use_bias=False,
norm_layer=(None, nn.BatchNorm2d),
act_layer=(None, None),
kernel_func=nn.ReLU,
scales=(5,),
eps=1e-5,
):
super(LiteMLA, self).__init__()
self.eps = eps
heads = heads or int(in_channels // dim * heads_ratio)
total_dim = heads * dim
use_bias = val2tuple(use_bias, 2)
norm_layer = val2tuple(norm_layer, 2)
act_layer = val2tuple(act_layer, 2)
self.dim = dim
self.qkv = ConvNormAct(
in_channels,
3 * total_dim,
1,
bias=use_bias[0],
norm_layer=norm_layer[0],
act_layer=act_layer[0],
)
self.aggreg = nn.ModuleList([
nn.Sequential(
nn.Conv2d(
3 * total_dim,
3 * total_dim,
scale,
padding=get_same_padding(scale),
groups=3 * total_dim,
bias=use_bias[0],
),
nn.Conv2d(3 * total_dim, 3 * total_dim, 1, groups=3 * heads, bias=use_bias[0]),
)
for scale in scales
])
self.kernel_func = kernel_func(inplace=False)
self.proj = ConvNormAct(
total_dim * (1 + len(scales)),
out_channels,
1,
bias=use_bias[1],
norm_layer=norm_layer[1],
act_layer=act_layer[1],
)
def _attn(self, q, k, v):
dtype = v.dtype
q, k, v = q.float(), k.float(), v.float()
kv = k.transpose(-1, -2) @ v
out = q @ kv
out = out[..., :-1] / (out[..., -1:] + self.eps)
return out.to(dtype)
def forward(self, x):
B, _, H, W = x.shape
# generate multi-scale q, k, v
qkv = self.qkv(x)
multi_scale_qkv = [qkv]
for op in self.aggreg:
multi_scale_qkv.append(op(qkv))
multi_scale_qkv = torch.cat(multi_scale_qkv, dim=1)
multi_scale_qkv = multi_scale_qkv.reshape(B, -1, 3 * self.dim, H * W).transpose(-1, -2)
q, k, v = multi_scale_qkv.chunk(3, dim=-1)
# lightweight global attention
q = self.kernel_func(q)
k = self.kernel_func(k)
v = F.pad(v, (0, 1), mode="constant", value=1.)
if not torch.jit.is_scripting():
with torch.autocast(device_type=v.device.type, enabled=False):
out = self._attn(q, k, v)
else:
out = self._attn(q, k, v)
# final projection
out = out.transpose(-1, -2).reshape(B, -1, H, W)
out = self.proj(out)
return out
register_notrace_module(LiteMLA)
class EfficientVitBlock(nn.Module):
def __init__(
self,
in_channels,
heads_ratio=1.0,
head_dim=32,
expand_ratio=4,
norm_layer=nn.BatchNorm2d,
act_layer=nn.Hardswish,
):
super(EfficientVitBlock, self).__init__()
self.context_module = ResidualBlock(
LiteMLA(
in_channels=in_channels,
out_channels=in_channels,
heads_ratio=heads_ratio,
dim=head_dim,
norm_layer=(None, norm_layer),
),
nn.Identity(),
)
self.local_module = ResidualBlock(
MBConv(
in_channels=in_channels,
out_channels=in_channels,
expand_ratio=expand_ratio,
use_bias=(True, True, False),
norm_layer=(None, None, norm_layer),
act_layer=(act_layer, act_layer, None),
),
nn.Identity(),
)
def forward(self, x):
x = self.context_module(x)
x = self.local_module(x)
return x
class ResidualBlock(nn.Module):
def __init__(
self,
main: Optional[nn.Module],
shortcut: Optional[nn.Module] = None,
pre_norm: Optional[nn.Module] = None,
):
super(ResidualBlock, self).__init__()
self.pre_norm = pre_norm if pre_norm is not None else nn.Identity()
self.main = main
self.shortcut = shortcut
def forward(self, x):
res = self.main(self.pre_norm(x))
if self.shortcut is not None:
res = res + self.shortcut(x)
return res
def build_local_block(
in_channels: int,
out_channels: int,
stride: int,
expand_ratio: float,
norm_layer: str,
act_layer: str,
fewer_norm: bool = False,
block_type: str = "default",
):
assert block_type in ["default", "large", "fused"]
if expand_ratio == 1:
if block_type == "default":
block = DSConv(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
use_bias=(True, False) if fewer_norm else False,
norm_layer=(None, norm_layer) if fewer_norm else norm_layer,
act_layer=(act_layer, None),
)
else:
block = ConvBlock(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
use_bias=(True, False) if fewer_norm else False,
norm_layer=(None, norm_layer) if fewer_norm else norm_layer,
act_layer=(act_layer, None),
)
else:
if block_type == "default":
block = MBConv(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
expand_ratio=expand_ratio,
use_bias=(True, True, False) if fewer_norm else False,
norm_layer=(None, None, norm_layer) if fewer_norm else norm_layer,
act_layer=(act_layer, act_layer, None),
)
else:
block = FusedMBConv(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
expand_ratio=expand_ratio,
use_bias=(True, False) if fewer_norm else False,
norm_layer=(None, norm_layer) if fewer_norm else norm_layer,
act_layer=(act_layer, None),
)
return block
class Stem(nn.Sequential):
def __init__(self, in_chs, out_chs, depth, norm_layer, act_layer, block_type='default'):
super().__init__()
self.stride = 2
self.add_module(
'in_conv',
ConvNormAct(
in_chs, out_chs,
kernel_size=3, stride=2, norm_layer=norm_layer, act_layer=act_layer,
)
)
stem_block = 0
for _ in range(depth):
self.add_module(f'res{stem_block}', ResidualBlock(
build_local_block(
in_channels=out_chs,
out_channels=out_chs,
stride=1,
expand_ratio=1,
norm_layer=norm_layer,
act_layer=act_layer,
block_type=block_type,
),
nn.Identity(),
))
stem_block += 1
class EfficientVitStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
depth,
norm_layer,
act_layer,
expand_ratio,
head_dim,
vit_stage=False,
):
super(EfficientVitStage, self).__init__()
blocks = [ResidualBlock(
build_local_block(
in_channels=in_chs,
out_channels=out_chs,
stride=2,
expand_ratio=expand_ratio,
norm_layer=norm_layer,
act_layer=act_layer,
fewer_norm=vit_stage,
),
None,
)]
in_chs = out_chs
if vit_stage:
# for stage 3, 4
for _ in range(depth):
blocks.append(
EfficientVitBlock(
in_channels=in_chs,
head_dim=head_dim,
expand_ratio=expand_ratio,
norm_layer=norm_layer,
act_layer=act_layer,
)
)
else:
# for stage 1, 2
for i in range(1, depth):
blocks.append(ResidualBlock(
build_local_block(
in_channels=in_chs,
out_channels=out_chs,
stride=1,
expand_ratio=expand_ratio,
norm_layer=norm_layer,
act_layer=act_layer
),
nn.Identity(),
))
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
return self.blocks(x)
class EfficientVitLargeStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
depth,
norm_layer,
act_layer,
head_dim,
vit_stage=False,
fewer_norm=False,
):
super(EfficientVitLargeStage, self).__init__()
blocks = [ResidualBlock(
build_local_block(
in_channels=in_chs,
out_channels=out_chs,
stride=2,
expand_ratio=24 if vit_stage else 16,
norm_layer=norm_layer,
act_layer=act_layer,
fewer_norm=vit_stage or fewer_norm,
block_type='default' if fewer_norm else 'fused',
),
None,
)]
in_chs = out_chs
if vit_stage:
# for stage 4
for _ in range(depth):
blocks.append(
EfficientVitBlock(
in_channels=in_chs,
head_dim=head_dim,
expand_ratio=6,
norm_layer=norm_layer,
act_layer=act_layer,
)
)
else:
# for stage 1, 2, 3
for i in range(depth):
blocks.append(ResidualBlock(
build_local_block(
in_channels=in_chs,
out_channels=out_chs,
stride=1,
expand_ratio=4,
norm_layer=norm_layer,
act_layer=act_layer,
fewer_norm=fewer_norm,
block_type='default' if fewer_norm else 'fused',
),
nn.Identity(),
))
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
return self.blocks(x)
class ClassifierHead(nn.Module):
def __init__(
self,
in_channels,
widths,
n_classes=1000,
dropout=0.,
norm_layer=nn.BatchNorm2d,
act_layer=nn.Hardswish,
global_pool='avg',
norm_eps=1e-5,
):
super(ClassifierHead, self).__init__()
self.in_conv = ConvNormAct(in_channels, widths[0], 1, norm_layer=norm_layer, act_layer=act_layer)
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW')
self.classifier = nn.Sequential(
nn.Linear(widths[0], widths[1], bias=False),
nn.LayerNorm(widths[1], eps=norm_eps),
act_layer(inplace=True) if act_layer is not None else nn.Identity(),
nn.Dropout(dropout, inplace=False),
nn.Linear(widths[1], n_classes, bias=True),
)
def forward(self, x, pre_logits: bool = False):
x = self.in_conv(x)
x = self.global_pool(x)
if pre_logits:
return x
x = self.classifier(x)
return x
class EfficientVit(nn.Module):
def __init__(
self,
in_chans=3,
widths=(),
depths=(),
head_dim=32,
expand_ratio=4,
norm_layer=nn.BatchNorm2d,
act_layer=nn.Hardswish,
global_pool='avg',
head_widths=(),
drop_rate=0.0,
num_classes=1000,
):
super(EfficientVit, self).__init__()
self.grad_checkpointing = False
self.global_pool = global_pool
self.num_classes = num_classes
# input stem
self.stem = Stem(in_chans, widths[0], depths[0], norm_layer, act_layer)
stride = self.stem.stride
# stages
self.feature_info = []
self.stages = nn.Sequential()
in_channels = widths[0]
for i, (w, d) in enumerate(zip(widths[1:], depths[1:])):
self.stages.append(EfficientVitStage(
in_channels,
w,
depth=d,
norm_layer=norm_layer,
act_layer=act_layer,
expand_ratio=expand_ratio,
head_dim=head_dim,
vit_stage=i >= 2,
))
stride *= 2
in_channels = w
self.feature_info += [dict(num_chs=in_channels, reduction=stride, module=f'stages.{i}')]
self.num_features = in_channels
self.head_widths = head_widths
self.head_dropout = drop_rate
if num_classes > 0:
self.head = ClassifierHead(
self.num_features,
self.head_widths,
n_classes=num_classes,
dropout=self.head_dropout,
global_pool=self.global_pool,
)
else:
if self.global_pool == 'avg':
self.head = SelectAdaptivePool2d(pool_type=global_pool, flatten=True)
else:
self.head = nn.Identity()
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+).downsample', (0,)),
(r'^stages\.(\d+)\.\w+\.(\d+)', None),
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.classifier[-1]
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
if num_classes > 0:
self.head = ClassifierHead(
self.num_features,
self.head_widths,
n_classes=num_classes,
dropout=self.head_dropout,
global_pool=self.global_pool,
)
else:
if self.global_pool == 'avg':
self.head = SelectAdaptivePool2d(pool_type=self.global_pool, flatten=True)
else:
self.head = nn.Identity()
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
class EfficientVitLarge(nn.Module):
def __init__(
self,
in_chans=3,
widths=(),
depths=(),
head_dim=32,
norm_layer=nn.BatchNorm2d,
act_layer=GELUTanh,
global_pool='avg',
head_widths=(),
drop_rate=0.0,
num_classes=1000,
norm_eps=1e-7,
):
super(EfficientVitLarge, self).__init__()
self.grad_checkpointing = False
self.global_pool = global_pool
self.num_classes = num_classes
self.norm_eps = norm_eps
norm_layer = partial(norm_layer, eps=self.norm_eps)
# input stem
self.stem = Stem(in_chans, widths[0], depths[0], norm_layer, act_layer, block_type='large')
stride = self.stem.stride
# stages
self.feature_info = []
self.stages = nn.Sequential()
in_channels = widths[0]
for i, (w, d) in enumerate(zip(widths[1:], depths[1:])):
self.stages.append(EfficientVitLargeStage(
in_channels,
w,
depth=d,
norm_layer=norm_layer,
act_layer=act_layer,
head_dim=head_dim,
vit_stage=i >= 3,
fewer_norm=i >= 2,
))
stride *= 2
in_channels = w
self.feature_info += [dict(num_chs=in_channels, reduction=stride, module=f'stages.{i}')]
self.num_features = in_channels
self.head_widths = head_widths
self.head_dropout = drop_rate
if num_classes > 0:
self.head = ClassifierHead(
self.num_features,
self.head_widths,
n_classes=num_classes,
dropout=self.head_dropout,
global_pool=self.global_pool,
act_layer=act_layer,
norm_eps=self.norm_eps,
)
else:
if self.global_pool == 'avg':
self.head = SelectAdaptivePool2d(pool_type=global_pool, flatten=True)
else:
self.head = nn.Identity()
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+).downsample', (0,)),
(r'^stages\.(\d+)\.\w+\.(\d+)', None),
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.classifier[-1]
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
if num_classes > 0:
self.head = ClassifierHead(
self.num_features,
self.head_widths,
n_classes=num_classes,
dropout=self.head_dropout,
global_pool=self.global_pool,
norm_eps=self.norm_eps
)
else:
if self.global_pool == 'avg':
self.head = SelectAdaptivePool2d(pool_type=self.global_pool, flatten=True)
else:
self.head = nn.Identity()
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'mean': IMAGENET_DEFAULT_MEAN,
'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.in_conv.conv',
'classifier': 'head.classifier.4',
'crop_pct': 0.95,
'input_size': (3, 224, 224),
'pool_size': (7, 7),
**kwargs,
}
default_cfgs = generate_default_cfgs({
'efficientvit_b0.r224_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientvit_b1.r224_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientvit_b1.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_b1.r288_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0,
),
'efficientvit_b2.r224_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientvit_b2.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_b2.r288_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0,
),
'efficientvit_b3.r224_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientvit_b3.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_b3.r288_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0,
),
'efficientvit_l1.r224_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=1.0,
),
'efficientvit_l2.r224_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=1.0,
),
'efficientvit_l2.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_l2.r288_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0,
),
'efficientvit_l2.r384_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
'efficientvit_l3.r224_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=1.0,
),
'efficientvit_l3.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_l3.r320_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0,
),
'efficientvit_l3.r384_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
# 'efficientvit_l0_sam.sam': _cfg(
# # hf_hub_id='timm/',
# input_size=(3, 512, 512), crop_pct=1.0,
# num_classes=0,
# ),
# 'efficientvit_l1_sam.sam': _cfg(
# # hf_hub_id='timm/',
# input_size=(3, 512, 512), crop_pct=1.0,
# num_classes=0,
# ),
# 'efficientvit_l2_sam.sam': _cfg(
# # hf_hub_id='timm/',f
# input_size=(3, 512, 512), crop_pct=1.0,
# num_classes=0,
# ),
})
def _create_efficientvit(variant, pretrained=False, **kwargs):
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
model = build_model_with_cfg(
EfficientVit,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs
)
return model
def _create_efficientvit_large(variant, pretrained=False, **kwargs):
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
model = build_model_with_cfg(
EfficientVitLarge,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs
)
return model
@register_model
def efficientvit_b0(pretrained=False, **kwargs):
model_args = dict(
widths=(8, 16, 32, 64, 128), depths=(1, 2, 2, 2, 2), head_dim=16, head_widths=(1024, 1280))
return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_b1(pretrained=False, **kwargs):
model_args = dict(
widths=(16, 32, 64, 128, 256), depths=(1, 2, 3, 3, 4), head_dim=16, head_widths=(1536, 1600))
return _create_efficientvit('efficientvit_b1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_b2(pretrained=False, **kwargs):
model_args = dict(
widths=(24, 48, 96, 192, 384), depths=(1, 3, 4, 4, 6), head_dim=32, head_widths=(2304, 2560))
return _create_efficientvit('efficientvit_b2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_b3(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 4, 6, 6, 9), head_dim=32, head_widths=(2304, 2560))
return _create_efficientvit('efficientvit_b3', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l1(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 6, 6), head_dim=32, head_widths=(3072, 3200))
return _create_efficientvit_large('efficientvit_l1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l2(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 2, 2, 8, 8), head_dim=32, head_widths=(3072, 3200))
return _create_efficientvit_large('efficientvit_l2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l3(pretrained=False, **kwargs):
model_args = dict(
widths=(64, 128, 256, 512, 1024), depths=(1, 2, 2, 8, 8), head_dim=32, head_widths=(6144, 6400))
return _create_efficientvit_large('efficientvit_l3', pretrained=pretrained, **dict(model_args, **kwargs))
# FIXME will wait for v2 SAM models which are pending
# @register_model
# def efficientvit_l0_sam(pretrained=False, **kwargs):
# # only backbone for segment-anything-model weights
# model_args = dict(
# widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 4, 4), head_dim=32, num_classes=0, norm_eps=1e-6)
# return _create_efficientvit_large('efficientvit_l0_sam', pretrained=pretrained, **dict(model_args, **kwargs))
#
#
# @register_model
# def efficientvit_l1_sam(pretrained=False, **kwargs):
# # only backbone for segment-anything-model weights
# model_args = dict(
# widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 6, 6), head_dim=32, num_classes=0, norm_eps=1e-6)
# return _create_efficientvit_large('efficientvit_l1_sam', pretrained=pretrained, **dict(model_args, **kwargs))
#
#
# @register_model
# def efficientvit_l2_sam(pretrained=False, **kwargs):
# # only backbone for segment-anything-model weights
# model_args = dict(
# widths=(32, 64, 128, 256, 512), depths=(1, 2, 2, 8, 8), head_dim=32, num_classes=0, norm_eps=1e-6)
# return _create_efficientvit_large('efficientvit_l2_sam', pretrained=pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/tnt.py
|
""" Transformer in Transformer (TNT) in PyTorch
A PyTorch implement of TNT as described in
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
The official mindspore code is released and available at
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
"""
import math
import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import Mlp, DropPath, trunc_normal_, _assert, to_2tuple
from ._builder import build_model_with_cfg
from ._registry import register_model
from .vision_transformer import resize_pos_embed
__all__ = ['TNT'] # model_registry will add each entrypoint fn to this
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'pixel_embed.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = {
'tnt_s_patch16_224': _cfg(
url='https://github.com/contrastive/pytorch-image-models/releases/download/TNT/tnt_s_patch16_224.pth.tar',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
),
'tnt_b_patch16_224': _cfg(
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
),
}
class Attention(nn.Module):
""" Multi-Head Attention
"""
def __init__(self, dim, hidden_dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
self.hidden_dim = hidden_dim
self.num_heads = num_heads
head_dim = hidden_dim // num_heads
self.head_dim = head_dim
self.scale = head_dim ** -0.5
self.qk = nn.Linear(dim, hidden_dim * 2, bias=qkv_bias)
self.v = nn.Linear(dim, dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop, inplace=True)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop, inplace=True)
def forward(self, x):
B, N, C = x.shape
qk = self.qk(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k = qk.unbind(0) # make torchscript happy (cannot use tensor as tuple)
v = self.v(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
""" TNT Block
"""
def __init__(
self,
dim,
dim_out,
num_pixel,
num_heads_in=4,
num_heads_out=12,
mlp_ratio=4.,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
# Inner transformer
self.norm_in = norm_layer(dim)
self.attn_in = Attention(
dim,
dim,
num_heads=num_heads_in,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.norm_mlp_in = norm_layer(dim)
self.mlp_in = Mlp(
in_features=dim,
hidden_features=int(dim * 4),
out_features=dim,
act_layer=act_layer,
drop=proj_drop,
)
self.norm1_proj = norm_layer(dim)
self.proj = nn.Linear(dim * num_pixel, dim_out, bias=True)
# Outer transformer
self.norm_out = norm_layer(dim_out)
self.attn_out = Attention(
dim_out,
dim_out,
num_heads=num_heads_out,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm_mlp = norm_layer(dim_out)
self.mlp = Mlp(
in_features=dim_out,
hidden_features=int(dim_out * mlp_ratio),
out_features=dim_out,
act_layer=act_layer,
drop=proj_drop,
)
def forward(self, pixel_embed, patch_embed):
# inner
pixel_embed = pixel_embed + self.drop_path(self.attn_in(self.norm_in(pixel_embed)))
pixel_embed = pixel_embed + self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed)))
# outer
B, N, C = patch_embed.size()
patch_embed = torch.cat(
[patch_embed[:, 0:1], patch_embed[:, 1:] + self.proj(self.norm1_proj(pixel_embed).reshape(B, N - 1, -1))],
dim=1)
patch_embed = patch_embed + self.drop_path(self.attn_out(self.norm_out(patch_embed)))
patch_embed = patch_embed + self.drop_path(self.mlp(self.norm_mlp(patch_embed)))
return pixel_embed, patch_embed
class PixelEmbed(nn.Module):
""" Image to Pixel Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, in_dim=48, stride=4):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
# grid_size property necessary for resizing positional embedding
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
num_patches = (self.grid_size[0]) * (self.grid_size[1])
self.img_size = img_size
self.num_patches = num_patches
self.in_dim = in_dim
new_patch_size = [math.ceil(ps / stride) for ps in patch_size]
self.new_patch_size = new_patch_size
self.proj = nn.Conv2d(in_chans, self.in_dim, kernel_size=7, padding=3, stride=stride)
self.unfold = nn.Unfold(kernel_size=new_patch_size, stride=new_patch_size)
def forward(self, x, pixel_pos):
B, C, H, W = x.shape
_assert(H == self.img_size[0],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
_assert(W == self.img_size[1],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
x = self.proj(x)
x = self.unfold(x)
x = x.transpose(1, 2).reshape(B * self.num_patches, self.in_dim, self.new_patch_size[0], self.new_patch_size[1])
x = x + pixel_pos
x = x.reshape(B * self.num_patches, self.in_dim, -1).transpose(1, 2)
return x
class TNT(nn.Module):
""" Transformer in Transformer - https://arxiv.org/abs/2103.00112
"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
global_pool='token',
embed_dim=768,
inner_dim=48,
depth=12,
num_heads_inner=4,
num_heads_outer=12,
mlp_ratio=4.,
qkv_bias=False,
drop_rate=0.,
pos_drop_rate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=nn.LayerNorm,
first_stride=4,
):
super().__init__()
assert global_pool in ('', 'token', 'avg')
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.grad_checkpointing = False
self.pixel_embed = PixelEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
in_dim=inner_dim,
stride=first_stride,
)
num_patches = self.pixel_embed.num_patches
self.num_patches = num_patches
new_patch_size = self.pixel_embed.new_patch_size
num_pixel = new_patch_size[0] * new_patch_size[1]
self.norm1_proj = norm_layer(num_pixel * inner_dim)
self.proj = nn.Linear(num_pixel * inner_dim, embed_dim)
self.norm2_proj = norm_layer(embed_dim)
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.patch_pos = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
self.pixel_pos = nn.Parameter(torch.zeros(1, inner_dim, new_patch_size[0], new_patch_size[1]))
self.pos_drop = nn.Dropout(p=pos_drop_rate)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
blocks = []
for i in range(depth):
blocks.append(Block(
dim=inner_dim,
dim_out=embed_dim,
num_pixel=num_pixel,
num_heads_in=num_heads_inner,
num_heads_out=num_heads_outer,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
))
self.blocks = nn.ModuleList(blocks)
self.norm = norm_layer(embed_dim)
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
trunc_normal_(self.cls_token, std=.02)
trunc_normal_(self.patch_pos, std=.02)
trunc_normal_(self.pixel_pos, std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'patch_pos', 'pixel_pos', 'cls_token'}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^cls_token|patch_pos|pixel_pos|pixel_embed|norm[12]_proj|proj', # stem and embed / pos
blocks=[
(r'^blocks\.(\d+)', None),
(r'^norm', (99999,)),
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'token', 'avg')
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
B = x.shape[0]
pixel_embed = self.pixel_embed(x, self.pixel_pos)
patch_embed = self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape(B, self.num_patches, -1))))
patch_embed = torch.cat((self.cls_token.expand(B, -1, -1), patch_embed), dim=1)
patch_embed = patch_embed + self.patch_pos
patch_embed = self.pos_drop(patch_embed)
if self.grad_checkpointing and not torch.jit.is_scripting():
for blk in self.blocks:
pixel_embed, patch_embed = checkpoint(blk, pixel_embed, patch_embed)
else:
for blk in self.blocks:
pixel_embed, patch_embed = blk(pixel_embed, patch_embed)
patch_embed = self.norm(patch_embed)
return patch_embed
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x[:, 1:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
if state_dict['patch_pos'].shape != model.patch_pos.shape:
state_dict['patch_pos'] = resize_pos_embed(state_dict['patch_pos'],
model.patch_pos, getattr(model, 'num_tokens', 1), model.pixel_embed.grid_size)
return state_dict
def _create_tnt(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
model = build_model_with_cfg(
TNT, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs)
return model
@register_model
def tnt_s_patch16_224(pretrained=False, **kwargs) -> TNT:
model_cfg = dict(
patch_size=16, embed_dim=384, inner_dim=24, depth=12, num_heads_outer=6,
qkv_bias=False)
model = _create_tnt('tnt_s_patch16_224', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def tnt_b_patch16_224(pretrained=False, **kwargs) -> TNT:
model_cfg = dict(
patch_size=16, embed_dim=640, inner_dim=40, depth=12, num_heads_outer=10,
qkv_bias=False)
model = _create_tnt('tnt_b_patch16_224', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/senet.py
|
"""
SEResNet implementation from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
Additional credit to https://github.com/creafz
Original model: https://github.com/hujie-frank/SENet
ResNet code gently borrowed from
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
FIXME I'm deprecating this model and moving them to ResNet as I don't want to maintain duplicate
support for extras like dilation, switchable BN/activations, feature extraction, etc that don't exist here.
"""
import math
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import create_classifier
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['SENet']
def _weight_init(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1.)
nn.init.constant_(m.bias, 0.)
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
module_input = x
x = x.mean((2, 3), keepdim=True)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
class Bottleneck(nn.Module):
"""
Base class for bottlenecks that implements `forward()` method.
"""
def forward(self, x):
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out = self.se_module(out) + shortcut
out = self.relu(out)
return out
class SEBottleneck(Bottleneck):
"""
Bottleneck for SENet154.
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None):
super(SEBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes * 2)
self.conv2 = nn.Conv2d(
planes * 2, planes * 4, kernel_size=3, stride=stride,
padding=1, groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(planes * 4)
self.conv3 = nn.Conv2d(planes * 4, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNetBottleneck(Bottleneck):
"""
ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
implementation and uses `stride=stride` in `conv1` and not in `conv2`
(the latter is used in the torchvision implementation of ResNet).
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None):
super(SEResNetBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False, stride=stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNeXtBottleneck(Bottleneck):
"""
ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None, base_width=4):
super(SEResNeXtBottleneck, self).__init__()
width = math.floor(planes * (base_width / 64)) * groups
self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
self.bn1 = nn.BatchNorm2d(width)
self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNetBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None):
super(SEResNetBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes, reduction=reduction)
self.downsample = downsample
self.stride = stride
def forward(self, x):
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out = self.se_module(out) + shortcut
out = self.relu(out)
return out
class SENet(nn.Module):
def __init__(
self, block, layers, groups, reduction, drop_rate=0.2,
in_chans=3, inplanes=64, input_3x3=False, downsample_kernel_size=1,
downsample_padding=0, num_classes=1000, global_pool='avg'):
"""
Parameters
----------
block (nn.Module): Bottleneck class.
- For SENet154: SEBottleneck
- For SE-ResNet models: SEResNetBottleneck
- For SE-ResNeXt models: SEResNeXtBottleneck
layers (list of ints): Number of residual blocks for 4 layers of the
network (layer1...layer4).
groups (int): Number of groups for the 3x3 convolution in each
bottleneck block.
- For SENet154: 64
- For SE-ResNet models: 1
- For SE-ResNeXt models: 32
reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
- For all models: 16
dropout_p (float or None): Drop probability for the Dropout layer.
If `None` the Dropout layer is not used.
- For SENet154: 0.2
- For SE-ResNet models: None
- For SE-ResNeXt models: None
inplanes (int): Number of input channels for layer1.
- For SENet154: 128
- For SE-ResNet models: 64
- For SE-ResNeXt models: 64
input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
a single 7x7 convolution in layer0.
- For SENet154: True
- For SE-ResNet models: False
- For SE-ResNeXt models: False
downsample_kernel_size (int): Kernel size for downsampling convolutions
in layer2, layer3 and layer4.
- For SENet154: 3
- For SE-ResNet models: 1
- For SE-ResNeXt models: 1
downsample_padding (int): Padding for downsampling convolutions in
layer2, layer3 and layer4.
- For SENet154: 1
- For SE-ResNet models: 0
- For SE-ResNeXt models: 0
num_classes (int): Number of outputs in `last_linear` layer.
- For all models: 1000
"""
super(SENet, self).__init__()
self.inplanes = inplanes
self.num_classes = num_classes
self.drop_rate = drop_rate
if input_3x3:
layer0_modules = [
('conv1', nn.Conv2d(in_chans, 64, 3, stride=2, padding=1, bias=False)),
('bn1', nn.BatchNorm2d(64)),
('relu1', nn.ReLU(inplace=True)),
('conv2', nn.Conv2d(64, 64, 3, stride=1, padding=1, bias=False)),
('bn2', nn.BatchNorm2d(64)),
('relu2', nn.ReLU(inplace=True)),
('conv3', nn.Conv2d(64, inplanes, 3, stride=1, padding=1, bias=False)),
('bn3', nn.BatchNorm2d(inplanes)),
('relu3', nn.ReLU(inplace=True)),
]
else:
layer0_modules = [
('conv1', nn.Conv2d(
in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)),
('bn1', nn.BatchNorm2d(inplanes)),
('relu1', nn.ReLU(inplace=True)),
]
self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
# To preserve compatibility with Caffe weights `ceil_mode=True` is used instead of `padding=1`.
self.pool0 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
self.feature_info = [dict(num_chs=inplanes, reduction=2, module='layer0')]
self.layer1 = self._make_layer(
block,
planes=64,
blocks=layers[0],
groups=groups,
reduction=reduction,
downsample_kernel_size=1,
downsample_padding=0
)
self.feature_info += [dict(num_chs=64 * block.expansion, reduction=4, module='layer1')]
self.layer2 = self._make_layer(
block,
planes=128,
blocks=layers[1],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.feature_info += [dict(num_chs=128 * block.expansion, reduction=8, module='layer2')]
self.layer3 = self._make_layer(
block,
planes=256,
blocks=layers[2],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.feature_info += [dict(num_chs=256 * block.expansion, reduction=16, module='layer3')]
self.layer4 = self._make_layer(
block,
planes=512,
blocks=layers[3],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.feature_info += [dict(num_chs=512 * block.expansion, reduction=32, module='layer4')]
self.num_features = 512 * block.expansion
self.global_pool, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
for m in self.modules():
_weight_init(m)
def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
downsample_kernel_size=1, downsample_padding=0):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes, planes * block.expansion, kernel_size=downsample_kernel_size,
stride=stride, padding=downsample_padding, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = [block(self.inplanes, planes, groups, reduction, stride, downsample)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, groups, reduction))
return nn.Sequential(*layers)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(stem=r'^layer0', blocks=r'^layer(\d+)' if coarse else r'^layer(\d+)\.(\d+)')
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.last_linear
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.layer0(x)
x = self.pool0(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return x if pre_logits else self.last_linear(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_senet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(SENet, variant, pretrained, **kwargs)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'layer0.conv1', 'classifier': 'last_linear',
**kwargs
}
default_cfgs = generate_default_cfgs({
'legacy_senet154.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_senet154-e9eb9fe6.pth'),
'legacy_seresnet18.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth',
interpolation='bicubic'),
'legacy_seresnet34.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet34-a4004e63.pth'),
'legacy_seresnet50.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pth'),
'legacy_seresnet101.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet101-7e38fcc6.pth'),
'legacy_seresnet152.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet152-d17c99b7.pth'),
'legacy_seresnext26_32x4d.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth',
interpolation='bicubic'),
'legacy_seresnext50_32x4d.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext50_32x4d-f3651bad.pth'),
'legacy_seresnext101_32x4d.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext101_32x4d-37725eac.pth'),
})
@register_model
def legacy_seresnet18(pretrained=False, **kwargs) -> SENet:
model_args = dict(
block=SEResNetBlock, layers=[2, 2, 2, 2], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet18', pretrained, **model_args)
@register_model
def legacy_seresnet34(pretrained=False, **kwargs) -> SENet:
model_args = dict(
block=SEResNetBlock, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet34', pretrained, **model_args)
@register_model
def legacy_seresnet50(pretrained=False, **kwargs) -> SENet:
model_args = dict(
block=SEResNetBottleneck, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet50', pretrained, **model_args)
@register_model
def legacy_seresnet101(pretrained=False, **kwargs) -> SENet:
model_args = dict(
block=SEResNetBottleneck, layers=[3, 4, 23, 3], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet101', pretrained, **model_args)
@register_model
def legacy_seresnet152(pretrained=False, **kwargs) -> SENet:
model_args = dict(
block=SEResNetBottleneck, layers=[3, 8, 36, 3], groups=1, reduction=16, **kwargs)
return _create_senet('legacy_seresnet152', pretrained, **model_args)
@register_model
def legacy_senet154(pretrained=False, **kwargs) -> SENet:
model_args = dict(
block=SEBottleneck, layers=[3, 8, 36, 3], groups=64, reduction=16,
downsample_kernel_size=3, downsample_padding=1, inplanes=128, input_3x3=True, **kwargs)
return _create_senet('legacy_senet154', pretrained, **model_args)
@register_model
def legacy_seresnext26_32x4d(pretrained=False, **kwargs) -> SENet:
model_args = dict(
block=SEResNeXtBottleneck, layers=[2, 2, 2, 2], groups=32, reduction=16, **kwargs)
return _create_senet('legacy_seresnext26_32x4d', pretrained, **model_args)
@register_model
def legacy_seresnext50_32x4d(pretrained=False, **kwargs) -> SENet:
model_args = dict(
block=SEResNeXtBottleneck, layers=[3, 4, 6, 3], groups=32, reduction=16, **kwargs)
return _create_senet('legacy_seresnext50_32x4d', pretrained, **model_args)
@register_model
def legacy_seresnext101_32x4d(pretrained=False, **kwargs) -> SENet:
model_args = dict(
block=SEResNeXtBottleneck, layers=[3, 4, 23, 3], groups=32, reduction=16, **kwargs)
return _create_senet('legacy_seresnext101_32x4d', pretrained, **model_args)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/_efficientnet_builder.py
|
""" EfficientNet, MobileNetV3, etc Builder
Assembles EfficieNet and related network feature blocks from string definitions.
Handles stride, dilation calculations, and selects feature extraction points.
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import math
import re
from copy import deepcopy
from functools import partial
from typing import Any, Dict, List
import torch.nn as nn
from ._efficientnet_blocks import *
from timm.layers import CondConv2d, get_condconv_initializer, get_act_layer, get_attn, make_divisible
__all__ = ["EfficientNetBuilder", "decode_arch_def", "efficientnet_init_weights",
'resolve_bn_args', 'resolve_act_layer', 'round_channels', 'BN_MOMENTUM_TF_DEFAULT', 'BN_EPS_TF_DEFAULT']
_logger = logging.getLogger(__name__)
_DEBUG_BUILDER = False
# Defaults used for Google/Tensorflow training of mobile networks /w RMSprop as per
# papers and TF reference implementations. PT momentum equiv for TF decay is (1 - TF decay)
# NOTE: momentum varies btw .99 and .9997 depending on source
# .99 in official TF TPU impl
# .9997 (/w .999 in search space) for paper
BN_MOMENTUM_TF_DEFAULT = 1 - 0.99
BN_EPS_TF_DEFAULT = 1e-3
_BN_ARGS_TF = dict(momentum=BN_MOMENTUM_TF_DEFAULT, eps=BN_EPS_TF_DEFAULT)
BlockArgs = List[List[Dict[str, Any]]]
def get_bn_args_tf():
return _BN_ARGS_TF.copy()
def resolve_bn_args(kwargs):
bn_args = {}
bn_momentum = kwargs.pop('bn_momentum', None)
if bn_momentum is not None:
bn_args['momentum'] = bn_momentum
bn_eps = kwargs.pop('bn_eps', None)
if bn_eps is not None:
bn_args['eps'] = bn_eps
return bn_args
def resolve_act_layer(kwargs, default='relu'):
return get_act_layer(kwargs.pop('act_layer', default))
def round_channels(channels, multiplier=1.0, divisor=8, channel_min=None, round_limit=0.9):
"""Round number of filters based on depth multiplier."""
if not multiplier:
return channels
return make_divisible(channels * multiplier, divisor, channel_min, round_limit=round_limit)
def _log_info_if(msg, condition):
if condition:
_logger.info(msg)
def _parse_ksize(ss):
if ss.isdigit():
return int(ss)
else:
return [int(k) for k in ss.split('.')]
def _decode_block_str(block_str):
""" Decode block definition string
Gets a list of block arg (dicts) through a string notation of arguments.
E.g. ir_r2_k3_s2_e1_i32_o16_se0.25_noskip
All args can exist in any order with the exception of the leading string which
is assumed to indicate the block type.
leading string - block type (
ir = InvertedResidual, ds = DepthwiseSep, dsa = DeptwhiseSep with pw act, cn = ConvBnAct)
r - number of repeat blocks,
k - kernel size,
s - strides (1-9),
e - expansion ratio,
c - output channels,
se - squeeze/excitation ratio
n - activation fn ('re', 'r6', 'hs', or 'sw')
Args:
block_str: a string representation of block arguments.
Returns:
A list of block args (dicts)
Raises:
ValueError: if the string def not properly specified (TODO)
"""
assert isinstance(block_str, str)
ops = block_str.split('_')
block_type = ops[0] # take the block type off the front
ops = ops[1:]
options = {}
skip = None
for op in ops:
# string options being checked on individual basis, combine if they grow
if op == 'noskip':
skip = False # force no skip connection
elif op == 'skip':
skip = True # force a skip connection
elif op.startswith('n'):
# activation fn
key = op[0]
v = op[1:]
if v == 're':
value = get_act_layer('relu')
elif v == 'r6':
value = get_act_layer('relu6')
elif v == 'hs':
value = get_act_layer('hard_swish')
elif v == 'sw':
value = get_act_layer('swish') # aka SiLU
elif v == 'mi':
value = get_act_layer('mish')
else:
continue
options[key] = value
else:
# all numeric options
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
# if act_layer is None, the model default (passed to model init) will be used
act_layer = options['n'] if 'n' in options else None
exp_kernel_size = _parse_ksize(options['a']) if 'a' in options else 1
pw_kernel_size = _parse_ksize(options['p']) if 'p' in options else 1
force_in_chs = int(options['fc']) if 'fc' in options else 0 # FIXME hack to deal with in_chs issue in TPU def
num_repeat = int(options['r'])
# each type of block has different valid arguments, fill accordingly
block_args = dict(
block_type=block_type,
out_chs=int(options['c']),
stride=int(options['s']),
act_layer=act_layer,
)
if block_type == 'ir':
block_args.update(dict(
dw_kernel_size=_parse_ksize(options['k']),
exp_kernel_size=exp_kernel_size,
pw_kernel_size=pw_kernel_size,
exp_ratio=float(options['e']),
se_ratio=float(options['se']) if 'se' in options else 0.,
noskip=skip is False,
))
if 'cc' in options:
block_args['num_experts'] = int(options['cc'])
elif block_type == 'ds' or block_type == 'dsa':
block_args.update(dict(
dw_kernel_size=_parse_ksize(options['k']),
pw_kernel_size=pw_kernel_size,
se_ratio=float(options['se']) if 'se' in options else 0.,
pw_act=block_type == 'dsa',
noskip=block_type == 'dsa' or skip is False,
))
elif block_type == 'er':
block_args.update(dict(
exp_kernel_size=_parse_ksize(options['k']),
pw_kernel_size=pw_kernel_size,
exp_ratio=float(options['e']),
force_in_chs=force_in_chs,
se_ratio=float(options['se']) if 'se' in options else 0.,
noskip=skip is False,
))
elif block_type == 'cn':
block_args.update(dict(
kernel_size=int(options['k']),
skip=skip is True,
))
else:
assert False, 'Unknown block type (%s)' % block_type
if 'gs' in options:
block_args['group_size'] = options['gs']
return block_args, num_repeat
def _scale_stage_depth(stack_args, repeats, depth_multiplier=1.0, depth_trunc='ceil'):
""" Per-stage depth scaling
Scales the block repeats in each stage. This depth scaling impl maintains
compatibility with the EfficientNet scaling method, while allowing sensible
scaling for other models that may have multiple block arg definitions in each stage.
"""
# We scale the total repeat count for each stage, there may be multiple
# block arg defs per stage so we need to sum.
num_repeat = sum(repeats)
if depth_trunc == 'round':
# Truncating to int by rounding allows stages with few repeats to remain
# proportionally smaller for longer. This is a good choice when stage definitions
# include single repeat stages that we'd prefer to keep that way as long as possible
num_repeat_scaled = max(1, round(num_repeat * depth_multiplier))
else:
# The default for EfficientNet truncates repeats to int via 'ceil'.
# Any multiplier > 1.0 will result in an increased depth for every stage.
num_repeat_scaled = int(math.ceil(num_repeat * depth_multiplier))
# Proportionally distribute repeat count scaling to each block definition in the stage.
# Allocation is done in reverse as it results in the first block being less likely to be scaled.
# The first block makes less sense to repeat in most of the arch definitions.
repeats_scaled = []
for r in repeats[::-1]:
rs = max(1, round((r / num_repeat * num_repeat_scaled)))
repeats_scaled.append(rs)
num_repeat -= r
num_repeat_scaled -= rs
repeats_scaled = repeats_scaled[::-1]
# Apply the calculated scaling to each block arg in the stage
sa_scaled = []
for ba, rep in zip(stack_args, repeats_scaled):
sa_scaled.extend([deepcopy(ba) for _ in range(rep)])
return sa_scaled
def decode_arch_def(
arch_def,
depth_multiplier=1.0,
depth_trunc='ceil',
experts_multiplier=1,
fix_first_last=False,
group_size=None,
):
""" Decode block architecture definition strings -> block kwargs
Args:
arch_def: architecture definition strings, list of list of strings
depth_multiplier: network depth multiplier
depth_trunc: networ depth truncation mode when applying multiplier
experts_multiplier: CondConv experts multiplier
fix_first_last: fix first and last block depths when multiplier is applied
group_size: group size override for all blocks that weren't explicitly set in arch string
Returns:
list of list of block kwargs
"""
arch_args = []
if isinstance(depth_multiplier, tuple):
assert len(depth_multiplier) == len(arch_def)
else:
depth_multiplier = (depth_multiplier,) * len(arch_def)
for stack_idx, (block_strings, multiplier) in enumerate(zip(arch_def, depth_multiplier)):
assert isinstance(block_strings, list)
stack_args = []
repeats = []
for block_str in block_strings:
assert isinstance(block_str, str)
ba, rep = _decode_block_str(block_str)
if ba.get('num_experts', 0) > 0 and experts_multiplier > 1:
ba['num_experts'] *= experts_multiplier
if group_size is not None:
ba.setdefault('group_size', group_size)
stack_args.append(ba)
repeats.append(rep)
if fix_first_last and (stack_idx == 0 or stack_idx == len(arch_def) - 1):
arch_args.append(_scale_stage_depth(stack_args, repeats, 1.0, depth_trunc))
else:
arch_args.append(_scale_stage_depth(stack_args, repeats, multiplier, depth_trunc))
return arch_args
class EfficientNetBuilder:
""" Build Trunk Blocks
This ended up being somewhat of a cross between
https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
and
https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_builder.py
"""
def __init__(self, output_stride=32, pad_type='', round_chs_fn=round_channels, se_from_exp=False,
act_layer=None, norm_layer=None, se_layer=None, drop_path_rate=0., feature_location=''):
self.output_stride = output_stride
self.pad_type = pad_type
self.round_chs_fn = round_chs_fn
self.se_from_exp = se_from_exp # calculate se channel reduction from expanded (mid) chs
self.act_layer = act_layer
self.norm_layer = norm_layer
self.se_layer = get_attn(se_layer)
try:
self.se_layer(8, rd_ratio=1.0) # test if attn layer accepts rd_ratio arg
self.se_has_ratio = True
except TypeError:
self.se_has_ratio = False
self.drop_path_rate = drop_path_rate
if feature_location == 'depthwise':
# old 'depthwise' mode renamed 'expansion' to match TF impl, old expansion mode didn't make sense
_logger.warning("feature_location=='depthwise' is deprecated, using 'expansion'")
feature_location = 'expansion'
self.feature_location = feature_location
assert feature_location in ('bottleneck', 'expansion', '')
self.verbose = _DEBUG_BUILDER
# state updated during build, consumed by model
self.in_chs = None
self.features = []
def _make_block(self, ba, block_idx, block_count):
drop_path_rate = self.drop_path_rate * block_idx / block_count
bt = ba.pop('block_type')
ba['in_chs'] = self.in_chs
ba['out_chs'] = self.round_chs_fn(ba['out_chs'])
if 'force_in_chs' in ba and ba['force_in_chs']:
# NOTE this is a hack to work around mismatch in TF EdgeEffNet impl
ba['force_in_chs'] = self.round_chs_fn(ba['force_in_chs'])
ba['pad_type'] = self.pad_type
# block act fn overrides the model default
ba['act_layer'] = ba['act_layer'] if ba['act_layer'] is not None else self.act_layer
assert ba['act_layer'] is not None
ba['norm_layer'] = self.norm_layer
ba['drop_path_rate'] = drop_path_rate
if bt != 'cn':
se_ratio = ba.pop('se_ratio')
if se_ratio and self.se_layer is not None:
if not self.se_from_exp:
# adjust se_ratio by expansion ratio if calculating se channels from block input
se_ratio /= ba.get('exp_ratio', 1.0)
if self.se_has_ratio:
ba['se_layer'] = partial(self.se_layer, rd_ratio=se_ratio)
else:
ba['se_layer'] = self.se_layer
if bt == 'ir':
_log_info_if(' InvertedResidual {}, Args: {}'.format(block_idx, str(ba)), self.verbose)
block = CondConvResidual(**ba) if ba.get('num_experts', 0) else InvertedResidual(**ba)
elif bt == 'ds' or bt == 'dsa':
_log_info_if(' DepthwiseSeparable {}, Args: {}'.format(block_idx, str(ba)), self.verbose)
block = DepthwiseSeparableConv(**ba)
elif bt == 'er':
_log_info_if(' EdgeResidual {}, Args: {}'.format(block_idx, str(ba)), self.verbose)
block = EdgeResidual(**ba)
elif bt == 'cn':
_log_info_if(' ConvBnAct {}, Args: {}'.format(block_idx, str(ba)), self.verbose)
block = ConvBnAct(**ba)
else:
assert False, 'Uknkown block type (%s) while building model.' % bt
self.in_chs = ba['out_chs'] # update in_chs for arg of next block
return block
def __call__(self, in_chs, model_block_args):
""" Build the blocks
Args:
in_chs: Number of input-channels passed to first block
model_block_args: A list of lists, outer list defines stages, inner
list contains strings defining block configuration(s)
Return:
List of block stacks (each stack wrapped in nn.Sequential)
"""
_log_info_if('Building model trunk with %d stages...' % len(model_block_args), self.verbose)
self.in_chs = in_chs
total_block_count = sum([len(x) for x in model_block_args])
total_block_idx = 0
current_stride = 2
current_dilation = 1
stages = []
if model_block_args[0][0]['stride'] > 1:
# if the first block starts with a stride, we need to extract first level feat from stem
feature_info = dict(module='bn1', num_chs=in_chs, stage=0, reduction=current_stride)
self.features.append(feature_info)
# outer list of block_args defines the stacks
for stack_idx, stack_args in enumerate(model_block_args):
last_stack = stack_idx + 1 == len(model_block_args)
_log_info_if('Stack: {}'.format(stack_idx), self.verbose)
assert isinstance(stack_args, list)
blocks = []
# each stack (stage of blocks) contains a list of block arguments
for block_idx, block_args in enumerate(stack_args):
last_block = block_idx + 1 == len(stack_args)
_log_info_if(' Block: {}'.format(block_idx), self.verbose)
assert block_args['stride'] in (1, 2)
if block_idx >= 1: # only the first block in any stack can have a stride > 1
block_args['stride'] = 1
extract_features = False
if last_block:
next_stack_idx = stack_idx + 1
extract_features = next_stack_idx >= len(model_block_args) or \
model_block_args[next_stack_idx][0]['stride'] > 1
next_dilation = current_dilation
if block_args['stride'] > 1:
next_output_stride = current_stride * block_args['stride']
if next_output_stride > self.output_stride:
next_dilation = current_dilation * block_args['stride']
block_args['stride'] = 1
_log_info_if(' Converting stride to dilation to maintain output_stride=={}'.format(
self.output_stride), self.verbose)
else:
current_stride = next_output_stride
block_args['dilation'] = current_dilation
if next_dilation != current_dilation:
current_dilation = next_dilation
# create the block
block = self._make_block(block_args, total_block_idx, total_block_count)
blocks.append(block)
# stash feature module name and channel info for model feature extraction
if extract_features:
feature_info = dict(
stage=stack_idx + 1,
reduction=current_stride,
**block.feature_info(self.feature_location),
)
leaf_name = feature_info.get('module', '')
if leaf_name:
feature_info['module'] = '.'.join([f'blocks.{stack_idx}.{block_idx}', leaf_name])
else:
assert last_block
feature_info['module'] = f'blocks.{stack_idx}'
self.features.append(feature_info)
total_block_idx += 1 # incr global block idx (across all stacks)
stages.append(nn.Sequential(*blocks))
return stages
def _init_weight_goog(m, n='', fix_group_fanout=True):
""" Weight initialization as per Tensorflow official implementations.
Args:
m (nn.Module): module to init
n (str): module name
fix_group_fanout (bool): enable correct (matching Tensorflow TPU impl) fanout calculation w/ group convs
Handles layers in EfficientNet, EfficientNet-CondConv, MixNet, MnasNet, MobileNetV3, etc:
* https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_model.py
* https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
"""
if isinstance(m, CondConv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
if fix_group_fanout:
fan_out //= m.groups
init_weight_fn = get_condconv_initializer(
lambda w: nn.init.normal_(w, 0, math.sqrt(2.0 / fan_out)), m.num_experts, m.weight_shape)
init_weight_fn(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
if fix_group_fanout:
fan_out //= m.groups
nn.init.normal_(m.weight, 0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
fan_out = m.weight.size(0) # fan-out
fan_in = 0
if 'routing_fn' in n:
fan_in = m.weight.size(1)
init_range = 1.0 / math.sqrt(fan_in + fan_out)
nn.init.uniform_(m.weight, -init_range, init_range)
nn.init.zeros_(m.bias)
def efficientnet_init_weights(model: nn.Module, init_fn=None):
init_fn = init_fn or _init_weight_goog
for n, m in model.named_modules():
init_fn(m, n)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/mlp_mixer.py
|
""" MLP-Mixer, ResMLP, and gMLP in PyTorch
This impl originally based on MLP-Mixer paper.
Official JAX impl: https://github.com/google-research/vision_transformer/blob/linen/vit_jax/models_mixer.py
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
@article{tolstikhin2021,
title={MLP-Mixer: An all-MLP Architecture for Vision},
author={Tolstikhin, Ilya and Houlsby, Neil and Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Unterthiner,
Thomas and Yung, Jessica and Keysers, Daniel and Uszkoreit, Jakob and Lucic, Mario and Dosovitskiy, Alexey},
journal={arXiv preprint arXiv:2105.01601},
year={2021}
}
Also supporting ResMlp, and a preliminary (not verified) implementations of gMLP
Code: https://github.com/facebookresearch/deit
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
@misc{touvron2021resmlp,
title={ResMLP: Feedforward networks for image classification with data-efficient training},
author={Hugo Touvron and Piotr Bojanowski and Mathilde Caron and Matthieu Cord and Alaaeldin El-Nouby and
Edouard Grave and Armand Joulin and Gabriel Synnaeve and Jakob Verbeek and Hervé Jégou},
year={2021},
eprint={2105.03404},
}
Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
@misc{liu2021pay,
title={Pay Attention to MLPs},
author={Hanxiao Liu and Zihang Dai and David R. So and Quoc V. Le},
year={2021},
eprint={2105.08050},
}
A thank you to paper authors for releasing code and weights.
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import PatchEmbed, Mlp, GluMlp, GatedMlp, DropPath, lecun_normal_, to_2tuple
from ._builder import build_model_with_cfg
from ._manipulate import named_apply, checkpoint_seq
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
__all__ = ['MixerBlock', 'MlpMixer'] # model_registry will add each entrypoint fn to this
class MixerBlock(nn.Module):
""" Residual Block w/ token mixing and channel MLPs
Based on: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
def __init__(
self,
dim,
seq_len,
mlp_ratio=(0.5, 4.0),
mlp_layer=Mlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU,
drop=0.,
drop_path=0.,
):
super().__init__()
tokens_dim, channels_dim = [int(x * dim) for x in to_2tuple(mlp_ratio)]
self.norm1 = norm_layer(dim)
self.mlp_tokens = mlp_layer(seq_len, tokens_dim, act_layer=act_layer, drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp_channels = mlp_layer(dim, channels_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
x = x + self.drop_path(self.mlp_tokens(self.norm1(x).transpose(1, 2)).transpose(1, 2))
x = x + self.drop_path(self.mlp_channels(self.norm2(x)))
return x
class Affine(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones((1, 1, dim)))
self.beta = nn.Parameter(torch.zeros((1, 1, dim)))
def forward(self, x):
return torch.addcmul(self.beta, self.alpha, x)
class ResBlock(nn.Module):
""" Residual MLP block w/ LayerScale and Affine 'norm'
Based on: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
def __init__(
self,
dim,
seq_len,
mlp_ratio=4,
mlp_layer=Mlp,
norm_layer=Affine,
act_layer=nn.GELU,
init_values=1e-4,
drop=0.,
drop_path=0.,
):
super().__init__()
channel_dim = int(dim * mlp_ratio)
self.norm1 = norm_layer(dim)
self.linear_tokens = nn.Linear(seq_len, seq_len)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp_channels = mlp_layer(dim, channel_dim, act_layer=act_layer, drop=drop)
self.ls1 = nn.Parameter(init_values * torch.ones(dim))
self.ls2 = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
x = x + self.drop_path(self.ls1 * self.linear_tokens(self.norm1(x).transpose(1, 2)).transpose(1, 2))
x = x + self.drop_path(self.ls2 * self.mlp_channels(self.norm2(x)))
return x
class SpatialGatingUnit(nn.Module):
""" Spatial Gating Unit
Based on: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
"""
def __init__(self, dim, seq_len, norm_layer=nn.LayerNorm):
super().__init__()
gate_dim = dim // 2
self.norm = norm_layer(gate_dim)
self.proj = nn.Linear(seq_len, seq_len)
def init_weights(self):
# special init for the projection gate, called as override by base model init
nn.init.normal_(self.proj.weight, std=1e-6)
nn.init.ones_(self.proj.bias)
def forward(self, x):
u, v = x.chunk(2, dim=-1)
v = self.norm(v)
v = self.proj(v.transpose(-1, -2))
return u * v.transpose(-1, -2)
class SpatialGatingBlock(nn.Module):
""" Residual Block w/ Spatial Gating
Based on: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
"""
def __init__(
self,
dim,
seq_len,
mlp_ratio=4,
mlp_layer=GatedMlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU,
drop=0.,
drop_path=0.,
):
super().__init__()
channel_dim = int(dim * mlp_ratio)
self.norm = norm_layer(dim)
sgu = partial(SpatialGatingUnit, seq_len=seq_len)
self.mlp_channels = mlp_layer(dim, channel_dim, act_layer=act_layer, gate_layer=sgu, drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
x = x + self.drop_path(self.mlp_channels(self.norm(x)))
return x
class MlpMixer(nn.Module):
def __init__(
self,
num_classes=1000,
img_size=224,
in_chans=3,
patch_size=16,
num_blocks=8,
embed_dim=512,
mlp_ratio=(0.5, 4.0),
block_layer=MixerBlock,
mlp_layer=Mlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU,
drop_rate=0.,
proj_drop_rate=0.,
drop_path_rate=0.,
nlhb=False,
stem_norm=False,
global_pool='avg',
):
super().__init__()
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.grad_checkpointing = False
self.stem = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer if stem_norm else None,
)
# FIXME drop_path (stochastic depth scaling rule or all the same?)
self.blocks = nn.Sequential(*[
block_layer(
embed_dim,
self.stem.num_patches,
mlp_ratio,
mlp_layer=mlp_layer,
norm_layer=norm_layer,
act_layer=act_layer,
drop=proj_drop_rate,
drop_path=drop_path_rate,
)
for _ in range(num_blocks)])
self.norm = norm_layer(embed_dim)
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
self.init_weights(nlhb=nlhb)
@torch.jit.ignore
def init_weights(self, nlhb=False):
head_bias = -math.log(self.num_classes) if nlhb else 0.
named_apply(partial(_init_weights, head_bias=head_bias), module=self) # depth-first
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'avg')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool == 'avg':
x = x.mean(dim=1)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=False):
""" Mixer weight initialization (trying to match Flax defaults)
"""
if isinstance(module, nn.Linear):
if name.startswith('head'):
nn.init.zeros_(module.weight)
nn.init.constant_(module.bias, head_bias)
else:
if flax:
# Flax defaults
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
else:
# like MLP init in vit (my original init)
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
if 'mlp' in name:
nn.init.normal_(module.bias, std=1e-6)
else:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
# NOTE if a parent module contains init_weights method, it can override the init of the
# child modules as this will be called in depth-first order.
module.init_weights()
def checkpoint_filter_fn(state_dict, model):
""" Remap checkpoints if needed """
if 'patch_embed.proj.weight' in state_dict:
# Remap FB ResMlp models -> timm
out_dict = {}
for k, v in state_dict.items():
k = k.replace('patch_embed.', 'stem.')
k = k.replace('attn.', 'linear_tokens.')
k = k.replace('mlp.', 'mlp_channels.')
k = k.replace('gamma_', 'ls')
if k.endswith('.alpha') or k.endswith('.beta'):
v = v.reshape(1, 1, -1)
out_dict[k] = v
return out_dict
return state_dict
def _create_mixer(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for MLP-Mixer models.')
model = build_model_with_cfg(
MlpMixer,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs,
)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': 0.875, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
'first_conv': 'stem.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = generate_default_cfgs({
'mixer_s32_224.untrained': _cfg(),
'mixer_s16_224.untrained': _cfg(),
'mixer_b32_224.untrained': _cfg(),
'mixer_b16_224.goog_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224-76587d61.pth',
),
'mixer_b16_224.goog_in21k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224_in21k-617b3de2.pth',
num_classes=21843
),
'mixer_l32_224.untrained': _cfg(),
'mixer_l16_224.goog_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224-92f9adc4.pth',
),
'mixer_l16_224.goog_in21k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224_in21k-846aa33c.pth',
num_classes=21843
),
# Mixer ImageNet-21K-P pretraining
'mixer_b16_224.miil_in21k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mixer_b16_224_miil_in21k-2a558a71.pth',
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
),
'mixer_b16_224.miil_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mixer_b16_224_miil-9229a591.pth',
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear',
),
'gmixer_12_224.untrained': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'gmixer_24_224.ra3_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmixer_24_224_raa-7daf7ae6.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_12_224.fb_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlp_12_no_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_24_224.fb_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlp_24_no_dist.pth',
#url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resmlp_24_224_raa-a8256759.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_36_224.fb_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlp_36_no_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_big_24_224.fb_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_no_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_12_224.fb_distilled_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_24_224.fb_distilled_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_36_224.fb_distilled_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlp_36_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_big_24_224.fb_distilled_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_big_24_224.fb_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_22k.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_12_224.fb_dino': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dino.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'resmlp_24_224.fb_dino': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dino.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'gmlp_ti16_224.untrained': _cfg(),
'gmlp_s16_224.ra3_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmlp_s16_224_raa-10536d42.pth',
),
'gmlp_b16_224.untrained': _cfg(),
})
@register_model
def mixer_s32_224(pretrained=False, **kwargs) -> MlpMixer:
""" Mixer-S/32 224x224
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=32, num_blocks=8, embed_dim=512, **kwargs)
model = _create_mixer('mixer_s32_224', pretrained=pretrained, **model_args)
return model
@register_model
def mixer_s16_224(pretrained=False, **kwargs) -> MlpMixer:
""" Mixer-S/16 224x224
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=16, num_blocks=8, embed_dim=512, **kwargs)
model = _create_mixer('mixer_s16_224', pretrained=pretrained, **model_args)
return model
@register_model
def mixer_b32_224(pretrained=False, **kwargs) -> MlpMixer:
""" Mixer-B/32 224x224
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=32, num_blocks=12, embed_dim=768, **kwargs)
model = _create_mixer('mixer_b32_224', pretrained=pretrained, **model_args)
return model
@register_model
def mixer_b16_224(pretrained=False, **kwargs) -> MlpMixer:
""" Mixer-B/16 224x224. ImageNet-1k pretrained weights.
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
model = _create_mixer('mixer_b16_224', pretrained=pretrained, **model_args)
return model
@register_model
def mixer_l32_224(pretrained=False, **kwargs) -> MlpMixer:
""" Mixer-L/32 224x224.
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=32, num_blocks=24, embed_dim=1024, **kwargs)
model = _create_mixer('mixer_l32_224', pretrained=pretrained, **model_args)
return model
@register_model
def mixer_l16_224(pretrained=False, **kwargs) -> MlpMixer:
""" Mixer-L/16 224x224. ImageNet-1k pretrained weights.
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=16, num_blocks=24, embed_dim=1024, **kwargs)
model = _create_mixer('mixer_l16_224', pretrained=pretrained, **model_args)
return model
@register_model
def gmixer_12_224(pretrained=False, **kwargs) -> MlpMixer:
""" Glu-Mixer-12 224x224
Experiment by Ross Wightman, adding SwiGLU to MLP-Mixer
"""
model_args = dict(
patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=(1.0, 4.0),
mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs)
model = _create_mixer('gmixer_12_224', pretrained=pretrained, **model_args)
return model
@register_model
def gmixer_24_224(pretrained=False, **kwargs) -> MlpMixer:
""" Glu-Mixer-24 224x224
Experiment by Ross Wightman, adding SwiGLU to MLP-Mixer
"""
model_args = dict(
patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=(1.0, 4.0),
mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs)
model = _create_mixer('gmixer_24_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_12_224(pretrained=False, **kwargs) -> MlpMixer:
""" ResMLP-12
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_12_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_24_224(pretrained=False, **kwargs) -> MlpMixer:
""" ResMLP-24
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-5), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_24_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_36_224(pretrained=False, **kwargs) -> MlpMixer:
""" ResMLP-36
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=16, num_blocks=36, embed_dim=384, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_36_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_big_24_224(pretrained=False, **kwargs) -> MlpMixer:
""" ResMLP-B-24
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_big_24_224', pretrained=pretrained, **model_args)
return model
@register_model
def gmlp_ti16_224(pretrained=False, **kwargs) -> MlpMixer:
""" gMLP-Tiny
Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
"""
model_args = dict(
patch_size=16, num_blocks=30, embed_dim=128, mlp_ratio=6, block_layer=SpatialGatingBlock,
mlp_layer=GatedMlp, **kwargs)
model = _create_mixer('gmlp_ti16_224', pretrained=pretrained, **model_args)
return model
@register_model
def gmlp_s16_224(pretrained=False, **kwargs) -> MlpMixer:
""" gMLP-Small
Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
"""
model_args = dict(
patch_size=16, num_blocks=30, embed_dim=256, mlp_ratio=6, block_layer=SpatialGatingBlock,
mlp_layer=GatedMlp, **kwargs)
model = _create_mixer('gmlp_s16_224', pretrained=pretrained, **model_args)
return model
@register_model
def gmlp_b16_224(pretrained=False, **kwargs) -> MlpMixer:
""" gMLP-Base
Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
"""
model_args = dict(
patch_size=16, num_blocks=30, embed_dim=512, mlp_ratio=6, block_layer=SpatialGatingBlock,
mlp_layer=GatedMlp, **kwargs)
model = _create_mixer('gmlp_b16_224', pretrained=pretrained, **model_args)
return model
register_model_deprecations(__name__, {
'mixer_b16_224_in21k': 'mixer_b16_224.goog_in21k_ft_in1k',
'mixer_l16_224_in21k': 'mixer_l16_224.goog_in21k_ft_in1k',
'mixer_b16_224_miil': 'mixer_b16_224.miil_in21k_ft_in1k',
'mixer_b16_224_miil_in21k': 'mixer_b16_224.miil_in21k',
'resmlp_12_distilled_224': 'resmlp_12_224.fb_distilled_in1k',
'resmlp_24_distilled_224': 'resmlp_24_224.fb_distilled_in1k',
'resmlp_36_distilled_224': 'resmlp_36_224.fb_distilled_in1k',
'resmlp_big_24_distilled_224': 'resmlp_big_24_224.fb_distilled_in1k',
'resmlp_big_24_224_in22ft1k': 'resmlp_big_24_224.fb_in22k_ft_in1k',
'resmlp_12_224_dino': 'resmlp_12_224',
'resmlp_24_224_dino': 'resmlp_24_224',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/registry.py
|
from ._registry import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/mvitv2.py
|
""" Multi-Scale Vision Transformer v2
@inproceedings{li2021improved,
title={MViTv2: Improved multiscale vision transformers for classification and detection},
author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
booktitle={CVPR},
year={2022}
}
Code adapted from original Apache 2.0 licensed impl at https://github.com/facebookresearch/mvit
Original copyright below.
Modifications and timm support by / Copyright 2022, Ross Wightman
"""
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved. All Rights Reserved.
import operator
from collections import OrderedDict
from dataclasses import dataclass
from functools import partial, reduce
from typing import Union, List, Tuple, Optional
import torch
import torch.utils.checkpoint as checkpoint
from torch import nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import Mlp, DropPath, trunc_normal_tf_, get_norm_layer, to_2tuple
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._registry import register_model, register_model_deprecations, generate_default_cfgs
__all__ = ['MultiScaleVit', 'MultiScaleVitCfg'] # model_registry will add each entrypoint fn to this
@dataclass
class MultiScaleVitCfg:
depths: Tuple[int, ...] = (2, 3, 16, 3)
embed_dim: Union[int, Tuple[int, ...]] = 96
num_heads: Union[int, Tuple[int, ...]] = 1
mlp_ratio: float = 4.
pool_first: bool = False
expand_attn: bool = True
qkv_bias: bool = True
use_cls_token: bool = False
use_abs_pos: bool = False
residual_pooling: bool = True
mode: str = 'conv'
kernel_qkv: Tuple[int, int] = (3, 3)
stride_q: Optional[Tuple[Tuple[int, int]]] = ((1, 1), (2, 2), (2, 2), (2, 2))
stride_kv: Optional[Tuple[Tuple[int, int]]] = None
stride_kv_adaptive: Optional[Tuple[int, int]] = (4, 4)
patch_kernel: Tuple[int, int] = (7, 7)
patch_stride: Tuple[int, int] = (4, 4)
patch_padding: Tuple[int, int] = (3, 3)
pool_type: str = 'max'
rel_pos_type: str = 'spatial'
act_layer: Union[str, Tuple[str, str]] = 'gelu'
norm_layer: Union[str, Tuple[str, str]] = 'layernorm'
norm_eps: float = 1e-6
def __post_init__(self):
num_stages = len(self.depths)
if not isinstance(self.embed_dim, (tuple, list)):
self.embed_dim = tuple(self.embed_dim * 2 ** i for i in range(num_stages))
assert len(self.embed_dim) == num_stages
if not isinstance(self.num_heads, (tuple, list)):
self.num_heads = tuple(self.num_heads * 2 ** i for i in range(num_stages))
assert len(self.num_heads) == num_stages
if self.stride_kv_adaptive is not None and self.stride_kv is None:
_stride_kv = self.stride_kv_adaptive
pool_kv_stride = []
for i in range(num_stages):
if min(self.stride_q[i]) > 1:
_stride_kv = [
max(_stride_kv[d] // self.stride_q[i][d], 1)
for d in range(len(_stride_kv))
]
pool_kv_stride.append(tuple(_stride_kv))
self.stride_kv = tuple(pool_kv_stride)
def prod(iterable):
return reduce(operator.mul, iterable, 1)
class PatchEmbed(nn.Module):
"""
PatchEmbed.
"""
def __init__(
self,
dim_in=3,
dim_out=768,
kernel=(7, 7),
stride=(4, 4),
padding=(3, 3),
):
super().__init__()
self.proj = nn.Conv2d(
dim_in,
dim_out,
kernel_size=kernel,
stride=stride,
padding=padding,
)
def forward(self, x) -> Tuple[torch.Tensor, List[int]]:
x = self.proj(x)
# B C H W -> B HW C
return x.flatten(2).transpose(1, 2), x.shape[-2:]
@register_notrace_function
def reshape_pre_pool(
x,
feat_size: List[int],
has_cls_token: bool = True
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
H, W = feat_size
if has_cls_token:
cls_tok, x = x[:, :, :1, :], x[:, :, 1:, :]
else:
cls_tok = None
x = x.reshape(-1, H, W, x.shape[-1]).permute(0, 3, 1, 2).contiguous()
return x, cls_tok
@register_notrace_function
def reshape_post_pool(
x,
num_heads: int,
cls_tok: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, List[int]]:
feat_size = [x.shape[2], x.shape[3]]
L_pooled = x.shape[2] * x.shape[3]
x = x.reshape(-1, num_heads, x.shape[1], L_pooled).transpose(2, 3)
if cls_tok is not None:
x = torch.cat((cls_tok, x), dim=2)
return x, feat_size
@register_notrace_function
def cal_rel_pos_type(
attn: torch.Tensor,
q: torch.Tensor,
has_cls_token: bool,
q_size: List[int],
k_size: List[int],
rel_pos_h: torch.Tensor,
rel_pos_w: torch.Tensor,
):
"""
Spatial Relative Positional Embeddings.
"""
sp_idx = 1 if has_cls_token else 0
q_h, q_w = q_size
k_h, k_w = k_size
# Scale up rel pos if shapes for q and k are different.
q_h_ratio = max(k_h / q_h, 1.0)
k_h_ratio = max(q_h / k_h, 1.0)
dist_h = (
torch.arange(q_h, device=q.device).unsqueeze(-1) * q_h_ratio -
torch.arange(k_h, device=q.device).unsqueeze(0) * k_h_ratio
)
dist_h += (k_h - 1) * k_h_ratio
q_w_ratio = max(k_w / q_w, 1.0)
k_w_ratio = max(q_w / k_w, 1.0)
dist_w = (
torch.arange(q_w, device=q.device).unsqueeze(-1) * q_w_ratio -
torch.arange(k_w, device=q.device).unsqueeze(0) * k_w_ratio
)
dist_w += (k_w - 1) * k_w_ratio
rel_h = rel_pos_h[dist_h.long()]
rel_w = rel_pos_w[dist_w.long()]
B, n_head, q_N, dim = q.shape
r_q = q[:, :, sp_idx:].reshape(B, n_head, q_h, q_w, dim)
rel_h = torch.einsum("byhwc,hkc->byhwk", r_q, rel_h)
rel_w = torch.einsum("byhwc,wkc->byhwk", r_q, rel_w)
attn[:, :, sp_idx:, sp_idx:] = (
attn[:, :, sp_idx:, sp_idx:].view(B, -1, q_h, q_w, k_h, k_w)
+ rel_h.unsqueeze(-1)
+ rel_w.unsqueeze(-2)
).view(B, -1, q_h * q_w, k_h * k_w)
return attn
class MultiScaleAttentionPoolFirst(nn.Module):
def __init__(
self,
dim,
dim_out,
feat_size,
num_heads=8,
qkv_bias=True,
mode="conv",
kernel_q=(1, 1),
kernel_kv=(1, 1),
stride_q=(1, 1),
stride_kv=(1, 1),
has_cls_token=True,
rel_pos_type='spatial',
residual_pooling=True,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.num_heads = num_heads
self.dim_out = dim_out
self.head_dim = dim_out // num_heads
self.scale = self.head_dim ** -0.5
self.has_cls_token = has_cls_token
padding_q = tuple([int(q // 2) for q in kernel_q])
padding_kv = tuple([int(kv // 2) for kv in kernel_kv])
self.q = nn.Linear(dim, dim_out, bias=qkv_bias)
self.k = nn.Linear(dim, dim_out, bias=qkv_bias)
self.v = nn.Linear(dim, dim_out, bias=qkv_bias)
self.proj = nn.Linear(dim_out, dim_out)
# Skip pooling with kernel and stride size of (1, 1, 1).
if prod(kernel_q) == 1 and prod(stride_q) == 1:
kernel_q = None
if prod(kernel_kv) == 1 and prod(stride_kv) == 1:
kernel_kv = None
self.mode = mode
self.unshared = mode == 'conv_unshared'
self.pool_q, self.pool_k, self.pool_v = None, None, None
self.norm_q, self.norm_k, self.norm_v = None, None, None
if mode in ("avg", "max"):
pool_op = nn.MaxPool2d if mode == "max" else nn.AvgPool2d
if kernel_q:
self.pool_q = pool_op(kernel_q, stride_q, padding_q)
if kernel_kv:
self.pool_k = pool_op(kernel_kv, stride_kv, padding_kv)
self.pool_v = pool_op(kernel_kv, stride_kv, padding_kv)
elif mode == "conv" or mode == "conv_unshared":
dim_conv = dim // num_heads if mode == "conv" else dim
if kernel_q:
self.pool_q = nn.Conv2d(
dim_conv,
dim_conv,
kernel_q,
stride=stride_q,
padding=padding_q,
groups=dim_conv,
bias=False,
)
self.norm_q = norm_layer(dim_conv)
if kernel_kv:
self.pool_k = nn.Conv2d(
dim_conv,
dim_conv,
kernel_kv,
stride=stride_kv,
padding=padding_kv,
groups=dim_conv,
bias=False,
)
self.norm_k = norm_layer(dim_conv)
self.pool_v = nn.Conv2d(
dim_conv,
dim_conv,
kernel_kv,
stride=stride_kv,
padding=padding_kv,
groups=dim_conv,
bias=False,
)
self.norm_v = norm_layer(dim_conv)
else:
raise NotImplementedError(f"Unsupported model {mode}")
# relative pos embedding
self.rel_pos_type = rel_pos_type
if self.rel_pos_type == 'spatial':
assert feat_size[0] == feat_size[1]
size = feat_size[0]
q_size = size // stride_q[1] if len(stride_q) > 0 else size
kv_size = size // stride_kv[1] if len(stride_kv) > 0 else size
rel_sp_dim = 2 * max(q_size, kv_size) - 1
self.rel_pos_h = nn.Parameter(torch.zeros(rel_sp_dim, self.head_dim))
self.rel_pos_w = nn.Parameter(torch.zeros(rel_sp_dim, self.head_dim))
trunc_normal_tf_(self.rel_pos_h, std=0.02)
trunc_normal_tf_(self.rel_pos_w, std=0.02)
self.residual_pooling = residual_pooling
def forward(self, x, feat_size: List[int]):
B, N, _ = x.shape
fold_dim = 1 if self.unshared else self.num_heads
x = x.reshape(B, N, fold_dim, -1).permute(0, 2, 1, 3)
q = k = v = x
if self.pool_q is not None:
q, q_tok = reshape_pre_pool(q, feat_size, self.has_cls_token)
q = self.pool_q(q)
q, q_size = reshape_post_pool(q, self.num_heads, q_tok)
else:
q_size = feat_size
if self.norm_q is not None:
q = self.norm_q(q)
if self.pool_k is not None:
k, k_tok = reshape_pre_pool(k, feat_size, self.has_cls_token)
k = self.pool_k(k)
k, k_size = reshape_post_pool(k, self.num_heads, k_tok)
else:
k_size = feat_size
if self.norm_k is not None:
k = self.norm_k(k)
if self.pool_v is not None:
v, v_tok = reshape_pre_pool(v, feat_size, self.has_cls_token)
v = self.pool_v(v)
v, v_size = reshape_post_pool(v, self.num_heads, v_tok)
else:
v_size = feat_size
if self.norm_v is not None:
v = self.norm_v(v)
q_N = q_size[0] * q_size[1] + int(self.has_cls_token)
q = q.transpose(1, 2).reshape(B, q_N, -1)
q = self.q(q).reshape(B, q_N, self.num_heads, -1).transpose(1, 2)
k_N = k_size[0] * k_size[1] + int(self.has_cls_token)
k = k.transpose(1, 2).reshape(B, k_N, -1)
k = self.k(k).reshape(B, k_N, self.num_heads, -1)
v_N = v_size[0] * v_size[1] + int(self.has_cls_token)
v = v.transpose(1, 2).reshape(B, v_N, -1)
v = self.v(v).reshape(B, v_N, self.num_heads, -1).transpose(1, 2)
attn = (q * self.scale) @ k
if self.rel_pos_type == 'spatial':
attn = cal_rel_pos_type(
attn,
q,
self.has_cls_token,
q_size,
k_size,
self.rel_pos_h,
self.rel_pos_w,
)
attn = attn.softmax(dim=-1)
x = attn @ v
if self.residual_pooling:
x = x + q
x = x.transpose(1, 2).reshape(B, -1, self.dim_out)
x = self.proj(x)
return x, q_size
class MultiScaleAttention(nn.Module):
def __init__(
self,
dim,
dim_out,
feat_size,
num_heads=8,
qkv_bias=True,
mode="conv",
kernel_q=(1, 1),
kernel_kv=(1, 1),
stride_q=(1, 1),
stride_kv=(1, 1),
has_cls_token=True,
rel_pos_type='spatial',
residual_pooling=True,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.num_heads = num_heads
self.dim_out = dim_out
self.head_dim = dim_out // num_heads
self.scale = self.head_dim ** -0.5
self.has_cls_token = has_cls_token
padding_q = tuple([int(q // 2) for q in kernel_q])
padding_kv = tuple([int(kv // 2) for kv in kernel_kv])
self.qkv = nn.Linear(dim, dim_out * 3, bias=qkv_bias)
self.proj = nn.Linear(dim_out, dim_out)
# Skip pooling with kernel and stride size of (1, 1, 1).
if prod(kernel_q) == 1 and prod(stride_q) == 1:
kernel_q = None
if prod(kernel_kv) == 1 and prod(stride_kv) == 1:
kernel_kv = None
self.mode = mode
self.unshared = mode == 'conv_unshared'
self.norm_q, self.norm_k, self.norm_v = None, None, None
self.pool_q, self.pool_k, self.pool_v = None, None, None
if mode in ("avg", "max"):
pool_op = nn.MaxPool2d if mode == "max" else nn.AvgPool2d
if kernel_q:
self.pool_q = pool_op(kernel_q, stride_q, padding_q)
if kernel_kv:
self.pool_k = pool_op(kernel_kv, stride_kv, padding_kv)
self.pool_v = pool_op(kernel_kv, stride_kv, padding_kv)
elif mode == "conv" or mode == "conv_unshared":
dim_conv = dim_out // num_heads if mode == "conv" else dim_out
if kernel_q:
self.pool_q = nn.Conv2d(
dim_conv,
dim_conv,
kernel_q,
stride=stride_q,
padding=padding_q,
groups=dim_conv,
bias=False,
)
self.norm_q = norm_layer(dim_conv)
if kernel_kv:
self.pool_k = nn.Conv2d(
dim_conv,
dim_conv,
kernel_kv,
stride=stride_kv,
padding=padding_kv,
groups=dim_conv,
bias=False,
)
self.norm_k = norm_layer(dim_conv)
self.pool_v = nn.Conv2d(
dim_conv,
dim_conv,
kernel_kv,
stride=stride_kv,
padding=padding_kv,
groups=dim_conv,
bias=False,
)
self.norm_v = norm_layer(dim_conv)
else:
raise NotImplementedError(f"Unsupported model {mode}")
# relative pos embedding
self.rel_pos_type = rel_pos_type
if self.rel_pos_type == 'spatial':
assert feat_size[0] == feat_size[1]
size = feat_size[0]
q_size = size // stride_q[1] if len(stride_q) > 0 else size
kv_size = size // stride_kv[1] if len(stride_kv) > 0 else size
rel_sp_dim = 2 * max(q_size, kv_size) - 1
self.rel_pos_h = nn.Parameter(torch.zeros(rel_sp_dim, self.head_dim))
self.rel_pos_w = nn.Parameter(torch.zeros(rel_sp_dim, self.head_dim))
trunc_normal_tf_(self.rel_pos_h, std=0.02)
trunc_normal_tf_(self.rel_pos_w, std=0.02)
self.residual_pooling = residual_pooling
def forward(self, x, feat_size: List[int]):
B, N, _ = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(dim=0)
if self.pool_q is not None:
q, q_tok = reshape_pre_pool(q, feat_size, self.has_cls_token)
q = self.pool_q(q)
q, q_size = reshape_post_pool(q, self.num_heads, q_tok)
else:
q_size = feat_size
if self.norm_q is not None:
q = self.norm_q(q)
if self.pool_k is not None:
k, k_tok = reshape_pre_pool(k, feat_size, self.has_cls_token)
k = self.pool_k(k)
k, k_size = reshape_post_pool(k, self.num_heads, k_tok)
else:
k_size = feat_size
if self.norm_k is not None:
k = self.norm_k(k)
if self.pool_v is not None:
v, v_tok = reshape_pre_pool(v, feat_size, self.has_cls_token)
v = self.pool_v(v)
v, _ = reshape_post_pool(v, self.num_heads, v_tok)
if self.norm_v is not None:
v = self.norm_v(v)
attn = (q * self.scale) @ k.transpose(-2, -1)
if self.rel_pos_type == 'spatial':
attn = cal_rel_pos_type(
attn,
q,
self.has_cls_token,
q_size,
k_size,
self.rel_pos_h,
self.rel_pos_w,
)
attn = attn.softmax(dim=-1)
x = attn @ v
if self.residual_pooling:
x = x + q
x = x.transpose(1, 2).reshape(B, -1, self.dim_out)
x = self.proj(x)
return x, q_size
class MultiScaleBlock(nn.Module):
def __init__(
self,
dim,
dim_out,
num_heads,
feat_size,
mlp_ratio=4.0,
qkv_bias=True,
drop_path=0.0,
norm_layer=nn.LayerNorm,
kernel_q=(1, 1),
kernel_kv=(1, 1),
stride_q=(1, 1),
stride_kv=(1, 1),
mode="conv",
has_cls_token=True,
expand_attn=False,
pool_first=False,
rel_pos_type='spatial',
residual_pooling=True,
):
super().__init__()
proj_needed = dim != dim_out
self.dim = dim
self.dim_out = dim_out
self.has_cls_token = has_cls_token
self.norm1 = norm_layer(dim)
self.shortcut_proj_attn = nn.Linear(dim, dim_out) if proj_needed and expand_attn else None
if stride_q and prod(stride_q) > 1:
kernel_skip = [s + 1 if s > 1 else s for s in stride_q]
stride_skip = stride_q
padding_skip = [int(skip // 2) for skip in kernel_skip]
self.shortcut_pool_attn = nn.MaxPool2d(kernel_skip, stride_skip, padding_skip)
else:
self.shortcut_pool_attn = None
att_dim = dim_out if expand_attn else dim
attn_layer = MultiScaleAttentionPoolFirst if pool_first else MultiScaleAttention
self.attn = attn_layer(
dim,
att_dim,
num_heads=num_heads,
feat_size=feat_size,
qkv_bias=qkv_bias,
kernel_q=kernel_q,
kernel_kv=kernel_kv,
stride_q=stride_q,
stride_kv=stride_kv,
norm_layer=norm_layer,
has_cls_token=has_cls_token,
mode=mode,
rel_pos_type=rel_pos_type,
residual_pooling=residual_pooling,
)
self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(att_dim)
mlp_dim_out = dim_out
self.shortcut_proj_mlp = nn.Linear(dim, dim_out) if proj_needed and not expand_attn else None
self.mlp = Mlp(
in_features=att_dim,
hidden_features=int(att_dim * mlp_ratio),
out_features=mlp_dim_out,
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def _shortcut_pool(self, x, feat_size: List[int]):
if self.shortcut_pool_attn is None:
return x
if self.has_cls_token:
cls_tok, x = x[:, :1, :], x[:, 1:, :]
else:
cls_tok = None
B, L, C = x.shape
H, W = feat_size
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2).contiguous()
x = self.shortcut_pool_attn(x)
x = x.reshape(B, C, -1).transpose(1, 2)
if cls_tok is not None:
x = torch.cat((cls_tok, x), dim=1)
return x
def forward(self, x, feat_size: List[int]):
x_norm = self.norm1(x)
# NOTE as per the original impl, this seems odd, but shortcut uses un-normalized input if no proj
x_shortcut = x if self.shortcut_proj_attn is None else self.shortcut_proj_attn(x_norm)
x_shortcut = self._shortcut_pool(x_shortcut, feat_size)
x, feat_size_new = self.attn(x_norm, feat_size)
x = x_shortcut + self.drop_path1(x)
x_norm = self.norm2(x)
x_shortcut = x if self.shortcut_proj_mlp is None else self.shortcut_proj_mlp(x_norm)
x = x_shortcut + self.drop_path2(self.mlp(x_norm))
return x, feat_size_new
class MultiScaleVitStage(nn.Module):
def __init__(
self,
dim,
dim_out,
depth,
num_heads,
feat_size,
mlp_ratio=4.0,
qkv_bias=True,
mode="conv",
kernel_q=(1, 1),
kernel_kv=(1, 1),
stride_q=(1, 1),
stride_kv=(1, 1),
has_cls_token=True,
expand_attn=False,
pool_first=False,
rel_pos_type='spatial',
residual_pooling=True,
norm_layer=nn.LayerNorm,
drop_path=0.0,
):
super().__init__()
self.grad_checkpointing = False
self.blocks = nn.ModuleList()
if expand_attn:
out_dims = (dim_out,) * depth
else:
out_dims = (dim,) * (depth - 1) + (dim_out,)
for i in range(depth):
attention_block = MultiScaleBlock(
dim=dim,
dim_out=out_dims[i],
num_heads=num_heads,
feat_size=feat_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
kernel_q=kernel_q,
kernel_kv=kernel_kv,
stride_q=stride_q if i == 0 else (1, 1),
stride_kv=stride_kv,
mode=mode,
has_cls_token=has_cls_token,
pool_first=pool_first,
rel_pos_type=rel_pos_type,
residual_pooling=residual_pooling,
expand_attn=expand_attn,
norm_layer=norm_layer,
drop_path=drop_path[i] if isinstance(drop_path, (list, tuple)) else drop_path,
)
dim = out_dims[i]
self.blocks.append(attention_block)
if i == 0:
feat_size = tuple([size // stride for size, stride in zip(feat_size, stride_q)])
self.feat_size = feat_size
def forward(self, x, feat_size: List[int]):
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x, feat_size = checkpoint.checkpoint(blk, x, feat_size)
else:
x, feat_size = blk(x, feat_size)
return x, feat_size
class MultiScaleVit(nn.Module):
"""
Improved Multiscale Vision Transformers for Classification and Detection
Yanghao Li*, Chao-Yuan Wu*, Haoqi Fan, Karttikeya Mangalam, Bo Xiong, Jitendra Malik,
Christoph Feichtenhofer*
https://arxiv.org/abs/2112.01526
Multiscale Vision Transformers
Haoqi Fan*, Bo Xiong*, Karttikeya Mangalam*, Yanghao Li*, Zhicheng Yan, Jitendra Malik,
Christoph Feichtenhofer*
https://arxiv.org/abs/2104.11227
"""
def __init__(
self,
cfg: MultiScaleVitCfg,
img_size: Tuple[int, int] = (224, 224),
in_chans: int = 3,
global_pool: Optional[str] = None,
num_classes: int = 1000,
drop_path_rate: float = 0.,
drop_rate: float = 0.,
):
super().__init__()
img_size = to_2tuple(img_size)
norm_layer = partial(get_norm_layer(cfg.norm_layer), eps=cfg.norm_eps)
self.num_classes = num_classes
self.drop_rate = drop_rate
if global_pool is None:
global_pool = 'token' if cfg.use_cls_token else 'avg'
self.global_pool = global_pool
self.depths = tuple(cfg.depths)
self.expand_attn = cfg.expand_attn
embed_dim = cfg.embed_dim[0]
self.patch_embed = PatchEmbed(
dim_in=in_chans,
dim_out=embed_dim,
kernel=cfg.patch_kernel,
stride=cfg.patch_stride,
padding=cfg.patch_padding,
)
patch_dims = (img_size[0] // cfg.patch_stride[0], img_size[1] // cfg.patch_stride[1])
num_patches = prod(patch_dims)
if cfg.use_cls_token:
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.num_prefix_tokens = 1
pos_embed_dim = num_patches + 1
else:
self.num_prefix_tokens = 0
self.cls_token = None
pos_embed_dim = num_patches
if cfg.use_abs_pos:
self.pos_embed = nn.Parameter(torch.zeros(1, pos_embed_dim, embed_dim))
else:
self.pos_embed = None
num_stages = len(cfg.embed_dim)
feat_size = patch_dims
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg.depths)).split(cfg.depths)]
self.stages = nn.ModuleList()
for i in range(num_stages):
if cfg.expand_attn:
dim_out = cfg.embed_dim[i]
else:
dim_out = cfg.embed_dim[min(i + 1, num_stages - 1)]
stage = MultiScaleVitStage(
dim=embed_dim,
dim_out=dim_out,
depth=cfg.depths[i],
num_heads=cfg.num_heads[i],
feat_size=feat_size,
mlp_ratio=cfg.mlp_ratio,
qkv_bias=cfg.qkv_bias,
mode=cfg.mode,
pool_first=cfg.pool_first,
expand_attn=cfg.expand_attn,
kernel_q=cfg.kernel_qkv,
kernel_kv=cfg.kernel_qkv,
stride_q=cfg.stride_q[i],
stride_kv=cfg.stride_kv[i],
has_cls_token=cfg.use_cls_token,
rel_pos_type=cfg.rel_pos_type,
residual_pooling=cfg.residual_pooling,
norm_layer=norm_layer,
drop_path=dpr[i],
)
embed_dim = dim_out
feat_size = stage.feat_size
self.stages.append(stage)
self.num_features = embed_dim
self.norm = norm_layer(embed_dim)
self.head = nn.Sequential(OrderedDict([
('drop', nn.Dropout(self.drop_rate)),
('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())
]))
if self.pos_embed is not None:
trunc_normal_tf_(self.pos_embed, std=0.02)
if self.cls_token is not None:
trunc_normal_tf_(self.cls_token, std=0.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_tf_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0.0)
@torch.jit.ignore
def no_weight_decay(self):
return {k for k, _ in self.named_parameters()
if any(n in k for n in ["pos_embed", "rel_pos_h", "rel_pos_w", "cls_token"])}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^patch_embed', # stem and embed
blocks=[(r'^stages\.(\d+)', None), (r'^norm', (99999,))]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = nn.Sequential(OrderedDict([
('drop', nn.Dropout(self.drop_rate)),
('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())
]))
def forward_features(self, x):
x, feat_size = self.patch_embed(x)
B, N, C = x.shape
if self.cls_token is not None:
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
if self.pos_embed is not None:
x = x + self.pos_embed
for stage in self.stages:
x, feat_size = stage(x, feat_size)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
if self.global_pool == 'avg':
x = x[:, self.num_prefix_tokens:].mean(1)
else:
x = x[:, 0]
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
if 'stages.0.blocks.0.norm1.weight' in state_dict:
return state_dict
import re
if 'model_state' in state_dict:
state_dict = state_dict['model_state']
depths = getattr(model, 'depths', None)
expand_attn = getattr(model, 'expand_attn', True)
assert depths is not None, 'model requires depth attribute to remap checkpoints'
depth_map = {}
block_idx = 0
for stage_idx, d in enumerate(depths):
depth_map.update({i: (stage_idx, i - block_idx) for i in range(block_idx, block_idx + d)})
block_idx += d
out_dict = {}
for k, v in state_dict.items():
k = re.sub(
r'blocks\.(\d+)',
lambda x: f'stages.{depth_map[int(x.group(1))][0]}.blocks.{depth_map[int(x.group(1))][1]}',
k)
if expand_attn:
k = re.sub(r'stages\.(\d+).blocks\.(\d+).proj', f'stages.\\1.blocks.\\2.shortcut_proj_attn', k)
else:
k = re.sub(r'stages\.(\d+).blocks\.(\d+).proj', f'stages.\\1.blocks.\\2.shortcut_proj_mlp', k)
if 'head' in k:
k = k.replace('head.projection', 'head.fc')
out_dict[k] = v
# for k, v in state_dict.items():
# if model.pos_embed is not None and k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
# # To resize pos embedding when using model at different size from pretrained weights
# v = resize_pos_embed(
# v,
# model.pos_embed,
# 0 if getattr(model, 'no_embed_class') else getattr(model, 'num_prefix_tokens', 1),
# model.patch_embed.grid_size
# )
return out_dict
model_cfgs = dict(
mvitv2_tiny=MultiScaleVitCfg(
depths=(1, 2, 5, 2),
),
mvitv2_small=MultiScaleVitCfg(
depths=(1, 2, 11, 2),
),
mvitv2_base=MultiScaleVitCfg(
depths=(2, 3, 16, 3),
),
mvitv2_large=MultiScaleVitCfg(
depths=(2, 6, 36, 4),
embed_dim=144,
num_heads=2,
expand_attn=False,
),
mvitv2_small_cls=MultiScaleVitCfg(
depths=(1, 2, 11, 2),
use_cls_token=True,
),
mvitv2_base_cls=MultiScaleVitCfg(
depths=(2, 3, 16, 3),
use_cls_token=True,
),
mvitv2_large_cls=MultiScaleVitCfg(
depths=(2, 6, 36, 4),
embed_dim=144,
num_heads=2,
use_cls_token=True,
expand_attn=True,
),
mvitv2_huge_cls=MultiScaleVitCfg(
depths=(4, 8, 60, 8),
embed_dim=192,
num_heads=3,
use_cls_token=True,
expand_attn=True,
),
)
def _create_mvitv2(variant, cfg_variant=None, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Multiscale Vision Transformer models.')
return build_model_with_cfg(
MultiScaleVit,
variant,
pretrained,
model_cfg=model_cfgs[variant] if not cfg_variant else model_cfgs[cfg_variant],
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head.fc',
'fixed_input_size': True,
**kwargs
}
default_cfgs = generate_default_cfgs({
'mvitv2_tiny.fb_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_T_in1k.pyth',
hf_hub_id='timm/'),
'mvitv2_small.fb_in1k': _cfg(url='https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_S_in1k.pyth',
hf_hub_id='timm/'),
'mvitv2_base.fb_in1k': _cfg(url='https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_B_in1k.pyth',
hf_hub_id='timm/'),
'mvitv2_large.fb_in1k': _cfg(url='https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_L_in1k.pyth',
hf_hub_id='timm/'),
'mvitv2_small_cls': _cfg(url=''),
'mvitv2_base_cls.fb_inw21k': _cfg(
url='https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_B_in21k.pyth',
hf_hub_id='timm/',
num_classes=19168),
'mvitv2_large_cls.fb_inw21k': _cfg(
url='https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_L_in21k.pyth',
hf_hub_id='timm/',
num_classes=19168),
'mvitv2_huge_cls.fb_inw21k': _cfg(
url='https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_H_in21k.pyth',
hf_hub_id='timm/',
num_classes=19168),
})
@register_model
def mvitv2_tiny(pretrained=False, **kwargs) -> MultiScaleVit:
return _create_mvitv2('mvitv2_tiny', pretrained=pretrained, **kwargs)
@register_model
def mvitv2_small(pretrained=False, **kwargs) -> MultiScaleVit:
return _create_mvitv2('mvitv2_small', pretrained=pretrained, **kwargs)
@register_model
def mvitv2_base(pretrained=False, **kwargs) -> MultiScaleVit:
return _create_mvitv2('mvitv2_base', pretrained=pretrained, **kwargs)
@register_model
def mvitv2_large(pretrained=False, **kwargs) -> MultiScaleVit:
return _create_mvitv2('mvitv2_large', pretrained=pretrained, **kwargs)
@register_model
def mvitv2_small_cls(pretrained=False, **kwargs) -> MultiScaleVit:
return _create_mvitv2('mvitv2_small_cls', pretrained=pretrained, **kwargs)
@register_model
def mvitv2_base_cls(pretrained=False, **kwargs) -> MultiScaleVit:
return _create_mvitv2('mvitv2_base_cls', pretrained=pretrained, **kwargs)
@register_model
def mvitv2_large_cls(pretrained=False, **kwargs) -> MultiScaleVit:
return _create_mvitv2('mvitv2_large_cls', pretrained=pretrained, **kwargs)
@register_model
def mvitv2_huge_cls(pretrained=False, **kwargs) -> MultiScaleVit:
return _create_mvitv2('mvitv2_huge_cls', pretrained=pretrained, **kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/inception_resnet_v2.py
|
""" Pytorch Inception-Resnet-V2 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import create_classifier, ConvNormAct
from ._builder import build_model_with_cfg
from ._manipulate import flatten_modules
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
__all__ = ['InceptionResnetV2']
class Mixed_5b(nn.Module):
def __init__(self, conv_block=None):
super(Mixed_5b, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch0 = conv_block(192, 96, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
conv_block(192, 48, kernel_size=1, stride=1),
conv_block(48, 64, kernel_size=5, stride=1, padding=2)
)
self.branch2 = nn.Sequential(
conv_block(192, 64, kernel_size=1, stride=1),
conv_block(64, 96, kernel_size=3, stride=1, padding=1),
conv_block(96, 96, kernel_size=3, stride=1, padding=1)
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
conv_block(192, 64, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Block35(nn.Module):
def __init__(self, scale=1.0, conv_block=None):
super(Block35, self).__init__()
self.scale = scale
conv_block = conv_block or ConvNormAct
self.branch0 = conv_block(320, 32, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
conv_block(320, 32, kernel_size=1, stride=1),
conv_block(32, 32, kernel_size=3, stride=1, padding=1)
)
self.branch2 = nn.Sequential(
conv_block(320, 32, kernel_size=1, stride=1),
conv_block(32, 48, kernel_size=3, stride=1, padding=1),
conv_block(48, 64, kernel_size=3, stride=1, padding=1)
)
self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
self.act = nn.ReLU()
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
out = self.conv2d(out)
out = out * self.scale + x
out = self.act(out)
return out
class Mixed_6a(nn.Module):
def __init__(self, conv_block=None):
super(Mixed_6a, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch0 = conv_block(320, 384, kernel_size=3, stride=2)
self.branch1 = nn.Sequential(
conv_block(320, 256, kernel_size=1, stride=1),
conv_block(256, 256, kernel_size=3, stride=1, padding=1),
conv_block(256, 384, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class Block17(nn.Module):
def __init__(self, scale=1.0, conv_block=None):
super(Block17, self).__init__()
self.scale = scale
conv_block = conv_block or ConvNormAct
self.branch0 = conv_block(1088, 192, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
conv_block(1088, 128, kernel_size=1, stride=1),
conv_block(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
conv_block(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0))
)
self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
self.act = nn.ReLU()
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
out = self.conv2d(out)
out = out * self.scale + x
out = self.act(out)
return out
class Mixed_7a(nn.Module):
def __init__(self, conv_block=None):
super(Mixed_7a, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch0 = nn.Sequential(
conv_block(1088, 256, kernel_size=1, stride=1),
conv_block(256, 384, kernel_size=3, stride=2)
)
self.branch1 = nn.Sequential(
conv_block(1088, 256, kernel_size=1, stride=1),
conv_block(256, 288, kernel_size=3, stride=2)
)
self.branch2 = nn.Sequential(
conv_block(1088, 256, kernel_size=1, stride=1),
conv_block(256, 288, kernel_size=3, stride=1, padding=1),
conv_block(288, 320, kernel_size=3, stride=2)
)
self.branch3 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Block8(nn.Module):
def __init__(self, scale=1.0, no_relu=False, conv_block=None):
super(Block8, self).__init__()
self.scale = scale
conv_block = conv_block or ConvNormAct
self.branch0 = conv_block(2080, 192, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
conv_block(2080, 192, kernel_size=1, stride=1),
conv_block(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
conv_block(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
)
self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
self.relu = None if no_relu else nn.ReLU()
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
out = self.conv2d(out)
out = out * self.scale + x
if self.relu is not None:
out = self.relu(out)
return out
class InceptionResnetV2(nn.Module):
def __init__(
self,
num_classes=1000,
in_chans=3,
drop_rate=0.,
output_stride=32,
global_pool='avg',
norm_layer='batchnorm2d',
norm_eps=1e-3,
act_layer='relu',
):
super(InceptionResnetV2, self).__init__()
self.num_classes = num_classes
self.num_features = 1536
assert output_stride == 32
conv_block = partial(
ConvNormAct,
padding=0,
norm_layer=norm_layer,
act_layer=act_layer,
norm_kwargs=dict(eps=norm_eps),
act_kwargs=dict(inplace=True),
)
self.conv2d_1a = conv_block(in_chans, 32, kernel_size=3, stride=2)
self.conv2d_2a = conv_block(32, 32, kernel_size=3, stride=1)
self.conv2d_2b = conv_block(32, 64, kernel_size=3, stride=1, padding=1)
self.feature_info = [dict(num_chs=64, reduction=2, module='conv2d_2b')]
self.maxpool_3a = nn.MaxPool2d(3, stride=2)
self.conv2d_3b = conv_block(64, 80, kernel_size=1, stride=1)
self.conv2d_4a = conv_block(80, 192, kernel_size=3, stride=1)
self.feature_info += [dict(num_chs=192, reduction=4, module='conv2d_4a')]
self.maxpool_5a = nn.MaxPool2d(3, stride=2)
self.mixed_5b = Mixed_5b(conv_block=conv_block)
self.repeat = nn.Sequential(*[Block35(scale=0.17, conv_block=conv_block) for _ in range(10)])
self.feature_info += [dict(num_chs=320, reduction=8, module='repeat')]
self.mixed_6a = Mixed_6a(conv_block=conv_block)
self.repeat_1 = nn.Sequential(*[Block17(scale=0.10, conv_block=conv_block) for _ in range(20)])
self.feature_info += [dict(num_chs=1088, reduction=16, module='repeat_1')]
self.mixed_7a = Mixed_7a(conv_block=conv_block)
self.repeat_2 = nn.Sequential(*[Block8(scale=0.20, conv_block=conv_block) for _ in range(9)])
self.block8 = Block8(no_relu=True, conv_block=conv_block)
self.conv2d_7b = conv_block(2080, self.num_features, kernel_size=1, stride=1)
self.feature_info += [dict(num_chs=self.num_features, reduction=32, module='conv2d_7b')]
self.global_pool, self.head_drop, self.classif = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, drop_rate=drop_rate)
@torch.jit.ignore
def group_matcher(self, coarse=False):
module_map = {k: i for i, (k, _) in enumerate(flatten_modules(self.named_children(), prefix=()))}
module_map.pop(('classif',))
def _matcher(name):
if any([name.startswith(n) for n in ('conv2d_1', 'conv2d_2')]):
return 0
elif any([name.startswith(n) for n in ('conv2d_3', 'conv2d_4')]):
return 1
elif any([name.startswith(n) for n in ('block8', 'conv2d_7')]):
return len(module_map) + 1
else:
for k in module_map.keys():
if k == tuple(name.split('.')[:len(k)]):
return module_map[k]
return float('inf')
return _matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, "checkpointing not supported"
@torch.jit.ignore
def get_classifier(self):
return self.classif
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.classif = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.conv2d_1a(x)
x = self.conv2d_2a(x)
x = self.conv2d_2b(x)
x = self.maxpool_3a(x)
x = self.conv2d_3b(x)
x = self.conv2d_4a(x)
x = self.maxpool_5a(x)
x = self.mixed_5b(x)
x = self.repeat(x)
x = self.mixed_6a(x)
x = self.repeat_1(x)
x = self.mixed_7a(x)
x = self.repeat_2(x)
x = self.block8(x)
x = self.conv2d_7b(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.head_drop(x)
return x if pre_logits else self.classif(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_inception_resnet_v2(variant, pretrained=False, **kwargs):
return build_model_with_cfg(InceptionResnetV2, variant, pretrained, **kwargs)
default_cfgs = generate_default_cfgs({
# ported from http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz
'inception_resnet_v2.tf_in1k': {
'hf_hub_id': 'timm/',
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
'crop_pct': 0.8975, 'interpolation': 'bicubic',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'conv2d_1a.conv', 'classifier': 'classif',
},
# As per https://arxiv.org/abs/1705.07204 and
# ported from http://download.tensorflow.org/models/ens_adv_inception_resnet_v2_2017_08_18.tar.gz
'inception_resnet_v2.tf_ens_adv_in1k': {
'hf_hub_id': 'timm/',
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
'crop_pct': 0.8975, 'interpolation': 'bicubic',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'conv2d_1a.conv', 'classifier': 'classif',
}
})
@register_model
def inception_resnet_v2(pretrained=False, **kwargs) -> InceptionResnetV2:
return _create_inception_resnet_v2('inception_resnet_v2', pretrained=pretrained, **kwargs)
register_model_deprecations(__name__, {
'ens_adv_inception_resnet_v2': 'inception_resnet_v2.tf_ens_adv_in1k',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/rexnet.py
|
""" ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channel variant hacked together by Ross Wightman
Copyright 2020 Ross Wightman
"""
from functools import partial
from math import ceil
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import ClassifierHead, create_act_layer, ConvNormAct, DropPath, make_divisible, SEModule
from ._builder import build_model_with_cfg
from ._efficientnet_builder import efficientnet_init_weights
from ._manipulate import checkpoint_seq
from ._registry import generate_default_cfgs, register_model
__all__ = ['RexNet'] # model_registry will add each entrypoint fn to this
SEWithNorm = partial(SEModule, norm_layer=nn.BatchNorm2d)
class LinearBottleneck(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride,
dilation=(1, 1),
exp_ratio=1.0,
se_ratio=0.,
ch_div=1,
act_layer='swish',
dw_act_layer='relu6',
drop_path=None,
):
super(LinearBottleneck, self).__init__()
self.use_shortcut = stride == 1 and dilation[0] == dilation[1] and in_chs <= out_chs
self.in_channels = in_chs
self.out_channels = out_chs
if exp_ratio != 1.:
dw_chs = make_divisible(round(in_chs * exp_ratio), divisor=ch_div)
self.conv_exp = ConvNormAct(in_chs, dw_chs, act_layer=act_layer)
else:
dw_chs = in_chs
self.conv_exp = None
self.conv_dw = ConvNormAct(
dw_chs,
dw_chs,
kernel_size=3,
stride=stride,
dilation=dilation[0],
groups=dw_chs,
apply_act=False,
)
if se_ratio > 0:
self.se = SEWithNorm(dw_chs, rd_channels=make_divisible(int(dw_chs * se_ratio), ch_div))
else:
self.se = None
self.act_dw = create_act_layer(dw_act_layer)
self.conv_pwl = ConvNormAct(dw_chs, out_chs, 1, apply_act=False)
self.drop_path = drop_path
def feat_channels(self, exp=False):
return self.conv_dw.out_channels if exp else self.out_channels
def forward(self, x):
shortcut = x
if self.conv_exp is not None:
x = self.conv_exp(x)
x = self.conv_dw(x)
if self.se is not None:
x = self.se(x)
x = self.act_dw(x)
x = self.conv_pwl(x)
if self.use_shortcut:
if self.drop_path is not None:
x = self.drop_path(x)
x = torch.cat([x[:, 0:self.in_channels] + shortcut, x[:, self.in_channels:]], dim=1)
return x
def _block_cfg(
width_mult=1.0,
depth_mult=1.0,
initial_chs=16,
final_chs=180,
se_ratio=0.,
ch_div=1,
):
layers = [1, 2, 2, 3, 3, 5]
strides = [1, 2, 2, 2, 1, 2]
layers = [ceil(element * depth_mult) for element in layers]
strides = sum([[element] + [1] * (layers[idx] - 1) for idx, element in enumerate(strides)], [])
exp_ratios = [1] * layers[0] + [6] * sum(layers[1:])
depth = sum(layers[:]) * 3
base_chs = initial_chs / width_mult if width_mult < 1.0 else initial_chs
# The following channel configuration is a simple instance to make each layer become an expand layer.
out_chs_list = []
for i in range(depth // 3):
out_chs_list.append(make_divisible(round(base_chs * width_mult), divisor=ch_div))
base_chs += final_chs / (depth // 3 * 1.0)
se_ratios = [0.] * (layers[0] + layers[1]) + [se_ratio] * sum(layers[2:])
return list(zip(out_chs_list, exp_ratios, strides, se_ratios))
def _build_blocks(
block_cfg,
prev_chs,
width_mult,
ch_div=1,
output_stride=32,
act_layer='swish',
dw_act_layer='relu6',
drop_path_rate=0.,
):
feat_chs = [prev_chs]
feature_info = []
curr_stride = 2
dilation = 1
features = []
num_blocks = len(block_cfg)
for block_idx, (chs, exp_ratio, stride, se_ratio) in enumerate(block_cfg):
next_dilation = dilation
if stride > 1:
fname = 'stem' if block_idx == 0 else f'features.{block_idx - 1}'
feature_info += [dict(num_chs=feat_chs[-1], reduction=curr_stride, module=fname)]
if curr_stride >= output_stride:
next_dilation = dilation * stride
stride = 1
block_dpr = drop_path_rate * block_idx / (num_blocks - 1) # stochastic depth linear decay rule
drop_path = DropPath(block_dpr) if block_dpr > 0. else None
features.append(LinearBottleneck(
in_chs=prev_chs,
out_chs=chs,
exp_ratio=exp_ratio,
stride=stride,
dilation=(dilation, next_dilation),
se_ratio=se_ratio,
ch_div=ch_div,
act_layer=act_layer,
dw_act_layer=dw_act_layer,
drop_path=drop_path,
))
curr_stride *= stride
dilation = next_dilation
prev_chs = chs
feat_chs += [features[-1].feat_channels()]
pen_chs = make_divisible(1280 * width_mult, divisor=ch_div)
feature_info += [dict(num_chs=feat_chs[-1], reduction=curr_stride, module=f'features.{len(features) - 1}')]
features.append(ConvNormAct(prev_chs, pen_chs, act_layer=act_layer))
return features, feature_info
class RexNet(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
output_stride=32,
initial_chs=16,
final_chs=180,
width_mult=1.0,
depth_mult=1.0,
se_ratio=1/12.,
ch_div=1,
act_layer='swish',
dw_act_layer='relu6',
drop_rate=0.2,
drop_path_rate=0.,
):
super(RexNet, self).__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
assert output_stride in (32, 16, 8)
stem_base_chs = 32 / width_mult if width_mult < 1.0 else 32
stem_chs = make_divisible(round(stem_base_chs * width_mult), divisor=ch_div)
self.stem = ConvNormAct(in_chans, stem_chs, 3, stride=2, act_layer=act_layer)
block_cfg = _block_cfg(width_mult, depth_mult, initial_chs, final_chs, se_ratio, ch_div)
features, self.feature_info = _build_blocks(
block_cfg,
stem_chs,
width_mult,
ch_div,
output_stride,
act_layer,
dw_act_layer,
drop_path_rate,
)
self.num_features = features[-1].out_channels
self.features = nn.Sequential(*features)
self.head = ClassifierHead(self.num_features, num_classes, global_pool, drop_rate)
efficientnet_init_weights(self)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem',
blocks=r'^features\.(\d+)',
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.features, x, flatten=True)
else:
x = self.features(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_rexnet(variant, pretrained, **kwargs):
feature_cfg = dict(flatten_sequential=True)
return build_model_with_cfg(
RexNet,
variant,
pretrained,
feature_cfg=feature_cfg,
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
'license': 'mit', **kwargs
}
default_cfgs = generate_default_cfgs({
'rexnet_100.nav_in1k': _cfg(hf_hub_id='timm/'),
'rexnet_130.nav_in1k': _cfg(hf_hub_id='timm/'),
'rexnet_150.nav_in1k': _cfg(hf_hub_id='timm/'),
'rexnet_200.nav_in1k': _cfg(hf_hub_id='timm/'),
'rexnet_300.nav_in1k': _cfg(hf_hub_id='timm/'),
'rexnetr_100.untrained': _cfg(),
'rexnetr_130.untrained': _cfg(),
'rexnetr_150.untrained': _cfg(),
'rexnetr_200.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_crop_pct=1.0, test_input_size=(3, 288, 288), license='apache-2.0'),
'rexnetr_300.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_crop_pct=1.0, test_input_size=(3, 288, 288), license='apache-2.0'),
'rexnetr_200.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821,
crop_pct=0.95, test_crop_pct=1.0, test_input_size=(3, 288, 288), license='apache-2.0'),
'rexnetr_300.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821,
crop_pct=0.95, test_crop_pct=1.0, test_input_size=(3, 288, 288), license='apache-2.0'),
})
@register_model
def rexnet_100(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 1.0x"""
return _create_rexnet('rexnet_100', pretrained, **kwargs)
@register_model
def rexnet_130(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 1.3x"""
return _create_rexnet('rexnet_130', pretrained, width_mult=1.3, **kwargs)
@register_model
def rexnet_150(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 1.5x"""
return _create_rexnet('rexnet_150', pretrained, width_mult=1.5, **kwargs)
@register_model
def rexnet_200(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 2.0x"""
return _create_rexnet('rexnet_200', pretrained, width_mult=2.0, **kwargs)
@register_model
def rexnet_300(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 3.0x"""
return _create_rexnet('rexnet_300', pretrained, width_mult=3.0, **kwargs)
@register_model
def rexnetr_100(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 1.0x w/ rounded (mod 8) channels"""
return _create_rexnet('rexnetr_100', pretrained, ch_div=8, **kwargs)
@register_model
def rexnetr_130(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 1.3x w/ rounded (mod 8) channels"""
return _create_rexnet('rexnetr_130', pretrained, width_mult=1.3, ch_div=8, **kwargs)
@register_model
def rexnetr_150(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 1.5x w/ rounded (mod 8) channels"""
return _create_rexnet('rexnetr_150', pretrained, width_mult=1.5, ch_div=8, **kwargs)
@register_model
def rexnetr_200(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 2.0x w/ rounded (mod 8) channels"""
return _create_rexnet('rexnetr_200', pretrained, width_mult=2.0, ch_div=8, **kwargs)
@register_model
def rexnetr_300(pretrained=False, **kwargs) -> RexNet:
"""ReXNet V1 3.0x w/ rounded (mod 16) channels"""
return _create_rexnet('rexnetr_300', pretrained, width_mult=3.0, ch_div=16, **kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/vision_transformer.py
|
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
- https://arxiv.org/abs/2106.10270
`FlexiViT: One Model for All Patch Sizes`
- https://arxiv.org/abs/2212.08013
The official jax code is released and available at
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision
Acknowledgments:
* The paper authors for releasing code and weights, thanks!
* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch
* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
Hacked together by / Copyright 2020, Ross Wightman
"""
import logging
import math
from collections import OrderedDict
from functools import partial
from typing import Any, Callable, Dict, Optional, Sequence, Set, Tuple, Type, Union, List
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.jit import Final
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD, \
OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
from timm.layers import PatchEmbed, Mlp, DropPath, AttentionPoolLatent, RmsNorm, PatchDropout, SwiGLUPacked, \
trunc_normal_, lecun_normal_, resample_patch_embed, resample_abs_pos_embed, use_fused_attn, \
get_act_layer, get_norm_layer, LayerType
from ._builder import build_model_with_cfg
from ._manipulate import named_apply, checkpoint_seq, adapt_input_conv
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
__all__ = ['VisionTransformer'] # model_registry will add each entrypoint fn to this
_logger = logging.getLogger(__name__)
class Attention(nn.Module):
fused_attn: Final[bool]
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_norm: bool = False,
attn_drop: float = 0.,
proj_drop: float = 0.,
norm_layer: nn.Module = nn.LayerNorm,
) -> None:
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
q, k = self.q_norm(q), self.k_norm(k)
if self.fused_attn:
x = F.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScale(nn.Module):
def __init__(
self,
dim: int,
init_values: float = 1e-5,
inplace: bool = False,
) -> None:
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class Block(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.,
qkv_bias: bool = False,
qk_norm: bool = False,
proj_drop: float = 0.,
attn_drop: float = 0.,
init_values: Optional[float] = None,
drop_path: float = 0.,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.LayerNorm,
mlp_layer: nn.Module = Mlp,
) -> None:
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
attn_drop=attn_drop,
proj_drop=proj_drop,
norm_layer=norm_layer,
)
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = mlp_layer(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class ResPostBlock(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.,
qkv_bias: bool = False,
qk_norm: bool = False,
proj_drop: float = 0.,
attn_drop: float = 0.,
init_values: Optional[float] = None,
drop_path: float = 0.,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.LayerNorm,
mlp_layer: nn.Module = Mlp,
) -> None:
super().__init__()
self.init_values = init_values
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
attn_drop=attn_drop,
proj_drop=proj_drop,
norm_layer=norm_layer,
)
self.norm1 = norm_layer(dim)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.mlp = mlp_layer(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.norm2 = norm_layer(dim)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.init_weights()
def init_weights(self) -> None:
# NOTE this init overrides that base model init with specific changes for the block type
if self.init_values is not None:
nn.init.constant_(self.norm1.weight, self.init_values)
nn.init.constant_(self.norm2.weight, self.init_values)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.drop_path1(self.norm1(self.attn(x)))
x = x + self.drop_path2(self.norm2(self.mlp(x)))
return x
class ParallelScalingBlock(nn.Module):
""" Parallel ViT block (MLP & Attention in parallel)
Based on:
'Scaling Vision Transformers to 22 Billion Parameters` - https://arxiv.org/abs/2302.05442
"""
fused_attn: Final[bool]
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.,
qkv_bias: bool = False,
qk_norm: bool = False,
proj_drop: float = 0.,
attn_drop: float = 0.,
init_values: Optional[float] = None,
drop_path: float = 0.,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.LayerNorm,
mlp_layer: Optional[nn.Module] = None,
) -> None:
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.fused_attn = use_fused_attn()
mlp_hidden_dim = int(mlp_ratio * dim)
in_proj_out_dim = mlp_hidden_dim + 3 * dim
self.in_norm = norm_layer(dim)
self.in_proj = nn.Linear(dim, in_proj_out_dim, bias=qkv_bias)
self.in_split = [mlp_hidden_dim] + [dim] * 3
if qkv_bias:
self.register_buffer('qkv_bias', None)
self.register_parameter('mlp_bias', None)
else:
self.register_buffer('qkv_bias', torch.zeros(3 * dim), persistent=False)
self.mlp_bias = nn.Parameter(torch.zeros(mlp_hidden_dim))
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.attn_out_proj = nn.Linear(dim, dim)
self.mlp_drop = nn.Dropout(proj_drop)
self.mlp_act = act_layer()
self.mlp_out_proj = nn.Linear(mlp_hidden_dim, dim)
self.ls = LayerScale(dim, init_values=init_values) if init_values is not None else nn.Identity()
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, N, C = x.shape
# Combined MLP fc1 & qkv projections
y = self.in_norm(x)
if self.mlp_bias is not None:
# Concat constant zero-bias for qkv w/ trainable mlp_bias.
# Appears faster than adding to x_mlp separately
y = F.linear(y, self.in_proj.weight, torch.cat((self.qkv_bias, self.mlp_bias)))
else:
y = self.in_proj(y)
x_mlp, q, k, v = torch.split(y, self.in_split, dim=-1)
# Dot product attention w/ qk norm
q = self.q_norm(q.view(B, N, self.num_heads, self.head_dim)).transpose(1, 2)
k = self.k_norm(k.view(B, N, self.num_heads, self.head_dim)).transpose(1, 2)
v = v.view(B, N, self.num_heads, self.head_dim).transpose(1, 2)
if self.fused_attn:
x_attn = F.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x_attn = attn @ v
x_attn = x_attn.transpose(1, 2).reshape(B, N, C)
x_attn = self.attn_out_proj(x_attn)
# MLP activation, dropout, fc2
x_mlp = self.mlp_act(x_mlp)
x_mlp = self.mlp_drop(x_mlp)
x_mlp = self.mlp_out_proj(x_mlp)
# Add residual w/ drop path & layer scale applied
y = self.drop_path(self.ls(x_attn + x_mlp))
x = x + y
return x
class ParallelThingsBlock(nn.Module):
""" Parallel ViT block (N parallel attention followed by N parallel MLP)
Based on:
`Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
"""
def __init__(
self,
dim: int,
num_heads: int,
num_parallel: int = 2,
mlp_ratio: float = 4.,
qkv_bias: bool = False,
qk_norm: bool = False,
init_values: Optional[float] = None,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.LayerNorm,
mlp_layer: nn.Module = Mlp,
) -> None:
super().__init__()
self.num_parallel = num_parallel
self.attns = nn.ModuleList()
self.ffns = nn.ModuleList()
for _ in range(num_parallel):
self.attns.append(nn.Sequential(OrderedDict([
('norm', norm_layer(dim)),
('attn', Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
attn_drop=attn_drop,
proj_drop=proj_drop,
norm_layer=norm_layer,
)),
('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
])))
self.ffns.append(nn.Sequential(OrderedDict([
('norm', norm_layer(dim)),
('mlp', mlp_layer(
dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)),
('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
])))
def _forward_jit(self, x: torch.Tensor) -> torch.Tensor:
x = x + torch.stack([attn(x) for attn in self.attns]).sum(dim=0)
x = x + torch.stack([ffn(x) for ffn in self.ffns]).sum(dim=0)
return x
@torch.jit.ignore
def _forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + sum(attn(x) for attn in self.attns)
x = x + sum(ffn(x) for ffn in self.ffns)
return x
def forward(self, x: torch.Tensor) -> torch.Tensor:
if torch.jit.is_scripting() or torch.jit.is_tracing():
return self._forward_jit(x)
else:
return self._forward(x)
class VisionTransformer(nn.Module):
""" Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
- https://arxiv.org/abs/2010.11929
"""
dynamic_img_size: Final[bool]
def __init__(
self,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: Literal['', 'avg', 'token', 'map'] = 'token',
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
qk_norm: bool = False,
init_values: Optional[float] = None,
class_token: bool = True,
no_embed_class: bool = False,
reg_tokens: int = 0,
pre_norm: bool = False,
fc_norm: Optional[bool] = None,
dynamic_img_size: bool = False,
dynamic_img_pad: bool = False,
drop_rate: float = 0.,
pos_drop_rate: float = 0.,
patch_drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
weight_init: Literal['skip', 'jax', 'jax_nlhb', 'moco', ''] = '',
embed_layer: Callable = PatchEmbed,
norm_layer: Optional[LayerType] = None,
act_layer: Optional[LayerType] = None,
block_fn: Type[nn.Module] = Block,
mlp_layer: Type[nn.Module] = Mlp,
) -> None:
"""
Args:
img_size: Input image size.
patch_size: Patch size.
in_chans: Number of image input channels.
num_classes: Mumber of classes for classification head.
global_pool: Type of global pooling for final sequence (default: 'token').
embed_dim: Transformer embedding dimension.
depth: Depth of transformer.
num_heads: Number of attention heads.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: Enable bias for qkv projections if True.
init_values: Layer-scale init values (layer-scale enabled if not None).
class_token: Use class token.
no_embed_class: Don't include position embeddings for class (or reg) tokens.
reg_tokens: Number of register tokens.
fc_norm: Pre head norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
drop_rate: Head dropout rate.
pos_drop_rate: Position embedding dropout rate.
attn_drop_rate: Attention dropout rate.
drop_path_rate: Stochastic depth rate.
weight_init: Weight initialization scheme.
embed_layer: Patch embedding layer.
norm_layer: Normalization layer.
act_layer: MLP activation layer.
block_fn: Transformer block layer.
"""
super().__init__()
assert global_pool in ('', 'avg', 'token', 'map')
assert class_token or global_pool != 'token'
use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
norm_layer = get_norm_layer(norm_layer) or partial(nn.LayerNorm, eps=1e-6)
act_layer = get_act_layer(act_layer) or nn.GELU
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.num_prefix_tokens = 1 if class_token else 0
self.num_prefix_tokens += reg_tokens
self.num_reg_tokens = reg_tokens
self.has_class_token = class_token
self.no_embed_class = no_embed_class # don't embed prefix positions (includes reg)
self.dynamic_img_size = dynamic_img_size
self.grad_checkpointing = False
embed_args = {}
if dynamic_img_size:
# flatten deferred until after pos embed
embed_args.update(dict(strict_img_size=False, output_fmt='NHWC'))
self.patch_embed = embed_layer(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
bias=not pre_norm, # disable bias if pre-norm is used (e.g. CLIP)
dynamic_img_pad=dynamic_img_pad,
**embed_args,
)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
self.reg_token = nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
embed_len = num_patches if no_embed_class else num_patches + self.num_prefix_tokens
self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * .02)
self.pos_drop = nn.Dropout(p=pos_drop_rate)
if patch_drop_rate > 0:
self.patch_drop = PatchDropout(
patch_drop_rate,
num_prefix_tokens=self.num_prefix_tokens,
)
else:
self.patch_drop = nn.Identity()
self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.Sequential(*[
block_fn(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
init_values=init_values,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer,
mlp_layer=mlp_layer,
)
for i in range(depth)])
self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
# Classifier Head
if global_pool == 'map':
self.attn_pool = AttentionPoolLatent(
self.embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
norm_layer=norm_layer,
)
else:
self.attn_pool = None
self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if weight_init != 'skip':
self.init_weights(weight_init)
def init_weights(self, mode: Literal['jax', 'jax_nlhb', 'moco', ''] = '') -> None:
assert mode in ('jax', 'jax_nlhb', 'moco', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
trunc_normal_(self.pos_embed, std=.02)
if self.cls_token is not None:
nn.init.normal_(self.cls_token, std=1e-6)
named_apply(get_init_weights_vit(mode, head_bias), self)
def _init_weights(self, m: nn.Module) -> None:
# this fn left here for compat with downstream users
init_weights_vit_timm(m)
@torch.jit.ignore()
def load_pretrained(self, checkpoint_path: str, prefix: str = '') -> None:
_load_weights(self, checkpoint_path, prefix)
@torch.jit.ignore
def no_weight_decay(self) -> Set:
return {'pos_embed', 'cls_token', 'dist_token'}
@torch.jit.ignore
def group_matcher(self, coarse: bool = False) -> Dict:
return dict(
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable: bool = True) -> None:
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head
def reset_classifier(self, num_classes: int, global_pool = None) -> None:
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'avg', 'token', 'map')
if global_pool == 'map' and self.attn_pool is None:
assert False, "Cannot currently add attention pooling in reset_classifier()."
elif global_pool != 'map ' and self.attn_pool is not None:
self.attn_pool = None # remove attention pooling
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
if self.dynamic_img_size:
B, H, W, C = x.shape
pos_embed = resample_abs_pos_embed(
self.pos_embed,
(H, W),
num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
)
x = x.view(B, -1, C)
else:
pos_embed = self.pos_embed
to_cat = []
if self.cls_token is not None:
to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
if self.reg_token is not None:
to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
if self.no_embed_class:
# deit-3, updated JAX (big vision)
# position embedding does not overlap with class token, add then concat
x = x + pos_embed
if to_cat:
x = torch.cat(to_cat + [x], dim=1)
else:
# original timm, JAX, and deit vit impl
# pos_embed has entry for class token, concat then add
if to_cat:
x = torch.cat(to_cat + [x], dim=1)
x = x + pos_embed
return self.pos_drop(x)
def _intermediate_layers(
self,
x: torch.Tensor,
n: Union[int, Sequence] = 1,
) -> List[torch.Tensor]:
outputs, num_blocks = [], len(self.blocks)
take_indices = set(range(num_blocks - n, num_blocks) if isinstance(n, int) else n)
# forward pass
x = self.patch_embed(x)
x = self._pos_embed(x)
x = self.patch_drop(x)
x = self.norm_pre(x)
for i, blk in enumerate(self.blocks):
x = blk(x)
if i in take_indices:
outputs.append(x)
return outputs
def get_intermediate_layers(
self,
x: torch.Tensor,
n: Union[int, Sequence] = 1,
reshape: bool = False,
return_prefix_tokens: bool = False,
norm: bool = False,
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
""" Intermediate layer accessor (NOTE: This is a WIP experiment).
Inspired by DINO / DINOv2 interface
"""
# take last n blocks if n is an int, if in is a sequence, select by matching indices
outputs = self._intermediate_layers(x, n)
if norm:
outputs = [self.norm(out) for out in outputs]
prefix_tokens = [out[:, 0:self.num_prefix_tokens] for out in outputs]
outputs = [out[:, self.num_prefix_tokens:] for out in outputs]
if reshape:
grid_size = self.patch_embed.grid_size
outputs = [
out.reshape(x.shape[0], grid_size[0], grid_size[1], -1).permute(0, 3, 1, 2).contiguous()
for out in outputs
]
if return_prefix_tokens:
return tuple(zip(outputs, prefix_tokens))
return tuple(outputs)
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
x = self.patch_embed(x)
x = self._pos_embed(x)
x = self.patch_drop(x)
x = self.norm_pre(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
x = self.norm(x)
return x
def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
if self.attn_pool is not None:
x = self.attn_pool(x)
elif self.global_pool == 'avg':
x = x[:, self.num_prefix_tokens:].mean(dim=1)
elif self.global_pool:
x = x[:, 0] # class token
x = self.fc_norm(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
x = self.forward_head(x)
return x
def init_weights_vit_timm(module: nn.Module, name: str = '') -> None:
""" ViT weight initialization, original timm impl (for reproducibility) """
if isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
module.init_weights()
def init_weights_vit_jax(module: nn.Module, name: str = '', head_bias: float = 0.0) -> None:
""" ViT weight initialization, matching JAX (Flax) impl """
if isinstance(module, nn.Linear):
if name.startswith('head'):
nn.init.zeros_(module.weight)
nn.init.constant_(module.bias, head_bias)
else:
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.normal_(module.bias, std=1e-6) if 'mlp' in name else nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
module.init_weights()
def init_weights_vit_moco(module: nn.Module, name: str = '') -> None:
""" ViT weight initialization, matching moco-v3 impl minus fixed PatchEmbed """
if isinstance(module, nn.Linear):
if 'qkv' in name:
# treat the weights of Q, K, V separately
val = math.sqrt(6. / float(module.weight.shape[0] // 3 + module.weight.shape[1]))
nn.init.uniform_(module.weight, -val, val)
else:
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
module.init_weights()
def get_init_weights_vit(mode: str = 'jax', head_bias: float = 0.0) -> None:
if 'jax' in mode:
return partial(init_weights_vit_jax, head_bias=head_bias)
elif 'moco' in mode:
return init_weights_vit_moco
else:
return init_weights_vit_timm
def resize_pos_embed(
posemb: torch.Tensor,
posemb_new: torch.Tensor,
num_prefix_tokens: int = 1,
gs_new: Tuple[int, int] = (),
interpolation: str = 'bicubic',
antialias: bool = False,
) -> torch.Tensor:
""" Rescale the grid of position embeddings when loading from state_dict.
*DEPRECATED* This function is being deprecated in favour of resample_abs_pos_embed
Adapted from:
https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
"""
ntok_new = posemb_new.shape[1]
if num_prefix_tokens:
posemb_prefix, posemb_grid = posemb[:, :num_prefix_tokens], posemb[0, num_prefix_tokens:]
ntok_new -= num_prefix_tokens
else:
posemb_prefix, posemb_grid = posemb[:, :0], posemb[0]
gs_old = int(math.sqrt(len(posemb_grid)))
if not len(gs_new): # backwards compatibility
gs_new = [int(math.sqrt(ntok_new))] * 2
assert len(gs_new) >= 2
_logger.info(f'Resized position embedding: {posemb.shape} ({[gs_old, gs_old]}) to {posemb_new.shape} ({gs_new}).')
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode=interpolation, antialias=antialias, align_corners=False)
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
posemb = torch.cat([posemb_prefix, posemb_grid], dim=1)
return posemb
@torch.no_grad()
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = '') -> None:
""" Load weights from .npz checkpoints for official Google Brain Flax implementation
"""
import numpy as np
def _n2p(w, t=True):
if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
w = w.flatten()
if t:
if w.ndim == 4:
w = w.transpose([3, 2, 0, 1])
elif w.ndim == 3:
w = w.transpose([2, 0, 1])
elif w.ndim == 2:
w = w.transpose([1, 0])
return torch.from_numpy(w)
w = np.load(checkpoint_path)
interpolation = 'bilinear'
antialias = False
big_vision = False
if not prefix:
if 'opt/target/embedding/kernel' in w:
prefix = 'opt/target/'
elif 'params/embedding/kernel' in w:
prefix = 'params/'
big_vision = True
elif 'params/img/embedding/kernel' in w:
prefix = 'params/img/'
big_vision = True
if hasattr(model.patch_embed, 'backbone'):
# hybrid
backbone = model.patch_embed.backbone
stem_only = not hasattr(backbone, 'stem')
stem = backbone if stem_only else backbone.stem
stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
if not stem_only:
for i, stage in enumerate(backbone.stages):
for j, block in enumerate(stage.blocks):
bp = f'{prefix}block{i + 1}/unit{j + 1}/'
for r in range(3):
getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
if block.downsample is not None:
block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
else:
embed_conv_w = adapt_input_conv(
model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
if embed_conv_w.shape[-2:] != model.patch_embed.proj.weight.shape[-2:]:
embed_conv_w = resample_patch_embed(
embed_conv_w,
model.patch_embed.proj.weight.shape[-2:],
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
model.patch_embed.proj.weight.copy_(embed_conv_w)
model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
if model.cls_token is not None:
model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
if big_vision:
pos_embed_w = _n2p(w[f'{prefix}pos_embedding'], t=False)
else:
pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
if pos_embed_w.shape != model.pos_embed.shape:
old_shape = pos_embed_w.shape
num_prefix_tokens = 0 if getattr(model, 'no_embed_class', False) else getattr(model, 'num_prefix_tokens', 1)
pos_embed_w = resample_abs_pos_embed( # resize pos embedding when different size from pretrained weights
pos_embed_w,
new_size=model.patch_embed.grid_size,
num_prefix_tokens=num_prefix_tokens,
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
model.pos_embed.copy_(pos_embed_w)
model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
if (isinstance(model.head, nn.Linear) and
f'{prefix}head/bias' in w and
model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]):
model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
# NOTE representation layer has been removed, not used in latest 21k/1k pretrained weights
# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
if model.attn_pool is not None:
block_prefix = f'{prefix}MAPHead_0/'
mha_prefix = block_prefix + f'MultiHeadDotProductAttention_0/'
model.attn_pool.latent.copy_(_n2p(w[f'{block_prefix}probe'], t=False))
model.attn_pool.kv.weight.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('key', 'value')]))
model.attn_pool.kv.bias.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('key', 'value')]))
model.attn_pool.q.weight.copy_(_n2p(w[f'{mha_prefix}query/kernel'], t=False).flatten(1).T)
model.attn_pool.q.bias.copy_(_n2p(w[f'{mha_prefix}query/bias'], t=False).reshape(-1))
model.attn_pool.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
model.attn_pool.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
model.attn_pool.norm.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
model.attn_pool.norm.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
for r in range(2):
getattr(model.attn_pool.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_{r}/kernel']))
getattr(model.attn_pool.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_{r}/bias']))
mha_sub, b_sub, ln1_sub = (0, 0, 1) if big_vision else (1, 3, 2)
for i, block in enumerate(model.blocks.children()):
block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
mha_prefix = block_prefix + f'MultiHeadDotProductAttention_{mha_sub}/'
block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
block.attn.qkv.weight.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
block.attn.qkv.bias.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/scale']))
block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/bias']))
for r in range(2):
getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/kernel']))
getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/bias']))
def _convert_openai_clip(
state_dict: Dict[str, torch.Tensor],
model: VisionTransformer,
prefix: str = 'visual.',
) -> Dict[str, torch.Tensor]:
out_dict = {}
swaps = [
('conv1', 'patch_embed.proj'),
('positional_embedding', 'pos_embed'),
('transformer.resblocks.', 'blocks.'),
('ln_pre', 'norm_pre'),
('ln_post', 'norm'),
('ln_', 'norm'),
('in_proj_', 'qkv.'),
('out_proj', 'proj'),
('mlp.c_fc', 'mlp.fc1'),
('mlp.c_proj', 'mlp.fc2'),
]
for k, v in state_dict.items():
if not k.startswith(prefix):
continue
k = k.replace(prefix, '')
for sp in swaps:
k = k.replace(sp[0], sp[1])
if k == 'proj':
k = 'head.weight'
v = v.transpose(0, 1)
out_dict['head.bias'] = torch.zeros(v.shape[0])
elif k == 'class_embedding':
k = 'cls_token'
v = v.unsqueeze(0).unsqueeze(1)
elif k == 'pos_embed':
v = v.unsqueeze(0)
if v.shape[1] != model.pos_embed.shape[1]:
# To resize pos embedding when using model at different size from pretrained weights
v = resize_pos_embed(
v,
model.pos_embed,
0 if getattr(model, 'no_embed_class') else getattr(model, 'num_prefix_tokens', 1),
model.patch_embed.grid_size
)
out_dict[k] = v
return out_dict
def _convert_dinov2(
state_dict: Dict[str, torch.Tensor],
model: VisionTransformer,
) -> Dict[str, torch.Tensor]:
import re
out_dict = {}
state_dict.pop("mask_token", None)
if 'register_tokens' in state_dict:
# convert dinov2 w/ registers to no_embed_class timm model (neither cls or reg tokens overlap pos embed)
out_dict['reg_token'] = state_dict.pop('register_tokens')
out_dict['cls_token'] = state_dict.pop('cls_token') + state_dict['pos_embed'][:, 0]
out_dict['pos_embed'] = state_dict.pop('pos_embed')[:, 1:]
for k, v in state_dict.items():
if re.match(r"blocks\.(\d+)\.mlp\.w12\.(?:weight|bias)", k):
out_dict[k.replace("w12", "fc1")] = v
continue
elif re.match(r"blocks\.(\d+)\.mlp\.w3\.(?:weight|bias)", k):
out_dict[k.replace("w3", "fc2")] = v
continue
out_dict[k] = v
return out_dict
def checkpoint_filter_fn(
state_dict: Dict[str, torch.Tensor],
model: VisionTransformer,
adapt_layer_scale: bool = False,
interpolation: str = 'bicubic',
antialias: bool = True,
) -> Dict[str, torch.Tensor]:
""" convert patch embedding weight from manual patchify + linear proj to conv"""
import re
out_dict = {}
state_dict = state_dict.get('model', state_dict)
state_dict = state_dict.get('state_dict', state_dict)
prefix = ''
if 'visual.class_embedding' in state_dict:
return _convert_openai_clip(state_dict, model)
elif 'module.visual.class_embedding' in state_dict:
return _convert_openai_clip(state_dict, model, prefix='module.visual.')
if "mask_token" in state_dict:
state_dict = _convert_dinov2(state_dict, model)
if "encoder" in state_dict:
state_dict = state_dict['encoder']
prefix = 'module.'
if 'visual.trunk.pos_embed' in state_dict:
# convert an OpenCLIP model with timm vision encoder
# FIXME remap final nn.Linear if it exists outside of the timm .trunk (ie in visual.head.proj)
prefix = 'visual.trunk.'
if prefix:
# filter on & remove prefix string from keys
state_dict = {k[len(prefix):]: v for k, v in state_dict.items() if k.startswith(prefix)}
for k, v in state_dict.items():
if 'patch_embed.proj.weight' in k:
O, I, H, W = model.patch_embed.proj.weight.shape
if len(v.shape) < 4:
# For old models that I trained prior to conv based patchification
O, I, H, W = model.patch_embed.proj.weight.shape
v = v.reshape(O, -1, H, W)
if v.shape[-1] != W or v.shape[-2] != H:
v = resample_patch_embed(
v,
(H, W),
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
# To resize pos embedding when using model at different size from pretrained weights
num_prefix_tokens = 0 if getattr(model, 'no_embed_class', False) else getattr(model, 'num_prefix_tokens', 1)
v = resample_abs_pos_embed(
v,
new_size=model.patch_embed.grid_size,
num_prefix_tokens=num_prefix_tokens,
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
elif adapt_layer_scale and 'gamma_' in k:
# remap layer-scale gamma into sub-module (deit3 models)
k = re.sub(r'gamma_([0-9])', r'ls\1.gamma', k)
elif 'pre_logits' in k:
# NOTE representation layer removed as not used in latest 21k/1k pretrained weights
continue
out_dict[k] = v
return out_dict
def _cfg(url: str = '', **kwargs) -> Dict[str, Any]:
return {
'url': url,
'num_classes': 1000,
'input_size': (3, 224, 224),
'pool_size': None,
'crop_pct': 0.9,
'interpolation': 'bicubic',
'fixed_input_size': True,
'mean': IMAGENET_INCEPTION_MEAN,
'std': IMAGENET_INCEPTION_STD,
'first_conv': 'patch_embed.proj',
'classifier': 'head',
**kwargs,
}
default_cfgs = {
# re-finetuned augreg 21k FT on in1k weights
'vit_base_patch16_224.augreg2_in21k_ft_in1k': _cfg(
hf_hub_id='timm/'),
'vit_base_patch16_384.augreg2_in21k_ft_in1k': _cfg(),
'vit_base_patch8_224.augreg2_in21k_ft_in1k': _cfg(
hf_hub_id='timm/'),
# How to train your ViT (augreg) weights, pretrained on 21k FT on in1k
'vit_tiny_patch16_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_tiny_patch16_384.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_small_patch32_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_small_patch32_384.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_small_patch16_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_small_patch16_384.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch32_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_base_patch32_384.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch16_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_base_patch16_384.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch8_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_large_patch16_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_large_patch16_384.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
# patch models (weights from official Google JAX impl) pretrained on in21k FT on in1k
'vit_base_patch16_224.orig_in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
hf_hub_id='timm/'),
'vit_base_patch16_384.orig_in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_large_patch32_384.orig_in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0),
# How to train your ViT (augreg) weights trained on in1k only
'vit_small_patch16_224.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_16-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_small_patch16_384.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_16-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch32_224.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_32-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_base_patch32_384.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_32-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch16_224.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_16-i1k-300ep-lr_0.001-aug_strong2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_base_patch16_384.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_16-i1k-300ep-lr_0.001-aug_strong2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_large_patch14_224.untrained': _cfg(url=''),
'vit_huge_patch14_224.untrained': _cfg(url=''),
'vit_giant_patch14_224.untrained': _cfg(url=''),
'vit_gigantic_patch14_224.untrained': _cfg(url=''),
# patch models, imagenet21k (weights from official Google JAX impl), classifier not valid
'vit_base_patch32_224.orig_in21k': _cfg(
#url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth',
hf_hub_id='timm/',
num_classes=0),
'vit_base_patch16_224.orig_in21k': _cfg(
#url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth',
hf_hub_id='timm/',
num_classes=0),
'vit_large_patch32_224.orig_in21k': _cfg(
#url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
hf_hub_id='timm/',
num_classes=0),
'vit_large_patch16_224.orig_in21k': _cfg(
#url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth',
hf_hub_id='timm/',
num_classes=0),
'vit_huge_patch14_224.orig_in21k': _cfg(
hf_hub_id='timm/',
num_classes=0),
# How to train your ViT (augreg) weights, pretrained on in21k
'vit_tiny_patch16_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_small_patch32_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_small_patch16_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_base_patch32_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0.npz',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_base_patch16_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_base_patch8_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_large_patch16_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1.npz',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
# SAM trained models (https://arxiv.org/abs/2106.01548)
'vit_base_patch32_224.sam_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz', custom_load=True,
hf_hub_id='timm/'),
'vit_base_patch16_224.sam_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz', custom_load=True,
hf_hub_id='timm/'),
# DINO pretrained - https://arxiv.org/abs/2104.14294 (no classifier head, for fine-tune only)
'vit_small_patch16_224.dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_small_patch8_224.dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_base_patch16_224.dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_base_patch8_224.dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
# DINOv2 pretrained - https://arxiv.org/abs/2304.07193 (no classifier head, for fine-tune/features only)
'vit_small_patch14_dinov2.lvd142m': _cfg(
url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth',
hf_hub_id='timm/',
license='apache-2.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
input_size=(3, 518, 518), crop_pct=1.0),
'vit_base_patch14_dinov2.lvd142m': _cfg(
url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_pretrain.pth',
hf_hub_id='timm/',
license='apache-2.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
input_size=(3, 518, 518), crop_pct=1.0),
'vit_large_patch14_dinov2.lvd142m': _cfg(
url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth',
hf_hub_id='timm/',
license='apache-2.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
input_size=(3, 518, 518), crop_pct=1.0),
'vit_giant_patch14_dinov2.lvd142m': _cfg(
url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth',
hf_hub_id='timm/',
license='apache-2.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
input_size=(3, 518, 518), crop_pct=1.0),
# DINOv2 pretrained w/ registers - https://arxiv.org/abs/2309.16588 (no classifier head, for fine-tune/features only)
'vit_small_patch14_reg4_dinov2.lvd142m': _cfg(
url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_reg4_pretrain.pth',
hf_hub_id='timm/',
license='apache-2.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
input_size=(3, 518, 518), crop_pct=1.0),
'vit_base_patch14_reg4_dinov2.lvd142m': _cfg(
url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_reg4_pretrain.pth',
hf_hub_id='timm/',
license='apache-2.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
input_size=(3, 518, 518), crop_pct=1.0),
'vit_large_patch14_reg4_dinov2.lvd142m': _cfg(
url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_reg4_pretrain.pth',
hf_hub_id='timm/',
license='apache-2.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
input_size=(3, 518, 518), crop_pct=1.0),
'vit_giant_patch14_reg4_dinov2.lvd142m': _cfg(
url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_reg4_pretrain.pth',
hf_hub_id='timm/',
license='apache-2.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
input_size=(3, 518, 518), crop_pct=1.0),
# ViT ImageNet-21K-P pretraining by MILL
'vit_base_patch16_224_miil.in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_in21k_miil-887286df.pth',
hf_hub_id='timm/',
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221),
'vit_base_patch16_224_miil.in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_1k_miil_84_4-2deb18e3.pth',
hf_hub_id='timm/',
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear'),
# Custom timm variants
'vit_base_patch16_rpn_224.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth',
hf_hub_id='timm/'),
'vit_medium_patch16_gap_240.sw_in12k': _cfg(
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=11821),
'vit_medium_patch16_gap_256.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), crop_pct=0.95),
'vit_medium_patch16_gap_384.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=0.95, crop_mode='squash'),
'vit_base_patch16_gap_224': _cfg(),
# CLIP pretrained image tower and related fine-tuned weights
'vit_base_patch32_clip_224.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch32_clip_384.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, input_size=(3, 384, 384)),
'vit_base_patch32_clip_448.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, input_size=(3, 448, 448)),
'vit_base_patch16_clip_224.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95),
'vit_base_patch16_clip_384.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'),
'vit_large_patch14_clip_224.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0),
'vit_large_patch14_clip_336.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_base_patch32_clip_224.openai_ft_in12k_in1k': _cfg(
# hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k_in1k', # FIXME weight exists, need to push
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch32_clip_384.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'),
'vit_base_patch16_clip_224.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95),
'vit_base_patch16_clip_384.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'),
'vit_large_patch14_clip_224.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_large_patch14_clip_336.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_base_patch32_clip_224.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch16_clip_224.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_base_patch16_clip_384.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'),
'vit_large_patch14_clip_224.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0),
'vit_large_patch14_clip_336.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_huge_patch14_clip_224.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_huge_patch14_clip_336.laion2b_ft_in1k': _cfg(
hf_hub_id='',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_base_patch32_clip_224.openai_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch16_clip_224.openai_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch16_clip_384.openai_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'),
'vit_large_patch14_clip_224.openai_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_base_patch32_clip_224.laion2b_ft_in12k': _cfg(
#hf_hub_id='timm/vit_base_patch32_clip_224.laion2b_ft_in12k', # FIXME weight exists, need to push
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
'vit_base_patch16_clip_224.laion2b_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
'vit_large_patch14_clip_224.laion2b_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=11821),
'vit_huge_patch14_clip_224.laion2b_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821),
'vit_base_patch32_clip_224.openai_ft_in12k': _cfg(
# hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k', # FIXME weight exists, need to push
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
'vit_base_patch16_clip_224.openai_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
'vit_large_patch14_clip_224.openai_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821),
'vit_base_patch32_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-B-32-laion2B-s34B-b79K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
'vit_base_patch16_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-B-16-laion2B-s34B-b88K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
'vit_large_patch14_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-L-14-laion2B-s32B-b82K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=768),
'vit_huge_patch14_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-H-14-laion2B-s32B-b79K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
'vit_giant_patch14_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-g-14-laion2B-s12B-b42K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
'vit_gigantic_patch14_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1280),
'vit_base_patch32_clip_224.datacompxl': _cfg(
hf_hub_id='laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
'vit_base_patch32_clip_256.datacompxl': _cfg(
hf_hub_id='laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 256, 256), num_classes=512),
'vit_base_patch16_clip_224.datacompxl': _cfg(
hf_hub_id='laion/CLIP-ViT-B-16-DataComp.XL-s13B-b90K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
'vit_large_patch14_clip_224.datacompxl': _cfg(
hf_hub_id='laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
'vit_base_patch16_clip_224.dfn2b': _cfg(
hf_hub_id='apple/DFN2B-CLIP-ViT-B-16',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
'vit_large_patch14_clip_224.dfn2b': _cfg(
hf_hub_id='apple/DFN2B-CLIP-ViT-L-14',
hf_hub_filename='open_clip_pytorch_model.bin',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
'vit_huge_patch14_clip_224.dfn5b': _cfg(
hf_hub_id='apple/DFN5B-CLIP-ViT-H-14',
hf_hub_filename='open_clip_pytorch_model.bin',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
'vit_huge_patch14_clip_378.dfn5b': _cfg(
hf_hub_id='apple/DFN5B-CLIP-ViT-H-14-378',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
notes=('natively QuickGELU, use quickgelu model variant for original results',),
crop_pct=1.0, input_size=(3, 378, 378), num_classes=1024),
'vit_base_patch32_clip_224.metaclip_2pt5b': _cfg(
hf_hub_id='facebook/metaclip-b32-fullcc2.5b',
hf_hub_filename='metaclip_b32_fullcc2.5b.bin',
license='cc-by-nc-4.0',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
'vit_base_patch16_clip_224.metaclip_2pt5b': _cfg(
hf_hub_id='facebook/metaclip-b16-fullcc2.5b',
hf_hub_filename='metaclip_b16_fullcc2.5b.bin',
license='cc-by-nc-4.0',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
'vit_large_patch14_clip_224.metaclip_2pt5b': _cfg(
hf_hub_id='facebook/metaclip-l14-fullcc2.5b',
hf_hub_filename='metaclip_l14_fullcc2.5b.bin',
license='cc-by-nc-4.0',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
'vit_huge_patch14_clip_224.metaclip_2pt5b': _cfg(
hf_hub_id='facebook/metaclip-h14-fullcc2.5b',
hf_hub_filename='metaclip_h14_fullcc2.5b.bin',
license='cc-by-nc-4.0',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
'vit_base_patch32_clip_224.openai': _cfg(
hf_hub_id='timm/vit_base_patch32_clip_224.openai',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
'vit_base_patch16_clip_224.openai': _cfg(
hf_hub_id='timm/vit_base_patch16_clip_224.openai',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
'vit_large_patch14_clip_224.openai': _cfg(
hf_hub_id='timm/vit_large_patch14_clip_224.openai',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
'vit_large_patch14_clip_336.openai': _cfg(
hf_hub_id='timm/vit_large_patch14_clip_336.openai', hf_hub_filename='open_clip_pytorch_model.bin',
notes=('natively QuickGELU, use quickgelu model variant for original results',),
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 336, 336), num_classes=768),
# experimental (may be removed)
'vit_base_patch32_plus_256.untrained': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
'vit_base_patch16_plus_240.untrained': _cfg(url='', input_size=(3, 240, 240), crop_pct=0.95),
'vit_small_patch16_36x1_224.untrained': _cfg(url=''),
'vit_small_patch16_18x2_224.untrained': _cfg(url=''),
'vit_base_patch16_18x2_224.untrained': _cfg(url=''),
# EVA fine-tuned weights from MAE style MIM - EVA-CLIP target pretrain
# https://github.com/baaivision/EVA/blob/7ecf2c0a370d97967e86d047d7af9188f78d2df3/eva/README.md#eva-l-learning-better-mim-representations-from-eva-clip
'eva_large_patch14_196.in22k_ft_in22k_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_196px_21k_to_1k_ft_88p6.pt',
hf_hub_id='timm/', license='mit',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 196, 196), crop_pct=1.0),
'eva_large_patch14_336.in22k_ft_in22k_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_336px_21k_to_1k_ft_89p2.pt',
hf_hub_id='timm/', license='mit',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),
'eva_large_patch14_196.in22k_ft_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_196px_1k_ft_88p0.pt',
hf_hub_id='timm/', license='mit',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 196, 196), crop_pct=1.0),
'eva_large_patch14_336.in22k_ft_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_336px_1k_ft_88p65.pt',
hf_hub_id='timm/', license='mit',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),
'flexivit_small.1200ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_s_i1k.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_small.600ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_s_i1k_600ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_small.300ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_s_i1k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.1200ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i1k.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.600ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i1k_600ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.300ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i1k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.1000ep_in21k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i21k_1000ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843),
'flexivit_base.300ep_in21k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i21k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843),
'flexivit_large.1200ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_l_i1k.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_large.600ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_l_i1k_600ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_large.300ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_l_i1k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.patch16_in21k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/vit_b16_i21k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843),
'flexivit_base.patch30_in21k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/vit_b30_i21k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843),
'vit_base_patch16_xp_224.untrained': _cfg(url=''),
'vit_large_patch14_xp_224.untrained': _cfg(url=''),
'vit_huge_patch14_xp_224.untrained': _cfg(url=''),
'vit_base_patch16_224.mae': _cfg(
url='https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth',
hf_hub_id='timm/',
license='cc-by-nc-4.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_large_patch16_224.mae': _cfg(
url='https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_large.pth',
hf_hub_id='timm/',
license='cc-by-nc-4.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_huge_patch14_224.mae': _cfg(
url='https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_huge.pth',
hf_hub_id='timm/',
license='cc-by-nc-4.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_huge_patch14_gap_224.in1k_ijepa': _cfg(
url='https://dl.fbaipublicfiles.com/ijepa/IN1K-vit.h.14-300e.pth.tar',
# hf_hub_id='timm/',
license='cc-by-nc-4.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_huge_patch14_gap_224.in22k_ijepa': _cfg(
url='https://dl.fbaipublicfiles.com/ijepa/IN22K-vit.h.14-900e.pth.tar',
# hf_hub_id='timm/',
license='cc-by-nc-4.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_huge_patch16_gap_448.in1k_ijepa': _cfg(
url='https://dl.fbaipublicfiles.com/ijepa/IN1K-vit.h.16-448px-300e.pth.tar',
# hf_hub_id='timm/',
license='cc-by-nc-4.0',
input_size=(3, 448, 448), crop_pct=1.0,
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_giant_patch16_gap_224.in22k_ijepa': _cfg(
url='https://dl.fbaipublicfiles.com/ijepa/IN22K-vit.g.16-600e.pth.tar',
# hf_hub_id='timm/',
license='cc-by-nc-4.0',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_base_patch16_siglip_224.webli': _cfg(
hf_hub_id='timm/ViT-B-16-SigLIP',
hf_hub_filename='open_clip_pytorch_model.bin',
num_classes=0),
'vit_base_patch16_siglip_256.webli': _cfg(
hf_hub_id='timm/ViT-B-16-SigLIP-256',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 256, 256),
num_classes=0),
'vit_base_patch16_siglip_384.webli': _cfg(
hf_hub_id='timm/ViT-B-16-SigLIP-384',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 384, 384),
num_classes=0),
'vit_base_patch16_siglip_512.webli': _cfg(
hf_hub_id='timm/ViT-B-16-SigLIP-512',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 512, 512),
num_classes=0),
'vit_large_patch16_siglip_256.webli': _cfg(
hf_hub_id='timm/ViT-L-16-SigLIP-256',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 256, 256),
num_classes=0),
'vit_large_patch16_siglip_384.webli': _cfg(
hf_hub_id='timm/ViT-L-16-SigLIP-384',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 384, 384),
num_classes=0),
'vit_so400m_patch14_siglip_224.webli': _cfg(
hf_hub_id='timm/ViT-SO400M-14-SigLIP',
hf_hub_filename='open_clip_pytorch_model.bin',
num_classes=0),
'vit_so400m_patch14_siglip_384.webli': _cfg(
hf_hub_id='timm/ViT-SO400M-14-SigLIP-384',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 384, 384),
num_classes=0),
'vit_medium_patch16_reg4_256': _cfg(
input_size=(3, 256, 256)),
'vit_medium_patch16_reg4_gap_256': _cfg(
input_size=(3, 256, 256)),
'vit_base_patch16_reg8_gap_256': _cfg(input_size=(3, 256, 256)),
}
_quick_gelu_cfgs = [
'vit_large_patch14_clip_224.dfn2b',
'vit_huge_patch14_clip_224.dfn5b',
'vit_huge_patch14_clip_378.dfn5b',
'vit_base_patch32_clip_224.metaclip_2pt5b',
'vit_base_patch16_clip_224.metaclip_2pt5b',
'vit_large_patch14_clip_224.metaclip_2pt5b',
'vit_huge_patch14_clip_224.metaclip_2pt5b',
'vit_base_patch32_clip_224.openai',
'vit_base_patch16_clip_224.openai',
'vit_large_patch14_clip_224.openai',
'vit_large_patch14_clip_336.openai',
]
default_cfgs.update({
n.replace('_clip_', '_clip_quickgelu_'): default_cfgs[n] for n in _quick_gelu_cfgs
})
default_cfgs = generate_default_cfgs(default_cfgs)
def _create_vision_transformer(variant: str, pretrained: bool = False, **kwargs) -> VisionTransformer:
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
if 'flexi' in variant:
# FIXME Google FlexiViT pretrained models have a strong preference for bilinear patch / embed
# interpolation, other pretrained models resize better w/ anti-aliased bicubic interpolation.
_filter_fn = partial(checkpoint_filter_fn, interpolation='bilinear', antialias=False)
else:
_filter_fn = checkpoint_filter_fn
# FIXME attn pool (currently only in siglip) params removed if pool disabled, is there a better soln?
strict = True
if 'siglip' in variant and kwargs.get('global_pool', None) != 'map':
strict = False
return build_model_with_cfg(
VisionTransformer,
variant,
pretrained,
pretrained_filter_fn=_filter_fn,
pretrained_strict=strict,
**kwargs,
)
@register_model
def vit_tiny_patch16_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Tiny (Vit-Ti/16)
"""
model_args = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3)
model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_tiny_patch16_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Tiny (Vit-Ti/16) @ 384x384.
"""
model_args = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3)
model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch32_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Small (ViT-S/32)
"""
model_args = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch32_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Small (ViT-S/32) at 384x384.
"""
model_args = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch16_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Small (ViT-S/16)
"""
model_args = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch16_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Small (ViT-S/16)
"""
model_args = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch8_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Small (ViT-S/8)
"""
model_args = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer('vit_small_patch8_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch32_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k, source https://github.com/google-research/vision_transformer.
"""
model_args = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch32_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
"""
model_args = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
"""
model_args = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
"""
model_args = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch8_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
"""
model_args = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch32_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
"""
model_args = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16)
model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch32_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
"""
model_args = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16)
model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch16_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
"""
model_args = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16)
model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch16_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
"""
model_args = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16)
model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch14_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/14)
"""
model_args = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16)
model = _create_vision_transformer('vit_large_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch14_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
"""
model_args = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16)
model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_giant_patch14_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Giant (little-g) model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
"""
model_args = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16)
model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_gigantic_patch14_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Gigantic (big-G) model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
"""
model_args = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16)
model = _create_vision_transformer(
'vit_gigantic_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_224_miil(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
"""
model_args = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False)
model = _create_vision_transformer(
'vit_base_patch16_224_miil', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_medium_patch16_gap_240(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Medium (ViT-M/16) w/o class token, w/ avg-pool @ 240x240
"""
model_args = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False)
model = _create_vision_transformer(
'vit_medium_patch16_gap_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_medium_patch16_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Medium (ViT-M/16) w/o class token, w/ avg-pool @ 256x256
"""
model_args = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False)
model = _create_vision_transformer(
'vit_medium_patch16_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_medium_patch16_gap_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Medium (ViT-M/16) w/o class token, w/ avg-pool @ 384x384
"""
model_args = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False)
model = _create_vision_transformer(
'vit_medium_patch16_gap_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_gap_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base (ViT-B/16) w/o class token, w/ avg-pool @ 224x224
"""
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=16, class_token=False, global_pool='avg', fc_norm=False)
model = _create_vision_transformer(
'vit_base_patch16_gap_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch14_gap_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/14) w/ no class token, avg pool
"""
model_args = dict(
patch_size=14, embed_dim=1280, depth=32, num_heads=16, class_token=False, global_pool='avg', fc_norm=False)
model = _create_vision_transformer(
'vit_huge_patch14_gap_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch16_gap_448(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/16) w/ no class token, avg pool @ 448x448
"""
model_args = dict(
patch_size=16, embed_dim=1280, depth=32, num_heads=16, class_token=False, global_pool='avg', fc_norm=False)
model = _create_vision_transformer(
'vit_huge_patch16_gap_448', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_giant_patch16_gap_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Giant (little-gg) model (ViT-g/16) w/ no class token, avg pool
"""
model_args = dict(
patch_size=16, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=48/11,
class_token=False, global_pool='avg', fc_norm=False)
model = _create_vision_transformer(
'vit_giant_patch16_gap_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch32_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/32 CLIP image tower @ 224x224
"""
model_args = dict(
patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch32_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch32_clip_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/32 CLIP image tower @ 256x256
"""
model_args = dict(
patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch32_clip_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch32_clip_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/32 CLIP image tower @ 384x384
"""
model_args = dict(
patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch32_clip_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch32_clip_448(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/32 CLIP image tower @ 448x448
"""
model_args = dict(
patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch32_clip_448', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/16 CLIP image tower
"""
model_args = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch16_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_clip_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/16 CLIP image tower @ 384x384
"""
model_args = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch16_clip_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch14_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/14) CLIP image tower
"""
model_args = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_large_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch14_clip_336(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/14) CLIP image tower @ 336x336
"""
model_args = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_large_patch14_clip_336', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch14_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/14) CLIP image tower.
"""
model_args = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_huge_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch14_clip_336(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/14) CLIP image tower @ 336x336
"""
model_args = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_huge_patch14_clip_336', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch14_clip_378(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/14) CLIP image tower @ 378x378
"""
model_args = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_huge_patch14_clip_378', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_giant_patch14_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Giant (little-g) model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
Pretrained weights from CLIP image tower.
"""
model_args = dict(
patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_giant_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_gigantic_patch14_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-bigG model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
Pretrained weights from CLIP image tower.
"""
model_args = dict(
patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_gigantic_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch32_clip_quickgelu_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/32 CLIP image tower @ 224x224
"""
model_args = dict(
patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True,
norm_layer=nn.LayerNorm, act_layer='quick_gelu')
model = _create_vision_transformer(
'vit_base_patch32_clip_quickgelu_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_clip_quickgelu_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/16 CLIP image tower w/ QuickGELU act
"""
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True,
norm_layer=nn.LayerNorm, act_layer='quick_gelu')
model = _create_vision_transformer(
'vit_base_patch16_clip_quickgelu_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch14_clip_quickgelu_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/14) CLIP image tower w/ QuickGELU act
"""
from timm.layers import get_act_layer
model_args = dict(
patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True,
norm_layer=nn.LayerNorm, act_layer='quick_gelu')
model = _create_vision_transformer(
'vit_large_patch14_clip_quickgelu_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch14_clip_quickgelu_336(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/14) CLIP image tower @ 336x336 w/ QuickGELU act
"""
model_args = dict(
patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True,
norm_layer=nn.LayerNorm, act_layer='quick_gelu')
model = _create_vision_transformer(
'vit_large_patch14_clip_quickgelu_336', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch14_clip_quickgelu_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/14) CLIP image tower w/ QuickGELU act.
"""
model_args = dict(
patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True,
norm_layer=nn.LayerNorm, act_layer='quick_gelu')
model = _create_vision_transformer(
'vit_huge_patch14_clip_quickgelu_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch14_clip_quickgelu_378(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/14) CLIP image tower @ 378x378 w/ QuickGELU act
"""
model_args = dict(
patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True,
norm_layer=nn.LayerNorm, act_layer='quick_gelu')
model = _create_vision_transformer(
'vit_huge_patch14_clip_quickgelu_378', pretrained=pretrained, **dict(model_args, **kwargs))
return model
# Experimental models below
@register_model
def vit_base_patch32_plus_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base (ViT-B/32+)
"""
model_args = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5)
model = _create_vision_transformer(
'vit_base_patch32_plus_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_plus_240(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base (ViT-B/16+)
"""
model_args = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5)
model = _create_vision_transformer(
'vit_base_patch16_plus_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_rpn_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base (ViT-B/16) w/ residual post-norm
"""
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5,
class_token=False, block_fn=ResPostBlock, global_pool='avg')
model = _create_vision_transformer(
'vit_base_patch16_rpn_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch16_36x1_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base w/ LayerScale + 36 x 1 (36 block serial) config. Experimental, may remove.
Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
"""
model_args = dict(patch_size=16, embed_dim=384, depth=36, num_heads=6, init_values=1e-5)
model = _create_vision_transformer(
'vit_small_patch16_36x1_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch16_18x2_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Small w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
"""
model_args = dict(
patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelThingsBlock)
model = _create_vision_transformer(
'vit_small_patch16_18x2_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_18x2_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Base w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
"""
model_args = dict(
patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelThingsBlock)
model = _create_vision_transformer(
'vit_base_patch16_18x2_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva_large_patch14_196(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" EVA-large model https://arxiv.org/abs/2211.07636 /via MAE MIM pretrain"""
model_args = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg')
model = _create_vision_transformer(
'eva_large_patch14_196', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva_large_patch14_336(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" EVA-large model https://arxiv.org/abs/2211.07636 via MAE MIM pretrain"""
model_args = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg')
model = _create_vision_transformer('eva_large_patch14_336', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def flexivit_small(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" FlexiViT-Small
"""
model_args = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, no_embed_class=True)
model = _create_vision_transformer('flexivit_small', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def flexivit_base(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" FlexiViT-Base
"""
model_args = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, no_embed_class=True)
model = _create_vision_transformer('flexivit_base', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def flexivit_large(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" FlexiViT-Large
"""
model_args = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True)
model = _create_vision_transformer('flexivit_large', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_xp_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/14) w/ parallel blocks and qk norm enabled.
"""
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, no_embed_class=True,
norm_layer=RmsNorm, block_fn=ParallelScalingBlock, qkv_bias=False, qk_norm=True,
)
model = _create_vision_transformer(
'vit_base_patch16_xp_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch14_xp_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Large model (ViT-L/14) w/ parallel blocks and qk norm enabled.
"""
model_args = dict(
patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, no_embed_class=True,
norm_layer=RmsNorm, block_fn=ParallelScalingBlock, qkv_bias=False, qk_norm=True,
)
model = _create_vision_transformer(
'vit_large_patch14_xp_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_huge_patch14_xp_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Huge model (ViT-H/14) w/ parallel blocks and qk norm enabled.
"""
model_args = dict(
patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, no_embed_class=True,
norm_layer=RmsNorm, block_fn=ParallelScalingBlock, qkv_bias=False, qk_norm=True,
)
model = _create_vision_transformer(
'vit_huge_patch14_xp_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch14_dinov2(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-S/14 for DINOv2
"""
model_args = dict(patch_size=14, embed_dim=384, depth=12, num_heads=6, init_values=1e-5, img_size=518)
model = _create_vision_transformer(
'vit_small_patch14_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch14_dinov2(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/14 for DINOv2
"""
model_args = dict(patch_size=14, embed_dim=768, depth=12, num_heads=12, init_values=1e-5, img_size=518)
model = _create_vision_transformer(
'vit_base_patch14_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch14_dinov2(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-L/14 for DINOv2
"""
model_args = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, init_values=1e-5, img_size=518)
model = _create_vision_transformer(
'vit_large_patch14_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_giant_patch14_dinov2(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-G/14 for DINOv2
"""
# The hidden_features of SwiGLU is calculated by:
# hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
# When embed_dim=1536, hidden_features=4096
# With SwiGLUPacked, we need to set hidden_features = 2 * 4096 = 8192
model_args = dict(
patch_size=14, embed_dim=1536, depth=40, num_heads=24, init_values=1e-5,
mlp_ratio=2.66667 * 2, mlp_layer=SwiGLUPacked, img_size=518, act_layer=nn.SiLU
)
model = _create_vision_transformer(
'vit_giant_patch14_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_patch14_reg4_dinov2(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-S/14 for DINOv2 w/ 4 registers
"""
model_args = dict(
patch_size=14, embed_dim=384, depth=12, num_heads=6, init_values=1e-5,
reg_tokens=4, no_embed_class=True,
)
model = _create_vision_transformer(
'vit_small_patch14_reg4_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch14_reg4_dinov2(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-B/14 for DINOv2 w/ 4 registers
"""
model_args = dict(
patch_size=14, embed_dim=768, depth=12, num_heads=12, init_values=1e-5,
reg_tokens=4, no_embed_class=True,
)
model = _create_vision_transformer(
'vit_base_patch14_reg4_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch14_reg4_dinov2(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-L/14 for DINOv2 w/ 4 registers
"""
model_args = dict(
patch_size=14, embed_dim=1024, depth=24, num_heads=16, init_values=1e-5,
reg_tokens=4, no_embed_class=True,
)
model = _create_vision_transformer(
'vit_large_patch14_reg4_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_giant_patch14_reg4_dinov2(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-G/14 for DINOv2
"""
# The hidden_features of SwiGLU is calculated by:
# hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
# When embed_dim=1536, hidden_features=4096
# With SwiGLUPacked, we need to set hidden_features = 2 * 4096 = 8192
model_args = dict(
patch_size=14, embed_dim=1536, depth=40, num_heads=24, init_values=1e-5, mlp_ratio=2.66667 * 2,
mlp_layer=SwiGLUPacked, act_layer=nn.SiLU, reg_tokens=4, no_embed_class=True,
)
model = _create_vision_transformer(
'vit_giant_patch14_reg4_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_siglip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_base_patch16_siglip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_siglip_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_base_patch16_siglip_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_siglip_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_base_patch16_siglip_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_siglip_512(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_base_patch16_siglip_512', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch16_siglip_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_large_patch16_siglip_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_patch16_siglip_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_large_patch16_siglip_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_so400m_patch14_siglip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=14, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_so400m_patch14_siglip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_so400m_patch14_siglip_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=14, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_so400m_patch14_siglip_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_medium_patch16_reg4_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=True,
no_embed_class=True, reg_tokens=4,
)
model = _create_vision_transformer(
'vit_medium_patch16_reg4_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_medium_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8,
class_token=False, no_embed_class=True, reg_tokens=4, global_pool='avg',
)
model = _create_vision_transformer(
'vit_medium_patch16_reg4_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_patch16_reg8_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False,
no_embed_class=True, global_pool='avg', reg_tokens=8,
)
model = _create_vision_transformer(
'vit_base_patch16_reg8_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
register_model_deprecations(__name__, {
'vit_tiny_patch16_224_in21k': 'vit_tiny_patch16_224.augreg_in21k',
'vit_small_patch32_224_in21k': 'vit_small_patch32_224.augreg_in21k',
'vit_small_patch16_224_in21k': 'vit_small_patch16_224.augreg_in21k',
'vit_base_patch32_224_in21k': 'vit_base_patch32_224.augreg_in21k',
'vit_base_patch16_224_in21k': 'vit_base_patch16_224.augreg_in21k',
'vit_base_patch8_224_in21k': 'vit_base_patch8_224.augreg_in21k',
'vit_large_patch32_224_in21k': 'vit_large_patch32_224.orig_in21k',
'vit_large_patch16_224_in21k': 'vit_large_patch16_224.augreg_in21k',
'vit_huge_patch14_224_in21k': 'vit_huge_patch14_224.orig_in21k',
'vit_base_patch32_224_sam': 'vit_base_patch32_224.sam',
'vit_base_patch16_224_sam': 'vit_base_patch16_224.sam',
'vit_small_patch16_224_dino': 'vit_small_patch16_224.dino',
'vit_small_patch8_224_dino': 'vit_small_patch8_224.dino',
'vit_base_patch16_224_dino': 'vit_base_patch16_224.dino',
'vit_base_patch8_224_dino': 'vit_base_patch8_224.dino',
'vit_base_patch16_224_miil_in21k': 'vit_base_patch16_224_miil.in21k',
'vit_base_patch32_224_clip_laion2b': 'vit_base_patch32_clip_224.laion2b',
'vit_large_patch14_224_clip_laion2b': 'vit_large_patch14_clip_224.laion2b',
'vit_huge_patch14_224_clip_laion2b': 'vit_huge_patch14_clip_224.laion2b',
'vit_giant_patch14_224_clip_laion2b': 'vit_giant_patch14_clip_224.laion2b',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/coat.py
|
"""
CoaT architecture.
Paper: Co-Scale Conv-Attentional Image Transformers - https://arxiv.org/abs/2104.06399
Official CoaT code at: https://github.com/mlpc-ucsd/CoaT
Modified from timm/models/vision_transformer.py
"""
from functools import partial
from typing import Tuple, List, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert, LayerNorm
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['CoaT']
class ConvRelPosEnc(nn.Module):
""" Convolutional relative position encoding. """
def __init__(self, head_chs, num_heads, window):
"""
Initialization.
Ch: Channels per head.
h: Number of heads.
window: Window size(s) in convolutional relative positional encoding. It can have two forms:
1. An integer of window size, which assigns all attention heads with the same window s
size in ConvRelPosEnc.
2. A dict mapping window size to #attention head splits (
e.g. {window size 1: #attention head split 1, window size 2: #attention head split 2})
It will apply different window size to the attention head splits.
"""
super().__init__()
if isinstance(window, int):
# Set the same window size for all attention heads.
window = {window: num_heads}
self.window = window
elif isinstance(window, dict):
self.window = window
else:
raise ValueError()
self.conv_list = nn.ModuleList()
self.head_splits = []
for cur_window, cur_head_split in window.items():
dilation = 1
# Determine padding size.
# Ref: https://discuss.pytorch.org/t/how-to-keep-the-shape-of-input-and-output-same-when-dilation-conv/14338
padding_size = (cur_window + (cur_window - 1) * (dilation - 1)) // 2
cur_conv = nn.Conv2d(
cur_head_split * head_chs,
cur_head_split * head_chs,
kernel_size=(cur_window, cur_window),
padding=(padding_size, padding_size),
dilation=(dilation, dilation),
groups=cur_head_split * head_chs,
)
self.conv_list.append(cur_conv)
self.head_splits.append(cur_head_split)
self.channel_splits = [x * head_chs for x in self.head_splits]
def forward(self, q, v, size: Tuple[int, int]):
B, num_heads, N, C = q.shape
H, W = size
_assert(N == 1 + H * W, '')
# Convolutional relative position encoding.
q_img = q[:, :, 1:, :] # [B, h, H*W, Ch]
v_img = v[:, :, 1:, :] # [B, h, H*W, Ch]
v_img = v_img.transpose(-1, -2).reshape(B, num_heads * C, H, W)
v_img_list = torch.split(v_img, self.channel_splits, dim=1) # Split according to channels
conv_v_img_list = []
for i, conv in enumerate(self.conv_list):
conv_v_img_list.append(conv(v_img_list[i]))
conv_v_img = torch.cat(conv_v_img_list, dim=1)
conv_v_img = conv_v_img.reshape(B, num_heads, C, H * W).transpose(-1, -2)
EV_hat = q_img * conv_v_img
EV_hat = F.pad(EV_hat, (0, 0, 1, 0, 0, 0)) # [B, h, N, Ch].
return EV_hat
class FactorAttnConvRelPosEnc(nn.Module):
""" Factorized attention with convolutional relative position encoding class. """
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
shared_crpe=None,
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop) # Note: attn_drop is actually not used.
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
# Shared convolutional relative position encoding.
self.crpe = shared_crpe
def forward(self, x, size: Tuple[int, int]):
B, N, C = x.shape
# Generate Q, K, V.
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0) # [B, h, N, Ch]
# Factorized attention.
k_softmax = k.softmax(dim=2)
factor_att = k_softmax.transpose(-1, -2) @ v
factor_att = q @ factor_att
# Convolutional relative position encoding.
crpe = self.crpe(q, v, size=size) # [B, h, N, Ch]
# Merge and reshape.
x = self.scale * factor_att + crpe
x = x.transpose(1, 2).reshape(B, N, C) # [B, h, N, Ch] -> [B, N, h, Ch] -> [B, N, C]
# Output projection.
x = self.proj(x)
x = self.proj_drop(x)
return x
class ConvPosEnc(nn.Module):
""" Convolutional Position Encoding.
Note: This module is similar to the conditional position encoding in CPVT.
"""
def __init__(self, dim, k=3):
super(ConvPosEnc, self).__init__()
self.proj = nn.Conv2d(dim, dim, k, 1, k//2, groups=dim)
def forward(self, x, size: Tuple[int, int]):
B, N, C = x.shape
H, W = size
_assert(N == 1 + H * W, '')
# Extract CLS token and image tokens.
cls_token, img_tokens = x[:, :1], x[:, 1:] # [B, 1, C], [B, H*W, C]
# Depthwise convolution.
feat = img_tokens.transpose(1, 2).view(B, C, H, W)
x = self.proj(feat) + feat
x = x.flatten(2).transpose(1, 2)
# Combine with CLS token.
x = torch.cat((cls_token, x), dim=1)
return x
class SerialBlock(nn.Module):
""" Serial block class.
Note: In this implementation, each serial block only contains a conv-attention and a FFN (MLP) module. """
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
shared_cpe=None,
shared_crpe=None,
):
super().__init__()
# Conv-Attention.
self.cpe = shared_cpe
self.norm1 = norm_layer(dim)
self.factoratt_crpe = FactorAttnConvRelPosEnc(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
shared_crpe=shared_crpe,
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# MLP.
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=proj_drop,
)
def forward(self, x, size: Tuple[int, int]):
# Conv-Attention.
x = self.cpe(x, size)
cur = self.norm1(x)
cur = self.factoratt_crpe(cur, size)
x = x + self.drop_path(cur)
# MLP.
cur = self.norm2(x)
cur = self.mlp(cur)
x = x + self.drop_path(cur)
return x
class ParallelBlock(nn.Module):
""" Parallel block class. """
def __init__(
self,
dims,
num_heads,
mlp_ratios=[],
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
shared_crpes=None,
):
super().__init__()
# Conv-Attention.
self.norm12 = norm_layer(dims[1])
self.norm13 = norm_layer(dims[2])
self.norm14 = norm_layer(dims[3])
self.factoratt_crpe2 = FactorAttnConvRelPosEnc(
dims[1],
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
shared_crpe=shared_crpes[1],
)
self.factoratt_crpe3 = FactorAttnConvRelPosEnc(
dims[2],
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
shared_crpe=shared_crpes[2],
)
self.factoratt_crpe4 = FactorAttnConvRelPosEnc(
dims[3],
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
shared_crpe=shared_crpes[3],
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# MLP.
self.norm22 = norm_layer(dims[1])
self.norm23 = norm_layer(dims[2])
self.norm24 = norm_layer(dims[3])
# In parallel block, we assume dimensions are the same and share the linear transformation.
assert dims[1] == dims[2] == dims[3]
assert mlp_ratios[1] == mlp_ratios[2] == mlp_ratios[3]
mlp_hidden_dim = int(dims[1] * mlp_ratios[1])
self.mlp2 = self.mlp3 = self.mlp4 = Mlp(
in_features=dims[1],
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=proj_drop,
)
def upsample(self, x, factor: float, size: Tuple[int, int]):
""" Feature map up-sampling. """
return self.interpolate(x, scale_factor=factor, size=size)
def downsample(self, x, factor: float, size: Tuple[int, int]):
""" Feature map down-sampling. """
return self.interpolate(x, scale_factor=1.0/factor, size=size)
def interpolate(self, x, scale_factor: float, size: Tuple[int, int]):
""" Feature map interpolation. """
B, N, C = x.shape
H, W = size
_assert(N == 1 + H * W, '')
cls_token = x[:, :1, :]
img_tokens = x[:, 1:, :]
img_tokens = img_tokens.transpose(1, 2).reshape(B, C, H, W)
img_tokens = F.interpolate(
img_tokens,
scale_factor=scale_factor,
recompute_scale_factor=False,
mode='bilinear',
align_corners=False,
)
img_tokens = img_tokens.reshape(B, C, -1).transpose(1, 2)
out = torch.cat((cls_token, img_tokens), dim=1)
return out
def forward(self, x1, x2, x3, x4, sizes: List[Tuple[int, int]]):
_, S2, S3, S4 = sizes
cur2 = self.norm12(x2)
cur3 = self.norm13(x3)
cur4 = self.norm14(x4)
cur2 = self.factoratt_crpe2(cur2, size=S2)
cur3 = self.factoratt_crpe3(cur3, size=S3)
cur4 = self.factoratt_crpe4(cur4, size=S4)
upsample3_2 = self.upsample(cur3, factor=2., size=S3)
upsample4_3 = self.upsample(cur4, factor=2., size=S4)
upsample4_2 = self.upsample(cur4, factor=4., size=S4)
downsample2_3 = self.downsample(cur2, factor=2., size=S2)
downsample3_4 = self.downsample(cur3, factor=2., size=S3)
downsample2_4 = self.downsample(cur2, factor=4., size=S2)
cur2 = cur2 + upsample3_2 + upsample4_2
cur3 = cur3 + upsample4_3 + downsample2_3
cur4 = cur4 + downsample3_4 + downsample2_4
x2 = x2 + self.drop_path(cur2)
x3 = x3 + self.drop_path(cur3)
x4 = x4 + self.drop_path(cur4)
# MLP.
cur2 = self.norm22(x2)
cur3 = self.norm23(x3)
cur4 = self.norm24(x4)
cur2 = self.mlp2(cur2)
cur3 = self.mlp3(cur3)
cur4 = self.mlp4(cur4)
x2 = x2 + self.drop_path(cur2)
x3 = x3 + self.drop_path(cur3)
x4 = x4 + self.drop_path(cur4)
return x1, x2, x3, x4
class CoaT(nn.Module):
""" CoaT class. """
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
embed_dims=(64, 128, 320, 512),
serial_depths=(3, 4, 6, 3),
parallel_depth=0,
num_heads=8,
mlp_ratios=(4, 4, 4, 4),
qkv_bias=True,
drop_rate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=LayerNorm,
return_interm_layers=False,
out_features=None,
crpe_window=None,
global_pool='token',
):
super().__init__()
assert global_pool in ('token', 'avg')
crpe_window = crpe_window or {3: 2, 5: 3, 7: 3}
self.return_interm_layers = return_interm_layers
self.out_features = out_features
self.embed_dims = embed_dims
self.num_features = embed_dims[-1]
self.num_classes = num_classes
self.global_pool = global_pool
# Patch embeddings.
img_size = to_2tuple(img_size)
self.patch_embed1 = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=in_chans,
embed_dim=embed_dims[0], norm_layer=nn.LayerNorm)
self.patch_embed2 = PatchEmbed(
img_size=[x // 4 for x in img_size], patch_size=2, in_chans=embed_dims[0],
embed_dim=embed_dims[1], norm_layer=nn.LayerNorm)
self.patch_embed3 = PatchEmbed(
img_size=[x // 8 for x in img_size], patch_size=2, in_chans=embed_dims[1],
embed_dim=embed_dims[2], norm_layer=nn.LayerNorm)
self.patch_embed4 = PatchEmbed(
img_size=[x // 16 for x in img_size], patch_size=2, in_chans=embed_dims[2],
embed_dim=embed_dims[3], norm_layer=nn.LayerNorm)
# Class tokens.
self.cls_token1 = nn.Parameter(torch.zeros(1, 1, embed_dims[0]))
self.cls_token2 = nn.Parameter(torch.zeros(1, 1, embed_dims[1]))
self.cls_token3 = nn.Parameter(torch.zeros(1, 1, embed_dims[2]))
self.cls_token4 = nn.Parameter(torch.zeros(1, 1, embed_dims[3]))
# Convolutional position encodings.
self.cpe1 = ConvPosEnc(dim=embed_dims[0], k=3)
self.cpe2 = ConvPosEnc(dim=embed_dims[1], k=3)
self.cpe3 = ConvPosEnc(dim=embed_dims[2], k=3)
self.cpe4 = ConvPosEnc(dim=embed_dims[3], k=3)
# Convolutional relative position encodings.
self.crpe1 = ConvRelPosEnc(head_chs=embed_dims[0] // num_heads, num_heads=num_heads, window=crpe_window)
self.crpe2 = ConvRelPosEnc(head_chs=embed_dims[1] // num_heads, num_heads=num_heads, window=crpe_window)
self.crpe3 = ConvRelPosEnc(head_chs=embed_dims[2] // num_heads, num_heads=num_heads, window=crpe_window)
self.crpe4 = ConvRelPosEnc(head_chs=embed_dims[3] // num_heads, num_heads=num_heads, window=crpe_window)
# Disable stochastic depth.
dpr = drop_path_rate
assert dpr == 0.0
skwargs = dict(
num_heads=num_heads,
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr,
norm_layer=norm_layer,
)
# Serial blocks 1.
self.serial_blocks1 = nn.ModuleList([
SerialBlock(
dim=embed_dims[0],
mlp_ratio=mlp_ratios[0],
shared_cpe=self.cpe1,
shared_crpe=self.crpe1,
**skwargs,
)
for _ in range(serial_depths[0])]
)
# Serial blocks 2.
self.serial_blocks2 = nn.ModuleList([
SerialBlock(
dim=embed_dims[1],
mlp_ratio=mlp_ratios[1],
shared_cpe=self.cpe2,
shared_crpe=self.crpe2,
**skwargs,
)
for _ in range(serial_depths[1])]
)
# Serial blocks 3.
self.serial_blocks3 = nn.ModuleList([
SerialBlock(
dim=embed_dims[2],
mlp_ratio=mlp_ratios[2],
shared_cpe=self.cpe3,
shared_crpe=self.crpe3,
**skwargs,
)
for _ in range(serial_depths[2])]
)
# Serial blocks 4.
self.serial_blocks4 = nn.ModuleList([
SerialBlock(
dim=embed_dims[3],
mlp_ratio=mlp_ratios[3],
shared_cpe=self.cpe4,
shared_crpe=self.crpe4,
**skwargs,
)
for _ in range(serial_depths[3])]
)
# Parallel blocks.
self.parallel_depth = parallel_depth
if self.parallel_depth > 0:
self.parallel_blocks = nn.ModuleList([
ParallelBlock(
dims=embed_dims,
mlp_ratios=mlp_ratios,
shared_crpes=(self.crpe1, self.crpe2, self.crpe3, self.crpe4),
**skwargs,
)
for _ in range(parallel_depth)]
)
else:
self.parallel_blocks = None
# Classification head(s).
if not self.return_interm_layers:
if self.parallel_blocks is not None:
self.norm2 = norm_layer(embed_dims[1])
self.norm3 = norm_layer(embed_dims[2])
else:
self.norm2 = self.norm3 = None
self.norm4 = norm_layer(embed_dims[3])
if self.parallel_depth > 0:
# CoaT series: Aggregate features of last three scales for classification.
assert embed_dims[1] == embed_dims[2] == embed_dims[3]
self.aggregate = torch.nn.Conv1d(in_channels=3, out_channels=1, kernel_size=1)
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
else:
# CoaT-Lite series: Use feature of last scale for classification.
self.aggregate = None
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
# Initialize weights.
trunc_normal_(self.cls_token1, std=.02)
trunc_normal_(self.cls_token2, std=.02)
trunc_normal_(self.cls_token3, std=.02)
trunc_normal_(self.cls_token4, std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'cls_token1', 'cls_token2', 'cls_token3', 'cls_token4'}
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem1=r'^cls_token1|patch_embed1|crpe1|cpe1',
serial_blocks1=r'^serial_blocks1\.(\d+)',
stem2=r'^cls_token2|patch_embed2|crpe2|cpe2',
serial_blocks2=r'^serial_blocks2\.(\d+)',
stem3=r'^cls_token3|patch_embed3|crpe3|cpe3',
serial_blocks3=r'^serial_blocks3\.(\d+)',
stem4=r'^cls_token4|patch_embed4|crpe4|cpe4',
serial_blocks4=r'^serial_blocks4\.(\d+)',
parallel_blocks=[ # FIXME (partially?) overlap parallel w/ serial blocks??
(r'^parallel_blocks\.(\d+)', None),
(r'^norm|aggregate', (99999,)),
]
)
return matcher
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('token', 'avg')
self.global_pool = global_pool
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x0):
B = x0.shape[0]
# Serial blocks 1.
x1 = self.patch_embed1(x0)
H1, W1 = self.patch_embed1.grid_size
x1 = insert_cls(x1, self.cls_token1)
for blk in self.serial_blocks1:
x1 = blk(x1, size=(H1, W1))
x1_nocls = remove_cls(x1).reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
# Serial blocks 2.
x2 = self.patch_embed2(x1_nocls)
H2, W2 = self.patch_embed2.grid_size
x2 = insert_cls(x2, self.cls_token2)
for blk in self.serial_blocks2:
x2 = blk(x2, size=(H2, W2))
x2_nocls = remove_cls(x2).reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
# Serial blocks 3.
x3 = self.patch_embed3(x2_nocls)
H3, W3 = self.patch_embed3.grid_size
x3 = insert_cls(x3, self.cls_token3)
for blk in self.serial_blocks3:
x3 = blk(x3, size=(H3, W3))
x3_nocls = remove_cls(x3).reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()
# Serial blocks 4.
x4 = self.patch_embed4(x3_nocls)
H4, W4 = self.patch_embed4.grid_size
x4 = insert_cls(x4, self.cls_token4)
for blk in self.serial_blocks4:
x4 = blk(x4, size=(H4, W4))
x4_nocls = remove_cls(x4).reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()
# Only serial blocks: Early return.
if self.parallel_blocks is None:
if not torch.jit.is_scripting() and self.return_interm_layers:
# Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
feat_out = {}
if 'x1_nocls' in self.out_features:
feat_out['x1_nocls'] = x1_nocls
if 'x2_nocls' in self.out_features:
feat_out['x2_nocls'] = x2_nocls
if 'x3_nocls' in self.out_features:
feat_out['x3_nocls'] = x3_nocls
if 'x4_nocls' in self.out_features:
feat_out['x4_nocls'] = x4_nocls
return feat_out
else:
# Return features for classification.
x4 = self.norm4(x4)
return x4
# Parallel blocks.
for blk in self.parallel_blocks:
x2, x3, x4 = self.cpe2(x2, (H2, W2)), self.cpe3(x3, (H3, W3)), self.cpe4(x4, (H4, W4))
x1, x2, x3, x4 = blk(x1, x2, x3, x4, sizes=[(H1, W1), (H2, W2), (H3, W3), (H4, W4)])
if not torch.jit.is_scripting() and self.return_interm_layers:
# Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
feat_out = {}
if 'x1_nocls' in self.out_features:
x1_nocls = remove_cls(x1).reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
feat_out['x1_nocls'] = x1_nocls
if 'x2_nocls' in self.out_features:
x2_nocls = remove_cls(x2).reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
feat_out['x2_nocls'] = x2_nocls
if 'x3_nocls' in self.out_features:
x3_nocls = remove_cls(x3).reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()
feat_out['x3_nocls'] = x3_nocls
if 'x4_nocls' in self.out_features:
x4_nocls = remove_cls(x4).reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()
feat_out['x4_nocls'] = x4_nocls
return feat_out
else:
x2 = self.norm2(x2)
x3 = self.norm3(x3)
x4 = self.norm4(x4)
return [x2, x3, x4]
def forward_head(self, x_feat: Union[torch.Tensor, List[torch.Tensor]], pre_logits: bool = False):
if isinstance(x_feat, list):
assert self.aggregate is not None
if self.global_pool == 'avg':
x = torch.cat([xl[:, 1:].mean(dim=1, keepdim=True) for xl in x_feat], dim=1) # [B, 3, C]
else:
x = torch.stack([xl[:, 0] for xl in x_feat], dim=1) # [B, 3, C]
x = self.aggregate(x).squeeze(dim=1) # Shape: [B, C]
else:
x = x_feat[:, 1:].mean(dim=1) if self.global_pool == 'avg' else x_feat[:, 0]
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x) -> torch.Tensor:
if not torch.jit.is_scripting() and self.return_interm_layers:
# Return intermediate features (for down-stream tasks).
return self.forward_features(x)
else:
# Return features for classification.
x_feat = self.forward_features(x)
x = self.forward_head(x_feat)
return x
def insert_cls(x, cls_token):
""" Insert CLS token. """
cls_tokens = cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
return x
def remove_cls(x):
""" Remove CLS token. """
return x[:, 1:, :]
def checkpoint_filter_fn(state_dict, model):
out_dict = {}
state_dict = state_dict.get('model', state_dict)
for k, v in state_dict.items():
# original model had unused norm layers, removing them requires filtering pretrained checkpoints
if k.startswith('norm1') or \
(k.startswith('norm2') and getattr(model, 'norm2', None) is None) or \
(k.startswith('norm3') and getattr(model, 'norm3', None) is None) or \
(k.startswith('norm4') and getattr(model, 'norm4', None) is None) or \
(k.startswith('aggregate') and getattr(model, 'aggregate', None) is None) or \
(k.startswith('head') and getattr(model, 'head', None) is None):
continue
out_dict[k] = v
return out_dict
def _create_coat(variant, pretrained=False, default_cfg=None, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
model = build_model_with_cfg(
CoaT,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs,
)
return model
def _cfg_coat(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed1.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = generate_default_cfgs({
'coat_tiny.in1k': _cfg_coat(hf_hub_id='timm/'),
'coat_mini.in1k': _cfg_coat(hf_hub_id='timm/'),
'coat_small.in1k': _cfg_coat(hf_hub_id='timm/'),
'coat_lite_tiny.in1k': _cfg_coat(hf_hub_id='timm/'),
'coat_lite_mini.in1k': _cfg_coat(hf_hub_id='timm/'),
'coat_lite_small.in1k': _cfg_coat(hf_hub_id='timm/'),
'coat_lite_medium.in1k': _cfg_coat(hf_hub_id='timm/'),
'coat_lite_medium_384.in1k': _cfg_coat(
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0, crop_mode='squash',
),
})
@register_model
def coat_tiny(pretrained=False, **kwargs) -> CoaT:
model_cfg = dict(
patch_size=4, embed_dims=[152, 152, 152, 152], serial_depths=[2, 2, 2, 2], parallel_depth=6)
model = _create_coat('coat_tiny', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def coat_mini(pretrained=False, **kwargs) -> CoaT:
model_cfg = dict(
patch_size=4, embed_dims=[152, 216, 216, 216], serial_depths=[2, 2, 2, 2], parallel_depth=6)
model = _create_coat('coat_mini', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def coat_small(pretrained=False, **kwargs) -> CoaT:
model_cfg = dict(
patch_size=4, embed_dims=[152, 320, 320, 320], serial_depths=[2, 2, 2, 2], parallel_depth=6, **kwargs)
model = _create_coat('coat_small', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def coat_lite_tiny(pretrained=False, **kwargs) -> CoaT:
model_cfg = dict(
patch_size=4, embed_dims=[64, 128, 256, 320], serial_depths=[2, 2, 2, 2], mlp_ratios=[8, 8, 4, 4])
model = _create_coat('coat_lite_tiny', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def coat_lite_mini(pretrained=False, **kwargs) -> CoaT:
model_cfg = dict(
patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[2, 2, 2, 2], mlp_ratios=[8, 8, 4, 4])
model = _create_coat('coat_lite_mini', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def coat_lite_small(pretrained=False, **kwargs) -> CoaT:
model_cfg = dict(
patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[3, 4, 6, 3], mlp_ratios=[8, 8, 4, 4])
model = _create_coat('coat_lite_small', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def coat_lite_medium(pretrained=False, **kwargs) -> CoaT:
model_cfg = dict(
patch_size=4, embed_dims=[128, 256, 320, 512], serial_depths=[3, 6, 10, 8])
model = _create_coat('coat_lite_medium', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def coat_lite_medium_384(pretrained=False, **kwargs) -> CoaT:
model_cfg = dict(
img_size=384, patch_size=4, embed_dims=[128, 256, 320, 512], serial_depths=[3, 6, 10, 8])
model = _create_coat('coat_lite_medium_384', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/mobilenetv3.py
|
""" MobileNet V3
A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
Hacked together by / Copyright 2019, Ross Wightman
"""
from functools import partial
from typing import Callable, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import SelectAdaptivePool2d, Linear, LayerType, PadType, create_conv2d, get_norm_act_layer
from ._builder import build_model_with_cfg, pretrained_cfg_for_features
from ._efficientnet_blocks import SqueezeExcite
from ._efficientnet_builder import BlockArgs, EfficientNetBuilder, decode_arch_def, efficientnet_init_weights, \
round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
from ._features import FeatureInfo, FeatureHooks
from ._manipulate import checkpoint_seq
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
__all__ = ['MobileNetV3', 'MobileNetV3Features']
class MobileNetV3(nn.Module):
""" MobiletNet-V3
Based on my EfficientNet implementation and building blocks, this model utilizes the MobileNet-v3 specific
'efficient head', where global pooling is done before the head convolution without a final batch-norm
layer before the classifier.
Paper: `Searching for MobileNetV3` - https://arxiv.org/abs/1905.02244
Other architectures utilizing MobileNet-V3 efficient head that are supported by this impl include:
* HardCoRe-NAS - https://arxiv.org/abs/2102.11646 (defn in hardcorenas.py uses this class)
* FBNet-V3 - https://arxiv.org/abs/2006.02049
* LCNet - https://arxiv.org/abs/2109.15099
"""
def __init__(
self,
block_args: BlockArgs,
num_classes: int = 1000,
in_chans: int = 3,
stem_size: int = 16,
fix_stem: bool = False,
num_features: int = 1280,
head_bias: bool = True,
pad_type: PadType = '',
act_layer: Optional[LayerType] = None,
norm_layer: Optional[LayerType] = None,
se_layer: Optional[LayerType] = None,
se_from_exp: bool = True,
round_chs_fn: Callable = round_channels,
drop_rate: float = 0.,
drop_path_rate: float = 0.,
global_pool: str = 'avg',
):
"""
Args:
block_args: Arguments for blocks of the network.
num_classes: Number of classes for classification head.
in_chans: Number of input image channels.
stem_size: Number of output channels of the initial stem convolution.
fix_stem: If True, don't scale stem by round_chs_fn.
num_features: Number of output channels of the conv head layer.
head_bias: If True, add a learnable bias to the conv head layer.
pad_type: Type of padding to use for convolution layers.
act_layer: Type of activation layer.
norm_layer: Type of normalization layer.
se_layer: Type of Squeeze-and-Excite layer.
se_from_exp: If True, calculate SE channel reduction from expanded mid channels.
round_chs_fn: Callable to round number of filters based on depth multiplier.
drop_rate: Dropout rate.
drop_path_rate: Stochastic depth rate.
global_pool: Type of pooling to use for global pooling features of the FC head.
"""
super(MobileNetV3, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
se_layer = se_layer or SqueezeExcite
self.num_classes = num_classes
self.num_features = num_features
self.drop_rate = drop_rate
self.grad_checkpointing = False
# Stem
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_act_layer(stem_size, inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
output_stride=32,
pad_type=pad_type,
round_chs_fn=round_chs_fn,
se_from_exp=se_from_exp,
act_layer=act_layer,
norm_layer=norm_layer,
se_layer=se_layer,
drop_path_rate=drop_path_rate,
)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = builder.features
head_chs = builder.in_chs
# Head + Pooling
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
num_pooled_chs = head_chs * self.global_pool.feat_mult()
self.conv_head = create_conv2d(num_pooled_chs, self.num_features, 1, padding=pad_type, bias=head_bias)
self.act2 = act_layer(inplace=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
efficientnet_init_weights(self)
def as_sequential(self):
layers = [self.conv_stem, self.bn1]
layers.extend(self.blocks)
layers.extend([self.global_pool, self.conv_head, self.act2])
layers.extend([nn.Flatten(), nn.Dropout(self.drop_rate), self.classifier])
return nn.Sequential(*layers)
@torch.jit.ignore
def group_matcher(self, coarse: bool = False):
return dict(
stem=r'^conv_stem|bn1',
blocks=r'^blocks\.(\d+)' if coarse else r'^blocks\.(\d+)\.(\d+)'
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable: bool = True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes: int, global_pool: str = 'avg'):
self.num_classes = num_classes
# cannot meaningfully change pooling of efficient head after creation
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv_stem(x)
x = self.bn1(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x, flatten=True)
else:
x = self.blocks(x)
return x
def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
x = self.global_pool(x)
x = self.conv_head(x)
x = self.act2(x)
x = self.flatten(x)
if pre_logits:
return x
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return self.classifier(x)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
x = self.forward_head(x)
return x
class MobileNetV3Features(nn.Module):
""" MobileNetV3 Feature Extractor
A work-in-progress feature extraction module for MobileNet-V3 to use as a backbone for segmentation
and object detection models.
"""
def __init__(
self,
block_args: BlockArgs,
out_indices: Tuple[int, ...] = (0, 1, 2, 3, 4),
feature_location: str = 'bottleneck',
in_chans: int = 3,
stem_size: int = 16,
fix_stem: bool = False,
output_stride: int = 32,
pad_type: PadType = '',
round_chs_fn: Callable = round_channels,
se_from_exp: bool = True,
act_layer: Optional[LayerType] = None,
norm_layer: Optional[LayerType] = None,
se_layer: Optional[LayerType] = None,
drop_rate: float = 0.,
drop_path_rate: float = 0.,
):
"""
Args:
block_args: Arguments for blocks of the network.
out_indices: Output from stages at indices.
feature_location: Location of feature before/after each block, must be in ['bottleneck', 'expansion']
in_chans: Number of input image channels.
stem_size: Number of output channels of the initial stem convolution.
fix_stem: If True, don't scale stem by round_chs_fn.
output_stride: Output stride of the network.
pad_type: Type of padding to use for convolution layers.
round_chs_fn: Callable to round number of filters based on depth multiplier.
se_from_exp: If True, calculate SE channel reduction from expanded mid channels.
act_layer: Type of activation layer.
norm_layer: Type of normalization layer.
se_layer: Type of Squeeze-and-Excite layer.
drop_rate: Dropout rate.
drop_path_rate: Stochastic depth rate.
"""
super(MobileNetV3Features, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
se_layer = se_layer or SqueezeExcite
self.drop_rate = drop_rate
self.grad_checkpointing = False
# Stem
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
output_stride=output_stride,
pad_type=pad_type,
round_chs_fn=round_chs_fn,
se_from_exp=se_from_exp,
act_layer=act_layer,
norm_layer=norm_layer,
se_layer=se_layer,
drop_path_rate=drop_path_rate,
feature_location=feature_location,
)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = FeatureInfo(builder.features, out_indices)
self._stage_out_idx = {f['stage']: f['index'] for f in self.feature_info.get_dicts()}
efficientnet_init_weights(self)
# Register feature extraction hooks with FeatureHooks helper
self.feature_hooks = None
if feature_location != 'bottleneck':
hooks = self.feature_info.get_dicts(keys=('module', 'hook_type'))
self.feature_hooks = FeatureHooks(hooks, self.named_modules())
@torch.jit.ignore
def set_grad_checkpointing(self, enable: bool = True):
self.grad_checkpointing = enable
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
x = self.conv_stem(x)
x = self.bn1(x)
x = self.act1(x)
if self.feature_hooks is None:
features = []
if 0 in self._stage_out_idx:
features.append(x) # add stem out
for i, b in enumerate(self.blocks):
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(b, x)
else:
x = b(x)
if i + 1 in self._stage_out_idx:
features.append(x)
return features
else:
self.blocks(x)
out = self.feature_hooks.get_output(x.device)
return list(out.values())
def _create_mnv3(variant: str, pretrained: bool = False, **kwargs) -> MobileNetV3:
features_mode = ''
model_cls = MobileNetV3
kwargs_filter = None
if kwargs.pop('features_only', False):
if 'feature_cfg' in kwargs:
features_mode = 'cfg'
else:
kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'head_bias', 'global_pool')
model_cls = MobileNetV3Features
features_mode = 'cls'
model = build_model_with_cfg(
model_cls,
variant,
pretrained,
features_only=features_mode == 'cfg',
pretrained_strict=features_mode != 'cls',
kwargs_filter=kwargs_filter,
**kwargs,
)
if features_mode == 'cls':
model.default_cfg = pretrained_cfg_for_features(model.default_cfg)
return model
def _gen_mobilenet_v3_rw(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3:
"""Creates a MobileNet-V3 model.
Ref impl: ?
Paper: https://arxiv.org/abs/1905.02244
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16_nre_noskip'], # relu
# stage 1, 112x112 in
['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'], # relu
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40_se0.25_nre'], # relu
# stage 3, 28x28 in
['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'], # hard-swish
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112_se0.25'], # hard-swish
# stage 5, 14x14in
['ir_r3_k5_s2_e6_c160_se0.25'], # hard-swish
# stage 6, 7x7 in
['cn_r1_k1_s1_c960'], # hard-swish
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
head_bias=False,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_layer=partial(SqueezeExcite, gate_layer='hard_sigmoid'),
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
return model
def _gen_mobilenet_v3(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3:
"""Creates a MobileNet-V3 model.
Ref impl: ?
Paper: https://arxiv.org/abs/1905.02244
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
if 'small' in variant:
num_features = 1024
if 'minimal' in variant:
act_layer = resolve_act_layer(kwargs, 'relu')
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s2_e1_c16'],
# stage 1, 56x56 in
['ir_r1_k3_s2_e4.5_c24', 'ir_r1_k3_s1_e3.67_c24'],
# stage 2, 28x28 in
['ir_r1_k3_s2_e4_c40', 'ir_r2_k3_s1_e6_c40'],
# stage 3, 14x14 in
['ir_r2_k3_s1_e3_c48'],
# stage 4, 14x14in
['ir_r3_k3_s2_e6_c96'],
# stage 6, 7x7 in
['cn_r1_k1_s1_c576'],
]
else:
act_layer = resolve_act_layer(kwargs, 'hard_swish')
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s2_e1_c16_se0.25_nre'], # relu
# stage 1, 56x56 in
['ir_r1_k3_s2_e4.5_c24_nre', 'ir_r1_k3_s1_e3.67_c24_nre'], # relu
# stage 2, 28x28 in
['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r2_k5_s1_e6_c40_se0.25'], # hard-swish
# stage 3, 14x14 in
['ir_r2_k5_s1_e3_c48_se0.25'], # hard-swish
# stage 4, 14x14in
['ir_r3_k5_s2_e6_c96_se0.25'], # hard-swish
# stage 6, 7x7 in
['cn_r1_k1_s1_c576'], # hard-swish
]
else:
num_features = 1280
if 'minimal' in variant:
act_layer = resolve_act_layer(kwargs, 'relu')
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16'],
# stage 1, 112x112 in
['ir_r1_k3_s2_e4_c24', 'ir_r1_k3_s1_e3_c24'],
# stage 2, 56x56 in
['ir_r3_k3_s2_e3_c40'],
# stage 3, 28x28 in
['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'],
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112'],
# stage 5, 14x14in
['ir_r3_k3_s2_e6_c160'],
# stage 6, 7x7 in
['cn_r1_k1_s1_c960'],
]
else:
act_layer = resolve_act_layer(kwargs, 'hard_swish')
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16_nre'], # relu
# stage 1, 112x112 in
['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'], # relu
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40_se0.25_nre'], # relu
# stage 3, 28x28 in
['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'], # hard-swish
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112_se0.25'], # hard-swish
# stage 5, 14x14in
['ir_r3_k5_s2_e6_c160_se0.25'], # hard-swish
# stage 6, 7x7 in
['cn_r1_k1_s1_c960'], # hard-swish
]
se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU, rd_round_fn=round_channels)
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
num_features=num_features,
stem_size=16,
fix_stem=channel_multiplier < 0.75,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=act_layer,
se_layer=se_layer,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
return model
def _gen_fbnetv3(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs):
""" FBNetV3
Paper: `FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining`
- https://arxiv.org/abs/2006.02049
FIXME untested, this is a preliminary impl of some FBNet-V3 variants.
"""
vl = variant.split('_')[-1]
if vl in ('a', 'b'):
stem_size = 16
arch_def = [
['ds_r2_k3_s1_e1_c16'],
['ir_r1_k5_s2_e4_c24', 'ir_r3_k5_s1_e2_c24'],
['ir_r1_k5_s2_e5_c40_se0.25', 'ir_r4_k5_s1_e3_c40_se0.25'],
['ir_r1_k5_s2_e5_c72', 'ir_r4_k3_s1_e3_c72'],
['ir_r1_k3_s1_e5_c120_se0.25', 'ir_r5_k5_s1_e3_c120_se0.25'],
['ir_r1_k3_s2_e6_c184_se0.25', 'ir_r5_k5_s1_e4_c184_se0.25', 'ir_r1_k5_s1_e6_c224_se0.25'],
['cn_r1_k1_s1_c1344'],
]
elif vl == 'd':
stem_size = 24
arch_def = [
['ds_r2_k3_s1_e1_c16'],
['ir_r1_k3_s2_e5_c24', 'ir_r5_k3_s1_e2_c24'],
['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r4_k3_s1_e3_c40_se0.25'],
['ir_r1_k3_s2_e5_c72', 'ir_r4_k3_s1_e3_c72'],
['ir_r1_k3_s1_e5_c128_se0.25', 'ir_r6_k5_s1_e3_c128_se0.25'],
['ir_r1_k3_s2_e6_c208_se0.25', 'ir_r5_k5_s1_e5_c208_se0.25', 'ir_r1_k5_s1_e6_c240_se0.25'],
['cn_r1_k1_s1_c1440'],
]
elif vl == 'g':
stem_size = 32
arch_def = [
['ds_r3_k3_s1_e1_c24'],
['ir_r1_k5_s2_e4_c40', 'ir_r4_k5_s1_e2_c40'],
['ir_r1_k5_s2_e4_c56_se0.25', 'ir_r4_k5_s1_e3_c56_se0.25'],
['ir_r1_k5_s2_e5_c104', 'ir_r4_k3_s1_e3_c104'],
['ir_r1_k3_s1_e5_c160_se0.25', 'ir_r8_k5_s1_e3_c160_se0.25'],
['ir_r1_k3_s2_e6_c264_se0.25', 'ir_r6_k5_s1_e5_c264_se0.25', 'ir_r2_k5_s1_e6_c288_se0.25'],
['cn_r1_k1_s1_c1728'],
]
else:
raise NotImplemented
round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.95)
se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', rd_round_fn=round_chs_fn)
act_layer = resolve_act_layer(kwargs, 'hard_swish')
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
num_features=1984,
head_bias=False,
stem_size=stem_size,
round_chs_fn=round_chs_fn,
se_from_exp=False,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=act_layer,
se_layer=se_layer,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
return model
def _gen_lcnet(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs):
""" LCNet
Essentially a MobileNet-V3 crossed with a MobileNet-V1
Paper: `PP-LCNet: A Lightweight CPU Convolutional Neural Network` - https://arxiv.org/abs/2109.15099
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['dsa_r1_k3_s1_c32'],
# stage 1, 112x112 in
['dsa_r2_k3_s2_c64'],
# stage 2, 56x56 in
['dsa_r2_k3_s2_c128'],
# stage 3, 28x28 in
['dsa_r1_k3_s2_c256', 'dsa_r1_k5_s1_c256'],
# stage 4, 14x14in
['dsa_r4_k5_s1_c256'],
# stage 5, 14x14in
['dsa_r2_k5_s2_c512_se0.25'],
# 7x7
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=16,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_layer=partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU),
num_features=1280,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
return model
def _gen_lcnet(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs):
""" LCNet
Essentially a MobileNet-V3 crossed with a MobileNet-V1
Paper: `PP-LCNet: A Lightweight CPU Convolutional Neural Network` - https://arxiv.org/abs/2109.15099
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['dsa_r1_k3_s1_c32'],
# stage 1, 112x112 in
['dsa_r2_k3_s2_c64'],
# stage 2, 56x56 in
['dsa_r2_k3_s2_c128'],
# stage 3, 28x28 in
['dsa_r1_k3_s2_c256', 'dsa_r1_k5_s1_c256'],
# stage 4, 14x14in
['dsa_r4_k5_s1_c256'],
# stage 5, 14x14in
['dsa_r2_k5_s2_c512_se0.25'],
# 7x7
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=16,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_layer=partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU),
num_features=1280,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
return model
def _cfg(url: str = '', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv_stem', 'classifier': 'classifier',
**kwargs
}
default_cfgs = generate_default_cfgs({
'mobilenetv3_large_075.untrained': _cfg(url=''),
'mobilenetv3_large_100.ra_in1k': _cfg(
interpolation='bicubic',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth',
hf_hub_id='timm/'),
'mobilenetv3_large_100.miil_in21k_ft_in1k': _cfg(
interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.),
origin_url='https://github.com/Alibaba-MIIL/ImageNet21K',
paper_ids='arXiv:2104.10972v4',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_1k_miil_78_0-66471c13.pth',
hf_hub_id='timm/'),
'mobilenetv3_large_100.miil_in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_in21k_miil-d71cc17b.pth',
hf_hub_id='timm/',
origin_url='https://github.com/Alibaba-MIIL/ImageNet21K',
paper_ids='arXiv:2104.10972v4',
interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), num_classes=11221),
'mobilenetv3_small_050.lamb_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_050_lambc-4b7bbe87.pth',
hf_hub_id='timm/',
interpolation='bicubic'),
'mobilenetv3_small_075.lamb_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_075_lambc-384766db.pth',
hf_hub_id='timm/',
interpolation='bicubic'),
'mobilenetv3_small_100.lamb_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_100_lamb-266a294c.pth',
hf_hub_id='timm/',
interpolation='bicubic'),
'mobilenetv3_rw.rmsp_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth',
hf_hub_id='timm/',
interpolation='bicubic'),
'tf_mobilenetv3_large_075.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_large_100.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_large_minimal_100.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_small_075.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_small_100.in1k': _cfg(
url= 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_small_minimal_100.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth',
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'fbnetv3_b.ra2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_b_224-ead5d2a1.pth',
hf_hub_id='timm/',
test_input_size=(3, 256, 256), crop_pct=0.95),
'fbnetv3_d.ra2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_d_224-c98bce42.pth',
hf_hub_id='timm/',
test_input_size=(3, 256, 256), crop_pct=0.95),
'fbnetv3_g.ra2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_g_240-0b1df83b.pth',
hf_hub_id='timm/',
input_size=(3, 240, 240), test_input_size=(3, 288, 288), crop_pct=0.95, pool_size=(8, 8)),
"lcnet_035.untrained": _cfg(),
"lcnet_050.ra2_in1k": _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_050-f447553b.pth',
hf_hub_id='timm/',
interpolation='bicubic',
),
"lcnet_075.ra2_in1k": _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_075-318cad2c.pth',
hf_hub_id='timm/',
interpolation='bicubic',
),
"lcnet_100.ra2_in1k": _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_100-a929038c.pth',
hf_hub_id='timm/',
interpolation='bicubic',
),
"lcnet_150.untrained": _cfg(),
})
@register_model
def mobilenetv3_large_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_large_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_small_050(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_small_050', 0.50, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_small_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_small_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_rw(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
model = _gen_mobilenet_v3_rw('mobilenetv3_rw', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_large_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_mobilenet_v3('tf_mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_large_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_mobilenet_v3('tf_mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_large_minimal_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_mobilenet_v3('tf_mobilenetv3_large_minimal_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_small_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_mobilenet_v3('tf_mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_small_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_mobilenet_v3('tf_mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_small_minimal_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V3 """
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
kwargs.setdefault('pad_type', 'same')
model = _gen_mobilenet_v3('tf_mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def fbnetv3_b(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" FBNetV3-B """
model = _gen_fbnetv3('fbnetv3_b', pretrained=pretrained, **kwargs)
return model
@register_model
def fbnetv3_d(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" FBNetV3-D """
model = _gen_fbnetv3('fbnetv3_d', pretrained=pretrained, **kwargs)
return model
@register_model
def fbnetv3_g(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" FBNetV3-G """
model = _gen_fbnetv3('fbnetv3_g', pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_035(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" PP-LCNet 0.35"""
model = _gen_lcnet('lcnet_035', 0.35, pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_050(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" PP-LCNet 0.5"""
model = _gen_lcnet('lcnet_050', 0.5, pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" PP-LCNet 1.0"""
model = _gen_lcnet('lcnet_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" PP-LCNet 1.0"""
model = _gen_lcnet('lcnet_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_150(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" PP-LCNet 1.5"""
model = _gen_lcnet('lcnet_150', 1.5, pretrained=pretrained, **kwargs)
return model
register_model_deprecations(__name__, {
'mobilenetv3_large_100_miil': 'mobilenetv3_large_100.miil_in21k_ft_in1k',
'mobilenetv3_large_100_miil_in21k': 'mobilenetv3_large_100.miil_in21k',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/_hub.py
|
import hashlib
import json
import logging
import os
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Iterable, Optional, Union
import torch
from torch.hub import HASH_REGEX, download_url_to_file, urlparse
try:
from torch.hub import get_dir
except ImportError:
from torch.hub import _get_torch_home as get_dir
try:
import safetensors.torch
_has_safetensors = True
except ImportError:
_has_safetensors = False
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
from timm import __version__
from timm.models._pretrained import filter_pretrained_cfg
try:
from huggingface_hub import (
create_repo, get_hf_file_metadata,
hf_hub_download, hf_hub_url,
repo_type_and_id_from_hf_id, upload_folder)
from huggingface_hub.utils import EntryNotFoundError
hf_hub_download = partial(hf_hub_download, library_name="timm", library_version=__version__)
_has_hf_hub = True
except ImportError:
hf_hub_download = None
_has_hf_hub = False
_logger = logging.getLogger(__name__)
__all__ = ['get_cache_dir', 'download_cached_file', 'has_hf_hub', 'hf_split', 'load_model_config_from_hf',
'load_state_dict_from_hf', 'save_for_hf', 'push_to_hf_hub']
# Default name for a weights file hosted on the Huggingface Hub.
HF_WEIGHTS_NAME = "pytorch_model.bin" # default pytorch pkl
HF_SAFE_WEIGHTS_NAME = "model.safetensors" # safetensors version
HF_OPEN_CLIP_WEIGHTS_NAME = "open_clip_pytorch_model.bin" # default pytorch pkl
HF_OPEN_CLIP_SAFE_WEIGHTS_NAME = "open_clip_model.safetensors" # safetensors version
def get_cache_dir(child_dir=''):
"""
Returns the location of the directory where models are cached (and creates it if necessary).
"""
# Issue warning to move data if old env is set
if os.getenv('TORCH_MODEL_ZOO'):
_logger.warning('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead')
hub_dir = get_dir()
child_dir = () if not child_dir else (child_dir,)
model_dir = os.path.join(hub_dir, 'checkpoints', *child_dir)
os.makedirs(model_dir, exist_ok=True)
return model_dir
def download_cached_file(url, check_hash=True, progress=False):
if isinstance(url, (list, tuple)):
url, filename = url
else:
parts = urlparse(url)
filename = os.path.basename(parts.path)
cached_file = os.path.join(get_cache_dir(), filename)
if not os.path.exists(cached_file):
_logger.info('Downloading: "{}" to {}\n'.format(url, cached_file))
hash_prefix = None
if check_hash:
r = HASH_REGEX.search(filename) # r is Optional[Match[str]]
hash_prefix = r.group(1) if r else None
download_url_to_file(url, cached_file, hash_prefix, progress=progress)
return cached_file
def check_cached_file(url, check_hash=True):
if isinstance(url, (list, tuple)):
url, filename = url
else:
parts = urlparse(url)
filename = os.path.basename(parts.path)
cached_file = os.path.join(get_cache_dir(), filename)
if os.path.exists(cached_file):
if check_hash:
r = HASH_REGEX.search(filename) # r is Optional[Match[str]]
hash_prefix = r.group(1) if r else None
if hash_prefix:
with open(cached_file, 'rb') as f:
hd = hashlib.sha256(f.read()).hexdigest()
if hd[:len(hash_prefix)] != hash_prefix:
return False
return True
return False
def has_hf_hub(necessary=False):
if not _has_hf_hub and necessary:
# if no HF Hub module installed, and it is necessary to continue, raise error
raise RuntimeError(
'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
return _has_hf_hub
def hf_split(hf_id: str):
# FIXME I may change @ -> # and be parsed as fragment in a URI model name scheme
rev_split = hf_id.split('@')
assert 0 < len(rev_split) <= 2, 'hf_hub id should only contain one @ character to identify revision.'
hf_model_id = rev_split[0]
hf_revision = rev_split[-1] if len(rev_split) > 1 else None
return hf_model_id, hf_revision
def load_cfg_from_json(json_file: Union[str, os.PathLike]):
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
def download_from_hf(model_id: str, filename: str):
hf_model_id, hf_revision = hf_split(model_id)
return hf_hub_download(hf_model_id, filename, revision=hf_revision)
def load_model_config_from_hf(model_id: str):
assert has_hf_hub(True)
cached_file = download_from_hf(model_id, 'config.json')
hf_config = load_cfg_from_json(cached_file)
if 'pretrained_cfg' not in hf_config:
# old form, pull pretrain_cfg out of the base dict
pretrained_cfg = hf_config
hf_config = {}
hf_config['architecture'] = pretrained_cfg.pop('architecture')
hf_config['num_features'] = pretrained_cfg.pop('num_features', None)
if 'labels' in pretrained_cfg: # deprecated name for 'label_names'
pretrained_cfg['label_names'] = pretrained_cfg.pop('labels')
hf_config['pretrained_cfg'] = pretrained_cfg
# NOTE currently discarding parent config as only arch name and pretrained_cfg used in timm right now
pretrained_cfg = hf_config['pretrained_cfg']
pretrained_cfg['hf_hub_id'] = model_id # insert hf_hub id for pretrained weight load during model creation
pretrained_cfg['source'] = 'hf-hub'
# model should be created with base config num_classes if its exist
if 'num_classes' in hf_config:
pretrained_cfg['num_classes'] = hf_config['num_classes']
# label meta-data in base config overrides saved pretrained_cfg on load
if 'label_names' in hf_config:
pretrained_cfg['label_names'] = hf_config.pop('label_names')
if 'label_descriptions' in hf_config:
pretrained_cfg['label_descriptions'] = hf_config.pop('label_descriptions')
model_args = hf_config.get('model_args', {})
model_name = hf_config['architecture']
return pretrained_cfg, model_name, model_args
def load_state_dict_from_hf(model_id: str, filename: str = HF_WEIGHTS_NAME):
assert has_hf_hub(True)
hf_model_id, hf_revision = hf_split(model_id)
# Look for .safetensors alternatives and load from it if it exists
if _has_safetensors:
for safe_filename in _get_safe_alternatives(filename):
try:
cached_safe_file = hf_hub_download(repo_id=hf_model_id, filename=safe_filename, revision=hf_revision)
_logger.info(
f"[{model_id}] Safe alternative available for '{filename}' "
f"(as '{safe_filename}'). Loading weights using safetensors.")
return safetensors.torch.load_file(cached_safe_file, device="cpu")
except EntryNotFoundError:
pass
# Otherwise, load using pytorch.load
cached_file = hf_hub_download(hf_model_id, filename=filename, revision=hf_revision)
_logger.debug(f"[{model_id}] Safe alternative not found for '{filename}'. Loading weights using default pytorch.")
return torch.load(cached_file, map_location='cpu')
def save_config_for_hf(
model,
config_path: str,
model_config: Optional[dict] = None,
model_args: Optional[dict] = None
):
model_config = model_config or {}
hf_config = {}
pretrained_cfg = filter_pretrained_cfg(model.pretrained_cfg, remove_source=True, remove_null=True)
# set some values at root config level
hf_config['architecture'] = pretrained_cfg.pop('architecture')
hf_config['num_classes'] = model_config.pop('num_classes', model.num_classes)
# NOTE these attr saved for informational purposes, do not impact model build
hf_config['num_features'] = model_config.pop('num_features', model.num_features)
global_pool_type = model_config.pop('global_pool', getattr(model, 'global_pool', None))
if isinstance(global_pool_type, str) and global_pool_type:
hf_config['global_pool'] = global_pool_type
# Save class label info
if 'labels' in model_config:
_logger.warning(
"'labels' as a config field for is deprecated. Please use 'label_names' and 'label_descriptions'."
" Renaming provided 'labels' field to 'label_names'.")
model_config.setdefault('label_names', model_config.pop('labels'))
label_names = model_config.pop('label_names', None)
if label_names:
assert isinstance(label_names, (dict, list, tuple))
# map label id (classifier index) -> unique label name (ie synset for ImageNet, MID for OpenImages)
# can be a dict id: name if there are id gaps, or tuple/list if no gaps.
hf_config['label_names'] = label_names
label_descriptions = model_config.pop('label_descriptions', None)
if label_descriptions:
assert isinstance(label_descriptions, dict)
# maps label names -> descriptions
hf_config['label_descriptions'] = label_descriptions
if model_args:
hf_config['model_args'] = model_args
hf_config['pretrained_cfg'] = pretrained_cfg
hf_config.update(model_config)
with config_path.open('w') as f:
json.dump(hf_config, f, indent=2)
def save_for_hf(
model,
save_directory: str,
model_config: Optional[dict] = None,
model_args: Optional[dict] = None,
safe_serialization: Union[bool, Literal["both"]] = False,
):
assert has_hf_hub(True)
save_directory = Path(save_directory)
save_directory.mkdir(exist_ok=True, parents=True)
# Save model weights, either safely (using safetensors), or using legacy pytorch approach or both.
tensors = model.state_dict()
if safe_serialization is True or safe_serialization == "both":
assert _has_safetensors, "`pip install safetensors` to use .safetensors"
safetensors.torch.save_file(tensors, save_directory / HF_SAFE_WEIGHTS_NAME)
if safe_serialization is False or safe_serialization == "both":
torch.save(tensors, save_directory / HF_WEIGHTS_NAME)
config_path = save_directory / 'config.json'
save_config_for_hf(
model,
config_path,
model_config=model_config,
model_args=model_args,
)
def push_to_hf_hub(
model: torch.nn.Module,
repo_id: str,
commit_message: str = 'Add model',
token: Optional[str] = None,
revision: Optional[str] = None,
private: bool = False,
create_pr: bool = False,
model_config: Optional[dict] = None,
model_card: Optional[dict] = None,
model_args: Optional[dict] = None,
safe_serialization: Union[bool, Literal["both"]] = False,
):
"""
Arguments:
(...)
safe_serialization (`bool` or `"both"`, *optional*, defaults to `False`):
Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
Can be set to `"both"` in order to push both safe and unsafe weights.
"""
# Create repo if it doesn't exist yet
repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True)
# Infer complete repo_id from repo_url
# Can be different from the input `repo_id` if repo_owner was implicit
_, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)
repo_id = f"{repo_owner}/{repo_name}"
# Check if README file already exist in repo
try:
get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision))
has_readme = True
except EntryNotFoundError:
has_readme = False
# Dump model and push to Hub
with TemporaryDirectory() as tmpdir:
# Save model weights and config.
save_for_hf(
model,
tmpdir,
model_config=model_config,
model_args=model_args,
safe_serialization=safe_serialization,
)
# Add readme if it does not exist
if not has_readme:
model_card = model_card or {}
model_name = repo_id.split('/')[-1]
readme_path = Path(tmpdir) / "README.md"
readme_text = generate_readme(model_card, model_name)
readme_path.write_text(readme_text)
# Upload model and return
return upload_folder(
repo_id=repo_id,
folder_path=tmpdir,
revision=revision,
create_pr=create_pr,
commit_message=commit_message,
)
def generate_readme(model_card: dict, model_name: str):
readme_text = "---\n"
readme_text += "tags:\n- image-classification\n- timm\n"
readme_text += "library_name: timm\n"
readme_text += f"license: {model_card.get('license', 'apache-2.0')}\n"
if 'details' in model_card and 'Dataset' in model_card['details']:
readme_text += 'datasets:\n'
if isinstance(model_card['details']['Dataset'], (tuple, list)):
for d in model_card['details']['Dataset']:
readme_text += f"- {d.lower()}\n"
else:
readme_text += f"- {model_card['details']['Dataset'].lower()}\n"
if 'Pretrain Dataset' in model_card['details']:
if isinstance(model_card['details']['Pretrain Dataset'], (tuple, list)):
for d in model_card['details']['Pretrain Dataset']:
readme_text += f"- {d.lower()}\n"
else:
readme_text += f"- {model_card['details']['Pretrain Dataset'].lower()}\n"
readme_text += "---\n"
readme_text += f"# Model card for {model_name}\n"
if 'description' in model_card:
readme_text += f"\n{model_card['description']}\n"
if 'details' in model_card:
readme_text += f"\n## Model Details\n"
for k, v in model_card['details'].items():
if isinstance(v, (list, tuple)):
readme_text += f"- **{k}:**\n"
for vi in v:
readme_text += f" - {vi}\n"
elif isinstance(v, dict):
readme_text += f"- **{k}:**\n"
for ki, vi in v.items():
readme_text += f" - {ki}: {vi}\n"
else:
readme_text += f"- **{k}:** {v}\n"
if 'usage' in model_card:
readme_text += f"\n## Model Usage\n"
readme_text += model_card['usage']
readme_text += '\n'
if 'comparison' in model_card:
readme_text += f"\n## Model Comparison\n"
readme_text += model_card['comparison']
readme_text += '\n'
if 'citation' in model_card:
readme_text += f"\n## Citation\n"
if not isinstance(model_card['citation'], (list, tuple)):
citations = [model_card['citation']]
else:
citations = model_card['citation']
for c in citations:
readme_text += f"```bibtex\n{c}\n```\n"
return readme_text
def _get_safe_alternatives(filename: str) -> Iterable[str]:
"""Returns potential safetensors alternatives for a given filename.
Use case:
When downloading a model from the Huggingface Hub, we first look if a .safetensors file exists and if yes, we use it.
Main use case is filename "pytorch_model.bin" => check for "model.safetensors" or "pytorch_model.safetensors".
"""
if filename == HF_WEIGHTS_NAME:
yield HF_SAFE_WEIGHTS_NAME
if filename == HF_OPEN_CLIP_WEIGHTS_NAME:
yield HF_OPEN_CLIP_SAFE_WEIGHTS_NAME
if filename not in (HF_WEIGHTS_NAME, HF_OPEN_CLIP_WEIGHTS_NAME) and filename.endswith(".bin"):
yield filename[:-4] + ".safetensors"
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/levit.py
|
""" LeViT
Paper: `LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference`
- https://arxiv.org/abs/2104.01136
@article{graham2021levit,
title={LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference},
author={Benjamin Graham and Alaaeldin El-Nouby and Hugo Touvron and Pierre Stock and Armand Joulin and Herv\'e J\'egou and Matthijs Douze},
journal={arXiv preprint arXiv:22104.01136},
year={2021}
}
Adapted from official impl at https://github.com/facebookresearch/LeViT, original copyright bellow.
This version combines both conv/linear models and fixes torchscript compatibility.
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
# Modified from
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# Copyright 2020 Ross Wightman, Apache-2.0 License
from collections import OrderedDict
from functools import partial
from typing import Dict
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN
from timm.layers import to_ntuple, to_2tuple, get_act_layer, DropPath, trunc_normal_
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import generate_default_cfgs, register_model
__all__ = ['Levit']
class ConvNorm(nn.Module):
def __init__(
self, in_chs, out_chs, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bn_weight_init=1):
super().__init__()
self.linear = nn.Conv2d(in_chs, out_chs, kernel_size, stride, padding, dilation, groups, bias=False)
self.bn = nn.BatchNorm2d(out_chs)
nn.init.constant_(self.bn.weight, bn_weight_init)
@torch.no_grad()
def fuse(self):
c, bn = self.linear, self.bn
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
m = nn.Conv2d(
w.size(1), w.size(0), w.shape[2:], stride=self.linear.stride,
padding=self.linear.padding, dilation=self.linear.dilation, groups=self.linear.groups)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
def forward(self, x):
return self.bn(self.linear(x))
class LinearNorm(nn.Module):
def __init__(self, in_features, out_features, bn_weight_init=1):
super().__init__()
self.linear = nn.Linear(in_features, out_features, bias=False)
self.bn = nn.BatchNorm1d(out_features)
nn.init.constant_(self.bn.weight, bn_weight_init)
@torch.no_grad()
def fuse(self):
l, bn = self.linear, self.bn
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = l.weight * w[:, None]
b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
m = nn.Linear(w.size(1), w.size(0))
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
def forward(self, x):
x = self.linear(x)
return self.bn(x.flatten(0, 1)).reshape_as(x)
class NormLinear(nn.Module):
def __init__(self, in_features, out_features, bias=True, std=0.02, drop=0.):
super().__init__()
self.bn = nn.BatchNorm1d(in_features)
self.drop = nn.Dropout(drop)
self.linear = nn.Linear(in_features, out_features, bias=bias)
trunc_normal_(self.linear.weight, std=std)
if self.linear.bias is not None:
nn.init.constant_(self.linear.bias, 0)
@torch.no_grad()
def fuse(self):
bn, l = self.bn, self.linear
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
b = bn.bias - self.bn.running_mean * self.bn.weight / (bn.running_var + bn.eps) ** 0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.linear.weight.T
else:
b = (l.weight @ b[:, None]).view(-1) + self.linear.bias
m = nn.Linear(w.size(1), w.size(0))
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
def forward(self, x):
return self.linear(self.drop(self.bn(x)))
class Stem8(nn.Sequential):
def __init__(self, in_chs, out_chs, act_layer):
super().__init__()
self.stride = 8
self.add_module('conv1', ConvNorm(in_chs, out_chs // 4, 3, stride=2, padding=1))
self.add_module('act1', act_layer())
self.add_module('conv2', ConvNorm(out_chs // 4, out_chs // 2, 3, stride=2, padding=1))
self.add_module('act2', act_layer())
self.add_module('conv3', ConvNorm(out_chs // 2, out_chs, 3, stride=2, padding=1))
class Stem16(nn.Sequential):
def __init__(self, in_chs, out_chs, act_layer):
super().__init__()
self.stride = 16
self.add_module('conv1', ConvNorm(in_chs, out_chs // 8, 3, stride=2, padding=1))
self.add_module('act1', act_layer())
self.add_module('conv2', ConvNorm(out_chs // 8, out_chs // 4, 3, stride=2, padding=1))
self.add_module('act2', act_layer())
self.add_module('conv3', ConvNorm(out_chs // 4, out_chs // 2, 3, stride=2, padding=1))
self.add_module('act3', act_layer())
self.add_module('conv4', ConvNorm(out_chs // 2, out_chs, 3, stride=2, padding=1))
class Downsample(nn.Module):
def __init__(self, stride, resolution, use_pool=False):
super().__init__()
self.stride = stride
self.resolution = to_2tuple(resolution)
self.pool = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False) if use_pool else None
def forward(self, x):
B, N, C = x.shape
x = x.view(B, self.resolution[0], self.resolution[1], C)
if self.pool is not None:
x = self.pool(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
else:
x = x[:, ::self.stride, ::self.stride]
return x.reshape(B, -1, C)
class Attention(nn.Module):
attention_bias_cache: Dict[str, torch.Tensor]
def __init__(
self,
dim,
key_dim,
num_heads=8,
attn_ratio=4.,
resolution=14,
use_conv=False,
act_layer=nn.SiLU,
):
super().__init__()
ln_layer = ConvNorm if use_conv else LinearNorm
resolution = to_2tuple(resolution)
self.use_conv = use_conv
self.num_heads = num_heads
self.scale = key_dim ** -0.5
self.key_dim = key_dim
self.key_attn_dim = key_dim * num_heads
self.val_dim = int(attn_ratio * key_dim)
self.val_attn_dim = int(attn_ratio * key_dim) * num_heads
self.qkv = ln_layer(dim, self.val_attn_dim + self.key_attn_dim * 2)
self.proj = nn.Sequential(OrderedDict([
('act', act_layer()),
('ln', ln_layer(self.val_attn_dim, dim, bn_weight_init=0))
]))
self.attention_biases = nn.Parameter(torch.zeros(num_heads, resolution[0] * resolution[1]))
pos = torch.stack(torch.meshgrid(torch.arange(resolution[0]), torch.arange(resolution[1]))).flatten(1)
rel_pos = (pos[..., :, None] - pos[..., None, :]).abs()
rel_pos = (rel_pos[0] * resolution[1]) + rel_pos[1]
self.register_buffer('attention_bias_idxs', rel_pos, persistent=False)
self.attention_bias_cache = {}
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and self.attention_bias_cache:
self.attention_bias_cache = {} # clear ab cache
def get_attention_biases(self, device: torch.device) -> torch.Tensor:
if torch.jit.is_tracing() or self.training:
return self.attention_biases[:, self.attention_bias_idxs]
else:
device_key = str(device)
if device_key not in self.attention_bias_cache:
self.attention_bias_cache[device_key] = self.attention_biases[:, self.attention_bias_idxs]
return self.attention_bias_cache[device_key]
def forward(self, x): # x (B,C,H,W)
if self.use_conv:
B, C, H, W = x.shape
q, k, v = self.qkv(x).view(
B, self.num_heads, -1, H * W).split([self.key_dim, self.key_dim, self.val_dim], dim=2)
attn = (q.transpose(-2, -1) @ k) * self.scale + self.get_attention_biases(x.device)
attn = attn.softmax(dim=-1)
x = (v @ attn.transpose(-2, -1)).view(B, -1, H, W)
else:
B, N, C = x.shape
q, k, v = self.qkv(x).view(
B, N, self.num_heads, -1).split([self.key_dim, self.key_dim, self.val_dim], dim=3)
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 3, 1)
v = v.permute(0, 2, 1, 3)
attn = q @ k * self.scale + self.get_attention_biases(x.device)
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N, self.val_attn_dim)
x = self.proj(x)
return x
class AttentionDownsample(nn.Module):
attention_bias_cache: Dict[str, torch.Tensor]
def __init__(
self,
in_dim,
out_dim,
key_dim,
num_heads=8,
attn_ratio=2.0,
stride=2,
resolution=14,
use_conv=False,
use_pool=False,
act_layer=nn.SiLU,
):
super().__init__()
resolution = to_2tuple(resolution)
self.stride = stride
self.resolution = resolution
self.num_heads = num_heads
self.key_dim = key_dim
self.key_attn_dim = key_dim * num_heads
self.val_dim = int(attn_ratio * key_dim)
self.val_attn_dim = self.val_dim * self.num_heads
self.scale = key_dim ** -0.5
self.use_conv = use_conv
if self.use_conv:
ln_layer = ConvNorm
sub_layer = partial(
nn.AvgPool2d,
kernel_size=3 if use_pool else 1, padding=1 if use_pool else 0, count_include_pad=False)
else:
ln_layer = LinearNorm
sub_layer = partial(Downsample, resolution=resolution, use_pool=use_pool)
self.kv = ln_layer(in_dim, self.val_attn_dim + self.key_attn_dim)
self.q = nn.Sequential(OrderedDict([
('down', sub_layer(stride=stride)),
('ln', ln_layer(in_dim, self.key_attn_dim))
]))
self.proj = nn.Sequential(OrderedDict([
('act', act_layer()),
('ln', ln_layer(self.val_attn_dim, out_dim))
]))
self.attention_biases = nn.Parameter(torch.zeros(num_heads, resolution[0] * resolution[1]))
k_pos = torch.stack(torch.meshgrid(torch.arange(resolution[0]), torch.arange(resolution[1]))).flatten(1)
q_pos = torch.stack(torch.meshgrid(
torch.arange(0, resolution[0], step=stride),
torch.arange(0, resolution[1], step=stride))).flatten(1)
rel_pos = (q_pos[..., :, None] - k_pos[..., None, :]).abs()
rel_pos = (rel_pos[0] * resolution[1]) + rel_pos[1]
self.register_buffer('attention_bias_idxs', rel_pos, persistent=False)
self.attention_bias_cache = {} # per-device attention_biases cache
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and self.attention_bias_cache:
self.attention_bias_cache = {} # clear ab cache
def get_attention_biases(self, device: torch.device) -> torch.Tensor:
if torch.jit.is_tracing() or self.training:
return self.attention_biases[:, self.attention_bias_idxs]
else:
device_key = str(device)
if device_key not in self.attention_bias_cache:
self.attention_bias_cache[device_key] = self.attention_biases[:, self.attention_bias_idxs]
return self.attention_bias_cache[device_key]
def forward(self, x):
if self.use_conv:
B, C, H, W = x.shape
HH, WW = (H - 1) // self.stride + 1, (W - 1) // self.stride + 1
k, v = self.kv(x).view(B, self.num_heads, -1, H * W).split([self.key_dim, self.val_dim], dim=2)
q = self.q(x).view(B, self.num_heads, self.key_dim, -1)
attn = (q.transpose(-2, -1) @ k) * self.scale + self.get_attention_biases(x.device)
attn = attn.softmax(dim=-1)
x = (v @ attn.transpose(-2, -1)).reshape(B, self.val_attn_dim, HH, WW)
else:
B, N, C = x.shape
k, v = self.kv(x).view(B, N, self.num_heads, -1).split([self.key_dim, self.val_dim], dim=3)
k = k.permute(0, 2, 3, 1) # BHCN
v = v.permute(0, 2, 1, 3) # BHNC
q = self.q(x).view(B, -1, self.num_heads, self.key_dim).permute(0, 2, 1, 3)
attn = q @ k * self.scale + self.get_attention_biases(x.device)
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, -1, self.val_attn_dim)
x = self.proj(x)
return x
class LevitMlp(nn.Module):
""" MLP for Levit w/ normalization + ability to switch btw conv and linear
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
use_conv=False,
act_layer=nn.SiLU,
drop=0.
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
ln_layer = ConvNorm if use_conv else LinearNorm
self.ln1 = ln_layer(in_features, hidden_features)
self.act = act_layer()
self.drop = nn.Dropout(drop)
self.ln2 = ln_layer(hidden_features, out_features, bn_weight_init=0)
def forward(self, x):
x = self.ln1(x)
x = self.act(x)
x = self.drop(x)
x = self.ln2(x)
return x
class LevitDownsample(nn.Module):
def __init__(
self,
in_dim,
out_dim,
key_dim,
num_heads=8,
attn_ratio=4.,
mlp_ratio=2.,
act_layer=nn.SiLU,
attn_act_layer=None,
resolution=14,
use_conv=False,
use_pool=False,
drop_path=0.,
):
super().__init__()
attn_act_layer = attn_act_layer or act_layer
self.attn_downsample = AttentionDownsample(
in_dim=in_dim,
out_dim=out_dim,
key_dim=key_dim,
num_heads=num_heads,
attn_ratio=attn_ratio,
act_layer=attn_act_layer,
resolution=resolution,
use_conv=use_conv,
use_pool=use_pool,
)
self.mlp = LevitMlp(
out_dim,
int(out_dim * mlp_ratio),
use_conv=use_conv,
act_layer=act_layer
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
x = self.attn_downsample(x)
x = x + self.drop_path(self.mlp(x))
return x
class LevitBlock(nn.Module):
def __init__(
self,
dim,
key_dim,
num_heads=8,
attn_ratio=4.,
mlp_ratio=2.,
resolution=14,
use_conv=False,
act_layer=nn.SiLU,
attn_act_layer=None,
drop_path=0.,
):
super().__init__()
attn_act_layer = attn_act_layer or act_layer
self.attn = Attention(
dim=dim,
key_dim=key_dim,
num_heads=num_heads,
attn_ratio=attn_ratio,
resolution=resolution,
use_conv=use_conv,
act_layer=attn_act_layer,
)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.mlp = LevitMlp(
dim,
int(dim * mlp_ratio),
use_conv=use_conv,
act_layer=act_layer
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
x = x + self.drop_path1(self.attn(x))
x = x + self.drop_path2(self.mlp(x))
return x
class LevitStage(nn.Module):
def __init__(
self,
in_dim,
out_dim,
key_dim,
depth=4,
num_heads=8,
attn_ratio=4.0,
mlp_ratio=4.0,
act_layer=nn.SiLU,
attn_act_layer=None,
resolution=14,
downsample='',
use_conv=False,
drop_path=0.,
):
super().__init__()
resolution = to_2tuple(resolution)
if downsample:
self.downsample = LevitDownsample(
in_dim,
out_dim,
key_dim=key_dim,
num_heads=in_dim // key_dim,
attn_ratio=4.,
mlp_ratio=2.,
act_layer=act_layer,
attn_act_layer=attn_act_layer,
resolution=resolution,
use_conv=use_conv,
drop_path=drop_path,
)
resolution = [(r - 1) // 2 + 1 for r in resolution]
else:
assert in_dim == out_dim
self.downsample = nn.Identity()
blocks = []
for _ in range(depth):
blocks += [LevitBlock(
out_dim,
key_dim,
num_heads=num_heads,
attn_ratio=attn_ratio,
mlp_ratio=mlp_ratio,
act_layer=act_layer,
attn_act_layer=attn_act_layer,
resolution=resolution,
use_conv=use_conv,
drop_path=drop_path,
)]
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
x = self.downsample(x)
x = self.blocks(x)
return x
class Levit(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
NOTE: distillation is defaulted to True since pretrained weights use it, will cause problems
w/ train scripts that don't take tuple outputs,
"""
def __init__(
self,
img_size=224,
in_chans=3,
num_classes=1000,
embed_dim=(192,),
key_dim=64,
depth=(12,),
num_heads=(3,),
attn_ratio=2.,
mlp_ratio=2.,
stem_backbone=None,
stem_stride=None,
stem_type='s16',
down_op='subsample',
act_layer='hard_swish',
attn_act_layer=None,
use_conv=False,
global_pool='avg',
drop_rate=0.,
drop_path_rate=0.):
super().__init__()
act_layer = get_act_layer(act_layer)
attn_act_layer = get_act_layer(attn_act_layer or act_layer)
self.use_conv = use_conv
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = embed_dim[-1]
self.embed_dim = embed_dim
self.drop_rate = drop_rate
self.grad_checkpointing = False
self.feature_info = []
num_stages = len(embed_dim)
assert len(depth) == num_stages
num_heads = to_ntuple(num_stages)(num_heads)
attn_ratio = to_ntuple(num_stages)(attn_ratio)
mlp_ratio = to_ntuple(num_stages)(mlp_ratio)
if stem_backbone is not None:
assert stem_stride >= 2
self.stem = stem_backbone
stride = stem_stride
else:
assert stem_type in ('s16', 's8')
if stem_type == 's16':
self.stem = Stem16(in_chans, embed_dim[0], act_layer=act_layer)
else:
self.stem = Stem8(in_chans, embed_dim[0], act_layer=act_layer)
stride = self.stem.stride
resolution = tuple([i // p for i, p in zip(to_2tuple(img_size), to_2tuple(stride))])
in_dim = embed_dim[0]
stages = []
for i in range(num_stages):
stage_stride = 2 if i > 0 else 1
stages += [LevitStage(
in_dim,
embed_dim[i],
key_dim,
depth=depth[i],
num_heads=num_heads[i],
attn_ratio=attn_ratio[i],
mlp_ratio=mlp_ratio[i],
act_layer=act_layer,
attn_act_layer=attn_act_layer,
resolution=resolution,
use_conv=use_conv,
downsample=down_op if stage_stride == 2 else '',
drop_path=drop_path_rate
)]
stride *= stage_stride
resolution = tuple([(r - 1) // stage_stride + 1 for r in resolution])
self.feature_info += [dict(num_chs=embed_dim[i], reduction=stride, module=f'stages.{i}')]
in_dim = embed_dim[i]
self.stages = nn.Sequential(*stages)
# Classifier head
self.head = NormLinear(embed_dim[-1], num_classes, drop=drop_rate) if num_classes > 0 else nn.Identity()
@torch.jit.ignore
def no_weight_decay(self):
return {x for x in self.state_dict().keys() if 'attention_biases' in x}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None, distillation=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = NormLinear(
self.embed_dim[-1], num_classes, drop=self.drop_rate) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.stem(x)
if not self.use_conv:
x = x.flatten(2).transpose(1, 2)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool == 'avg':
x = x.mean(dim=(-2, -1)) if self.use_conv else x.mean(dim=1)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
class LevitDistilled(Levit):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.head_dist = NormLinear(self.num_features, self.num_classes) if self.num_classes > 0 else nn.Identity()
self.distilled_training = False # must set this True to train w/ distillation token
@torch.jit.ignore
def get_classifier(self):
return self.head, self.head_dist
def reset_classifier(self, num_classes, global_pool=None, distillation=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = NormLinear(
self.num_features, num_classes, drop=self.drop_rate) if num_classes > 0 else nn.Identity()
self.head_dist = NormLinear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
@torch.jit.ignore
def set_distilled_training(self, enable=True):
self.distilled_training = enable
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool == 'avg':
x = x.mean(dim=(-2, -1)) if self.use_conv else x.mean(dim=1)
if pre_logits:
return x
x, x_dist = self.head(x), self.head_dist(x)
if self.distilled_training and self.training and not torch.jit.is_scripting():
# only return separate classification predictions when training in distilled mode
return x, x_dist
else:
# during standard train/finetune, inference average the classifier predictions
return (x + x_dist) / 2
def checkpoint_filter_fn(state_dict, model):
if 'model' in state_dict:
state_dict = state_dict['model']
# filter out attn biases, should not have been persistent
state_dict = {k: v for k, v in state_dict.items() if 'attention_bias_idxs' not in k}
D = model.state_dict()
out_dict = {}
for ka, kb, va, vb in zip(D.keys(), state_dict.keys(), D.values(), state_dict.values()):
if va.ndim == 4 and vb.ndim == 2:
vb = vb[:, :, None, None]
if va.shape != vb.shape:
# head or first-conv shapes may change for fine-tune
assert 'head' in ka or 'stem.conv1.linear' in ka
out_dict[ka] = vb
return out_dict
model_cfgs = dict(
levit_128s=dict(
embed_dim=(128, 256, 384), key_dim=16, num_heads=(4, 6, 8), depth=(2, 3, 4)),
levit_128=dict(
embed_dim=(128, 256, 384), key_dim=16, num_heads=(4, 8, 12), depth=(4, 4, 4)),
levit_192=dict(
embed_dim=(192, 288, 384), key_dim=32, num_heads=(3, 5, 6), depth=(4, 4, 4)),
levit_256=dict(
embed_dim=(256, 384, 512), key_dim=32, num_heads=(4, 6, 8), depth=(4, 4, 4)),
levit_384=dict(
embed_dim=(384, 512, 768), key_dim=32, num_heads=(6, 9, 12), depth=(4, 4, 4)),
# stride-8 stem experiments
levit_384_s8=dict(
embed_dim=(384, 512, 768), key_dim=32, num_heads=(6, 9, 12), depth=(4, 4, 4),
act_layer='silu', stem_type='s8'),
levit_512_s8=dict(
embed_dim=(512, 640, 896), key_dim=64, num_heads=(8, 10, 14), depth=(4, 4, 4),
act_layer='silu', stem_type='s8'),
# wider experiments
levit_512=dict(
embed_dim=(512, 768, 1024), key_dim=64, num_heads=(8, 12, 16), depth=(4, 4, 4), act_layer='silu'),
# deeper experiments
levit_256d=dict(
embed_dim=(256, 384, 512), key_dim=32, num_heads=(4, 6, 8), depth=(4, 8, 6), act_layer='silu'),
levit_512d=dict(
embed_dim=(512, 640, 768), key_dim=64, num_heads=(8, 10, 12), depth=(4, 8, 6), act_layer='silu'),
)
def create_levit(variant, cfg_variant=None, pretrained=False, distilled=True, **kwargs):
is_conv = '_conv' in variant
out_indices = kwargs.pop('out_indices', (0, 1, 2))
if kwargs.get('features_only', None):
if not is_conv:
raise RuntimeError('features_only not implemented for LeVit in non-convolutional mode.')
if cfg_variant is None:
if variant in model_cfgs:
cfg_variant = variant
elif is_conv:
cfg_variant = variant.replace('_conv', '')
model_cfg = dict(model_cfgs[cfg_variant], **kwargs)
model = build_model_with_cfg(
LevitDistilled if distilled else Levit,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**model_cfg,
)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1.linear', 'classifier': ('head.linear', 'head_dist.linear'),
**kwargs
}
default_cfgs = generate_default_cfgs({
# weights in nn.Linear mode
'levit_128s.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
),
'levit_128.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
),
'levit_192.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
),
'levit_256.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
),
'levit_384.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
),
# weights in nn.Conv2d mode
'levit_conv_128s.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
pool_size=(4, 4),
),
'levit_conv_128.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
pool_size=(4, 4),
),
'levit_conv_192.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
pool_size=(4, 4),
),
'levit_conv_256.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
pool_size=(4, 4),
),
'levit_conv_384.fb_dist_in1k': _cfg(
hf_hub_id='timm/',
pool_size=(4, 4),
),
'levit_384_s8.untrained': _cfg(classifier='head.linear'),
'levit_512_s8.untrained': _cfg(classifier='head.linear'),
'levit_512.untrained': _cfg(classifier='head.linear'),
'levit_256d.untrained': _cfg(classifier='head.linear'),
'levit_512d.untrained': _cfg(classifier='head.linear'),
'levit_conv_384_s8.untrained': _cfg(classifier='head.linear'),
'levit_conv_512_s8.untrained': _cfg(classifier='head.linear'),
'levit_conv_512.untrained': _cfg(classifier='head.linear'),
'levit_conv_256d.untrained': _cfg(classifier='head.linear'),
'levit_conv_512d.untrained': _cfg(classifier='head.linear'),
})
@register_model
def levit_128s(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_128s', pretrained=pretrained, **kwargs)
@register_model
def levit_128(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_128', pretrained=pretrained, **kwargs)
@register_model
def levit_192(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_192', pretrained=pretrained, **kwargs)
@register_model
def levit_256(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_256', pretrained=pretrained, **kwargs)
@register_model
def levit_384(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_384', pretrained=pretrained, **kwargs)
@register_model
def levit_384_s8(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_384_s8', pretrained=pretrained, **kwargs)
@register_model
def levit_512_s8(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_512_s8', pretrained=pretrained, distilled=False, **kwargs)
@register_model
def levit_512(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_512', pretrained=pretrained, distilled=False, **kwargs)
@register_model
def levit_256d(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_256d', pretrained=pretrained, distilled=False, **kwargs)
@register_model
def levit_512d(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_512d', pretrained=pretrained, distilled=False, **kwargs)
@register_model
def levit_conv_128s(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_128s', pretrained=pretrained, use_conv=True, **kwargs)
@register_model
def levit_conv_128(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_128', pretrained=pretrained, use_conv=True, **kwargs)
@register_model
def levit_conv_192(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_192', pretrained=pretrained, use_conv=True, **kwargs)
@register_model
def levit_conv_256(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_256', pretrained=pretrained, use_conv=True, **kwargs)
@register_model
def levit_conv_384(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_384', pretrained=pretrained, use_conv=True, **kwargs)
@register_model
def levit_conv_384_s8(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_384_s8', pretrained=pretrained, use_conv=True, **kwargs)
@register_model
def levit_conv_512_s8(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_512_s8', pretrained=pretrained, use_conv=True, distilled=False, **kwargs)
@register_model
def levit_conv_512(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_512', pretrained=pretrained, use_conv=True, distilled=False, **kwargs)
@register_model
def levit_conv_256d(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_256d', pretrained=pretrained, use_conv=True, distilled=False, **kwargs)
@register_model
def levit_conv_512d(pretrained=False, **kwargs) -> Levit:
return create_levit('levit_conv_512d', pretrained=pretrained, use_conv=True, distilled=False, **kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/beit.py
|
""" BEiT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
Model from official source: https://github.com/microsoft/unilm/tree/master/beit
@inproceedings{beit,
title={{BEiT}: {BERT} Pre-Training of Image Transformers},
author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=p-BhZSz59o4}
}
BEiT-v2 from https://github.com/microsoft/unilm/tree/master/beit2
@article{beitv2,
title={{BEiT v2}: Masked Image Modeling with Vector-Quantized Visual Tokenizers},
author={Zhiliang Peng and Li Dong and Hangbo Bao and Qixiang Ye and Furu Wei},
year={2022},
eprint={2208.06366},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
At this point only the 1k fine-tuned classification weights and model configs have been added,
see original source above for pre-training models and procedure.
Modifications by / Copyright 2021 Ross Wightman, original copyrights below
"""
# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# Based on timm and DeiT code bases
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit/
# https://github.com/facebookresearch/dino
# --------------------------------------------------------'
import math
from typing import Callable, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import PatchEmbed, Mlp, SwiGLU, LayerNorm, DropPath, trunc_normal_, use_fused_attn
from timm.layers import resample_patch_embed, resample_abs_pos_embed, resize_rel_pos_bias_table
from ._builder import build_model_with_cfg
from ._registry import generate_default_cfgs, register_model
from .vision_transformer import checkpoint_filter_fn
__all__ = ['Beit']
def gen_relative_position_index(window_size: Tuple[int, int]) -> torch.Tensor:
num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
window_area = window_size[0] * window_size[1]
coords = torch.stack(torch.meshgrid(
[torch.arange(window_size[0]),
torch.arange(window_size[1])])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = torch.zeros(size=(window_area + 1,) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = num_relative_distance - 3
relative_position_index[0:, 0] = num_relative_distance - 2
relative_position_index[0, 0] = num_relative_distance - 1
return relative_position_index
class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
attn_drop: float = 0.,
proj_drop: float = 0.,
window_size: Optional[Tuple[int, int]] = None,
attn_head_dim: Optional[int] = None,
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
if attn_head_dim is not None:
head_dim = attn_head_dim
all_head_dim = head_dim * self.num_heads
self.scale = head_dim ** -0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
if qkv_bias:
self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
self.register_buffer('k_bias', torch.zeros(all_head_dim), persistent=False)
self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
else:
self.q_bias = None
self.k_bias = None
self.v_bias = None
if window_size:
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn.Parameter(
torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
self.register_buffer("relative_position_index", gen_relative_position_index(window_size), persistent=False)
else:
self.window_size = None
self.relative_position_bias_table = None
self.relative_position_index = None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(all_head_dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def _get_rel_pos_bias(self):
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1] + 1,
self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
return relative_position_bias.unsqueeze(0)
def forward(self, x, shared_rel_pos_bias: Optional[torch.Tensor] = None):
B, N, C = x.shape
qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) if self.q_bias is not None else None
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0) # B, num_heads, N, head_dim
if self.fused_attn:
rel_pos_bias = None
if self.relative_position_bias_table is not None:
rel_pos_bias = self._get_rel_pos_bias()
if shared_rel_pos_bias is not None:
rel_pos_bias = rel_pos_bias + shared_rel_pos_bias
elif shared_rel_pos_bias is not None:
rel_pos_bias = shared_rel_pos_bias
x = F.scaled_dot_product_attention(
q, k, v,
attn_mask=rel_pos_bias,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
if self.relative_position_bias_table is not None:
attn = attn + self._get_rel_pos_bias()
if shared_rel_pos_bias is not None:
attn = attn + shared_rel_pos_bias
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
qkv_bias: bool = False,
mlp_ratio: float = 4.,
scale_mlp: bool = False,
swiglu_mlp: bool = False,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
init_values: Optional[float] = None,
act_layer: Callable = nn.GELU,
norm_layer: Callable = LayerNorm,
window_size: Optional[Tuple[int, int]] = None,
attn_head_dim: Optional[int] = None,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
window_size=window_size,
attn_head_dim=attn_head_dim,
)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
if swiglu_mlp:
self.mlp = SwiGLU(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
norm_layer=norm_layer if scale_mlp else None,
drop=proj_drop,
)
else:
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
norm_layer=norm_layer if scale_mlp else None,
drop=proj_drop,
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
if init_values:
self.gamma_1 = nn.Parameter(init_values * torch.ones(dim))
self.gamma_2 = nn.Parameter(init_values * torch.ones(dim))
else:
self.gamma_1, self.gamma_2 = None, None
def forward(self, x, shared_rel_pos_bias: Optional[torch.Tensor] = None):
if self.gamma_1 is None:
x = x + self.drop_path1(self.attn(self.norm1(x), shared_rel_pos_bias=shared_rel_pos_bias))
x = x + self.drop_path2(self.mlp(self.norm2(x)))
else:
x = x + self.drop_path1(self.gamma_1 * self.attn(self.norm1(x), shared_rel_pos_bias=shared_rel_pos_bias))
x = x + self.drop_path2(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class RelativePositionBias(nn.Module):
def __init__(self, window_size, num_heads):
super().__init__()
self.window_size = window_size
self.window_area = window_size[0] * window_size[1]
num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads))
# trunc_normal_(self.relative_position_bias_table, std=.02)
self.register_buffer("relative_position_index", gen_relative_position_index(window_size))
def forward(self):
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_area + 1, self.window_area + 1, -1) # Wh*Ww,Wh*Ww,nH
return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
class Beit(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(
self,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = 'avg',
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
qkv_bias: bool = True,
mlp_ratio: float = 4.,
swiglu_mlp: bool = False,
scale_mlp: bool = False,
drop_rate: float = 0.,
pos_drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
norm_layer: Callable = LayerNorm,
init_values: Optional[float] = None,
use_abs_pos_emb: bool = True,
use_rel_pos_bias: bool = False,
use_shared_rel_pos_bias: bool = False,
head_init_scale: float = 0.001,
):
super().__init__()
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.num_prefix_tokens = 1
self.grad_checkpointing = False
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
# self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) if use_abs_pos_emb else None
self.pos_drop = nn.Dropout(p=pos_drop_rate)
if use_shared_rel_pos_bias:
self.rel_pos_bias = RelativePositionBias(
window_size=self.patch_embed.grid_size,
num_heads=num_heads,
)
else:
self.rel_pos_bias = None
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
mlp_ratio=mlp_ratio,
scale_mlp=scale_mlp,
swiglu_mlp=swiglu_mlp,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
init_values=init_values,
window_size=self.patch_embed.grid_size if use_rel_pos_bias else None,
)
for i in range(depth)])
use_fc_norm = self.global_pool == 'avg'
self.norm = nn.Identity() if use_fc_norm else norm_layer(embed_dim)
self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
self.fix_init_weight()
if isinstance(self.head, nn.Linear):
trunc_normal_(self.head.weight, std=.02)
self.head.weight.data.mul_(head_init_scale)
self.head.bias.data.mul_(head_init_scale)
def fix_init_weight(self):
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.blocks):
rescale(layer.attn.proj.weight.data, layer_id + 1)
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
nwd = {'pos_embed', 'cls_token'}
for n, _ in self.named_parameters():
if 'relative_position_bias_table' in n:
nwd.add(n)
return nwd
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^cls_token|pos_embed|patch_embed|rel_pos_bias', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))],
)
return matcher
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.patch_embed(x)
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
if self.pos_embed is not None:
x = x + self.pos_embed
x = self.pos_drop(x)
rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(blk, x, shared_rel_pos_bias=rel_pos_bias)
else:
x = blk(x, shared_rel_pos_bias=rel_pos_bias)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = self.fc_norm(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = generate_default_cfgs({
'beit_base_patch16_224.in22k_ft_in22k_in1k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
hf_hub_id='timm/'),
'beit_base_patch16_384.in22k_ft_in22k_in1k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_384_pt22k_ft22kto1k.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0,
),
'beit_base_patch16_224.in22k_ft_in22k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22k.pth',
hf_hub_id='timm/',
num_classes=21841,
),
'beit_large_patch16_224.in22k_ft_in22k_in1k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth',
hf_hub_id='timm/'),
'beit_large_patch16_384.in22k_ft_in22k_in1k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_384_pt22k_ft22kto1k.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0,
),
'beit_large_patch16_512.in22k_ft_in22k_in1k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_512_pt22k_ft22kto1k.pth',
hf_hub_id='timm/',
input_size=(3, 512, 512), crop_pct=1.0,
),
'beit_large_patch16_224.in22k_ft_in22k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth',
hf_hub_id='timm/',
num_classes=21841,
),
'beitv2_base_patch16_224.in1k_ft_in22k_in1k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21kto1k.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
'beitv2_base_patch16_224.in1k_ft_in1k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft1k.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
'beitv2_base_patch16_224.in1k_ft_in22k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21k.pth',
hf_hub_id='timm/',
num_classes=21841, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
'beitv2_large_patch16_224.in1k_ft_in22k_in1k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21kto1k.pth',
hf_hub_id='timm/',
crop_pct=0.95, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
'beitv2_large_patch16_224.in1k_ft_in1k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft1k.pth',
hf_hub_id='timm/',
crop_pct=0.95, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
'beitv2_large_patch16_224.in1k_ft_in22k': _cfg(
#url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21k.pth',
hf_hub_id='timm/',
num_classes=21841, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
),
})
def _beit_checkpoint_filter_fn(state_dict, model, interpolation='bicubic', antialias=True):
state_dict = state_dict.get('model', state_dict)
state_dict = state_dict.get('module', state_dict)
# beit v2 didn't strip module
out_dict = {}
for k, v in state_dict.items():
if 'relative_position_index' in k:
continue
if 'patch_embed.proj.weight' in k:
O, I, H, W = model.patch_embed.proj.weight.shape
if v.shape[-1] != W or v.shape[-2] != H:
v = resample_patch_embed(
v,
(H, W),
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
# To resize pos embedding when using model at different size from pretrained weights
num_prefix_tokens = 1
v = resample_abs_pos_embed(
v,
new_size=model.patch_embed.grid_size,
num_prefix_tokens=num_prefix_tokens,
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
elif k.endswith('relative_position_bias_table'):
m = model.get_submodule(k[:-29])
if v.shape != m.relative_position_bias_table.shape or m.window_size[0] != m.window_size[1]:
v = resize_rel_pos_bias_table(
v,
new_window_size=m.window_size,
new_bias_shape=m.relative_position_bias_table.shape,
)
out_dict[k] = v
return out_dict
def _create_beit(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for BEiT models.')
model = build_model_with_cfg(
Beit, variant, pretrained,
# FIXME an updated filter fn needed to interpolate rel pos emb if fine tuning to diff model sizes
pretrained_filter_fn=_beit_checkpoint_filter_fn,
**kwargs)
return model
@register_model
def beit_base_patch16_224(pretrained=False, **kwargs) -> Beit:
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1)
model = _create_beit('beit_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def beit_base_patch16_384(pretrained=False, **kwargs) -> Beit:
model_args = dict(
img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1)
model = _create_beit('beit_base_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def beit_large_patch16_224(pretrained=False, **kwargs) -> Beit:
model_args = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
model = _create_beit('beit_large_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def beit_large_patch16_384(pretrained=False, **kwargs) -> Beit:
model_args = dict(
img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
model = _create_beit('beit_large_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def beit_large_patch16_512(pretrained=False, **kwargs) -> Beit:
model_args = dict(
img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
model = _create_beit('beit_large_patch16_512', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def beitv2_base_patch16_224(pretrained=False, **kwargs) -> Beit:
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
model = _create_beit('beitv2_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def beitv2_large_patch16_224(pretrained=False, **kwargs) -> Beit:
model_args = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
model = _create_beit('beitv2_large_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/resnet.py
|
"""PyTorch ResNet
This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2019, Ross Wightman
"""
import math
from functools import partial
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, GroupNorm, LayerType, create_attn, \
get_attn, get_act_layer, get_norm_layer, create_classifier
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
__all__ = ['ResNet', 'BasicBlock', 'Bottleneck'] # model_registry will add each entrypoint fn to this
def get_padding(kernel_size: int, stride: int, dilation: int = 1) -> int:
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
def create_aa(aa_layer: Type[nn.Module], channels: int, stride: int = 2, enable: bool = True) -> nn.Module:
if not aa_layer or not enable:
return nn.Identity()
if issubclass(aa_layer, nn.AvgPool2d):
return aa_layer(stride)
else:
return aa_layer(channels=channels, stride=stride)
class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
cardinality: int = 1,
base_width: int = 64,
reduce_first: int = 1,
dilation: int = 1,
first_dilation: Optional[int] = None,
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
attn_layer: Optional[Type[nn.Module]] = None,
aa_layer: Optional[Type[nn.Module]] = None,
drop_block: Optional[Type[nn.Module]] = None,
drop_path: Optional[nn.Module] = None,
):
"""
Args:
inplanes: Input channel dimensionality.
planes: Used to determine output channel dimensionalities.
stride: Stride used in convolution layers.
downsample: Optional downsample layer for residual path.
cardinality: Number of convolution groups.
base_width: Base width used to determine output channel dimensionality.
reduce_first: Reduction factor for first convolution output width of residual blocks.
dilation: Dilation rate for convolution layers.
first_dilation: Dilation rate for first convolution layer.
act_layer: Activation layer.
norm_layer: Normalization layer.
attn_layer: Attention layer.
aa_layer: Anti-aliasing layer.
drop_block: Class for DropBlock layer.
drop_path: Optional DropPath layer.
"""
super(BasicBlock, self).__init__()
assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
assert base_width == 64, 'BasicBlock does not support changing base width'
first_planes = planes // reduce_first
outplanes = planes * self.expansion
first_dilation = first_dilation or dilation
use_aa = aa_layer is not None and (stride == 2 or first_dilation != dilation)
self.conv1 = nn.Conv2d(
inplanes, first_planes, kernel_size=3, stride=1 if use_aa else stride, padding=first_dilation,
dilation=first_dilation, bias=False)
self.bn1 = norm_layer(first_planes)
self.drop_block = drop_block() if drop_block is not None else nn.Identity()
self.act1 = act_layer(inplace=True)
self.aa = create_aa(aa_layer, channels=first_planes, stride=stride, enable=use_aa)
self.conv2 = nn.Conv2d(
first_planes, outplanes, kernel_size=3, padding=dilation, dilation=dilation, bias=False)
self.bn2 = norm_layer(outplanes)
self.se = create_attn(attn_layer, outplanes)
self.act2 = act_layer(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
self.drop_path = drop_path
def zero_init_last(self):
if getattr(self.bn2, 'weight', None) is not None:
nn.init.zeros_(self.bn2.weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.drop_block(x)
x = self.act1(x)
x = self.aa(x)
x = self.conv2(x)
x = self.bn2(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
x += shortcut
x = self.act2(x)
return x
class Bottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
cardinality: int = 1,
base_width: int = 64,
reduce_first: int = 1,
dilation: int = 1,
first_dilation: Optional[int] = None,
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
attn_layer: Optional[Type[nn.Module]] = None,
aa_layer: Optional[Type[nn.Module]] = None,
drop_block: Optional[Type[nn.Module]] = None,
drop_path: Optional[nn.Module] = None,
):
"""
Args:
inplanes: Input channel dimensionality.
planes: Used to determine output channel dimensionalities.
stride: Stride used in convolution layers.
downsample: Optional downsample layer for residual path.
cardinality: Number of convolution groups.
base_width: Base width used to determine output channel dimensionality.
reduce_first: Reduction factor for first convolution output width of residual blocks.
dilation: Dilation rate for convolution layers.
first_dilation: Dilation rate for first convolution layer.
act_layer: Activation layer.
norm_layer: Normalization layer.
attn_layer: Attention layer.
aa_layer: Anti-aliasing layer.
drop_block: Class for DropBlock layer.
drop_path: Optional DropPath layer.
"""
super(Bottleneck, self).__init__()
width = int(math.floor(planes * (base_width / 64)) * cardinality)
first_planes = width // reduce_first
outplanes = planes * self.expansion
first_dilation = first_dilation or dilation
use_aa = aa_layer is not None and (stride == 2 or first_dilation != dilation)
self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False)
self.bn1 = norm_layer(first_planes)
self.act1 = act_layer(inplace=True)
self.conv2 = nn.Conv2d(
first_planes, width, kernel_size=3, stride=1 if use_aa else stride,
padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False)
self.bn2 = norm_layer(width)
self.drop_block = drop_block() if drop_block is not None else nn.Identity()
self.act2 = act_layer(inplace=True)
self.aa = create_aa(aa_layer, channels=width, stride=stride, enable=use_aa)
self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
self.bn3 = norm_layer(outplanes)
self.se = create_attn(attn_layer, outplanes)
self.act3 = act_layer(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
self.drop_path = drop_path
def zero_init_last(self):
if getattr(self.bn3, 'weight', None) is not None:
nn.init.zeros_(self.bn3.weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.drop_block(x)
x = self.act2(x)
x = self.aa(x)
x = self.conv3(x)
x = self.bn3(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
x += shortcut
x = self.act3(x)
return x
def downsample_conv(
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
dilation: int = 1,
first_dilation: Optional[int] = None,
norm_layer: Optional[Type[nn.Module]] = None,
) -> nn.Module:
norm_layer = norm_layer or nn.BatchNorm2d
kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
first_dilation = (first_dilation or dilation) if kernel_size > 1 else 1
p = get_padding(kernel_size, stride, first_dilation)
return nn.Sequential(*[
nn.Conv2d(
in_channels, out_channels, kernel_size, stride=stride, padding=p, dilation=first_dilation, bias=False),
norm_layer(out_channels)
])
def downsample_avg(
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
dilation: int = 1,
first_dilation: Optional[int] = None,
norm_layer: Optional[Type[nn.Module]] = None,
) -> nn.Module:
norm_layer = norm_layer or nn.BatchNorm2d
avg_stride = stride if dilation == 1 else 1
if stride == 1 and dilation == 1:
pool = nn.Identity()
else:
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
return nn.Sequential(*[
pool,
nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False),
norm_layer(out_channels)
])
def drop_blocks(drop_prob: float = 0.):
return [
None, None,
partial(DropBlock2d, drop_prob=drop_prob, block_size=5, gamma_scale=0.25) if drop_prob else None,
partial(DropBlock2d, drop_prob=drop_prob, block_size=3, gamma_scale=1.00) if drop_prob else None]
def make_blocks(
block_fn: Union[BasicBlock, Bottleneck],
channels: List[int],
block_repeats: List[int],
inplanes: int,
reduce_first: int = 1,
output_stride: int = 32,
down_kernel_size: int = 1,
avg_down: bool = False,
drop_block_rate: float = 0.,
drop_path_rate: float = 0.,
**kwargs,
) -> Tuple[List[Tuple[str, nn.Module]], List[Dict[str, Any]]]:
stages = []
feature_info = []
net_num_blocks = sum(block_repeats)
net_block_idx = 0
net_stride = 4
dilation = prev_dilation = 1
for stage_idx, (planes, num_blocks, db) in enumerate(zip(channels, block_repeats, drop_blocks(drop_block_rate))):
stage_name = f'layer{stage_idx + 1}' # never liked this name, but weight compat requires it
stride = 1 if stage_idx == 0 else 2
if net_stride >= output_stride:
dilation *= stride
stride = 1
else:
net_stride *= stride
downsample = None
if stride != 1 or inplanes != planes * block_fn.expansion:
down_kwargs = dict(
in_channels=inplanes,
out_channels=planes * block_fn.expansion,
kernel_size=down_kernel_size,
stride=stride,
dilation=dilation,
first_dilation=prev_dilation,
norm_layer=kwargs.get('norm_layer'),
)
downsample = downsample_avg(**down_kwargs) if avg_down else downsample_conv(**down_kwargs)
block_kwargs = dict(reduce_first=reduce_first, dilation=dilation, drop_block=db, **kwargs)
blocks = []
for block_idx in range(num_blocks):
downsample = downsample if block_idx == 0 else None
stride = stride if block_idx == 0 else 1
block_dpr = drop_path_rate * net_block_idx / (net_num_blocks - 1) # stochastic depth linear decay rule
blocks.append(block_fn(
inplanes,
planes,
stride,
downsample,
first_dilation=prev_dilation,
drop_path=DropPath(block_dpr) if block_dpr > 0. else None,
**block_kwargs,
))
prev_dilation = dilation
inplanes = planes * block_fn.expansion
net_block_idx += 1
stages.append((stage_name, nn.Sequential(*blocks)))
feature_info.append(dict(num_chs=inplanes, reduction=net_stride, module=stage_name))
return stages, feature_info
class ResNet(nn.Module):
"""ResNet / ResNeXt / SE-ResNeXt / SE-Net
This class implements all variants of ResNet, ResNeXt, SE-ResNeXt, and SENet that
* have > 1 stride in the 3x3 conv layer of bottleneck
* have conv-bn-act ordering
This ResNet impl supports a number of stem and downsample options based on the v1c, v1d, v1e, and v1s
variants included in the MXNet Gluon ResNetV1b model. The C and D variants are also discussed in the
'Bag of Tricks' paper: https://arxiv.org/pdf/1812.01187. The B variant is equivalent to torchvision default.
ResNet variants (the same modifications can be used in SE/ResNeXt models as well):
* normal, b - 7x7 stem, stem_width = 64, same as torchvision ResNet, NVIDIA ResNet 'v1.5', Gluon v1b
* c - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64)
* d - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64), average pool in downsample
* e - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128), average pool in downsample
* s - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128)
* t - 3 layer deep 3x3 stem, stem width = 32 (24, 48, 64), average pool in downsample
* tn - 3 layer deep 3x3 stem, stem width = 32 (24, 32, 64), average pool in downsample
ResNeXt
* normal - 7x7 stem, stem_width = 64, standard cardinality and base widths
* same c,d, e, s variants as ResNet can be enabled
SE-ResNeXt
* normal - 7x7 stem, stem_width = 64
* same c, d, e, s variants as ResNet can be enabled
SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64,
reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
"""
def __init__(
self,
block: Union[BasicBlock, Bottleneck],
layers: List[int],
num_classes: int = 1000,
in_chans: int = 3,
output_stride: int = 32,
global_pool: str = 'avg',
cardinality: int = 1,
base_width: int = 64,
stem_width: int = 64,
stem_type: str = '',
replace_stem_pool: bool = False,
block_reduce_first: int = 1,
down_kernel_size: int = 1,
avg_down: bool = False,
act_layer: LayerType = nn.ReLU,
norm_layer: LayerType = nn.BatchNorm2d,
aa_layer: Optional[Type[nn.Module]] = None,
drop_rate: float = 0.0,
drop_path_rate: float = 0.,
drop_block_rate: float = 0.,
zero_init_last: bool = True,
block_args: Optional[Dict[str, Any]] = None,
):
"""
Args:
block (nn.Module): class for the residual block. Options are BasicBlock, Bottleneck.
layers (List[int]) : number of layers in each block
num_classes (int): number of classification classes (default 1000)
in_chans (int): number of input (color) channels. (default 3)
output_stride (int): output stride of the network, 32, 16, or 8. (default 32)
global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg')
cardinality (int): number of convolution groups for 3x3 conv in Bottleneck. (default 1)
base_width (int): bottleneck channels factor. `planes * base_width / 64 * cardinality` (default 64)
stem_width (int): number of channels in stem convolutions (default 64)
stem_type (str): The type of stem (default ''):
* '', default - a single 7x7 conv with a width of stem_width
* 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2
* 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
replace_stem_pool (bool): replace stem max-pooling layer with a 3x3 stride-2 convolution
block_reduce_first (int): Reduction factor for first convolution output width of residual blocks,
1 for all archs except senets, where 2 (default 1)
down_kernel_size (int): kernel size of residual block downsample path,
1x1 for most, 3x3 for senets (default: 1)
avg_down (bool): use avg pooling for projection skip connection between stages/downsample (default False)
act_layer (str, nn.Module): activation layer
norm_layer (str, nn.Module): normalization layer
aa_layer (nn.Module): anti-aliasing layer
drop_rate (float): Dropout probability before classifier, for training (default 0.)
drop_path_rate (float): Stochastic depth drop-path rate (default 0.)
drop_block_rate (float): Drop block rate (default 0.)
zero_init_last (bool): zero-init the last weight in residual path (usually last BN affine weight)
block_args (dict): Extra kwargs to pass through to block module
"""
super(ResNet, self).__init__()
block_args = block_args or dict()
assert output_stride in (8, 16, 32)
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
act_layer = get_act_layer(act_layer)
norm_layer = get_norm_layer(norm_layer)
# Stem
deep_stem = 'deep' in stem_type
inplanes = stem_width * 2 if deep_stem else 64
if deep_stem:
stem_chs = (stem_width, stem_width)
if 'tiered' in stem_type:
stem_chs = (3 * (stem_width // 4), stem_width)
self.conv1 = nn.Sequential(*[
nn.Conv2d(in_chans, stem_chs[0], 3, stride=2, padding=1, bias=False),
norm_layer(stem_chs[0]),
act_layer(inplace=True),
nn.Conv2d(stem_chs[0], stem_chs[1], 3, stride=1, padding=1, bias=False),
norm_layer(stem_chs[1]),
act_layer(inplace=True),
nn.Conv2d(stem_chs[1], inplanes, 3, stride=1, padding=1, bias=False)])
else:
self.conv1 = nn.Conv2d(in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = norm_layer(inplanes)
self.act1 = act_layer(inplace=True)
self.feature_info = [dict(num_chs=inplanes, reduction=2, module='act1')]
# Stem pooling. The name 'maxpool' remains for weight compatibility.
if replace_stem_pool:
self.maxpool = nn.Sequential(*filter(None, [
nn.Conv2d(inplanes, inplanes, 3, stride=1 if aa_layer else 2, padding=1, bias=False),
create_aa(aa_layer, channels=inplanes, stride=2) if aa_layer is not None else None,
norm_layer(inplanes),
act_layer(inplace=True),
]))
else:
if aa_layer is not None:
if issubclass(aa_layer, nn.AvgPool2d):
self.maxpool = aa_layer(2)
else:
self.maxpool = nn.Sequential(*[
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
aa_layer(channels=inplanes, stride=2)])
else:
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# Feature Blocks
channels = [64, 128, 256, 512]
stage_modules, stage_feature_info = make_blocks(
block,
channels,
layers,
inplanes,
cardinality=cardinality,
base_width=base_width,
output_stride=output_stride,
reduce_first=block_reduce_first,
avg_down=avg_down,
down_kernel_size=down_kernel_size,
act_layer=act_layer,
norm_layer=norm_layer,
aa_layer=aa_layer,
drop_block_rate=drop_block_rate,
drop_path_rate=drop_path_rate,
**block_args,
)
for stage in stage_modules:
self.add_module(*stage) # layer1, layer2, etc
self.feature_info.extend(stage_feature_info)
# Head (Pooling and Classifier)
self.num_features = 512 * block.expansion
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
self.init_weights(zero_init_last=zero_init_last)
@torch.jit.ignore
def init_weights(self, zero_init_last: bool = True):
for n, m in self.named_modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if zero_init_last:
for m in self.modules():
if hasattr(m, 'zero_init_last'):
m.zero_init_last()
@torch.jit.ignore
def group_matcher(self, coarse: bool = False):
matcher = dict(stem=r'^conv1|bn1|maxpool', blocks=r'^layer(\d+)' if coarse else r'^layer(\d+)\.(\d+)')
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable: bool = True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self, name_only: bool = False):
return 'fc' if name_only else self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.maxpool(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq([self.layer1, self.layer2, self.layer3, self.layer4], x, flatten=True)
else:
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
x = self.global_pool(x)
if self.drop_rate:
x = F.dropout(x, p=float(self.drop_rate), training=self.training)
return x if pre_logits else self.fc(x)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_resnet(variant, pretrained: bool = False, **kwargs) -> ResNet:
return build_model_with_cfg(ResNet, variant, pretrained, **kwargs)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1', 'classifier': 'fc',
**kwargs
}
def _tcfg(url='', **kwargs):
return _cfg(url=url, **dict({'interpolation': 'bicubic'}, **kwargs))
def _ttcfg(url='', **kwargs):
return _cfg(url=url, **dict({
'interpolation': 'bicubic', 'test_input_size': (3, 288, 288), 'test_crop_pct': 0.95,
'origin_url': 'https://github.com/huggingface/pytorch-image-models',
}, **kwargs))
def _rcfg(url='', **kwargs):
return _cfg(url=url, **dict({
'interpolation': 'bicubic', 'crop_pct': 0.95, 'test_input_size': (3, 288, 288), 'test_crop_pct': 1.0,
'origin_url': 'https://github.com/huggingface/pytorch-image-models', 'paper_ids': 'arXiv:2110.00476'
}, **kwargs))
def _r3cfg(url='', **kwargs):
return _cfg(url=url, **dict({
'interpolation': 'bicubic', 'input_size': (3, 160, 160), 'pool_size': (5, 5),
'crop_pct': 0.95, 'test_input_size': (3, 224, 224), 'test_crop_pct': 0.95,
'origin_url': 'https://github.com/huggingface/pytorch-image-models', 'paper_ids': 'arXiv:2110.00476',
}, **kwargs))
def _gcfg(url='', **kwargs):
return _cfg(url=url, **dict({
'interpolation': 'bicubic',
'origin_url': 'https://cv.gluon.ai/model_zoo/classification.html',
}, **kwargs))
default_cfgs = generate_default_cfgs({
# ResNet and Wide ResNet trained w/ timm (RSB paper and others)
'resnet10t.c3_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet10t_176_c3-f3215ab1.pth',
input_size=(3, 176, 176), pool_size=(6, 6), test_crop_pct=0.95, test_input_size=(3, 224, 224),
first_conv='conv1.0'),
'resnet14t.c3_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet14t_176_c3-c4ed2c37.pth',
input_size=(3, 176, 176), pool_size=(6, 6), test_crop_pct=0.95, test_input_size=(3, 224, 224),
first_conv='conv1.0'),
'resnet18.a1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet18_a1_0-d63eafa0.pth'),
'resnet18.a2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet18_a2_0-b61bd467.pth'),
'resnet18.a3_in1k': _r3cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet18_a3_0-40c531c8.pth'),
'resnet18d.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet18d_ra2-48a79e06.pth',
first_conv='conv1.0'),
'resnet34.a1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet34_a1_0-46f8f793.pth'),
'resnet34.a2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet34_a2_0-82d47d71.pth'),
'resnet34.a3_in1k': _r3cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet34_a3_0-a20cabb6.pth',
crop_pct=0.95),
'resnet34.bt_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth'),
'resnet34d.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34d_ra2-f8dcfcaf.pth',
first_conv='conv1.0'),
'resnet26.bt_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26-9aa10e23.pth'),
'resnet26d.bt_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth',
first_conv='conv1.0'),
'resnet26t.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet26t_256_ra2-6f6fa748.pth',
first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=0.94, test_input_size=(3, 320, 320), test_crop_pct=1.0),
'resnet50.a1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth'),
'resnet50.a1h_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth',
input_size=(3, 176, 176), pool_size=(6, 6), crop_pct=0.9, test_input_size=(3, 224, 224), test_crop_pct=1.0),
'resnet50.a2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a2_0-a2746f79.pth'),
'resnet50.a3_in1k': _r3cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a3_0-59cae1ef.pth'),
'resnet50.b1k_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_b1k-532a802a.pth'),
'resnet50.b2k_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_b2k-1ba180c1.pth'),
'resnet50.c1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_c1-5ba5e060.pth'),
'resnet50.c2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_c2-d01e05b2.pth'),
'resnet50.d_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_d-f39db8af.pth'),
'resnet50.ram_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/resnet50_ram-a26f946b.pth'),
'resnet50.am_in1k': _tcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/resnet50_am-6c502b37.pth'),
'resnet50.ra_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/resnet50_ra-85ebb6e5.pth'),
'resnet50.bt_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/rw_resnet50-86acaeed.pth'),
'resnet50d.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth',
first_conv='conv1.0'),
'resnet50d.a1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50d_a1_0-e20cff14.pth',
first_conv='conv1.0'),
'resnet50d.a2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50d_a2_0-a3adc64d.pth',
first_conv='conv1.0'),
'resnet50d.a3_in1k': _r3cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50d_a3_0-403fdfad.pth',
first_conv='conv1.0'),
'resnet50t.untrained': _ttcfg(first_conv='conv1.0'),
'resnet101.a1h_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a1h-36d3f2aa.pth'),
'resnet101.a1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a1_0-cdcb52a9.pth'),
'resnet101.a2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a2_0-6edb36c7.pth'),
'resnet101.a3_in1k': _r3cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a3_0-1db14157.pth'),
'resnet101d.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95,
test_crop_pct=1.0, test_input_size=(3, 320, 320)),
'resnet152.a1h_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet152_a1h-dc400468.pth'),
'resnet152.a1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet152_a1_0-2eee8a7a.pth'),
'resnet152.a2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet152_a2_0-b4c6978f.pth'),
'resnet152.a3_in1k': _r3cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet152_a3_0-134d4688.pth'),
'resnet152d.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95,
test_crop_pct=1.0, test_input_size=(3, 320, 320)),
'resnet200.untrained': _ttcfg(),
'resnet200d.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth',
first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95,
test_crop_pct=1.0, test_input_size=(3, 320, 320)),
'wide_resnet50_2.racm_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/wide_resnet50_racm-8234f177.pth'),
# torchvision resnet weights
'resnet18.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnet18-5c106cde.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnet34.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnet34-333f7ec4.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnet50.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnet50-19c8e357.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnet50.tv2_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnet50-11ad3fa6.pth',
input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnet101.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnet101.tv2_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnet101-cd907fc2.pth',
input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnet152.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnet152-b121ed2d.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnet152.tv2_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnet152-f82ba261.pth',
input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'wide_resnet50_2.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'wide_resnet50_2.tv2_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/wide_resnet50_2-9ba9bcbe.pth',
input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'wide_resnet101_2.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'wide_resnet101_2.tv2_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/wide_resnet101_2-d733dc28.pth',
input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
# ResNets w/ alternative norm layers
'resnet50_gn.a1h_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_gn_a1h2-8fe6c4d0.pth',
crop_pct=0.94),
# ResNeXt trained in timm (RSB paper and others)
'resnext50_32x4d.a1h_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a1h-0146ab0a.pth'),
'resnext50_32x4d.a1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a1_0-b5a91a1d.pth'),
'resnext50_32x4d.a2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a2_0-efc76add.pth'),
'resnext50_32x4d.a3_in1k': _r3cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a3_0-3e450271.pth'),
'resnext50_32x4d.ra_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d_ra-d733960d.pth'),
'resnext50d_32x4d.bt_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth',
first_conv='conv1.0'),
'resnext101_32x4d.untrained': _ttcfg(),
'resnext101_64x4d.c1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/resnext101_64x4d_c-0d0e0cc0.pth'),
# torchvision ResNeXt weights
'resnext50_32x4d.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnext101_32x8d.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnext101_64x4d.tv_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnext101_64x4d-173b62eb.pth',
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnext50_32x4d.tv2_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnext50_32x4d-1a0047aa.pth',
input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
'resnext101_32x8d.tv2_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/resnext101_32x8d-110c445d.pth',
input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
# ResNeXt models - Weakly Supervised Pretraining on Instagram Hashtags
# from https://github.com/facebookresearch/WSL-Images
# Please note the CC-BY-NC 4.0 license on these weights, non-commercial use only.
'resnext101_32x8d.fb_wsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/WSL-Images'),
'resnext101_32x16d.fb_wsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/WSL-Images'),
'resnext101_32x32d.fb_wsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/WSL-Images'),
'resnext101_32x48d.fb_wsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/WSL-Images'),
# Semi-Supervised ResNe*t models from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models
# Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
'resnet18.fb_ssl_yfcc100m_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnet50.fb_ssl_yfcc100m_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
# Semi-Weakly Supervised ResNe*t models from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models
# Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
'resnet18.fb_swsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnet50.fb_swsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnext50_32x4d.fb_swsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnext101_32x4d.fb_swsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnext101_32x8d.fb_swsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
'resnext101_32x16d.fb_swsl_ig1b_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth',
license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
# Efficient Channel Attention ResNets
'ecaresnet26t.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet26t_ra2-46609757.pth',
first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
test_crop_pct=0.95, test_input_size=(3, 320, 320)),
'ecaresnetlight.miil_in1k': _tcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnetlight-75a9c627.pth',
test_crop_pct=0.95, test_input_size=(3, 288, 288)),
'ecaresnet50d.miil_in1k': _tcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet50d-93c81e3b.pth',
first_conv='conv1.0', test_crop_pct=0.95, test_input_size=(3, 288, 288)),
'ecaresnet50d_pruned.miil_in1k': _tcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet50d_p-e4fa23c2.pth',
first_conv='conv1.0', test_crop_pct=0.95, test_input_size=(3, 288, 288)),
'ecaresnet50t.ra2_in1k': _tcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet50t_ra2-f7ac63c4.pth',
first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
test_crop_pct=0.95, test_input_size=(3, 320, 320)),
'ecaresnet50t.a1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/ecaresnet50t_a1_0-99bd76a8.pth',
first_conv='conv1.0'),
'ecaresnet50t.a2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/ecaresnet50t_a2_0-b1c7b745.pth',
first_conv='conv1.0'),
'ecaresnet50t.a3_in1k': _r3cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/ecaresnet50t_a3_0-8cc311f1.pth',
first_conv='conv1.0'),
'ecaresnet101d.miil_in1k': _tcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet101d-153dad65.pth',
first_conv='conv1.0', test_crop_pct=0.95, test_input_size=(3, 288, 288)),
'ecaresnet101d_pruned.miil_in1k': _tcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet101d_p-9e74cb91.pth',
first_conv='conv1.0', test_crop_pct=0.95, test_input_size=(3, 288, 288)),
'ecaresnet200d.untrained': _ttcfg(
first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.95, pool_size=(8, 8)),
'ecaresnet269d.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet269d_320_ra2-7baa55cb.pth',
first_conv='conv1.0', input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=0.95,
test_crop_pct=1.0, test_input_size=(3, 352, 352)),
# Efficient Channel Attention ResNeXts
'ecaresnext26t_32x4d.untrained': _tcfg(first_conv='conv1.0'),
'ecaresnext50t_32x4d.untrained': _tcfg(first_conv='conv1.0'),
# Squeeze-Excitation ResNets, to eventually replace the models in senet.py
'seresnet18.untrained': _ttcfg(),
'seresnet34.untrained': _ttcfg(),
'seresnet50.a1_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/seresnet50_a1_0-ffa00869.pth',
crop_pct=0.95),
'seresnet50.a2_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/seresnet50_a2_0-850de0d9.pth',
crop_pct=0.95),
'seresnet50.a3_in1k': _r3cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/seresnet50_a3_0-317ecd56.pth',
crop_pct=0.95),
'seresnet50.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth'),
'seresnet50t.untrained': _ttcfg(
first_conv='conv1.0'),
'seresnet101.untrained': _ttcfg(),
'seresnet152.untrained': _ttcfg(),
'seresnet152d.ra2_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95,
test_crop_pct=1.0, test_input_size=(3, 320, 320)
),
'seresnet200d.untrained': _ttcfg(
first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8)),
'seresnet269d.untrained': _ttcfg(
first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8)),
# Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py
'seresnext26d_32x4d.bt_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth',
first_conv='conv1.0'),
'seresnext26t_32x4d.bt_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth',
first_conv='conv1.0'),
'seresnext50_32x4d.racm_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext50_32x4d_racm-a304a460.pth'),
'seresnext101_32x4d.untrained': _ttcfg(),
'seresnext101_32x8d.ah_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnext101_32x8d_ah-e6bc4c0a.pth'),
'seresnext101d_32x8d.ah_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnext101d_32x8d_ah-191d7b94.pth',
first_conv='conv1.0'),
# ResNets with anti-aliasing / blur pool
'resnetaa50d.sw_in12k_ft_in1k': _ttcfg(
hf_hub_id='timm/',
first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
'resnetaa101d.sw_in12k_ft_in1k': _ttcfg(
hf_hub_id='timm/',
first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
'seresnextaa101d_32x8d.sw_in12k_ft_in1k_288': _ttcfg(
hf_hub_id='timm/',
crop_pct=0.95, input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), test_crop_pct=1.0,
first_conv='conv1.0'),
'seresnextaa101d_32x8d.sw_in12k_ft_in1k': _ttcfg(
hf_hub_id='timm/',
first_conv='conv1.0', test_crop_pct=1.0),
'seresnextaa201d_32x8d.sw_in12k_ft_in1k_384': _cfg(
hf_hub_id='timm/',
interpolation='bicubic', first_conv='conv1.0', pool_size=(12, 12), input_size=(3, 384, 384), crop_pct=1.0),
'seresnextaa201d_32x8d.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821, interpolation='bicubic', first_conv='conv1.0',
crop_pct=0.95, input_size=(3, 320, 320), pool_size=(10, 10), test_input_size=(3, 384, 384), test_crop_pct=1.0),
'resnetaa50d.sw_in12k': _ttcfg(
hf_hub_id='timm/',
num_classes=11821, first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
'resnetaa50d.d_in12k': _ttcfg(
hf_hub_id='timm/',
num_classes=11821, first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
'resnetaa101d.sw_in12k': _ttcfg(
hf_hub_id='timm/',
num_classes=11821, first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
'seresnextaa101d_32x8d.sw_in12k': _ttcfg(
hf_hub_id='timm/',
num_classes=11821, first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
'resnetblur18.untrained': _ttcfg(),
'resnetblur50.bt_in1k': _ttcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth'),
'resnetblur50d.untrained': _ttcfg(first_conv='conv1.0'),
'resnetblur101d.untrained': _ttcfg(first_conv='conv1.0'),
'resnetaa34d.untrained': _ttcfg(first_conv='conv1.0'),
'resnetaa50.a1h_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetaa50_a1h-4cf422b3.pth'),
'seresnetaa50d.untrained': _ttcfg(first_conv='conv1.0'),
'seresnextaa101d_32x8d.ah_in1k': _rcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnextaa101d_32x8d_ah-83c8ae12.pth',
first_conv='conv1.0'),
# ResNet-RS models
'resnetrs50.tf_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs50_ema-6b53758b.pth',
input_size=(3, 160, 160), pool_size=(5, 5), crop_pct=0.91, test_input_size=(3, 224, 224),
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs101.tf_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs101_i192_ema-1509bbf6.pth',
input_size=(3, 192, 192), pool_size=(6, 6), crop_pct=0.94, test_input_size=(3, 288, 288),
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs152.tf_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs152_i256_ema-a9aff7f9.pth',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320),
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs200.tf_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/resnetrs200_c-6b698b88.pth',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320),
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs270.tf_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs270_ema-b40e674c.pth',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 352, 352),
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs350.tf_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs350_i256_ema-5a1aa8f1.pth',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0, test_input_size=(3, 384, 384),
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs420.tf_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs420_ema-972dee69.pth',
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0, test_input_size=(3, 416, 416),
interpolation='bicubic', first_conv='conv1.0'),
# gluon resnet weights
'resnet18.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth'),
'resnet34.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth'),
'resnet50.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth'),
'resnet101.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth'),
'resnet152.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth'),
'resnet50c.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth',
first_conv='conv1.0'),
'resnet101c.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth',
first_conv='conv1.0'),
'resnet152c.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth',
first_conv='conv1.0'),
'resnet50d.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth',
first_conv='conv1.0'),
'resnet101d.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth',
first_conv='conv1.0'),
'resnet152d.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth',
first_conv='conv1.0'),
'resnet50s.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth',
first_conv='conv1.0'),
'resnet101s.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth',
first_conv='conv1.0'),
'resnet152s.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth',
first_conv='conv1.0'),
'resnext50_32x4d.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth'),
'resnext101_32x4d.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth'),
'resnext101_64x4d.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth'),
'seresnext50_32x4d.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth'),
'seresnext101_32x4d.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth'),
'seresnext101_64x4d.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth'),
'senet154.gluon_in1k': _gcfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth',
first_conv='conv1.0'),
})
@register_model
def resnet10t(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-10-T model.
"""
model_args = dict(block=BasicBlock, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True)
return _create_resnet('resnet10t', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet14t(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-14-T model.
"""
model_args = dict(block=Bottleneck, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True)
return _create_resnet('resnet14t', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet18(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-18 model.
"""
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2])
return _create_resnet('resnet18', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet18d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-18-D model.
"""
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet18d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet34(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-34 model.
"""
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3])
return _create_resnet('resnet34', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet34d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-34-D model.
"""
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet34d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet26(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-26 model.
"""
model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2])
return _create_resnet('resnet26', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet26t(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-26-T model.
"""
model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep_tiered', avg_down=True)
return _create_resnet('resnet26t', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet26d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-26-D model.
"""
model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet26d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet50(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50 model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3])
return _create_resnet('resnet50', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet50c(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-C model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep')
return _create_resnet('resnet50c', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet50d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet50d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet50s(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-S model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=64, stem_type='deep')
return _create_resnet('resnet50s', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet50t(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-T model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True)
return _create_resnet('resnet50t', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet101(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-101 model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3])
return _create_resnet('resnet101', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet101c(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-101-C model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep')
return _create_resnet('resnet101c', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet101d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-101-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet101d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet101s(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-101-S model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=64, stem_type='deep')
return _create_resnet('resnet101s', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet152(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-152 model.
"""
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3])
return _create_resnet('resnet152', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet152c(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-152-C model.
"""
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep')
return _create_resnet('resnet152c', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet152d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-152-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet152d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet152s(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-152-S model.
"""
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=64, stem_type='deep')
return _create_resnet('resnet152s', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet200(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-200 model.
"""
model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3])
return _create_resnet('resnet200', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet200d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-200-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnet200d', pretrained, **dict(model_args, **kwargs))
@register_model
def wide_resnet50_2(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a Wide ResNet-50-2 model.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], base_width=128)
return _create_resnet('wide_resnet50_2', pretrained, **dict(model_args, **kwargs))
@register_model
def wide_resnet101_2(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a Wide ResNet-101-2 model.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], base_width=128)
return _create_resnet('wide_resnet101_2', pretrained, **dict(model_args, **kwargs))
@register_model
def resnet50_gn(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50 model w/ GroupNorm
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], norm_layer='groupnorm')
return _create_resnet('resnet50_gn', pretrained, **dict(model_args, **kwargs))
@register_model
def resnext50_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNeXt50-32x4d model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4)
return _create_resnet('resnext50_32x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnext50d_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNeXt50d-32x4d model. ResNext50 w/ deep stem & avg pool downsample
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnext50d_32x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnext101_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNeXt-101 32x4d model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4)
return _create_resnet('resnext101_32x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnext101_32x8d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNeXt-101 32x8d model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8)
return _create_resnet('resnext101_32x8d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnext101_32x16d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNeXt-101 32x16d model
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16)
return _create_resnet('resnext101_32x16d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnext101_32x32d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNeXt-101 32x32d model
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=32)
return _create_resnet('resnext101_32x32d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnext101_64x4d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNeXt101-64x4d model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4)
return _create_resnet('resnext101_64x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def ecaresnet26t(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs an ECA-ResNeXt-26-T model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem and ECA attn.
"""
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32,
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnet26t', pretrained, **dict(model_args, **kwargs))
@register_model
def ecaresnet50d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-D model with eca.
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnet50d', pretrained, **dict(model_args, **kwargs))
@register_model
def ecaresnet50d_pruned(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-D model pruned with eca.
The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnet50d_pruned', pretrained, pruned=True, **dict(model_args, **kwargs))
@register_model
def ecaresnet50t(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs an ECA-ResNet-50-T model.
Like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem and ECA attn.
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32,
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnet50t', pretrained, **dict(model_args, **kwargs))
@register_model
def ecaresnetlight(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-D light model with eca.
"""
model_args = dict(
block=Bottleneck, layers=[1, 1, 11, 3], stem_width=32, avg_down=True,
block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnetlight', pretrained, **dict(model_args, **kwargs))
@register_model
def ecaresnet101d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-101-D model with eca.
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnet101d', pretrained, **dict(model_args, **kwargs))
@register_model
def ecaresnet101d_pruned(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-101-D model pruned with eca.
The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnet101d_pruned', pretrained, pruned=True, **dict(model_args, **kwargs))
@register_model
def ecaresnet200d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-200-D model with ECA.
"""
model_args = dict(
block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnet200d', pretrained, **dict(model_args, **kwargs))
@register_model
def ecaresnet269d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-269-D model with ECA.
"""
model_args = dict(
block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnet269d', pretrained, **dict(model_args, **kwargs))
@register_model
def ecaresnext26t_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs an ECA-ResNeXt-26-T model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem. This model replaces SE module with the ECA module
"""
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnext26t_32x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def ecaresnext50t_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs an ECA-ResNeXt-50-T model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem. This model replaces SE module with the ECA module
"""
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'))
return _create_resnet('ecaresnext50t_32x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnet18(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], block_args=dict(attn_layer='se'))
return _create_resnet('seresnet18', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnet34(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se'))
return _create_resnet('seresnet34', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnet50(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se'))
return _create_resnet('seresnet50', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnet50t(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered',
avg_down=True, block_args=dict(attn_layer='se'))
return _create_resnet('seresnet50t', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnet101(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], block_args=dict(attn_layer='se'))
return _create_resnet('seresnet101', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnet152(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], block_args=dict(attn_layer='se'))
return _create_resnet('seresnet152', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnet152d(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep',
avg_down=True, block_args=dict(attn_layer='se'))
return _create_resnet('seresnet152d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnet200d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-200-D model with SE attn.
"""
model_args = dict(
block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep',
avg_down=True, block_args=dict(attn_layer='se'))
return _create_resnet('seresnet200d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnet269d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-269-D model with SE attn.
"""
model_args = dict(
block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep',
avg_down=True, block_args=dict(attn_layer='se'))
return _create_resnet('seresnet269d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnext26d_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a SE-ResNeXt-26-D model.`
This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for
combination of deep stem and avg_pool in downsample.
"""
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
stem_type='deep', avg_down=True, block_args=dict(attn_layer='se'))
return _create_resnet('seresnext26d_32x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnext26t_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a SE-ResNet-26-T model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem.
"""
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='se'))
return _create_resnet('seresnext26t_32x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnext50_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
block_args=dict(attn_layer='se'))
return _create_resnet('seresnext50_32x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnext101_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4,
block_args=dict(attn_layer='se'))
return _create_resnet('seresnext101_32x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnext101_32x8d(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8,
block_args=dict(attn_layer='se'))
return _create_resnet('seresnext101_32x8d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnext101d_32x8d(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8,
stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(attn_layer='se'))
return _create_resnet('seresnext101d_32x8d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnext101_64x4d(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4,
block_args=dict(attn_layer='se'))
return _create_resnet('seresnext101_64x4d', pretrained, **dict(model_args, **kwargs))
@register_model
def senet154(pretrained: bool = False, **kwargs) -> ResNet:
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep',
down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer='se'))
return _create_resnet('senet154', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetblur18(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-18 model with blur anti-aliasing
"""
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], aa_layer=BlurPool2d)
return _create_resnet('resnetblur18', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetblur50(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50 model with blur anti-aliasing
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=BlurPool2d)
return _create_resnet('resnetblur50', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetblur50d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-D model with blur anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=BlurPool2d,
stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnetblur50d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetblur101d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-101-D model with blur anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], aa_layer=BlurPool2d,
stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnetblur101d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetaa34d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-34-D model w/ avgpool anti-aliasing
"""
model_args = dict(
block=BasicBlock, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d, stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnetaa34d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetaa50(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50 model with avgpool anti-aliasing
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d)
return _create_resnet('resnetaa50', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetaa50d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-50-D model with avgpool anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d,
stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnetaa50d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetaa101d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-101-D model with avgpool anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], aa_layer=nn.AvgPool2d,
stem_width=32, stem_type='deep', avg_down=True)
return _create_resnet('resnetaa101d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnetaa50d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a SE=ResNet-50-D model with avgpool anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d,
stem_width=32, stem_type='deep', avg_down=True, block_args=dict(attn_layer='se'))
return _create_resnet('seresnetaa50d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnextaa101d_32x8d(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a SE=ResNeXt-101-D 32x8d model with avgpool anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8,
stem_width=32, stem_type='deep', avg_down=True, aa_layer=nn.AvgPool2d,
block_args=dict(attn_layer='se'))
return _create_resnet('seresnextaa101d_32x8d', pretrained, **dict(model_args, **kwargs))
@register_model
def seresnextaa201d_32x8d(pretrained: bool = False, **kwargs):
"""Constructs a SE=ResNeXt-101-D 32x8d model with avgpool anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 24, 36, 4], cardinality=32, base_width=8,
stem_width=64, stem_type='deep', avg_down=True, aa_layer=nn.AvgPool2d,
block_args=dict(attn_layer='se'))
return _create_resnet('seresnextaa201d_32x8d', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetrs50(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-RS-50 model.
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
"""
attn_layer = partial(get_attn('se'), rd_ratio=0.25)
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
avg_down=True, block_args=dict(attn_layer=attn_layer))
return _create_resnet('resnetrs50', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetrs101(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-RS-101 model.
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
"""
attn_layer = partial(get_attn('se'), rd_ratio=0.25)
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
avg_down=True, block_args=dict(attn_layer=attn_layer))
return _create_resnet('resnetrs101', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetrs152(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-RS-152 model.
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
"""
attn_layer = partial(get_attn('se'), rd_ratio=0.25)
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
avg_down=True, block_args=dict(attn_layer=attn_layer))
return _create_resnet('resnetrs152', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetrs200(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-RS-200 model.
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
"""
attn_layer = partial(get_attn('se'), rd_ratio=0.25)
model_args = dict(
block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
avg_down=True, block_args=dict(attn_layer=attn_layer))
return _create_resnet('resnetrs200', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetrs270(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-RS-270 model.
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
"""
attn_layer = partial(get_attn('se'), rd_ratio=0.25)
model_args = dict(
block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', replace_stem_pool=True,
avg_down=True, block_args=dict(attn_layer=attn_layer))
return _create_resnet('resnetrs270', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetrs350(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-RS-350 model.
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
"""
attn_layer = partial(get_attn('se'), rd_ratio=0.25)
model_args = dict(
block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', replace_stem_pool=True,
avg_down=True, block_args=dict(attn_layer=attn_layer))
return _create_resnet('resnetrs350', pretrained, **dict(model_args, **kwargs))
@register_model
def resnetrs420(pretrained: bool = False, **kwargs) -> ResNet:
"""Constructs a ResNet-RS-420 model
Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
"""
attn_layer = partial(get_attn('se'), rd_ratio=0.25)
model_args = dict(
block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', replace_stem_pool=True,
avg_down=True, block_args=dict(attn_layer=attn_layer))
return _create_resnet('resnetrs420', pretrained, **dict(model_args, **kwargs))
register_model_deprecations(__name__, {
'tv_resnet34': 'resnet34.tv_in1k',
'tv_resnet50': 'resnet50.tv_in1k',
'tv_resnet101': 'resnet101.tv_in1k',
'tv_resnet152': 'resnet152.tv_in1k',
'tv_resnext50_32x4d' : 'resnext50_32x4d.tv_in1k',
'ig_resnext101_32x8d': 'resnext101_32x8d.fb_wsl_ig1b_ft_in1k',
'ig_resnext101_32x16d': 'resnext101_32x8d.fb_wsl_ig1b_ft_in1k',
'ig_resnext101_32x32d': 'resnext101_32x8d.fb_wsl_ig1b_ft_in1k',
'ig_resnext101_32x48d': 'resnext101_32x8d.fb_wsl_ig1b_ft_in1k',
'ssl_resnet18': 'resnet18.fb_ssl_yfcc100m_ft_in1k',
'ssl_resnet50': 'resnet50.fb_ssl_yfcc100m_ft_in1k',
'ssl_resnext50_32x4d': 'resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k',
'ssl_resnext101_32x4d': 'resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k',
'ssl_resnext101_32x8d': 'resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k',
'ssl_resnext101_32x16d': 'resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k',
'swsl_resnet18': 'resnet18.fb_swsl_ig1b_ft_in1k',
'swsl_resnet50': 'resnet50.fb_swsl_ig1b_ft_in1k',
'swsl_resnext50_32x4d': 'resnext50_32x4d.fb_swsl_ig1b_ft_in1k',
'swsl_resnext101_32x4d': 'resnext101_32x4d.fb_swsl_ig1b_ft_in1k',
'swsl_resnext101_32x8d': 'resnext101_32x8d.fb_swsl_ig1b_ft_in1k',
'swsl_resnext101_32x16d': 'resnext101_32x16d.fb_swsl_ig1b_ft_in1k',
'gluon_resnet18_v1b': 'resnet18.gluon_in1k',
'gluon_resnet34_v1b': 'resnet34.gluon_in1k',
'gluon_resnet50_v1b': 'resnet50.gluon_in1k',
'gluon_resnet101_v1b': 'resnet101.gluon_in1k',
'gluon_resnet152_v1b': 'resnet152.gluon_in1k',
'gluon_resnet50_v1c': 'resnet50c.gluon_in1k',
'gluon_resnet101_v1c': 'resnet101c.gluon_in1k',
'gluon_resnet152_v1c': 'resnet152c.gluon_in1k',
'gluon_resnet50_v1d': 'resnet50d.gluon_in1k',
'gluon_resnet101_v1d': 'resnet101d.gluon_in1k',
'gluon_resnet152_v1d': 'resnet152d.gluon_in1k',
'gluon_resnet50_v1s': 'resnet50s.gluon_in1k',
'gluon_resnet101_v1s': 'resnet101s.gluon_in1k',
'gluon_resnet152_v1s': 'resnet152s.gluon_in1k',
'gluon_resnext50_32x4d': 'resnext50_32x4d.gluon_in1k',
'gluon_resnext101_32x4d': 'resnext101_32x4d.gluon_in1k',
'gluon_resnext101_64x4d': 'resnext101_64x4d.gluon_in1k',
'gluon_seresnext50_32x4d': 'seresnext50_32x4d.gluon_in1k',
'gluon_seresnext101_32x4d': 'seresnext101_32x4d.gluon_in1k',
'gluon_seresnext101_64x4d': 'seresnext101_64x4d.gluon_in1k',
'gluon_senet154': 'senet154.gluon_in1k',
'seresnext26tn_32x4d': 'seresnext26t_32x4d',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/dla.py
|
""" Deep Layer Aggregation and DLA w/ Res2Net
DLA original adapted from Official Pytorch impl at: https://github.com/ucbdrive/dla
DLA Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484
Res2Net additions from: https://github.com/gasvn/Res2Net/
Res2Net Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
"""
import math
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import create_classifier
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['DLA']
class DlaBasic(nn.Module):
"""DLA Basic"""
def __init__(self, inplanes, planes, stride=1, dilation=1, **_):
super(DlaBasic, self).__init__()
self.conv1 = nn.Conv2d(
inplanes, planes, kernel_size=3,
stride=stride, padding=dilation, bias=False, dilation=dilation)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(
planes, planes, kernel_size=3,
stride=1, padding=dilation, bias=False, dilation=dilation)
self.bn2 = nn.BatchNorm2d(planes)
self.stride = stride
def forward(self, x, shortcut: Optional[torch.Tensor] = None, children: Optional[List[torch.Tensor]] = None):
if shortcut is None:
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += shortcut
out = self.relu(out)
return out
class DlaBottleneck(nn.Module):
"""DLA/DLA-X Bottleneck"""
expansion = 2
def __init__(self, inplanes, outplanes, stride=1, dilation=1, cardinality=1, base_width=64):
super(DlaBottleneck, self).__init__()
self.stride = stride
mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality)
mid_planes = mid_planes // self.expansion
self.conv1 = nn.Conv2d(inplanes, mid_planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(mid_planes)
self.conv2 = nn.Conv2d(
mid_planes, mid_planes, kernel_size=3,
stride=stride, padding=dilation, bias=False, dilation=dilation, groups=cardinality)
self.bn2 = nn.BatchNorm2d(mid_planes)
self.conv3 = nn.Conv2d(mid_planes, outplanes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(outplanes)
self.relu = nn.ReLU(inplace=True)
def forward(self, x, shortcut: Optional[torch.Tensor] = None, children: Optional[List[torch.Tensor]] = None):
if shortcut is None:
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
out += shortcut
out = self.relu(out)
return out
class DlaBottle2neck(nn.Module):
""" Res2Net/Res2NeXT DLA Bottleneck
Adapted from https://github.com/gasvn/Res2Net/blob/master/dla.py
"""
expansion = 2
def __init__(self, inplanes, outplanes, stride=1, dilation=1, scale=4, cardinality=8, base_width=4):
super(DlaBottle2neck, self).__init__()
self.is_first = stride > 1
self.scale = scale
mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality)
mid_planes = mid_planes // self.expansion
self.width = mid_planes
self.conv1 = nn.Conv2d(inplanes, mid_planes * scale, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(mid_planes * scale)
num_scale_convs = max(1, scale - 1)
convs = []
bns = []
for _ in range(num_scale_convs):
convs.append(nn.Conv2d(
mid_planes, mid_planes, kernel_size=3,
stride=stride, padding=dilation, dilation=dilation, groups=cardinality, bias=False))
bns.append(nn.BatchNorm2d(mid_planes))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1) if self.is_first else None
self.conv3 = nn.Conv2d(mid_planes * scale, outplanes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(outplanes)
self.relu = nn.ReLU(inplace=True)
def forward(self, x, shortcut: Optional[torch.Tensor] = None, children: Optional[List[torch.Tensor]] = None):
if shortcut is None:
shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out, self.width, 1)
spo = []
sp = spx[0] # redundant, for torchscript
for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
if i == 0 or self.is_first:
sp = spx[i]
else:
sp = sp + spx[i]
sp = conv(sp)
sp = bn(sp)
sp = self.relu(sp)
spo.append(sp)
if self.scale > 1:
if self.pool is not None: # self.is_first == True, None check for torchscript
spo.append(self.pool(spx[-1]))
else:
spo.append(spx[-1])
out = torch.cat(spo, 1)
out = self.conv3(out)
out = self.bn3(out)
out += shortcut
out = self.relu(out)
return out
class DlaRoot(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, shortcut):
super(DlaRoot, self).__init__()
self.conv = nn.Conv2d(
in_channels, out_channels, 1, stride=1, bias=False, padding=(kernel_size - 1) // 2)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.shortcut = shortcut
def forward(self, x_children: List[torch.Tensor]):
x = self.conv(torch.cat(x_children, 1))
x = self.bn(x)
if self.shortcut:
x += x_children[0]
x = self.relu(x)
return x
class DlaTree(nn.Module):
def __init__(
self,
levels,
block,
in_channels,
out_channels,
stride=1,
dilation=1,
cardinality=1,
base_width=64,
level_root=False,
root_dim=0,
root_kernel_size=1,
root_shortcut=False,
):
super(DlaTree, self).__init__()
if root_dim == 0:
root_dim = 2 * out_channels
if level_root:
root_dim += in_channels
self.downsample = nn.MaxPool2d(stride, stride=stride) if stride > 1 else nn.Identity()
self.project = nn.Identity()
cargs = dict(dilation=dilation, cardinality=cardinality, base_width=base_width)
if levels == 1:
self.tree1 = block(in_channels, out_channels, stride, **cargs)
self.tree2 = block(out_channels, out_channels, 1, **cargs)
if in_channels != out_channels:
# NOTE the official impl/weights have project layers in levels > 1 case that are never
# used, I've moved the project layer here to avoid wasted params but old checkpoints will
# need strict=False while loading.
self.project = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
nn.BatchNorm2d(out_channels))
self.root = DlaRoot(root_dim, out_channels, root_kernel_size, root_shortcut)
else:
cargs.update(dict(root_kernel_size=root_kernel_size, root_shortcut=root_shortcut))
self.tree1 = DlaTree(
levels - 1,
block,
in_channels,
out_channels,
stride,
root_dim=0,
**cargs,
)
self.tree2 = DlaTree(
levels - 1,
block,
out_channels,
out_channels,
root_dim=root_dim + out_channels,
**cargs,
)
self.root = None
self.level_root = level_root
self.root_dim = root_dim
self.levels = levels
def forward(self, x, shortcut: Optional[torch.Tensor] = None, children: Optional[List[torch.Tensor]] = None):
if children is None:
children = []
bottom = self.downsample(x)
shortcut = self.project(bottom)
if self.level_root:
children.append(bottom)
x1 = self.tree1(x, shortcut)
if self.root is not None: # levels == 1
x2 = self.tree2(x1)
x = self.root([x2, x1] + children)
else:
children.append(x1)
x = self.tree2(x1, None, children)
return x
class DLA(nn.Module):
def __init__(
self,
levels,
channels,
output_stride=32,
num_classes=1000,
in_chans=3,
global_pool='avg',
cardinality=1,
base_width=64,
block=DlaBottle2neck,
shortcut_root=False,
drop_rate=0.0,
):
super(DLA, self).__init__()
self.channels = channels
self.num_classes = num_classes
self.cardinality = cardinality
self.base_width = base_width
assert output_stride == 32 # FIXME support dilation
self.base_layer = nn.Sequential(
nn.Conv2d(in_chans, channels[0], kernel_size=7, stride=1, padding=3, bias=False),
nn.BatchNorm2d(channels[0]),
nn.ReLU(inplace=True),
)
self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
self.level1 = self._make_conv_level(channels[0], channels[1], levels[1], stride=2)
cargs = dict(cardinality=cardinality, base_width=base_width, root_shortcut=shortcut_root)
self.level2 = DlaTree(levels[2], block, channels[1], channels[2], 2, level_root=False, **cargs)
self.level3 = DlaTree(levels[3], block, channels[2], channels[3], 2, level_root=True, **cargs)
self.level4 = DlaTree(levels[4], block, channels[3], channels[4], 2, level_root=True, **cargs)
self.level5 = DlaTree(levels[5], block, channels[4], channels[5], 2, level_root=True, **cargs)
self.feature_info = [
dict(num_chs=channels[0], reduction=1, module='level0'), # rare to have a meaningful stride 1 level
dict(num_chs=channels[1], reduction=2, module='level1'),
dict(num_chs=channels[2], reduction=4, module='level2'),
dict(num_chs=channels[3], reduction=8, module='level3'),
dict(num_chs=channels[4], reduction=16, module='level4'),
dict(num_chs=channels[5], reduction=32, module='level5'),
]
self.num_features = channels[-1]
self.global_pool, self.head_drop, self.fc = create_classifier(
self.num_features,
self.num_classes,
pool_type=global_pool,
use_conv=True,
drop_rate=drop_rate,
)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1):
modules = []
for i in range(convs):
modules.extend([
nn.Conv2d(
inplanes, planes, kernel_size=3,
stride=stride if i == 0 else 1,
padding=dilation, bias=False, dilation=dilation),
nn.BatchNorm2d(planes),
nn.ReLU(inplace=True)])
inplanes = planes
return nn.Sequential(*modules)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^base_layer',
blocks=r'^level(\d+)' if coarse else [
# an unusual arch, this achieves somewhat more granularity without getting super messy
(r'^level(\d+)\.tree(\d+)', None),
(r'^level(\d+)\.root', (2,)),
(r'^level(\d+)', (1,))
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.fc = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
def forward_features(self, x):
x = self.base_layer(x)
x = self.level0(x)
x = self.level1(x)
x = self.level2(x)
x = self.level3(x)
x = self.level4(x)
x = self.level5(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.head_drop(x)
if pre_logits:
return self.flatten(x)
x = self.fc(x)
return self.flatten(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_dla(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
DLA,
variant,
pretrained,
pretrained_strict=False,
feature_cfg=dict(out_indices=(1, 2, 3, 4, 5)),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'base_layer.0', 'classifier': 'fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'dla34.in1k': _cfg(hf_hub_id='timm/'),
'dla46_c.in1k': _cfg(hf_hub_id='timm/'),
'dla46x_c.in1k': _cfg(hf_hub_id='timm/'),
'dla60x_c.in1k': _cfg(hf_hub_id='timm/'),
'dla60.in1k': _cfg(hf_hub_id='timm/'),
'dla60x.in1k': _cfg(hf_hub_id='timm/'),
'dla102.in1k': _cfg(hf_hub_id='timm/'),
'dla102x.in1k': _cfg(hf_hub_id='timm/'),
'dla102x2.in1k': _cfg(hf_hub_id='timm/'),
'dla169.in1k': _cfg(hf_hub_id='timm/'),
'dla60_res2net.in1k': _cfg(hf_hub_id='timm/'),
'dla60_res2next.in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def dla60_res2net(pretrained=False, **kwargs) -> DLA:
model_args = dict(
levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottle2neck, cardinality=1, base_width=28)
return _create_dla('dla60_res2net', pretrained, **dict(model_args, **kwargs))
@register_model
def dla60_res2next(pretrained=False,**kwargs):
model_args = dict(
levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottle2neck, cardinality=8, base_width=4)
return _create_dla('dla60_res2next', pretrained, **dict(model_args, **kwargs))
@register_model
def dla34(pretrained=False, **kwargs) -> DLA: # DLA-34
model_args = dict(
levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 128, 256, 512], block=DlaBasic)
return _create_dla('dla34', pretrained, **dict(model_args, **kwargs))
@register_model
def dla46_c(pretrained=False, **kwargs) -> DLA: # DLA-46-C
model_args = dict(
levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256], block=DlaBottleneck)
return _create_dla('dla46_c', pretrained, **dict(model_args, **kwargs))
@register_model
def dla46x_c(pretrained=False, **kwargs) -> DLA: # DLA-X-46-C
model_args = dict(
levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
block=DlaBottleneck, cardinality=32, base_width=4)
return _create_dla('dla46x_c', pretrained, **dict(model_args, **kwargs))
@register_model
def dla60x_c(pretrained=False, **kwargs) -> DLA: # DLA-X-60-C
model_args = dict(
levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 64, 64, 128, 256],
block=DlaBottleneck, cardinality=32, base_width=4)
return _create_dla('dla60x_c', pretrained, **dict(model_args, **kwargs))
@register_model
def dla60(pretrained=False, **kwargs) -> DLA: # DLA-60
model_args = dict(
levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
block=DlaBottleneck)
return _create_dla('dla60', pretrained, **dict(model_args, **kwargs))
@register_model
def dla60x(pretrained=False, **kwargs) -> DLA: # DLA-X-60
model_args = dict(
levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
block=DlaBottleneck, cardinality=32, base_width=4)
return _create_dla('dla60x', pretrained, **dict(model_args, **kwargs))
@register_model
def dla102(pretrained=False, **kwargs) -> DLA: # DLA-102
model_args = dict(
levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
block=DlaBottleneck, shortcut_root=True)
return _create_dla('dla102', pretrained, **dict(model_args, **kwargs))
@register_model
def dla102x(pretrained=False, **kwargs) -> DLA: # DLA-X-102
model_args = dict(
levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
block=DlaBottleneck, cardinality=32, base_width=4, shortcut_root=True)
return _create_dla('dla102x', pretrained, **dict(model_args, **kwargs))
@register_model
def dla102x2(pretrained=False, **kwargs) -> DLA: # DLA-X-102 64
model_args = dict(
levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
block=DlaBottleneck, cardinality=64, base_width=4, shortcut_root=True)
return _create_dla('dla102x2', pretrained, **dict(model_args, **kwargs))
@register_model
def dla169(pretrained=False, **kwargs) -> DLA: # DLA-169
model_args = dict(
levels=[1, 1, 2, 3, 5, 1], channels=[16, 32, 128, 256, 512, 1024],
block=DlaBottleneck, shortcut_root=True)
return _create_dla('dla169', pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/hub.py
|
from ._hub import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/eva.py
|
""" EVA
EVA from https://github.com/baaivision/EVA , paper: https://arxiv.org/abs/2211.07636
@article{EVA,
title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang,
Tiejun and Wang, Xinlong and Cao, Yue},
journal={arXiv preprint arXiv:2211.07636},
year={2022}
}
EVA-02: A Visual Representation for Neon Genesis - https://arxiv.org/abs/2303.11331
@article{EVA02,
title={EVA-02: A Visual Representation for Neon Genesis},
author={Fang, Yuxin and Sun, Quan and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
journal={arXiv preprint arXiv:2303.11331},
year={2023}
}
This file contains EVA & EVA02 model implementations evolved from BEiT, additional models in vision_transformer.py.
Modifications by / Copyright 2023 Ross Wightman, original copyrights below
"""
# EVA models Copyright (c) 2022 BAAI-Vision
# EVA02 models Copyright (c) 2023 BAAI-Vision
import math
from typing import Callable, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
from timm.layers import PatchEmbed, Mlp, GluMlp, SwiGLU, LayerNorm, DropPath, PatchDropout, RotaryEmbeddingCat, \
apply_rot_embed_cat, apply_keep_indices_nlc, trunc_normal_, resample_patch_embed, resample_abs_pos_embed, \
to_2tuple, use_fused_attn
from ._builder import build_model_with_cfg
from ._registry import generate_default_cfgs, register_model
__all__ = ['Eva']
class EvaAttention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = True,
qkv_fused: bool = True,
attn_drop: float = 0.,
proj_drop: float = 0.,
attn_head_dim: Optional[int] = None,
norm_layer: Optional[Callable] = None,
):
"""
Args:
dim:
num_heads:
qkv_bias:
qkv_fused:
attn_drop:
proj_drop:
attn_head_dim:
norm_layer:
"""
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
if attn_head_dim is not None:
head_dim = attn_head_dim
all_head_dim = head_dim * self.num_heads
self.scale = head_dim ** -0.5
self.fused_attn = use_fused_attn()
if qkv_fused:
self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
self.q_proj = self.k_proj = self.v_proj = None
if qkv_bias:
self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
self.register_buffer('k_bias', torch.zeros(all_head_dim), persistent=False)
self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
else:
self.q_bias = self.k_bias = self.v_bias = None
else:
self.q_proj = nn.Linear(dim, all_head_dim, bias=qkv_bias)
self.k_proj = nn.Linear(dim, all_head_dim, bias=False)
self.v_proj = nn.Linear(dim, all_head_dim, bias=qkv_bias)
self.qkv = None
self.q_bias = self.k_bias = self.v_bias = None
self.attn_drop = nn.Dropout(attn_drop)
self.norm = norm_layer(all_head_dim) if norm_layer is not None else nn.Identity()
self.proj = nn.Linear(all_head_dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(
self,
x,
rope: Optional[torch.Tensor] = None,
attn_mask: Optional[torch.Tensor] = None,
):
B, N, C = x.shape
if self.qkv is not None:
qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) if self.q_bias is not None else None
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0) # B, num_heads, N, head_dim
else:
q = self.q_proj(x).reshape(B, N, self.num_heads, -1).transpose(1, 2) # B, num_heads, N, C
k = self.k_proj(x).reshape(B, N, self.num_heads, -1).transpose(1, 2)
v = self.v_proj(x).reshape(B, N, self.num_heads, -1).transpose(1, 2)
if rope is not None:
q = torch.cat([q[:, :, :1, :], apply_rot_embed_cat(q[:, :, 1:, :], rope)], 2).type_as(v)
k = torch.cat([k[:, :, :1, :], apply_rot_embed_cat(k[:, :, 1:, :], rope)], 2).type_as(v)
if self.fused_attn:
x = F.scaled_dot_product_attention(
q, k, v,
attn_mask=attn_mask,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
attn = attn.softmax(dim=-1)
if attn_mask is not None:
attn_mask = attn_mask.to(torch.bool)
attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf"))
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.norm(x)
x = self.proj(x)
x = self.proj_drop(x)
return x
class EvaBlock(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
qkv_bias: bool = True,
qkv_fused: bool = True,
mlp_ratio: float = 4.,
swiglu_mlp: bool = False,
scale_mlp: bool = False,
scale_attn_inner: bool = False,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
init_values: Optional[float] = None,
act_layer: Callable = nn.GELU,
norm_layer: Callable = LayerNorm,
attn_head_dim: Optional[int] = None,
):
"""
Args:
dim:
num_heads:
qkv_bias:
qkv_fused:
mlp_ratio:
swiglu_mlp:
scale_mlp:
scale_attn_inner:
proj_drop:
attn_drop:
drop_path:
init_values:
act_layer:
norm_layer:
attn_head_dim:
"""
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = EvaAttention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qkv_fused=qkv_fused,
attn_drop=attn_drop,
proj_drop=proj_drop,
attn_head_dim=attn_head_dim,
norm_layer=norm_layer if scale_attn_inner else None,
)
self.gamma_1 = nn.Parameter(init_values * torch.ones(dim)) if init_values is not None else None
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
hidden_features = int(dim * mlp_ratio)
if swiglu_mlp:
if scale_mlp:
# when norm in SwiGLU used, an impl with separate fc for gate & x is used
self.mlp = SwiGLU(
in_features=dim,
hidden_features=hidden_features,
norm_layer=norm_layer if scale_mlp else None,
drop=proj_drop,
)
else:
# w/o any extra norm, an impl with packed weights is used, matches existing GluMLP
self.mlp = GluMlp(
in_features=dim,
hidden_features=hidden_features * 2,
norm_layer=norm_layer if scale_mlp else None,
act_layer=nn.SiLU,
gate_last=False,
drop=proj_drop,
)
else:
self.mlp = Mlp(
in_features=dim,
hidden_features=hidden_features,
act_layer=act_layer,
norm_layer=norm_layer if scale_mlp else None,
drop=proj_drop,
)
self.gamma_2 = nn.Parameter(init_values * torch.ones(dim)) if init_values is not None else None
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x, rope: Optional[torch.Tensor] = None, attn_mask: Optional[torch.Tensor] = None):
if self.gamma_1 is None:
x = x + self.drop_path1(self.attn(self.norm1(x), rope=rope, attn_mask=attn_mask))
x = x + self.drop_path2(self.mlp(self.norm2(x)))
else:
x = x + self.drop_path1(self.gamma_1 * self.attn(self.norm1(x), rope=rope, attn_mask=attn_mask))
x = x + self.drop_path2(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class EvaBlockPostNorm(nn.Module):
""" EVA block w/ post-norm and support for swiglu, MLP norm scale, ROPE. """
def __init__(
self,
dim: int,
num_heads: int,
qkv_bias: bool = True,
qkv_fused: bool = True,
mlp_ratio: float = 4.,
swiglu_mlp: bool = False,
scale_mlp: bool = False,
scale_attn_inner: bool = False,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
init_values: Optional[float] = None, # ignore for post-norm
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
attn_head_dim: Optional[int] = None,
):
"""
Args:
dim:
num_heads:
qkv_bias:
qkv_fused:
mlp_ratio:
swiglu_mlp:
scale_mlp:
scale_attn_inner:
proj_drop:
attn_drop:
drop_path:
init_values:
act_layer:
norm_layer:
attn_head_dim:
"""
super().__init__()
self.attn = EvaAttention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qkv_fused=qkv_fused,
attn_drop=attn_drop,
proj_drop=proj_drop,
attn_head_dim=attn_head_dim,
norm_layer=norm_layer if scale_attn_inner else None,
)
self.norm1 = norm_layer(dim)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
hidden_features = int(dim * mlp_ratio)
if swiglu_mlp:
if scale_mlp:
# when norm in SwiGLU used, an impl with separate fc for gate & x is used
self.mlp = SwiGLU(
in_features=dim,
hidden_features=hidden_features,
norm_layer=norm_layer if scale_mlp else None,
drop=proj_drop,
)
else:
# w/o any extra norm, an impl with packed fc1 weights is used, matches existing GluMLP
self.mlp = GluMlp(
in_features=dim,
hidden_features=hidden_features * 2,
norm_layer=norm_layer if scale_mlp else None,
act_layer=nn.SiLU,
gate_last=False,
drop=proj_drop,
)
else:
self.mlp = Mlp(
in_features=dim,
hidden_features=hidden_features,
act_layer=act_layer,
norm_layer=norm_layer if scale_mlp else None,
drop=proj_drop,
)
self.norm2 = norm_layer(dim)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x, rope: Optional[torch.Tensor] = None, attn_mask: Optional[torch.Tensor] = None):
x = x + self.drop_path1(self.norm1(self.attn(x, rope=rope, attn_mask=attn_mask)))
x = x + self.drop_path2(self.norm2(self.mlp(x)))
return x
class Eva(nn.Module):
""" Eva Vision Transformer w/ Abs & Rotary Pos Embed
This class implements the EVA and EVA02 models that were based on the BEiT ViT variant
* EVA - abs pos embed, global avg pool
* EVA02 - abs + rope pos embed, global avg pool, SwiGLU, scale Norm in MLP (ala normformer)
"""
def __init__(
self,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = 'avg',
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
qkv_bias: bool = True,
qkv_fused: bool = True,
mlp_ratio: float = 4.,
swiglu_mlp: bool = False,
scale_mlp: bool = False,
scale_attn_inner: bool = False,
drop_rate: float = 0.,
pos_drop_rate: float = 0.,
patch_drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
norm_layer: Callable = LayerNorm,
init_values: Optional[float] = None,
class_token: bool = True,
use_abs_pos_emb: bool = True,
use_rot_pos_emb: bool = False,
use_post_norm: bool = False,
dynamic_img_size: bool = False,
dynamic_img_pad: bool = False,
ref_feat_shape: Optional[Union[Tuple[int, int], int]] = None,
head_init_scale: float = 0.001,
):
"""
Args:
img_size:
patch_size:
in_chans:
num_classes:
global_pool:
embed_dim:
depth:
num_heads:
qkv_bias:
qkv_fused:
mlp_ratio:
swiglu_mlp:
scale_mlp:
scale_attn_inner:
drop_rate:
pos_drop_rate:
proj_drop_rate:
attn_drop_rate:
drop_path_rate:
norm_layer:
init_values:
class_token:
use_abs_pos_emb:
use_rot_pos_emb:
use_post_norm:
ref_feat_shape:
head_init_scale:
"""
super().__init__()
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.num_prefix_tokens = 1 if class_token else 0
self.dynamic_img_size = dynamic_img_size
self.grad_checkpointing = False
embed_args = {}
if dynamic_img_size:
# flatten deferred until after pos embed
embed_args.update(dict(strict_img_size=False, output_fmt='NHWC'))
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
dynamic_img_pad=dynamic_img_pad,
**embed_args,
)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
self.pos_embed = nn.Parameter(
torch.zeros(1, num_patches + self.num_prefix_tokens, embed_dim)) if use_abs_pos_emb else None
self.pos_drop = nn.Dropout(p=pos_drop_rate)
if patch_drop_rate > 0:
self.patch_drop = PatchDropout(
patch_drop_rate,
num_prefix_tokens=self.num_prefix_tokens,
return_indices=True,
)
else:
self.patch_drop = None
if use_rot_pos_emb:
ref_feat_shape = to_2tuple(ref_feat_shape) if ref_feat_shape is not None else None
self.rope = RotaryEmbeddingCat(
embed_dim // num_heads,
in_pixels=False,
feat_shape=None if dynamic_img_size else self.patch_embed.grid_size,
ref_feat_shape=ref_feat_shape,
)
else:
self.rope = None
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
block_fn = EvaBlockPostNorm if use_post_norm else EvaBlock
self.blocks = nn.ModuleList([
block_fn(
dim=embed_dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qkv_fused=qkv_fused,
mlp_ratio=mlp_ratio,
swiglu_mlp=swiglu_mlp,
scale_mlp=scale_mlp,
scale_attn_inner=scale_attn_inner,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
init_values=init_values,
)
for i in range(depth)])
use_fc_norm = self.global_pool == 'avg'
self.norm = nn.Identity() if use_fc_norm else norm_layer(embed_dim)
self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
self.fix_init_weight()
if isinstance(self.head, nn.Linear):
trunc_normal_(self.head.weight, std=.02)
self.head.weight.data.mul_(head_init_scale)
self.head.bias.data.mul_(head_init_scale)
def fix_init_weight(self):
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.blocks):
rescale(layer.attn.proj.weight.data, layer_id + 1)
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.zeros_(m.bias)
@torch.jit.ignore
def no_weight_decay(self):
nwd = {'pos_embed', 'cls_token'}
return nwd
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))],
)
return matcher
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def _pos_embed(self, x) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
if self.dynamic_img_size:
B, H, W, C = x.shape
if self.pos_embed is not None:
pos_embed = resample_abs_pos_embed(
self.pos_embed,
(H, W),
num_prefix_tokens=self.num_prefix_tokens,
)
else:
pos_embed = None
x = x.view(B, -1, C)
rot_pos_embed = self.rope.get_embed(shape=(H, W)) if self.rope is not None else None
else:
pos_embed = self.pos_embed
rot_pos_embed = self.rope.get_embed() if self.rope is not None else None
if self.cls_token is not None:
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
if pos_embed is not None:
x = x + pos_embed
x = self.pos_drop(x)
# obtain shared rotary position embedding and apply patch dropout
if self.patch_drop is not None:
x, keep_indices = self.patch_drop(x)
if rot_pos_embed is not None and keep_indices is not None:
rot_pos_embed = apply_keep_indices_nlc(x, rot_pos_embed, keep_indices)
return x, rot_pos_embed
def forward_features(self, x):
x = self.patch_embed(x)
x, rot_pos_embed = self._pos_embed(x)
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(blk, x, rope=rot_pos_embed)
else:
x = blk(x, rope=rot_pos_embed)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = self.fc_norm(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(
state_dict,
model,
interpolation='bicubic',
antialias=True,
):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
out_dict = {}
state_dict = state_dict.get('model_ema', state_dict)
state_dict = state_dict.get('model', state_dict)
state_dict = state_dict.get('module', state_dict)
state_dict = state_dict.get('state_dict', state_dict)
# prefix for loading OpenCLIP compatible weights
if 'visual.trunk.pos_embed' in state_dict:
prefix = 'visual.trunk.'
elif 'visual.pos_embed' in state_dict:
prefix = 'visual.'
else:
prefix = ''
mim_weights = prefix + 'mask_token' in state_dict
no_qkv = prefix + 'blocks.0.attn.q_proj.weight' in state_dict
len_prefix = len(prefix)
for k, v in state_dict.items():
if prefix:
if k.startswith(prefix):
k = k[len_prefix:]
else:
continue
if 'rope' in k:
# fixed embedding no need to load buffer from checkpoint
continue
if 'patch_embed.proj.weight' in k:
_, _, H, W = model.patch_embed.proj.weight.shape
if v.shape[-1] != W or v.shape[-2] != H:
v = resample_patch_embed(
v,
(H, W),
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
# To resize pos embedding when using model at different size from pretrained weights
num_prefix_tokens = 0 if getattr(model, 'no_embed_class', False) else getattr(model, 'num_prefix_tokens', 1)
v = resample_abs_pos_embed(
v,
new_size=model.patch_embed.grid_size,
num_prefix_tokens=num_prefix_tokens,
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
k = k.replace('mlp.ffn_ln', 'mlp.norm')
k = k.replace('attn.inner_attn_ln', 'attn.norm')
k = k.replace('mlp.w12', 'mlp.fc1')
k = k.replace('mlp.w1', 'mlp.fc1_g')
k = k.replace('mlp.w2', 'mlp.fc1_x')
k = k.replace('mlp.w3', 'mlp.fc2')
if no_qkv:
k = k.replace('q_bias', 'q_proj.bias')
k = k.replace('v_bias', 'v_proj.bias')
if mim_weights and k in ('mask_token', 'lm_head.weight', 'lm_head.bias', 'norm.weight', 'norm.bias'):
if k == 'norm.weight' or k == 'norm.bias':
# try moving norm -> fc norm on fine-tune, probably a better starting point than new init
k = k.replace('norm', 'fc_norm')
else:
# skip pretrain mask token & head weights
continue
out_dict[k] = v
return out_dict
def _create_eva(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Eva models.')
model = build_model_with_cfg(
Eva, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': OPENAI_CLIP_MEAN, 'std': OPENAI_CLIP_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
'license': 'mit', **kwargs
}
default_cfgs = generate_default_cfgs({
# EVA 01 CLIP fine-tuned on imagenet-1k
'eva_giant_patch14_224.clip_ft_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz224_ftcls_89p1.pt',
hf_hub_id='timm/',
),
'eva_giant_patch14_336.clip_ft_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz336_ftcls_89p4.pt',
hf_hub_id='timm/',
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),
# MIM EVA 01 pretrain, ft on in22k -> in1k
'eva_giant_patch14_336.m30m_ft_in22k_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_21k_1k_336px_psz14_ema_89p6.pt',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),
'eva_giant_patch14_560.m30m_ft_in22k_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_21k_1k_560px_psz14_ema_89p7.pt',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
input_size=(3, 560, 560), crop_pct=1.0, crop_mode='squash'),
# in22k or m38m MIM pretrain w/ intermediate in22k fine-tune and final in1k fine-tune
'eva02_base_patch14_448.mim_in22k_ft_in22k_in1k': _cfg(
# hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_B_pt_in21k_medft_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash',
),
'eva02_large_patch14_448.mim_in22k_ft_in22k_in1k': _cfg(
# hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_L_pt_in21k_medft_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash',
),
'eva02_large_patch14_448.mim_m38m_ft_in22k_in1k': _cfg(
hf_hub_id='timm/',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_L_pt_m38m_medft_in21k_ft_in1k_p14.pt',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash',
),
# in22k or m3m MIM pretrain w/ in1k fine-tune
'eva02_tiny_patch14_336.mim_in22k_ft_in1k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_Ti_pt_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 336, 336), crop_pct=1.0,
),
'eva02_small_patch14_336.mim_in22k_ft_in1k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_S_pt_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 336, 336), crop_pct=1.0,
),
'eva02_base_patch14_448.mim_in22k_ft_in1k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_B_pt_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0,
),
'eva02_large_patch14_448.mim_in22k_ft_in1k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_L_pt_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0,
),
'eva02_large_patch14_448.mim_m38m_ft_in1k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_L_pt_m38m_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0,
),
# in22k or m3m MIM pretrain w/ in22k fine-tune
'eva02_base_patch14_448.mim_in22k_ft_in22k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_B_pt_in21k_medft_in21k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash', num_classes=21841,
),
'eva02_large_patch14_448.mim_in22k_ft_in22k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_L_pt_in21k_medft_in21k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash', num_classes=21841,
),
'eva02_large_patch14_448.mim_m38m_ft_in22k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_L_pt_m38m_medft_in21k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash', num_classes=21841,
),
# in22k or m38m MIM pretrain
'eva02_tiny_patch14_224.mim_in22k': _cfg(
# hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_Ti_pt_in21k_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
'eva02_small_patch14_224.mim_in22k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_S_pt_in21k_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
'eva02_base_patch14_224.mim_in22k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_B_pt_in21k_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
'eva02_large_patch14_224.mim_in22k': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_L_pt_in21k_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
'eva02_large_patch14_224.mim_m38m': _cfg(
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_L_pt_m38m_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
# EVA01 and EVA02 CLIP image towers
'eva_giant_patch14_clip_224.laion400m': _cfg(
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA01_CLIP_g_14_plus_psz14_s11B.pt',
hf_hub_id='timm/eva_giant_patch14_clip_224.laion400m_s11b_b41k', # float16 weights
hf_hub_filename='open_clip_pytorch_model.bin',
num_classes=1024,
),
'eva_giant_patch14_clip_224.merged2b': _cfg(
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA01_CLIP_g_14_plus_psz14_s11B.pt',
hf_hub_id='timm/eva_giant_patch14_plus_clip_224.merged2b_s11b_b114k', # float16 weights
hf_hub_filename='open_clip_pytorch_model.bin',
num_classes=1024,
),
'eva02_base_patch16_clip_224.merged2b': _cfg(
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
hf_hub_id='timm/eva02_base_patch16_clip_224.merged2b_s8b_b131k', # float16 weights
hf_hub_filename='open_clip_pytorch_model.bin',
num_classes=512,
),
'eva02_large_patch14_clip_224.merged2b': _cfg(
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
hf_hub_id='timm/eva02_large_patch14_clip_224.merged2b_s4b_b131k', # float16 weights
hf_hub_filename='open_clip_pytorch_model.bin',
num_classes=768,
),
'eva02_large_patch14_clip_336.merged2b': _cfg(
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
hf_hub_id='timm/eva02_large_patch14_clip_336.merged2b_s6b_b61k', # float16 weights
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 336, 336), crop_pct=1.0,
num_classes=768,
),
'eva02_enormous_patch14_clip_224.laion2b': _cfg(
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_E_psz14_plus_s9B.pt',
hf_hub_id='timm/eva02_enormous_patch14_clip_224.laion2b_s4b_b115k', # float16 weights
hf_hub_filename='open_clip_pytorch_model.bin',
num_classes=1024,
),
'eva02_enormous_patch14_clip_224.laion2b_plus': _cfg(
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_E_psz14_plus_s9B.pt',
hf_hub_id='timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k', # bfloat16 weights
hf_hub_filename='open_clip_pytorch_model.bin',
num_classes=1024,
),
'eva02_enormous_patch14_clip_224.pretrain': _cfg(
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_E_psz14.pt',
num_classes=0,
),
})
@register_model
def eva_giant_patch14_224(pretrained=False, **kwargs) -> Eva:
""" EVA-g model https://arxiv.org/abs/2211.07636 """
model_args = dict(patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408)
model = _create_eva('eva_giant_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva_giant_patch14_336(pretrained=False, **kwargs) -> Eva:
""" EVA-g model https://arxiv.org/abs/2211.07636 """
model_args = dict(patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408)
model = _create_eva('eva_giant_patch14_336', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva_giant_patch14_560(pretrained=False, **kwargs) -> Eva:
""" EVA-g model https://arxiv.org/abs/2211.07636 """
model_args = dict(patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408)
model = _create_eva('eva_giant_patch14_560', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_tiny_patch14_224(pretrained=False, **kwargs) -> Eva:
model_args = dict(
img_size=224,
patch_size=14,
embed_dim=192,
depth=12,
num_heads=3,
mlp_ratio=4 * 2 / 3,
swiglu_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
model = _create_eva('eva02_tiny_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_small_patch14_224(pretrained=False, **kwargs) -> Eva:
model_args = dict(
img_size=224,
patch_size=14,
embed_dim=384,
depth=12,
num_heads=6,
mlp_ratio=4 * 2 / 3,
swiglu_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
model = _create_eva('eva02_small_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_base_patch14_224(pretrained=False, **kwargs) -> Eva:
model_args = dict(
img_size=224,
patch_size=14,
embed_dim=768,
depth=12,
num_heads=12,
qkv_fused=False,
mlp_ratio=4 * 2 / 3,
swiglu_mlp=True,
scale_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
model = _create_eva('eva02_base_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_large_patch14_224(pretrained=False, **kwargs) -> Eva:
model_args = dict(
img_size=224,
patch_size=14,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4 * 2 / 3,
qkv_fused=False,
swiglu_mlp=True,
scale_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
model = _create_eva('eva02_large_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_tiny_patch14_336(pretrained=False, **kwargs) -> Eva:
model_args = dict(
img_size=336,
patch_size=14,
embed_dim=192,
depth=12,
num_heads=3,
mlp_ratio=4 * 2 / 3,
swiglu_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
model = _create_eva('eva02_tiny_patch14_336', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_small_patch14_336(pretrained=False, **kwargs) -> Eva:
model_args = dict(
img_size=336,
patch_size=14,
embed_dim=384,
depth=12,
num_heads=6,
mlp_ratio=4 * 2 / 3,
swiglu_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
model = _create_eva('eva02_small_patch14_336', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_base_patch14_448(pretrained=False, **kwargs) -> Eva:
model_args = dict(
img_size=448,
patch_size=14,
embed_dim=768,
depth=12,
num_heads=12,
qkv_fused=False,
mlp_ratio=4 * 2 / 3,
swiglu_mlp=True,
scale_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
model = _create_eva('eva02_base_patch14_448', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_large_patch14_448(pretrained=False, **kwargs) -> Eva:
model_args = dict(
img_size=448,
patch_size=14,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4 * 2 / 3,
qkv_fused=False,
swiglu_mlp=True,
scale_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
model = _create_eva('eva02_large_patch14_448', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva_giant_patch14_clip_224(pretrained=False, **kwargs) -> Eva:
""" EVA-g CLIP model (only difference from non-CLIP is the pooling) """
model_args = dict(
patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408,
global_pool=kwargs.pop('global_pool', 'token'))
model = _create_eva('eva_giant_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_base_patch16_clip_224(pretrained=False, **kwargs) -> Eva:
""" A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_base """
model_args = dict(
img_size=224,
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
qkv_fused=False,
mlp_ratio=4 * 2 / 3,
swiglu_mlp=True,
scale_mlp=True,
scale_attn_inner=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
global_pool=kwargs.pop('global_pool', 'token'),
)
model = _create_eva('eva02_base_patch16_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_large_patch14_clip_224(pretrained=False, **kwargs) -> Eva:
""" A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_large """
model_args = dict(
img_size=224,
patch_size=14,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4 * 2 / 3,
qkv_fused=False,
swiglu_mlp=True,
scale_mlp=True,
scale_attn_inner=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
global_pool=kwargs.pop('global_pool', 'token'),
)
model = _create_eva('eva02_large_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_large_patch14_clip_336(pretrained=False, **kwargs) -> Eva:
""" A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_large """
model_args = dict(
img_size=336,
patch_size=14,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4 * 2 / 3,
qkv_fused=False,
swiglu_mlp=True,
scale_mlp=True,
scale_attn_inner=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
global_pool=kwargs.pop('global_pool', 'token'),
)
model = _create_eva('eva02_large_patch14_clip_336', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def eva02_enormous_patch14_clip_224(pretrained=False, **kwargs) -> Eva:
""" A EVA-CLIP specific variant that uses residual post-norm in blocks """
model_args = dict(
img_size=224,
patch_size=14,
embed_dim=1792,
depth=64,
num_heads=16,
mlp_ratio=15360 / 1792,
use_post_norm=True,
global_pool=kwargs.pop('global_pool', 'token'),
)
model = _create_eva('eva02_enormous_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/tiny_vit.py
|
""" TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import LayerNorm2d, NormMlpClassifierHead, DropPath,\
trunc_normal_, resize_rel_pos_bias_table_levit, use_fused_attn
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_module
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
class ConvNorm(torch.nn.Sequential):
def __init__(self, in_chs, out_chs, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
super().__init__()
self.conv = nn.Conv2d(in_chs, out_chs, ks, stride, pad, dilation, groups, bias=False)
self.bn = nn.BatchNorm2d(out_chs)
torch.nn.init.constant_(self.bn.weight, bn_weight_init)
torch.nn.init.constant_(self.bn.bias, 0)
@torch.no_grad()
def fuse(self):
c, bn = self.conv, self.bn
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / \
(bn.running_var + bn.eps) ** 0.5
m = torch.nn.Conv2d(
w.size(1) * self.conv.groups, w.size(0), w.shape[2:],
stride=self.conv.stride, padding=self.conv.padding, dilation=self.conv.dilation, groups=self.conv.groups)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class PatchEmbed(nn.Module):
def __init__(self, in_chs, out_chs, act_layer):
super().__init__()
self.stride = 4
self.conv1 = ConvNorm(in_chs, out_chs // 2, 3, 2, 1)
self.act = act_layer()
self.conv2 = ConvNorm(out_chs // 2, out_chs, 3, 2, 1)
def forward(self, x):
x = self.conv1(x)
x = self.act(x)
x = self.conv2(x)
return x
class MBConv(nn.Module):
def __init__(self, in_chs, out_chs, expand_ratio, act_layer, drop_path):
super().__init__()
mid_chs = int(in_chs * expand_ratio)
self.conv1 = ConvNorm(in_chs, mid_chs, ks=1)
self.act1 = act_layer()
self.conv2 = ConvNorm(mid_chs, mid_chs, ks=3, stride=1, pad=1, groups=mid_chs)
self.act2 = act_layer()
self.conv3 = ConvNorm(mid_chs, out_chs, ks=1, bn_weight_init=0.0)
self.act3 = act_layer()
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
x = self.drop_path(x)
x += shortcut
x = self.act3(x)
return x
class PatchMerging(nn.Module):
def __init__(self, dim, out_dim, act_layer):
super().__init__()
self.conv1 = ConvNorm(dim, out_dim, 1, 1, 0)
self.act1 = act_layer()
self.conv2 = ConvNorm(out_dim, out_dim, 3, 2, 1, groups=out_dim)
self.act2 = act_layer()
self.conv3 = ConvNorm(out_dim, out_dim, 1, 1, 0)
def forward(self, x):
x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
return x
class ConvLayer(nn.Module):
def __init__(
self,
dim,
depth,
act_layer,
drop_path=0.,
conv_expand_ratio=4.,
):
super().__init__()
self.dim = dim
self.depth = depth
self.blocks = nn.Sequential(*[
MBConv(
dim, dim, conv_expand_ratio, act_layer,
drop_path[i] if isinstance(drop_path, list) else drop_path,
)
for i in range(depth)
])
def forward(self, x):
x = self.blocks(x)
return x
class NormMlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
norm_layer=nn.LayerNorm,
act_layer=nn.GELU,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.norm = norm_layer(in_features)
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.drop1 = nn.Dropout(drop)
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop2 = nn.Dropout(drop)
def forward(self, x):
x = self.norm(x)
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class Attention(torch.nn.Module):
fused_attn: torch.jit.Final[bool]
attention_bias_cache: Dict[str, torch.Tensor]
def __init__(
self,
dim,
key_dim,
num_heads=8,
attn_ratio=4,
resolution=(14, 14),
):
super().__init__()
assert isinstance(resolution, tuple) and len(resolution) == 2
self.num_heads = num_heads
self.scale = key_dim ** -0.5
self.key_dim = key_dim
self.val_dim = int(attn_ratio * key_dim)
self.out_dim = self.val_dim * num_heads
self.attn_ratio = attn_ratio
self.resolution = resolution
self.fused_attn = use_fused_attn()
self.norm = nn.LayerNorm(dim)
self.qkv = nn.Linear(dim, num_heads * (self.val_dim + 2 * key_dim))
self.proj = nn.Linear(self.out_dim, dim)
points = list(itertools.product(range(resolution[0]), range(resolution[1])))
N = len(points)
attention_offsets = {}
idxs = []
for p1 in points:
for p2 in points:
offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
if offset not in attention_offsets:
attention_offsets[offset] = len(attention_offsets)
idxs.append(attention_offsets[offset])
self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
self.register_buffer('attention_bias_idxs', torch.LongTensor(idxs).view(N, N), persistent=False)
self.attention_bias_cache = {}
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and self.attention_bias_cache:
self.attention_bias_cache = {} # clear ab cache
def get_attention_biases(self, device: torch.device) -> torch.Tensor:
if torch.jit.is_tracing() or self.training:
return self.attention_biases[:, self.attention_bias_idxs]
else:
device_key = str(device)
if device_key not in self.attention_bias_cache:
self.attention_bias_cache[device_key] = self.attention_biases[:, self.attention_bias_idxs]
return self.attention_bias_cache[device_key]
def forward(self, x):
attn_bias = self.get_attention_biases(x.device)
B, N, _ = x.shape
# Normalization
x = self.norm(x)
qkv = self.qkv(x)
# (B, N, num_heads, d)
q, k, v = qkv.view(B, N, self.num_heads, -1).split([self.key_dim, self.key_dim, self.val_dim], dim=3)
# (B, num_heads, N, d)
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
if self.fused_attn:
x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn + attn_bias
attn = attn.softmax(dim=-1)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, self.out_dim)
x = self.proj(x)
return x
class TinyVitBlock(nn.Module):
""" TinyViT Block.
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
window_size (int): Window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
drop (float, optional): Dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
local_conv_size (int): the kernel size of the convolution between
Attention and MLP. Default: 3
act_layer: the activation function. Default: nn.GELU
"""
def __init__(
self,
dim,
num_heads,
window_size=7,
mlp_ratio=4.,
drop=0.,
drop_path=0.,
local_conv_size=3,
act_layer=nn.GELU
):
super().__init__()
self.dim = dim
self.num_heads = num_heads
assert window_size > 0, 'window_size must be greater than 0'
self.window_size = window_size
self.mlp_ratio = mlp_ratio
assert dim % num_heads == 0, 'dim must be divisible by num_heads'
head_dim = dim // num_heads
window_resolution = (window_size, window_size)
self.attn = Attention(dim, head_dim, num_heads, attn_ratio=1, resolution=window_resolution)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.mlp = NormMlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=drop,
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
pad = local_conv_size // 2
self.local_conv = ConvNorm(dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)
def forward(self, x):
B, H, W, C = x.shape
L = H * W
shortcut = x
if H == self.window_size and W == self.window_size:
x = x.reshape(B, L, C)
x = self.attn(x)
x = x.view(B, H, W, C)
else:
pad_b = (self.window_size - H % self.window_size) % self.window_size
pad_r = (self.window_size - W % self.window_size) % self.window_size
padding = pad_b > 0 or pad_r > 0
if padding:
x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))
# window partition
pH, pW = H + pad_b, W + pad_r
nH = pH // self.window_size
nW = pW // self.window_size
x = x.view(B, nH, self.window_size, nW, self.window_size, C).transpose(2, 3).reshape(
B * nH * nW, self.window_size * self.window_size, C
)
x = self.attn(x)
# window reverse
x = x.view(B, nH, nW, self.window_size, self.window_size, C).transpose(2, 3).reshape(B, pH, pW, C)
if padding:
x = x[:, :H, :W].contiguous()
x = shortcut + self.drop_path1(x)
x = x.permute(0, 3, 1, 2)
x = self.local_conv(x)
x = x.reshape(B, C, L).transpose(1, 2)
x = x + self.drop_path2(self.mlp(x))
return x.view(B, H, W, C)
def extra_repr(self) -> str:
return f"dim={self.dim}, num_heads={self.num_heads}, " \
f"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}"
register_notrace_module(TinyVitBlock)
class TinyVitStage(nn.Module):
""" A basic TinyViT layer for one stage.
Args:
dim (int): Number of input channels.
out_dim: the output dimension of the layer
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
drop (float, optional): Dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
local_conv_size: the kernel size of the depthwise convolution between attention and MLP. Default: 3
act_layer: the activation function. Default: nn.GELU
"""
def __init__(
self,
dim,
out_dim,
depth,
num_heads,
window_size,
mlp_ratio=4.,
drop=0.,
drop_path=0.,
downsample=None,
local_conv_size=3,
act_layer=nn.GELU,
):
super().__init__()
self.depth = depth
self.out_dim = out_dim
# patch merging layer
if downsample is not None:
self.downsample = downsample(
dim=dim,
out_dim=out_dim,
act_layer=act_layer,
)
else:
self.downsample = nn.Identity()
assert dim == out_dim
# build blocks
self.blocks = nn.Sequential(*[
TinyVitBlock(
dim=out_dim,
num_heads=num_heads,
window_size=window_size,
mlp_ratio=mlp_ratio,
drop=drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
local_conv_size=local_conv_size,
act_layer=act_layer,
)
for i in range(depth)])
def forward(self, x):
x = self.downsample(x)
x = x.permute(0, 2, 3, 1) # BCHW -> BHWC
x = self.blocks(x)
x = x.permute(0, 3, 1, 2) # BHWC -> BCHW
return x
def extra_repr(self) -> str:
return f"dim={self.out_dim}, depth={self.depth}"
class TinyVit(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
embed_dims=(96, 192, 384, 768),
depths=(2, 2, 6, 2),
num_heads=(3, 6, 12, 24),
window_sizes=(7, 7, 14, 7),
mlp_ratio=4.,
drop_rate=0.,
drop_path_rate=0.1,
use_checkpoint=False,
mbconv_expand_ratio=4.0,
local_conv_size=3,
act_layer=nn.GELU,
):
super().__init__()
self.num_classes = num_classes
self.depths = depths
self.num_stages = len(depths)
self.mlp_ratio = mlp_ratio
self.grad_checkpointing = use_checkpoint
self.patch_embed = PatchEmbed(
in_chs=in_chans,
out_chs=embed_dims[0],
act_layer=act_layer,
)
# stochastic depth rate rule
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
# build stages
self.stages = nn.Sequential()
stride = self.patch_embed.stride
prev_dim = embed_dims[0]
self.feature_info = []
for stage_idx in range(self.num_stages):
if stage_idx == 0:
stage = ConvLayer(
dim=prev_dim,
depth=depths[stage_idx],
act_layer=act_layer,
drop_path=dpr[:depths[stage_idx]],
conv_expand_ratio=mbconv_expand_ratio,
)
else:
out_dim = embed_dims[stage_idx]
drop_path_rate = dpr[sum(depths[:stage_idx]):sum(depths[:stage_idx + 1])]
stage = TinyVitStage(
dim=embed_dims[stage_idx - 1],
out_dim=out_dim,
depth=depths[stage_idx],
num_heads=num_heads[stage_idx],
window_size=window_sizes[stage_idx],
mlp_ratio=self.mlp_ratio,
drop=drop_rate,
local_conv_size=local_conv_size,
drop_path=drop_path_rate,
downsample=PatchMerging,
act_layer=act_layer,
)
prev_dim = out_dim
stride *= 2
self.stages.append(stage)
self.feature_info += [dict(num_chs=prev_dim, reduction=stride, module=f'stages.{stage_idx}')]
# Classifier head
self.num_features = embed_dims[-1]
norm_layer_cf = partial(LayerNorm2d, eps=1e-5)
self.head = NormMlpClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
norm_layer=norm_layer_cf,
)
# init weights
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def no_weight_decay_keywords(self):
return {'attention_biases'}
@torch.jit.ignore
def no_weight_decay(self):
return {x for x in self.state_dict().keys() if 'attention_biases' in x}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^patch_embed',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+).downsample', (0,)),
(r'^stages\.(\d+)\.\w+\.(\d+)', None),
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
self.head.reset(num_classes, global_pool=global_pool)
def forward_features(self, x):
x = self.patch_embed(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
return x
def forward_head(self, x):
x = self.head(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
if 'model' in state_dict.keys():
state_dict = state_dict['model']
target_sd = model.state_dict()
out_dict = {}
for k, v in state_dict.items():
if k.endswith('attention_bias_idxs'):
continue
if 'attention_biases' in k:
# TODO: whether move this func into model for dynamic input resolution? (high risk)
v = resize_rel_pos_bias_table_levit(v.T, target_sd[k].shape[::-1]).T
out_dict[k] = v
return out_dict
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'mean': IMAGENET_DEFAULT_MEAN,
'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.conv1.conv',
'classifier': 'head.fc',
'pool_size': (7, 7),
'input_size': (3, 224, 224),
'crop_pct': 0.95,
**kwargs,
}
default_cfgs = generate_default_cfgs({
'tiny_vit_5m_224.dist_in22k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_5m_22k_distill.pth',
num_classes=21841
),
'tiny_vit_5m_224.dist_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_5m_22kto1k_distill.pth'
),
'tiny_vit_5m_224.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_5m_1k.pth'
),
'tiny_vit_11m_224.dist_in22k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_11m_22k_distill.pth',
num_classes=21841
),
'tiny_vit_11m_224.dist_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_11m_22kto1k_distill.pth'
),
'tiny_vit_11m_224.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_11m_1k.pth'
),
'tiny_vit_21m_224.dist_in22k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_21m_22k_distill.pth',
num_classes=21841
),
'tiny_vit_21m_224.dist_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_21m_22kto1k_distill.pth'
),
'tiny_vit_21m_224.in1k': _cfg(
hf_hub_id='timm/',
#url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_21m_1k.pth'
),
'tiny_vit_21m_384.dist_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_21m_22kto1k_384_distill.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
'tiny_vit_21m_512.dist_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_21m_22kto1k_512_distill.pth',
input_size=(3, 512, 512), pool_size=(16, 16), crop_pct=1.0, crop_mode='squash',
),
})
def _create_tiny_vit(variant, pretrained=False, **kwargs):
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
model = build_model_with_cfg(
TinyVit,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs
)
return model
@register_model
def tiny_vit_5m_224(pretrained=False, **kwargs):
model_kwargs = dict(
embed_dims=[64, 128, 160, 320],
depths=[2, 2, 6, 2],
num_heads=[2, 4, 5, 10],
window_sizes=[7, 7, 14, 7],
drop_path_rate=0.0,
)
model_kwargs.update(kwargs)
return _create_tiny_vit('tiny_vit_5m_224', pretrained, **model_kwargs)
@register_model
def tiny_vit_11m_224(pretrained=False, **kwargs):
model_kwargs = dict(
embed_dims=[64, 128, 256, 448],
depths=[2, 2, 6, 2],
num_heads=[2, 4, 8, 14],
window_sizes=[7, 7, 14, 7],
drop_path_rate=0.1,
)
model_kwargs.update(kwargs)
return _create_tiny_vit('tiny_vit_11m_224', pretrained, **model_kwargs)
@register_model
def tiny_vit_21m_224(pretrained=False, **kwargs):
model_kwargs = dict(
embed_dims=[96, 192, 384, 576],
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 18],
window_sizes=[7, 7, 14, 7],
drop_path_rate=0.2,
)
model_kwargs.update(kwargs)
return _create_tiny_vit('tiny_vit_21m_224', pretrained, **model_kwargs)
@register_model
def tiny_vit_21m_384(pretrained=False, **kwargs):
model_kwargs = dict(
embed_dims=[96, 192, 384, 576],
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 18],
window_sizes=[12, 12, 24, 12],
drop_path_rate=0.1,
)
model_kwargs.update(kwargs)
return _create_tiny_vit('tiny_vit_21m_384', pretrained, **model_kwargs)
@register_model
def tiny_vit_21m_512(pretrained=False, **kwargs):
model_kwargs = dict(
embed_dims=[96, 192, 384, 576],
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 18],
window_sizes=[16, 16, 32, 16],
drop_path_rate=0.1,
)
model_kwargs.update(kwargs)
return _create_tiny_vit('tiny_vit_21m_512', pretrained, **model_kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/convit.py
|
""" ConViT Model
@article{d2021convit,
title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
journal={arXiv preprint arXiv:2103.10697},
year={2021}
}
Paper link: https://arxiv.org/abs/2103.10697
Original code: https://github.com/facebookresearch/convit, original copyright below
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
'''These modules are adapted from those of timm, see
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
'''
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, trunc_normal_, PatchEmbed, Mlp, LayerNorm
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_module
from ._registry import register_model, generate_default_cfgs
from .vision_transformer_hybrid import HybridEmbed
__all__ = ['ConVit']
@register_notrace_module # reason: FX can't symbolically trace control flow in forward method
class GPSA(nn.Module):
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
locality_strength=1.,
):
super().__init__()
self.num_heads = num_heads
self.dim = dim
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.locality_strength = locality_strength
self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.v = nn.Linear(dim, dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.pos_proj = nn.Linear(3, num_heads)
self.proj_drop = nn.Dropout(proj_drop)
self.gating_param = nn.Parameter(torch.ones(self.num_heads))
self.rel_indices: torch.Tensor = torch.zeros(1, 1, 1, 3) # silly torchscript hack, won't work with None
def forward(self, x):
B, N, C = x.shape
if self.rel_indices is None or self.rel_indices.shape[1] != N:
self.rel_indices = self.get_rel_indices(N)
attn = self.get_attention(x)
v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
def get_attention(self, x):
B, N, C = x.shape
qk = self.qk(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k = qk[0], qk[1]
pos_score = self.rel_indices.expand(B, -1, -1, -1)
pos_score = self.pos_proj(pos_score).permute(0, 3, 1, 2)
patch_score = (q @ k.transpose(-2, -1)) * self.scale
patch_score = patch_score.softmax(dim=-1)
pos_score = pos_score.softmax(dim=-1)
gating = self.gating_param.view(1, -1, 1, 1)
attn = (1. - torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score
attn /= attn.sum(dim=-1).unsqueeze(-1)
attn = self.attn_drop(attn)
return attn
def get_attention_map(self, x, return_map=False):
attn_map = self.get_attention(x).mean(0) # average over batch
distances = self.rel_indices.squeeze()[:, :, -1] ** .5
dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / distances.size(0)
if return_map:
return dist, attn_map
else:
return dist
def local_init(self):
self.v.weight.data.copy_(torch.eye(self.dim))
locality_distance = 1 # max(1,1/locality_strength**.5)
kernel_size = int(self.num_heads ** .5)
center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2
for h1 in range(kernel_size):
for h2 in range(kernel_size):
position = h1 + kernel_size * h2
self.pos_proj.weight.data[position, 2] = -1
self.pos_proj.weight.data[position, 1] = 2 * (h1 - center) * locality_distance
self.pos_proj.weight.data[position, 0] = 2 * (h2 - center) * locality_distance
self.pos_proj.weight.data *= self.locality_strength
def get_rel_indices(self, num_patches: int) -> torch.Tensor:
img_size = int(num_patches ** .5)
rel_indices = torch.zeros(1, num_patches, num_patches, 3)
ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
indx = ind.repeat(img_size, img_size)
indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
indd = indx ** 2 + indy ** 2
rel_indices[:, :, :, 2] = indd.unsqueeze(0)
rel_indices[:, :, :, 1] = indy.unsqueeze(0)
rel_indices[:, :, :, 0] = indx.unsqueeze(0)
device = self.qk.weight.device
return rel_indices.to(device)
class MHSA(nn.Module):
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def get_attention_map(self, x, return_map=False):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn_map = (q @ k.transpose(-2, -1)) * self.scale
attn_map = attn_map.softmax(dim=-1).mean(0)
img_size = int(N ** .5)
ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
indx = ind.repeat(img_size, img_size)
indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
indd = indx ** 2 + indy ** 2
distances = indd ** .5
distances = distances.to(x.device)
dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / N
if return_map:
return dist, attn_map
else:
return dist
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=LayerNorm,
use_gpsa=True,
locality_strength=1.,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.use_gpsa = use_gpsa
if self.use_gpsa:
self.attn = GPSA(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
locality_strength=locality_strength,
)
else:
self.attn = MHSA(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=proj_drop,
)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class ConVit(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
global_pool='token',
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.,
qkv_bias=False,
drop_rate=0.,
pos_drop_rate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
hybrid_backbone=None,
norm_layer=LayerNorm,
local_up_to_layer=3,
locality_strength=1.,
use_pos_embed=True,
):
super().__init__()
assert global_pool in ('', 'avg', 'token')
embed_dim *= num_heads
self.num_classes = num_classes
self.global_pool = global_pool
self.local_up_to_layer = local_up_to_layer
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.locality_strength = locality_strength
self.use_pos_embed = use_pos_embed
if hybrid_backbone is not None:
self.patch_embed = HybridEmbed(
hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
else:
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
)
num_patches = self.patch_embed.num_patches
self.num_patches = num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_drop = nn.Dropout(p=pos_drop_rate)
if self.use_pos_embed:
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
trunc_normal_(self.pos_embed, std=.02)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
use_gpsa=i < local_up_to_layer,
locality_strength=locality_strength,
) for i in range(depth)])
self.norm = norm_layer(embed_dim)
# Classifier head
self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
trunc_normal_(self.cls_token, std=.02)
self.apply(self._init_weights)
for n, m in self.named_modules():
if hasattr(m, 'local_init'):
m.local_init()
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'token', 'avg')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.patch_embed(x)
if self.use_pos_embed:
x = x + self.pos_embed
x = self.pos_drop(x)
cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
for u, blk in enumerate(self.blocks):
if u == self.local_up_to_layer:
x = torch.cat((cls_tokens, x), dim=1)
x = blk(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x[:, 1:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_convit(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
return build_model_with_cfg(ConVit, variant, pretrained, **kwargs)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = generate_default_cfgs({
# ConViT
'convit_tiny.fb_in1k': _cfg(hf_hub_id='timm/'),
'convit_small.fb_in1k': _cfg(hf_hub_id='timm/'),
'convit_base.fb_in1k': _cfg(hf_hub_id='timm/')
})
@register_model
def convit_tiny(pretrained=False, **kwargs) -> ConVit:
model_args = dict(
local_up_to_layer=10, locality_strength=1.0, embed_dim=48, num_heads=4)
model = _create_convit(variant='convit_tiny', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convit_small(pretrained=False, **kwargs) -> ConVit:
model_args = dict(
local_up_to_layer=10, locality_strength=1.0, embed_dim=48, num_heads=9)
model = _create_convit(variant='convit_small', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convit_base(pretrained=False, **kwargs) -> ConVit:
model_args = dict(
local_up_to_layer=10, locality_strength=1.0, embed_dim=48, num_heads=16)
model = _create_convit(variant='convit_base', pretrained=pretrained, **dict(model_args, **kwargs))
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/xception.py
|
"""
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Keras implementation. Achieves the following performance on the validation set:
Loss:0.9173 Prec@1:78.892 Prec@5:94.292
REMEMBER to set your image size to 3x299x299 for both test and validation
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])
The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
"""
import torch.jit
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import create_classifier
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
__all__ = ['Xception']
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1):
super(SeparableConv2d, self).__init__()
self.conv1 = nn.Conv2d(
in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=False)
self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, 1, 1, bias=False)
def forward(self, x):
x = self.conv1(x)
x = self.pointwise(x)
return x
class Block(nn.Module):
def __init__(self, in_channels, out_channels, reps, strides=1, start_with_relu=True, grow_first=True):
super(Block, self).__init__()
if out_channels != in_channels or strides != 1:
self.skip = nn.Conv2d(in_channels, out_channels, 1, stride=strides, bias=False)
self.skipbn = nn.BatchNorm2d(out_channels)
else:
self.skip = None
rep = []
for i in range(reps):
if grow_first:
inc = in_channels if i == 0 else out_channels
outc = out_channels
else:
inc = in_channels
outc = in_channels if i < (reps - 1) else out_channels
rep.append(nn.ReLU(inplace=True))
rep.append(SeparableConv2d(inc, outc, 3, stride=1, padding=1))
rep.append(nn.BatchNorm2d(outc))
if not start_with_relu:
rep = rep[1:]
else:
rep[0] = nn.ReLU(inplace=False)
if strides != 1:
rep.append(nn.MaxPool2d(3, strides, 1))
self.rep = nn.Sequential(*rep)
def forward(self, inp):
x = self.rep(inp)
if self.skip is not None:
skip = self.skip(inp)
skip = self.skipbn(skip)
else:
skip = inp
x += skip
return x
class Xception(nn.Module):
"""
Xception optimized for the ImageNet dataset, as specified in
https://arxiv.org/pdf/1610.02357.pdf
"""
def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'):
""" Constructor
Args:
num_classes: number of classes
"""
super(Xception, self).__init__()
self.drop_rate = drop_rate
self.global_pool = global_pool
self.num_classes = num_classes
self.num_features = 2048
self.conv1 = nn.Conv2d(in_chans, 32, 3, 2, 0, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.act1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
self.bn2 = nn.BatchNorm2d(64)
self.act2 = nn.ReLU(inplace=True)
self.block1 = Block(64, 128, 2, 2, start_with_relu=False)
self.block2 = Block(128, 256, 2, 2)
self.block3 = Block(256, 728, 2, 2)
self.block4 = Block(728, 728, 3, 1)
self.block5 = Block(728, 728, 3, 1)
self.block6 = Block(728, 728, 3, 1)
self.block7 = Block(728, 728, 3, 1)
self.block8 = Block(728, 728, 3, 1)
self.block9 = Block(728, 728, 3, 1)
self.block10 = Block(728, 728, 3, 1)
self.block11 = Block(728, 728, 3, 1)
self.block12 = Block(728, 1024, 2, 2, grow_first=False)
self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
self.bn3 = nn.BatchNorm2d(1536)
self.act3 = nn.ReLU(inplace=True)
self.conv4 = SeparableConv2d(1536, self.num_features, 3, 1, 1)
self.bn4 = nn.BatchNorm2d(self.num_features)
self.act4 = nn.ReLU(inplace=True)
self.feature_info = [
dict(num_chs=64, reduction=2, module='act2'),
dict(num_chs=128, reduction=4, module='block2.rep.0'),
dict(num_chs=256, reduction=8, module='block3.rep.0'),
dict(num_chs=728, reduction=16, module='block12.rep.0'),
dict(num_chs=2048, reduction=32, module='act4'),
]
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
# #------- init weights --------
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^conv[12]|bn[12]',
blocks=[
(r'^block(\d+)', None),
(r'^conv[34]|bn[34]', (99,)),
],
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, "gradient checkpointing not supported"
@torch.jit.ignore
def get_classifier(self):
return self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.block6(x)
x = self.block7(x)
x = self.block8(x)
x = self.block9(x)
x = self.block10(x)
x = self.block11(x)
x = self.block12(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.act3(x)
x = self.conv4(x)
x = self.bn4(x)
x = self.act4(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
if self.drop_rate:
F.dropout(x, self.drop_rate, training=self.training)
return x if pre_logits else self.fc(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _xception(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
Xception, variant, pretrained,
feature_cfg=dict(feature_cls='hook'),
**kwargs)
default_cfgs = generate_default_cfgs({
'legacy_xception.tf_in1k': {
'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth',
'input_size': (3, 299, 299),
'pool_size': (10, 10),
'crop_pct': 0.8975,
'interpolation': 'bicubic',
'mean': (0.5, 0.5, 0.5),
'std': (0.5, 0.5, 0.5),
'num_classes': 1000,
'first_conv': 'conv1',
'classifier': 'fc'
# The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
}
})
@register_model
def legacy_xception(pretrained=False, **kwargs) -> Xception:
return _xception('legacy_xception', pretrained=pretrained, **kwargs)
register_model_deprecations(__name__, {
'xception': 'legacy_xception',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/byoanet.py
|
""" Bring-Your-Own-Attention Network
A flexible network w/ dataclass based config for stacking NN blocks including
self-attention (or similar) layers.
Currently used to implement experimental variants of:
* Bottleneck Transformers
* Lambda ResNets
* HaloNets
Consider all of the models definitions here as experimental WIP and likely to change.
Hacked together by / copyright Ross Wightman, 2021.
"""
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
from .byobnet import ByoBlockCfg, ByoModelCfg, ByobNet, interleave_blocks
__all__ = []
model_cfgs = dict(
botnet26t=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
fixed_input_size=True,
self_attn_layer='bottleneck',
self_attn_kwargs=dict()
),
sebotnet33ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg('self_attn', d=2, c=1536, s=2, gs=0, br=0.333),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
act_layer='silu',
num_features=1280,
attn_layer='se',
self_attn_layer='bottleneck',
self_attn_kwargs=dict()
),
botnet50ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=4, d=4, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
fixed_input_size=True,
self_attn_layer='bottleneck',
self_attn_kwargs=dict()
),
eca_botnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=16, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=16, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
fixed_input_size=True,
act_layer='silu',
attn_layer='eca',
self_attn_layer='bottleneck',
self_attn_kwargs=dict(dim_head=16)
),
halonet_h1=ByoModelCfg(
blocks=(
ByoBlockCfg(type='self_attn', d=3, c=64, s=1, gs=0, br=1.0),
ByoBlockCfg(type='self_attn', d=3, c=128, s=2, gs=0, br=1.0),
ByoBlockCfg(type='self_attn', d=10, c=256, s=2, gs=0, br=1.0),
ByoBlockCfg(type='self_attn', d=3, c=512, s=2, gs=0, br=1.0),
),
stem_chs=64,
stem_type='7x7',
stem_pool='maxpool',
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=8, halo_size=3),
),
halonet26t=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=8, halo_size=2)
),
sehalonet33ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg('self_attn', d=2, c=1536, s=2, gs=0, br=0.333),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
act_layer='silu',
num_features=1280,
attn_layer='se',
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=8, halo_size=3)
),
halonet50ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
interleave_blocks(
types=('bottle', 'self_attn'), every=4, d=4, c=512, s=2, gs=0, br=0.25,
self_attn_layer='halo', self_attn_kwargs=dict(block_size=8, halo_size=3, num_heads=4)),
interleave_blocks(types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=8, halo_size=3)
),
eca_halonext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=16, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=16, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
attn_layer='eca',
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=8, halo_size=2, dim_head=16)
),
lambda_resnet26t=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
self_attn_layer='lambda',
self_attn_kwargs=dict(r=9)
),
lambda_resnet50ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=4, d=4, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
act_layer='silu',
self_attn_layer='lambda',
self_attn_kwargs=dict(r=9)
),
lambda_resnet26rpt_256=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
self_attn_layer='lambda',
self_attn_kwargs=dict(r=None)
),
# experimental
haloregnetz_b=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=3),
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=3),
interleave_blocks(types=('bottle', 'self_attn'), every=3, d=12, c=192, s=2, gs=16, br=3),
ByoBlockCfg('self_attn', d=2, c=288, s=2, gs=16, br=3),
),
stem_chs=32,
stem_pool='',
downsample='',
num_features=1536,
act_layer='silu',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
block_kwargs=dict(bottle_in=True, linear_out=True),
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=7, halo_size=2, qk_ratio=0.33)
),
# experimental
lamhalobotnet50ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
interleave_blocks(
types=('bottle', 'self_attn'), d=4, c=512, s=2, gs=0, br=0.25,
self_attn_layer='lambda', self_attn_kwargs=dict(r=13)),
interleave_blocks(
types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25,
self_attn_layer='halo', self_attn_kwargs=dict(halo_size=3)),
interleave_blocks(
types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25,
self_attn_layer='bottleneck', self_attn_kwargs=dict()),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
act_layer='silu',
),
halo2botnet50ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
interleave_blocks(
types=('bottle', 'self_attn'), d=4, c=512, s=2, gs=0, br=0.25,
self_attn_layer='halo', self_attn_kwargs=dict(halo_size=3)),
interleave_blocks(
types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25,
self_attn_layer='halo', self_attn_kwargs=dict(halo_size=3)),
interleave_blocks(
types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25,
self_attn_layer='bottleneck', self_attn_kwargs=dict()),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
act_layer='silu',
),
)
def _create_byoanet(variant, cfg_variant=None, pretrained=False, **kwargs):
return build_model_with_cfg(
ByobNet, variant, pretrained,
model_cfg=model_cfgs[variant] if not cfg_variant else model_cfgs[cfg_variant],
feature_cfg=dict(flatten_sequential=True),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.95, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
'fixed_input_size': False, 'min_input_size': (3, 224, 224),
**kwargs
}
default_cfgs = generate_default_cfgs({
# GPU-Efficient (ResNet) weights
'botnet26t_256.c1_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/botnet26t_c1_256-167a0e9f.pth',
hf_hub_id='timm/',
fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'sebotnet33ts_256.a1h_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/sebotnet33ts_a1h2_256-957e3c3e.pth',
hf_hub_id='timm/',
fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
'botnet50ts_256.untrained': _cfg(
fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'eca_botnext26ts_256.c1_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_botnext26ts_c_256-95a898f6.pth',
hf_hub_id='timm/',
fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'halonet_h1.untrained': _cfg(input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
'halonet26t.a1h_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halonet26t_a1h_256-3083328c.pth',
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
'sehalonet33ts.ra2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/sehalonet33ts_256-87e053f9.pth',
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
'halonet50ts.a1h_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halonet50ts_a1h2_256-f3a3daee.pth',
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
'eca_halonext26ts.c1_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_halonext26ts_c_256-06906299.pth',
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
'lambda_resnet26t.c1_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26t_c_256-e5a5c857.pth',
hf_hub_id='timm/',
min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
'lambda_resnet50ts.a1h_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet50ts_a1h_256-b87370f7.pth',
hf_hub_id='timm/',
min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
'lambda_resnet26rpt_256.c1_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26rpt_c_256-ab00292d.pth',
hf_hub_id='timm/',
fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
'haloregnetz_b.ra3_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/haloregnetz_c_raa_256-c8ad7616.pth',
hf_hub_id='timm/',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
first_conv='stem.conv', input_size=(3, 224, 224), pool_size=(7, 7), min_input_size=(3, 224, 224), crop_pct=0.94),
'lamhalobotnet50ts_256.a1h_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lamhalobotnet50ts_a1h2_256-fe3d9445.pth',
hf_hub_id='timm/',
fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'halo2botnet50ts_256.a1h_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halo2botnet50ts_a1h2_256-fd9c11a3.pth',
hf_hub_id='timm/',
fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
})
@register_model
def botnet26t_256(pretrained=False, **kwargs) -> ByobNet:
""" Bottleneck Transformer w/ ResNet26-T backbone.
"""
kwargs.setdefault('img_size', 256)
return _create_byoanet('botnet26t_256', 'botnet26t', pretrained=pretrained, **kwargs)
@register_model
def sebotnet33ts_256(pretrained=False, **kwargs) -> ByobNet:
""" Bottleneck Transformer w/ a ResNet33-t backbone, SE attn for non Halo blocks, SiLU,
"""
return _create_byoanet('sebotnet33ts_256', 'sebotnet33ts', pretrained=pretrained, **kwargs)
@register_model
def botnet50ts_256(pretrained=False, **kwargs) -> ByobNet:
""" Bottleneck Transformer w/ ResNet50-T backbone, silu act.
"""
kwargs.setdefault('img_size', 256)
return _create_byoanet('botnet50ts_256', 'botnet50ts', pretrained=pretrained, **kwargs)
@register_model
def eca_botnext26ts_256(pretrained=False, **kwargs) -> ByobNet:
""" Bottleneck Transformer w/ ResNet26-T backbone, silu act.
"""
kwargs.setdefault('img_size', 256)
return _create_byoanet('eca_botnext26ts_256', 'eca_botnext26ts', pretrained=pretrained, **kwargs)
@register_model
def halonet_h1(pretrained=False, **kwargs) -> ByobNet:
""" HaloNet-H1. Halo attention in all stages as per the paper.
NOTE: This runs very slowly!
"""
return _create_byoanet('halonet_h1', pretrained=pretrained, **kwargs)
@register_model
def halonet26t(pretrained=False, **kwargs) -> ByobNet:
""" HaloNet w/ a ResNet26-t backbone. Halo attention in final two stages
"""
return _create_byoanet('halonet26t', pretrained=pretrained, **kwargs)
@register_model
def sehalonet33ts(pretrained=False, **kwargs) -> ByobNet:
""" HaloNet w/ a ResNet33-t backbone, SE attn for non Halo blocks, SiLU, 1-2 Halo in stage 2,3,4.
"""
return _create_byoanet('sehalonet33ts', pretrained=pretrained, **kwargs)
@register_model
def halonet50ts(pretrained=False, **kwargs) -> ByobNet:
""" HaloNet w/ a ResNet50-t backbone, silu act. Halo attention in final two stages
"""
return _create_byoanet('halonet50ts', pretrained=pretrained, **kwargs)
@register_model
def eca_halonext26ts(pretrained=False, **kwargs) -> ByobNet:
""" HaloNet w/ a ResNet26-t backbone, silu act. Halo attention in final two stages
"""
return _create_byoanet('eca_halonext26ts', pretrained=pretrained, **kwargs)
@register_model
def lambda_resnet26t(pretrained=False, **kwargs) -> ByobNet:
""" Lambda-ResNet-26-T. Lambda layers w/ conv pos in last two stages.
"""
return _create_byoanet('lambda_resnet26t', pretrained=pretrained, **kwargs)
@register_model
def lambda_resnet50ts(pretrained=False, **kwargs) -> ByobNet:
""" Lambda-ResNet-50-TS. SiLU act. Lambda layers w/ conv pos in last two stages.
"""
return _create_byoanet('lambda_resnet50ts', pretrained=pretrained, **kwargs)
@register_model
def lambda_resnet26rpt_256(pretrained=False, **kwargs) -> ByobNet:
""" Lambda-ResNet-26-R-T. Lambda layers w/ rel pos embed in last two stages.
"""
kwargs.setdefault('img_size', 256)
return _create_byoanet('lambda_resnet26rpt_256', pretrained=pretrained, **kwargs)
@register_model
def haloregnetz_b(pretrained=False, **kwargs) -> ByobNet:
""" Halo + RegNetZ
"""
return _create_byoanet('haloregnetz_b', pretrained=pretrained, **kwargs)
@register_model
def lamhalobotnet50ts_256(pretrained=False, **kwargs) -> ByobNet:
""" Combo Attention (Lambda + Halo + Bot) Network
"""
return _create_byoanet('lamhalobotnet50ts_256', 'lamhalobotnet50ts', pretrained=pretrained, **kwargs)
@register_model
def halo2botnet50ts_256(pretrained=False, **kwargs) -> ByobNet:
""" Combo Attention (Halo + Halo + Bot) Network
"""
return _create_byoanet('halo2botnet50ts_256', 'halo2botnet50ts', pretrained=pretrained, **kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/crossvit.py
|
""" CrossViT Model
@inproceedings{
chen2021crossvit,
title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
booktitle={International Conference on Computer Vision (ICCV)},
year={2021}
}
Paper link: https://arxiv.org/abs/2103.14899
Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
NOTE: model names have been renamed from originals to represent actual input res all *_224 -> *_240 and *_384 -> *_408
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# Copyright IBM All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Modifed from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
from functools import partial
from typing import List
from typing import Tuple
import torch
import torch.hub
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, to_2tuple, trunc_normal_, _assert
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._registry import register_model, generate_default_cfgs
from .vision_transformer import Block
__all__ = ['CrossVit'] # model_registry will add each entrypoint fn to this
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, multi_conv=False):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
if multi_conv:
if patch_size[0] == 12:
self.proj = nn.Sequential(
nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
nn.ReLU(inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=3, padding=0),
nn.ReLU(inplace=True),
nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=1, padding=1),
)
elif patch_size[0] == 16:
self.proj = nn.Sequential(
nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
nn.ReLU(inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=2, padding=1),
)
else:
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
_assert(H == self.img_size[0],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
_assert(W == self.img_size[1],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class CrossAttention(nn.Module):
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = head_dim ** -0.5
self.wq = nn.Linear(dim, dim, bias=qkv_bias)
self.wk = nn.Linear(dim, dim, bias=qkv_bias)
self.wv = nn.Linear(dim, dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
# B1C -> B1H(C/H) -> BH1(C/H)
q = self.wq(x[:, 0:1, ...]).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
# BNC -> BNH(C/H) -> BHN(C/H)
k = self.wk(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
# BNC -> BNH(C/H) -> BHN(C/H)
v = self.wv(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
attn = (q @ k.transpose(-2, -1)) * self.scale # BH1(C/H) @ BH(C/H)N -> BH1N
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, 1, C) # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
x = self.proj(x)
x = self.proj_drop(x)
return x
class CrossAttentionBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = CrossAttention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
x = x[:, 0:1, ...] + self.drop_path(self.attn(self.norm1(x)))
return x
class MultiScaleBlock(nn.Module):
def __init__(
self,
dim,
patches,
depth,
num_heads,
mlp_ratio,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
num_branches = len(dim)
self.num_branches = num_branches
# different branch could have different embedding size, the first one is the base
self.blocks = nn.ModuleList()
for d in range(num_branches):
tmp = []
for i in range(depth[d]):
tmp.append(Block(
dim=dim[d],
num_heads=num_heads[d],
mlp_ratio=mlp_ratio[d],
qkv_bias=qkv_bias,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[i],
norm_layer=norm_layer,
))
if len(tmp) != 0:
self.blocks.append(nn.Sequential(*tmp))
if len(self.blocks) == 0:
self.blocks = None
self.projs = nn.ModuleList()
for d in range(num_branches):
if dim[d] == dim[(d + 1) % num_branches] and False:
tmp = [nn.Identity()]
else:
tmp = [norm_layer(dim[d]), act_layer(), nn.Linear(dim[d], dim[(d + 1) % num_branches])]
self.projs.append(nn.Sequential(*tmp))
self.fusion = nn.ModuleList()
for d in range(num_branches):
d_ = (d + 1) % num_branches
nh = num_heads[d_]
if depth[-1] == 0: # backward capability:
self.fusion.append(
CrossAttentionBlock(
dim=dim[d_],
num_heads=nh,
mlp_ratio=mlp_ratio[d],
qkv_bias=qkv_bias,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[-1],
norm_layer=norm_layer,
))
else:
tmp = []
for _ in range(depth[-1]):
tmp.append(CrossAttentionBlock(
dim=dim[d_],
num_heads=nh,
mlp_ratio=mlp_ratio[d],
qkv_bias=qkv_bias,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[-1],
norm_layer=norm_layer,
))
self.fusion.append(nn.Sequential(*tmp))
self.revert_projs = nn.ModuleList()
for d in range(num_branches):
if dim[(d + 1) % num_branches] == dim[d] and False:
tmp = [nn.Identity()]
else:
tmp = [norm_layer(dim[(d + 1) % num_branches]), act_layer(),
nn.Linear(dim[(d + 1) % num_branches], dim[d])]
self.revert_projs.append(nn.Sequential(*tmp))
def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
outs_b = []
for i, block in enumerate(self.blocks):
outs_b.append(block(x[i]))
# only take the cls token out
proj_cls_token = torch.jit.annotate(List[torch.Tensor], [])
for i, proj in enumerate(self.projs):
proj_cls_token.append(proj(outs_b[i][:, 0:1, ...]))
# cross attention
outs = []
for i, (fusion, revert_proj) in enumerate(zip(self.fusion, self.revert_projs)):
tmp = torch.cat((proj_cls_token[i], outs_b[(i + 1) % self.num_branches][:, 1:, ...]), dim=1)
tmp = fusion(tmp)
reverted_proj_cls_token = revert_proj(tmp[:, 0:1, ...])
tmp = torch.cat((reverted_proj_cls_token, outs_b[i][:, 1:, ...]), dim=1)
outs.append(tmp)
return outs
def _compute_num_patches(img_size, patches):
return [i[0] // p * i[1] // p for i, p in zip(img_size, patches)]
@register_notrace_function
def scale_image(x, ss: Tuple[int, int], crop_scale: bool = False): # annotations for torchscript
"""
Pulled out of CrossViT.forward_features to bury conditional logic in a leaf node for FX tracing.
Args:
x (Tensor): input image
ss (tuple[int, int]): height and width to scale to
crop_scale (bool): whether to crop instead of interpolate to achieve the desired scale. Defaults to False
Returns:
Tensor: the "scaled" image batch tensor
"""
H, W = x.shape[-2:]
if H != ss[0] or W != ss[1]:
if crop_scale and ss[0] <= H and ss[1] <= W:
cu, cl = int(round((H - ss[0]) / 2.)), int(round((W - ss[1]) / 2.))
x = x[:, :, cu:cu + ss[0], cl:cl + ss[1]]
else:
x = torch.nn.functional.interpolate(x, size=ss, mode='bicubic', align_corners=False)
return x
class CrossVit(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(
self,
img_size=224,
img_scale=(1.0, 1.0),
patch_size=(8, 16),
in_chans=3,
num_classes=1000,
embed_dim=(192, 384),
depth=((1, 3, 1), (1, 3, 1), (1, 3, 1)),
num_heads=(6, 12),
mlp_ratio=(2., 2., 4.),
multi_conv=False,
crop_scale=False,
qkv_bias=True,
drop_rate=0.,
pos_drop_rate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
global_pool='token',
):
super().__init__()
assert global_pool in ('token', 'avg')
self.num_classes = num_classes
self.global_pool = global_pool
self.img_size = to_2tuple(img_size)
img_scale = to_2tuple(img_scale)
self.img_size_scaled = [tuple([int(sj * si) for sj in self.img_size]) for si in img_scale]
self.crop_scale = crop_scale # crop instead of interpolate for scale
num_patches = _compute_num_patches(self.img_size_scaled, patch_size)
self.num_branches = len(patch_size)
self.embed_dim = embed_dim
self.num_features = sum(embed_dim)
self.patch_embed = nn.ModuleList()
# hard-coded for torch jit script
for i in range(self.num_branches):
setattr(self, f'pos_embed_{i}', nn.Parameter(torch.zeros(1, 1 + num_patches[i], embed_dim[i])))
setattr(self, f'cls_token_{i}', nn.Parameter(torch.zeros(1, 1, embed_dim[i])))
for im_s, p, d in zip(self.img_size_scaled, patch_size, embed_dim):
self.patch_embed.append(
PatchEmbed(
img_size=im_s,
patch_size=p,
in_chans=in_chans,
embed_dim=d,
multi_conv=multi_conv,
))
self.pos_drop = nn.Dropout(p=pos_drop_rate)
total_depth = sum([sum(x[-2:]) for x in depth])
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, total_depth)] # stochastic depth decay rule
dpr_ptr = 0
self.blocks = nn.ModuleList()
for idx, block_cfg in enumerate(depth):
curr_depth = max(block_cfg[:-1]) + block_cfg[-1]
dpr_ = dpr[dpr_ptr:dpr_ptr + curr_depth]
blk = MultiScaleBlock(
embed_dim,
num_patches,
block_cfg,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr_,
norm_layer=norm_layer,
)
dpr_ptr += curr_depth
self.blocks.append(blk)
self.norm = nn.ModuleList([norm_layer(embed_dim[i]) for i in range(self.num_branches)])
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.ModuleList([
nn.Linear(embed_dim[i], num_classes) if num_classes > 0 else nn.Identity()
for i in range(self.num_branches)])
for i in range(self.num_branches):
trunc_normal_(getattr(self, f'pos_embed_{i}'), std=.02)
trunc_normal_(getattr(self, f'cls_token_{i}'), std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
out = set()
for i in range(self.num_branches):
out.add(f'cls_token_{i}')
pe = getattr(self, f'pos_embed_{i}', None)
if pe is not None and pe.requires_grad:
out.add(f'pos_embed_{i}')
return out
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('token', 'avg')
self.global_pool = global_pool
self.head = nn.ModuleList(
[nn.Linear(self.embed_dim[i], num_classes) if num_classes > 0 else nn.Identity() for i in
range(self.num_branches)])
def forward_features(self, x) -> List[torch.Tensor]:
B = x.shape[0]
xs = []
for i, patch_embed in enumerate(self.patch_embed):
x_ = x
ss = self.img_size_scaled[i]
x_ = scale_image(x_, ss, self.crop_scale)
x_ = patch_embed(x_)
cls_tokens = self.cls_token_0 if i == 0 else self.cls_token_1 # hard-coded for torch jit script
cls_tokens = cls_tokens.expand(B, -1, -1)
x_ = torch.cat((cls_tokens, x_), dim=1)
pos_embed = self.pos_embed_0 if i == 0 else self.pos_embed_1 # hard-coded for torch jit script
x_ = x_ + pos_embed
x_ = self.pos_drop(x_)
xs.append(x_)
for i, blk in enumerate(self.blocks):
xs = blk(xs)
# NOTE: was before branch token section, move to here to assure all branch token are before layer norm
xs = [norm(xs[i]) for i, norm in enumerate(self.norm)]
return xs
def forward_head(self, xs: List[torch.Tensor], pre_logits: bool = False) -> torch.Tensor:
xs = [x[:, 1:].mean(dim=1) for x in xs] if self.global_pool == 'avg' else [x[:, 0] for x in xs]
xs = [self.head_drop(x) for x in xs]
if pre_logits or isinstance(self.head[0], nn.Identity):
return torch.cat([x for x in xs], dim=1)
return torch.mean(torch.stack([head(xs[i]) for i, head in enumerate(self.head)], dim=0), dim=0)
def forward(self, x):
xs = self.forward_features(x)
x = self.forward_head(xs)
return x
def _create_crossvit(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
def pretrained_filter_fn(state_dict):
new_state_dict = {}
for key in state_dict.keys():
if 'pos_embed' in key or 'cls_token' in key:
new_key = key.replace(".", "_")
else:
new_key = key
new_state_dict[new_key] = state_dict[key]
return new_state_dict
return build_model_with_cfg(
CrossVit,
variant,
pretrained,
pretrained_filter_fn=pretrained_filter_fn,
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None, 'crop_pct': 0.875,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
'first_conv': ('patch_embed.0.proj', 'patch_embed.1.proj'),
'classifier': ('head.0', 'head.1'),
**kwargs
}
default_cfgs = generate_default_cfgs({
'crossvit_15_240.in1k': _cfg(hf_hub_id='timm/'),
'crossvit_15_dagger_240.in1k': _cfg(
hf_hub_id='timm/',
first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
),
'crossvit_15_dagger_408.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'), crop_pct=1.0,
),
'crossvit_18_240.in1k': _cfg(hf_hub_id='timm/'),
'crossvit_18_dagger_240.in1k': _cfg(
hf_hub_id='timm/',
first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
),
'crossvit_18_dagger_408.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'), crop_pct=1.0,
),
'crossvit_9_240.in1k': _cfg(hf_hub_id='timm/'),
'crossvit_9_dagger_240.in1k': _cfg(
hf_hub_id='timm/',
first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
),
'crossvit_base_240.in1k': _cfg(hf_hub_id='timm/'),
'crossvit_small_240.in1k': _cfg(hf_hub_id='timm/'),
'crossvit_tiny_240.in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def crossvit_tiny_240(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[96, 192], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
num_heads=[3, 3], mlp_ratio=[4, 4, 1])
model = _create_crossvit(variant='crossvit_tiny_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_small_240(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
num_heads=[6, 6], mlp_ratio=[4, 4, 1])
model = _create_crossvit(variant='crossvit_small_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_base_240(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[384, 768], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
num_heads=[12, 12], mlp_ratio=[4, 4, 1])
model = _create_crossvit(variant='crossvit_base_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_9_240(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
num_heads=[4, 4], mlp_ratio=[3, 3, 1])
model = _create_crossvit(variant='crossvit_9_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_15_240(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
num_heads=[6, 6], mlp_ratio=[3, 3, 1])
model = _create_crossvit(variant='crossvit_15_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_18_240(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 224 / 240), patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
num_heads=[7, 7], mlp_ratio=[3, 3, 1], **kwargs)
model = _create_crossvit(variant='crossvit_18_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_9_dagger_240(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 224 / 240), patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
num_heads=[4, 4], mlp_ratio=[3, 3, 1], multi_conv=True)
model = _create_crossvit(variant='crossvit_9_dagger_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_15_dagger_240(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
num_heads=[6, 6], mlp_ratio=[3, 3, 1], multi_conv=True)
model = _create_crossvit(variant='crossvit_15_dagger_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_15_dagger_408(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 384/408), patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
num_heads=[6, 6], mlp_ratio=[3, 3, 1], multi_conv=True)
model = _create_crossvit(variant='crossvit_15_dagger_408', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_18_dagger_240(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
num_heads=[7, 7], mlp_ratio=[3, 3, 1], multi_conv=True)
model = _create_crossvit(variant='crossvit_18_dagger_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def crossvit_18_dagger_408(pretrained=False, **kwargs) -> CrossVit:
model_args = dict(
img_scale=(1.0, 384/408), patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
num_heads=[7, 7], mlp_ratio=[3, 3, 1], multi_conv=True)
model = _create_crossvit(variant='crossvit_18_dagger_408', pretrained=pretrained, **dict(model_args, **kwargs))
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/visformer.py
|
""" Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import to_2tuple, trunc_normal_, DropPath, PatchEmbed, LayerNorm2d, create_classifier, use_fused_attn
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
__all__ = ['Visformer']
class SpatialMlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.,
group=8,
spatial_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
drop_probs = to_2tuple(drop)
self.in_features = in_features
self.out_features = out_features
self.spatial_conv = spatial_conv
if self.spatial_conv:
if group < 2: # net setting
hidden_features = in_features * 5 // 6
else:
hidden_features = in_features * 2
self.hidden_features = hidden_features
self.group = group
self.conv1 = nn.Conv2d(in_features, hidden_features, 1, stride=1, padding=0, bias=False)
self.act1 = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
if self.spatial_conv:
self.conv2 = nn.Conv2d(
hidden_features, hidden_features, 3, stride=1, padding=1, groups=self.group, bias=False)
self.act2 = act_layer()
else:
self.conv2 = None
self.act2 = None
self.conv3 = nn.Conv2d(hidden_features, out_features, 1, stride=1, padding=0, bias=False)
self.drop3 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.conv1(x)
x = self.act1(x)
x = self.drop1(x)
if self.conv2 is not None:
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
x = self.drop3(x)
return x
class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, head_dim_ratio=1., attn_drop=0., proj_drop=0.):
super().__init__()
self.dim = dim
self.num_heads = num_heads
head_dim = round(dim // num_heads * head_dim_ratio)
self.head_dim = head_dim
self.scale = head_dim ** -0.5
self.fused_attn = use_fused_attn(experimental=True)
self.qkv = nn.Conv2d(dim, head_dim * num_heads * 3, 1, stride=1, padding=0, bias=False)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Conv2d(self.head_dim * self.num_heads, dim, 1, stride=1, padding=0, bias=False)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, C, H, W = x.shape
x = self.qkv(x).reshape(B, 3, self.num_heads, self.head_dim, -1).permute(1, 0, 2, 4, 3)
q, k, v = x.unbind(0)
if self.fused_attn:
x = torch.nn.functional.scaled_dot_product_attention(
q.contiguous(), k.contiguous(), v.contiguous(),
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.permute(0, 1, 3, 2).reshape(B, -1, H, W)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
head_dim_ratio=1.,
mlp_ratio=4.,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=LayerNorm2d,
group=8,
attn_disabled=False,
spatial_conv=False,
):
super().__init__()
self.spatial_conv = spatial_conv
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
if attn_disabled:
self.norm1 = None
self.attn = None
else:
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
head_dim_ratio=head_dim_ratio,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.norm2 = norm_layer(dim)
self.mlp = SpatialMlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
group=group,
spatial_conv=spatial_conv,
)
def forward(self, x):
if self.attn is not None:
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class Visformer(nn.Module):
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
init_channels=32,
embed_dim=384,
depth=12,
num_heads=6,
mlp_ratio=4.,
drop_rate=0.,
pos_drop_rate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=LayerNorm2d,
attn_stage='111',
use_pos_embed=True,
spatial_conv='111',
vit_stem=False,
group=8,
global_pool='avg',
conv_init=False,
embed_norm=None,
):
super().__init__()
img_size = to_2tuple(img_size)
self.num_classes = num_classes
self.embed_dim = embed_dim
self.init_channels = init_channels
self.img_size = img_size
self.vit_stem = vit_stem
self.conv_init = conv_init
if isinstance(depth, (list, tuple)):
self.stage_num1, self.stage_num2, self.stage_num3 = depth
depth = sum(depth)
else:
self.stage_num1 = self.stage_num3 = depth // 3
self.stage_num2 = depth - self.stage_num1 - self.stage_num3
self.use_pos_embed = use_pos_embed
self.grad_checkpointing = False
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
# stage 1
if self.vit_stem:
self.stem = None
self.patch_embed1 = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=embed_norm,
flatten=False,
)
img_size = [x // patch_size for x in img_size]
else:
if self.init_channels is None:
self.stem = None
self.patch_embed1 = PatchEmbed(
img_size=img_size,
patch_size=patch_size // 2,
in_chans=in_chans,
embed_dim=embed_dim // 2,
norm_layer=embed_norm,
flatten=False,
)
img_size = [x // (patch_size // 2) for x in img_size]
else:
self.stem = nn.Sequential(
nn.Conv2d(in_chans, self.init_channels, 7, stride=2, padding=3, bias=False),
nn.BatchNorm2d(self.init_channels),
nn.ReLU(inplace=True)
)
img_size = [x // 2 for x in img_size]
self.patch_embed1 = PatchEmbed(
img_size=img_size,
patch_size=patch_size // 4,
in_chans=self.init_channels,
embed_dim=embed_dim // 2,
norm_layer=embed_norm,
flatten=False,
)
img_size = [x // (patch_size // 4) for x in img_size]
if self.use_pos_embed:
if self.vit_stem:
self.pos_embed1 = nn.Parameter(torch.zeros(1, embed_dim, *img_size))
else:
self.pos_embed1 = nn.Parameter(torch.zeros(1, embed_dim//2, *img_size))
self.pos_drop = nn.Dropout(p=pos_drop_rate)
else:
self.pos_embed1 = None
self.stage1 = nn.Sequential(*[
Block(
dim=embed_dim//2,
num_heads=num_heads,
head_dim_ratio=0.5,
mlp_ratio=mlp_ratio,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
group=group,
attn_disabled=(attn_stage[0] == '0'),
spatial_conv=(spatial_conv[0] == '1'),
)
for i in range(self.stage_num1)
])
# stage2
if not self.vit_stem:
self.patch_embed2 = PatchEmbed(
img_size=img_size,
patch_size=patch_size // 8,
in_chans=embed_dim // 2,
embed_dim=embed_dim,
norm_layer=embed_norm,
flatten=False,
)
img_size = [x // (patch_size // 8) for x in img_size]
if self.use_pos_embed:
self.pos_embed2 = nn.Parameter(torch.zeros(1, embed_dim, *img_size))
else:
self.pos_embed2 = None
else:
self.patch_embed2 = None
self.stage2 = nn.Sequential(*[
Block(
dim=embed_dim,
num_heads=num_heads,
head_dim_ratio=1.0,
mlp_ratio=mlp_ratio,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
group=group,
attn_disabled=(attn_stage[1] == '0'),
spatial_conv=(spatial_conv[1] == '1'),
)
for i in range(self.stage_num1, self.stage_num1+self.stage_num2)
])
# stage 3
if not self.vit_stem:
self.patch_embed3 = PatchEmbed(
img_size=img_size,
patch_size=patch_size // 8,
in_chans=embed_dim,
embed_dim=embed_dim * 2,
norm_layer=embed_norm,
flatten=False,
)
img_size = [x // (patch_size // 8) for x in img_size]
if self.use_pos_embed:
self.pos_embed3 = nn.Parameter(torch.zeros(1, embed_dim*2, *img_size))
else:
self.pos_embed3 = None
else:
self.patch_embed3 = None
self.stage3 = nn.Sequential(*[
Block(
dim=embed_dim * 2,
num_heads=num_heads,
head_dim_ratio=1.0,
mlp_ratio=mlp_ratio,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
group=group,
attn_disabled=(attn_stage[2] == '0'),
spatial_conv=(spatial_conv[2] == '1'),
)
for i in range(self.stage_num1+self.stage_num2, depth)
])
self.num_features = embed_dim if self.vit_stem else embed_dim * 2
self.norm = norm_layer(self.num_features)
# head
global_pool, head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
self.global_pool = global_pool
self.head_drop = nn.Dropout(drop_rate)
self.head = head
# weights init
if self.use_pos_embed:
trunc_normal_(self.pos_embed1, std=0.02)
if not self.vit_stem:
trunc_normal_(self.pos_embed2, std=0.02)
trunc_normal_(self.pos_embed3, std=0.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
if self.conv_init:
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
else:
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0.)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^patch_embed1|pos_embed1|stem', # stem and embed
blocks=[
(r'^stage(\d+)\.(\d+)' if coarse else r'^stage(\d+)\.(\d+)', None),
(r'^(?:patch_embed|pos_embed)(\d+)', (0,)),
(r'^norm', (99999,))
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
if self.stem is not None:
x = self.stem(x)
# stage 1
x = self.patch_embed1(x)
if self.pos_embed1 is not None:
x = self.pos_drop(x + self.pos_embed1)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stage1, x)
else:
x = self.stage1(x)
# stage 2
if self.patch_embed2 is not None:
x = self.patch_embed2(x)
if self.pos_embed2 is not None:
x = self.pos_drop(x + self.pos_embed2)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stage2, x)
else:
x = self.stage2(x)
# stage3
if self.patch_embed3 is not None:
x = self.patch_embed3(x)
if self.pos_embed3 is not None:
x = self.pos_drop(x + self.pos_embed3)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stage3, x)
else:
x = self.stage3(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_visformer(variant, pretrained=False, default_cfg=None, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
model = build_model_with_cfg(Visformer, variant, pretrained, **kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0', 'classifier': 'head',
**kwargs
}
default_cfgs = generate_default_cfgs({
'visformer_tiny.in1k': _cfg(hf_hub_id='timm/'),
'visformer_small.in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def visformer_tiny(pretrained=False, **kwargs) -> Visformer:
model_cfg = dict(
init_channels=16, embed_dim=192, depth=(7, 4, 4), num_heads=3, mlp_ratio=4., group=8,
attn_stage='011', spatial_conv='100', norm_layer=nn.BatchNorm2d, conv_init=True,
embed_norm=nn.BatchNorm2d)
model = _create_visformer('visformer_tiny', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def visformer_small(pretrained=False, **kwargs) -> Visformer:
model_cfg = dict(
init_channels=32, embed_dim=384, depth=(7, 4, 4), num_heads=6, mlp_ratio=4., group=8,
attn_stage='011', spatial_conv='100', norm_layer=nn.BatchNorm2d, conv_init=True,
embed_norm=nn.BatchNorm2d)
model = _create_visformer('visformer_small', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
# @register_model
# def visformer_net1(pretrained=False, **kwargs):
# model = Visformer(
# init_channels=None, embed_dim=384, depth=(0, 12, 0), num_heads=6, mlp_ratio=4., attn_stage='111',
# spatial_conv='000', vit_stem=True, conv_init=True, **kwargs)
# model.default_cfg = _cfg()
# return model
#
#
# @register_model
# def visformer_net2(pretrained=False, **kwargs):
# model = Visformer(
# init_channels=32, embed_dim=384, depth=(0, 12, 0), num_heads=6, mlp_ratio=4., attn_stage='111',
# spatial_conv='000', vit_stem=False, conv_init=True, **kwargs)
# model.default_cfg = _cfg()
# return model
#
#
# @register_model
# def visformer_net3(pretrained=False, **kwargs):
# model = Visformer(
# init_channels=32, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4., attn_stage='111',
# spatial_conv='000', vit_stem=False, conv_init=True, **kwargs)
# model.default_cfg = _cfg()
# return model
#
#
# @register_model
# def visformer_net4(pretrained=False, **kwargs):
# model = Visformer(
# init_channels=32, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4., attn_stage='111',
# spatial_conv='000', vit_stem=False, conv_init=True, **kwargs)
# model.default_cfg = _cfg()
# return model
#
#
# @register_model
# def visformer_net5(pretrained=False, **kwargs):
# model = Visformer(
# init_channels=32, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4., group=1, attn_stage='111',
# spatial_conv='111', vit_stem=False, conv_init=True, **kwargs)
# model.default_cfg = _cfg()
# return model
#
#
# @register_model
# def visformer_net6(pretrained=False, **kwargs):
# model = Visformer(
# init_channels=32, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4., group=1, attn_stage='111',
# pos_embed=False, spatial_conv='111', conv_init=True, **kwargs)
# model.default_cfg = _cfg()
# return model
#
#
# @register_model
# def visformer_net7(pretrained=False, **kwargs):
# model = Visformer(
# init_channels=32, embed_dim=384, depth=(6, 7, 7), num_heads=6, group=1, attn_stage='000',
# pos_embed=False, spatial_conv='111', conv_init=True, **kwargs)
# model.default_cfg = _cfg()
# return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/hardcorenas.py
|
from functools import partial
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from ._builder import build_model_with_cfg
from ._builder import pretrained_cfg_for_features
from ._efficientnet_blocks import SqueezeExcite
from ._efficientnet_builder import decode_arch_def, resolve_act_layer, resolve_bn_args, round_channels
from ._registry import register_model, generate_default_cfgs
from .mobilenetv3 import MobileNetV3, MobileNetV3Features
__all__ = [] # model_registry will add each entrypoint fn to this
def _gen_hardcorenas(pretrained, variant, arch_def, **kwargs):
"""Creates a hardcorenas model
Ref impl: https://github.com/Alibaba-MIIL/HardCoReNAS
Paper: https://arxiv.org/abs/2102.11646
"""
num_features = 1280
se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU, rd_round_fn=round_channels)
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
num_features=num_features,
stem_size=32,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_layer=se_layer,
**kwargs,
)
features_only = False
model_cls = MobileNetV3
kwargs_filter = None
if model_kwargs.pop('features_only', False):
features_only = True
kwargs_filter = ('num_classes', 'num_features', 'global_pool', 'head_conv', 'head_bias', 'global_pool')
model_cls = MobileNetV3Features
model = build_model_with_cfg(
model_cls,
variant,
pretrained,
pretrained_strict=not features_only,
kwargs_filter=kwargs_filter,
**model_kwargs,
)
if features_only:
model.default_cfg = pretrained_cfg_for_features(model.default_cfg)
return model
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv_stem', 'classifier': 'classifier',
**kwargs
}
default_cfgs = generate_default_cfgs({
'hardcorenas_a.miil_green_in1k': _cfg(hf_hub_id='timm/'),
'hardcorenas_b.miil_green_in1k': _cfg(hf_hub_id='timm/'),
'hardcorenas_c.miil_green_in1k': _cfg(hf_hub_id='timm/'),
'hardcorenas_d.miil_green_in1k': _cfg(hf_hub_id='timm/'),
'hardcorenas_e.miil_green_in1k': _cfg(hf_hub_id='timm/'),
'hardcorenas_f.miil_green_in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def hardcorenas_a(pretrained=False, **kwargs) -> MobileNetV3:
""" hardcorenas_A """
arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre', 'ir_r1_k5_s1_e3_c24_nre_se0.25'],
['ir_r1_k5_s2_e3_c40_nre', 'ir_r1_k5_s1_e6_c40_nre_se0.25'],
['ir_r1_k5_s2_e6_c80_se0.25', 'ir_r1_k5_s1_e6_c80_se0.25'],
['ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25'],
['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25'], ['cn_r1_k1_s1_c960']]
model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_a', arch_def=arch_def, **kwargs)
return model
@register_model
def hardcorenas_b(pretrained=False, **kwargs) -> MobileNetV3:
""" hardcorenas_B """
arch_def = [['ds_r1_k3_s1_e1_c16_nre'],
['ir_r1_k5_s2_e3_c24_nre', 'ir_r1_k5_s1_e3_c24_nre_se0.25', 'ir_r1_k3_s1_e3_c24_nre'],
['ir_r1_k5_s2_e3_c40_nre', 'ir_r1_k5_s1_e3_c40_nre', 'ir_r1_k5_s1_e3_c40_nre'],
['ir_r1_k5_s2_e3_c80', 'ir_r1_k5_s1_e3_c80', 'ir_r1_k3_s1_e3_c80', 'ir_r1_k3_s1_e3_c80'],
['ir_r1_k5_s1_e3_c112', 'ir_r1_k3_s1_e3_c112', 'ir_r1_k3_s1_e3_c112', 'ir_r1_k3_s1_e3_c112'],
['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k3_s1_e3_c192_se0.25'],
['cn_r1_k1_s1_c960']]
model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_b', arch_def=arch_def, **kwargs)
return model
@register_model
def hardcorenas_c(pretrained=False, **kwargs) -> MobileNetV3:
""" hardcorenas_C """
arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre', 'ir_r1_k5_s1_e3_c24_nre_se0.25'],
['ir_r1_k5_s2_e3_c40_nre', 'ir_r1_k5_s1_e3_c40_nre', 'ir_r1_k5_s1_e3_c40_nre',
'ir_r1_k5_s1_e3_c40_nre'],
['ir_r1_k5_s2_e4_c80', 'ir_r1_k5_s1_e6_c80_se0.25', 'ir_r1_k3_s1_e3_c80', 'ir_r1_k3_s1_e3_c80'],
['ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k3_s1_e3_c112', 'ir_r1_k3_s1_e3_c112', 'ir_r1_k3_s1_e3_c112'],
['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k3_s1_e3_c192_se0.25'],
['cn_r1_k1_s1_c960']]
model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_c', arch_def=arch_def, **kwargs)
return model
@register_model
def hardcorenas_d(pretrained=False, **kwargs) -> MobileNetV3:
""" hardcorenas_D """
arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre_se0.25', 'ir_r1_k5_s1_e3_c24_nre_se0.25'],
['ir_r1_k5_s2_e3_c40_nre_se0.25', 'ir_r1_k5_s1_e4_c40_nre_se0.25', 'ir_r1_k3_s1_e3_c40_nre_se0.25'],
['ir_r1_k5_s2_e4_c80_se0.25', 'ir_r1_k3_s1_e3_c80_se0.25', 'ir_r1_k3_s1_e3_c80_se0.25',
'ir_r1_k3_s1_e3_c80_se0.25'],
['ir_r1_k3_s1_e4_c112_se0.25', 'ir_r1_k5_s1_e4_c112_se0.25', 'ir_r1_k3_s1_e3_c112_se0.25',
'ir_r1_k5_s1_e3_c112_se0.25'],
['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25',
'ir_r1_k3_s1_e6_c192_se0.25'], ['cn_r1_k1_s1_c960']]
model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_d', arch_def=arch_def, **kwargs)
return model
@register_model
def hardcorenas_e(pretrained=False, **kwargs) -> MobileNetV3:
""" hardcorenas_E """
arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre_se0.25', 'ir_r1_k5_s1_e3_c24_nre_se0.25'],
['ir_r1_k5_s2_e6_c40_nre_se0.25', 'ir_r1_k5_s1_e4_c40_nre_se0.25', 'ir_r1_k5_s1_e4_c40_nre_se0.25',
'ir_r1_k3_s1_e3_c40_nre_se0.25'], ['ir_r1_k5_s2_e4_c80_se0.25', 'ir_r1_k3_s1_e6_c80_se0.25'],
['ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25',
'ir_r1_k5_s1_e3_c112_se0.25'],
['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25',
'ir_r1_k3_s1_e6_c192_se0.25'], ['cn_r1_k1_s1_c960']]
model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_e', arch_def=arch_def, **kwargs)
return model
@register_model
def hardcorenas_f(pretrained=False, **kwargs) -> MobileNetV3:
""" hardcorenas_F """
arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre_se0.25', 'ir_r1_k5_s1_e3_c24_nre_se0.25'],
['ir_r1_k5_s2_e6_c40_nre_se0.25', 'ir_r1_k5_s1_e6_c40_nre_se0.25'],
['ir_r1_k5_s2_e6_c80_se0.25', 'ir_r1_k5_s1_e6_c80_se0.25', 'ir_r1_k3_s1_e3_c80_se0.25',
'ir_r1_k3_s1_e3_c80_se0.25'],
['ir_r1_k3_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25',
'ir_r1_k3_s1_e3_c112_se0.25'],
['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k3_s1_e6_c192_se0.25',
'ir_r1_k3_s1_e6_c192_se0.25'], ['cn_r1_k1_s1_c960']]
model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_f', arch_def=arch_def, **kwargs)
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/vovnet.py
|
""" VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNet.pytorch/blob/master/models_vovnet/vovnet.py
for some reference, rewrote most of the code.
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import List
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import ConvNormAct, SeparableConvNormAct, BatchNormAct2d, ClassifierHead, DropPath, \
create_attn, create_norm_act_layer
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
__all__ = ['VovNet'] # model_registry will add each entrypoint fn to this
class SequentialAppendList(nn.Sequential):
def __init__(self, *args):
super(SequentialAppendList, self).__init__(*args)
def forward(self, x: torch.Tensor, concat_list: List[torch.Tensor]) -> torch.Tensor:
for i, module in enumerate(self):
if i == 0:
concat_list.append(module(x))
else:
concat_list.append(module(concat_list[-1]))
x = torch.cat(concat_list, dim=1)
return x
class OsaBlock(nn.Module):
def __init__(
self,
in_chs,
mid_chs,
out_chs,
layer_per_block,
residual=False,
depthwise=False,
attn='',
norm_layer=BatchNormAct2d,
act_layer=nn.ReLU,
drop_path=None,
):
super(OsaBlock, self).__init__()
self.residual = residual
self.depthwise = depthwise
conv_kwargs = dict(norm_layer=norm_layer, act_layer=act_layer)
next_in_chs = in_chs
if self.depthwise and next_in_chs != mid_chs:
assert not residual
self.conv_reduction = ConvNormAct(next_in_chs, mid_chs, 1, **conv_kwargs)
else:
self.conv_reduction = None
mid_convs = []
for i in range(layer_per_block):
if self.depthwise:
conv = SeparableConvNormAct(mid_chs, mid_chs, **conv_kwargs)
else:
conv = ConvNormAct(next_in_chs, mid_chs, 3, **conv_kwargs)
next_in_chs = mid_chs
mid_convs.append(conv)
self.conv_mid = SequentialAppendList(*mid_convs)
# feature aggregation
next_in_chs = in_chs + layer_per_block * mid_chs
self.conv_concat = ConvNormAct(next_in_chs, out_chs, **conv_kwargs)
self.attn = create_attn(attn, out_chs) if attn else None
self.drop_path = drop_path
def forward(self, x):
output = [x]
if self.conv_reduction is not None:
x = self.conv_reduction(x)
x = self.conv_mid(x, output)
x = self.conv_concat(x)
if self.attn is not None:
x = self.attn(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.residual:
x = x + output[0]
return x
class OsaStage(nn.Module):
def __init__(
self,
in_chs,
mid_chs,
out_chs,
block_per_stage,
layer_per_block,
downsample=True,
residual=True,
depthwise=False,
attn='ese',
norm_layer=BatchNormAct2d,
act_layer=nn.ReLU,
drop_path_rates=None,
):
super(OsaStage, self).__init__()
self.grad_checkpointing = False
if downsample:
self.pool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
else:
self.pool = None
blocks = []
for i in range(block_per_stage):
last_block = i == block_per_stage - 1
if drop_path_rates is not None and drop_path_rates[i] > 0.:
drop_path = DropPath(drop_path_rates[i])
else:
drop_path = None
blocks += [OsaBlock(
in_chs, mid_chs, out_chs, layer_per_block, residual=residual and i > 0, depthwise=depthwise,
attn=attn if last_block else '', norm_layer=norm_layer, act_layer=act_layer, drop_path=drop_path)
]
in_chs = out_chs
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
if self.pool is not None:
x = self.pool(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class VovNet(nn.Module):
def __init__(
self,
cfg,
in_chans=3,
num_classes=1000,
global_pool='avg',
output_stride=32,
norm_layer=BatchNormAct2d,
act_layer=nn.ReLU,
drop_rate=0.,
drop_path_rate=0.,
**kwargs,
):
"""
Args:
cfg (dict): Model architecture configuration
in_chans (int): Number of input channels (default: 3)
num_classes (int): Number of classifier classes (default: 1000)
global_pool (str): Global pooling type (default: 'avg')
output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32)
norm_layer (Union[str, nn.Module]): normalization layer
act_layer (Union[str, nn.Module]): activation layer
drop_rate (float): Dropout rate (default: 0.)
drop_path_rate (float): Stochastic depth drop-path rate (default: 0.)
kwargs (dict): Extra kwargs overlayed onto cfg
"""
super(VovNet, self).__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
assert output_stride == 32 # FIXME support dilation
cfg = dict(cfg, **kwargs)
stem_stride = cfg.get("stem_stride", 4)
stem_chs = cfg["stem_chs"]
stage_conv_chs = cfg["stage_conv_chs"]
stage_out_chs = cfg["stage_out_chs"]
block_per_stage = cfg["block_per_stage"]
layer_per_block = cfg["layer_per_block"]
conv_kwargs = dict(norm_layer=norm_layer, act_layer=act_layer)
# Stem module
last_stem_stride = stem_stride // 2
conv_type = SeparableConvNormAct if cfg["depthwise"] else ConvNormAct
self.stem = nn.Sequential(*[
ConvNormAct(in_chans, stem_chs[0], 3, stride=2, **conv_kwargs),
conv_type(stem_chs[0], stem_chs[1], 3, stride=1, **conv_kwargs),
conv_type(stem_chs[1], stem_chs[2], 3, stride=last_stem_stride, **conv_kwargs),
])
self.feature_info = [dict(
num_chs=stem_chs[1], reduction=2, module=f'stem.{1 if stem_stride == 4 else 2}')]
current_stride = stem_stride
# OSA stages
stage_dpr = torch.split(torch.linspace(0, drop_path_rate, sum(block_per_stage)), block_per_stage)
in_ch_list = stem_chs[-1:] + stage_out_chs[:-1]
stage_args = dict(residual=cfg["residual"], depthwise=cfg["depthwise"], attn=cfg["attn"], **conv_kwargs)
stages = []
for i in range(4): # num_stages
downsample = stem_stride == 2 or i > 0 # first stage has no stride/downsample if stem_stride is 4
stages += [OsaStage(
in_ch_list[i],
stage_conv_chs[i],
stage_out_chs[i],
block_per_stage[i],
layer_per_block,
downsample=downsample,
drop_path_rates=stage_dpr[i],
**stage_args,
)]
self.num_features = stage_out_chs[i]
current_stride *= 2 if downsample else 1
self.feature_info += [dict(num_chs=self.num_features, reduction=current_stride, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
for n, m in self.named_modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.Linear):
nn.init.zeros_(m.bias)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else r'^stages\.(\d+).blocks\.(\d+)',
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
def forward_features(self, x):
x = self.stem(x)
return self.stages(x)
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
# model cfgs adapted from https://github.com/youngwanLEE/vovnet-detectron2 &
# https://github.com/stigma0617/VoVNet.pytorch/blob/master/models_vovnet/vovnet.py
model_cfgs = dict(
vovnet39a=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 2, 2],
residual=False,
depthwise=False,
attn='',
),
vovnet57a=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 4, 3],
residual=False,
depthwise=False,
attn='',
),
ese_vovnet19b_slim_dw=dict(
stem_chs=[64, 64, 64],
stage_conv_chs=[64, 80, 96, 112],
stage_out_chs=[112, 256, 384, 512],
layer_per_block=3,
block_per_stage=[1, 1, 1, 1],
residual=True,
depthwise=True,
attn='ese',
),
ese_vovnet19b_dw=dict(
stem_chs=[64, 64, 64],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=3,
block_per_stage=[1, 1, 1, 1],
residual=True,
depthwise=True,
attn='ese',
),
ese_vovnet19b_slim=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[64, 80, 96, 112],
stage_out_chs=[112, 256, 384, 512],
layer_per_block=3,
block_per_stage=[1, 1, 1, 1],
residual=True,
depthwise=False,
attn='ese',
),
ese_vovnet19b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=3,
block_per_stage=[1, 1, 1, 1],
residual=True,
depthwise=False,
attn='ese',
),
ese_vovnet39b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 2, 2],
residual=True,
depthwise=False,
attn='ese',
),
ese_vovnet57b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 4, 3],
residual=True,
depthwise=False,
attn='ese',
),
ese_vovnet99b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 3, 9, 3],
residual=True,
depthwise=False,
attn='ese',
),
eca_vovnet39b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 2, 2],
residual=True,
depthwise=False,
attn='eca',
),
)
model_cfgs['ese_vovnet39b_evos'] = model_cfgs['ese_vovnet39b']
def _create_vovnet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
VovNet,
variant,
pretrained,
model_cfg=model_cfgs[variant],
feature_cfg=dict(flatten_sequential=True),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0.conv', 'classifier': 'head.fc', **kwargs,
}
default_cfgs = generate_default_cfgs({
'vovnet39a.untrained': _cfg(url=''),
'vovnet57a.untrained': _cfg(url=''),
'ese_vovnet19b_slim_dw.untrained': _cfg(url=''),
'ese_vovnet19b_dw.ra_in1k': _cfg(
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'ese_vovnet19b_slim.untrained': _cfg(url=''),
'ese_vovnet39b.ra_in1k': _cfg(
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'ese_vovnet57b.untrained': _cfg(url=''),
'ese_vovnet99b.untrained': _cfg(url=''),
'eca_vovnet39b.untrained': _cfg(url=''),
'ese_vovnet39b_evos.untrained': _cfg(url=''),
})
@register_model
def vovnet39a(pretrained=False, **kwargs) -> VovNet:
return _create_vovnet('vovnet39a', pretrained=pretrained, **kwargs)
@register_model
def vovnet57a(pretrained=False, **kwargs) -> VovNet:
return _create_vovnet('vovnet57a', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet19b_slim_dw(pretrained=False, **kwargs) -> VovNet:
return _create_vovnet('ese_vovnet19b_slim_dw', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet19b_dw(pretrained=False, **kwargs) -> VovNet:
return _create_vovnet('ese_vovnet19b_dw', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet19b_slim(pretrained=False, **kwargs) -> VovNet:
return _create_vovnet('ese_vovnet19b_slim', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet39b(pretrained=False, **kwargs) -> VovNet:
return _create_vovnet('ese_vovnet39b', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet57b(pretrained=False, **kwargs) -> VovNet:
return _create_vovnet('ese_vovnet57b', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet99b(pretrained=False, **kwargs) -> VovNet:
return _create_vovnet('ese_vovnet99b', pretrained=pretrained, **kwargs)
@register_model
def eca_vovnet39b(pretrained=False, **kwargs) -> VovNet:
return _create_vovnet('eca_vovnet39b', pretrained=pretrained, **kwargs)
# Experimental Models
@register_model
def ese_vovnet39b_evos(pretrained=False, **kwargs) -> VovNet:
def norm_act_fn(num_features, **nkwargs):
return create_norm_act_layer('evonorms0', num_features, jit=False, **nkwargs)
return _create_vovnet('ese_vovnet39b_evos', pretrained=pretrained, norm_layer=norm_act_fn, **kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/convnext.py
|
""" ConvNeXt
Papers:
* `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
@Article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
* `ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
@article{Woo2023ConvNeXtV2,
title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie},
year={2023},
journal={arXiv preprint arXiv:2301.00808},
}
Original code and weights from:
* https://github.com/facebookresearch/ConvNeXt, original copyright below
* https://github.com/facebookresearch/ConvNeXt-V2, original copyright below
Model defs atto, femto, pico, nano and _ols / _hnf variants are timm originals.
Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
"""
# ConvNeXt
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the MIT license
# ConvNeXt-V2
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree (Attribution-NonCommercial 4.0 International (CC BY-NC 4.0))
# No code was used directly from ConvNeXt-V2, however the weights are CC BY-NC 4.0 so beware if using commercially.
from collections import OrderedDict
from functools import partial
from typing import Callable, Optional, Tuple, Union
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
from timm.layers import trunc_normal_, AvgPool2dSame, DropPath, Mlp, GlobalResponseNormMlp, \
LayerNorm2d, LayerNorm, create_conv2d, get_act_layer, make_divisible, to_ntuple
from timm.layers import NormMlpClassifierHead, ClassifierHead
from ._builder import build_model_with_cfg
from ._manipulate import named_apply, checkpoint_seq
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
__all__ = ['ConvNeXt'] # model_registry will add each entrypoint fn to this
class Downsample(nn.Module):
def __init__(self, in_chs, out_chs, stride=1, dilation=1):
super().__init__()
avg_stride = stride if dilation == 1 else 1
if stride > 1 or dilation > 1:
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
else:
self.pool = nn.Identity()
if in_chs != out_chs:
self.conv = create_conv2d(in_chs, out_chs, 1, stride=1)
else:
self.conv = nn.Identity()
def forward(self, x):
x = self.pool(x)
x = self.conv(x)
return x
class ConvNeXtBlock(nn.Module):
""" ConvNeXt Block
There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
Unlike the official impl, this one allows choice of 1 or 2, 1x1 conv can be faster with appropriate
choice of LayerNorm impl, however as model size increases the tradeoffs appear to change and nn.Linear
is a better choice. This was observed with PyTorch 1.10 on 3090 GPU, it could change over time & w/ different HW.
"""
def __init__(
self,
in_chs: int,
out_chs: Optional[int] = None,
kernel_size: int = 7,
stride: int = 1,
dilation: Union[int, Tuple[int, int]] = (1, 1),
mlp_ratio: float = 4,
conv_mlp: bool = False,
conv_bias: bool = True,
use_grn: bool = False,
ls_init_value: Optional[float] = 1e-6,
act_layer: Union[str, Callable] = 'gelu',
norm_layer: Optional[Callable] = None,
drop_path: float = 0.,
):
"""
Args:
in_chs: Block input channels.
out_chs: Block output channels (same as in_chs if None).
kernel_size: Depthwise convolution kernel size.
stride: Stride of depthwise convolution.
dilation: Tuple specifying input and output dilation of block.
mlp_ratio: MLP expansion ratio.
conv_mlp: Use 1x1 convolutions for MLP and a NCHW compatible norm layer if True.
conv_bias: Apply bias for all convolution (linear) layers.
use_grn: Use GlobalResponseNorm in MLP (from ConvNeXt-V2)
ls_init_value: Layer-scale init values, layer-scale applied if not None.
act_layer: Activation layer.
norm_layer: Normalization layer (defaults to LN if not specified).
drop_path: Stochastic depth probability.
"""
super().__init__()
out_chs = out_chs or in_chs
dilation = to_ntuple(2)(dilation)
act_layer = get_act_layer(act_layer)
if not norm_layer:
norm_layer = LayerNorm2d if conv_mlp else LayerNorm
mlp_layer = partial(GlobalResponseNormMlp if use_grn else Mlp, use_conv=conv_mlp)
self.use_conv_mlp = conv_mlp
self.conv_dw = create_conv2d(
in_chs,
out_chs,
kernel_size=kernel_size,
stride=stride,
dilation=dilation[0],
depthwise=True,
bias=conv_bias,
)
self.norm = norm_layer(out_chs)
self.mlp = mlp_layer(out_chs, int(mlp_ratio * out_chs), act_layer=act_layer)
self.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value is not None else None
if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
self.shortcut = Downsample(in_chs, out_chs, stride=stride, dilation=dilation[0])
else:
self.shortcut = nn.Identity()
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.conv_dw(x)
if self.use_conv_mlp:
x = self.norm(x)
x = self.mlp(x)
else:
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
x = self.mlp(x)
x = x.permute(0, 3, 1, 2)
if self.gamma is not None:
x = x.mul(self.gamma.reshape(1, -1, 1, 1))
x = self.drop_path(x) + self.shortcut(shortcut)
return x
class ConvNeXtStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
kernel_size=7,
stride=2,
depth=2,
dilation=(1, 1),
drop_path_rates=None,
ls_init_value=1.0,
conv_mlp=False,
conv_bias=True,
use_grn=False,
act_layer='gelu',
norm_layer=None,
norm_layer_cl=None
):
super().__init__()
self.grad_checkpointing = False
if in_chs != out_chs or stride > 1 or dilation[0] != dilation[1]:
ds_ks = 2 if stride > 1 or dilation[0] != dilation[1] else 1
pad = 'same' if dilation[1] > 1 else 0 # same padding needed if dilation used
self.downsample = nn.Sequential(
norm_layer(in_chs),
create_conv2d(
in_chs,
out_chs,
kernel_size=ds_ks,
stride=stride,
dilation=dilation[0],
padding=pad,
bias=conv_bias,
),
)
in_chs = out_chs
else:
self.downsample = nn.Identity()
drop_path_rates = drop_path_rates or [0.] * depth
stage_blocks = []
for i in range(depth):
stage_blocks.append(ConvNeXtBlock(
in_chs=in_chs,
out_chs=out_chs,
kernel_size=kernel_size,
dilation=dilation[1],
drop_path=drop_path_rates[i],
ls_init_value=ls_init_value,
conv_mlp=conv_mlp,
conv_bias=conv_bias,
use_grn=use_grn,
act_layer=act_layer,
norm_layer=norm_layer if conv_mlp else norm_layer_cl,
))
in_chs = out_chs
self.blocks = nn.Sequential(*stage_blocks)
def forward(self, x):
x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class ConvNeXt(nn.Module):
r""" ConvNeXt
A PyTorch impl of : `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
"""
def __init__(
self,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = 'avg',
output_stride: int = 32,
depths: Tuple[int, ...] = (3, 3, 9, 3),
dims: Tuple[int, ...] = (96, 192, 384, 768),
kernel_sizes: Union[int, Tuple[int, ...]] = 7,
ls_init_value: Optional[float] = 1e-6,
stem_type: str = 'patch',
patch_size: int = 4,
head_init_scale: float = 1.,
head_norm_first: bool = False,
head_hidden_size: Optional[int] = None,
conv_mlp: bool = False,
conv_bias: bool = True,
use_grn: bool = False,
act_layer: Union[str, Callable] = 'gelu',
norm_layer: Optional[Union[str, Callable]] = None,
norm_eps: Optional[float] = None,
drop_rate: float = 0.,
drop_path_rate: float = 0.,
):
"""
Args:
in_chans: Number of input image channels.
num_classes: Number of classes for classification head.
global_pool: Global pooling type.
output_stride: Output stride of network, one of (8, 16, 32).
depths: Number of blocks at each stage.
dims: Feature dimension at each stage.
kernel_sizes: Depthwise convolution kernel-sizes for each stage.
ls_init_value: Init value for Layer Scale, disabled if None.
stem_type: Type of stem.
patch_size: Stem patch size for patch stem.
head_init_scale: Init scaling value for classifier weights and biases.
head_norm_first: Apply normalization before global pool + head.
head_hidden_size: Size of MLP hidden layer in head if not None and head_norm_first == False.
conv_mlp: Use 1x1 conv in MLP, improves speed for small networks w/ chan last.
conv_bias: Use bias layers w/ all convolutions.
use_grn: Use Global Response Norm (ConvNeXt-V2) in MLP.
act_layer: Activation layer type.
norm_layer: Normalization layer type.
drop_rate: Head pre-classifier dropout rate.
drop_path_rate: Stochastic depth drop rate.
"""
super().__init__()
assert output_stride in (8, 16, 32)
kernel_sizes = to_ntuple(4)(kernel_sizes)
if norm_layer is None:
norm_layer = LayerNorm2d
norm_layer_cl = norm_layer if conv_mlp else LayerNorm
if norm_eps is not None:
norm_layer = partial(norm_layer, eps=norm_eps)
norm_layer_cl = partial(norm_layer_cl, eps=norm_eps)
else:
assert conv_mlp,\
'If a norm_layer is specified, conv MLP must be used so all norm expect rank-4, channels-first input'
norm_layer_cl = norm_layer
if norm_eps is not None:
norm_layer_cl = partial(norm_layer_cl, eps=norm_eps)
self.num_classes = num_classes
self.drop_rate = drop_rate
self.feature_info = []
assert stem_type in ('patch', 'overlap', 'overlap_tiered')
if stem_type == 'patch':
# NOTE: this stem is a minimal form of ViT PatchEmbed, as used in SwinTransformer w/ patch_size = 4
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=patch_size, stride=patch_size, bias=conv_bias),
norm_layer(dims[0]),
)
stem_stride = patch_size
else:
mid_chs = make_divisible(dims[0] // 2) if 'tiered' in stem_type else dims[0]
self.stem = nn.Sequential(
nn.Conv2d(in_chans, mid_chs, kernel_size=3, stride=2, padding=1, bias=conv_bias),
nn.Conv2d(mid_chs, dims[0], kernel_size=3, stride=2, padding=1, bias=conv_bias),
norm_layer(dims[0]),
)
stem_stride = 4
self.stages = nn.Sequential()
dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
stages = []
prev_chs = dims[0]
curr_stride = stem_stride
dilation = 1
# 4 feature resolution stages, each consisting of multiple residual blocks
for i in range(4):
stride = 2 if curr_stride == 2 or i > 0 else 1
if curr_stride >= output_stride and stride > 1:
dilation *= stride
stride = 1
curr_stride *= stride
first_dilation = 1 if dilation in (1, 2) else 2
out_chs = dims[i]
stages.append(ConvNeXtStage(
prev_chs,
out_chs,
kernel_size=kernel_sizes[i],
stride=stride,
dilation=(first_dilation, dilation),
depth=depths[i],
drop_path_rates=dp_rates[i],
ls_init_value=ls_init_value,
conv_mlp=conv_mlp,
conv_bias=conv_bias,
use_grn=use_grn,
act_layer=act_layer,
norm_layer=norm_layer,
norm_layer_cl=norm_layer_cl,
))
prev_chs = out_chs
# NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2
self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
self.num_features = prev_chs
# if head_norm_first == true, norm -> global pool -> fc ordering, like most other nets
# otherwise pool -> norm -> fc, the default ConvNeXt ordering (pretrained FB weights)
if head_norm_first:
assert not head_hidden_size
self.norm_pre = norm_layer(self.num_features)
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=self.drop_rate,
)
else:
self.norm_pre = nn.Identity()
self.head = NormMlpClassifierHead(
self.num_features,
num_classes,
hidden_size=head_hidden_size,
pool_type=global_pool,
drop_rate=self.drop_rate,
norm_layer=norm_layer,
act_layer='gelu',
)
named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+)\.downsample', (0,)), # blocks
(r'^stages\.(\d+)\.blocks\.(\d+)', None),
(r'^norm_pre', (99999,))
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes=0, global_pool=None):
self.head.reset(num_classes, global_pool)
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
x = self.norm_pre(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=True) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module, name=None, head_init_scale=1.0):
if isinstance(module, nn.Conv2d):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=.02)
nn.init.zeros_(module.bias)
if name and 'head.' in name:
module.weight.data.mul_(head_init_scale)
module.bias.data.mul_(head_init_scale)
def checkpoint_filter_fn(state_dict, model):
""" Remap FB checkpoints -> timm """
if 'head.norm.weight' in state_dict or 'norm_pre.weight' in state_dict:
return state_dict # non-FB checkpoint
if 'model' in state_dict:
state_dict = state_dict['model']
out_dict = {}
if 'visual.trunk.stem.0.weight' in state_dict:
out_dict = {k.replace('visual.trunk.', ''): v for k, v in state_dict.items() if k.startswith('visual.trunk.')}
if 'visual.head.proj.weight' in state_dict:
out_dict['head.fc.weight'] = state_dict['visual.head.proj.weight']
out_dict['head.fc.bias'] = torch.zeros(state_dict['visual.head.proj.weight'].shape[0])
elif 'visual.head.mlp.fc1.weight' in state_dict:
out_dict['head.pre_logits.fc.weight'] = state_dict['visual.head.mlp.fc1.weight']
out_dict['head.pre_logits.fc.bias'] = state_dict['visual.head.mlp.fc1.bias']
out_dict['head.fc.weight'] = state_dict['visual.head.mlp.fc2.weight']
out_dict['head.fc.bias'] = torch.zeros(state_dict['visual.head.mlp.fc2.weight'].shape[0])
return out_dict
import re
for k, v in state_dict.items():
k = k.replace('downsample_layers.0.', 'stem.')
k = re.sub(r'stages.([0-9]+).([0-9]+)', r'stages.\1.blocks.\2', k)
k = re.sub(r'downsample_layers.([0-9]+).([0-9]+)', r'stages.\1.downsample.\2', k)
k = k.replace('dwconv', 'conv_dw')
k = k.replace('pwconv', 'mlp.fc')
if 'grn' in k:
k = k.replace('grn.beta', 'mlp.grn.bias')
k = k.replace('grn.gamma', 'mlp.grn.weight')
v = v.reshape(v.shape[-1])
k = k.replace('head.', 'head.fc.')
if k.startswith('norm.'):
k = k.replace('norm', 'head.norm')
if v.ndim == 2 and 'head' not in k:
model_shape = model.state_dict()[k].shape
v = v.reshape(model_shape)
out_dict[k] = v
return out_dict
def _create_convnext(variant, pretrained=False, **kwargs):
if kwargs.get('pretrained_cfg', '') == 'fcmae':
# NOTE fcmae pretrained weights have no classifier or final norm-layer (`head.norm`)
# This is workaround loading with num_classes=0 w/o removing norm-layer.
kwargs.setdefault('pretrained_strict', False)
model = build_model_with_cfg(
ConvNeXt, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
**kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0', 'classifier': 'head.fc',
**kwargs
}
def _cfgv2(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0', 'classifier': 'head.fc',
'license': 'cc-by-nc-4.0', 'paper_ids': 'arXiv:2301.00808',
'paper_name': 'ConvNeXt-V2: Co-designing and Scaling ConvNets with Masked Autoencoders',
'origin_url': 'https://github.com/facebookresearch/ConvNeXt-V2',
**kwargs
}
default_cfgs = generate_default_cfgs({
# timm specific variants
'convnext_tiny.in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_small.in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_atto.d2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_d2-01bb0f51.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnext_atto_ols.a2_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_ols_a2-78d1c8f3.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnext_femto.d1_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_femto_d1-d71d5b4c.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnext_femto_ols.d1_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_femto_ols_d1-246bf2ed.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnext_pico.d1_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_pico_d1-10ad7f0d.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnext_pico_ols.d1_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_pico_ols_d1-611f0ca7.pth',
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_nano.in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_nano.d1h_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_nano_d1h-7eb4bdea.pth',
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_nano_ols.d1h_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_nano_ols_d1h-ae424a9a.pth',
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_tiny_hnf.a2h_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_tiny_hnf_a2h-ab7e9df2.pth',
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_tiny.in12k_ft_in1k_384': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_small.in12k_ft_in1k_384': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_nano.in12k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, num_classes=11821),
'convnext_tiny.in12k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, num_classes=11821),
'convnext_small.in12k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, num_classes=11821),
'convnext_tiny.fb_in22k_ft_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_224.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_small.fb_in22k_ft_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_224.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_base.fb_in22k_ft_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_large.fb_in22k_ft_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_xlarge.fb_in22k_ft_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_tiny.fb_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_small.fb_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_base.fb_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_large.fb_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnext_tiny.fb_in22k_ft_in1k_384': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_384.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_small.fb_in22k_ft_in1k_384': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_384.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_base.fb_in22k_ft_in1k_384': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_large.fb_in22k_ft_in1k_384': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_xlarge.fb_in22k_ft_in1k_384': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_tiny.fb_in22k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth",
hf_hub_id='timm/',
num_classes=21841),
'convnext_small.fb_in22k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth",
hf_hub_id='timm/',
num_classes=21841),
'convnext_base.fb_in22k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth",
hf_hub_id='timm/',
num_classes=21841),
'convnext_large.fb_in22k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth",
hf_hub_id='timm/',
num_classes=21841),
'convnext_xlarge.fb_in22k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth",
hf_hub_id='timm/',
num_classes=21841),
'convnextv2_nano.fcmae_ft_in22k_in1k': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_nano.fcmae_ft_in22k_in1k_384': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt',
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnextv2_tiny.fcmae_ft_in22k_in1k': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_tiny.fcmae_ft_in22k_in1k_384': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt",
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnextv2_base.fcmae_ft_in22k_in1k': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_base.fcmae_ft_in22k_in1k_384': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt",
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnextv2_large.fcmae_ft_in22k_in1k': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_large.fcmae_ft_in22k_in1k_384': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt",
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnextv2_huge.fcmae_ft_in22k_in1k_384': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt",
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnextv2_huge.fcmae_ft_in22k_in1k_512': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt",
hf_hub_id='timm/',
input_size=(3, 512, 512), pool_size=(15, 15), crop_pct=1.0, crop_mode='squash'),
'convnextv2_atto.fcmae_ft_in1k': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnextv2_femto.fcmae_ft_in1k': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnextv2_pico.fcmae_ft_in1k': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnextv2_nano.fcmae_ft_in1k': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt',
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_tiny.fcmae_ft_in1k': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_base.fcmae_ft_in1k': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_large.fcmae_ft_in1k': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_huge.fcmae_ft_in1k': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt",
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_atto.fcmae': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_atto_1k_224_fcmae.pt',
hf_hub_id='timm/',
num_classes=0),
'convnextv2_femto.fcmae': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_femto_1k_224_fcmae.pt',
hf_hub_id='timm/',
num_classes=0),
'convnextv2_pico.fcmae': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_pico_1k_224_fcmae.pt',
hf_hub_id='timm/',
num_classes=0),
'convnextv2_nano.fcmae': _cfgv2(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_nano_1k_224_fcmae.pt',
hf_hub_id='timm/',
num_classes=0),
'convnextv2_tiny.fcmae': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_tiny_1k_224_fcmae.pt",
hf_hub_id='timm/',
num_classes=0),
'convnextv2_base.fcmae': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_base_1k_224_fcmae.pt",
hf_hub_id='timm/',
num_classes=0),
'convnextv2_large.fcmae': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_large_1k_224_fcmae.pt",
hf_hub_id='timm/',
num_classes=0),
'convnextv2_huge.fcmae': _cfgv2(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_huge_1k_224_fcmae.pt",
hf_hub_id='timm/',
num_classes=0),
'convnextv2_small.untrained': _cfg(),
# CLIP weights, fine-tuned on in1k or in12k + in1k
'convnext_base.clip_laion2b_augreg_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0),
'convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0),
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_base.clip_laion2b_augreg_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0),
'convnext_base.clip_laiona_augreg_ft_in1k_384': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'convnext_large_mlp.clip_laion2b_augreg_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0
),
'convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'
),
'convnext_xxlarge.clip_laion2b_soup_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0),
'convnext_base.clip_laion2b_augreg_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0),
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_320': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821,
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0),
'convnext_large_mlp.clip_laion2b_augreg_ft_in12k_384': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821,
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_384': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821,
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'convnext_xxlarge.clip_laion2b_soup_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0),
# CLIP original image tower weights
'convnext_base.clip_laion2b': _cfg(
hf_hub_id='laion/CLIP-convnext_base_w-laion2B-s13B-b82K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=640),
'convnext_base.clip_laion2b_augreg': _cfg(
hf_hub_id='laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=640),
'convnext_base.clip_laiona': _cfg(
hf_hub_id='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=640),
'convnext_base.clip_laiona_320': _cfg(
hf_hub_id='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0, num_classes=640),
'convnext_base.clip_laiona_augreg_320': _cfg(
hf_hub_id='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0, num_classes=640),
'convnext_large_mlp.clip_laion2b_augreg': _cfg(
hf_hub_id='laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=768),
'convnext_large_mlp.clip_laion2b_ft_320': _cfg(
hf_hub_id='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0, num_classes=768),
'convnext_large_mlp.clip_laion2b_ft_soup_320': _cfg(
hf_hub_id='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0, num_classes=768),
'convnext_xxlarge.clip_laion2b_soup': _cfg(
hf_hub_id='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=1024),
'convnext_xxlarge.clip_laion2b_rewind': _cfg(
hf_hub_id='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=1024),
})
@register_model
def convnext_atto(pretrained=False, **kwargs) -> ConvNeXt:
# timm femto variant (NOTE: still tweaking depths, will vary between 3-4M param, current is 3.7M
model_args = dict(depths=(2, 2, 6, 2), dims=(40, 80, 160, 320), conv_mlp=True)
model = _create_convnext('convnext_atto', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_atto_ols(pretrained=False, **kwargs) -> ConvNeXt:
# timm femto variant with overlapping 3x3 conv stem, wider than non-ols femto above, current param count 3.7M
model_args = dict(depths=(2, 2, 6, 2), dims=(40, 80, 160, 320), conv_mlp=True, stem_type='overlap_tiered')
model = _create_convnext('convnext_atto_ols', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_femto(pretrained=False, **kwargs) -> ConvNeXt:
# timm femto variant
model_args = dict(depths=(2, 2, 6, 2), dims=(48, 96, 192, 384), conv_mlp=True)
model = _create_convnext('convnext_femto', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_femto_ols(pretrained=False, **kwargs) -> ConvNeXt:
# timm femto variant
model_args = dict(depths=(2, 2, 6, 2), dims=(48, 96, 192, 384), conv_mlp=True, stem_type='overlap_tiered')
model = _create_convnext('convnext_femto_ols', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_pico(pretrained=False, **kwargs) -> ConvNeXt:
# timm pico variant
model_args = dict(depths=(2, 2, 6, 2), dims=(64, 128, 256, 512), conv_mlp=True)
model = _create_convnext('convnext_pico', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_pico_ols(pretrained=False, **kwargs) -> ConvNeXt:
# timm nano variant with overlapping 3x3 conv stem
model_args = dict(depths=(2, 2, 6, 2), dims=(64, 128, 256, 512), conv_mlp=True, stem_type='overlap_tiered')
model = _create_convnext('convnext_pico_ols', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_nano(pretrained=False, **kwargs) -> ConvNeXt:
# timm nano variant with standard stem and head
model_args = dict(depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), conv_mlp=True)
model = _create_convnext('convnext_nano', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_nano_ols(pretrained=False, **kwargs) -> ConvNeXt:
# experimental nano variant with overlapping conv stem
model_args = dict(depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), conv_mlp=True, stem_type='overlap')
model = _create_convnext('convnext_nano_ols', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_tiny_hnf(pretrained=False, **kwargs) -> ConvNeXt:
# experimental tiny variant with norm before pooling in head (head norm first)
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True)
model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_tiny(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768))
model = _create_convnext('convnext_tiny', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_small(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768])
model = _create_convnext('convnext_small', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_base(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024])
model = _create_convnext('convnext_base', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_large(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536])
model = _create_convnext('convnext_large', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_large_mlp(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], head_hidden_size=1536)
model = _create_convnext('convnext_large_mlp', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_xlarge(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048])
model = _create_convnext('convnext_xlarge', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnext_xxlarge(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 4, 30, 3], dims=[384, 768, 1536, 3072], norm_eps=kwargs.pop('norm_eps', 1e-5))
model = _create_convnext('convnext_xxlarge', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnextv2_atto(pretrained=False, **kwargs) -> ConvNeXt:
# timm femto variant (NOTE: still tweaking depths, will vary between 3-4M param, current is 3.7M
model_args = dict(
depths=(2, 2, 6, 2), dims=(40, 80, 160, 320), use_grn=True, ls_init_value=None, conv_mlp=True)
model = _create_convnext('convnextv2_atto', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnextv2_femto(pretrained=False, **kwargs) -> ConvNeXt:
# timm femto variant
model_args = dict(
depths=(2, 2, 6, 2), dims=(48, 96, 192, 384), use_grn=True, ls_init_value=None, conv_mlp=True)
model = _create_convnext('convnextv2_femto', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnextv2_pico(pretrained=False, **kwargs) -> ConvNeXt:
# timm pico variant
model_args = dict(
depths=(2, 2, 6, 2), dims=(64, 128, 256, 512), use_grn=True, ls_init_value=None, conv_mlp=True)
model = _create_convnext('convnextv2_pico', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnextv2_nano(pretrained=False, **kwargs) -> ConvNeXt:
# timm nano variant with standard stem and head
model_args = dict(
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), use_grn=True, ls_init_value=None, conv_mlp=True)
model = _create_convnext('convnextv2_nano', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnextv2_tiny(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), use_grn=True, ls_init_value=None)
model = _create_convnext('convnextv2_tiny', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnextv2_small(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], use_grn=True, ls_init_value=None)
model = _create_convnext('convnextv2_small', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnextv2_base(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], use_grn=True, ls_init_value=None)
model = _create_convnext('convnextv2_base', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnextv2_large(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], use_grn=True, ls_init_value=None)
model = _create_convnext('convnextv2_large', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def convnextv2_huge(pretrained=False, **kwargs) -> ConvNeXt:
model_args = dict(depths=[3, 3, 27, 3], dims=[352, 704, 1408, 2816], use_grn=True, ls_init_value=None)
model = _create_convnext('convnextv2_huge', pretrained=pretrained, **dict(model_args, **kwargs))
return model
register_model_deprecations(__name__, {
'convnext_tiny_in22ft1k': 'convnext_tiny.fb_in22k_ft_in1k',
'convnext_small_in22ft1k': 'convnext_small.fb_in22k_ft_in1k',
'convnext_base_in22ft1k': 'convnext_base.fb_in22k_ft_in1k',
'convnext_large_in22ft1k': 'convnext_large.fb_in22k_ft_in1k',
'convnext_xlarge_in22ft1k': 'convnext_xlarge.fb_in22k_ft_in1k',
'convnext_tiny_384_in22ft1k': 'convnext_tiny.fb_in22k_ft_in1k_384',
'convnext_small_384_in22ft1k': 'convnext_small.fb_in22k_ft_in1k_384',
'convnext_base_384_in22ft1k': 'convnext_base.fb_in22k_ft_in1k_384',
'convnext_large_384_in22ft1k': 'convnext_large.fb_in22k_ft_in1k_384',
'convnext_xlarge_384_in22ft1k': 'convnext_xlarge.fb_in22k_ft_in1k_384',
'convnext_tiny_in22k': 'convnext_tiny.fb_in22k',
'convnext_small_in22k': 'convnext_small.fb_in22k',
'convnext_base_in22k': 'convnext_base.fb_in22k',
'convnext_large_in22k': 'convnext_large.fb_in22k',
'convnext_xlarge_in22k': 'convnext_xlarge.fb_in22k',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/dpn.py
|
""" PyTorch implementation of DualPathNetworks
Based on original MXNet implementation https://github.com/cypw/DPNs with
many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs.
This implementation is compatible with the pretrained weights from cypw's MXNet implementation.
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DPN_MEAN, IMAGENET_DPN_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import BatchNormAct2d, ConvNormAct, create_conv2d, create_classifier, get_norm_act_layer
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['DPN']
class CatBnAct(nn.Module):
def __init__(self, in_chs, norm_layer=BatchNormAct2d):
super(CatBnAct, self).__init__()
self.bn = norm_layer(in_chs, eps=0.001)
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (Tuple[torch.Tensor, torch.Tensor]) -> (torch.Tensor)
pass
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (torch.Tensor) -> (torch.Tensor)
pass
def forward(self, x):
if isinstance(x, tuple):
x = torch.cat(x, dim=1)
return self.bn(x)
class BnActConv2d(nn.Module):
def __init__(self, in_chs, out_chs, kernel_size, stride, groups=1, norm_layer=BatchNormAct2d):
super(BnActConv2d, self).__init__()
self.bn = norm_layer(in_chs, eps=0.001)
self.conv = create_conv2d(in_chs, out_chs, kernel_size, stride=stride, groups=groups)
def forward(self, x):
return self.conv(self.bn(x))
class DualPathBlock(nn.Module):
def __init__(
self,
in_chs,
num_1x1_a,
num_3x3_b,
num_1x1_c,
inc,
groups,
block_type='normal',
b=False,
):
super(DualPathBlock, self).__init__()
self.num_1x1_c = num_1x1_c
self.inc = inc
self.b = b
if block_type == 'proj':
self.key_stride = 1
self.has_proj = True
elif block_type == 'down':
self.key_stride = 2
self.has_proj = True
else:
assert block_type == 'normal'
self.key_stride = 1
self.has_proj = False
self.c1x1_w_s1 = None
self.c1x1_w_s2 = None
if self.has_proj:
# Using different member names here to allow easier parameter key matching for conversion
if self.key_stride == 2:
self.c1x1_w_s2 = BnActConv2d(
in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=2)
else:
self.c1x1_w_s1 = BnActConv2d(
in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=1)
self.c1x1_a = BnActConv2d(in_chs=in_chs, out_chs=num_1x1_a, kernel_size=1, stride=1)
self.c3x3_b = BnActConv2d(
in_chs=num_1x1_a, out_chs=num_3x3_b, kernel_size=3, stride=self.key_stride, groups=groups)
if b:
self.c1x1_c = CatBnAct(in_chs=num_3x3_b)
self.c1x1_c1 = create_conv2d(num_3x3_b, num_1x1_c, kernel_size=1)
self.c1x1_c2 = create_conv2d(num_3x3_b, inc, kernel_size=1)
else:
self.c1x1_c = BnActConv2d(in_chs=num_3x3_b, out_chs=num_1x1_c + inc, kernel_size=1, stride=1)
self.c1x1_c1 = None
self.c1x1_c2 = None
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]
pass
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
pass
def forward(self, x) -> Tuple[torch.Tensor, torch.Tensor]:
if isinstance(x, tuple):
x_in = torch.cat(x, dim=1)
else:
x_in = x
if self.c1x1_w_s1 is None and self.c1x1_w_s2 is None:
# self.has_proj == False, torchscript requires condition on module == None
x_s1 = x[0]
x_s2 = x[1]
else:
# self.has_proj == True
if self.c1x1_w_s1 is not None:
# self.key_stride = 1
x_s = self.c1x1_w_s1(x_in)
else:
# self.key_stride = 2
x_s = self.c1x1_w_s2(x_in)
x_s1 = x_s[:, :self.num_1x1_c, :, :]
x_s2 = x_s[:, self.num_1x1_c:, :, :]
x_in = self.c1x1_a(x_in)
x_in = self.c3x3_b(x_in)
x_in = self.c1x1_c(x_in)
if self.c1x1_c1 is not None:
# self.b == True, using None check for torchscript compat
out1 = self.c1x1_c1(x_in)
out2 = self.c1x1_c2(x_in)
else:
out1 = x_in[:, :self.num_1x1_c, :, :]
out2 = x_in[:, self.num_1x1_c:, :, :]
resid = x_s1 + out1
dense = torch.cat([x_s2, out2], dim=1)
return resid, dense
class DPN(nn.Module):
def __init__(
self,
k_sec=(3, 4, 20, 3),
inc_sec=(16, 32, 24, 128),
k_r=96,
groups=32,
num_classes=1000,
in_chans=3,
output_stride=32,
global_pool='avg',
small=False,
num_init_features=64,
b=False,
drop_rate=0.,
norm_layer='batchnorm2d',
act_layer='relu',
fc_act_layer='elu',
):
super(DPN, self).__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
self.b = b
assert output_stride == 32 # FIXME look into dilation support
norm_layer = partial(get_norm_act_layer(norm_layer, act_layer=act_layer), eps=.001)
fc_norm_layer = partial(get_norm_act_layer(norm_layer, act_layer=fc_act_layer), eps=.001, inplace=False)
bw_factor = 1 if small else 4
blocks = OrderedDict()
# conv1
blocks['conv1_1'] = ConvNormAct(
in_chans, num_init_features, kernel_size=3 if small else 7, stride=2, norm_layer=norm_layer)
blocks['conv1_pool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.feature_info = [dict(num_chs=num_init_features, reduction=2, module='features.conv1_1')]
# conv2
bw = 64 * bw_factor
inc = inc_sec[0]
r = (k_r * bw) // (64 * bw_factor)
blocks['conv2_1'] = DualPathBlock(num_init_features, r, r, bw, inc, groups, 'proj', b)
in_chs = bw + 3 * inc
for i in range(2, k_sec[0] + 1):
blocks['conv2_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
self.feature_info += [dict(num_chs=in_chs, reduction=4, module=f'features.conv2_{k_sec[0]}')]
# conv3
bw = 128 * bw_factor
inc = inc_sec[1]
r = (k_r * bw) // (64 * bw_factor)
blocks['conv3_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b)
in_chs = bw + 3 * inc
for i in range(2, k_sec[1] + 1):
blocks['conv3_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
self.feature_info += [dict(num_chs=in_chs, reduction=8, module=f'features.conv3_{k_sec[1]}')]
# conv4
bw = 256 * bw_factor
inc = inc_sec[2]
r = (k_r * bw) // (64 * bw_factor)
blocks['conv4_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b)
in_chs = bw + 3 * inc
for i in range(2, k_sec[2] + 1):
blocks['conv4_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
self.feature_info += [dict(num_chs=in_chs, reduction=16, module=f'features.conv4_{k_sec[2]}')]
# conv5
bw = 512 * bw_factor
inc = inc_sec[3]
r = (k_r * bw) // (64 * bw_factor)
blocks['conv5_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b)
in_chs = bw + 3 * inc
for i in range(2, k_sec[3] + 1):
blocks['conv5_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
self.feature_info += [dict(num_chs=in_chs, reduction=32, module=f'features.conv5_{k_sec[3]}')]
blocks['conv5_bn_ac'] = CatBnAct(in_chs, norm_layer=fc_norm_layer)
self.num_features = in_chs
self.features = nn.Sequential(blocks)
# Using 1x1 conv for the FC layer to allow the extra pooling scheme
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^features\.conv1',
blocks=[
(r'^features\.conv(\d+)' if coarse else r'^features\.conv(\d+)_(\d+)', None),
(r'^features\.conv5_bn_ac', (99999,))
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
def forward_features(self, x):
return self.features(x)
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
if pre_logits:
return self.flatten(x)
x = self.classifier(x)
return self.flatten(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_dpn(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
DPN,
variant,
pretrained,
feature_cfg=dict(feature_concat=True, flatten_sequential=True),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DPN_MEAN, 'std': IMAGENET_DPN_STD,
'first_conv': 'features.conv1_1.conv', 'classifier': 'classifier',
**kwargs
}
default_cfgs = generate_default_cfgs({
'dpn48b.untrained': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'dpn68.mx_in1k': _cfg(hf_hub_id='timm/'),
'dpn68b.ra_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'dpn68b.mx_in1k': _cfg(hf_hub_id='timm/'),
'dpn92.mx_in1k': _cfg(hf_hub_id='timm/'),
'dpn98.mx_in1k': _cfg(hf_hub_id='timm/'),
'dpn131.mx_in1k': _cfg(hf_hub_id='timm/'),
'dpn107.mx_in1k': _cfg(hf_hub_id='timm/')
})
@register_model
def dpn48b(pretrained=False, **kwargs) -> DPN:
model_args = dict(
small=True, num_init_features=10, k_r=128, groups=32,
b=True, k_sec=(3, 4, 6, 3), inc_sec=(16, 32, 32, 64), act_layer='silu')
return _create_dpn('dpn48b', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def dpn68(pretrained=False, **kwargs) -> DPN:
model_args = dict(
small=True, num_init_features=10, k_r=128, groups=32,
k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64))
return _create_dpn('dpn68', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def dpn68b(pretrained=False, **kwargs) -> DPN:
model_args = dict(
small=True, num_init_features=10, k_r=128, groups=32,
b=True, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64))
return _create_dpn('dpn68b', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def dpn92(pretrained=False, **kwargs) -> DPN:
model_args = dict(
num_init_features=64, k_r=96, groups=32,
k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128))
return _create_dpn('dpn92', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def dpn98(pretrained=False, **kwargs) -> DPN:
model_args = dict(
num_init_features=96, k_r=160, groups=40,
k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128))
return _create_dpn('dpn98', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def dpn131(pretrained=False, **kwargs) -> DPN:
model_args = dict(
num_init_features=128, k_r=160, groups=40,
k_sec=(4, 8, 28, 3), inc_sec=(16, 32, 32, 128))
return _create_dpn('dpn131', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def dpn107(pretrained=False, **kwargs) -> DPN:
model_args = dict(
num_init_features=128, k_r=200, groups=50,
k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128))
return _create_dpn('dpn107', pretrained=pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/selecsls.py
|
"""PyTorch SelecSLS Net example for ImageNet Classification
License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode)
Author: Dushyant Mehta (@mehtadushy)
SelecSLS (core) Network Architecture as proposed in "XNect: Real-time Multi-person 3D
Human Pose Estimation with a Single RGB Camera, Mehta et al."
https://arxiv.org/abs/1907.00837
Based on ResNet implementation in https://github.com/rwightman/pytorch-image-models
and SelecSLS Net implementation in https://github.com/mehtadushy/SelecSLS-Pytorch
"""
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import create_classifier
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['SelecSls'] # model_registry will add each entrypoint fn to this
class SequentialList(nn.Sequential):
def __init__(self, *args):
super(SequentialList, self).__init__(*args)
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (List[torch.Tensor]) -> (List[torch.Tensor])
pass
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (torch.Tensor) -> (List[torch.Tensor])
pass
def forward(self, x) -> List[torch.Tensor]:
for module in self:
x = module(x)
return x
class SelectSeq(nn.Module):
def __init__(self, mode='index', index=0):
super(SelectSeq, self).__init__()
self.mode = mode
self.index = index
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (List[torch.Tensor]) -> (torch.Tensor)
pass
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (Tuple[torch.Tensor]) -> (torch.Tensor)
pass
def forward(self, x) -> torch.Tensor:
if self.mode == 'index':
return x[self.index]
else:
return torch.cat(x, dim=1)
def conv_bn(in_chs, out_chs, k=3, stride=1, padding=None, dilation=1):
if padding is None:
padding = ((stride - 1) + dilation * (k - 1)) // 2
return nn.Sequential(
nn.Conv2d(in_chs, out_chs, k, stride, padding=padding, dilation=dilation, bias=False),
nn.BatchNorm2d(out_chs),
nn.ReLU(inplace=True)
)
class SelecSlsBlock(nn.Module):
def __init__(self, in_chs, skip_chs, mid_chs, out_chs, is_first, stride, dilation=1):
super(SelecSlsBlock, self).__init__()
self.stride = stride
self.is_first = is_first
assert stride in [1, 2]
# Process input with 4 conv blocks with the same number of input and output channels
self.conv1 = conv_bn(in_chs, mid_chs, 3, stride, dilation=dilation)
self.conv2 = conv_bn(mid_chs, mid_chs, 1)
self.conv3 = conv_bn(mid_chs, mid_chs // 2, 3)
self.conv4 = conv_bn(mid_chs // 2, mid_chs, 1)
self.conv5 = conv_bn(mid_chs, mid_chs // 2, 3)
self.conv6 = conv_bn(2 * mid_chs + (0 if is_first else skip_chs), out_chs, 1)
def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
if not isinstance(x, list):
x = [x]
assert len(x) in [1, 2]
d1 = self.conv1(x[0])
d2 = self.conv3(self.conv2(d1))
d3 = self.conv5(self.conv4(d2))
if self.is_first:
out = self.conv6(torch.cat([d1, d2, d3], 1))
return [out, out]
else:
return [self.conv6(torch.cat([d1, d2, d3, x[1]], 1)), x[1]]
class SelecSls(nn.Module):
"""SelecSls42 / SelecSls60 / SelecSls84
Parameters
----------
cfg : network config dictionary specifying block type, feature, and head args
num_classes : int, default 1000
Number of classification classes.
in_chans : int, default 3
Number of input (color) channels.
drop_rate : float, default 0.
Dropout probability before classifier, for training
global_pool : str, default 'avg'
Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
"""
def __init__(self, cfg, num_classes=1000, in_chans=3, drop_rate=0.0, global_pool='avg'):
self.num_classes = num_classes
super(SelecSls, self).__init__()
self.stem = conv_bn(in_chans, 32, stride=2)
self.features = SequentialList(*[cfg['block'](*block_args) for block_args in cfg['features']])
self.from_seq = SelectSeq() # from List[tensor] -> Tensor in module compatible way
self.head = nn.Sequential(*[conv_bn(*conv_args) for conv_args in cfg['head']])
self.num_features = cfg['num_features']
self.feature_info = cfg['feature_info']
self.global_pool, self.head_drop, self.fc = create_classifier(
self.num_features,
self.num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
)
for n, m in self.named_modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=r'^features\.(\d+)',
blocks_head=r'^head'
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.stem(x)
x = self.features(x)
x = self.head(self.from_seq(x))
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.head_drop(x)
return x if pre_logits else self.fc(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_selecsls(variant, pretrained, **kwargs):
cfg = {}
feature_info = [dict(num_chs=32, reduction=2, module='stem.2')]
if variant.startswith('selecsls42'):
cfg['block'] = SelecSlsBlock
# Define configuration of the network after the initial neck
cfg['features'] = [
# in_chs, skip_chs, mid_chs, out_chs, is_first, stride
(32, 0, 64, 64, True, 2),
(64, 64, 64, 128, False, 1),
(128, 0, 144, 144, True, 2),
(144, 144, 144, 288, False, 1),
(288, 0, 304, 304, True, 2),
(304, 304, 304, 480, False, 1),
]
feature_info.extend([
dict(num_chs=128, reduction=4, module='features.1'),
dict(num_chs=288, reduction=8, module='features.3'),
dict(num_chs=480, reduction=16, module='features.5'),
])
# Head can be replaced with alternative configurations depending on the problem
feature_info.append(dict(num_chs=1024, reduction=32, module='head.1'))
if variant == 'selecsls42b':
cfg['head'] = [
(480, 960, 3, 2),
(960, 1024, 3, 1),
(1024, 1280, 3, 2),
(1280, 1024, 1, 1),
]
feature_info.append(dict(num_chs=1024, reduction=64, module='head.3'))
cfg['num_features'] = 1024
else:
cfg['head'] = [
(480, 960, 3, 2),
(960, 1024, 3, 1),
(1024, 1024, 3, 2),
(1024, 1280, 1, 1),
]
feature_info.append(dict(num_chs=1280, reduction=64, module='head.3'))
cfg['num_features'] = 1280
elif variant.startswith('selecsls60'):
cfg['block'] = SelecSlsBlock
# Define configuration of the network after the initial neck
cfg['features'] = [
# in_chs, skip_chs, mid_chs, out_chs, is_first, stride
(32, 0, 64, 64, True, 2),
(64, 64, 64, 128, False, 1),
(128, 0, 128, 128, True, 2),
(128, 128, 128, 128, False, 1),
(128, 128, 128, 288, False, 1),
(288, 0, 288, 288, True, 2),
(288, 288, 288, 288, False, 1),
(288, 288, 288, 288, False, 1),
(288, 288, 288, 416, False, 1),
]
feature_info.extend([
dict(num_chs=128, reduction=4, module='features.1'),
dict(num_chs=288, reduction=8, module='features.4'),
dict(num_chs=416, reduction=16, module='features.8'),
])
# Head can be replaced with alternative configurations depending on the problem
feature_info.append(dict(num_chs=1024, reduction=32, module='head.1'))
if variant == 'selecsls60b':
cfg['head'] = [
(416, 756, 3, 2),
(756, 1024, 3, 1),
(1024, 1280, 3, 2),
(1280, 1024, 1, 1),
]
feature_info.append(dict(num_chs=1024, reduction=64, module='head.3'))
cfg['num_features'] = 1024
else:
cfg['head'] = [
(416, 756, 3, 2),
(756, 1024, 3, 1),
(1024, 1024, 3, 2),
(1024, 1280, 1, 1),
]
feature_info.append(dict(num_chs=1280, reduction=64, module='head.3'))
cfg['num_features'] = 1280
elif variant == 'selecsls84':
cfg['block'] = SelecSlsBlock
# Define configuration of the network after the initial neck
cfg['features'] = [
# in_chs, skip_chs, mid_chs, out_chs, is_first, stride
(32, 0, 64, 64, True, 2),
(64, 64, 64, 144, False, 1),
(144, 0, 144, 144, True, 2),
(144, 144, 144, 144, False, 1),
(144, 144, 144, 144, False, 1),
(144, 144, 144, 144, False, 1),
(144, 144, 144, 304, False, 1),
(304, 0, 304, 304, True, 2),
(304, 304, 304, 304, False, 1),
(304, 304, 304, 304, False, 1),
(304, 304, 304, 304, False, 1),
(304, 304, 304, 304, False, 1),
(304, 304, 304, 512, False, 1),
]
feature_info.extend([
dict(num_chs=144, reduction=4, module='features.1'),
dict(num_chs=304, reduction=8, module='features.6'),
dict(num_chs=512, reduction=16, module='features.12'),
])
# Head can be replaced with alternative configurations depending on the problem
cfg['head'] = [
(512, 960, 3, 2),
(960, 1024, 3, 1),
(1024, 1024, 3, 2),
(1024, 1280, 3, 1),
]
cfg['num_features'] = 1280
feature_info.extend([
dict(num_chs=1024, reduction=32, module='head.1'),
dict(num_chs=1280, reduction=64, module='head.3')
])
else:
raise ValueError('Invalid net configuration ' + variant + ' !!!')
cfg['feature_info'] = feature_info
# this model can do 6 feature levels by default, unlike most others, leave as 0-4 to avoid surprises?
return build_model_with_cfg(
SelecSls,
variant,
pretrained,
model_cfg=cfg,
feature_cfg=dict(out_indices=(0, 1, 2, 3, 4), flatten_sequential=True),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (4, 4),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0', 'classifier': 'fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'selecsls42.untrained': _cfg(
interpolation='bicubic'),
'selecsls42b.in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic'),
'selecsls60.in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic'),
'selecsls60b.in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic'),
'selecsls84.untrained': _cfg(
interpolation='bicubic'),
})
@register_model
def selecsls42(pretrained=False, **kwargs) -> SelecSls:
"""Constructs a SelecSls42 model.
"""
return _create_selecsls('selecsls42', pretrained, **kwargs)
@register_model
def selecsls42b(pretrained=False, **kwargs) -> SelecSls:
"""Constructs a SelecSls42_B model.
"""
return _create_selecsls('selecsls42b', pretrained, **kwargs)
@register_model
def selecsls60(pretrained=False, **kwargs) -> SelecSls:
"""Constructs a SelecSls60 model.
"""
return _create_selecsls('selecsls60', pretrained, **kwargs)
@register_model
def selecsls60b(pretrained=False, **kwargs) -> SelecSls:
"""Constructs a SelecSls60_B model.
"""
return _create_selecsls('selecsls60b', pretrained, **kwargs)
@register_model
def selecsls84(pretrained=False, **kwargs) -> SelecSls:
"""Constructs a SelecSls84 model.
"""
return _create_selecsls('selecsls84', pretrained, **kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/_prune.py
|
import os
import pkgutil
from copy import deepcopy
from torch import nn as nn
from timm.layers import Conv2dSame, BatchNormAct2d, Linear
__all__ = ['extract_layer', 'set_layer', 'adapt_model_from_string', 'adapt_model_from_file']
def extract_layer(model, layer):
layer = layer.split('.')
module = model
if hasattr(model, 'module') and layer[0] != 'module':
module = model.module
if not hasattr(model, 'module') and layer[0] == 'module':
layer = layer[1:]
for l in layer:
if hasattr(module, l):
if not l.isdigit():
module = getattr(module, l)
else:
module = module[int(l)]
else:
return module
return module
def set_layer(model, layer, val):
layer = layer.split('.')
module = model
if hasattr(model, 'module') and layer[0] != 'module':
module = model.module
lst_index = 0
module2 = module
for l in layer:
if hasattr(module2, l):
if not l.isdigit():
module2 = getattr(module2, l)
else:
module2 = module2[int(l)]
lst_index += 1
lst_index -= 1
for l in layer[:lst_index]:
if not l.isdigit():
module = getattr(module, l)
else:
module = module[int(l)]
l = layer[lst_index]
setattr(module, l, val)
def adapt_model_from_string(parent_module, model_string):
separator = '***'
state_dict = {}
lst_shape = model_string.split(separator)
for k in lst_shape:
k = k.split(':')
key = k[0]
shape = k[1][1:-1].split(',')
if shape[0] != '':
state_dict[key] = [int(i) for i in shape]
new_module = deepcopy(parent_module)
for n, m in parent_module.named_modules():
old_module = extract_layer(parent_module, n)
if isinstance(old_module, nn.Conv2d) or isinstance(old_module, Conv2dSame):
if isinstance(old_module, Conv2dSame):
conv = Conv2dSame
else:
conv = nn.Conv2d
s = state_dict[n + '.weight']
in_channels = s[1]
out_channels = s[0]
g = 1
if old_module.groups > 1:
in_channels = out_channels
g = in_channels
new_conv = conv(
in_channels=in_channels, out_channels=out_channels, kernel_size=old_module.kernel_size,
bias=old_module.bias is not None, padding=old_module.padding, dilation=old_module.dilation,
groups=g, stride=old_module.stride)
set_layer(new_module, n, new_conv)
elif isinstance(old_module, BatchNormAct2d):
new_bn = BatchNormAct2d(
state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
affine=old_module.affine, track_running_stats=True)
new_bn.drop = old_module.drop
new_bn.act = old_module.act
set_layer(new_module, n, new_bn)
elif isinstance(old_module, nn.BatchNorm2d):
new_bn = nn.BatchNorm2d(
num_features=state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
affine=old_module.affine, track_running_stats=True)
set_layer(new_module, n, new_bn)
elif isinstance(old_module, nn.Linear):
# FIXME extra checks to ensure this is actually the FC classifier layer and not a diff Linear layer?
num_features = state_dict[n + '.weight'][1]
new_fc = Linear(
in_features=num_features, out_features=old_module.out_features, bias=old_module.bias is not None)
set_layer(new_module, n, new_fc)
if hasattr(new_module, 'num_features'):
new_module.num_features = num_features
new_module.eval()
parent_module.eval()
return new_module
def adapt_model_from_file(parent_module, model_variant):
adapt_data = pkgutil.get_data(__name__, os.path.join('_pruned', model_variant + '.txt'))
return adapt_model_from_string(parent_module, adapt_data.decode('utf-8').strip())
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/regnet.py
|
"""RegNet X, Y, Z, and more
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
Original Impl: None
Based on original PyTorch impl linked above, but re-wrote to use my own blocks (adapted from ResNet here)
and cleaned up with more descriptive variable names.
Weights from original pycls impl have been modified:
* first layer from BGR -> RGB as most PyTorch models are
* removed training specific dict entries from checkpoints and keep model state_dict only
* remap names to match the ones here
Supports weight loading from torchvision and classy-vision (incl VISSL SEER)
A number of custom timm model definitions additions including:
* stochastic depth, gradient checkpointing, layer-decay, configurable dilation
* a pre-activation 'V' variant
* only known RegNet-Z model definitions with pretrained weights
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from dataclasses import dataclass, replace
from functools import partial
from typing import Optional, Union, Callable
import numpy as np
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import ClassifierHead, AvgPool2dSame, ConvNormAct, SEModule, DropPath, GroupNormAct
from timm.layers import get_act_layer, get_norm_act_layer, create_conv2d, make_divisible
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq, named_apply
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
__all__ = ['RegNet', 'RegNetCfg'] # model_registry will add each entrypoint fn to this
@dataclass
class RegNetCfg:
depth: int = 21
w0: int = 80
wa: float = 42.63
wm: float = 2.66
group_size: int = 24
bottle_ratio: float = 1.
se_ratio: float = 0.
group_min_ratio: float = 0.
stem_width: int = 32
downsample: Optional[str] = 'conv1x1'
linear_out: bool = False
preact: bool = False
num_features: int = 0
act_layer: Union[str, Callable] = 'relu'
norm_layer: Union[str, Callable] = 'batchnorm'
def quantize_float(f, q):
"""Converts a float to the closest non-zero int divisible by q."""
return int(round(f / q) * q)
def adjust_widths_groups_comp(widths, bottle_ratios, groups, min_ratio=0.):
"""Adjusts the compatibility of widths and groups."""
bottleneck_widths = [int(w * b) for w, b in zip(widths, bottle_ratios)]
groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_widths)]
if min_ratio:
# torchvision uses a different rounding scheme for ensuring bottleneck widths divisible by group widths
bottleneck_widths = [make_divisible(w_bot, g, min_ratio) for w_bot, g in zip(bottleneck_widths, groups)]
else:
bottleneck_widths = [quantize_float(w_bot, g) for w_bot, g in zip(bottleneck_widths, groups)]
widths = [int(w_bot / b) for w_bot, b in zip(bottleneck_widths, bottle_ratios)]
return widths, groups
def generate_regnet(width_slope, width_initial, width_mult, depth, group_size, quant=8):
"""Generates per block widths from RegNet parameters."""
assert width_slope >= 0 and width_initial > 0 and width_mult > 1 and width_initial % quant == 0
# TODO dWr scaling?
# depth = int(depth * (scale ** 0.1))
# width_scale = scale ** 0.4 # dWr scale, exp 0.8 / 2, applied to both group and layer widths
widths_cont = np.arange(depth) * width_slope + width_initial
width_exps = np.round(np.log(widths_cont / width_initial) / np.log(width_mult))
widths = np.round(np.divide(width_initial * np.power(width_mult, width_exps), quant)) * quant
num_stages, max_stage = len(np.unique(widths)), width_exps.max() + 1
groups = np.array([group_size for _ in range(num_stages)])
return widths.astype(int).tolist(), num_stages, groups.astype(int).tolist()
def downsample_conv(
in_chs,
out_chs,
kernel_size=1,
stride=1,
dilation=1,
norm_layer=None,
preact=False,
):
norm_layer = norm_layer or nn.BatchNorm2d
kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
dilation = dilation if kernel_size > 1 else 1
if preact:
return create_conv2d(
in_chs,
out_chs,
kernel_size,
stride=stride,
dilation=dilation,
)
else:
return ConvNormAct(
in_chs,
out_chs,
kernel_size,
stride=stride,
dilation=dilation,
norm_layer=norm_layer,
apply_act=False,
)
def downsample_avg(
in_chs,
out_chs,
kernel_size=1,
stride=1,
dilation=1,
norm_layer=None,
preact=False,
):
""" AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment."""
norm_layer = norm_layer or nn.BatchNorm2d
avg_stride = stride if dilation == 1 else 1
pool = nn.Identity()
if stride > 1 or dilation > 1:
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
if preact:
conv = create_conv2d(in_chs, out_chs, 1, stride=1)
else:
conv = ConvNormAct(in_chs, out_chs, 1, stride=1, norm_layer=norm_layer, apply_act=False)
return nn.Sequential(*[pool, conv])
def create_shortcut(
downsample_type,
in_chs,
out_chs,
kernel_size,
stride,
dilation=(1, 1),
norm_layer=None,
preact=False,
):
assert downsample_type in ('avg', 'conv1x1', '', None)
if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
dargs = dict(stride=stride, dilation=dilation[0], norm_layer=norm_layer, preact=preact)
if not downsample_type:
return None # no shortcut, no downsample
elif downsample_type == 'avg':
return downsample_avg(in_chs, out_chs, **dargs)
else:
return downsample_conv(in_chs, out_chs, kernel_size=kernel_size, **dargs)
else:
return nn.Identity() # identity shortcut (no downsample)
class Bottleneck(nn.Module):
""" RegNet Bottleneck
This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
"""
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=(1, 1),
bottle_ratio=1,
group_size=1,
se_ratio=0.25,
downsample='conv1x1',
linear_out=False,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
drop_block=None,
drop_path_rate=0.,
):
super(Bottleneck, self).__init__()
act_layer = get_act_layer(act_layer)
bottleneck_chs = int(round(out_chs * bottle_ratio))
groups = bottleneck_chs // group_size
cargs = dict(act_layer=act_layer, norm_layer=norm_layer)
self.conv1 = ConvNormAct(in_chs, bottleneck_chs, kernel_size=1, **cargs)
self.conv2 = ConvNormAct(
bottleneck_chs,
bottleneck_chs,
kernel_size=3,
stride=stride,
dilation=dilation[0],
groups=groups,
drop_layer=drop_block,
**cargs,
)
if se_ratio:
se_channels = int(round(in_chs * se_ratio))
self.se = SEModule(bottleneck_chs, rd_channels=se_channels, act_layer=act_layer)
else:
self.se = nn.Identity()
self.conv3 = ConvNormAct(bottleneck_chs, out_chs, kernel_size=1, apply_act=False, **cargs)
self.act3 = nn.Identity() if linear_out else act_layer()
self.downsample = create_shortcut(
downsample,
in_chs,
out_chs,
kernel_size=1,
stride=stride,
dilation=dilation,
norm_layer=norm_layer,
)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
def zero_init_last(self):
nn.init.zeros_(self.conv3.bn.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.conv2(x)
x = self.se(x)
x = self.conv3(x)
if self.downsample is not None:
# NOTE stuck with downsample as the attr name due to weight compatibility
# now represents the shortcut, no shortcut if None, and non-downsample shortcut == nn.Identity()
x = self.drop_path(x) + self.downsample(shortcut)
x = self.act3(x)
return x
class PreBottleneck(nn.Module):
""" RegNet Bottleneck
This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
"""
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=(1, 1),
bottle_ratio=1,
group_size=1,
se_ratio=0.25,
downsample='conv1x1',
linear_out=False,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
drop_block=None,
drop_path_rate=0.,
):
super(PreBottleneck, self).__init__()
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
bottleneck_chs = int(round(out_chs * bottle_ratio))
groups = bottleneck_chs // group_size
self.norm1 = norm_act_layer(in_chs)
self.conv1 = create_conv2d(in_chs, bottleneck_chs, kernel_size=1)
self.norm2 = norm_act_layer(bottleneck_chs)
self.conv2 = create_conv2d(
bottleneck_chs,
bottleneck_chs,
kernel_size=3,
stride=stride,
dilation=dilation[0],
groups=groups,
)
if se_ratio:
se_channels = int(round(in_chs * se_ratio))
self.se = SEModule(bottleneck_chs, rd_channels=se_channels, act_layer=act_layer)
else:
self.se = nn.Identity()
self.norm3 = norm_act_layer(bottleneck_chs)
self.conv3 = create_conv2d(bottleneck_chs, out_chs, kernel_size=1)
self.downsample = create_shortcut(
downsample,
in_chs,
out_chs,
kernel_size=1,
stride=stride,
dilation=dilation,
preact=True,
)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
def zero_init_last(self):
pass
def forward(self, x):
x = self.norm1(x)
shortcut = x
x = self.conv1(x)
x = self.norm2(x)
x = self.conv2(x)
x = self.se(x)
x = self.norm3(x)
x = self.conv3(x)
if self.downsample is not None:
# NOTE stuck with downsample as the attr name due to weight compatibility
# now represents the shortcut, no shortcut if None, and non-downsample shortcut == nn.Identity()
x = self.drop_path(x) + self.downsample(shortcut)
return x
class RegStage(nn.Module):
"""Stage (sequence of blocks w/ the same output shape)."""
def __init__(
self,
depth,
in_chs,
out_chs,
stride,
dilation,
drop_path_rates=None,
block_fn=Bottleneck,
**block_kwargs,
):
super(RegStage, self).__init__()
self.grad_checkpointing = False
first_dilation = 1 if dilation in (1, 2) else 2
for i in range(depth):
block_stride = stride if i == 0 else 1
block_in_chs = in_chs if i == 0 else out_chs
block_dilation = (first_dilation, dilation)
dpr = drop_path_rates[i] if drop_path_rates is not None else 0.
name = "b{}".format(i + 1)
self.add_module(
name,
block_fn(
block_in_chs,
out_chs,
stride=block_stride,
dilation=block_dilation,
drop_path_rate=dpr,
**block_kwargs,
)
)
first_dilation = dilation
def forward(self, x):
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.children(), x)
else:
for block in self.children():
x = block(x)
return x
class RegNet(nn.Module):
"""RegNet-X, Y, and Z Models
Paper: https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
"""
def __init__(
self,
cfg: RegNetCfg,
in_chans=3,
num_classes=1000,
output_stride=32,
global_pool='avg',
drop_rate=0.,
drop_path_rate=0.,
zero_init_last=True,
**kwargs,
):
"""
Args:
cfg (RegNetCfg): Model architecture configuration
in_chans (int): Number of input channels (default: 3)
num_classes (int): Number of classifier classes (default: 1000)
output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32)
global_pool (str): Global pooling type (default: 'avg')
drop_rate (float): Dropout rate (default: 0.)
drop_path_rate (float): Stochastic depth drop-path rate (default: 0.)
zero_init_last (bool): Zero-init last weight of residual path
kwargs (dict): Extra kwargs overlayed onto cfg
"""
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
assert output_stride in (8, 16, 32)
cfg = replace(cfg, **kwargs) # update cfg with extra passed kwargs
# Construct the stem
stem_width = cfg.stem_width
na_args = dict(act_layer=cfg.act_layer, norm_layer=cfg.norm_layer)
if cfg.preact:
self.stem = create_conv2d(in_chans, stem_width, 3, stride=2)
else:
self.stem = ConvNormAct(in_chans, stem_width, 3, stride=2, **na_args)
self.feature_info = [dict(num_chs=stem_width, reduction=2, module='stem')]
# Construct the stages
prev_width = stem_width
curr_stride = 2
per_stage_args, common_args = self._get_stage_args(
cfg,
output_stride=output_stride,
drop_path_rate=drop_path_rate,
)
assert len(per_stage_args) == 4
block_fn = PreBottleneck if cfg.preact else Bottleneck
for i, stage_args in enumerate(per_stage_args):
stage_name = "s{}".format(i + 1)
self.add_module(
stage_name,
RegStage(
in_chs=prev_width,
block_fn=block_fn,
**stage_args,
**common_args,
)
)
prev_width = stage_args['out_chs']
curr_stride *= stage_args['stride']
self.feature_info += [dict(num_chs=prev_width, reduction=curr_stride, module=stage_name)]
# Construct the head
if cfg.num_features:
self.final_conv = ConvNormAct(prev_width, cfg.num_features, kernel_size=1, **na_args)
self.num_features = cfg.num_features
else:
final_act = cfg.linear_out or cfg.preact
self.final_conv = get_act_layer(cfg.act_layer)() if final_act else nn.Identity()
self.num_features = prev_width
self.head = ClassifierHead(
in_features=self.num_features,
num_classes=num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
)
named_apply(partial(_init_weights, zero_init_last=zero_init_last), self)
def _get_stage_args(self, cfg: RegNetCfg, default_stride=2, output_stride=32, drop_path_rate=0.):
# Generate RegNet ws per block
widths, num_stages, stage_gs = generate_regnet(cfg.wa, cfg.w0, cfg.wm, cfg.depth, cfg.group_size)
# Convert to per stage format
stage_widths, stage_depths = np.unique(widths, return_counts=True)
stage_br = [cfg.bottle_ratio for _ in range(num_stages)]
stage_strides = []
stage_dilations = []
net_stride = 2
dilation = 1
for _ in range(num_stages):
if net_stride >= output_stride:
dilation *= default_stride
stride = 1
else:
stride = default_stride
net_stride *= stride
stage_strides.append(stride)
stage_dilations.append(dilation)
stage_dpr = np.split(np.linspace(0, drop_path_rate, sum(stage_depths)), np.cumsum(stage_depths[:-1]))
# Adjust the compatibility of ws and gws
stage_widths, stage_gs = adjust_widths_groups_comp(
stage_widths, stage_br, stage_gs, min_ratio=cfg.group_min_ratio)
arg_names = ['out_chs', 'stride', 'dilation', 'depth', 'bottle_ratio', 'group_size', 'drop_path_rates']
per_stage_args = [
dict(zip(arg_names, params)) for params in
zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_br, stage_gs, stage_dpr)
]
common_args = dict(
downsample=cfg.downsample,
se_ratio=cfg.se_ratio,
linear_out=cfg.linear_out,
act_layer=cfg.act_layer,
norm_layer=cfg.norm_layer,
)
return per_stage_args, common_args
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=r'^s(\d+)' if coarse else r'^s(\d+)\.b(\d+)',
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in list(self.children())[1:-1]:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.head.reset(num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.stem(x)
x = self.s1(x)
x = self.s2(x)
x = self.s3(x)
x = self.s4(x)
x = self.final_conv(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module, name='', zero_init_last=False):
if isinstance(module, nn.Conv2d):
fan_out = module.kernel_size[0] * module.kernel_size[1] * module.out_channels
fan_out //= module.groups
module.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.01)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif zero_init_last and hasattr(module, 'zero_init_last'):
module.zero_init_last()
def _filter_fn(state_dict):
state_dict = state_dict.get('model', state_dict)
replaces = [
('f.a.0', 'conv1.conv'),
('f.a.1', 'conv1.bn'),
('f.b.0', 'conv2.conv'),
('f.b.1', 'conv2.bn'),
('f.final_bn', 'conv3.bn'),
('f.se.excitation.0', 'se.fc1'),
('f.se.excitation.2', 'se.fc2'),
('f.se', 'se'),
('f.c.0', 'conv3.conv'),
('f.c.1', 'conv3.bn'),
('f.c', 'conv3.conv'),
('proj.0', 'downsample.conv'),
('proj.1', 'downsample.bn'),
('proj', 'downsample.conv'),
]
if 'classy_state_dict' in state_dict:
# classy-vision & vissl (SEER) weights
import re
state_dict = state_dict['classy_state_dict']['base_model']['model']
out = {}
for k, v in state_dict['trunk'].items():
k = k.replace('_feature_blocks.conv1.stem.0', 'stem.conv')
k = k.replace('_feature_blocks.conv1.stem.1', 'stem.bn')
k = re.sub(
r'^_feature_blocks.res\d.block(\d)-(\d+)',
lambda x: f's{int(x.group(1))}.b{int(x.group(2)) + 1}', k)
k = re.sub(r's(\d)\.b(\d+)\.bn', r's\1.b\2.downsample.bn', k)
for s, r in replaces:
k = k.replace(s, r)
out[k] = v
for k, v in state_dict['heads'].items():
if 'projection_head' in k or 'prototypes' in k:
continue
k = k.replace('0.clf.0', 'head.fc')
out[k] = v
return out
if 'stem.0.weight' in state_dict:
# torchvision weights
import re
out = {}
for k, v in state_dict.items():
k = k.replace('stem.0', 'stem.conv')
k = k.replace('stem.1', 'stem.bn')
k = re.sub(
r'trunk_output.block(\d)\.block(\d+)\-(\d+)',
lambda x: f's{int(x.group(1))}.b{int(x.group(3)) + 1}', k)
for s, r in replaces:
k = k.replace(s, r)
k = k.replace('fc.', 'head.fc.')
out[k] = v
return out
return state_dict
# Model FLOPS = three trailing digits * 10^8
model_cfgs = dict(
# RegNet-X
regnetx_002=RegNetCfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13),
regnetx_004=RegNetCfg(w0=24, wa=24.48, wm=2.54, group_size=16, depth=22),
regnetx_004_tv=RegNetCfg(w0=24, wa=24.48, wm=2.54, group_size=16, depth=22, group_min_ratio=0.9),
regnetx_006=RegNetCfg(w0=48, wa=36.97, wm=2.24, group_size=24, depth=16),
regnetx_008=RegNetCfg(w0=56, wa=35.73, wm=2.28, group_size=16, depth=16),
regnetx_016=RegNetCfg(w0=80, wa=34.01, wm=2.25, group_size=24, depth=18),
regnetx_032=RegNetCfg(w0=88, wa=26.31, wm=2.25, group_size=48, depth=25),
regnetx_040=RegNetCfg(w0=96, wa=38.65, wm=2.43, group_size=40, depth=23),
regnetx_064=RegNetCfg(w0=184, wa=60.83, wm=2.07, group_size=56, depth=17),
regnetx_080=RegNetCfg(w0=80, wa=49.56, wm=2.88, group_size=120, depth=23),
regnetx_120=RegNetCfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19),
regnetx_160=RegNetCfg(w0=216, wa=55.59, wm=2.1, group_size=128, depth=22),
regnetx_320=RegNetCfg(w0=320, wa=69.86, wm=2.0, group_size=168, depth=23),
# RegNet-Y
regnety_002=RegNetCfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13, se_ratio=0.25),
regnety_004=RegNetCfg(w0=48, wa=27.89, wm=2.09, group_size=8, depth=16, se_ratio=0.25),
regnety_006=RegNetCfg(w0=48, wa=32.54, wm=2.32, group_size=16, depth=15, se_ratio=0.25),
regnety_008=RegNetCfg(w0=56, wa=38.84, wm=2.4, group_size=16, depth=14, se_ratio=0.25),
regnety_008_tv=RegNetCfg(w0=56, wa=38.84, wm=2.4, group_size=16, depth=14, se_ratio=0.25, group_min_ratio=0.9),
regnety_016=RegNetCfg(w0=48, wa=20.71, wm=2.65, group_size=24, depth=27, se_ratio=0.25),
regnety_032=RegNetCfg(w0=80, wa=42.63, wm=2.66, group_size=24, depth=21, se_ratio=0.25),
regnety_040=RegNetCfg(w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25),
regnety_064=RegNetCfg(w0=112, wa=33.22, wm=2.27, group_size=72, depth=25, se_ratio=0.25),
regnety_080=RegNetCfg(w0=192, wa=76.82, wm=2.19, group_size=56, depth=17, se_ratio=0.25),
regnety_080_tv=RegNetCfg(w0=192, wa=76.82, wm=2.19, group_size=56, depth=17, se_ratio=0.25, group_min_ratio=0.9),
regnety_120=RegNetCfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19, se_ratio=0.25),
regnety_160=RegNetCfg(w0=200, wa=106.23, wm=2.48, group_size=112, depth=18, se_ratio=0.25),
regnety_320=RegNetCfg(w0=232, wa=115.89, wm=2.53, group_size=232, depth=20, se_ratio=0.25),
regnety_640=RegNetCfg(w0=352, wa=147.48, wm=2.4, group_size=328, depth=20, se_ratio=0.25),
regnety_1280=RegNetCfg(w0=456, wa=160.83, wm=2.52, group_size=264, depth=27, se_ratio=0.25),
regnety_2560=RegNetCfg(w0=640, wa=230.83, wm=2.53, group_size=373, depth=27, se_ratio=0.25),
#regnety_2560=RegNetCfg(w0=640, wa=124.47, wm=2.04, group_size=848, depth=27, se_ratio=0.25),
# Experimental
regnety_040_sgn=RegNetCfg(
w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25,
act_layer='silu', norm_layer=partial(GroupNormAct, group_size=16)),
# regnetv = 'preact regnet y'
regnetv_040=RegNetCfg(
depth=22, w0=96, wa=31.41, wm=2.24, group_size=64, se_ratio=0.25, preact=True, act_layer='silu'),
regnetv_064=RegNetCfg(
depth=25, w0=112, wa=33.22, wm=2.27, group_size=72, se_ratio=0.25, preact=True, act_layer='silu',
downsample='avg'),
# RegNet-Z (unverified)
regnetz_005=RegNetCfg(
depth=21, w0=16, wa=10.7, wm=2.51, group_size=4, bottle_ratio=4.0, se_ratio=0.25,
downsample=None, linear_out=True, num_features=1024, act_layer='silu',
),
regnetz_040=RegNetCfg(
depth=28, w0=48, wa=14.5, wm=2.226, group_size=8, bottle_ratio=4.0, se_ratio=0.25,
downsample=None, linear_out=True, num_features=0, act_layer='silu',
),
regnetz_040_h=RegNetCfg(
depth=28, w0=48, wa=14.5, wm=2.226, group_size=8, bottle_ratio=4.0, se_ratio=0.25,
downsample=None, linear_out=True, num_features=1536, act_layer='silu',
),
)
def _create_regnet(variant, pretrained, **kwargs):
return build_model_with_cfg(
RegNet, variant, pretrained,
model_cfg=model_cfgs[variant],
pretrained_filter_fn=_filter_fn,
**kwargs)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'test_input_size': (3, 288, 288), 'crop_pct': 0.95, 'test_crop_pct': 1.0,
'interpolation': 'bicubic', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
**kwargs
}
def _cfgpyc(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
'license': 'mit', 'origin_url': 'https://github.com/facebookresearch/pycls', **kwargs
}
def _cfgtv2(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.965, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
'license': 'bsd-3-clause', 'origin_url': 'https://github.com/pytorch/vision', **kwargs
}
default_cfgs = generate_default_cfgs({
# timm trained models
'regnety_032.ra_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth'),
'regnety_040.ra3_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnety_040_ra3-670e1166.pth'),
'regnety_064.ra3_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnety_064_ra3-aa26dc7d.pth'),
'regnety_080.ra3_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnety_080_ra3-1fdc4344.pth'),
'regnety_120.sw_in12k_ft_in1k': _cfg(hf_hub_id='timm/'),
'regnety_160.sw_in12k_ft_in1k': _cfg(hf_hub_id='timm/'),
'regnety_160.lion_in12k_ft_in1k': _cfg(hf_hub_id='timm/'),
# timm in12k pretrain
'regnety_120.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821),
'regnety_160.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821),
# timm custom arch (v and z guess) + trained models
'regnety_040_sgn.untrained': _cfg(url=''),
'regnetv_040.ra3_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetv_040_ra3-c248f51f.pth',
first_conv='stem'),
'regnetv_064.ra3_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetv_064_ra3-530616c2.pth',
first_conv='stem'),
'regnetz_005.untrained': _cfg(url=''),
'regnetz_040.ra3_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetz_040_ra3-9007edf5.pth',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320)),
'regnetz_040_h.ra3_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-tpu-weights/regnetz_040h_ra3-f594343b.pth',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320)),
# used in DeiT for distillation (from Facebook DeiT GitHub repository)
'regnety_160.deit_in1k': _cfg(
hf_hub_id='timm/', url='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth'),
'regnetx_004_tv.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_x_400mf-62229a5f.pth'),
'regnetx_008.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_x_800mf-94a99ebd.pth'),
'regnetx_016.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_x_1_6gf-a12f2b72.pth'),
'regnetx_032.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_x_3_2gf-7071aa85.pth'),
'regnetx_080.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_x_8gf-2b70d774.pth'),
'regnetx_160.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_x_16gf-ba3796d7.pth'),
'regnetx_320.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_x_32gf-6eb8fdc6.pth'),
'regnety_004.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_400mf-e6988f5f.pth'),
'regnety_008_tv.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_800mf-58fc7688.pth'),
'regnety_016.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_1_6gf-0d7bc02a.pth'),
'regnety_032.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_3_2gf-9180c971.pth'),
'regnety_080_tv.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_8gf-dc2b1b54.pth'),
'regnety_160.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_16gf-3e4a00f9.pth'),
'regnety_320.tv2_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_32gf-8db6d4b5.pth'),
'regnety_160.swag_ft_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_16gf_swag-43afe44d.pth', license='cc-by-nc-4.0',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'regnety_320.swag_ft_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_32gf_swag-04fdfa75.pth', license='cc-by-nc-4.0',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'regnety_1280.swag_ft_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_128gf_swag-c8ce3e52.pth', license='cc-by-nc-4.0',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'regnety_160.swag_lc_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_16gf_lc_swag-f3ec0043.pth', license='cc-by-nc-4.0'),
'regnety_320.swag_lc_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_32gf_lc_swag-e1583746.pth', license='cc-by-nc-4.0'),
'regnety_1280.swag_lc_in1k': _cfgtv2(
hf_hub_id='timm/',
url='https://download.pytorch.org/models/regnet_y_128gf_lc_swag-cbe8ce12.pth', license='cc-by-nc-4.0'),
'regnety_320.seer_ft_in1k': _cfgtv2(
hf_hub_id='timm/',
license='other', origin_url='https://github.com/facebookresearch/vissl',
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'regnety_640.seer_ft_in1k': _cfgtv2(
hf_hub_id='timm/',
license='other', origin_url='https://github.com/facebookresearch/vissl',
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'regnety_1280.seer_ft_in1k': _cfgtv2(
hf_hub_id='timm/',
license='other', origin_url='https://github.com/facebookresearch/vissl',
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'regnety_2560.seer_ft_in1k': _cfgtv2(
hf_hub_id='timm/',
license='other', origin_url='https://github.com/facebookresearch/vissl',
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet256_finetuned_in1k_model_final_checkpoint_phase38.torch',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'regnety_320.seer': _cfgtv2(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch',
num_classes=0, license='other', origin_url='https://github.com/facebookresearch/vissl'),
'regnety_640.seer': _cfgtv2(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch',
num_classes=0, license='other', origin_url='https://github.com/facebookresearch/vissl'),
'regnety_1280.seer': _cfgtv2(
hf_hub_id='timm/',
url='https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch',
num_classes=0, license='other', origin_url='https://github.com/facebookresearch/vissl'),
# FIXME invalid weight <-> model match, mistake on their end
#'regnety_2560.seer': _cfgtv2(
# url='https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_cosine_rg256gf_noBNhead_wd1e5_fairstore_bs16_node64_sinkhorn10_proto16k_apex_syncBN64_warmup8k/model_final_checkpoint_phase0.torch',
# num_classes=0, license='other', origin_url='https://github.com/facebookresearch/vissl'),
'regnetx_002.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_004.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_006.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_008.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_016.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_032.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_040.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_064.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_080.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_120.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_160.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnetx_320.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_002.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_004.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_006.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_008.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_016.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_032.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_040.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_064.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_080.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_120.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_160.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
'regnety_320.pycls_in1k': _cfgpyc(hf_hub_id='timm/'),
})
@register_model
def regnetx_002(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-200MF"""
return _create_regnet('regnetx_002', pretrained, **kwargs)
@register_model
def regnetx_004(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-400MF"""
return _create_regnet('regnetx_004', pretrained, **kwargs)
@register_model
def regnetx_004_tv(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-400MF w/ torchvision group rounding"""
return _create_regnet('regnetx_004_tv', pretrained, **kwargs)
@register_model
def regnetx_006(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-600MF"""
return _create_regnet('regnetx_006', pretrained, **kwargs)
@register_model
def regnetx_008(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-800MF"""
return _create_regnet('regnetx_008', pretrained, **kwargs)
@register_model
def regnetx_016(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-1.6GF"""
return _create_regnet('regnetx_016', pretrained, **kwargs)
@register_model
def regnetx_032(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-3.2GF"""
return _create_regnet('regnetx_032', pretrained, **kwargs)
@register_model
def regnetx_040(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-4.0GF"""
return _create_regnet('regnetx_040', pretrained, **kwargs)
@register_model
def regnetx_064(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-6.4GF"""
return _create_regnet('regnetx_064', pretrained, **kwargs)
@register_model
def regnetx_080(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-8.0GF"""
return _create_regnet('regnetx_080', pretrained, **kwargs)
@register_model
def regnetx_120(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-12GF"""
return _create_regnet('regnetx_120', pretrained, **kwargs)
@register_model
def regnetx_160(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-16GF"""
return _create_regnet('regnetx_160', pretrained, **kwargs)
@register_model
def regnetx_320(pretrained=False, **kwargs) -> RegNet:
"""RegNetX-32GF"""
return _create_regnet('regnetx_320', pretrained, **kwargs)
@register_model
def regnety_002(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-200MF"""
return _create_regnet('regnety_002', pretrained, **kwargs)
@register_model
def regnety_004(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-400MF"""
return _create_regnet('regnety_004', pretrained, **kwargs)
@register_model
def regnety_006(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-600MF"""
return _create_regnet('regnety_006', pretrained, **kwargs)
@register_model
def regnety_008(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-800MF"""
return _create_regnet('regnety_008', pretrained, **kwargs)
@register_model
def regnety_008_tv(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-800MF w/ torchvision group rounding"""
return _create_regnet('regnety_008_tv', pretrained, **kwargs)
@register_model
def regnety_016(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-1.6GF"""
return _create_regnet('regnety_016', pretrained, **kwargs)
@register_model
def regnety_032(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-3.2GF"""
return _create_regnet('regnety_032', pretrained, **kwargs)
@register_model
def regnety_040(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-4.0GF"""
return _create_regnet('regnety_040', pretrained, **kwargs)
@register_model
def regnety_064(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-6.4GF"""
return _create_regnet('regnety_064', pretrained, **kwargs)
@register_model
def regnety_080(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-8.0GF"""
return _create_regnet('regnety_080', pretrained, **kwargs)
@register_model
def regnety_080_tv(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-8.0GF w/ torchvision group rounding"""
return _create_regnet('regnety_080_tv', pretrained, **kwargs)
@register_model
def regnety_120(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-12GF"""
return _create_regnet('regnety_120', pretrained, **kwargs)
@register_model
def regnety_160(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-16GF"""
return _create_regnet('regnety_160', pretrained, **kwargs)
@register_model
def regnety_320(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-32GF"""
return _create_regnet('regnety_320', pretrained, **kwargs)
@register_model
def regnety_640(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-64GF"""
return _create_regnet('regnety_640', pretrained, **kwargs)
@register_model
def regnety_1280(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-128GF"""
return _create_regnet('regnety_1280', pretrained, **kwargs)
@register_model
def regnety_2560(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-256GF"""
return _create_regnet('regnety_2560', pretrained, **kwargs)
@register_model
def regnety_040_sgn(pretrained=False, **kwargs) -> RegNet:
"""RegNetY-4.0GF w/ GroupNorm """
return _create_regnet('regnety_040_sgn', pretrained, **kwargs)
@register_model
def regnetv_040(pretrained=False, **kwargs) -> RegNet:
"""RegNetV-4.0GF (pre-activation)"""
return _create_regnet('regnetv_040', pretrained, **kwargs)
@register_model
def regnetv_064(pretrained=False, **kwargs) -> RegNet:
"""RegNetV-6.4GF (pre-activation)"""
return _create_regnet('regnetv_064', pretrained, **kwargs)
@register_model
def regnetz_005(pretrained=False, **kwargs) -> RegNet:
"""RegNetZ-500MF
NOTE: config found in https://github.com/facebookresearch/ClassyVision/blob/main/classy_vision/models/regnet.py
but it's not clear it is equivalent to paper model as not detailed in the paper.
"""
return _create_regnet('regnetz_005', pretrained, zero_init_last=False, **kwargs)
@register_model
def regnetz_040(pretrained=False, **kwargs) -> RegNet:
"""RegNetZ-4.0GF
NOTE: config found in https://github.com/facebookresearch/ClassyVision/blob/main/classy_vision/models/regnet.py
but it's not clear it is equivalent to paper model as not detailed in the paper.
"""
return _create_regnet('regnetz_040', pretrained, zero_init_last=False, **kwargs)
@register_model
def regnetz_040_h(pretrained=False, **kwargs) -> RegNet:
"""RegNetZ-4.0GF
NOTE: config found in https://github.com/facebookresearch/ClassyVision/blob/main/classy_vision/models/regnet.py
but it's not clear it is equivalent to paper model as not detailed in the paper.
"""
return _create_regnet('regnetz_040_h', pretrained, zero_init_last=False, **kwargs)
register_model_deprecations(__name__, {
'regnetz_040h': 'regnetz_040_h',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/inception_next.py
|
"""
InceptionNeXt paper: https://arxiv.org/abs/2303.16900
Original implementation & weights from: https://github.com/sail-sg/inceptionnext
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, DropPath, to_2tuple, get_padding, SelectAdaptivePool2d
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
class InceptionDWConv2d(nn.Module):
""" Inception depthwise convolution
"""
def __init__(
self,
in_chs,
square_kernel_size=3,
band_kernel_size=11,
branch_ratio=0.125,
dilation=1,
):
super().__init__()
gc = int(in_chs * branch_ratio) # channel numbers of a convolution branch
square_padding = get_padding(square_kernel_size, dilation=dilation)
band_padding = get_padding(band_kernel_size, dilation=dilation)
self.dwconv_hw = nn.Conv2d(
gc, gc, square_kernel_size,
padding=square_padding, dilation=dilation, groups=gc)
self.dwconv_w = nn.Conv2d(
gc, gc, (1, band_kernel_size),
padding=(0, band_padding), dilation=(1, dilation), groups=gc)
self.dwconv_h = nn.Conv2d(
gc, gc, (band_kernel_size, 1),
padding=(band_padding, 0), dilation=(dilation, 1), groups=gc)
self.split_indexes = (in_chs - 3 * gc, gc, gc, gc)
def forward(self, x):
x_id, x_hw, x_w, x_h = torch.split(x, self.split_indexes, dim=1)
return torch.cat((
x_id,
self.dwconv_hw(x_hw),
self.dwconv_w(x_w),
self.dwconv_h(x_h)
), dim=1,
)
class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
copied from timm: https://github.com/huggingface/pytorch-image-models/blob/v0.6.11/timm/models/layers/mlp.py
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.ReLU,
norm_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
self.act = act_layer()
self.drop = nn.Dropout(drop)
self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])
def forward(self, x):
x = self.fc1(x)
x = self.norm(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
return x
class MlpClassifierHead(nn.Module):
""" MLP classification head
"""
def __init__(
self,
dim,
num_classes=1000,
pool_type='avg',
mlp_ratio=3,
act_layer=nn.GELU,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
drop=0.,
bias=True
):
super().__init__()
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type, flatten=True)
in_features = dim * self.global_pool.feat_mult()
hidden_features = int(mlp_ratio * in_features)
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
self.act = act_layer()
self.norm = norm_layer(hidden_features)
self.fc2 = nn.Linear(hidden_features, num_classes, bias=bias)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.global_pool(x)
x = self.fc1(x)
x = self.act(x)
x = self.norm(x)
x = self.drop(x)
x = self.fc2(x)
return x
class MetaNeXtBlock(nn.Module):
""" MetaNeXtBlock Block
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
def __init__(
self,
dim,
dilation=1,
token_mixer=InceptionDWConv2d,
norm_layer=nn.BatchNorm2d,
mlp_layer=ConvMlp,
mlp_ratio=4,
act_layer=nn.GELU,
ls_init_value=1e-6,
drop_path=0.,
):
super().__init__()
self.token_mixer = token_mixer(dim, dilation=dilation)
self.norm = norm_layer(dim)
self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=act_layer)
self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.token_mixer(x)
x = self.norm(x)
x = self.mlp(x)
if self.gamma is not None:
x = x.mul(self.gamma.reshape(1, -1, 1, 1))
x = self.drop_path(x) + shortcut
return x
class MetaNeXtStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=2,
depth=2,
dilation=(1, 1),
drop_path_rates=None,
ls_init_value=1.0,
token_mixer=InceptionDWConv2d,
act_layer=nn.GELU,
norm_layer=None,
mlp_ratio=4,
):
super().__init__()
self.grad_checkpointing = False
if stride > 1 or dilation[0] != dilation[1]:
self.downsample = nn.Sequential(
norm_layer(in_chs),
nn.Conv2d(
in_chs,
out_chs,
kernel_size=2,
stride=stride,
dilation=dilation[0],
),
)
else:
self.downsample = nn.Identity()
drop_path_rates = drop_path_rates or [0.] * depth
stage_blocks = []
for i in range(depth):
stage_blocks.append(MetaNeXtBlock(
dim=out_chs,
dilation=dilation[1],
drop_path=drop_path_rates[i],
ls_init_value=ls_init_value,
token_mixer=token_mixer,
act_layer=act_layer,
norm_layer=norm_layer,
mlp_ratio=mlp_ratio,
))
self.blocks = nn.Sequential(*stage_blocks)
def forward(self, x):
x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class MetaNeXt(nn.Module):
r""" MetaNeXt
A PyTorch impl of : `InceptionNeXt: When Inception Meets ConvNeXt` - https://arxiv.org/abs/2303.16900
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
depths (tuple(int)): Number of blocks at each stage. Default: (3, 3, 9, 3)
dims (tuple(int)): Feature dimension at each stage. Default: (96, 192, 384, 768)
token_mixers: Token mixer function. Default: nn.Identity
norm_layer: Normalization layer. Default: nn.BatchNorm2d
act_layer: Activation function for MLP. Default: nn.GELU
mlp_ratios (int or tuple(int)): MLP ratios. Default: (4, 4, 4, 3)
head_fn: classifier head
drop_rate (float): Head dropout rate
drop_path_rate (float): Stochastic depth rate. Default: 0.
ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
output_stride=32,
depths=(3, 3, 9, 3),
dims=(96, 192, 384, 768),
token_mixers=InceptionDWConv2d,
norm_layer=nn.BatchNorm2d,
act_layer=nn.GELU,
mlp_ratios=(4, 4, 4, 3),
head_fn=MlpClassifierHead,
drop_rate=0.,
drop_path_rate=0.,
ls_init_value=1e-6,
):
super().__init__()
num_stage = len(depths)
if not isinstance(token_mixers, (list, tuple)):
token_mixers = [token_mixers] * num_stage
if not isinstance(mlp_ratios, (list, tuple)):
mlp_ratios = [mlp_ratios] * num_stage
self.num_classes = num_classes
self.global_pool = global_pool
self.drop_rate = drop_rate
self.feature_info = []
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
norm_layer(dims[0])
)
dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
prev_chs = dims[0]
curr_stride = 4
dilation = 1
# feature resolution stages, each consisting of multiple residual blocks
self.stages = nn.Sequential()
for i in range(num_stage):
stride = 2 if curr_stride == 2 or i > 0 else 1
if curr_stride >= output_stride and stride > 1:
dilation *= stride
stride = 1
curr_stride *= stride
first_dilation = 1 if dilation in (1, 2) else 2
out_chs = dims[i]
self.stages.append(MetaNeXtStage(
prev_chs,
out_chs,
stride=stride if i > 0 else 1,
dilation=(first_dilation, dilation),
depth=depths[i],
drop_path_rates=dp_rates[i],
ls_init_value=ls_init_value,
act_layer=act_layer,
token_mixer=token_mixers[i],
norm_layer=norm_layer,
mlp_ratio=mlp_ratios[i],
))
prev_chs = out_chs
self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{i}')]
self.num_features = prev_chs
if self.num_classes > 0:
if issubclass(head_fn, MlpClassifierHead):
assert self.global_pool, 'Cannot disable global pooling with MLP head present.'
self.head = head_fn(self.num_features, num_classes, pool_type=self.global_pool, drop=drop_rate)
else:
if self.global_pool:
self.head = SelectAdaptivePool2d(pool_type=self.global_pool, flatten=True)
else:
self.head = nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+)\.downsample', (0,)), # blocks
(r'^stages\.(\d+)\.blocks\.(\d+)', None),
]
)
@torch.jit.ignore
def get_classifier(self):
return self.head.fc2
def reset_classifier(self, num_classes=0, global_pool=None, head_fn=MlpClassifierHead):
if global_pool is not None:
self.global_pool = global_pool
if num_classes > 0:
if issubclass(head_fn, MlpClassifierHead):
assert self.global_pool, 'Cannot disable global pooling with MLP head present.'
self.head = head_fn(self.num_features, num_classes, pool_type=self.global_pool, drop=self.drop_rate)
else:
if self.global_pool:
self.head = SelectAdaptivePool2d(pool_type=self.global_pool, flatten=True)
else:
self.head = nn.Identity()
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def no_weight_decay(self):
return set()
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if pre_logits:
if hasattr(self.head, 'global_pool'):
x = self.head.global_pool(x)
return x
return self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0', 'classifier': 'head.fc2',
**kwargs
}
default_cfgs = generate_default_cfgs({
'inception_next_tiny.sail_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_tiny.pth',
),
'inception_next_small.sail_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_small.pth',
),
'inception_next_base.sail_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base.pth',
crop_pct=0.95,
),
'inception_next_base.sail_in1k_384': _cfg(
hf_hub_id='timm/',
# url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
})
def _create_inception_next(variant, pretrained=False, **kwargs):
model = build_model_with_cfg(
MetaNeXt, variant, pretrained,
feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
**kwargs,
)
return model
@register_model
def inception_next_tiny(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768),
token_mixers=InceptionDWConv2d,
)
return _create_inception_next('inception_next_tiny', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def inception_next_small(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 27, 3), dims=(96, 192, 384, 768),
token_mixers=InceptionDWConv2d,
)
return _create_inception_next('inception_next_small', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def inception_next_base(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 27, 3), dims=(128, 256, 512, 1024),
token_mixers=InceptionDWConv2d,
)
return _create_inception_next('inception_next_base', pretrained=pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/features.py
|
from ._features import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/vision_transformer_hybrid.py
|
""" Hybrid Vision Transformer (ViT) in PyTorch
A PyTorch implement of the Hybrid Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
- https://arxiv.org/abs/2106.10270
NOTE These hybrid model definitions depend on code in vision_transformer.py.
They were moved here to keep file sizes sane.
Hacked together by / Copyright 2020, Ross Wightman
"""
from functools import partial
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import StdConv2dSame, StdConv2d, to_2tuple, Format, nchw_to
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
from .resnet import resnet26d, resnet50d
from .resnetv2 import ResNetV2, create_resnetv2_stem
from .vision_transformer import _create_vision_transformer, VisionTransformer
class HybridEmbed(nn.Module):
""" CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
output_fmt: Format
dynamic_img_pad: torch.jit.Final[bool]
def __init__(
self,
backbone,
img_size=224,
patch_size=1,
feature_size=None,
in_chans=3,
embed_dim=768,
bias=True,
flatten: bool = True,
output_fmt: Optional[str] = None,
strict_img_size: bool = True,
dynamic_img_pad: bool = False,
):
super().__init__()
assert isinstance(backbone, nn.Module)
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.backbone = backbone
if feature_size is None:
with torch.no_grad():
# NOTE Most reliable way of determining output dims is to run forward pass
training = backbone.training
if training:
backbone.eval()
o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
if isinstance(o, (list, tuple)):
o = o[-1] # last feature if backbone outputs list/tuple of features
feature_size = o.shape[-2:]
feature_dim = o.shape[1]
backbone.train(training)
else:
feature_size = to_2tuple(feature_size)
if hasattr(self.backbone, 'feature_info'):
feature_dim = self.backbone.feature_info.channels()[-1]
else:
feature_dim = self.backbone.num_features
if not dynamic_img_pad:
assert feature_size[0] % patch_size[0] == 0 and feature_size[1] % patch_size[1] == 0
self.grid_size = (feature_size[0] // patch_size[0], feature_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
if output_fmt is not None:
self.flatten = False
self.output_fmt = Format(output_fmt)
else:
# flatten spatial dim and transpose to channels last, kept for bwd compat
self.flatten = flatten
self.output_fmt = Format.NCHW
self.strict_img_size = strict_img_size
self.dynamic_img_pad = dynamic_img_pad
self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
def forward(self, x):
x = self.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
_, _, H, W = x.shape
if self.dynamic_img_pad:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
x = F.pad(x, (0, pad_w, 0, pad_h))
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
elif self.output_fmt != Format.NCHW:
x = nchw_to(x, self.output_fmt)
return x
class HybridEmbedWithSize(nn.Module):
""" CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
def __init__(
self,
backbone,
img_size=224,
patch_size=1,
feature_size=None,
in_chans=3,
embed_dim=768,
bias=True,
):
super().__init__(
backbone=backbone,
img_size=img_size,
patch_size=patch_size,
feature_size=feature_size,
in_chans=in_chans,
embed_dim=embed_dim,
bias=bias,
)
def forward(self, x) -> Tuple[torch.Tensor, List[int]]:
x = self.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
x = self.proj(x)
return x.flatten(2).transpose(1, 2), x.shape[-2:]
def _create_vision_transformer_hybrid(variant, backbone, pretrained=False, **kwargs):
embed_layer = partial(HybridEmbed, backbone=backbone)
kwargs.setdefault('patch_size', 1) # default patch size for hybrid models if not set
return _create_vision_transformer(variant, pretrained=pretrained, embed_layer=embed_layer, **kwargs)
def _resnetv2(layers=(3, 4, 9), **kwargs):
""" ResNet-V2 backbone helper"""
padding_same = kwargs.get('padding_same', True)
stem_type = 'same' if padding_same else ''
conv_layer = partial(StdConv2dSame, eps=1e-8) if padding_same else partial(StdConv2d, eps=1e-8)
if len(layers):
backbone = ResNetV2(
layers=layers, num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
preact=False, stem_type=stem_type, conv_layer=conv_layer)
else:
backbone = create_resnetv2_stem(
kwargs.get('in_chans', 3), stem_type=stem_type, preact=False, conv_layer=conv_layer)
return backbone
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
'first_conv': 'patch_embed.backbone.stem.conv', 'classifier': 'head',
**kwargs
}
default_cfgs = generate_default_cfgs({
# hybrid in-1k models (weights from official JAX impl where they exist)
'vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
hf_hub_id='timm/',
custom_load=True,
first_conv='patch_embed.backbone.conv'),
'vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
hf_hub_id='timm/',
first_conv='patch_embed.backbone.conv', input_size=(3, 384, 384), crop_pct=1.0, custom_load=True),
'vit_small_r26_s32_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_light0-wd_0.03-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.03-res_224.npz',
hf_hub_id='timm/',
custom_load=True,
),
'vit_small_r26_s32_384.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0, custom_load=True),
'vit_base_r26_s32_224.untrained': _cfg(),
'vit_base_r50_s16_384.orig_in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_large_r50_s32_224.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True,
),
'vit_large_r50_s32_384.augreg_in21k_ft_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0, custom_load=True,
),
# hybrid in-21k models (weights from official Google JAX impl where they exist)
'vit_tiny_r_s16_p8_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
hf_hub_id='timm/',
num_classes=21843, crop_pct=0.9, first_conv='patch_embed.backbone.conv', custom_load=True),
'vit_small_r26_s32_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0.npz',
hf_hub_id='timm/',
num_classes=21843, crop_pct=0.9, custom_load=True),
'vit_base_r50_s16_224.orig_in21k': _cfg(
#url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
hf_hub_id='timm/',
num_classes=0, crop_pct=0.9),
'vit_large_r50_s32_224.augreg_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0.npz',
hf_hub_id='timm/',
num_classes=21843, crop_pct=0.9, custom_load=True),
# hybrid models (using timm resnet backbones)
'vit_small_resnet26d_224.untrained': _cfg(
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
'vit_small_resnet50d_s16_224.untrained': _cfg(
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
'vit_base_resnet26d_224.untrained': _cfg(
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
'vit_base_resnet50d_224.untrained': _cfg(
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
})
@register_model
def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs) -> VisionTransformer:
""" R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
"""
backbone = _resnetv2(layers=(), **kwargs)
model_args = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3)
model = _create_vision_transformer_hybrid(
'vit_tiny_r_s16_p8_224', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_tiny_r_s16_p8_384(pretrained=False, **kwargs) -> VisionTransformer:
""" R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 384 x 384.
"""
backbone = _resnetv2(layers=(), **kwargs)
model_args = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3)
model = _create_vision_transformer_hybrid(
'vit_tiny_r_s16_p8_384', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_r26_s32_224(pretrained=False, **kwargs) -> VisionTransformer:
""" R26+ViT-S/S32 hybrid.
"""
backbone = _resnetv2((2, 2, 2, 2), **kwargs)
model_args = dict(embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer_hybrid(
'vit_small_r26_s32_224', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_r26_s32_384(pretrained=False, **kwargs) -> VisionTransformer:
""" R26+ViT-S/S32 hybrid.
"""
backbone = _resnetv2((2, 2, 2, 2), **kwargs)
model_args = dict(embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer_hybrid(
'vit_small_r26_s32_384', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_r26_s32_224(pretrained=False, **kwargs) -> VisionTransformer:
""" R26+ViT-B/S32 hybrid.
"""
backbone = _resnetv2((2, 2, 2, 2), **kwargs)
model_args = dict(embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer_hybrid(
'vit_base_r26_s32_224', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_r50_s16_224(pretrained=False, **kwargs) -> VisionTransformer:
""" R50+ViT-B/S16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
"""
backbone = _resnetv2((3, 4, 9), **kwargs)
model_args = dict(embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer_hybrid(
'vit_base_r50_s16_224', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_r50_s16_384(pretrained=False, **kwargs) -> VisionTransformer:
""" R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
"""
backbone = _resnetv2((3, 4, 9), **kwargs)
model_args = dict(embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer_hybrid(
'vit_base_r50_s16_384', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_r50_s32_224(pretrained=False, **kwargs) -> VisionTransformer:
""" R50+ViT-L/S32 hybrid.
"""
backbone = _resnetv2((3, 4, 6, 3), **kwargs)
model_args = dict(embed_dim=1024, depth=24, num_heads=16)
model = _create_vision_transformer_hybrid(
'vit_large_r50_s32_224', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_large_r50_s32_384(pretrained=False, **kwargs) -> VisionTransformer:
""" R50+ViT-L/S32 hybrid.
"""
backbone = _resnetv2((3, 4, 6, 3), **kwargs)
model_args = dict(embed_dim=1024, depth=24, num_heads=16)
model = _create_vision_transformer_hybrid(
'vit_large_r50_s32_384', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_resnet26d_224(pretrained=False, **kwargs) -> VisionTransformer:
""" Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.
"""
backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
model_args = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3)
model = _create_vision_transformer_hybrid(
'vit_small_resnet26d_224', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_small_resnet50d_s16_224(pretrained=False, **kwargs) -> VisionTransformer:
""" Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.
"""
backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3])
model_args = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3)
model = _create_vision_transformer_hybrid(
'vit_small_resnet50d_s16_224', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_resnet26d_224(pretrained=False, **kwargs) -> VisionTransformer:
""" Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights.
"""
backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
model_args = dict(embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer_hybrid(
'vit_base_resnet26d_224', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_base_resnet50d_224(pretrained=False, **kwargs) -> VisionTransformer:
""" Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights.
"""
backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
model_args = dict(embed_dim=768, depth=12, num_heads=12)
model = _create_vision_transformer_hybrid(
'vit_base_resnet50d_224', backbone=backbone, pretrained=pretrained, **dict(model_args, **kwargs))
return model
register_model_deprecations(__name__, {
'vit_tiny_r_s16_p8_224_in21k': 'vit_tiny_r_s16_p8_224.augreg_in21k',
'vit_small_r26_s32_224_in21k': 'vit_small_r26_s32_224.augreg_in21k',
'vit_base_r50_s16_224_in21k': 'vit_base_r50_s16_224.orig_in21k',
'vit_base_resnet50_224_in21k': 'vit_base_r50_s16_224.orig_in21k',
'vit_large_r50_s32_224_in21k': 'vit_large_r50_s32_224.augreg_in21k',
'vit_base_resnet50_384': 'vit_base_r50_s16_384.orig_in21k_ft_in1k'
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/maxxvit.py
|
""" MaxVit and CoAtNet Vision Transformer - CNN Hybrids in PyTorch
This is a from-scratch implementation of both CoAtNet and MaxVit in PyTorch.
99% of the implementation was done from papers, however last minute some adjustments were made
based on the (as yet unfinished?) public code release https://github.com/google-research/maxvit
There are multiple sets of models defined for both architectures. Typically, names with a
`_rw` suffix are my own original configs prior to referencing https://github.com/google-research/maxvit.
These configs work well and appear to be a bit faster / lower resource than the paper.
The models without extra prefix / suffix' (coatnet_0_224, maxvit_tiny_224, etc), are intended to
match paper, BUT, without any official pretrained weights it's difficult to confirm a 100% match.
Papers:
MaxViT: Multi-Axis Vision Transformer - https://arxiv.org/abs/2204.01697
@article{tu2022maxvit,
title={MaxViT: Multi-Axis Vision Transformer},
author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
journal={ECCV},
year={2022},
}
CoAtNet: Marrying Convolution and Attention for All Data Sizes - https://arxiv.org/abs/2106.04803
@article{DBLP:journals/corr/abs-2106-04803,
author = {Zihang Dai and Hanxiao Liu and Quoc V. Le and Mingxing Tan},
title = {CoAtNet: Marrying Convolution and Attention for All Data Sizes},
journal = {CoRR},
volume = {abs/2106.04803},
year = {2021}
}
Hacked together by / Copyright 2022, Ross Wightman
"""
import math
from collections import OrderedDict
from dataclasses import dataclass, replace, field
from functools import partial
from typing import Callable, Optional, Union, Tuple, List
import torch
from torch import nn
from torch.jit import Final
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import Mlp, ConvMlp, DropPath, LayerNorm, ClassifierHead, NormMlpClassifierHead
from timm.layers import create_attn, get_act_layer, get_norm_layer, get_norm_act_layer, create_conv2d, create_pool2d
from timm.layers import trunc_normal_tf_, to_2tuple, extend_tuple, make_divisible, _assert
from timm.layers import RelPosMlp, RelPosBias, RelPosBiasTf, use_fused_attn, resize_rel_pos_bias_table
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._manipulate import named_apply, checkpoint_seq
from ._registry import generate_default_cfgs, register_model
__all__ = ['MaxxVitCfg', 'MaxxVitConvCfg', 'MaxxVitTransformerCfg', 'MaxxVit']
@dataclass
class MaxxVitTransformerCfg:
dim_head: int = 32
head_first: bool = True # head ordering in qkv channel dim
expand_ratio: float = 4.0
expand_first: bool = True
shortcut_bias: bool = True
attn_bias: bool = True
attn_drop: float = 0.
proj_drop: float = 0.
pool_type: str = 'avg2'
rel_pos_type: str = 'bias'
rel_pos_dim: int = 512 # for relative position types w/ MLP
partition_ratio: int = 32
window_size: Optional[Tuple[int, int]] = None
grid_size: Optional[Tuple[int, int]] = None
no_block_attn: bool = False # disable window block attention for maxvit (ie only grid)
use_nchw_attn: bool = False # for MaxViT variants (not used for CoAt), keep tensors in NCHW order
init_values: Optional[float] = None
act_layer: str = 'gelu'
norm_layer: str = 'layernorm2d'
norm_layer_cl: str = 'layernorm'
norm_eps: float = 1e-6
def __post_init__(self):
if self.grid_size is not None:
self.grid_size = to_2tuple(self.grid_size)
if self.window_size is not None:
self.window_size = to_2tuple(self.window_size)
if self.grid_size is None:
self.grid_size = self.window_size
@dataclass
class MaxxVitConvCfg:
block_type: str = 'mbconv'
expand_ratio: float = 4.0
expand_output: bool = True # calculate expansion channels from output (vs input chs)
kernel_size: int = 3
group_size: int = 1 # 1 == depthwise
pre_norm_act: bool = False # activation after pre-norm
output_bias: bool = True # bias for shortcut + final 1x1 projection conv
stride_mode: str = 'dw' # stride done via one of 'pool', '1x1', 'dw'
pool_type: str = 'avg2'
downsample_pool_type: str = 'avg2'
padding: str = ''
attn_early: bool = False # apply attn between conv2 and norm2, instead of after norm2
attn_layer: str = 'se'
attn_act_layer: str = 'silu'
attn_ratio: float = 0.25
init_values: Optional[float] = 1e-6 # for ConvNeXt block, ignored by MBConv
act_layer: str = 'gelu'
norm_layer: str = ''
norm_layer_cl: str = ''
norm_eps: Optional[float] = None
def __post_init__(self):
# mbconv vs convnext blocks have different defaults, set in post_init to avoid explicit config args
assert self.block_type in ('mbconv', 'convnext')
use_mbconv = self.block_type == 'mbconv'
if not self.norm_layer:
self.norm_layer = 'batchnorm2d' if use_mbconv else 'layernorm2d'
if not self.norm_layer_cl and not use_mbconv:
self.norm_layer_cl = 'layernorm'
if self.norm_eps is None:
self.norm_eps = 1e-5 if use_mbconv else 1e-6
self.downsample_pool_type = self.downsample_pool_type or self.pool_type
@dataclass
class MaxxVitCfg:
embed_dim: Tuple[int, ...] = (96, 192, 384, 768)
depths: Tuple[int, ...] = (2, 3, 5, 2)
block_type: Tuple[Union[str, Tuple[str, ...]], ...] = ('C', 'C', 'T', 'T')
stem_width: Union[int, Tuple[int, int]] = 64
stem_bias: bool = False
conv_cfg: MaxxVitConvCfg = field(default_factory=MaxxVitConvCfg)
transformer_cfg: MaxxVitTransformerCfg = field(default_factory=MaxxVitTransformerCfg)
head_hidden_size: int = None
weight_init: str = 'vit_eff'
class Attention2d(nn.Module):
fused_attn: Final[bool]
""" multi-head attention for 2D NCHW tensors"""
def __init__(
self,
dim: int,
dim_out: Optional[int] = None,
dim_head: int = 32,
bias: bool = True,
expand_first: bool = True,
head_first: bool = True,
rel_pos_cls: Callable = None,
attn_drop: float = 0.,
proj_drop: float = 0.
):
super().__init__()
dim_out = dim_out or dim
dim_attn = dim_out if expand_first else dim
self.num_heads = dim_attn // dim_head
self.dim_head = dim_head
self.head_first = head_first
self.scale = dim_head ** -0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Conv2d(dim, dim_attn * 3, 1, bias=bias)
self.rel_pos = rel_pos_cls(num_heads=self.num_heads) if rel_pos_cls else None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Conv2d(dim_attn, dim_out, 1, bias=bias)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
B, C, H, W = x.shape
if self.head_first:
q, k, v = self.qkv(x).view(B, self.num_heads, self.dim_head * 3, -1).chunk(3, dim=2)
else:
q, k, v = self.qkv(x).reshape(B, 3, self.num_heads, self.dim_head, -1).unbind(1)
if self.fused_attn:
attn_bias = None
if self.rel_pos is not None:
attn_bias = self.rel_pos.get_bias()
elif shared_rel_pos is not None:
attn_bias = shared_rel_pos
x = torch.nn.functional.scaled_dot_product_attention(
q.transpose(-1, -2).contiguous(),
k.transpose(-1, -2).contiguous(),
v.transpose(-1, -2).contiguous(),
attn_mask=attn_bias,
dropout_p=self.attn_drop.p if self.training else 0.,
).transpose(-1, -2).reshape(B, -1, H, W)
else:
q = q * self.scale
attn = q.transpose(-2, -1) @ k
if self.rel_pos is not None:
attn = self.rel_pos(attn)
elif shared_rel_pos is not None:
attn = attn + shared_rel_pos
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (v @ attn.transpose(-2, -1)).view(B, -1, H, W)
x = self.proj(x)
x = self.proj_drop(x)
return x
class AttentionCl(nn.Module):
""" Channels-last multi-head attention (B, ..., C) """
fused_attn: Final[bool]
def __init__(
self,
dim: int,
dim_out: Optional[int] = None,
dim_head: int = 32,
bias: bool = True,
expand_first: bool = True,
head_first: bool = True,
rel_pos_cls: Callable = None,
attn_drop: float = 0.,
proj_drop: float = 0.
):
super().__init__()
dim_out = dim_out or dim
dim_attn = dim_out if expand_first and dim_out > dim else dim
assert dim_attn % dim_head == 0, 'attn dim should be divisible by head_dim'
self.num_heads = dim_attn // dim_head
self.dim_head = dim_head
self.head_first = head_first
self.scale = dim_head ** -0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, dim_attn * 3, bias=bias)
self.rel_pos = rel_pos_cls(num_heads=self.num_heads) if rel_pos_cls else None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim_attn, dim_out, bias=bias)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
B = x.shape[0]
restore_shape = x.shape[:-1]
if self.head_first:
q, k, v = self.qkv(x).view(B, -1, self.num_heads, self.dim_head * 3).transpose(1, 2).chunk(3, dim=3)
else:
q, k, v = self.qkv(x).reshape(B, -1, 3, self.num_heads, self.dim_head).transpose(1, 3).unbind(2)
if self.fused_attn:
attn_bias = None
if self.rel_pos is not None:
attn_bias = self.rel_pos.get_bias()
elif shared_rel_pos is not None:
attn_bias = shared_rel_pos
x = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
attn_mask=attn_bias,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
if self.rel_pos is not None:
attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
elif shared_rel_pos is not None:
attn = attn + shared_rel_pos
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(restore_shape + (-1,))
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
gamma = self.gamma
return x.mul_(gamma) if self.inplace else x * gamma
class LayerScale2d(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
gamma = self.gamma.view(1, -1, 1, 1)
return x.mul_(gamma) if self.inplace else x * gamma
class Downsample2d(nn.Module):
""" A downsample pooling module supporting several maxpool and avgpool modes
* 'max' - MaxPool2d w/ kernel_size 3, stride 2, padding 1
* 'max2' - MaxPool2d w/ kernel_size = stride = 2
* 'avg' - AvgPool2d w/ kernel_size 3, stride 2, padding 1
* 'avg2' - AvgPool2d w/ kernel_size = stride = 2
"""
def __init__(
self,
dim: int,
dim_out: int,
pool_type: str = 'avg2',
padding: str = '',
bias: bool = True,
):
super().__init__()
assert pool_type in ('max', 'max2', 'avg', 'avg2')
if pool_type == 'max':
self.pool = create_pool2d('max', kernel_size=3, stride=2, padding=padding or 1)
elif pool_type == 'max2':
self.pool = create_pool2d('max', 2, padding=padding or 0) # kernel_size == stride == 2
elif pool_type == 'avg':
self.pool = create_pool2d(
'avg', kernel_size=3, stride=2, count_include_pad=False, padding=padding or 1)
else:
self.pool = create_pool2d('avg', 2, padding=padding or 0)
if dim != dim_out:
self.expand = nn.Conv2d(dim, dim_out, 1, bias=bias)
else:
self.expand = nn.Identity()
def forward(self, x):
x = self.pool(x) # spatial downsample
x = self.expand(x) # expand chs
return x
def _init_transformer(module, name, scheme=''):
if isinstance(module, (nn.Conv2d, nn.Linear)):
if scheme == 'normal':
nn.init.normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif scheme == 'trunc_normal':
trunc_normal_tf_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif scheme == 'xavier_normal':
nn.init.xavier_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
else:
# vit like
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
if 'mlp' in name:
nn.init.normal_(module.bias, std=1e-6)
else:
nn.init.zeros_(module.bias)
class TransformerBlock2d(nn.Module):
""" Transformer block with 2D downsampling
'2D' NCHW tensor layout
Some gains can be seen on GPU using a 1D / CL block, BUT w/ the need to switch back/forth to NCHW
for spatial pooling, the benefit is minimal so ended up using just this variant for CoAt configs.
This impl was faster on TPU w/ PT XLA than the 1D experiment.
"""
def __init__(
self,
dim: int,
dim_out: int,
stride: int = 1,
rel_pos_cls: Callable = None,
cfg: MaxxVitTransformerCfg = MaxxVitTransformerCfg(),
drop_path: float = 0.,
):
super().__init__()
norm_layer = partial(get_norm_layer(cfg.norm_layer), eps=cfg.norm_eps)
act_layer = get_act_layer(cfg.act_layer)
if stride == 2:
self.shortcut = Downsample2d(dim, dim_out, pool_type=cfg.pool_type, bias=cfg.shortcut_bias)
self.norm1 = nn.Sequential(OrderedDict([
('norm', norm_layer(dim)),
('down', Downsample2d(dim, dim, pool_type=cfg.pool_type)),
]))
else:
assert dim == dim_out
self.shortcut = nn.Identity()
self.norm1 = norm_layer(dim)
self.attn = Attention2d(
dim,
dim_out,
dim_head=cfg.dim_head,
expand_first=cfg.expand_first,
bias=cfg.attn_bias,
rel_pos_cls=rel_pos_cls,
attn_drop=cfg.attn_drop,
proj_drop=cfg.proj_drop
)
self.ls1 = LayerScale2d(dim_out, init_values=cfg.init_values) if cfg.init_values else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim_out)
self.mlp = ConvMlp(
in_features=dim_out,
hidden_features=int(dim_out * cfg.expand_ratio),
act_layer=act_layer,
drop=cfg.proj_drop)
self.ls2 = LayerScale2d(dim_out, init_values=cfg.init_values) if cfg.init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def init_weights(self, scheme=''):
named_apply(partial(_init_transformer, scheme=scheme), self)
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
x = self.shortcut(x) + self.drop_path1(self.ls1(self.attn(self.norm1(x), shared_rel_pos=shared_rel_pos)))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
def _init_conv(module, name, scheme=''):
if isinstance(module, nn.Conv2d):
if scheme == 'normal':
nn.init.normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif scheme == 'trunc_normal':
trunc_normal_tf_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif scheme == 'xavier_normal':
nn.init.xavier_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
else:
# efficientnet like
fan_out = module.kernel_size[0] * module.kernel_size[1] * module.out_channels
fan_out //= module.groups
nn.init.normal_(module.weight, 0, math.sqrt(2.0 / fan_out))
if module.bias is not None:
nn.init.zeros_(module.bias)
def num_groups(group_size, channels):
if not group_size: # 0 or None
return 1 # normal conv with 1 group
else:
# NOTE group_size == 1 -> depthwise conv
assert channels % group_size == 0
return channels // group_size
class MbConvBlock(nn.Module):
""" Pre-Norm Conv Block - 1x1 - kxk - 1x1, w/ inverted bottleneck (expand)
"""
def __init__(
self,
in_chs: int,
out_chs: int,
stride: int = 1,
dilation: Tuple[int, int] = (1, 1),
cfg: MaxxVitConvCfg = MaxxVitConvCfg(),
drop_path: float = 0.
):
super(MbConvBlock, self).__init__()
norm_act_layer = partial(get_norm_act_layer(cfg.norm_layer, cfg.act_layer), eps=cfg.norm_eps)
mid_chs = make_divisible((out_chs if cfg.expand_output else in_chs) * cfg.expand_ratio)
groups = num_groups(cfg.group_size, mid_chs)
if stride == 2:
self.shortcut = Downsample2d(
in_chs, out_chs, pool_type=cfg.pool_type, bias=cfg.output_bias, padding=cfg.padding)
else:
self.shortcut = nn.Identity()
assert cfg.stride_mode in ('pool', '1x1', 'dw')
stride_pool, stride_1, stride_2 = 1, 1, 1
if cfg.stride_mode == 'pool':
# NOTE this is not described in paper, experiment to find faster option that doesn't stride in 1x1
stride_pool, dilation_2 = stride, dilation[1]
# FIXME handle dilation of avg pool
elif cfg.stride_mode == '1x1':
# NOTE I don't like this option described in paper, 1x1 w/ stride throws info away
stride_1, dilation_2 = stride, dilation[1]
else:
stride_2, dilation_2 = stride, dilation[0]
self.pre_norm = norm_act_layer(in_chs, apply_act=cfg.pre_norm_act)
if stride_pool > 1:
self.down = Downsample2d(in_chs, in_chs, pool_type=cfg.downsample_pool_type, padding=cfg.padding)
else:
self.down = nn.Identity()
self.conv1_1x1 = create_conv2d(in_chs, mid_chs, 1, stride=stride_1)
self.norm1 = norm_act_layer(mid_chs)
self.conv2_kxk = create_conv2d(
mid_chs, mid_chs, cfg.kernel_size,
stride=stride_2, dilation=dilation_2, groups=groups, padding=cfg.padding)
attn_kwargs = {}
if isinstance(cfg.attn_layer, str):
if cfg.attn_layer == 'se' or cfg.attn_layer == 'eca':
attn_kwargs['act_layer'] = cfg.attn_act_layer
attn_kwargs['rd_channels'] = int(cfg.attn_ratio * (out_chs if cfg.expand_output else mid_chs))
# two different orderings for SE and norm2 (due to some weights and trials using SE before norm2)
if cfg.attn_early:
self.se_early = create_attn(cfg.attn_layer, mid_chs, **attn_kwargs)
self.norm2 = norm_act_layer(mid_chs)
self.se = None
else:
self.se_early = None
self.norm2 = norm_act_layer(mid_chs)
self.se = create_attn(cfg.attn_layer, mid_chs, **attn_kwargs)
self.conv3_1x1 = create_conv2d(mid_chs, out_chs, 1, bias=cfg.output_bias)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def init_weights(self, scheme=''):
named_apply(partial(_init_conv, scheme=scheme), self)
def forward(self, x):
shortcut = self.shortcut(x)
x = self.pre_norm(x)
x = self.down(x)
# 1x1 expansion conv & norm-act
x = self.conv1_1x1(x)
x = self.norm1(x)
# depthwise / grouped 3x3 conv w/ SE (or other) channel attention & norm-act
x = self.conv2_kxk(x)
if self.se_early is not None:
x = self.se_early(x)
x = self.norm2(x)
if self.se is not None:
x = self.se(x)
# 1x1 linear projection to output width
x = self.conv3_1x1(x)
x = self.drop_path(x) + shortcut
return x
class ConvNeXtBlock(nn.Module):
""" ConvNeXt Block
"""
def __init__(
self,
in_chs: int,
out_chs: Optional[int] = None,
kernel_size: int = 7,
stride: int = 1,
dilation: Tuple[int, int] = (1, 1),
cfg: MaxxVitConvCfg = MaxxVitConvCfg(),
conv_mlp: bool = True,
drop_path: float = 0.
):
super().__init__()
out_chs = out_chs or in_chs
act_layer = get_act_layer(cfg.act_layer)
if conv_mlp:
norm_layer = partial(get_norm_layer(cfg.norm_layer), eps=cfg.norm_eps)
mlp_layer = ConvMlp
else:
assert 'layernorm' in cfg.norm_layer
norm_layer = LayerNorm
mlp_layer = Mlp
self.use_conv_mlp = conv_mlp
if stride == 2:
self.shortcut = Downsample2d(in_chs, out_chs)
elif in_chs != out_chs:
self.shortcut = nn.Conv2d(in_chs, out_chs, kernel_size=1, bias=cfg.output_bias)
else:
self.shortcut = nn.Identity()
assert cfg.stride_mode in ('pool', 'dw')
stride_pool, stride_dw = 1, 1
# FIXME handle dilation?
if cfg.stride_mode == 'pool':
stride_pool = stride
else:
stride_dw = stride
if stride_pool == 2:
self.down = Downsample2d(in_chs, in_chs, pool_type=cfg.downsample_pool_type)
else:
self.down = nn.Identity()
self.conv_dw = create_conv2d(
in_chs, out_chs, kernel_size=kernel_size, stride=stride_dw, dilation=dilation[1],
depthwise=True, bias=cfg.output_bias)
self.norm = norm_layer(out_chs)
self.mlp = mlp_layer(out_chs, int(cfg.expand_ratio * out_chs), bias=cfg.output_bias, act_layer=act_layer)
if conv_mlp:
self.ls = LayerScale2d(out_chs, cfg.init_values) if cfg.init_values else nn.Identity()
else:
self.ls = LayerScale(out_chs, cfg.init_values) if cfg.init_values else nn.Identity()
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = self.shortcut(x)
x = self.down(x)
x = self.conv_dw(x)
if self.use_conv_mlp:
x = self.norm(x)
x = self.mlp(x)
x = self.ls(x)
else:
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
x = self.mlp(x)
x = self.ls(x)
x = x.permute(0, 3, 1, 2)
x = self.drop_path(x) + shortcut
return x
def window_partition(x, window_size: List[int]):
B, H, W, C = x.shape
_assert(H % window_size[0] == 0, f'height ({H}) must be divisible by window ({window_size[0]})')
_assert(W % window_size[1] == 0, '')
x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
return windows
@register_notrace_function # reason: int argument is a Proxy
def window_reverse(windows, window_size: List[int], img_size: List[int]):
H, W = img_size
C = windows.shape[-1]
x = windows.view(-1, H // window_size[0], W // window_size[1], window_size[0], window_size[1], C)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
return x
def grid_partition(x, grid_size: List[int]):
B, H, W, C = x.shape
_assert(H % grid_size[0] == 0, f'height {H} must be divisible by grid {grid_size[0]}')
_assert(W % grid_size[1] == 0, '')
x = x.view(B, grid_size[0], H // grid_size[0], grid_size[1], W // grid_size[1], C)
windows = x.permute(0, 2, 4, 1, 3, 5).contiguous().view(-1, grid_size[0], grid_size[1], C)
return windows
@register_notrace_function # reason: int argument is a Proxy
def grid_reverse(windows, grid_size: List[int], img_size: List[int]):
H, W = img_size
C = windows.shape[-1]
x = windows.view(-1, H // grid_size[0], W // grid_size[1], grid_size[0], grid_size[1], C)
x = x.permute(0, 3, 1, 4, 2, 5).contiguous().view(-1, H, W, C)
return x
def get_rel_pos_cls(cfg: MaxxVitTransformerCfg, window_size):
rel_pos_cls = None
if cfg.rel_pos_type == 'mlp':
rel_pos_cls = partial(RelPosMlp, window_size=window_size, hidden_dim=cfg.rel_pos_dim)
elif cfg.rel_pos_type == 'bias':
rel_pos_cls = partial(RelPosBias, window_size=window_size)
elif cfg.rel_pos_type == 'bias_tf':
rel_pos_cls = partial(RelPosBiasTf, window_size=window_size)
return rel_pos_cls
class PartitionAttentionCl(nn.Module):
""" Grid or Block partition + Attn + FFN.
NxC 'channels last' tensor layout.
"""
def __init__(
self,
dim: int,
partition_type: str = 'block',
cfg: MaxxVitTransformerCfg = MaxxVitTransformerCfg(),
drop_path: float = 0.,
):
super().__init__()
norm_layer = partial(get_norm_layer(cfg.norm_layer_cl), eps=cfg.norm_eps) # NOTE this block is channels-last
act_layer = get_act_layer(cfg.act_layer)
self.partition_block = partition_type == 'block'
self.partition_size = to_2tuple(cfg.window_size if self.partition_block else cfg.grid_size)
rel_pos_cls = get_rel_pos_cls(cfg, self.partition_size)
self.norm1 = norm_layer(dim)
self.attn = AttentionCl(
dim,
dim,
dim_head=cfg.dim_head,
bias=cfg.attn_bias,
head_first=cfg.head_first,
rel_pos_cls=rel_pos_cls,
attn_drop=cfg.attn_drop,
proj_drop=cfg.proj_drop,
)
self.ls1 = LayerScale(dim, init_values=cfg.init_values) if cfg.init_values else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * cfg.expand_ratio),
act_layer=act_layer,
drop=cfg.proj_drop)
self.ls2 = LayerScale(dim, init_values=cfg.init_values) if cfg.init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def _partition_attn(self, x):
img_size = x.shape[1:3]
if self.partition_block:
partitioned = window_partition(x, self.partition_size)
else:
partitioned = grid_partition(x, self.partition_size)
partitioned = self.attn(partitioned)
if self.partition_block:
x = window_reverse(partitioned, self.partition_size, img_size)
else:
x = grid_reverse(partitioned, self.partition_size, img_size)
return x
def forward(self, x):
x = x + self.drop_path1(self.ls1(self._partition_attn(self.norm1(x))))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class ParallelPartitionAttention(nn.Module):
""" Experimental. Grid and Block partition + single FFN
NxC tensor layout.
"""
def __init__(
self,
dim: int,
cfg: MaxxVitTransformerCfg = MaxxVitTransformerCfg(),
drop_path: float = 0.,
):
super().__init__()
assert dim % 2 == 0
norm_layer = partial(get_norm_layer(cfg.norm_layer_cl), eps=cfg.norm_eps) # NOTE this block is channels-last
act_layer = get_act_layer(cfg.act_layer)
assert cfg.window_size == cfg.grid_size
self.partition_size = to_2tuple(cfg.window_size)
rel_pos_cls = get_rel_pos_cls(cfg, self.partition_size)
self.norm1 = norm_layer(dim)
self.attn_block = AttentionCl(
dim,
dim // 2,
dim_head=cfg.dim_head,
bias=cfg.attn_bias,
head_first=cfg.head_first,
rel_pos_cls=rel_pos_cls,
attn_drop=cfg.attn_drop,
proj_drop=cfg.proj_drop,
)
self.attn_grid = AttentionCl(
dim,
dim // 2,
dim_head=cfg.dim_head,
bias=cfg.attn_bias,
head_first=cfg.head_first,
rel_pos_cls=rel_pos_cls,
attn_drop=cfg.attn_drop,
proj_drop=cfg.proj_drop,
)
self.ls1 = LayerScale(dim, init_values=cfg.init_values) if cfg.init_values else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * cfg.expand_ratio),
out_features=dim,
act_layer=act_layer,
drop=cfg.proj_drop)
self.ls2 = LayerScale(dim, init_values=cfg.init_values) if cfg.init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def _partition_attn(self, x):
img_size = x.shape[1:3]
partitioned_block = window_partition(x, self.partition_size)
partitioned_block = self.attn_block(partitioned_block)
x_window = window_reverse(partitioned_block, self.partition_size, img_size)
partitioned_grid = grid_partition(x, self.partition_size)
partitioned_grid = self.attn_grid(partitioned_grid)
x_grid = grid_reverse(partitioned_grid, self.partition_size, img_size)
return torch.cat([x_window, x_grid], dim=-1)
def forward(self, x):
x = x + self.drop_path1(self.ls1(self._partition_attn(self.norm1(x))))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
def window_partition_nchw(x, window_size: List[int]):
B, C, H, W = x.shape
_assert(H % window_size[0] == 0, f'height ({H}) must be divisible by window ({window_size[0]})')
_assert(W % window_size[1] == 0, '')
x = x.view(B, C, H // window_size[0], window_size[0], W // window_size[1], window_size[1])
windows = x.permute(0, 2, 4, 1, 3, 5).contiguous().view(-1, C, window_size[0], window_size[1])
return windows
@register_notrace_function # reason: int argument is a Proxy
def window_reverse_nchw(windows, window_size: List[int], img_size: List[int]):
H, W = img_size
C = windows.shape[1]
x = windows.view(-1, H // window_size[0], W // window_size[1], C, window_size[0], window_size[1])
x = x.permute(0, 3, 1, 4, 2, 5).contiguous().view(-1, C, H, W)
return x
def grid_partition_nchw(x, grid_size: List[int]):
B, C, H, W = x.shape
_assert(H % grid_size[0] == 0, f'height {H} must be divisible by grid {grid_size[0]}')
_assert(W % grid_size[1] == 0, '')
x = x.view(B, C, grid_size[0], H // grid_size[0], grid_size[1], W // grid_size[1])
windows = x.permute(0, 3, 5, 1, 2, 4).contiguous().view(-1, C, grid_size[0], grid_size[1])
return windows
@register_notrace_function # reason: int argument is a Proxy
def grid_reverse_nchw(windows, grid_size: List[int], img_size: List[int]):
H, W = img_size
C = windows.shape[1]
x = windows.view(-1, H // grid_size[0], W // grid_size[1], C, grid_size[0], grid_size[1])
x = x.permute(0, 3, 4, 1, 5, 2).contiguous().view(-1, C, H, W)
return x
class PartitionAttention2d(nn.Module):
""" Grid or Block partition + Attn + FFN
'2D' NCHW tensor layout.
"""
def __init__(
self,
dim: int,
partition_type: str = 'block',
cfg: MaxxVitTransformerCfg = MaxxVitTransformerCfg(),
drop_path: float = 0.,
):
super().__init__()
norm_layer = partial(get_norm_layer(cfg.norm_layer), eps=cfg.norm_eps) # NOTE this block is channels-last
act_layer = get_act_layer(cfg.act_layer)
self.partition_block = partition_type == 'block'
self.partition_size = to_2tuple(cfg.window_size if self.partition_block else cfg.grid_size)
rel_pos_cls = get_rel_pos_cls(cfg, self.partition_size)
self.norm1 = norm_layer(dim)
self.attn = Attention2d(
dim,
dim,
dim_head=cfg.dim_head,
bias=cfg.attn_bias,
head_first=cfg.head_first,
rel_pos_cls=rel_pos_cls,
attn_drop=cfg.attn_drop,
proj_drop=cfg.proj_drop,
)
self.ls1 = LayerScale2d(dim, init_values=cfg.init_values) if cfg.init_values else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = ConvMlp(
in_features=dim,
hidden_features=int(dim * cfg.expand_ratio),
act_layer=act_layer,
drop=cfg.proj_drop)
self.ls2 = LayerScale2d(dim, init_values=cfg.init_values) if cfg.init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def _partition_attn(self, x):
img_size = x.shape[-2:]
if self.partition_block:
partitioned = window_partition_nchw(x, self.partition_size)
else:
partitioned = grid_partition_nchw(x, self.partition_size)
partitioned = self.attn(partitioned)
if self.partition_block:
x = window_reverse_nchw(partitioned, self.partition_size, img_size)
else:
x = grid_reverse_nchw(partitioned, self.partition_size, img_size)
return x
def forward(self, x):
x = x + self.drop_path1(self.ls1(self._partition_attn(self.norm1(x))))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class MaxxVitBlock(nn.Module):
""" MaxVit conv, window partition + FFN , grid partition + FFN
"""
def __init__(
self,
dim: int,
dim_out: int,
stride: int = 1,
conv_cfg: MaxxVitConvCfg = MaxxVitConvCfg(),
transformer_cfg: MaxxVitTransformerCfg = MaxxVitTransformerCfg(),
drop_path: float = 0.,
):
super().__init__()
self.nchw_attn = transformer_cfg.use_nchw_attn
conv_cls = ConvNeXtBlock if conv_cfg.block_type == 'convnext' else MbConvBlock
self.conv = conv_cls(dim, dim_out, stride=stride, cfg=conv_cfg, drop_path=drop_path)
attn_kwargs = dict(dim=dim_out, cfg=transformer_cfg, drop_path=drop_path)
partition_layer = PartitionAttention2d if self.nchw_attn else PartitionAttentionCl
self.attn_block = None if transformer_cfg.no_block_attn else partition_layer(**attn_kwargs)
self.attn_grid = partition_layer(partition_type='grid', **attn_kwargs)
def init_weights(self, scheme=''):
if self.attn_block is not None:
named_apply(partial(_init_transformer, scheme=scheme), self.attn_block)
named_apply(partial(_init_transformer, scheme=scheme), self.attn_grid)
named_apply(partial(_init_conv, scheme=scheme), self.conv)
def forward(self, x):
# NCHW format
x = self.conv(x)
if not self.nchw_attn:
x = x.permute(0, 2, 3, 1) # to NHWC (channels-last)
if self.attn_block is not None:
x = self.attn_block(x)
x = self.attn_grid(x)
if not self.nchw_attn:
x = x.permute(0, 3, 1, 2) # back to NCHW
return x
class ParallelMaxxVitBlock(nn.Module):
""" MaxVit block with parallel cat(window + grid), one FF
Experimental timm block.
"""
def __init__(
self,
dim,
dim_out,
stride=1,
num_conv=2,
conv_cfg: MaxxVitConvCfg = MaxxVitConvCfg(),
transformer_cfg: MaxxVitTransformerCfg = MaxxVitTransformerCfg(),
drop_path=0.,
):
super().__init__()
conv_cls = ConvNeXtBlock if conv_cfg.block_type == 'convnext' else MbConvBlock
if num_conv > 1:
convs = [conv_cls(dim, dim_out, stride=stride, cfg=conv_cfg, drop_path=drop_path)]
convs += [conv_cls(dim_out, dim_out, cfg=conv_cfg, drop_path=drop_path)] * (num_conv - 1)
self.conv = nn.Sequential(*convs)
else:
self.conv = conv_cls(dim, dim_out, stride=stride, cfg=conv_cfg, drop_path=drop_path)
self.attn = ParallelPartitionAttention(dim=dim_out, cfg=transformer_cfg, drop_path=drop_path)
def init_weights(self, scheme=''):
named_apply(partial(_init_transformer, scheme=scheme), self.attn)
named_apply(partial(_init_conv, scheme=scheme), self.conv)
def forward(self, x):
x = self.conv(x)
x = x.permute(0, 2, 3, 1)
x = self.attn(x)
x = x.permute(0, 3, 1, 2)
return x
class MaxxVitStage(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
stride: int = 2,
depth: int = 4,
feat_size: Tuple[int, int] = (14, 14),
block_types: Union[str, Tuple[str]] = 'C',
transformer_cfg: MaxxVitTransformerCfg = MaxxVitTransformerCfg(),
conv_cfg: MaxxVitConvCfg = MaxxVitConvCfg(),
drop_path: Union[float, List[float]] = 0.,
):
super().__init__()
self.grad_checkpointing = False
block_types = extend_tuple(block_types, depth)
blocks = []
for i, t in enumerate(block_types):
block_stride = stride if i == 0 else 1
assert t in ('C', 'T', 'M', 'PM')
if t == 'C':
conv_cls = ConvNeXtBlock if conv_cfg.block_type == 'convnext' else MbConvBlock
blocks += [conv_cls(
in_chs,
out_chs,
stride=block_stride,
cfg=conv_cfg,
drop_path=drop_path[i],
)]
elif t == 'T':
rel_pos_cls = get_rel_pos_cls(transformer_cfg, feat_size)
blocks += [TransformerBlock2d(
in_chs,
out_chs,
stride=block_stride,
rel_pos_cls=rel_pos_cls,
cfg=transformer_cfg,
drop_path=drop_path[i],
)]
elif t == 'M':
blocks += [MaxxVitBlock(
in_chs,
out_chs,
stride=block_stride,
conv_cfg=conv_cfg,
transformer_cfg=transformer_cfg,
drop_path=drop_path[i],
)]
elif t == 'PM':
blocks += [ParallelMaxxVitBlock(
in_chs,
out_chs,
stride=block_stride,
conv_cfg=conv_cfg,
transformer_cfg=transformer_cfg,
drop_path=drop_path[i],
)]
in_chs = out_chs
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class Stem(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
padding: str = '',
bias: bool = False,
act_layer: str = 'gelu',
norm_layer: str = 'batchnorm2d',
norm_eps: float = 1e-5,
):
super().__init__()
if not isinstance(out_chs, (list, tuple)):
out_chs = to_2tuple(out_chs)
norm_act_layer = partial(get_norm_act_layer(norm_layer, act_layer), eps=norm_eps)
self.out_chs = out_chs[-1]
self.stride = 2
self.conv1 = create_conv2d(in_chs, out_chs[0], kernel_size, stride=2, padding=padding, bias=bias)
self.norm1 = norm_act_layer(out_chs[0])
self.conv2 = create_conv2d(out_chs[0], out_chs[1], kernel_size, stride=1, padding=padding, bias=bias)
def init_weights(self, scheme=''):
named_apply(partial(_init_conv, scheme=scheme), self)
def forward(self, x):
x = self.conv1(x)
x = self.norm1(x)
x = self.conv2(x)
return x
def cfg_window_size(cfg: MaxxVitTransformerCfg, img_size: Tuple[int, int]):
if cfg.window_size is not None:
assert cfg.grid_size
return cfg
partition_size = img_size[0] // cfg.partition_ratio, img_size[1] // cfg.partition_ratio
cfg = replace(cfg, window_size=partition_size, grid_size=partition_size)
return cfg
def _overlay_kwargs(cfg: MaxxVitCfg, **kwargs):
transformer_kwargs = {}
conv_kwargs = {}
base_kwargs = {}
for k, v in kwargs.items():
if k.startswith('transformer_'):
transformer_kwargs[k.replace('transformer_', '')] = v
elif k.startswith('conv_'):
conv_kwargs[k.replace('conv_', '')] = v
else:
base_kwargs[k] = v
cfg = replace(
cfg,
transformer_cfg=replace(cfg.transformer_cfg, **transformer_kwargs),
conv_cfg=replace(cfg.conv_cfg, **conv_kwargs),
**base_kwargs
)
return cfg
class MaxxVit(nn.Module):
""" CoaTNet + MaxVit base model.
Highly configurable for different block compositions, tensor layouts, pooling types.
"""
def __init__(
self,
cfg: MaxxVitCfg,
img_size: Union[int, Tuple[int, int]] = 224,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = 'avg',
drop_rate: float = 0.,
drop_path_rate: float = 0.,
**kwargs,
):
super().__init__()
img_size = to_2tuple(img_size)
if kwargs:
cfg = _overlay_kwargs(cfg, **kwargs)
transformer_cfg = cfg_window_size(cfg.transformer_cfg, img_size)
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = cfg.embed_dim[-1]
self.drop_rate = drop_rate
self.grad_checkpointing = False
self.feature_info = []
self.stem = Stem(
in_chs=in_chans,
out_chs=cfg.stem_width,
padding=cfg.conv_cfg.padding,
bias=cfg.stem_bias,
act_layer=cfg.conv_cfg.act_layer,
norm_layer=cfg.conv_cfg.norm_layer,
norm_eps=cfg.conv_cfg.norm_eps,
)
stride = self.stem.stride
self.feature_info += [dict(num_chs=self.stem.out_chs, reduction=2, module='stem')]
feat_size = tuple([i // s for i, s in zip(img_size, to_2tuple(stride))])
num_stages = len(cfg.embed_dim)
assert len(cfg.depths) == num_stages
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg.depths)).split(cfg.depths)]
in_chs = self.stem.out_chs
stages = []
for i in range(num_stages):
stage_stride = 2
out_chs = cfg.embed_dim[i]
feat_size = tuple([(r - 1) // stage_stride + 1 for r in feat_size])
stages += [MaxxVitStage(
in_chs,
out_chs,
depth=cfg.depths[i],
block_types=cfg.block_type[i],
conv_cfg=cfg.conv_cfg,
transformer_cfg=transformer_cfg,
feat_size=feat_size,
drop_path=dpr[i],
)]
stride *= stage_stride
in_chs = out_chs
self.feature_info += [dict(num_chs=out_chs, reduction=stride, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
final_norm_layer = partial(get_norm_layer(cfg.transformer_cfg.norm_layer), eps=cfg.transformer_cfg.norm_eps)
self.head_hidden_size = cfg.head_hidden_size
if self.head_hidden_size:
self.norm = nn.Identity()
self.head = NormMlpClassifierHead(
self.num_features,
num_classes,
hidden_size=self.head_hidden_size,
pool_type=global_pool,
drop_rate=drop_rate,
norm_layer=final_norm_layer,
)
else:
# standard classifier head w/ norm, pooling, fc classifier
self.norm = final_norm_layer(self.num_features)
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
# Weight init (default PyTorch init works well for AdamW if scheme not set)
assert cfg.weight_init in ('', 'normal', 'trunc_normal', 'xavier_normal', 'vit_eff')
if cfg.weight_init:
named_apply(partial(self._init_weights, scheme=cfg.weight_init), self)
def _init_weights(self, module, name, scheme=''):
if hasattr(module, 'init_weights'):
try:
module.init_weights(scheme=scheme)
except TypeError:
module.init_weights()
@torch.jit.ignore
def no_weight_decay(self):
return {
k for k, _ in self.named_parameters()
if any(n in k for n in ["relative_position_bias_table", "rel_pos.mlp"])}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem', # stem and embed
blocks=[(r'^stages\.(\d+)', None), (r'^norm', (99999,))]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
self.head.reset(num_classes, global_pool)
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _rw_coat_cfg(
stride_mode='pool',
pool_type='avg2',
conv_output_bias=False,
conv_attn_early=False,
conv_attn_act_layer='relu',
conv_norm_layer='',
transformer_shortcut_bias=True,
transformer_norm_layer='layernorm2d',
transformer_norm_layer_cl='layernorm',
init_values=None,
rel_pos_type='bias',
rel_pos_dim=512,
):
# 'RW' timm variant models were created and trained before seeing https://github.com/google-research/maxvit
# Common differences for initial timm models:
# - pre-norm layer in MZBConv included an activation after norm
# - mbconv expansion calculated from input instead of output chs
# - mbconv shortcut and final 1x1 conv did not have a bias
# - SE act layer was relu, not silu
# - mbconv uses silu in timm, not gelu
# - expansion in attention block done via output proj, not input proj
# Variable differences (evolved over training initial models):
# - avg pool with kernel_size=2 favoured downsampling (instead of maxpool for coat)
# - SE attention was between conv2 and norm/act
# - default to avg pool for mbconv downsample instead of 1x1 or dw conv
# - transformer block shortcut has no bias
return dict(
conv_cfg=MaxxVitConvCfg(
stride_mode=stride_mode,
pool_type=pool_type,
pre_norm_act=True,
expand_output=False,
output_bias=conv_output_bias,
attn_early=conv_attn_early,
attn_act_layer=conv_attn_act_layer,
act_layer='silu',
norm_layer=conv_norm_layer,
),
transformer_cfg=MaxxVitTransformerCfg(
expand_first=False,
shortcut_bias=transformer_shortcut_bias,
pool_type=pool_type,
init_values=init_values,
norm_layer=transformer_norm_layer,
norm_layer_cl=transformer_norm_layer_cl,
rel_pos_type=rel_pos_type,
rel_pos_dim=rel_pos_dim,
),
)
def _rw_max_cfg(
stride_mode='dw',
pool_type='avg2',
conv_output_bias=False,
conv_attn_ratio=1 / 16,
conv_norm_layer='',
transformer_norm_layer='layernorm2d',
transformer_norm_layer_cl='layernorm',
window_size=None,
dim_head=32,
init_values=None,
rel_pos_type='bias',
rel_pos_dim=512,
):
# 'RW' timm variant models were created and trained before seeing https://github.com/google-research/maxvit
# Differences of initial timm models:
# - mbconv expansion calculated from input instead of output chs
# - mbconv shortcut and final 1x1 conv did not have a bias
# - mbconv uses silu in timm, not gelu
# - expansion in attention block done via output proj, not input proj
return dict(
conv_cfg=MaxxVitConvCfg(
stride_mode=stride_mode,
pool_type=pool_type,
expand_output=False,
output_bias=conv_output_bias,
attn_ratio=conv_attn_ratio,
act_layer='silu',
norm_layer=conv_norm_layer,
),
transformer_cfg=MaxxVitTransformerCfg(
expand_first=False,
pool_type=pool_type,
dim_head=dim_head,
window_size=window_size,
init_values=init_values,
norm_layer=transformer_norm_layer,
norm_layer_cl=transformer_norm_layer_cl,
rel_pos_type=rel_pos_type,
rel_pos_dim=rel_pos_dim,
),
)
def _next_cfg(
stride_mode='dw',
pool_type='avg2',
conv_norm_layer='layernorm2d',
conv_norm_layer_cl='layernorm',
transformer_norm_layer='layernorm2d',
transformer_norm_layer_cl='layernorm',
window_size=None,
no_block_attn=False,
init_values=1e-6,
rel_pos_type='mlp', # MLP by default for maxxvit
rel_pos_dim=512,
):
# For experimental models with convnext instead of mbconv
init_values = to_2tuple(init_values)
return dict(
conv_cfg=MaxxVitConvCfg(
block_type='convnext',
stride_mode=stride_mode,
pool_type=pool_type,
expand_output=False,
init_values=init_values[0],
norm_layer=conv_norm_layer,
norm_layer_cl=conv_norm_layer_cl,
),
transformer_cfg=MaxxVitTransformerCfg(
expand_first=False,
pool_type=pool_type,
window_size=window_size,
no_block_attn=no_block_attn, # enabled for MaxxViT-V2
init_values=init_values[1],
norm_layer=transformer_norm_layer,
norm_layer_cl=transformer_norm_layer_cl,
rel_pos_type=rel_pos_type,
rel_pos_dim=rel_pos_dim,
),
)
def _tf_cfg():
return dict(
conv_cfg=MaxxVitConvCfg(
norm_eps=1e-3,
act_layer='gelu_tanh',
padding='same',
),
transformer_cfg=MaxxVitTransformerCfg(
norm_eps=1e-5,
act_layer='gelu_tanh',
head_first=False, # heads are interleaved (q_nh, q_hdim, k_nh, q_hdim, ....)
rel_pos_type='bias_tf',
),
)
model_cfgs = dict(
# timm specific CoAtNet configs
coatnet_pico_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(2, 3, 5, 2),
stem_width=(32, 64),
**_rw_max_cfg( # using newer max defaults here
conv_output_bias=True,
conv_attn_ratio=0.25,
),
),
coatnet_nano_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(3, 4, 6, 3),
stem_width=(32, 64),
**_rw_max_cfg( # using newer max defaults here
stride_mode='pool',
conv_output_bias=True,
conv_attn_ratio=0.25,
),
),
coatnet_0_rw=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 3, 7, 2), # deeper than paper '0' model
stem_width=(32, 64),
**_rw_coat_cfg(
conv_attn_early=True,
transformer_shortcut_bias=False,
),
),
coatnet_1_rw=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 6, 14, 2),
stem_width=(32, 64),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_early=True,
transformer_shortcut_bias=False,
)
),
coatnet_2_rw=MaxxVitCfg(
embed_dim=(128, 256, 512, 1024),
depths=(2, 6, 14, 2),
stem_width=(64, 128),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_act_layer='silu',
#init_values=1e-6,
),
),
coatnet_3_rw=MaxxVitCfg(
embed_dim=(192, 384, 768, 1536),
depths=(2, 6, 14, 2),
stem_width=(96, 192),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_act_layer='silu',
init_values=1e-6,
),
),
# Experimental CoAtNet configs w/ ImageNet-1k train (different norm layers, MLP rel-pos)
coatnet_bn_0_rw=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 3, 7, 2), # deeper than paper '0' model
stem_width=(32, 64),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_early=True,
transformer_shortcut_bias=False,
transformer_norm_layer='batchnorm2d',
)
),
coatnet_rmlp_nano_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(3, 4, 6, 3),
stem_width=(32, 64),
**_rw_max_cfg(
conv_output_bias=True,
conv_attn_ratio=0.25,
rel_pos_type='mlp',
rel_pos_dim=384,
),
),
coatnet_rmlp_0_rw=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 3, 7, 2), # deeper than paper '0' model
stem_width=(32, 64),
**_rw_coat_cfg(
stride_mode='dw',
rel_pos_type='mlp',
),
),
coatnet_rmlp_1_rw=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 6, 14, 2),
stem_width=(32, 64),
**_rw_coat_cfg(
pool_type='max',
conv_attn_early=True,
transformer_shortcut_bias=False,
rel_pos_type='mlp',
rel_pos_dim=384, # was supposed to be 512, woops
),
),
coatnet_rmlp_1_rw2=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 6, 14, 2),
stem_width=(32, 64),
**_rw_coat_cfg(
stride_mode='dw',
rel_pos_type='mlp',
rel_pos_dim=512, # was supposed to be 512, woops
),
),
coatnet_rmlp_2_rw=MaxxVitCfg(
embed_dim=(128, 256, 512, 1024),
depths=(2, 6, 14, 2),
stem_width=(64, 128),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_act_layer='silu',
init_values=1e-6,
rel_pos_type='mlp'
),
),
coatnet_rmlp_3_rw=MaxxVitCfg(
embed_dim=(192, 384, 768, 1536),
depths=(2, 6, 14, 2),
stem_width=(96, 192),
**_rw_coat_cfg(
stride_mode='dw',
conv_attn_act_layer='silu',
init_values=1e-6,
rel_pos_type='mlp'
),
),
coatnet_nano_cc=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(3, 4, 6, 3),
stem_width=(32, 64),
block_type=('C', 'C', ('C', 'T'), ('C', 'T')),
**_rw_coat_cfg(),
),
coatnext_nano_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(3, 4, 6, 3),
stem_width=(32, 64),
weight_init='normal',
**_next_cfg(
rel_pos_type='bias',
init_values=(1e-5, None)
),
),
# Trying to be like the CoAtNet paper configs
coatnet_0=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 3, 5, 2),
stem_width=64,
head_hidden_size=768,
),
coatnet_1=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 6, 14, 2),
stem_width=64,
head_hidden_size=768,
),
coatnet_2=MaxxVitCfg(
embed_dim=(128, 256, 512, 1024),
depths=(2, 6, 14, 2),
stem_width=128,
head_hidden_size=1024,
),
coatnet_3=MaxxVitCfg(
embed_dim=(192, 384, 768, 1536),
depths=(2, 6, 14, 2),
stem_width=192,
head_hidden_size=1536,
),
coatnet_4=MaxxVitCfg(
embed_dim=(192, 384, 768, 1536),
depths=(2, 12, 28, 2),
stem_width=192,
head_hidden_size=1536,
),
coatnet_5=MaxxVitCfg(
embed_dim=(256, 512, 1280, 2048),
depths=(2, 12, 28, 2),
stem_width=192,
head_hidden_size=2048,
),
# Experimental MaxVit configs
maxvit_pico_rw=MaxxVitCfg(
embed_dim=(32, 64, 128, 256),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(24, 32),
**_rw_max_cfg(),
),
maxvit_nano_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(1, 2, 3, 1),
block_type=('M',) * 4,
stem_width=(32, 64),
**_rw_max_cfg(),
),
maxvit_tiny_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(32, 64),
**_rw_max_cfg(),
),
maxvit_tiny_pm=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(2, 2, 5, 2),
block_type=('PM',) * 4,
stem_width=(32, 64),
**_rw_max_cfg(),
),
maxvit_rmlp_pico_rw=MaxxVitCfg(
embed_dim=(32, 64, 128, 256),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(24, 32),
**_rw_max_cfg(rel_pos_type='mlp'),
),
maxvit_rmlp_nano_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(1, 2, 3, 1),
block_type=('M',) * 4,
stem_width=(32, 64),
**_rw_max_cfg(rel_pos_type='mlp'),
),
maxvit_rmlp_tiny_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(32, 64),
**_rw_max_cfg(rel_pos_type='mlp'),
),
maxvit_rmlp_small_rw=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(32, 64),
**_rw_max_cfg(
rel_pos_type='mlp',
init_values=1e-6,
),
),
maxvit_rmlp_base_rw=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 6, 14, 2),
block_type=('M',) * 4,
stem_width=(32, 64),
head_hidden_size=768,
**_rw_max_cfg(
rel_pos_type='mlp',
),
),
maxxvit_rmlp_nano_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(1, 2, 3, 1),
block_type=('M',) * 4,
stem_width=(32, 64),
weight_init='normal',
**_next_cfg(),
),
maxxvit_rmlp_tiny_rw=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(32, 64),
**_next_cfg(),
),
maxxvit_rmlp_small_rw=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=(48, 96),
**_next_cfg(),
),
maxxvitv2_nano_rw=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(1, 2, 3, 1),
block_type=('M',) * 4,
stem_width=(48, 96),
weight_init='normal',
**_next_cfg(
no_block_attn=True,
rel_pos_type='bias',
),
),
maxxvitv2_rmlp_base_rw=MaxxVitCfg(
embed_dim=(128, 256, 512, 1024),
depths=(2, 6, 12, 2),
block_type=('M',) * 4,
stem_width=(64, 128),
**_next_cfg(
no_block_attn=True,
),
),
maxxvitv2_rmlp_large_rw=MaxxVitCfg(
embed_dim=(160, 320, 640, 1280),
depths=(2, 6, 16, 2),
block_type=('M',) * 4,
stem_width=(80, 160),
head_hidden_size=1280,
**_next_cfg(
no_block_attn=True,
),
),
# Trying to be like the MaxViT paper configs
maxvit_tiny_tf=MaxxVitCfg(
embed_dim=(64, 128, 256, 512),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=64,
stem_bias=True,
head_hidden_size=512,
**_tf_cfg(),
),
maxvit_small_tf=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 2, 5, 2),
block_type=('M',) * 4,
stem_width=64,
stem_bias=True,
head_hidden_size=768,
**_tf_cfg(),
),
maxvit_base_tf=MaxxVitCfg(
embed_dim=(96, 192, 384, 768),
depths=(2, 6, 14, 2),
block_type=('M',) * 4,
stem_width=64,
stem_bias=True,
head_hidden_size=768,
**_tf_cfg(),
),
maxvit_large_tf=MaxxVitCfg(
embed_dim=(128, 256, 512, 1024),
depths=(2, 6, 14, 2),
block_type=('M',) * 4,
stem_width=128,
stem_bias=True,
head_hidden_size=1024,
**_tf_cfg(),
),
maxvit_xlarge_tf=MaxxVitCfg(
embed_dim=(192, 384, 768, 1536),
depths=(2, 6, 14, 2),
block_type=('M',) * 4,
stem_width=192,
stem_bias=True,
head_hidden_size=1536,
**_tf_cfg(),
),
)
def checkpoint_filter_fn(state_dict, model: nn.Module):
model_state_dict = model.state_dict()
out_dict = {}
for k, v in state_dict.items():
if k.endswith('relative_position_bias_table'):
m = model.get_submodule(k[:-29])
if v.shape != m.relative_position_bias_table.shape or m.window_size[0] != m.window_size[1]:
v = resize_rel_pos_bias_table(
v,
new_window_size=m.window_size,
new_bias_shape=m.relative_position_bias_table.shape,
)
if k in model_state_dict and v.ndim != model_state_dict[k].ndim and v.numel() == model_state_dict[k].numel():
# adapt between conv2d / linear layers
assert v.ndim in (2, 4)
v = v.reshape(model_state_dict[k].shape)
out_dict[k] = v
return out_dict
def _create_maxxvit(variant, cfg_variant=None, pretrained=False, **kwargs):
if cfg_variant is None:
if variant in model_cfgs:
cfg_variant = variant
else:
cfg_variant = '_'.join(variant.split('_')[:-1])
return build_model_with_cfg(
MaxxVit, variant, pretrained,
model_cfg=model_cfgs[cfg_variant],
feature_cfg=dict(flatten_sequential=True),
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.95, 'interpolation': 'bicubic',
'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
'first_conv': 'stem.conv1', 'classifier': 'head.fc',
'fixed_input_size': True,
**kwargs
}
default_cfgs = generate_default_cfgs({
# timm specific CoAtNet configs, ImageNet-1k pretrain, fixed rel-pos
'coatnet_pico_rw_224.untrained': _cfg(url=''),
'coatnet_nano_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_nano_rw_224_sw-f53093b4.pth',
crop_pct=0.9),
'coatnet_0_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_0_rw_224_sw-a6439706.pth'),
'coatnet_1_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_1_rw_224_sw-5cae1ea8.pth'
),
# timm specific CoAtNet configs, ImageNet-12k pretrain w/ 1k fine-tune, fixed rel-pos
'coatnet_2_rw_224.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/'),
#'coatnet_3_rw_224.untrained': _cfg(url=''),
# Experimental CoAtNet configs w/ ImageNet-12k pretrain -> 1k fine-tune (different norm layers, MLP rel-pos)
'coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/'),
'coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/'),
'coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
# Experimental CoAtNet configs w/ ImageNet-1k train (different norm layers, MLP rel-pos)
'coatnet_bn_0_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_bn_0_rw_224_sw-c228e218.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
crop_pct=0.95),
'coatnet_rmlp_nano_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_rmlp_nano_rw_224_sw-bd1d51b3.pth',
crop_pct=0.9),
'coatnet_rmlp_0_rw_224.untrained': _cfg(url=''),
'coatnet_rmlp_1_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_rmlp_1_rw_224_sw-9051e6c3.pth'),
'coatnet_rmlp_2_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_rmlp_2_rw_224_sw-5ccfac55.pth'),
'coatnet_rmlp_3_rw_224.untrained': _cfg(url=''),
'coatnet_nano_cc_224.untrained': _cfg(url=''),
'coatnext_nano_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnext_nano_rw_224_ad-22cb71c2.pth',
crop_pct=0.9),
# ImagenNet-12k pretrain CoAtNet
'coatnet_2_rw_224.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821),
'coatnet_3_rw_224.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821),
'coatnet_rmlp_1_rw2_224.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821),
'coatnet_rmlp_2_rw_224.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821),
# Trying to be like the CoAtNet paper configs (will adapt if 'tf' weights are ever released)
'coatnet_0_224.untrained': _cfg(url=''),
'coatnet_1_224.untrained': _cfg(url=''),
'coatnet_2_224.untrained': _cfg(url=''),
'coatnet_3_224.untrained': _cfg(url=''),
'coatnet_4_224.untrained': _cfg(url=''),
'coatnet_5_224.untrained': _cfg(url=''),
# timm specific MaxVit configs, ImageNet-1k pretrain or untrained
'maxvit_pico_rw_256.untrained': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
'maxvit_nano_rw_256.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_nano_rw_256_sw-fb127241.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxvit_tiny_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_tiny_rw_224_sw-7d0dffeb.pth'),
'maxvit_tiny_rw_256.untrained': _cfg(
url='',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxvit_tiny_pm_256.untrained': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
# timm specific MaxVit w/ MLP rel-pos, ImageNet-1k pretrain
'maxvit_rmlp_pico_rw_256.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_pico_rw_256_sw-8d82f2c6.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxvit_rmlp_nano_rw_256.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_nano_rw_256_sw-c17bb0d6.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxvit_rmlp_tiny_rw_256.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_tiny_rw_256_sw-bbef0ff5.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxvit_rmlp_small_rw_224.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_small_rw_224_sw-6ef0ae4f.pth',
crop_pct=0.9,
),
'maxvit_rmlp_small_rw_256.untrained': _cfg(
url='',
input_size=(3, 256, 256), pool_size=(8, 8)),
# timm specific MaxVit w/ ImageNet-12k pretrain and 1k fine-tune
'maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
),
'maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
# timm specific MaxVit w/ ImageNet-12k pretrain
'maxvit_rmlp_base_rw_224.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821,
),
# timm MaxxViT configs (ConvNeXt conv blocks mixed with MaxVit transformer blocks)
'maxxvit_rmlp_nano_rw_256.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxxvit_rmlp_nano_rw_256_sw-0325d459.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxxvit_rmlp_tiny_rw_256.untrained': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
'maxxvit_rmlp_small_rw_256.sw_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxxvit_rmlp_small_rw_256_sw-37e217ff.pth',
input_size=(3, 256, 256), pool_size=(8, 8)),
# timm MaxxViT-V2 configs (ConvNeXt conv blocks mixed with MaxVit transformer blocks, more width, no block attn)
'maxxvitv2_nano_rw_256.sw_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8)),
'maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/'),
'maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'maxxvitv2_rmlp_large_rw_224.untrained': _cfg(url=''),
'maxxvitv2_rmlp_base_rw_224.sw_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821),
# MaxViT models ported from official Tensorflow impl
'maxvit_tiny_tf_224.in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'maxvit_tiny_tf_384.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'maxvit_tiny_tf_512.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 512, 512), pool_size=(16, 16), crop_pct=1.0, crop_mode='squash'),
'maxvit_small_tf_224.in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'maxvit_small_tf_384.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'maxvit_small_tf_512.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 512, 512), pool_size=(16, 16), crop_pct=1.0, crop_mode='squash'),
'maxvit_base_tf_224.in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'maxvit_base_tf_384.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'maxvit_base_tf_512.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 512, 512), pool_size=(16, 16), crop_pct=1.0, crop_mode='squash'),
'maxvit_large_tf_224.in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'maxvit_large_tf_384.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'maxvit_large_tf_512.in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 512, 512), pool_size=(16, 16), crop_pct=1.0, crop_mode='squash'),
'maxvit_base_tf_224.in21k': _cfg(
hf_hub_id='timm/',
num_classes=21843),
'maxvit_base_tf_384.in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'maxvit_base_tf_512.in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 512, 512), pool_size=(16, 16), crop_pct=1.0, crop_mode='squash'),
'maxvit_large_tf_224.in21k': _cfg(
hf_hub_id='timm/',
num_classes=21843),
'maxvit_large_tf_384.in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'maxvit_large_tf_512.in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 512, 512), crop_pct=1.0, crop_mode='squash'),
'maxvit_xlarge_tf_224.in21k': _cfg(
hf_hub_id='timm/',
num_classes=21843),
'maxvit_xlarge_tf_384.in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
'maxvit_xlarge_tf_512.in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 512, 512), pool_size=(16, 16), crop_pct=1.0, crop_mode='squash'),
})
@register_model
def coatnet_pico_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_pico_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_nano_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_nano_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_0_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_0_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_1_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_1_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_2_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_2_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_3_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_3_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_bn_0_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_bn_0_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_rmlp_nano_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_rmlp_nano_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_rmlp_0_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_rmlp_0_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_rmlp_1_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_rmlp_1_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_rmlp_1_rw2_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_rmlp_1_rw2_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_rmlp_2_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_rmlp_2_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_rmlp_2_rw_384(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_rmlp_2_rw_384', pretrained=pretrained, **kwargs)
@register_model
def coatnet_rmlp_3_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_rmlp_3_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_nano_cc_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_nano_cc_224', pretrained=pretrained, **kwargs)
@register_model
def coatnext_nano_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnext_nano_rw_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_0_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_0_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_1_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_1_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_2_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_2_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_3_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_3_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_4_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_4_224', pretrained=pretrained, **kwargs)
@register_model
def coatnet_5_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('coatnet_5_224', pretrained=pretrained, **kwargs)
@register_model
def maxvit_pico_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_pico_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_nano_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_nano_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_tiny_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_tiny_rw_224', pretrained=pretrained, **kwargs)
@register_model
def maxvit_tiny_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_tiny_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_rmlp_pico_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_rmlp_pico_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_rmlp_nano_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_rmlp_nano_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_rmlp_tiny_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_rmlp_tiny_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_rmlp_small_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_rmlp_small_rw_224', pretrained=pretrained, **kwargs)
@register_model
def maxvit_rmlp_small_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_rmlp_small_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxvit_rmlp_base_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_rmlp_base_rw_224', pretrained=pretrained, **kwargs)
@register_model
def maxvit_rmlp_base_rw_384(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_rmlp_base_rw_384', pretrained=pretrained, **kwargs)
@register_model
def maxvit_tiny_pm_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_tiny_pm_256', pretrained=pretrained, **kwargs)
@register_model
def maxxvit_rmlp_nano_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxxvit_rmlp_nano_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxxvit_rmlp_tiny_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxxvit_rmlp_tiny_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxxvit_rmlp_small_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxxvit_rmlp_small_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxxvitv2_nano_rw_256(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxxvitv2_nano_rw_256', pretrained=pretrained, **kwargs)
@register_model
def maxxvitv2_rmlp_base_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxxvitv2_rmlp_base_rw_224', pretrained=pretrained, **kwargs)
@register_model
def maxxvitv2_rmlp_base_rw_384(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxxvitv2_rmlp_base_rw_384', pretrained=pretrained, **kwargs)
@register_model
def maxxvitv2_rmlp_large_rw_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxxvitv2_rmlp_large_rw_224', pretrained=pretrained, **kwargs)
@register_model
def maxvit_tiny_tf_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_tiny_tf_224', 'maxvit_tiny_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_tiny_tf_384(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_tiny_tf_384', 'maxvit_tiny_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_tiny_tf_512(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_tiny_tf_512', 'maxvit_tiny_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_small_tf_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_small_tf_224', 'maxvit_small_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_small_tf_384(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_small_tf_384', 'maxvit_small_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_small_tf_512(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_small_tf_512', 'maxvit_small_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_base_tf_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_base_tf_224', 'maxvit_base_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_base_tf_384(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_base_tf_384', 'maxvit_base_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_base_tf_512(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_base_tf_512', 'maxvit_base_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_large_tf_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_large_tf_224', 'maxvit_large_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_large_tf_384(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_large_tf_384', 'maxvit_large_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_large_tf_512(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_large_tf_512', 'maxvit_large_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_xlarge_tf_224(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_xlarge_tf_224', 'maxvit_xlarge_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_xlarge_tf_384(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_xlarge_tf_384', 'maxvit_xlarge_tf', pretrained=pretrained, **kwargs)
@register_model
def maxvit_xlarge_tf_512(pretrained=False, **kwargs) -> MaxxVit:
return _create_maxxvit('maxvit_xlarge_tf_512', 'maxvit_xlarge_tf', pretrained=pretrained, **kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/gcvit.py
|
""" Global Context ViT
From scratch implementation of GCViT in the style of timm swin_transformer_v2_cr.py
Global Context Vision Transformers -https://arxiv.org/abs/2206.09959
@article{hatamizadeh2022global,
title={Global Context Vision Transformers},
author={Hatamizadeh, Ali and Yin, Hongxu and Kautz, Jan and Molchanov, Pavlo},
journal={arXiv preprint arXiv:2206.09959},
year={2022}
}
Free of any code related to NVIDIA GCVit impl at https://github.com/NVlabs/GCVit.
The license for this code release is Apache 2.0 with no commercial restrictions.
However, weight files adapted from NVIDIA GCVit impl ARE under a non-commercial share-alike license
(https://creativecommons.org/licenses/by-nc-sa/4.0/) until I have a chance to train new ones...
Hacked together by / Copyright 2022, Ross Wightman
"""
import math
from functools import partial
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, to_2tuple, to_ntuple, Mlp, ClassifierHead, LayerNorm2d, \
get_attn, get_act_layer, get_norm_layer, RelPosBias, _assert
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._manipulate import named_apply
from ._registry import register_model, generate_default_cfgs
__all__ = ['GlobalContextVit']
class MbConvBlock(nn.Module):
""" A depthwise separable / fused mbconv style residual block with SE, `no norm.
"""
def __init__(
self,
in_chs,
out_chs=None,
expand_ratio=1.0,
attn_layer='se',
bias=False,
act_layer=nn.GELU,
):
super().__init__()
attn_kwargs = dict(act_layer=act_layer)
if isinstance(attn_layer, str) and attn_layer == 'se' or attn_layer == 'eca':
attn_kwargs['rd_ratio'] = 0.25
attn_kwargs['bias'] = False
attn_layer = get_attn(attn_layer)
out_chs = out_chs or in_chs
mid_chs = int(expand_ratio * in_chs)
self.conv_dw = nn.Conv2d(in_chs, mid_chs, 3, 1, 1, groups=in_chs, bias=bias)
self.act = act_layer()
self.se = attn_layer(mid_chs, **attn_kwargs)
self.conv_pw = nn.Conv2d(mid_chs, out_chs, 1, 1, 0, bias=bias)
def forward(self, x):
shortcut = x
x = self.conv_dw(x)
x = self.act(x)
x = self.se(x)
x = self.conv_pw(x)
x = x + shortcut
return x
class Downsample2d(nn.Module):
def __init__(
self,
dim,
dim_out=None,
reduction='conv',
act_layer=nn.GELU,
norm_layer=LayerNorm2d, # NOTE in NCHW
):
super().__init__()
dim_out = dim_out or dim
self.norm1 = norm_layer(dim) if norm_layer is not None else nn.Identity()
self.conv_block = MbConvBlock(dim, act_layer=act_layer)
assert reduction in ('conv', 'max', 'avg')
if reduction == 'conv':
self.reduction = nn.Conv2d(dim, dim_out, 3, 2, 1, bias=False)
elif reduction == 'max':
assert dim == dim_out
self.reduction = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
else:
assert dim == dim_out
self.reduction = nn.AvgPool2d(kernel_size=2)
self.norm2 = norm_layer(dim_out) if norm_layer is not None else nn.Identity()
def forward(self, x):
x = self.norm1(x)
x = self.conv_block(x)
x = self.reduction(x)
x = self.norm2(x)
return x
class FeatureBlock(nn.Module):
def __init__(
self,
dim,
levels=0,
reduction='max',
act_layer=nn.GELU,
):
super().__init__()
reductions = levels
levels = max(1, levels)
if reduction == 'avg':
pool_fn = partial(nn.AvgPool2d, kernel_size=2)
else:
pool_fn = partial(nn.MaxPool2d, kernel_size=3, stride=2, padding=1)
self.blocks = nn.Sequential()
for i in range(levels):
self.blocks.add_module(f'conv{i+1}', MbConvBlock(dim, act_layer=act_layer))
if reductions:
self.blocks.add_module(f'pool{i+1}', pool_fn())
reductions -= 1
def forward(self, x):
return self.blocks(x)
class Stem(nn.Module):
def __init__(
self,
in_chs: int = 3,
out_chs: int = 96,
act_layer: Callable = nn.GELU,
norm_layer: Callable = LayerNorm2d, # NOTE stem in NCHW
):
super().__init__()
self.conv1 = nn.Conv2d(in_chs, out_chs, kernel_size=3, stride=2, padding=1)
self.down = Downsample2d(out_chs, act_layer=act_layer, norm_layer=norm_layer)
def forward(self, x):
x = self.conv1(x)
x = self.down(x)
return x
class WindowAttentionGlobal(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
window_size: Tuple[int, int],
use_global: bool = True,
qkv_bias: bool = True,
attn_drop: float = 0.,
proj_drop: float = 0.,
):
super().__init__()
window_size = to_2tuple(window_size)
self.window_size = window_size
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.use_global = use_global
self.rel_pos = RelPosBias(window_size=window_size, num_heads=num_heads)
if self.use_global:
self.qkv = nn.Linear(dim, dim * 2, bias=qkv_bias)
else:
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, q_global: Optional[torch.Tensor] = None):
B, N, C = x.shape
if self.use_global and q_global is not None:
_assert(x.shape[-1] == q_global.shape[-1], 'x and q_global seq lengths should be equal')
kv = self.qkv(x)
kv = kv.reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k, v = kv.unbind(0)
q = q_global.repeat(B // q_global.shape[0], 1, 1, 1)
q = q.reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
else:
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
q = q * self.scale
attn = q @ k.transpose(-2, -1).contiguous() # NOTE contiguous() fixes an odd jit bug in PyTorch 2.0
attn = self.rel_pos(attn)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
def window_partition(x, window_size: Tuple[int, int]):
B, H, W, C = x.shape
x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
return windows
@register_notrace_function # reason: int argument is a Proxy
def window_reverse(windows, window_size: Tuple[int, int], img_size: Tuple[int, int]):
H, W = img_size
C = windows.shape[-1]
x = windows.view(-1, H // window_size[0], W // window_size[1], window_size[0], window_size[1], C)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
return x
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class GlobalContextVitBlock(nn.Module):
def __init__(
self,
dim: int,
feat_size: Tuple[int, int],
num_heads: int,
window_size: int = 7,
mlp_ratio: float = 4.,
use_global: bool = True,
qkv_bias: bool = True,
layer_scale: Optional[float] = None,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
attn_layer: Callable = WindowAttentionGlobal,
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
):
super().__init__()
feat_size = to_2tuple(feat_size)
window_size = to_2tuple(window_size)
self.window_size = window_size
self.num_windows = int((feat_size[0] // window_size[0]) * (feat_size[1] // window_size[1]))
self.norm1 = norm_layer(dim)
self.attn = attn_layer(
dim,
num_heads=num_heads,
window_size=window_size,
use_global=use_global,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.ls1 = LayerScale(dim, layer_scale) if layer_scale is not None else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=proj_drop)
self.ls2 = LayerScale(dim, layer_scale) if layer_scale is not None else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def _window_attn(self, x, q_global: Optional[torch.Tensor] = None):
B, H, W, C = x.shape
x_win = window_partition(x, self.window_size)
x_win = x_win.view(-1, self.window_size[0] * self.window_size[1], C)
attn_win = self.attn(x_win, q_global)
x = window_reverse(attn_win, self.window_size, (H, W))
return x
def forward(self, x, q_global: Optional[torch.Tensor] = None):
x = x + self.drop_path1(self.ls1(self._window_attn(self.norm1(x), q_global)))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class GlobalContextVitStage(nn.Module):
def __init__(
self,
dim,
depth: int,
num_heads: int,
feat_size: Tuple[int, int],
window_size: Tuple[int, int],
downsample: bool = True,
global_norm: bool = False,
stage_norm: bool = False,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
layer_scale: Optional[float] = None,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: Union[List[float], float] = 0.0,
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
norm_layer_cl: Callable = LayerNorm2d,
):
super().__init__()
if downsample:
self.downsample = Downsample2d(
dim=dim,
dim_out=dim * 2,
norm_layer=norm_layer,
)
dim = dim * 2
feat_size = (feat_size[0] // 2, feat_size[1] // 2)
else:
self.downsample = nn.Identity()
self.feat_size = feat_size
window_size = to_2tuple(window_size)
feat_levels = int(math.log2(min(feat_size) / min(window_size)))
self.global_block = FeatureBlock(dim, feat_levels)
self.global_norm = norm_layer_cl(dim) if global_norm else nn.Identity()
self.blocks = nn.ModuleList([
GlobalContextVitBlock(
dim=dim,
num_heads=num_heads,
feat_size=feat_size,
window_size=window_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
use_global=(i % 2 != 0),
layer_scale=layer_scale,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
act_layer=act_layer,
norm_layer=norm_layer_cl,
)
for i in range(depth)
])
self.norm = norm_layer_cl(dim) if stage_norm else nn.Identity()
self.dim = dim
self.feat_size = feat_size
self.grad_checkpointing = False
def forward(self, x):
# input NCHW, downsample & global block are 2d conv + pooling
x = self.downsample(x)
global_query = self.global_block(x)
# reshape NCHW --> NHWC for transformer blocks
x = x.permute(0, 2, 3, 1)
global_query = self.global_norm(global_query.permute(0, 2, 3, 1))
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x, global_query)
x = self.norm(x)
x = x.permute(0, 3, 1, 2).contiguous() # back to NCHW
return x
class GlobalContextVit(nn.Module):
def __init__(
self,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = 'avg',
img_size: Tuple[int, int] = 224,
window_ratio: Tuple[int, ...] = (32, 32, 16, 32),
window_size: Tuple[int, ...] = None,
embed_dim: int = 64,
depths: Tuple[int, ...] = (3, 4, 19, 5),
num_heads: Tuple[int, ...] = (2, 4, 8, 16),
mlp_ratio: float = 3.0,
qkv_bias: bool = True,
layer_scale: Optional[float] = None,
drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
weight_init='',
act_layer: str = 'gelu',
norm_layer: str = 'layernorm2d',
norm_layer_cl: str = 'layernorm',
norm_eps: float = 1e-5,
):
super().__init__()
act_layer = get_act_layer(act_layer)
norm_layer = partial(get_norm_layer(norm_layer), eps=norm_eps)
norm_layer_cl = partial(get_norm_layer(norm_layer_cl), eps=norm_eps)
img_size = to_2tuple(img_size)
feat_size = tuple(d // 4 for d in img_size) # stem reduction by 4
self.global_pool = global_pool
self.num_classes = num_classes
self.drop_rate = drop_rate
num_stages = len(depths)
self.num_features = int(embed_dim * 2 ** (num_stages - 1))
if window_size is not None:
window_size = to_ntuple(num_stages)(window_size)
else:
assert window_ratio is not None
window_size = tuple([(img_size[0] // r, img_size[1] // r) for r in to_ntuple(num_stages)(window_ratio)])
self.stem = Stem(
in_chs=in_chans,
out_chs=embed_dim,
act_layer=act_layer,
norm_layer=norm_layer
)
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
stages = []
for i in range(num_stages):
last_stage = i == num_stages - 1
stage_scale = 2 ** max(i - 1, 0)
stages.append(GlobalContextVitStage(
dim=embed_dim * stage_scale,
depth=depths[i],
num_heads=num_heads[i],
feat_size=(feat_size[0] // stage_scale, feat_size[1] // stage_scale),
window_size=window_size[i],
downsample=i != 0,
stage_norm=last_stage,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
layer_scale=layer_scale,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
act_layer=act_layer,
norm_layer=norm_layer,
norm_layer_cl=norm_layer_cl,
))
self.stages = nn.Sequential(*stages)
# Classifier head
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
if weight_init:
named_apply(partial(self._init_weights, scheme=weight_init), self)
def _init_weights(self, module, name, scheme='vit'):
# note Conv2d left as default init
if scheme == 'vit':
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
if 'mlp' in name:
nn.init.normal_(module.bias, std=1e-6)
else:
nn.init.zeros_(module.bias)
else:
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
@torch.jit.ignore
def no_weight_decay(self):
return {
k for k, _ in self.named_parameters()
if any(n in k for n in ["relative_position_bias_table", "rel_pos.mlp"])}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem', # stem and embed
blocks=r'^stages\.(\d+)'
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is None:
global_pool = self.head.global_pool.pool_type
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
x = self.stem(x)
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_gcvit(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
model = build_model_with_cfg(GlobalContextVit, variant, pretrained, **kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1', 'classifier': 'head.fc',
'fixed_input_size': True,
**kwargs
}
default_cfgs = generate_default_cfgs({
'gcvit_xxtiny.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_xxtiny_224_nvidia-d1d86009.pth'),
'gcvit_xtiny.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_xtiny_224_nvidia-274b92b7.pth'),
'gcvit_tiny.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_tiny_224_nvidia-ac783954.pth'),
'gcvit_small.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_small_224_nvidia-4e98afa2.pth'),
'gcvit_base.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-morevit/gcvit_base_224_nvidia-f009139b.pth'),
})
@register_model
def gcvit_xxtiny(pretrained=False, **kwargs) -> GlobalContextVit:
model_kwargs = dict(
depths=(2, 2, 6, 2),
num_heads=(2, 4, 8, 16),
**kwargs)
return _create_gcvit('gcvit_xxtiny', pretrained=pretrained, **model_kwargs)
@register_model
def gcvit_xtiny(pretrained=False, **kwargs) -> GlobalContextVit:
model_kwargs = dict(
depths=(3, 4, 6, 5),
num_heads=(2, 4, 8, 16),
**kwargs)
return _create_gcvit('gcvit_xtiny', pretrained=pretrained, **model_kwargs)
@register_model
def gcvit_tiny(pretrained=False, **kwargs) -> GlobalContextVit:
model_kwargs = dict(
depths=(3, 4, 19, 5),
num_heads=(2, 4, 8, 16),
**kwargs)
return _create_gcvit('gcvit_tiny', pretrained=pretrained, **model_kwargs)
@register_model
def gcvit_small(pretrained=False, **kwargs) -> GlobalContextVit:
model_kwargs = dict(
depths=(3, 4, 19, 5),
num_heads=(3, 6, 12, 24),
embed_dim=96,
mlp_ratio=2,
layer_scale=1e-5,
**kwargs)
return _create_gcvit('gcvit_small', pretrained=pretrained, **model_kwargs)
@register_model
def gcvit_base(pretrained=False, **kwargs) -> GlobalContextVit:
model_kwargs = dict(
depths=(3, 4, 19, 5),
num_heads=(4, 8, 16, 32),
embed_dim=128,
mlp_ratio=2,
layer_scale=1e-5,
**kwargs)
return _create_gcvit('gcvit_base', pretrained=pretrained, **model_kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/xception_aligned.py
|
"""Pytorch impl of Aligned Xception 41, 65, 71
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import ClassifierHead, ConvNormAct, create_conv2d, get_norm_act_layer
from timm.layers.helpers import to_3tuple
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
__all__ = ['XceptionAligned']
class SeparableConv2d(nn.Module):
def __init__(
self,
in_chs,
out_chs,
kernel_size=3,
stride=1,
dilation=1,
padding='',
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
):
super(SeparableConv2d, self).__init__()
self.kernel_size = kernel_size
self.dilation = dilation
# depthwise convolution
self.conv_dw = create_conv2d(
in_chs, in_chs, kernel_size, stride=stride,
padding=padding, dilation=dilation, depthwise=True)
self.bn_dw = norm_layer(in_chs)
self.act_dw = act_layer(inplace=True) if act_layer is not None else nn.Identity()
# pointwise convolution
self.conv_pw = create_conv2d(in_chs, out_chs, kernel_size=1)
self.bn_pw = norm_layer(out_chs)
self.act_pw = act_layer(inplace=True) if act_layer is not None else nn.Identity()
def forward(self, x):
x = self.conv_dw(x)
x = self.bn_dw(x)
x = self.act_dw(x)
x = self.conv_pw(x)
x = self.bn_pw(x)
x = self.act_pw(x)
return x
class PreSeparableConv2d(nn.Module):
def __init__(
self,
in_chs,
out_chs,
kernel_size=3,
stride=1,
dilation=1,
padding='',
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
first_act=True,
):
super(PreSeparableConv2d, self).__init__()
norm_act_layer = get_norm_act_layer(norm_layer, act_layer=act_layer)
self.kernel_size = kernel_size
self.dilation = dilation
self.norm = norm_act_layer(in_chs, inplace=True) if first_act else nn.Identity()
# depthwise convolution
self.conv_dw = create_conv2d(
in_chs, in_chs, kernel_size, stride=stride,
padding=padding, dilation=dilation, depthwise=True)
# pointwise convolution
self.conv_pw = create_conv2d(in_chs, out_chs, kernel_size=1)
def forward(self, x):
x = self.norm(x)
x = self.conv_dw(x)
x = self.conv_pw(x)
return x
class XceptionModule(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=1,
pad_type='',
start_with_relu=True,
no_skip=False,
act_layer=nn.ReLU,
norm_layer=None,
):
super(XceptionModule, self).__init__()
out_chs = to_3tuple(out_chs)
self.in_channels = in_chs
self.out_channels = out_chs[-1]
self.no_skip = no_skip
if not no_skip and (self.out_channels != self.in_channels or stride != 1):
self.shortcut = ConvNormAct(
in_chs, self.out_channels, 1, stride=stride, norm_layer=norm_layer, apply_act=False)
else:
self.shortcut = None
separable_act_layer = None if start_with_relu else act_layer
self.stack = nn.Sequential()
for i in range(3):
if start_with_relu:
self.stack.add_module(f'act{i + 1}', act_layer(inplace=i > 0))
self.stack.add_module(f'conv{i + 1}', SeparableConv2d(
in_chs, out_chs[i], 3, stride=stride if i == 2 else 1, dilation=dilation, padding=pad_type,
act_layer=separable_act_layer, norm_layer=norm_layer))
in_chs = out_chs[i]
def forward(self, x):
skip = x
x = self.stack(x)
if self.shortcut is not None:
skip = self.shortcut(skip)
if not self.no_skip:
x = x + skip
return x
class PreXceptionModule(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=1,
pad_type='',
no_skip=False,
act_layer=nn.ReLU,
norm_layer=None,
):
super(PreXceptionModule, self).__init__()
out_chs = to_3tuple(out_chs)
self.in_channels = in_chs
self.out_channels = out_chs[-1]
self.no_skip = no_skip
if not no_skip and (self.out_channels != self.in_channels or stride != 1):
self.shortcut = create_conv2d(in_chs, self.out_channels, 1, stride=stride)
else:
self.shortcut = nn.Identity()
self.norm = get_norm_act_layer(norm_layer, act_layer=act_layer)(in_chs, inplace=True)
self.stack = nn.Sequential()
for i in range(3):
self.stack.add_module(f'conv{i + 1}', PreSeparableConv2d(
in_chs,
out_chs[i],
3,
stride=stride if i == 2 else 1,
dilation=dilation,
padding=pad_type,
act_layer=act_layer,
norm_layer=norm_layer,
first_act=i > 0,
))
in_chs = out_chs[i]
def forward(self, x):
x = self.norm(x)
skip = x
x = self.stack(x)
if not self.no_skip:
x = x + self.shortcut(skip)
return x
class XceptionAligned(nn.Module):
"""Modified Aligned Xception
"""
def __init__(
self,
block_cfg,
num_classes=1000,
in_chans=3,
output_stride=32,
preact=False,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
drop_rate=0.,
global_pool='avg',
):
super(XceptionAligned, self).__init__()
assert output_stride in (8, 16, 32)
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
layer_args = dict(act_layer=act_layer, norm_layer=norm_layer)
self.stem = nn.Sequential(*[
ConvNormAct(in_chans, 32, kernel_size=3, stride=2, **layer_args),
create_conv2d(32, 64, kernel_size=3, stride=1) if preact else
ConvNormAct(32, 64, kernel_size=3, stride=1, **layer_args)
])
curr_dilation = 1
curr_stride = 2
self.feature_info = []
self.blocks = nn.Sequential()
module_fn = PreXceptionModule if preact else XceptionModule
for i, b in enumerate(block_cfg):
b['dilation'] = curr_dilation
if b['stride'] > 1:
name = f'blocks.{i}.stack.conv2' if preact else f'blocks.{i}.stack.act3'
self.feature_info += [dict(num_chs=to_3tuple(b['out_chs'])[-2], reduction=curr_stride, module=name)]
next_stride = curr_stride * b['stride']
if next_stride > output_stride:
curr_dilation *= b['stride']
b['stride'] = 1
else:
curr_stride = next_stride
self.blocks.add_module(str(i), module_fn(**b, **layer_args))
self.num_features = self.blocks[-1].out_channels
self.feature_info += [dict(
num_chs=self.num_features, reduction=curr_stride, module='blocks.' + str(len(self.blocks) - 1))]
self.act = act_layer(inplace=True) if preact else nn.Identity()
self.head = ClassifierHead(
in_features=self.num_features,
num_classes=num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=r'^blocks\.(\d+)',
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.head.reset(num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
x = self.act(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _xception(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
XceptionAligned,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True, feature_cls='hook'),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (10, 10),
'crop_pct': 0.903, 'interpolation': 'bicubic',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'stem.0.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'xception65.ra3_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.94,
),
'xception41.tf_in1k': _cfg(hf_hub_id='timm/'),
'xception65.tf_in1k': _cfg(hf_hub_id='timm/'),
'xception71.tf_in1k': _cfg(hf_hub_id='timm/'),
'xception41p.ra3_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.94,
),
'xception65p.ra3_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.94,
),
})
@register_model
def xception41(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-41
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 8),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
]
model_args = dict(block_cfg=block_cfg, norm_layer=partial(nn.BatchNorm2d, eps=.001, momentum=.1))
return _xception('xception41', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def xception65(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-65
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
]
model_args = dict(block_cfg=block_cfg, norm_layer=partial(nn.BatchNorm2d, eps=.001, momentum=.1))
return _xception('xception65', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def xception71(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-71
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=1),
dict(in_chs=256, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=1),
dict(in_chs=728, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
]
model_args = dict(block_cfg=block_cfg, norm_layer=partial(nn.BatchNorm2d, eps=.001, momentum=.1))
return _xception('xception71', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def xception41p(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-41 w/ Pre-Act
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 8),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), no_skip=True, stride=1),
]
model_args = dict(block_cfg=block_cfg, preact=True, norm_layer=nn.BatchNorm2d)
return _xception('xception41p', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def xception65p(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-65 w/ Pre-Act
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True),
]
model_args = dict(
block_cfg=block_cfg, preact=True, norm_layer=partial(nn.BatchNorm2d, eps=.001, momentum=.1))
return _xception('xception65p', pretrained=pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/pvt_v2.py
|
""" Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.13797},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Based on Apache 2.0 licensed code at https://github.com/whai362/PVT
Modifications and timm support by / Copyright 2022, Ross Wightman
"""
import math
from typing import Tuple, List, Callable, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, to_2tuple, to_ntuple, trunc_normal_, LayerNorm, use_fused_attn
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['PyramidVisionTransformerV2']
class MlpWithDepthwiseConv(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.,
extra_relu=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.relu = nn.ReLU() if extra_relu else nn.Identity()
self.dwconv = nn.Conv2d(hidden_features, hidden_features, 3, 1, 1, bias=True, groups=hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x, feat_size: List[int]):
x = self.fc1(x)
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, feat_size[0], feat_size[1])
x = self.relu(x)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim,
num_heads=8,
sr_ratio=1,
linear_attn=False,
qkv_bias=True,
attn_drop=0.,
proj_drop=0.
):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.fused_attn = use_fused_attn()
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
if not linear_attn:
self.pool = None
if sr_ratio > 1:
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)
else:
self.sr = None
self.norm = None
self.act = None
else:
self.pool = nn.AdaptiveAvgPool2d(7)
self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1)
self.norm = nn.LayerNorm(dim)
self.act = nn.GELU()
def forward(self, x, feat_size: List[int]):
B, N, C = x.shape
H, W = feat_size
q = self.q(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
if self.pool is not None:
x = x.permute(0, 2, 1).reshape(B, C, H, W)
x = self.sr(self.pool(x)).reshape(B, C, -1).permute(0, 2, 1)
x = self.norm(x)
x = self.act(x)
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
else:
if self.sr is not None:
x = x.permute(0, 2, 1).reshape(B, C, H, W)
x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
x = self.norm(x)
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
else:
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k, v = kv.unbind(0)
if self.fused_attn:
x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
sr_ratio=1,
linear_attn=False,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
sr_ratio=sr_ratio,
linear_attn=linear_attn,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = MlpWithDepthwiseConv(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
extra_relu=linear_attn,
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x, feat_size: List[int]):
x = x + self.drop_path1(self.attn(self.norm1(x), feat_size))
x = x + self.drop_path2(self.mlp(self.norm2(x), feat_size))
return x
class OverlapPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
patch_size = to_2tuple(patch_size)
assert max(patch_size) > stride, "Set larger patch_size than stride"
self.patch_size = patch_size
self.proj = nn.Conv2d(
in_chans, embed_dim, patch_size,
stride=stride, padding=(patch_size[0] // 2, patch_size[1] // 2))
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
x = self.proj(x)
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
return x
class PyramidVisionTransformerStage(nn.Module):
def __init__(
self,
dim: int,
dim_out: int,
depth: int,
downsample: bool = True,
num_heads: int = 8,
sr_ratio: int = 1,
linear_attn: bool = False,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: Union[List[float], float] = 0.0,
norm_layer: Callable = LayerNorm,
):
super().__init__()
self.grad_checkpointing = False
if downsample:
self.downsample = OverlapPatchEmbed(
patch_size=3,
stride=2,
in_chans=dim,
embed_dim=dim_out,
)
else:
assert dim == dim_out
self.downsample = None
self.blocks = nn.ModuleList([Block(
dim=dim_out,
num_heads=num_heads,
sr_ratio=sr_ratio,
linear_attn=linear_attn,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
) for i in range(depth)])
self.norm = norm_layer(dim_out)
def forward(self, x):
# x is either B, C, H, W (if downsample) or B, H, W, C if not
if self.downsample is not None:
# input to downsample is B, C, H, W
x = self.downsample(x) # output B, H, W, C
B, H, W, C = x.shape
feat_size = (H, W)
x = x.reshape(B, -1, C)
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint.checkpoint(blk, x, feat_size)
else:
x = blk(x, feat_size)
x = self.norm(x)
x = x.reshape(B, feat_size[0], feat_size[1], -1).permute(0, 3, 1, 2).contiguous()
return x
class PyramidVisionTransformerV2(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
depths=(3, 4, 6, 3),
embed_dims=(64, 128, 256, 512),
num_heads=(1, 2, 4, 8),
sr_ratios=(8, 4, 2, 1),
mlp_ratios=(8., 8., 4., 4.),
qkv_bias=True,
linear=False,
drop_rate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=LayerNorm,
):
super().__init__()
self.num_classes = num_classes
assert global_pool in ('avg', '')
self.global_pool = global_pool
self.depths = depths
num_stages = len(depths)
mlp_ratios = to_ntuple(num_stages)(mlp_ratios)
num_heads = to_ntuple(num_stages)(num_heads)
sr_ratios = to_ntuple(num_stages)(sr_ratios)
assert(len(embed_dims)) == num_stages
self.feature_info = []
self.patch_embed = OverlapPatchEmbed(
patch_size=7,
stride=4,
in_chans=in_chans,
embed_dim=embed_dims[0],
)
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
cur = 0
prev_dim = embed_dims[0]
stages = []
for i in range(num_stages):
stages += [PyramidVisionTransformerStage(
dim=prev_dim,
dim_out=embed_dims[i],
depth=depths[i],
downsample=i > 0,
num_heads=num_heads[i],
sr_ratio=sr_ratios[i],
mlp_ratio=mlp_ratios[i],
linear_attn=linear,
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
)]
prev_dim = embed_dims[i]
cur += depths[i]
self.feature_info += [dict(num_chs=prev_dim, reduction=4 * 2**i, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
# classification head
self.num_features = embed_dims[-1]
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def freeze_patch_emb(self):
self.patch_embed.requires_grad = False
@torch.jit.ignore
def no_weight_decay(self):
return {}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^patch_embed', # stem and embed
blocks=r'^stages\.(\d+)'
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('avg', '')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.patch_embed(x)
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x.mean(dim=(-1, -2))
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _checkpoint_filter_fn(state_dict, model):
""" Remap original checkpoints -> timm """
if 'patch_embed.proj.weight' in state_dict:
return state_dict # non-original checkpoint, no remapping needed
out_dict = {}
import re
for k, v in state_dict.items():
if k.startswith('patch_embed'):
k = k.replace('patch_embed1', 'patch_embed')
k = k.replace('patch_embed2', 'stages.1.downsample')
k = k.replace('patch_embed3', 'stages.2.downsample')
k = k.replace('patch_embed4', 'stages.3.downsample')
k = k.replace('dwconv.dwconv', 'dwconv')
k = re.sub(r'block(\d+).(\d+)', lambda x: f'stages.{int(x.group(1)) - 1}.blocks.{x.group(2)}', k)
k = re.sub(r'^norm(\d+)', lambda x: f'stages.{int(x.group(1)) - 1}.norm', k)
out_dict[k] = v
return out_dict
def _create_pvt2(variant, pretrained=False, **kwargs):
default_out_indices = tuple(range(4))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
PyramidVisionTransformerV2,
variant,
pretrained,
pretrained_filter_fn=_checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs,
)
return model
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head', 'fixed_input_size': False,
**kwargs
}
default_cfgs = generate_default_cfgs({
'pvt_v2_b0.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b1.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b2.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b3.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b4.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b5.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b2_li.in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def pvt_v2_b0(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(2, 2, 2, 2), embed_dims=(32, 64, 160, 256), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b0', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b1(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(2, 2, 2, 2), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b2(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(3, 4, 6, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b3(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(3, 4, 18, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b3', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b4(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(3, 8, 27, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b4', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b5(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(
depths=(3, 6, 40, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), mlp_ratios=(4, 4, 4, 4))
return _create_pvt2('pvt_v2_b5', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b2_li(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(
depths=(3, 4, 6, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), linear=True)
return _create_pvt2('pvt_v2_b2_li', pretrained=pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/volo.py
|
""" Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Copyright 2021 Sea Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, Mlp, to_2tuple, to_ntuple, trunc_normal_
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['VOLO'] # model_registry will add each entrypoint fn to this
class OutlookAttention(nn.Module):
def __init__(
self,
dim,
num_heads,
kernel_size=3,
padding=1,
stride=1,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
head_dim = dim // num_heads
self.num_heads = num_heads
self.kernel_size = kernel_size
self.padding = padding
self.stride = stride
self.scale = head_dim ** -0.5
self.v = nn.Linear(dim, dim, bias=qkv_bias)
self.attn = nn.Linear(dim, kernel_size ** 4 * num_heads)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.unfold = nn.Unfold(kernel_size=kernel_size, padding=padding, stride=stride)
self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True)
def forward(self, x):
B, H, W, C = x.shape
v = self.v(x).permute(0, 3, 1, 2) # B, C, H, W
h, w = math.ceil(H / self.stride), math.ceil(W / self.stride)
v = self.unfold(v).reshape(
B, self.num_heads, C // self.num_heads,
self.kernel_size * self.kernel_size, h * w).permute(0, 1, 4, 3, 2) # B,H,N,kxk,C/H
attn = self.pool(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
attn = self.attn(attn).reshape(
B, h * w, self.num_heads, self.kernel_size * self.kernel_size,
self.kernel_size * self.kernel_size).permute(0, 2, 1, 3, 4) # B,H,N,kxk,kxk
attn = attn * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).permute(0, 1, 4, 3, 2).reshape(B, C * self.kernel_size * self.kernel_size, h * w)
x = F.fold(x, output_size=(H, W), kernel_size=self.kernel_size, padding=self.padding, stride=self.stride)
x = self.proj(x.permute(0, 2, 3, 1))
x = self.proj_drop(x)
return x
class Outlooker(nn.Module):
def __init__(
self,
dim,
kernel_size,
padding,
stride=1,
num_heads=1,
mlp_ratio=3.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
qkv_bias=False,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = OutlookAttention(
dim,
num_heads,
kernel_size=kernel_size,
padding=padding,
stride=stride,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class Attention(nn.Module):
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, H, W, C = x.shape
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, H, W, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Transformer(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class ClassAttention(nn.Module):
def __init__(
self,
dim,
num_heads=8,
head_dim=None,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
self.num_heads = num_heads
if head_dim is not None:
self.head_dim = head_dim
else:
head_dim = dim // num_heads
self.head_dim = head_dim
self.scale = head_dim ** -0.5
self.kv = nn.Linear(dim, self.head_dim * self.num_heads * 2, bias=qkv_bias)
self.q = nn.Linear(dim, self.head_dim * self.num_heads, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(self.head_dim * self.num_heads, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
kv = self.kv(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k, v = kv.unbind(0)
q = self.q(x[:, :1, :]).reshape(B, self.num_heads, 1, self.head_dim)
attn = ((q * self.scale) @ k.transpose(-2, -1))
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
cls_embed = (attn @ v).transpose(1, 2).reshape(B, 1, self.head_dim * self.num_heads)
cls_embed = self.proj(cls_embed)
cls_embed = self.proj_drop(cls_embed)
return cls_embed
class ClassBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
head_dim=None,
mlp_ratio=4.,
qkv_bias=False,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = ClassAttention(
dim,
num_heads=num_heads,
head_dim=head_dim,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop,
)
# NOTE: drop path for stochastic depth
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop,
)
def forward(self, x):
cls_embed = x[:, :1]
cls_embed = cls_embed + self.drop_path(self.attn(self.norm1(x)))
cls_embed = cls_embed + self.drop_path(self.mlp(self.norm2(cls_embed)))
return torch.cat([cls_embed, x[:, 1:]], dim=1)
def get_block(block_type, **kargs):
if block_type == 'ca':
return ClassBlock(**kargs)
def rand_bbox(size, lam, scale=1):
"""
get bounding box as token labeling (https://github.com/zihangJiang/TokenLabeling)
return: bounding box
"""
W = size[1] // scale
H = size[2] // scale
cut_rat = np.sqrt(1. - lam)
cut_w = (W * cut_rat).astype(int)
cut_h = (H * cut_rat).astype(int)
# uniform
cx = np.random.randint(W)
cy = np.random.randint(H)
bbx1 = np.clip(cx - cut_w // 2, 0, W)
bby1 = np.clip(cy - cut_h // 2, 0, H)
bbx2 = np.clip(cx + cut_w // 2, 0, W)
bby2 = np.clip(cy + cut_h // 2, 0, H)
return bbx1, bby1, bbx2, bby2
class PatchEmbed(nn.Module):
""" Image to Patch Embedding.
Different with ViT use 1 conv layer, we use 4 conv layers to do patch embedding
"""
def __init__(
self,
img_size=224,
stem_conv=False,
stem_stride=1,
patch_size=8,
in_chans=3,
hidden_dim=64,
embed_dim=384,
):
super().__init__()
assert patch_size in [4, 8, 16]
if stem_conv:
self.conv = nn.Sequential(
nn.Conv2d(in_chans, hidden_dim, kernel_size=7, stride=stem_stride, padding=3, bias=False), # 112x112
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1, bias=False), # 112x112
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1, bias=False), # 112x112
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
)
else:
self.conv = None
self.proj = nn.Conv2d(
hidden_dim, embed_dim, kernel_size=patch_size // stem_stride, stride=patch_size // stem_stride)
self.num_patches = (img_size // patch_size) * (img_size // patch_size)
def forward(self, x):
if self.conv is not None:
x = self.conv(x)
x = self.proj(x) # B, C, H, W
return x
class Downsample(nn.Module):
""" Image to Patch Embedding, downsampling between stage1 and stage2
"""
def __init__(self, in_embed_dim, out_embed_dim, patch_size=2):
super().__init__()
self.proj = nn.Conv2d(in_embed_dim, out_embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
x = x.permute(0, 3, 1, 2)
x = self.proj(x) # B, C, H, W
x = x.permute(0, 2, 3, 1)
return x
def outlooker_blocks(
block_fn,
index,
dim,
layers,
num_heads=1,
kernel_size=3,
padding=1,
stride=2,
mlp_ratio=3.,
qkv_bias=False,
attn_drop=0,
drop_path_rate=0.,
**kwargs,
):
"""
generate outlooker layer in stage1
return: outlooker layers
"""
blocks = []
for block_idx in range(layers[index]):
block_dpr = drop_path_rate * (block_idx + sum(layers[:index])) / (sum(layers) - 1)
blocks.append(block_fn(
dim,
kernel_size=kernel_size,
padding=padding,
stride=stride,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
drop_path=block_dpr,
))
blocks = nn.Sequential(*blocks)
return blocks
def transformer_blocks(
block_fn,
index,
dim,
layers,
num_heads,
mlp_ratio=3.,
qkv_bias=False,
attn_drop=0,
drop_path_rate=0.,
**kwargs,
):
"""
generate transformer layers in stage2
return: transformer layers
"""
blocks = []
for block_idx in range(layers[index]):
block_dpr = drop_path_rate * (block_idx + sum(layers[:index])) / (sum(layers) - 1)
blocks.append(block_fn(
dim,
num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
drop_path=block_dpr,
))
blocks = nn.Sequential(*blocks)
return blocks
class VOLO(nn.Module):
"""
Vision Outlooker, the main class of our model
"""
def __init__(
self,
layers,
img_size=224,
in_chans=3,
num_classes=1000,
global_pool='token',
patch_size=8,
stem_hidden_dim=64,
embed_dims=None,
num_heads=None,
downsamples=(True, False, False, False),
outlook_attention=(True, False, False, False),
mlp_ratio=3.0,
qkv_bias=False,
drop_rate=0.,
pos_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=nn.LayerNorm,
post_layers=('ca', 'ca'),
use_aux_head=True,
use_mix_token=False,
pooling_scale=2,
):
super().__init__()
num_layers = len(layers)
mlp_ratio = to_ntuple(num_layers)(mlp_ratio)
img_size = to_2tuple(img_size)
self.num_classes = num_classes
self.global_pool = global_pool
self.mix_token = use_mix_token
self.pooling_scale = pooling_scale
self.num_features = embed_dims[-1]
if use_mix_token: # enable token mixing, see token labeling for details.
self.beta = 1.0
assert global_pool == 'token', "return all tokens if mix_token is enabled"
self.grad_checkpointing = False
self.patch_embed = PatchEmbed(
stem_conv=True,
stem_stride=2,
patch_size=patch_size,
in_chans=in_chans,
hidden_dim=stem_hidden_dim,
embed_dim=embed_dims[0],
)
# inital positional encoding, we add positional encoding after outlooker blocks
patch_grid = (img_size[0] // patch_size // pooling_scale, img_size[1] // patch_size // pooling_scale)
self.pos_embed = nn.Parameter(torch.zeros(1, patch_grid[0], patch_grid[1], embed_dims[-1]))
self.pos_drop = nn.Dropout(p=pos_drop_rate)
# set the main block in network
network = []
for i in range(len(layers)):
if outlook_attention[i]:
# stage 1
stage = outlooker_blocks(
Outlooker,
i,
embed_dims[i],
layers,
num_heads[i],
mlp_ratio=mlp_ratio[i],
qkv_bias=qkv_bias,
attn_drop=attn_drop_rate,
norm_layer=norm_layer,
)
network.append(stage)
else:
# stage 2
stage = transformer_blocks(
Transformer,
i,
embed_dims[i],
layers,
num_heads[i],
mlp_ratio=mlp_ratio[i],
qkv_bias=qkv_bias,
drop_path_rate=drop_path_rate,
attn_drop=attn_drop_rate,
norm_layer=norm_layer,
)
network.append(stage)
if downsamples[i]:
# downsampling between two stages
network.append(Downsample(embed_dims[i], embed_dims[i + 1], 2))
self.network = nn.ModuleList(network)
# set post block, for example, class attention layers
self.post_network = None
if post_layers is not None:
self.post_network = nn.ModuleList([
get_block(
post_layers[i],
dim=embed_dims[-1],
num_heads=num_heads[-1],
mlp_ratio=mlp_ratio[-1],
qkv_bias=qkv_bias,
attn_drop=attn_drop_rate,
drop_path=0.,
norm_layer=norm_layer)
for i in range(len(post_layers))
])
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims[-1]))
trunc_normal_(self.cls_token, std=.02)
# set output type
if use_aux_head:
self.aux_head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
else:
self.aux_head = None
self.norm = norm_layer(self.num_features)
# Classifier head
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
trunc_normal_(self.pos_embed, std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
blocks=[
(r'^network\.(\d+)\.(\d+)', None),
(r'^network\.(\d+)', (0,)),
],
blocks2=[
(r'^cls_token', (0,)),
(r'^post_network\.(\d+)', None),
(r'^norm', (99999,))
],
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
if self.aux_head is not None:
self.aux_head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_tokens(self, x):
for idx, block in enumerate(self.network):
if idx == 2:
# add positional encoding after outlooker blocks
x = x + self.pos_embed
x = self.pos_drop(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(block, x)
else:
x = block(x)
B, H, W, C = x.shape
x = x.reshape(B, -1, C)
return x
def forward_cls(self, x):
B, N, C = x.shape
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat([cls_tokens, x], dim=1)
for block in self.post_network:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(block, x)
else:
x = block(x)
return x
def forward_train(self, x):
""" A separate forward fn for training with mix_token (if a train script supports).
Combining multiple modes in as single forward with different return types is torchscript hell.
"""
x = self.patch_embed(x)
x = x.permute(0, 2, 3, 1) # B,C,H,W-> B,H,W,C
# mix token, see token labeling for details.
if self.mix_token and self.training:
lam = np.random.beta(self.beta, self.beta)
patch_h, patch_w = x.shape[1] // self.pooling_scale, x.shape[2] // self.pooling_scale
bbx1, bby1, bbx2, bby2 = rand_bbox(x.size(), lam, scale=self.pooling_scale)
temp_x = x.clone()
sbbx1, sbby1 = self.pooling_scale * bbx1, self.pooling_scale * bby1
sbbx2, sbby2 = self.pooling_scale * bbx2, self.pooling_scale * bby2
temp_x[:, sbbx1:sbbx2, sbby1:sbby2, :] = x.flip(0)[:, sbbx1:sbbx2, sbby1:sbby2, :]
x = temp_x
else:
bbx1, bby1, bbx2, bby2 = 0, 0, 0, 0
# step2: tokens learning in the two stages
x = self.forward_tokens(x)
# step3: post network, apply class attention or not
if self.post_network is not None:
x = self.forward_cls(x)
x = self.norm(x)
if self.global_pool == 'avg':
x_cls = x.mean(dim=1)
elif self.global_pool == 'token':
x_cls = x[:, 0]
else:
x_cls = x
if self.aux_head is None:
return x_cls
x_aux = self.aux_head(x[:, 1:]) # generate classes in all feature tokens, see token labeling
if not self.training:
return x_cls + 0.5 * x_aux.max(1)[0]
if self.mix_token and self.training: # reverse "mix token", see token labeling for details.
x_aux = x_aux.reshape(x_aux.shape[0], patch_h, patch_w, x_aux.shape[-1])
temp_x = x_aux.clone()
temp_x[:, bbx1:bbx2, bby1:bby2, :] = x_aux.flip(0)[:, bbx1:bbx2, bby1:bby2, :]
x_aux = temp_x
x_aux = x_aux.reshape(x_aux.shape[0], patch_h * patch_w, x_aux.shape[-1])
# return these: 1. class token, 2. classes from all feature tokens, 3. bounding box
return x_cls, x_aux, (bbx1, bby1, bbx2, bby2)
def forward_features(self, x):
x = self.patch_embed(x).permute(0, 2, 3, 1) # B,C,H,W-> B,H,W,C
# step2: tokens learning in the two stages
x = self.forward_tokens(x)
# step3: post network, apply class attention or not
if self.post_network is not None:
x = self.forward_cls(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool == 'avg':
out = x.mean(dim=1)
elif self.global_pool == 'token':
out = x[:, 0]
else:
out = x
x = self.head_drop(x)
if pre_logits:
return out
out = self.head(out)
if self.aux_head is not None:
# generate classes in all feature tokens, see token labeling
aux = self.aux_head(x[:, 1:])
out = out + 0.5 * aux.max(1)[0]
return out
def forward(self, x):
""" simplified forward (without mix token training) """
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_volo(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
return build_model_with_cfg(
VOLO,
variant,
pretrained,
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .96, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.conv.0', 'classifier': ('head', 'aux_head'),
**kwargs
}
default_cfgs = generate_default_cfgs({
'volo_d1_224.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d1_224_84.2.pth.tar',
crop_pct=0.96),
'volo_d1_384.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d1_384_85.2.pth.tar',
crop_pct=1.0, input_size=(3, 384, 384)),
'volo_d2_224.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d2_224_85.2.pth.tar',
crop_pct=0.96),
'volo_d2_384.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d2_384_86.0.pth.tar',
crop_pct=1.0, input_size=(3, 384, 384)),
'volo_d3_224.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d3_224_85.4.pth.tar',
crop_pct=0.96),
'volo_d3_448.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d3_448_86.3.pth.tar',
crop_pct=1.0, input_size=(3, 448, 448)),
'volo_d4_224.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d4_224_85.7.pth.tar',
crop_pct=0.96),
'volo_d4_448.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d4_448_86.79.pth.tar',
crop_pct=1.15, input_size=(3, 448, 448)),
'volo_d5_224.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d5_224_86.10.pth.tar',
crop_pct=0.96),
'volo_d5_448.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d5_448_87.0.pth.tar',
crop_pct=1.15, input_size=(3, 448, 448)),
'volo_d5_512.sail_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/sail-sg/volo/releases/download/volo_1/d5_512_87.07.pth.tar',
crop_pct=1.15, input_size=(3, 512, 512)),
})
@register_model
def volo_d1_224(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D1 model, Params: 27M """
model_args = dict(layers=(4, 4, 8, 2), embed_dims=(192, 384, 384, 384), num_heads=(6, 12, 12, 12), **kwargs)
model = _create_volo('volo_d1_224', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d1_384(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D1 model, Params: 27M """
model_args = dict(layers=(4, 4, 8, 2), embed_dims=(192, 384, 384, 384), num_heads=(6, 12, 12, 12), **kwargs)
model = _create_volo('volo_d1_384', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d2_224(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D2 model, Params: 59M """
model_args = dict(layers=(6, 4, 10, 4), embed_dims=(256, 512, 512, 512), num_heads=(8, 16, 16, 16), **kwargs)
model = _create_volo('volo_d2_224', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d2_384(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D2 model, Params: 59M """
model_args = dict(layers=(6, 4, 10, 4), embed_dims=(256, 512, 512, 512), num_heads=(8, 16, 16, 16), **kwargs)
model = _create_volo('volo_d2_384', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d3_224(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D3 model, Params: 86M """
model_args = dict(layers=(8, 8, 16, 4), embed_dims=(256, 512, 512, 512), num_heads=(8, 16, 16, 16), **kwargs)
model = _create_volo('volo_d3_224', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d3_448(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D3 model, Params: 86M """
model_args = dict(layers=(8, 8, 16, 4), embed_dims=(256, 512, 512, 512), num_heads=(8, 16, 16, 16), **kwargs)
model = _create_volo('volo_d3_448', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d4_224(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D4 model, Params: 193M """
model_args = dict(layers=(8, 8, 16, 4), embed_dims=(384, 768, 768, 768), num_heads=(12, 16, 16, 16), **kwargs)
model = _create_volo('volo_d4_224', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d4_448(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D4 model, Params: 193M """
model_args = dict(layers=(8, 8, 16, 4), embed_dims=(384, 768, 768, 768), num_heads=(12, 16, 16, 16), **kwargs)
model = _create_volo('volo_d4_448', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d5_224(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D5 model, Params: 296M
stem_hidden_dim=128, the dim in patch embedding is 128 for VOLO-D5
"""
model_args = dict(
layers=(12, 12, 20, 4), embed_dims=(384, 768, 768, 768), num_heads=(12, 16, 16, 16),
mlp_ratio=4, stem_hidden_dim=128, **kwargs)
model = _create_volo('volo_d5_224', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d5_448(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D5 model, Params: 296M
stem_hidden_dim=128, the dim in patch embedding is 128 for VOLO-D5
"""
model_args = dict(
layers=(12, 12, 20, 4), embed_dims=(384, 768, 768, 768), num_heads=(12, 16, 16, 16),
mlp_ratio=4, stem_hidden_dim=128, **kwargs)
model = _create_volo('volo_d5_448', pretrained=pretrained, **model_args)
return model
@register_model
def volo_d5_512(pretrained=False, **kwargs) -> VOLO:
""" VOLO-D5 model, Params: 296M
stem_hidden_dim=128, the dim in patch embedding is 128 for VOLO-D5
"""
model_args = dict(
layers=(12, 12, 20, 4), embed_dims=(384, 768, 768, 768), num_heads=(12, 16, 16, 16),
mlp_ratio=4, stem_hidden_dim=128, **kwargs)
model = _create_volo('volo_d5_512', pretrained=pretrained, **model_args)
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/pit.py
|
""" Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyright below.
Modifications for timm by / Copyright 2020 Ross Wightman
"""
# PiT
# Copyright 2021-present NAVER Corp.
# Apache License v2.0
import math
import re
from functools import partial
from typing import Sequence, Tuple
import torch
from torch import nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, to_2tuple, LayerNorm
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
from .vision_transformer import Block
__all__ = ['PoolingVisionTransformer'] # model_registry will add each entrypoint fn to this
class SequentialTuple(nn.Sequential):
""" This module exists to work around torchscript typing issues list -> list"""
def __init__(self, *args):
super(SequentialTuple, self).__init__(*args)
def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
for module in self:
x = module(x)
return x
class Transformer(nn.Module):
def __init__(
self,
base_dim,
depth,
heads,
mlp_ratio,
pool=None,
proj_drop=.0,
attn_drop=.0,
drop_path_prob=None,
norm_layer=None,
):
super(Transformer, self).__init__()
embed_dim = base_dim * heads
self.pool = pool
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
self.blocks = nn.Sequential(*[
Block(
dim=embed_dim,
num_heads=heads,
mlp_ratio=mlp_ratio,
qkv_bias=True,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path_prob[i],
norm_layer=partial(nn.LayerNorm, eps=1e-6)
)
for i in range(depth)])
def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
x, cls_tokens = x
token_length = cls_tokens.shape[1]
if self.pool is not None:
x, cls_tokens = self.pool(x, cls_tokens)
B, C, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = torch.cat((cls_tokens, x), dim=1)
x = self.norm(x)
x = self.blocks(x)
cls_tokens = x[:, :token_length]
x = x[:, token_length:]
x = x.transpose(1, 2).reshape(B, C, H, W)
return x, cls_tokens
class Pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super(Pooling, self).__init__()
self.conv = nn.Conv2d(
in_feature,
out_feature,
kernel_size=stride + 1,
padding=stride // 2,
stride=stride,
padding_mode=padding_mode,
groups=in_feature,
)
self.fc = nn.Linear(in_feature, out_feature)
def forward(self, x, cls_token) -> Tuple[torch.Tensor, torch.Tensor]:
x = self.conv(x)
cls_token = self.fc(cls_token)
return x, cls_token
class ConvEmbedding(nn.Module):
def __init__(
self,
in_channels,
out_channels,
img_size: int = 224,
patch_size: int = 16,
stride: int = 8,
padding: int = 0,
):
super(ConvEmbedding, self).__init__()
padding = padding
self.img_size = to_2tuple(img_size)
self.patch_size = to_2tuple(patch_size)
self.height = math.floor((self.img_size[0] + 2 * padding - self.patch_size[0]) / stride + 1)
self.width = math.floor((self.img_size[1] + 2 * padding - self.patch_size[1]) / stride + 1)
self.grid_size = (self.height, self.width)
self.conv = nn.Conv2d(
in_channels, out_channels, kernel_size=patch_size,
stride=stride, padding=padding, bias=True)
def forward(self, x):
x = self.conv(x)
return x
class PoolingVisionTransformer(nn.Module):
""" Pooling-based Vision Transformer
A PyTorch implement of 'Rethinking Spatial Dimensions of Vision Transformers'
- https://arxiv.org/abs/2103.16302
"""
def __init__(
self,
img_size: int = 224,
patch_size: int = 16,
stride: int = 8,
stem_type: str = 'overlap',
base_dims: Sequence[int] = (48, 48, 48),
depth: Sequence[int] = (2, 6, 4),
heads: Sequence[int] = (2, 4, 8),
mlp_ratio: float = 4,
num_classes=1000,
in_chans=3,
global_pool='token',
distilled=False,
drop_rate=0.,
pos_drop_drate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
):
super(PoolingVisionTransformer, self).__init__()
assert global_pool in ('token',)
self.base_dims = base_dims
self.heads = heads
embed_dim = base_dims[0] * heads[0]
self.num_classes = num_classes
self.global_pool = global_pool
self.num_tokens = 2 if distilled else 1
self.feature_info = []
self.patch_embed = ConvEmbedding(in_chans, embed_dim, img_size, patch_size, stride)
self.pos_embed = nn.Parameter(torch.randn(1, embed_dim, self.patch_embed.height, self.patch_embed.width))
self.cls_token = nn.Parameter(torch.randn(1, self.num_tokens, embed_dim))
self.pos_drop = nn.Dropout(p=pos_drop_drate)
transformers = []
# stochastic depth decay rule
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depth)).split(depth)]
prev_dim = embed_dim
for i in range(len(depth)):
pool = None
embed_dim = base_dims[i] * heads[i]
if i > 0:
pool = Pooling(
prev_dim,
embed_dim,
stride=2,
)
transformers += [Transformer(
base_dims[i],
depth[i],
heads[i],
mlp_ratio,
pool=pool,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path_prob=dpr[i],
)]
prev_dim = embed_dim
self.feature_info += [dict(num_chs=prev_dim, reduction=(stride - 1) * 2**i, module=f'transformers.{i}')]
self.transformers = SequentialTuple(*transformers)
self.norm = nn.LayerNorm(base_dims[-1] * heads[-1], eps=1e-6)
self.num_features = self.embed_dim = embed_dim
# Classifier head
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.head_dist = None
if distilled:
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
self.distilled_training = False # must set this True to train w/ distillation token
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
@torch.jit.ignore
def set_distilled_training(self, enable=True):
self.distilled_training = enable
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
def get_classifier(self):
if self.head_dist is not None:
return self.head, self.head_dist
else:
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if self.head_dist is not None:
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.patch_embed(x)
x = self.pos_drop(x + self.pos_embed)
cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
x, cls_tokens = self.transformers((x, cls_tokens))
cls_tokens = self.norm(cls_tokens)
return cls_tokens
def forward_head(self, x, pre_logits: bool = False) -> torch.Tensor:
if self.head_dist is not None:
assert self.global_pool == 'token'
x, x_dist = x[:, 0], x[:, 1]
x = self.head_drop(x)
x_dist = self.head_drop(x)
if not pre_logits:
x = self.head(x)
x_dist = self.head_dist(x_dist)
if self.distilled_training and self.training and not torch.jit.is_scripting():
# only return separate classification predictions when training in distilled mode
return x, x_dist
else:
# during standard train / finetune, inference average the classifier predictions
return (x + x_dist) / 2
else:
if self.global_pool == 'token':
x = x[:, 0]
x = self.head_drop(x)
if not pre_logits:
x = self.head(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
""" preprocess checkpoints """
out_dict = {}
p_blocks = re.compile(r'pools\.(\d)\.')
for k, v in state_dict.items():
# FIXME need to update resize for PiT impl
# if k == 'pos_embed' and v.shape != model.pos_embed.shape:
# # To resize pos embedding when using model at different size from pretrained weights
# v = resize_pos_embed(v, model.pos_embed)
k = p_blocks.sub(lambda exp: f'transformers.{int(exp.group(1)) + 1}.pool.', k)
out_dict[k] = v
return out_dict
def _create_pit(variant, pretrained=False, **kwargs):
default_out_indices = tuple(range(3))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
PoolingVisionTransformer,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(feature_cls='hook', no_rewrite=True, out_indices=out_indices),
**kwargs,
)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.conv', 'classifier': 'head',
**kwargs
}
default_cfgs = generate_default_cfgs({
# deit models (FB weights)
'pit_ti_224.in1k': _cfg(hf_hub_id='timm/'),
'pit_xs_224.in1k': _cfg(hf_hub_id='timm/'),
'pit_s_224.in1k': _cfg(hf_hub_id='timm/'),
'pit_b_224.in1k': _cfg(hf_hub_id='timm/'),
'pit_ti_distilled_224.in1k': _cfg(
hf_hub_id='timm/',
classifier=('head', 'head_dist')),
'pit_xs_distilled_224.in1k': _cfg(
hf_hub_id='timm/',
classifier=('head', 'head_dist')),
'pit_s_distilled_224.in1k': _cfg(
hf_hub_id='timm/',
classifier=('head', 'head_dist')),
'pit_b_distilled_224.in1k': _cfg(
hf_hub_id='timm/',
classifier=('head', 'head_dist')),
})
@register_model
def pit_b_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
model_args = dict(
patch_size=14,
stride=7,
base_dims=[64, 64, 64],
depth=[3, 6, 4],
heads=[4, 8, 16],
mlp_ratio=4,
)
return _create_pit('pit_b_224', pretrained, **dict(model_args, **kwargs))
@register_model
def pit_s_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
model_args = dict(
patch_size=16,
stride=8,
base_dims=[48, 48, 48],
depth=[2, 6, 4],
heads=[3, 6, 12],
mlp_ratio=4,
)
return _create_pit('pit_s_224', pretrained, **dict(model_args, **kwargs))
@register_model
def pit_xs_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
model_args = dict(
patch_size=16,
stride=8,
base_dims=[48, 48, 48],
depth=[2, 6, 4],
heads=[2, 4, 8],
mlp_ratio=4,
)
return _create_pit('pit_xs_224', pretrained, **dict(model_args, **kwargs))
@register_model
def pit_ti_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
model_args = dict(
patch_size=16,
stride=8,
base_dims=[32, 32, 32],
depth=[2, 6, 4],
heads=[2, 4, 8],
mlp_ratio=4,
)
return _create_pit('pit_ti_224', pretrained, **dict(model_args, **kwargs))
@register_model
def pit_b_distilled_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
model_args = dict(
patch_size=14,
stride=7,
base_dims=[64, 64, 64],
depth=[3, 6, 4],
heads=[4, 8, 16],
mlp_ratio=4,
distilled=True,
)
return _create_pit('pit_b_distilled_224', pretrained, **dict(model_args, **kwargs))
@register_model
def pit_s_distilled_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
model_args = dict(
patch_size=16,
stride=8,
base_dims=[48, 48, 48],
depth=[2, 6, 4],
heads=[3, 6, 12],
mlp_ratio=4,
distilled=True,
)
return _create_pit('pit_s_distilled_224', pretrained, **dict(model_args, **kwargs))
@register_model
def pit_xs_distilled_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
model_args = dict(
patch_size=16,
stride=8,
base_dims=[48, 48, 48],
depth=[2, 6, 4],
heads=[2, 4, 8],
mlp_ratio=4,
distilled=True,
)
return _create_pit('pit_xs_distilled_224', pretrained, **dict(model_args, **kwargs))
@register_model
def pit_ti_distilled_224(pretrained=False, **kwargs) -> PoolingVisionTransformer:
model_args = dict(
patch_size=16,
stride=8,
base_dims=[32, 32, 32],
depth=[2, 6, 4],
heads=[2, 4, 8],
mlp_ratio=4,
distilled=True,
)
return _create_pit('pit_ti_distilled_224', pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/_builder.py
|
import dataclasses
import logging
import os
from copy import deepcopy
from typing import Optional, Dict, Callable, Any, Tuple
from torch import nn as nn
from torch.hub import load_state_dict_from_url
from timm.models._features import FeatureListNet, FeatureHookNet
from timm.models._features_fx import FeatureGraphNet
from timm.models._helpers import load_state_dict
from timm.models._hub import has_hf_hub, download_cached_file, check_cached_file, load_state_dict_from_hf
from timm.models._manipulate import adapt_input_conv
from timm.models._pretrained import PretrainedCfg
from timm.models._prune import adapt_model_from_file
from timm.models._registry import get_pretrained_cfg
_logger = logging.getLogger(__name__)
# Global variables for rarely used pretrained checkpoint download progress and hash check.
# Use set_pretrained_download_progress / set_pretrained_check_hash functions to toggle.
_DOWNLOAD_PROGRESS = False
_CHECK_HASH = False
_USE_OLD_CACHE = int(os.environ.get('TIMM_USE_OLD_CACHE', 0)) > 0
__all__ = ['set_pretrained_download_progress', 'set_pretrained_check_hash', 'load_custom_pretrained', 'load_pretrained',
'pretrained_cfg_for_features', 'resolve_pretrained_cfg', 'build_model_with_cfg']
def _resolve_pretrained_source(pretrained_cfg):
cfg_source = pretrained_cfg.get('source', '')
pretrained_url = pretrained_cfg.get('url', None)
pretrained_file = pretrained_cfg.get('file', None)
pretrained_sd = pretrained_cfg.get('state_dict', None)
hf_hub_id = pretrained_cfg.get('hf_hub_id', None)
# resolve where to load pretrained weights from
load_from = ''
pretrained_loc = ''
if cfg_source == 'hf-hub' and has_hf_hub(necessary=True):
# hf-hub specified as source via model identifier
load_from = 'hf-hub'
assert hf_hub_id
pretrained_loc = hf_hub_id
else:
# default source == timm or unspecified
if pretrained_sd:
# direct state_dict pass through is the highest priority
load_from = 'state_dict'
pretrained_loc = pretrained_sd
assert isinstance(pretrained_loc, dict)
elif pretrained_file:
# file load override is the second-highest priority if set
load_from = 'file'
pretrained_loc = pretrained_file
else:
old_cache_valid = False
if _USE_OLD_CACHE:
# prioritized old cached weights if exists and env var enabled
old_cache_valid = check_cached_file(pretrained_url) if pretrained_url else False
if not old_cache_valid and hf_hub_id and has_hf_hub(necessary=True):
# hf-hub available as alternate weight source in default_cfg
load_from = 'hf-hub'
pretrained_loc = hf_hub_id
elif pretrained_url:
load_from = 'url'
pretrained_loc = pretrained_url
if load_from == 'hf-hub' and pretrained_cfg.get('hf_hub_filename', None):
# if a filename override is set, return tuple for location w/ (hub_id, filename)
pretrained_loc = pretrained_loc, pretrained_cfg['hf_hub_filename']
return load_from, pretrained_loc
def set_pretrained_download_progress(enable=True):
""" Set download progress for pretrained weights on/off (globally). """
global _DOWNLOAD_PROGRESS
_DOWNLOAD_PROGRESS = enable
def set_pretrained_check_hash(enable=True):
""" Set hash checking for pretrained weights on/off (globally). """
global _CHECK_HASH
_CHECK_HASH = enable
def load_custom_pretrained(
model: nn.Module,
pretrained_cfg: Optional[Dict] = None,
load_fn: Optional[Callable] = None,
):
r"""Loads a custom (read non .pth) weight file
Downloads checkpoint file into cache-dir like torch.hub based loaders, but calls
a passed in custom load fun, or the `load_pretrained` model member fn.
If the object is already present in `model_dir`, it's deserialized and returned.
The default value of `model_dir` is ``<hub_dir>/checkpoints`` where
`hub_dir` is the directory returned by :func:`~torch.hub.get_dir`.
Args:
model: The instantiated model to load weights into
pretrained_cfg (dict): Default pretrained model cfg
load_fn: An external standalone fn that loads weights into provided model, otherwise a fn named
'laod_pretrained' on the model will be called if it exists
"""
pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None)
if not pretrained_cfg:
_logger.warning("Invalid pretrained config, cannot load weights.")
return
load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)
if not load_from:
_logger.warning("No pretrained weights exist for this model. Using random initialization.")
return
if load_from == 'hf-hub':
_logger.warning("Hugging Face hub not currently supported for custom load pretrained models.")
elif load_from == 'url':
pretrained_loc = download_cached_file(
pretrained_loc,
check_hash=_CHECK_HASH,
progress=_DOWNLOAD_PROGRESS,
)
if load_fn is not None:
load_fn(model, pretrained_loc)
elif hasattr(model, 'load_pretrained'):
model.load_pretrained(pretrained_loc)
else:
_logger.warning("Valid function to load pretrained weights is not available, using random initialization.")
def load_pretrained(
model: nn.Module,
pretrained_cfg: Optional[Dict] = None,
num_classes: int = 1000,
in_chans: int = 3,
filter_fn: Optional[Callable] = None,
strict: bool = True,
):
""" Load pretrained checkpoint
Args:
model (nn.Module) : PyTorch model module
pretrained_cfg (Optional[Dict]): configuration for pretrained weights / target dataset
num_classes (int): num_classes for target model
in_chans (int): in_chans for target model
filter_fn (Optional[Callable]): state_dict filter fn for load (takes state_dict, model as args)
strict (bool): strict load of checkpoint
"""
pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None)
if not pretrained_cfg:
raise RuntimeError("Invalid pretrained config, cannot load weights. Use `pretrained=False` for random init.")
load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)
if load_from == 'state_dict':
_logger.info(f'Loading pretrained weights from state dict')
state_dict = pretrained_loc # pretrained_loc is the actual state dict for this override
elif load_from == 'file':
_logger.info(f'Loading pretrained weights from file ({pretrained_loc})')
if pretrained_cfg.get('custom_load', False):
model.load_pretrained(pretrained_loc)
return
else:
state_dict = load_state_dict(pretrained_loc)
elif load_from == 'url':
_logger.info(f'Loading pretrained weights from url ({pretrained_loc})')
if pretrained_cfg.get('custom_load', False):
pretrained_loc = download_cached_file(
pretrained_loc,
progress=_DOWNLOAD_PROGRESS,
check_hash=_CHECK_HASH,
)
model.load_pretrained(pretrained_loc)
return
else:
state_dict = load_state_dict_from_url(
pretrained_loc,
map_location='cpu',
progress=_DOWNLOAD_PROGRESS,
check_hash=_CHECK_HASH,
)
elif load_from == 'hf-hub':
_logger.info(f'Loading pretrained weights from Hugging Face hub ({pretrained_loc})')
if isinstance(pretrained_loc, (list, tuple)):
state_dict = load_state_dict_from_hf(*pretrained_loc)
else:
state_dict = load_state_dict_from_hf(pretrained_loc)
else:
model_name = pretrained_cfg.get('architecture', 'this model')
raise RuntimeError(f"No pretrained weights exist for {model_name}. Use `pretrained=False` for random init.")
if filter_fn is not None:
try:
state_dict = filter_fn(state_dict, model)
except TypeError as e:
# for backwards compat with filter fn that take one arg
state_dict = filter_fn(state_dict)
input_convs = pretrained_cfg.get('first_conv', None)
if input_convs is not None and in_chans != 3:
if isinstance(input_convs, str):
input_convs = (input_convs,)
for input_conv_name in input_convs:
weight_name = input_conv_name + '.weight'
try:
state_dict[weight_name] = adapt_input_conv(in_chans, state_dict[weight_name])
_logger.info(
f'Converted input conv {input_conv_name} pretrained weights from 3 to {in_chans} channel(s)')
except NotImplementedError as e:
del state_dict[weight_name]
strict = False
_logger.warning(
f'Unable to convert pretrained {input_conv_name} weights, using random init for this layer.')
classifiers = pretrained_cfg.get('classifier', None)
label_offset = pretrained_cfg.get('label_offset', 0)
if classifiers is not None:
if isinstance(classifiers, str):
classifiers = (classifiers,)
if num_classes != pretrained_cfg['num_classes']:
for classifier_name in classifiers:
# completely discard fully connected if model num_classes doesn't match pretrained weights
state_dict.pop(classifier_name + '.weight', None)
state_dict.pop(classifier_name + '.bias', None)
strict = False
elif label_offset > 0:
for classifier_name in classifiers:
# special case for pretrained weights with an extra background class in pretrained weights
classifier_weight = state_dict[classifier_name + '.weight']
state_dict[classifier_name + '.weight'] = classifier_weight[label_offset:]
classifier_bias = state_dict[classifier_name + '.bias']
state_dict[classifier_name + '.bias'] = classifier_bias[label_offset:]
model.load_state_dict(state_dict, strict=strict)
def pretrained_cfg_for_features(pretrained_cfg):
pretrained_cfg = deepcopy(pretrained_cfg)
# remove default pretrained cfg fields that don't have much relevance for feature backbone
to_remove = ('num_classes', 'classifier', 'global_pool') # add default final pool size?
for tr in to_remove:
pretrained_cfg.pop(tr, None)
return pretrained_cfg
def _filter_kwargs(kwargs, names):
if not kwargs or not names:
return
for n in names:
kwargs.pop(n, None)
def _update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter):
""" Update the default_cfg and kwargs before passing to model
Args:
pretrained_cfg: input pretrained cfg (updated in-place)
kwargs: keyword args passed to model build fn (updated in-place)
kwargs_filter: keyword arg keys that must be removed before model __init__
"""
# Set model __init__ args that can be determined by default_cfg (if not already passed as kwargs)
default_kwarg_names = ('num_classes', 'global_pool', 'in_chans')
if pretrained_cfg.get('fixed_input_size', False):
# if fixed_input_size exists and is True, model takes an img_size arg that fixes its input size
default_kwarg_names += ('img_size',)
for n in default_kwarg_names:
# for legacy reasons, model __init__args uses img_size + in_chans as separate args while
# pretrained_cfg has one input_size=(C, H ,W) entry
if n == 'img_size':
input_size = pretrained_cfg.get('input_size', None)
if input_size is not None:
assert len(input_size) == 3
kwargs.setdefault(n, input_size[-2:])
elif n == 'in_chans':
input_size = pretrained_cfg.get('input_size', None)
if input_size is not None:
assert len(input_size) == 3
kwargs.setdefault(n, input_size[0])
else:
default_val = pretrained_cfg.get(n, None)
if default_val is not None:
kwargs.setdefault(n, pretrained_cfg[n])
# Filter keyword args for task specific model variants (some 'features only' models, etc.)
_filter_kwargs(kwargs, names=kwargs_filter)
def resolve_pretrained_cfg(
variant: str,
pretrained_cfg=None,
pretrained_cfg_overlay=None,
) -> PretrainedCfg:
model_with_tag = variant
pretrained_tag = None
if pretrained_cfg:
if isinstance(pretrained_cfg, dict):
# pretrained_cfg dict passed as arg, validate by converting to PretrainedCfg
pretrained_cfg = PretrainedCfg(**pretrained_cfg)
elif isinstance(pretrained_cfg, str):
pretrained_tag = pretrained_cfg
pretrained_cfg = None
# fallback to looking up pretrained cfg in model registry by variant identifier
if not pretrained_cfg:
if pretrained_tag:
model_with_tag = '.'.join([variant, pretrained_tag])
pretrained_cfg = get_pretrained_cfg(model_with_tag)
if not pretrained_cfg:
_logger.warning(
f"No pretrained configuration specified for {model_with_tag} model. Using a default."
f" Please add a config to the model pretrained_cfg registry or pass explicitly.")
pretrained_cfg = PretrainedCfg() # instance with defaults
pretrained_cfg_overlay = pretrained_cfg_overlay or {}
if not pretrained_cfg.architecture:
pretrained_cfg_overlay.setdefault('architecture', variant)
pretrained_cfg = dataclasses.replace(pretrained_cfg, **pretrained_cfg_overlay)
return pretrained_cfg
def build_model_with_cfg(
model_cls: Callable,
variant: str,
pretrained: bool,
pretrained_cfg: Optional[Dict] = None,
pretrained_cfg_overlay: Optional[Dict] = None,
model_cfg: Optional[Any] = None,
feature_cfg: Optional[Dict] = None,
pretrained_strict: bool = True,
pretrained_filter_fn: Optional[Callable] = None,
kwargs_filter: Optional[Tuple[str]] = None,
**kwargs,
):
""" Build model with specified default_cfg and optional model_cfg
This helper fn aids in the construction of a model including:
* handling default_cfg and associated pretrained weight loading
* passing through optional model_cfg for models with config based arch spec
* features_only model adaptation
* pruning config / model adaptation
Args:
model_cls (nn.Module): model class
variant (str): model variant name
pretrained (bool): load pretrained weights
pretrained_cfg (dict): model's pretrained weight/task config
model_cfg (Optional[Dict]): model's architecture config
feature_cfg (Optional[Dict]: feature extraction adapter config
pretrained_strict (bool): load pretrained weights strictly
pretrained_filter_fn (Optional[Callable]): filter callable for pretrained weights
kwargs_filter (Optional[Tuple]): kwargs to filter before passing to model
**kwargs: model args passed through to model __init__
"""
pruned = kwargs.pop('pruned', False)
features = False
feature_cfg = feature_cfg or {}
# resolve and update model pretrained config and model kwargs
pretrained_cfg = resolve_pretrained_cfg(
variant,
pretrained_cfg=pretrained_cfg,
pretrained_cfg_overlay=pretrained_cfg_overlay
)
# FIXME converting back to dict, PretrainedCfg use should be propagated further, but not into model
pretrained_cfg = pretrained_cfg.to_dict()
_update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter)
# Setup for feature extraction wrapper done at end of this fn
if kwargs.pop('features_only', False):
features = True
feature_cfg.setdefault('out_indices', (0, 1, 2, 3, 4))
if 'out_indices' in kwargs:
feature_cfg['out_indices'] = kwargs.pop('out_indices')
# Instantiate the model
if model_cfg is None:
model = model_cls(**kwargs)
else:
model = model_cls(cfg=model_cfg, **kwargs)
model.pretrained_cfg = pretrained_cfg
model.default_cfg = model.pretrained_cfg # alias for backwards compat
if pruned:
model = adapt_model_from_file(model, variant)
# For classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats
num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
if pretrained:
load_pretrained(
model,
pretrained_cfg=pretrained_cfg,
num_classes=num_classes_pretrained,
in_chans=kwargs.get('in_chans', 3),
filter_fn=pretrained_filter_fn,
strict=pretrained_strict,
)
# Wrap the model in a feature extraction module if enabled
if features:
feature_cls = FeatureListNet
output_fmt = getattr(model, 'output_fmt', None)
if output_fmt is not None:
feature_cfg.setdefault('output_fmt', output_fmt)
if 'feature_cls' in feature_cfg:
feature_cls = feature_cfg.pop('feature_cls')
if isinstance(feature_cls, str):
feature_cls = feature_cls.lower()
if 'hook' in feature_cls:
feature_cls = FeatureHookNet
elif feature_cls == 'fx':
feature_cls = FeatureGraphNet
else:
assert False, f'Unknown feature class {feature_cls}'
model = feature_cls(model, **feature_cfg)
model.pretrained_cfg = pretrained_cfg_for_features(pretrained_cfg) # add back pretrained cfg
model.default_cfg = model.pretrained_cfg # alias for rename backwards compat (default_cfg -> pretrained_cfg)
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/sknet.py
|
""" Selective Kernel Networks (ResNet base)
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268)
and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building something closer
to the original paper with some modifications of my own to better balance param count vs accuracy.
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from torch import nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import SelectiveKernel, ConvNormAct, create_attn
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
from .resnet import ResNet
class SelectiveKernelBasic(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1,
base_width=64,
sk_kwargs=None,
reduce_first=1,
dilation=1,
first_dilation=None,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
attn_layer=None,
aa_layer=None,
drop_block=None,
drop_path=None,
):
super(SelectiveKernelBasic, self).__init__()
sk_kwargs = sk_kwargs or {}
conv_kwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
assert base_width == 64, 'BasicBlock doest not support changing base width'
first_planes = planes // reduce_first
outplanes = planes * self.expansion
first_dilation = first_dilation or dilation
self.conv1 = SelectiveKernel(
inplanes, first_planes, stride=stride, dilation=first_dilation,
aa_layer=aa_layer, drop_layer=drop_block, **conv_kwargs, **sk_kwargs)
self.conv2 = ConvNormAct(
first_planes, outplanes, kernel_size=3, dilation=dilation, apply_act=False, **conv_kwargs)
self.se = create_attn(attn_layer, outplanes)
self.act = act_layer(inplace=True)
self.downsample = downsample
self.drop_path = drop_path
def zero_init_last(self):
if getattr(self.conv2.bn, 'weight', None) is not None:
nn.init.zeros_(self.conv2.bn.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.conv2(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
x += shortcut
x = self.act(x)
return x
class SelectiveKernelBottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1,
base_width=64,
sk_kwargs=None,
reduce_first=1,
dilation=1,
first_dilation=None,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
attn_layer=None,
aa_layer=None,
drop_block=None,
drop_path=None,
):
super(SelectiveKernelBottleneck, self).__init__()
sk_kwargs = sk_kwargs or {}
conv_kwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
width = int(math.floor(planes * (base_width / 64)) * cardinality)
first_planes = width // reduce_first
outplanes = planes * self.expansion
first_dilation = first_dilation or dilation
self.conv1 = ConvNormAct(inplanes, first_planes, kernel_size=1, **conv_kwargs)
self.conv2 = SelectiveKernel(
first_planes, width, stride=stride, dilation=first_dilation, groups=cardinality,
aa_layer=aa_layer, drop_layer=drop_block, **conv_kwargs, **sk_kwargs)
self.conv3 = ConvNormAct(width, outplanes, kernel_size=1, apply_act=False, **conv_kwargs)
self.se = create_attn(attn_layer, outplanes)
self.act = act_layer(inplace=True)
self.downsample = downsample
self.drop_path = drop_path
def zero_init_last(self):
if getattr(self.conv3.bn, 'weight', None) is not None:
nn.init.zeros_(self.conv3.bn.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
x += shortcut
x = self.act(x)
return x
def _create_skresnet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
ResNet,
variant,
pretrained,
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1', 'classifier': 'fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'skresnet18.ra_in1k': _cfg(hf_hub_id='timm/'),
'skresnet34.ra_in1k': _cfg(hf_hub_id='timm/'),
'skresnet50.untrained': _cfg(),
'skresnet50d.untrained': _cfg(
first_conv='conv1.0'),
'skresnext50_32x4d.ra_in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def skresnet18(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Selective Kernel ResNet-18 model.
Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this
variation splits the input channels to the selective convolutions to keep param count down.
"""
sk_kwargs = dict(rd_ratio=1 / 8, rd_divisor=16, split_input=True)
model_args = dict(
block=SelectiveKernelBasic, layers=[2, 2, 2, 2], block_args=dict(sk_kwargs=sk_kwargs),
zero_init_last=False, **kwargs)
return _create_skresnet('skresnet18', pretrained, **model_args)
@register_model
def skresnet34(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Selective Kernel ResNet-34 model.
Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this
variation splits the input channels to the selective convolutions to keep param count down.
"""
sk_kwargs = dict(rd_ratio=1 / 8, rd_divisor=16, split_input=True)
model_args = dict(
block=SelectiveKernelBasic, layers=[3, 4, 6, 3], block_args=dict(sk_kwargs=sk_kwargs),
zero_init_last=False, **kwargs)
return _create_skresnet('skresnet34', pretrained, **model_args)
@register_model
def skresnet50(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Select Kernel ResNet-50 model.
Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this
variation splits the input channels to the selective convolutions to keep param count down.
"""
sk_kwargs = dict(split_input=True)
model_args = dict(
block=SelectiveKernelBottleneck, layers=[3, 4, 6, 3], block_args=dict(sk_kwargs=sk_kwargs),
zero_init_last=False, **kwargs)
return _create_skresnet('skresnet50', pretrained, **model_args)
@register_model
def skresnet50d(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Select Kernel ResNet-50-D model.
Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this
variation splits the input channels to the selective convolutions to keep param count down.
"""
sk_kwargs = dict(split_input=True)
model_args = dict(
block=SelectiveKernelBottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(sk_kwargs=sk_kwargs), zero_init_last=False, **kwargs)
return _create_skresnet('skresnet50d', pretrained, **model_args)
@register_model
def skresnext50_32x4d(pretrained=False, **kwargs) -> ResNet:
"""Constructs a Select Kernel ResNeXt50-32x4d model. This should be equivalent to
the SKNet-50 model in the Select Kernel Paper
"""
sk_kwargs = dict(rd_ratio=1/16, rd_divisor=32, split_input=False)
model_args = dict(
block=SelectiveKernelBottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
block_args=dict(sk_kwargs=sk_kwargs), zero_init_last=False, **kwargs)
return _create_skresnet('skresnext50_32x4d', pretrained, **model_args)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/densenet.py
|
"""Pytorch Densenet implementation w/ tweaks
This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
fixed kwargs passthrough and addition of dynamic global avg/max pool.
"""
import re
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from torch.jit.annotations import List
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import BatchNormAct2d, get_norm_act_layer, BlurPool2d, create_classifier
from ._builder import build_model_with_cfg
from ._manipulate import MATCH_PREV_GROUP
from ._registry import register_model, generate_default_cfgs
__all__ = ['DenseNet']
class DenseLayer(nn.Module):
def __init__(
self,
num_input_features,
growth_rate,
bn_size,
norm_layer=BatchNormAct2d,
drop_rate=0.,
grad_checkpointing=False,
):
super(DenseLayer, self).__init__()
self.add_module('norm1', norm_layer(num_input_features)),
self.add_module('conv1', nn.Conv2d(
num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)),
self.add_module('norm2', norm_layer(bn_size * growth_rate)),
self.add_module('conv2', nn.Conv2d(
bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)),
self.drop_rate = float(drop_rate)
self.grad_checkpointing = grad_checkpointing
def bottleneck_fn(self, xs):
# type: (List[torch.Tensor]) -> torch.Tensor
concated_features = torch.cat(xs, 1)
bottleneck_output = self.conv1(self.norm1(concated_features)) # noqa: T484
return bottleneck_output
# todo: rewrite when torchscript supports any
def any_requires_grad(self, x):
# type: (List[torch.Tensor]) -> bool
for tensor in x:
if tensor.requires_grad:
return True
return False
@torch.jit.unused # noqa: T484
def call_checkpoint_bottleneck(self, x):
# type: (List[torch.Tensor]) -> torch.Tensor
def closure(*xs):
return self.bottleneck_fn(xs)
return cp.checkpoint(closure, *x)
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (List[torch.Tensor]) -> (torch.Tensor)
pass
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (torch.Tensor) -> (torch.Tensor)
pass
# torchscript does not yet support *args, so we overload method
# allowing it to take either a List[Tensor] or single Tensor
def forward(self, x): # noqa: F811
if isinstance(x, torch.Tensor):
prev_features = [x]
else:
prev_features = x
if self.grad_checkpointing and self.any_requires_grad(prev_features):
if torch.jit.is_scripting():
raise Exception("Memory Efficient not supported in JIT")
bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
else:
bottleneck_output = self.bottleneck_fn(prev_features)
new_features = self.conv2(self.norm2(bottleneck_output))
if self.drop_rate > 0:
new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
return new_features
class DenseBlock(nn.ModuleDict):
_version = 2
def __init__(
self,
num_layers,
num_input_features,
bn_size,
growth_rate,
norm_layer=BatchNormAct2d,
drop_rate=0.,
grad_checkpointing=False,
):
super(DenseBlock, self).__init__()
for i in range(num_layers):
layer = DenseLayer(
num_input_features + i * growth_rate,
growth_rate=growth_rate,
bn_size=bn_size,
norm_layer=norm_layer,
drop_rate=drop_rate,
grad_checkpointing=grad_checkpointing,
)
self.add_module('denselayer%d' % (i + 1), layer)
def forward(self, init_features):
features = [init_features]
for name, layer in self.items():
new_features = layer(features)
features.append(new_features)
return torch.cat(features, 1)
class DenseTransition(nn.Sequential):
def __init__(
self,
num_input_features,
num_output_features,
norm_layer=BatchNormAct2d,
aa_layer=None,
):
super(DenseTransition, self).__init__()
self.add_module('norm', norm_layer(num_input_features))
self.add_module('conv', nn.Conv2d(
num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))
if aa_layer is not None:
self.add_module('pool', aa_layer(num_output_features, stride=2))
else:
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
class DenseNet(nn.Module):
r"""Densenet-BC model class, based on
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
growth_rate (int) - how many filters to add each layer (`k` in paper)
block_config (list of 4 ints) - how many layers in each pooling block
bn_size (int) - multiplicative factor for number of bottle neck layers
(i.e. bn_size * k features in the bottleneck layer)
drop_rate (float) - dropout rate before classifier layer
proj_drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
"""
def __init__(
self,
growth_rate=32,
block_config=(6, 12, 24, 16),
num_classes=1000,
in_chans=3,
global_pool='avg',
bn_size=4,
stem_type='',
act_layer='relu',
norm_layer='batchnorm2d',
aa_layer=None,
drop_rate=0.,
proj_drop_rate=0.,
memory_efficient=False,
aa_stem_only=True,
):
self.num_classes = num_classes
super(DenseNet, self).__init__()
norm_layer = get_norm_act_layer(norm_layer, act_layer=act_layer)
# Stem
deep_stem = 'deep' in stem_type # 3x3 deep stem
num_init_features = growth_rate * 2
if aa_layer is None:
stem_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
else:
stem_pool = nn.Sequential(*[
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
aa_layer(channels=num_init_features, stride=2)])
if deep_stem:
stem_chs_1 = stem_chs_2 = growth_rate
if 'tiered' in stem_type:
stem_chs_1 = 3 * (growth_rate // 4)
stem_chs_2 = num_init_features if 'narrow' in stem_type else 6 * (growth_rate // 4)
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False)),
('norm0', norm_layer(stem_chs_1)),
('conv1', nn.Conv2d(stem_chs_1, stem_chs_2, 3, stride=1, padding=1, bias=False)),
('norm1', norm_layer(stem_chs_2)),
('conv2', nn.Conv2d(stem_chs_2, num_init_features, 3, stride=1, padding=1, bias=False)),
('norm2', norm_layer(num_init_features)),
('pool0', stem_pool),
]))
else:
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(in_chans, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
('norm0', norm_layer(num_init_features)),
('pool0', stem_pool),
]))
self.feature_info = [
dict(num_chs=num_init_features, reduction=2, module=f'features.norm{2 if deep_stem else 0}')]
current_stride = 4
# DenseBlocks
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = DenseBlock(
num_layers=num_layers,
num_input_features=num_features,
bn_size=bn_size,
growth_rate=growth_rate,
norm_layer=norm_layer,
drop_rate=proj_drop_rate,
grad_checkpointing=memory_efficient,
)
module_name = f'denseblock{(i + 1)}'
self.features.add_module(module_name, block)
num_features = num_features + num_layers * growth_rate
transition_aa_layer = None if aa_stem_only else aa_layer
if i != len(block_config) - 1:
self.feature_info += [
dict(num_chs=num_features, reduction=current_stride, module='features.' + module_name)]
current_stride *= 2
trans = DenseTransition(
num_input_features=num_features,
num_output_features=num_features // 2,
norm_layer=norm_layer,
aa_layer=transition_aa_layer,
)
self.features.add_module(f'transition{i + 1}', trans)
num_features = num_features // 2
# Final batch norm
self.features.add_module('norm5', norm_layer(num_features))
self.feature_info += [dict(num_chs=num_features, reduction=current_stride, module='features.norm5')]
self.num_features = num_features
# Linear layer
global_pool, classifier = create_classifier(
self.num_features,
self.num_classes,
pool_type=global_pool,
)
self.global_pool = global_pool
self.head_drop = nn.Dropout(drop_rate)
self.classifier = classifier
# Official init from torch repo.
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^features\.conv[012]|features\.norm[012]|features\.pool[012]',
blocks=r'^features\.(?:denseblock|transition)(\d+)' if coarse else [
(r'^features\.denseblock(\d+)\.denselayer(\d+)', None),
(r'^features\.transition(\d+)', MATCH_PREV_GROUP) # FIXME combine with previous denselayer
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for b in self.features.modules():
if isinstance(b, DenseLayer):
b.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
return self.features(x)
def forward(self, x):
x = self.forward_features(x)
x = self.global_pool(x)
x = self.head_drop(x)
x = self.classifier(x)
return x
def _filter_torchvision_pretrained(state_dict):
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
for key in list(state_dict.keys()):
res = pattern.match(key)
if res:
new_key = res.group(1) + res.group(2)
state_dict[new_key] = state_dict[key]
del state_dict[key]
return state_dict
def _create_densenet(variant, growth_rate, block_config, pretrained, **kwargs):
kwargs['growth_rate'] = growth_rate
kwargs['block_config'] = block_config
return build_model_with_cfg(
DenseNet,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True),
pretrained_filter_fn=_filter_torchvision_pretrained,
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'features.conv0', 'classifier': 'classifier', **kwargs,
}
default_cfgs = generate_default_cfgs({
'densenet121.ra_in1k': _cfg(
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'densenetblur121d.ra_in1k': _cfg(
hf_hub_id='timm/',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'densenet264d.untrained': _cfg(),
'densenet121.tv_in1k': _cfg(hf_hub_id='timm/'),
'densenet169.tv_in1k': _cfg(hf_hub_id='timm/'),
'densenet201.tv_in1k': _cfg(hf_hub_id='timm/'),
'densenet161.tv_in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def densenet121(pretrained=False, **kwargs) -> DenseNet:
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model_args = dict(growth_rate=32, block_config=(6, 12, 24, 16))
model = _create_densenet('densenet121', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def densenetblur121d(pretrained=False, **kwargs) -> DenseNet:
r"""Densenet-121 w/ blur-pooling & 3-layer 3x3 stem
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model_args = dict(growth_rate=32, block_config=(6, 12, 24, 16), stem_type='deep', aa_layer=BlurPool2d)
model = _create_densenet('densenetblur121d', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def densenet169(pretrained=False, **kwargs) -> DenseNet:
r"""Densenet-169 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model_args = dict(growth_rate=32, block_config=(6, 12, 32, 32))
model = _create_densenet('densenet169', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def densenet201(pretrained=False, **kwargs) -> DenseNet:
r"""Densenet-201 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model_args = dict(growth_rate=32, block_config=(6, 12, 48, 32))
model = _create_densenet('densenet201', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def densenet161(pretrained=False, **kwargs) -> DenseNet:
r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model_args = dict(growth_rate=48, block_config=(6, 12, 36, 24))
model = _create_densenet('densenet161', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def densenet264d(pretrained=False, **kwargs) -> DenseNet:
r"""Densenet-264 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model_args = dict(growth_rate=48, block_config=(6, 12, 64, 48), stem_type='deep')
model = _create_densenet('densenet264d', pretrained=pretrained, **dict(model_args, **kwargs))
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/repghost.py
|
"""
An implementation of RepGhostNet Model as defined in:
RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. https://arxiv.org/abs/2211.06088
Original implementation: https://github.com/ChengpengChen/RepGhost
"""
import copy
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import SelectAdaptivePool2d, Linear, make_divisible
from ._builder import build_model_with_cfg
from ._efficientnet_blocks import SqueezeExcite, ConvBnAct
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
__all__ = ['RepGhostNet']
_SE_LAYER = partial(SqueezeExcite, gate_layer='hard_sigmoid', rd_round_fn=partial(make_divisible, divisor=4))
class RepGhostModule(nn.Module):
def __init__(
self,
in_chs,
out_chs,
kernel_size=1,
dw_size=3,
stride=1,
relu=True,
reparam=True,
):
super(RepGhostModule, self).__init__()
self.out_chs = out_chs
init_chs = out_chs
new_chs = out_chs
self.primary_conv = nn.Sequential(
nn.Conv2d(in_chs, init_chs, kernel_size, stride, kernel_size // 2, bias=False),
nn.BatchNorm2d(init_chs),
nn.ReLU(inplace=True) if relu else nn.Identity(),
)
fusion_conv = []
fusion_bn = []
if reparam:
fusion_conv.append(nn.Identity())
fusion_bn.append(nn.BatchNorm2d(init_chs))
self.fusion_conv = nn.Sequential(*fusion_conv)
self.fusion_bn = nn.Sequential(*fusion_bn)
self.cheap_operation = nn.Sequential(
nn.Conv2d(init_chs, new_chs, dw_size, 1, dw_size//2, groups=init_chs, bias=False),
nn.BatchNorm2d(new_chs),
# nn.ReLU(inplace=True) if relu else nn.Identity(),
)
self.relu = nn.ReLU(inplace=False) if relu else nn.Identity()
def forward(self, x):
x1 = self.primary_conv(x)
x2 = self.cheap_operation(x1)
for conv, bn in zip(self.fusion_conv, self.fusion_bn):
x2 = x2 + bn(conv(x1))
return self.relu(x2)
def get_equivalent_kernel_bias(self):
kernel3x3, bias3x3 = self._fuse_bn_tensor(self.cheap_operation[0], self.cheap_operation[1])
for conv, bn in zip(self.fusion_conv, self.fusion_bn):
kernel, bias = self._fuse_bn_tensor(conv, bn, kernel3x3.shape[0], kernel3x3.device)
kernel3x3 += self._pad_1x1_to_3x3_tensor(kernel)
bias3x3 += bias
return kernel3x3, bias3x3
@staticmethod
def _pad_1x1_to_3x3_tensor(kernel1x1):
if kernel1x1 is None:
return 0
else:
return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
@staticmethod
def _fuse_bn_tensor(conv, bn, in_channels=None, device=None):
in_channels = in_channels if in_channels else bn.running_mean.shape[0]
device = device if device else bn.weight.device
if isinstance(conv, nn.Conv2d):
kernel = conv.weight
assert conv.bias is None
else:
assert isinstance(conv, nn.Identity)
kernel = torch.ones(in_channels, 1, 1, 1, device=device)
if isinstance(bn, nn.BatchNorm2d):
running_mean = bn.running_mean
running_var = bn.running_var
gamma = bn.weight
beta = bn.bias
eps = bn.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
assert isinstance(bn, nn.Identity)
return kernel, torch.zeros(in_channels).to(kernel.device)
def switch_to_deploy(self):
if len(self.fusion_conv) == 0 and len(self.fusion_bn) == 0:
return
kernel, bias = self.get_equivalent_kernel_bias()
self.cheap_operation = nn.Conv2d(
in_channels=self.cheap_operation[0].in_channels,
out_channels=self.cheap_operation[0].out_channels,
kernel_size=self.cheap_operation[0].kernel_size,
padding=self.cheap_operation[0].padding,
dilation=self.cheap_operation[0].dilation,
groups=self.cheap_operation[0].groups,
bias=True)
self.cheap_operation.weight.data = kernel
self.cheap_operation.bias.data = bias
self.__delattr__('fusion_conv')
self.__delattr__('fusion_bn')
self.fusion_conv = []
self.fusion_bn = []
def reparameterize(self):
self.switch_to_deploy()
class RepGhostBottleneck(nn.Module):
""" RepGhost bottleneck w/ optional SE"""
def __init__(
self,
in_chs,
mid_chs,
out_chs,
dw_kernel_size=3,
stride=1,
act_layer=nn.ReLU,
se_ratio=0.,
reparam=True,
):
super(RepGhostBottleneck, self).__init__()
has_se = se_ratio is not None and se_ratio > 0.
self.stride = stride
# Point-wise expansion
self.ghost1 = RepGhostModule(in_chs, mid_chs, relu=True, reparam=reparam)
# Depth-wise convolution
if self.stride > 1:
self.conv_dw = nn.Conv2d(
mid_chs, mid_chs, dw_kernel_size, stride=stride,
padding=(dw_kernel_size-1)//2, groups=mid_chs, bias=False)
self.bn_dw = nn.BatchNorm2d(mid_chs)
else:
self.conv_dw = None
self.bn_dw = None
# Squeeze-and-excitation
self.se = _SE_LAYER(mid_chs, rd_ratio=se_ratio) if has_se else None
# Point-wise linear projection
self.ghost2 = RepGhostModule(mid_chs, out_chs, relu=False, reparam=reparam)
# shortcut
if in_chs == out_chs and self.stride == 1:
self.shortcut = nn.Sequential()
else:
self.shortcut = nn.Sequential(
nn.Conv2d(
in_chs, in_chs, dw_kernel_size, stride=stride,
padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False),
nn.BatchNorm2d(in_chs),
nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(out_chs),
)
def forward(self, x):
shortcut = x
# 1st ghost bottleneck
x = self.ghost1(x)
# Depth-wise convolution
if self.conv_dw is not None:
x = self.conv_dw(x)
x = self.bn_dw(x)
# Squeeze-and-excitation
if self.se is not None:
x = self.se(x)
# 2nd ghost bottleneck
x = self.ghost2(x)
x += self.shortcut(shortcut)
return x
class RepGhostNet(nn.Module):
def __init__(
self,
cfgs,
num_classes=1000,
width=1.0,
in_chans=3,
output_stride=32,
global_pool='avg',
drop_rate=0.2,
reparam=True,
):
super(RepGhostNet, self).__init__()
# setting of inverted residual blocks
assert output_stride == 32, 'only output_stride==32 is valid, dilation not supported'
self.cfgs = cfgs
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
self.feature_info = []
# building first layer
stem_chs = make_divisible(16 * width, 4)
self.conv_stem = nn.Conv2d(in_chans, stem_chs, 3, 2, 1, bias=False)
self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module=f'conv_stem'))
self.bn1 = nn.BatchNorm2d(stem_chs)
self.act1 = nn.ReLU(inplace=True)
prev_chs = stem_chs
# building inverted residual blocks
stages = nn.ModuleList([])
block = RepGhostBottleneck
stage_idx = 0
net_stride = 2
for cfg in self.cfgs:
layers = []
s = 1
for k, exp_size, c, se_ratio, s in cfg:
out_chs = make_divisible(c * width, 4)
mid_chs = make_divisible(exp_size * width, 4)
layers.append(block(prev_chs, mid_chs, out_chs, k, s, se_ratio=se_ratio, reparam=reparam))
prev_chs = out_chs
if s > 1:
net_stride *= 2
self.feature_info.append(dict(
num_chs=prev_chs, reduction=net_stride, module=f'blocks.{stage_idx}'))
stages.append(nn.Sequential(*layers))
stage_idx += 1
out_chs = make_divisible(exp_size * width * 2, 4)
stages.append(nn.Sequential(ConvBnAct(prev_chs, out_chs, 1)))
self.pool_dim = prev_chs = out_chs
self.blocks = nn.Sequential(*stages)
# building last several layers
self.num_features = out_chs = 1280
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.conv_head = nn.Conv2d(prev_chs, out_chs, 1, 1, 0, bias=True)
self.act2 = nn.ReLU(inplace=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(out_chs, num_classes) if num_classes > 0 else nn.Identity()
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^conv_stem|bn1',
blocks=[
(r'^blocks\.(\d+)' if coarse else r'^blocks\.(\d+)\.(\d+)', None),
(r'conv_head', (99999,))
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
# cannot meaningfully change pooling of efficient head after creation
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.conv_stem(x)
x = self.bn1(x)
x = self.act1(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x, flatten=True)
else:
x = self.blocks(x)
return x
def forward_head(self, x):
x = self.global_pool(x)
x = self.conv_head(x)
x = self.act2(x)
x = self.flatten(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.classifier(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def convert_to_deploy(self):
repghost_model_convert(self, do_copy=False)
def repghost_model_convert(model: torch.nn.Module, save_path=None, do_copy=True):
"""
taken from from https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
"""
if do_copy:
model = copy.deepcopy(model)
for module in model.modules():
if hasattr(module, 'switch_to_deploy'):
module.switch_to_deploy()
if save_path is not None:
torch.save(model.state_dict(), save_path)
return model
def _create_repghostnet(variant, width=1.0, pretrained=False, **kwargs):
"""
Constructs a RepGhostNet model
"""
cfgs = [
# k, t, c, SE, s
# stage1
[[3, 8, 16, 0, 1]],
# stage2
[[3, 24, 24, 0, 2]],
[[3, 36, 24, 0, 1]],
# stage3
[[5, 36, 40, 0.25, 2]],
[[5, 60, 40, 0.25, 1]],
# stage4
[[3, 120, 80, 0, 2]],
[[3, 100, 80, 0, 1],
[3, 120, 80, 0, 1],
[3, 120, 80, 0, 1],
[3, 240, 112, 0.25, 1],
[3, 336, 112, 0.25, 1]
],
# stage5
[[5, 336, 160, 0.25, 2]],
[[5, 480, 160, 0, 1],
[5, 480, 160, 0.25, 1],
[5, 480, 160, 0, 1],
[5, 480, 160, 0.25, 1]
]
]
model_kwargs = dict(
cfgs=cfgs,
width=width,
**kwargs,
)
return build_model_with_cfg(
RepGhostNet,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True),
**model_kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv_stem', 'classifier': 'classifier',
**kwargs
}
default_cfgs = generate_default_cfgs({
'repghostnet_050.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/ChengpengChen/RepGhost/releases/download/RepGhost/repghostnet_0_5x_43M_66.95.pth.tar'
),
'repghostnet_058.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/ChengpengChen/RepGhost/releases/download/RepGhost/repghostnet_0_58x_60M_68.94.pth.tar'
),
'repghostnet_080.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/ChengpengChen/RepGhost/releases/download/RepGhost/repghostnet_0_8x_96M_72.24.pth.tar'
),
'repghostnet_100.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/ChengpengChen/RepGhost/releases/download/RepGhost/repghostnet_1_0x_142M_74.22.pth.tar'
),
'repghostnet_111.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/ChengpengChen/RepGhost/releases/download/RepGhost/repghostnet_1_11x_170M_75.07.pth.tar'
),
'repghostnet_130.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/ChengpengChen/RepGhost/releases/download/RepGhost/repghostnet_1_3x_231M_76.37.pth.tar'
),
'repghostnet_150.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/ChengpengChen/RepGhost/releases/download/RepGhost/repghostnet_1_5x_301M_77.45.pth.tar'
),
'repghostnet_200.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/ChengpengChen/RepGhost/releases/download/RepGhost/repghostnet_2_0x_516M_78.81.pth.tar'
),
})
@register_model
def repghostnet_050(pretrained=False, **kwargs) -> RepGhostNet:
""" RepGhostNet-0.5x """
model = _create_repghostnet('repghostnet_050', width=0.5, pretrained=pretrained, **kwargs)
return model
@register_model
def repghostnet_058(pretrained=False, **kwargs) -> RepGhostNet:
""" RepGhostNet-0.58x """
model = _create_repghostnet('repghostnet_058', width=0.58, pretrained=pretrained, **kwargs)
return model
@register_model
def repghostnet_080(pretrained=False, **kwargs) -> RepGhostNet:
""" RepGhostNet-0.8x """
model = _create_repghostnet('repghostnet_080', width=0.8, pretrained=pretrained, **kwargs)
return model
@register_model
def repghostnet_100(pretrained=False, **kwargs) -> RepGhostNet:
""" RepGhostNet-1.0x """
model = _create_repghostnet('repghostnet_100', width=1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def repghostnet_111(pretrained=False, **kwargs) -> RepGhostNet:
""" RepGhostNet-1.11x """
model = _create_repghostnet('repghostnet_111', width=1.11, pretrained=pretrained, **kwargs)
return model
@register_model
def repghostnet_130(pretrained=False, **kwargs) -> RepGhostNet:
""" RepGhostNet-1.3x """
model = _create_repghostnet('repghostnet_130', width=1.3, pretrained=pretrained, **kwargs)
return model
@register_model
def repghostnet_150(pretrained=False, **kwargs) -> RepGhostNet:
""" RepGhostNet-1.5x """
model = _create_repghostnet('repghostnet_150', width=1.5, pretrained=pretrained, **kwargs)
return model
@register_model
def repghostnet_200(pretrained=False, **kwargs) -> RepGhostNet:
""" RepGhostNet-2.0x """
model = _create_repghostnet('repghostnet_200', width=2.0, pretrained=pretrained, **kwargs)
return model
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/swin_transformer.py
|
""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weights from
- https://github.com/microsoft/Cream/tree/main/AutoFormerV2
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import logging
import math
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import PatchEmbed, Mlp, DropPath, ClassifierHead, to_2tuple, to_ntuple, trunc_normal_, \
_assert, use_fused_attn, resize_rel_pos_bias_table, resample_patch_embed
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._manipulate import checkpoint_seq, named_apply
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
from .vision_transformer import get_init_weights_vit
__all__ = ['SwinTransformer'] # model_registry will add each entrypoint fn to this
_logger = logging.getLogger(__name__)
_int_or_tuple_2_t = Union[int, Tuple[int, int]]
def window_partition(
x: torch.Tensor,
window_size: Tuple[int, int],
) -> torch.Tensor:
"""
Partition into non-overlapping windows with padding if needed.
Args:
x (tensor): input tokens with [B, H, W, C].
window_size (int): window size.
Returns:
windows: windows after partition with [B * num_windows, window_size, window_size, C].
(Hp, Wp): padded height and width before partition
"""
B, H, W, C = x.shape
x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
return windows
@register_notrace_function # reason: int argument is a Proxy
def window_reverse(windows, window_size: Tuple[int, int], H: int, W: int):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
C = windows.shape[-1]
x = windows.view(-1, H // window_size[0], W // window_size[1], window_size[0], window_size[1], C)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
return x
def get_relative_position_index(win_h: int, win_w: int):
# get pair-wise relative position index for each token inside the window
coords = torch.stack(torch.meshgrid([torch.arange(win_h), torch.arange(win_w)])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += win_h - 1 # shift to start from 0
relative_coords[:, :, 1] += win_w - 1
relative_coords[:, :, 0] *= 2 * win_w - 1
return relative_coords.sum(-1) # Wh*Ww, Wh*Ww
class WindowAttention(nn.Module):
""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports shifted and non-shifted windows.
"""
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim: int,
num_heads: int,
head_dim: Optional[int] = None,
window_size: _int_or_tuple_2_t = 7,
qkv_bias: bool = True,
attn_drop: float = 0.,
proj_drop: float = 0.,
):
"""
Args:
dim: Number of input channels.
num_heads: Number of attention heads.
head_dim: Number of channels per head (dim // num_heads if not set)
window_size: The height and width of the window.
qkv_bias: If True, add a learnable bias to query, key, value.
attn_drop: Dropout ratio of attention weight.
proj_drop: Dropout ratio of output.
"""
super().__init__()
self.dim = dim
self.window_size = to_2tuple(window_size) # Wh, Ww
win_h, win_w = self.window_size
self.window_area = win_h * win_w
self.num_heads = num_heads
head_dim = head_dim or dim // num_heads
attn_dim = head_dim * num_heads
self.scale = head_dim ** -0.5
self.fused_attn = use_fused_attn(experimental=True) # NOTE not tested for prime-time yet
# define a parameter table of relative position bias, shape: 2*Wh-1 * 2*Ww-1, nH
self.relative_position_bias_table = nn.Parameter(torch.zeros((2 * win_h - 1) * (2 * win_w - 1), num_heads))
# get pair-wise relative position index for each token inside the window
self.register_buffer("relative_position_index", get_relative_position_index(win_h, win_w), persistent=False)
self.qkv = nn.Linear(dim, attn_dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(attn_dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
trunc_normal_(self.relative_position_bias_table, std=.02)
self.softmax = nn.Softmax(dim=-1)
def _get_rel_pos_bias(self) -> torch.Tensor:
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.view(-1)].view(self.window_area, self.window_area, -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
return relative_position_bias.unsqueeze(0)
def forward(self, x, mask: Optional[torch.Tensor] = None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
if self.fused_attn:
attn_mask = self._get_rel_pos_bias()
if mask is not None:
num_win = mask.shape[0]
mask = mask.view(1, num_win, 1, N, N).expand(B_ // num_win, -1, self.num_heads, -1, -1)
attn_mask = attn_mask + mask.reshape(-1, self.num_heads, N, N)
x = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
attn_mask=attn_mask,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn + self._get_rel_pos_bias()
if mask is not None:
num_win = mask.shape[0]
attn = attn.view(-1, num_win, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B_, N, -1)
x = self.proj(x)
x = self.proj_drop(x)
return x
class SwinTransformerBlock(nn.Module):
""" Swin Transformer Block.
"""
def __init__(
self,
dim: int,
input_resolution: _int_or_tuple_2_t,
num_heads: int = 4,
head_dim: Optional[int] = None,
window_size: _int_or_tuple_2_t = 7,
shift_size: int = 0,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
):
"""
Args:
dim: Number of input channels.
input_resolution: Input resolution.
window_size: Window size.
num_heads: Number of attention heads.
head_dim: Enforce the number of channels per head
shift_size: Shift size for SW-MSA.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: If True, add a learnable bias to query, key, value.
proj_drop: Dropout rate.
attn_drop: Attention dropout rate.
drop_path: Stochastic depth rate.
act_layer: Activation layer.
norm_layer: Normalization layer.
"""
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
ws, ss = self._calc_window_shift(window_size, shift_size)
self.window_size: Tuple[int, int] = ws
self.shift_size: Tuple[int, int] = ss
self.window_area = self.window_size[0] * self.window_size[1]
self.mlp_ratio = mlp_ratio
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
num_heads=num_heads,
head_dim=head_dim,
window_size=to_2tuple(self.window_size),
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
if any(self.shift_size):
# calculate attention mask for SW-MSA
H, W = self.input_resolution
H = math.ceil(H / self.window_size[0]) * self.window_size[0]
W = math.ceil(W / self.window_size[1]) * self.window_size[1]
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
cnt = 0
for h in (
slice(0, -self.window_size[0]),
slice(-self.window_size[0], -self.shift_size[0]),
slice(-self.shift_size[0], None)):
for w in (
slice(0, -self.window_size[1]),
slice(-self.window_size[1], -self.shift_size[1]),
slice(-self.shift_size[1], None)):
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.window_area)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask, persistent=False)
def _calc_window_shift(self, target_window_size, target_shift_size) -> Tuple[Tuple[int, int], Tuple[int, int]]:
target_window_size = to_2tuple(target_window_size)
target_shift_size = to_2tuple(target_shift_size)
window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)]
return tuple(window_size), tuple(shift_size)
def _attn(self, x):
B, H, W, C = x.shape
# cyclic shift
has_shift = any(self.shift_size)
if has_shift:
shifted_x = torch.roll(x, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2))
else:
shifted_x = x
# pad for resolution not divisible by window size
pad_h = (self.window_size[0] - H % self.window_size[0]) % self.window_size[0]
pad_w = (self.window_size[1] - W % self.window_size[1]) % self.window_size[1]
shifted_x = torch.nn.functional.pad(shifted_x, (0, 0, 0, pad_w, 0, pad_h))
Hp, Wp = H + pad_h, W + pad_w
# partition windows
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.view(-1, self.window_area, C) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C)
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
shifted_x = shifted_x[:, :H, :W, :].contiguous()
# reverse cyclic shift
if has_shift:
x = torch.roll(shifted_x, shifts=self.shift_size, dims=(1, 2))
else:
x = shifted_x
return x
def forward(self, x):
B, H, W, C = x.shape
x = x + self.drop_path1(self._attn(self.norm1(x)))
x = x.reshape(B, -1, C)
x = x + self.drop_path2(self.mlp(self.norm2(x)))
x = x.reshape(B, H, W, C)
return x
class PatchMerging(nn.Module):
""" Patch Merging Layer.
"""
def __init__(
self,
dim: int,
out_dim: Optional[int] = None,
norm_layer: Callable = nn.LayerNorm,
):
"""
Args:
dim: Number of input channels.
out_dim: Number of output channels (or 2 * dim if None)
norm_layer: Normalization layer.
"""
super().__init__()
self.dim = dim
self.out_dim = out_dim or 2 * dim
self.norm = norm_layer(4 * dim)
self.reduction = nn.Linear(4 * dim, self.out_dim, bias=False)
def forward(self, x):
B, H, W, C = x.shape
_assert(H % 2 == 0, f"x height ({H}) is not even.")
_assert(W % 2 == 0, f"x width ({W}) is not even.")
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3)
x = self.norm(x)
x = self.reduction(x)
return x
class SwinTransformerStage(nn.Module):
""" A basic Swin Transformer layer for one stage.
"""
def __init__(
self,
dim: int,
out_dim: int,
input_resolution: Tuple[int, int],
depth: int,
downsample: bool = True,
num_heads: int = 4,
head_dim: Optional[int] = None,
window_size: _int_or_tuple_2_t = 7,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: Union[List[float], float] = 0.,
norm_layer: Callable = nn.LayerNorm,
):
"""
Args:
dim: Number of input channels.
input_resolution: Input resolution.
depth: Number of blocks.
downsample: Downsample layer at the end of the layer.
num_heads: Number of attention heads.
head_dim: Channels per head (dim // num_heads if not set)
window_size: Local window size.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: If True, add a learnable bias to query, key, value.
proj_drop: Projection dropout rate.
attn_drop: Attention dropout rate.
drop_path: Stochastic depth rate.
norm_layer: Normalization layer.
"""
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.output_resolution = tuple(i // 2 for i in input_resolution) if downsample else input_resolution
self.depth = depth
self.grad_checkpointing = False
window_size = to_2tuple(window_size)
shift_size = tuple([w // 2 for w in window_size])
# patch merging layer
if downsample:
self.downsample = PatchMerging(
dim=dim,
out_dim=out_dim,
norm_layer=norm_layer,
)
else:
assert dim == out_dim
self.downsample = nn.Identity()
# build blocks
self.blocks = nn.Sequential(*[
SwinTransformerBlock(
dim=out_dim,
input_resolution=self.output_resolution,
num_heads=num_heads,
head_dim=head_dim,
window_size=window_size,
shift_size=0 if (i % 2 == 0) else shift_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
)
for i in range(depth)])
def forward(self, x):
x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class SwinTransformer(nn.Module):
""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
https://arxiv.org/pdf/2103.14030
"""
def __init__(
self,
img_size: _int_or_tuple_2_t = 224,
patch_size: int = 4,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = 'avg',
embed_dim: int = 96,
depths: Tuple[int, ...] = (2, 2, 6, 2),
num_heads: Tuple[int, ...] = (3, 6, 12, 24),
head_dim: Optional[int] = None,
window_size: _int_or_tuple_2_t = 7,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.1,
embed_layer: Callable = PatchEmbed,
norm_layer: Union[str, Callable] = nn.LayerNorm,
weight_init: str = '',
**kwargs,
):
"""
Args:
img_size: Input image size.
patch_size: Patch size.
in_chans: Number of input image channels.
num_classes: Number of classes for classification head.
embed_dim: Patch embedding dimension.
depths: Depth of each Swin Transformer layer.
num_heads: Number of attention heads in different layers.
head_dim: Dimension of self-attention heads.
window_size: Window size.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: If True, add a learnable bias to query, key, value.
drop_rate: Dropout rate.
attn_drop_rate (float): Attention dropout rate.
drop_path_rate (float): Stochastic depth rate.
embed_layer: Patch embedding layer.
norm_layer (nn.Module): Normalization layer.
"""
super().__init__()
assert global_pool in ('', 'avg')
self.num_classes = num_classes
self.global_pool = global_pool
self.output_fmt = 'NHWC'
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
self.feature_info = []
if not isinstance(embed_dim, (tuple, list)):
embed_dim = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
# split image into non-overlapping patches
self.patch_embed = embed_layer(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim[0],
norm_layer=norm_layer,
output_fmt='NHWC',
)
self.patch_grid = self.patch_embed.grid_size
# build layers
head_dim = to_ntuple(self.num_layers)(head_dim)
if not isinstance(window_size, (list, tuple)):
window_size = to_ntuple(self.num_layers)(window_size)
elif len(window_size) == 2:
window_size = (window_size,) * self.num_layers
assert len(window_size) == self.num_layers
mlp_ratio = to_ntuple(self.num_layers)(mlp_ratio)
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
layers = []
in_dim = embed_dim[0]
scale = 1
for i in range(self.num_layers):
out_dim = embed_dim[i]
layers += [SwinTransformerStage(
dim=in_dim,
out_dim=out_dim,
input_resolution=(
self.patch_grid[0] // scale,
self.patch_grid[1] // scale
),
depth=depths[i],
downsample=i > 0,
num_heads=num_heads[i],
head_dim=head_dim[i],
window_size=window_size[i],
mlp_ratio=mlp_ratio[i],
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
)]
in_dim = out_dim
if i > 0:
scale *= 2
self.feature_info += [dict(num_chs=out_dim, reduction=4 * scale, module=f'layers.{i}')]
self.layers = nn.Sequential(*layers)
self.norm = norm_layer(self.num_features)
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
input_fmt=self.output_fmt,
)
if weight_init != 'skip':
self.init_weights(weight_init)
@torch.jit.ignore
def init_weights(self, mode=''):
assert mode in ('jax', 'jax_nlhb', 'moco', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
named_apply(get_init_weights_vit(mode, head_bias=head_bias), self)
@torch.jit.ignore
def no_weight_decay(self):
nwd = set()
for n, _ in self.named_parameters():
if 'relative_position_bias_table' in n:
nwd.add(n)
return nwd
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^patch_embed', # stem and embed
blocks=r'^layers\.(\d+)' if coarse else [
(r'^layers\.(\d+).downsample', (0,)),
(r'^layers\.(\d+)\.\w+\.(\d+)', None),
(r'^norm', (99999,)),
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for l in self.layers:
l.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
self.head.reset(num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.patch_embed(x)
x = self.layers(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=True) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
old_weights = True
if 'head.fc.weight' in state_dict:
old_weights = False
import re
out_dict = {}
state_dict = state_dict.get('model', state_dict)
state_dict = state_dict.get('state_dict', state_dict)
for k, v in state_dict.items():
if any([n in k for n in ('relative_position_index', 'attn_mask')]):
continue # skip buffers that should not be persistent
if 'patch_embed.proj.weight' in k:
_, _, H, W = model.patch_embed.proj.weight.shape
if v.shape[-2] != H or v.shape[-1] != W:
v = resample_patch_embed(
v,
(H, W),
interpolation='bicubic',
antialias=True,
verbose=True,
)
if k.endswith('relative_position_bias_table'):
m = model.get_submodule(k[:-29])
if v.shape != m.relative_position_bias_table.shape or m.window_size[0] != m.window_size[1]:
v = resize_rel_pos_bias_table(
v,
new_window_size=m.window_size,
new_bias_shape=m.relative_position_bias_table.shape,
)
if old_weights:
k = re.sub(r'layers.(\d+).downsample', lambda x: f'layers.{int(x.group(1)) + 1}.downsample', k)
k = k.replace('head.', 'head.fc.')
out_dict[k] = v
return out_dict
def _create_swin_transformer(variant, pretrained=False, **kwargs):
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
SwinTransformer, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head.fc',
'license': 'mit', **kwargs
}
default_cfgs = generate_default_cfgs({
'swin_small_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22kto1k_finetune.pth', ),
'swin_base_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth',),
'swin_base_patch4_window12_384.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'swin_large_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth',),
'swin_large_patch4_window12_384.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'swin_tiny_patch4_window7_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',),
'swin_small_patch4_window7_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth',),
'swin_base_patch4_window7_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth',),
'swin_base_patch4_window12_384.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
# tiny 22k pretrain is worse than 1k, so moved after (untagged priority is based on order)
'swin_tiny_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22kto1k_finetune.pth',),
'swin_tiny_patch4_window7_224.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth',
num_classes=21841),
'swin_small_patch4_window7_224.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22k.pth',
num_classes=21841),
'swin_base_patch4_window7_224.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth',
num_classes=21841),
'swin_base_patch4_window12_384.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21841),
'swin_large_patch4_window7_224.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth',
num_classes=21841),
'swin_large_patch4_window12_384.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21841),
'swin_s3_tiny_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_t-1d53f6a8.pth'),
'swin_s3_small_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_s-3bb4c69d.pth'),
'swin_s3_base_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_b-a1e95db4.pth'),
})
@register_model
def swin_tiny_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-T @ 224x224, trained ImageNet-1k
"""
model_args = dict(patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer(
'swin_tiny_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_small_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-S @ 224x224
"""
model_args = dict(patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer(
'swin_small_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_base_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-B @ 224x224
"""
model_args = dict(patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
return _create_swin_transformer(
'swin_base_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_base_patch4_window12_384(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-B @ 384x384
"""
model_args = dict(patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
return _create_swin_transformer(
'swin_base_patch4_window12_384', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_large_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-L @ 224x224
"""
model_args = dict(patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48))
return _create_swin_transformer(
'swin_large_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_large_patch4_window12_384(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-L @ 384x384
"""
model_args = dict(patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48))
return _create_swin_transformer(
'swin_large_patch4_window12_384', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_s3_tiny_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-S3-T @ 224x224, https://arxiv.org/abs/2111.14725
"""
model_args = dict(
patch_size=4, window_size=(7, 7, 14, 7), embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer('swin_s3_tiny_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_s3_small_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-S3-S @ 224x224, https://arxiv.org/abs/2111.14725
"""
model_args = dict(
patch_size=4, window_size=(14, 14, 14, 7), embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer('swin_s3_small_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_s3_base_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-S3-B @ 224x224, https://arxiv.org/abs/2111.14725
"""
model_args = dict(
patch_size=4, window_size=(7, 7, 14, 7), embed_dim=96, depths=(2, 2, 30, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer('swin_s3_base_224', pretrained=pretrained, **dict(model_args, **kwargs))
register_model_deprecations(__name__, {
'swin_base_patch4_window7_224_in22k': 'swin_base_patch4_window7_224.ms_in22k',
'swin_base_patch4_window12_384_in22k': 'swin_base_patch4_window12_384.ms_in22k',
'swin_large_patch4_window7_224_in22k': 'swin_large_patch4_window7_224.ms_in22k',
'swin_large_patch4_window12_384_in22k': 'swin_large_patch4_window12_384.ms_in22k',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/inception_v4.py
|
""" Pytorch Inception-V4 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import create_classifier, ConvNormAct
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['InceptionV4']
class Mixed3a(nn.Module):
def __init__(self, conv_block=ConvNormAct):
super(Mixed3a, self).__init__()
self.maxpool = nn.MaxPool2d(3, stride=2)
self.conv = conv_block(64, 96, kernel_size=3, stride=2)
def forward(self, x):
x0 = self.maxpool(x)
x1 = self.conv(x)
out = torch.cat((x0, x1), 1)
return out
class Mixed4a(nn.Module):
def __init__(self, conv_block=ConvNormAct):
super(Mixed4a, self).__init__()
self.branch0 = nn.Sequential(
conv_block(160, 64, kernel_size=1, stride=1),
conv_block(64, 96, kernel_size=3, stride=1)
)
self.branch1 = nn.Sequential(
conv_block(160, 64, kernel_size=1, stride=1),
conv_block(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
conv_block(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
conv_block(64, 96, kernel_size=(3, 3), stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
return out
class Mixed5a(nn.Module):
def __init__(self, conv_block=ConvNormAct):
super(Mixed5a, self).__init__()
self.conv = conv_block(192, 192, kernel_size=3, stride=2)
self.maxpool = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.conv(x)
x1 = self.maxpool(x)
out = torch.cat((x0, x1), 1)
return out
class InceptionA(nn.Module):
def __init__(self, conv_block=ConvNormAct):
super(InceptionA, self).__init__()
self.branch0 = conv_block(384, 96, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
conv_block(384, 64, kernel_size=1, stride=1),
conv_block(64, 96, kernel_size=3, stride=1, padding=1)
)
self.branch2 = nn.Sequential(
conv_block(384, 64, kernel_size=1, stride=1),
conv_block(64, 96, kernel_size=3, stride=1, padding=1),
conv_block(96, 96, kernel_size=3, stride=1, padding=1)
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
conv_block(384, 96, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class ReductionA(nn.Module):
def __init__(self, conv_block=ConvNormAct):
super(ReductionA, self).__init__()
self.branch0 = conv_block(384, 384, kernel_size=3, stride=2)
self.branch1 = nn.Sequential(
conv_block(384, 192, kernel_size=1, stride=1),
conv_block(192, 224, kernel_size=3, stride=1, padding=1),
conv_block(224, 256, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class InceptionB(nn.Module):
def __init__(self, conv_block=ConvNormAct):
super(InceptionB, self).__init__()
self.branch0 = conv_block(1024, 384, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
conv_block(1024, 192, kernel_size=1, stride=1),
conv_block(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
conv_block(224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0))
)
self.branch2 = nn.Sequential(
conv_block(1024, 192, kernel_size=1, stride=1),
conv_block(192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)),
conv_block(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
conv_block(224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)),
conv_block(224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3))
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
conv_block(1024, 128, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class ReductionB(nn.Module):
def __init__(self, conv_block=ConvNormAct):
super(ReductionB, self).__init__()
self.branch0 = nn.Sequential(
conv_block(1024, 192, kernel_size=1, stride=1),
conv_block(192, 192, kernel_size=3, stride=2)
)
self.branch1 = nn.Sequential(
conv_block(1024, 256, kernel_size=1, stride=1),
conv_block(256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)),
conv_block(256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)),
conv_block(320, 320, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class InceptionC(nn.Module):
def __init__(self, conv_block=ConvNormAct):
super(InceptionC, self).__init__()
self.branch0 = conv_block(1536, 256, kernel_size=1, stride=1)
self.branch1_0 = conv_block(1536, 384, kernel_size=1, stride=1)
self.branch1_1a = conv_block(384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
self.branch1_1b = conv_block(384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
self.branch2_0 = conv_block(1536, 384, kernel_size=1, stride=1)
self.branch2_1 = conv_block(384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0))
self.branch2_2 = conv_block(448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1))
self.branch2_3a = conv_block(512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
self.branch2_3b = conv_block(512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
conv_block(1536, 256, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1_0 = self.branch1_0(x)
x1_1a = self.branch1_1a(x1_0)
x1_1b = self.branch1_1b(x1_0)
x1 = torch.cat((x1_1a, x1_1b), 1)
x2_0 = self.branch2_0(x)
x2_1 = self.branch2_1(x2_0)
x2_2 = self.branch2_2(x2_1)
x2_3a = self.branch2_3a(x2_2)
x2_3b = self.branch2_3b(x2_2)
x2 = torch.cat((x2_3a, x2_3b), 1)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class InceptionV4(nn.Module):
def __init__(
self,
num_classes=1000,
in_chans=3,
output_stride=32,
drop_rate=0.,
global_pool='avg',
norm_layer='batchnorm2d',
norm_eps=1e-3,
act_layer='relu',
):
super(InceptionV4, self).__init__()
assert output_stride == 32
self.num_classes = num_classes
self.num_features = 1536
conv_block = partial(
ConvNormAct,
padding=0,
norm_layer=norm_layer,
act_layer=act_layer,
norm_kwargs=dict(eps=norm_eps),
act_kwargs=dict(inplace=True),
)
features = [
conv_block(in_chans, 32, kernel_size=3, stride=2),
conv_block(32, 32, kernel_size=3, stride=1),
conv_block(32, 64, kernel_size=3, stride=1, padding=1),
Mixed3a(conv_block),
Mixed4a(conv_block),
Mixed5a(conv_block),
]
features += [InceptionA(conv_block) for _ in range(4)]
features += [ReductionA(conv_block)] # Mixed6a
features += [InceptionB(conv_block) for _ in range(7)]
features += [ReductionB(conv_block)] # Mixed7a
features += [InceptionC(conv_block) for _ in range(3)]
self.features = nn.Sequential(*features)
self.feature_info = [
dict(num_chs=64, reduction=2, module='features.2'),
dict(num_chs=160, reduction=4, module='features.3'),
dict(num_chs=384, reduction=8, module='features.9'),
dict(num_chs=1024, reduction=16, module='features.17'),
dict(num_chs=1536, reduction=32, module='features.21'),
]
self.global_pool, self.head_drop, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, drop_rate=drop_rate)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^features\.[012]\.',
blocks=r'^features\.(\d+)'
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.last_linear
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
return self.features(x)
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.head_drop(x)
return x if pre_logits else self.last_linear(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_inception_v4(variant, pretrained=False, **kwargs) -> InceptionV4:
return build_model_with_cfg(
InceptionV4,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True),
**kwargs,
)
default_cfgs = generate_default_cfgs({
'inception_v4.tf_in1k': {
'hf_hub_id': 'timm/',
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'features.0.conv', 'classifier': 'last_linear',
}
})
@register_model
def inception_v4(pretrained=False, **kwargs):
return _create_inception_v4('inception_v4', pretrained, **kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/resnetv2.py
|
"""Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their original .NPZ checkpoints.
Additionally, supports non pre-activation bottleneck for use as a backbone for Vision Transfomers (ViT) and
extra padding support to allow porting of official Hybrid ResNet pretrained weights from
https://github.com/google-research/vision_transformer
Thanks to the Google team for the above two repositories and associated papers:
* Big Transfer (BiT): General Visual Representation Learning - https://arxiv.org/abs/1912.11370
* An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale - https://arxiv.org/abs/2010.11929
* Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
Original copyright of Google code below, modifications by Ross Wightman, Copyright 2020.
"""
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict # pylint: disable=g-importing-member
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import GroupNormAct, BatchNormAct2d, EvoNorm2dS0, FilterResponseNormTlu2d, ClassifierHead, \
DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d, get_act_layer, get_norm_act_layer, make_divisible
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq, named_apply, adapt_input_conv
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
__all__ = ['ResNetV2'] # model_registry will add each entrypoint fn to this
class PreActBottleneck(nn.Module):
"""Pre-activation (v2) bottleneck block.
Follows the implementation of "Identity Mappings in Deep Residual Networks":
https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua
Except it puts the stride on 3x3 conv when available.
"""
def __init__(
self,
in_chs,
out_chs=None,
bottle_ratio=0.25,
stride=1,
dilation=1,
first_dilation=None,
groups=1,
act_layer=None,
conv_layer=None,
norm_layer=None,
proj_layer=None,
drop_path_rate=0.,
):
super().__init__()
first_dilation = first_dilation or dilation
conv_layer = conv_layer or StdConv2d
norm_layer = norm_layer or partial(GroupNormAct, num_groups=32)
out_chs = out_chs or in_chs
mid_chs = make_divisible(out_chs * bottle_ratio)
if proj_layer is not None:
self.downsample = proj_layer(
in_chs, out_chs, stride=stride, dilation=dilation, first_dilation=first_dilation, preact=True,
conv_layer=conv_layer, norm_layer=norm_layer)
else:
self.downsample = None
self.norm1 = norm_layer(in_chs)
self.conv1 = conv_layer(in_chs, mid_chs, 1)
self.norm2 = norm_layer(mid_chs)
self.conv2 = conv_layer(mid_chs, mid_chs, 3, stride=stride, dilation=first_dilation, groups=groups)
self.norm3 = norm_layer(mid_chs)
self.conv3 = conv_layer(mid_chs, out_chs, 1)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
def zero_init_last(self):
nn.init.zeros_(self.conv3.weight)
def forward(self, x):
x_preact = self.norm1(x)
# shortcut branch
shortcut = x
if self.downsample is not None:
shortcut = self.downsample(x_preact)
# residual branch
x = self.conv1(x_preact)
x = self.conv2(self.norm2(x))
x = self.conv3(self.norm3(x))
x = self.drop_path(x)
return x + shortcut
class Bottleneck(nn.Module):
"""Non Pre-activation bottleneck block, equiv to V1.5/V1b Bottleneck. Used for ViT.
"""
def __init__(
self,
in_chs,
out_chs=None,
bottle_ratio=0.25,
stride=1,
dilation=1,
first_dilation=None,
groups=1,
act_layer=None,
conv_layer=None,
norm_layer=None,
proj_layer=None,
drop_path_rate=0.,
):
super().__init__()
first_dilation = first_dilation or dilation
act_layer = act_layer or nn.ReLU
conv_layer = conv_layer or StdConv2d
norm_layer = norm_layer or partial(GroupNormAct, num_groups=32)
out_chs = out_chs or in_chs
mid_chs = make_divisible(out_chs * bottle_ratio)
if proj_layer is not None:
self.downsample = proj_layer(
in_chs, out_chs, stride=stride, dilation=dilation, preact=False,
conv_layer=conv_layer, norm_layer=norm_layer)
else:
self.downsample = None
self.conv1 = conv_layer(in_chs, mid_chs, 1)
self.norm1 = norm_layer(mid_chs)
self.conv2 = conv_layer(mid_chs, mid_chs, 3, stride=stride, dilation=first_dilation, groups=groups)
self.norm2 = norm_layer(mid_chs)
self.conv3 = conv_layer(mid_chs, out_chs, 1)
self.norm3 = norm_layer(out_chs, apply_act=False)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
self.act3 = act_layer(inplace=True)
def zero_init_last(self):
if getattr(self.norm3, 'weight', None) is not None:
nn.init.zeros_(self.norm3.weight)
def forward(self, x):
# shortcut branch
shortcut = x
if self.downsample is not None:
shortcut = self.downsample(x)
# residual
x = self.conv1(x)
x = self.norm1(x)
x = self.conv2(x)
x = self.norm2(x)
x = self.conv3(x)
x = self.norm3(x)
x = self.drop_path(x)
x = self.act3(x + shortcut)
return x
class DownsampleConv(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=1,
first_dilation=None,
preact=True,
conv_layer=None,
norm_layer=None,
):
super(DownsampleConv, self).__init__()
self.conv = conv_layer(in_chs, out_chs, 1, stride=stride)
self.norm = nn.Identity() if preact else norm_layer(out_chs, apply_act=False)
def forward(self, x):
return self.norm(self.conv(x))
class DownsampleAvg(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=1,
first_dilation=None,
preact=True,
conv_layer=None,
norm_layer=None,
):
""" AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment."""
super(DownsampleAvg, self).__init__()
avg_stride = stride if dilation == 1 else 1
if stride > 1 or dilation > 1:
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
else:
self.pool = nn.Identity()
self.conv = conv_layer(in_chs, out_chs, 1, stride=1)
self.norm = nn.Identity() if preact else norm_layer(out_chs, apply_act=False)
def forward(self, x):
return self.norm(self.conv(self.pool(x)))
class ResNetStage(nn.Module):
"""ResNet Stage."""
def __init__(
self,
in_chs,
out_chs,
stride,
dilation,
depth,
bottle_ratio=0.25,
groups=1,
avg_down=False,
block_dpr=None,
block_fn=PreActBottleneck,
act_layer=None,
conv_layer=None,
norm_layer=None,
**block_kwargs,
):
super(ResNetStage, self).__init__()
first_dilation = 1 if dilation in (1, 2) else 2
layer_kwargs = dict(act_layer=act_layer, conv_layer=conv_layer, norm_layer=norm_layer)
proj_layer = DownsampleAvg if avg_down else DownsampleConv
prev_chs = in_chs
self.blocks = nn.Sequential()
for block_idx in range(depth):
drop_path_rate = block_dpr[block_idx] if block_dpr else 0.
stride = stride if block_idx == 0 else 1
self.blocks.add_module(str(block_idx), block_fn(
prev_chs,
out_chs,
stride=stride,
dilation=dilation,
bottle_ratio=bottle_ratio,
groups=groups,
first_dilation=first_dilation,
proj_layer=proj_layer,
drop_path_rate=drop_path_rate,
**layer_kwargs,
**block_kwargs,
))
prev_chs = out_chs
first_dilation = dilation
proj_layer = None
def forward(self, x):
x = self.blocks(x)
return x
def is_stem_deep(stem_type):
return any([s in stem_type for s in ('deep', 'tiered')])
def create_resnetv2_stem(
in_chs,
out_chs=64,
stem_type='',
preact=True,
conv_layer=StdConv2d,
norm_layer=partial(GroupNormAct, num_groups=32),
):
stem = OrderedDict()
assert stem_type in ('', 'fixed', 'same', 'deep', 'deep_fixed', 'deep_same', 'tiered')
# NOTE conv padding mode can be changed by overriding the conv_layer def
if is_stem_deep(stem_type):
# A 3 deep 3x3 conv stack as in ResNet V1D models
if 'tiered' in stem_type:
stem_chs = (3 * out_chs // 8, out_chs // 2) # 'T' resnets in resnet.py
else:
stem_chs = (out_chs // 2, out_chs // 2) # 'D' ResNets
stem['conv1'] = conv_layer(in_chs, stem_chs[0], kernel_size=3, stride=2)
stem['norm1'] = norm_layer(stem_chs[0])
stem['conv2'] = conv_layer(stem_chs[0], stem_chs[1], kernel_size=3, stride=1)
stem['norm2'] = norm_layer(stem_chs[1])
stem['conv3'] = conv_layer(stem_chs[1], out_chs, kernel_size=3, stride=1)
if not preact:
stem['norm3'] = norm_layer(out_chs)
else:
# The usual 7x7 stem conv
stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=7, stride=2)
if not preact:
stem['norm'] = norm_layer(out_chs)
if 'fixed' in stem_type:
# 'fixed' SAME padding approximation that is used in BiT models
stem['pad'] = nn.ConstantPad2d(1, 0.)
stem['pool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
elif 'same' in stem_type:
# full, input size based 'SAME' padding, used in ViT Hybrid model
stem['pool'] = create_pool2d('max', kernel_size=3, stride=2, padding='same')
else:
# the usual PyTorch symmetric padding
stem['pool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
return nn.Sequential(stem)
class ResNetV2(nn.Module):
"""Implementation of Pre-activation (v2) ResNet mode.
"""
def __init__(
self,
layers,
channels=(256, 512, 1024, 2048),
num_classes=1000,
in_chans=3,
global_pool='avg',
output_stride=32,
width_factor=1,
stem_chs=64,
stem_type='',
avg_down=False,
preact=True,
act_layer=nn.ReLU,
norm_layer=partial(GroupNormAct, num_groups=32),
conv_layer=StdConv2d,
drop_rate=0.,
drop_path_rate=0.,
zero_init_last=False,
):
"""
Args:
layers (List[int]) : number of layers in each block
channels (List[int]) : number of channels in each block:
num_classes (int): number of classification classes (default 1000)
in_chans (int): number of input (color) channels. (default 3)
global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg')
output_stride (int): output stride of the network, 32, 16, or 8. (default 32)
width_factor (int): channel (width) multiplication factor
stem_chs (int): stem width (default: 64)
stem_type (str): stem type (default: '' == 7x7)
avg_down (bool): average pooling in residual downsampling (default: False)
preact (bool): pre-activiation (default: True)
act_layer (Union[str, nn.Module]): activation layer
norm_layer (Union[str, nn.Module]): normalization layer
conv_layer (nn.Module): convolution module
drop_rate: classifier dropout rate (default: 0.)
drop_path_rate: stochastic depth rate (default: 0.)
zero_init_last: zero-init last weight in residual path (default: False)
"""
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
wf = width_factor
norm_layer = get_norm_act_layer(norm_layer, act_layer=act_layer)
act_layer = get_act_layer(act_layer)
self.feature_info = []
stem_chs = make_divisible(stem_chs * wf)
self.stem = create_resnetv2_stem(
in_chans,
stem_chs,
stem_type,
preact,
conv_layer=conv_layer,
norm_layer=norm_layer,
)
stem_feat = ('stem.conv3' if is_stem_deep(stem_type) else 'stem.conv') if preact else 'stem.norm'
self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module=stem_feat))
prev_chs = stem_chs
curr_stride = 4
dilation = 1
block_dprs = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(layers)).split(layers)]
block_fn = PreActBottleneck if preact else Bottleneck
self.stages = nn.Sequential()
for stage_idx, (d, c, bdpr) in enumerate(zip(layers, channels, block_dprs)):
out_chs = make_divisible(c * wf)
stride = 1 if stage_idx == 0 else 2
if curr_stride >= output_stride:
dilation *= stride
stride = 1
stage = ResNetStage(
prev_chs,
out_chs,
stride=stride,
dilation=dilation,
depth=d,
avg_down=avg_down,
act_layer=act_layer,
conv_layer=conv_layer,
norm_layer=norm_layer,
block_dpr=bdpr,
block_fn=block_fn,
)
prev_chs = out_chs
curr_stride *= stride
self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{stage_idx}')]
self.stages.add_module(str(stage_idx), stage)
self.num_features = prev_chs
self.norm = norm_layer(self.num_features) if preact else nn.Identity()
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=self.drop_rate,
use_conv=True,
)
self.init_weights(zero_init_last=zero_init_last)
self.grad_checkpointing = False
@torch.jit.ignore
def init_weights(self, zero_init_last=True):
named_apply(partial(_init_weights, zero_init_last=zero_init_last), self)
@torch.jit.ignore()
def load_pretrained(self, checkpoint_path, prefix='resnet/'):
_load_weights(self, checkpoint_path, prefix)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+)\.blocks\.(\d+)', None),
(r'^norm', (99999,))
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.head.reset(num_classes, global_pool)
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x, flatten=True)
else:
x = self.stages(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module: nn.Module, name: str = '', zero_init_last=True):
if isinstance(module, nn.Linear) or ('head.fc' in name and isinstance(module, nn.Conv2d)):
nn.init.normal_(module.weight, mean=0.0, std=0.01)
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, (nn.BatchNorm2d, nn.LayerNorm, nn.GroupNorm)):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
elif zero_init_last and hasattr(module, 'zero_init_last'):
module.zero_init_last()
@torch.no_grad()
def _load_weights(model: nn.Module, checkpoint_path: str, prefix: str = 'resnet/'):
import numpy as np
def t2p(conv_weights):
"""Possibly convert HWIO to OIHW."""
if conv_weights.ndim == 4:
conv_weights = conv_weights.transpose([3, 2, 0, 1])
return torch.from_numpy(conv_weights)
weights = np.load(checkpoint_path)
stem_conv_w = adapt_input_conv(
model.stem.conv.weight.shape[1], t2p(weights[f'{prefix}root_block/standardized_conv2d/kernel']))
model.stem.conv.weight.copy_(stem_conv_w)
model.norm.weight.copy_(t2p(weights[f'{prefix}group_norm/gamma']))
model.norm.bias.copy_(t2p(weights[f'{prefix}group_norm/beta']))
if isinstance(getattr(model.head, 'fc', None), nn.Conv2d) and \
model.head.fc.weight.shape[0] == weights[f'{prefix}head/conv2d/kernel'].shape[-1]:
model.head.fc.weight.copy_(t2p(weights[f'{prefix}head/conv2d/kernel']))
model.head.fc.bias.copy_(t2p(weights[f'{prefix}head/conv2d/bias']))
for i, (sname, stage) in enumerate(model.stages.named_children()):
for j, (bname, block) in enumerate(stage.blocks.named_children()):
cname = 'standardized_conv2d'
block_prefix = f'{prefix}block{i + 1}/unit{j + 1:02d}/'
block.conv1.weight.copy_(t2p(weights[f'{block_prefix}a/{cname}/kernel']))
block.conv2.weight.copy_(t2p(weights[f'{block_prefix}b/{cname}/kernel']))
block.conv3.weight.copy_(t2p(weights[f'{block_prefix}c/{cname}/kernel']))
block.norm1.weight.copy_(t2p(weights[f'{block_prefix}a/group_norm/gamma']))
block.norm2.weight.copy_(t2p(weights[f'{block_prefix}b/group_norm/gamma']))
block.norm3.weight.copy_(t2p(weights[f'{block_prefix}c/group_norm/gamma']))
block.norm1.bias.copy_(t2p(weights[f'{block_prefix}a/group_norm/beta']))
block.norm2.bias.copy_(t2p(weights[f'{block_prefix}b/group_norm/beta']))
block.norm3.bias.copy_(t2p(weights[f'{block_prefix}c/group_norm/beta']))
if block.downsample is not None:
w = weights[f'{block_prefix}a/proj/{cname}/kernel']
block.downsample.conv.weight.copy_(t2p(w))
def _create_resnetv2(variant, pretrained=False, **kwargs):
feature_cfg = dict(flatten_sequential=True)
return build_model_with_cfg(
ResNetV2, variant, pretrained,
feature_cfg=feature_cfg,
**kwargs,
)
def _create_resnetv2_bit(variant, pretrained=False, **kwargs):
return _create_resnetv2(
variant,
pretrained=pretrained,
stem_type='fixed',
conv_layer=partial(StdConv2d, eps=1e-8),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
# Paper: Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
'resnetv2_50x1_bit.goog_distilled_in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic', custom_load=True),
'resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic', custom_load=True),
'resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, interpolation='bicubic', custom_load=True),
# pretrained on imagenet21k, finetuned on imagenet1k
'resnetv2_50x1_bit.goog_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_50x3_bit.goog_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_101x1_bit.goog_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_101x3_bit.goog_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_152x2_bit.goog_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_152x4_bit.goog_in21k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 480, 480), pool_size=(15, 15), crop_pct=1.0, custom_load=True), # only one at 480x480?
# trained on imagenet-21k
'resnetv2_50x1_bit.goog_in21k': _cfg(
hf_hub_id='timm/',
num_classes=21843, custom_load=True),
'resnetv2_50x3_bit.goog_in21k': _cfg(
hf_hub_id='timm/',
num_classes=21843, custom_load=True),
'resnetv2_101x1_bit.goog_in21k': _cfg(
hf_hub_id='timm/',
num_classes=21843, custom_load=True),
'resnetv2_101x3_bit.goog_in21k': _cfg(
hf_hub_id='timm/',
num_classes=21843, custom_load=True),
'resnetv2_152x2_bit.goog_in21k': _cfg(
hf_hub_id='timm/',
num_classes=21843, custom_load=True),
'resnetv2_152x4_bit.goog_in21k': _cfg(
hf_hub_id='timm/',
num_classes=21843, custom_load=True),
'resnetv2_50.a1h_in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic', crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'resnetv2_50d.untrained': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
'resnetv2_50t.untrained': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
'resnetv2_101.a1h_in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic', crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'resnetv2_101d.untrained': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
'resnetv2_152.untrained': _cfg(
interpolation='bicubic'),
'resnetv2_152d.untrained': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
'resnetv2_50d_gn.ah_in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic', first_conv='stem.conv1',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'resnetv2_50d_evos.ah_in1k': _cfg(
hf_hub_id='timm/',
interpolation='bicubic', first_conv='stem.conv1',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
'resnetv2_50d_frn.untrained': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
})
@register_model
def resnetv2_50x1_bit(pretrained=False, **kwargs) -> ResNetV2:
return _create_resnetv2_bit(
'resnetv2_50x1_bit', pretrained=pretrained, layers=[3, 4, 6, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_50x3_bit(pretrained=False, **kwargs) -> ResNetV2:
return _create_resnetv2_bit(
'resnetv2_50x3_bit', pretrained=pretrained, layers=[3, 4, 6, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_101x1_bit(pretrained=False, **kwargs) -> ResNetV2:
return _create_resnetv2_bit(
'resnetv2_101x1_bit', pretrained=pretrained, layers=[3, 4, 23, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_101x3_bit(pretrained=False, **kwargs) -> ResNetV2:
return _create_resnetv2_bit(
'resnetv2_101x3_bit', pretrained=pretrained, layers=[3, 4, 23, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_152x2_bit(pretrained=False, **kwargs) -> ResNetV2:
return _create_resnetv2_bit(
'resnetv2_152x2_bit', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=2, **kwargs)
@register_model
def resnetv2_152x4_bit(pretrained=False, **kwargs) -> ResNetV2:
return _create_resnetv2_bit(
'resnetv2_152x4_bit', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=4, **kwargs)
@register_model
def resnetv2_50(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d)
return _create_resnetv2('resnetv2_50', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def resnetv2_50d(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d,
stem_type='deep', avg_down=True)
return _create_resnetv2('resnetv2_50d', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def resnetv2_50t(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d,
stem_type='tiered', avg_down=True)
return _create_resnetv2('resnetv2_50t', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def resnetv2_101(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(layers=[3, 4, 23, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d)
return _create_resnetv2('resnetv2_101', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def resnetv2_101d(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(
layers=[3, 4, 23, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d,
stem_type='deep', avg_down=True)
return _create_resnetv2('resnetv2_101d', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def resnetv2_152(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(layers=[3, 8, 36, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d)
return _create_resnetv2('resnetv2_152', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def resnetv2_152d(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(
layers=[3, 8, 36, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d,
stem_type='deep', avg_down=True)
return _create_resnetv2('resnetv2_152d', pretrained=pretrained, **dict(model_args, **kwargs))
# Experimental configs (may change / be removed)
@register_model
def resnetv2_50d_gn(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=GroupNormAct,
stem_type='deep', avg_down=True)
return _create_resnetv2('resnetv2_50d_gn', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def resnetv2_50d_evos(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=EvoNorm2dS0,
stem_type='deep', avg_down=True)
return _create_resnetv2('resnetv2_50d_evos', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def resnetv2_50d_frn(pretrained=False, **kwargs) -> ResNetV2:
model_args = dict(
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=FilterResponseNormTlu2d,
stem_type='deep', avg_down=True)
return _create_resnetv2('resnetv2_50d_frn', pretrained=pretrained, **dict(model_args, **kwargs))
register_model_deprecations(__name__, {
'resnetv2_50x1_bitm': 'resnetv2_50x1_bit.goog_in21k_ft_in1k',
'resnetv2_50x3_bitm': 'resnetv2_50x3_bit.goog_in21k_ft_in1k',
'resnetv2_101x1_bitm': 'resnetv2_101x1_bit.goog_in21k_ft_in1k',
'resnetv2_101x3_bitm': 'resnetv2_101x3_bit.goog_in21k_ft_in1k',
'resnetv2_152x2_bitm': 'resnetv2_152x2_bit.goog_in21k_ft_in1k',
'resnetv2_152x4_bitm': 'resnetv2_152x4_bit.goog_in21k_ft_in1k',
'resnetv2_50x1_bitm_in21k': 'resnetv2_50x1_bit.goog_in21k',
'resnetv2_50x3_bitm_in21k': 'resnetv2_50x3_bit.goog_in21k',
'resnetv2_101x1_bitm_in21k': 'resnetv2_101x1_bit.goog_in21k',
'resnetv2_101x3_bitm_in21k': 'resnetv2_101x3_bit.goog_in21k',
'resnetv2_152x2_bitm_in21k': 'resnetv2_152x2_bit.goog_in21k',
'resnetv2_152x4_bitm_in21k': 'resnetv2_152x4_bit.goog_in21k',
'resnetv2_50x1_bit_distilled': 'resnetv2_50x1_bit.goog_distilled_in1k',
'resnetv2_152x2_bit_teacher': 'resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k',
'resnetv2_152x2_bit_teacher_384': 'resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/efficientformer.py
|
""" EfficientFormer
@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov,
Sergey and Wang, Yanzhi and Ren, Jian},
journal={arXiv preprint arXiv:2206.01191},
year={2022}
}
Based on Apache 2.0 licensed code at https://github.com/snap-research/EfficientFormer, Copyright (c) 2022 Snap Inc.
Modifications and timm support by / Copyright 2022, Ross Wightman
"""
from typing import Dict
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, trunc_normal_, to_2tuple, Mlp
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import generate_default_cfgs, register_model
__all__ = ['EfficientFormer'] # model_registry will add each entrypoint fn to this
EfficientFormer_width = {
'l1': (48, 96, 224, 448),
'l3': (64, 128, 320, 512),
'l7': (96, 192, 384, 768),
}
EfficientFormer_depth = {
'l1': (3, 2, 6, 4),
'l3': (4, 4, 12, 6),
'l7': (6, 6, 18, 8),
}
class Attention(torch.nn.Module):
attention_bias_cache: Dict[str, torch.Tensor]
def __init__(
self,
dim=384,
key_dim=32,
num_heads=8,
attn_ratio=4,
resolution=7
):
super().__init__()
self.num_heads = num_heads
self.scale = key_dim ** -0.5
self.key_dim = key_dim
self.key_attn_dim = key_dim * num_heads
self.val_dim = int(attn_ratio * key_dim)
self.val_attn_dim = self.val_dim * num_heads
self.attn_ratio = attn_ratio
self.qkv = nn.Linear(dim, self.key_attn_dim * 2 + self.val_attn_dim)
self.proj = nn.Linear(self.val_attn_dim, dim)
resolution = to_2tuple(resolution)
pos = torch.stack(torch.meshgrid(torch.arange(resolution[0]), torch.arange(resolution[1]))).flatten(1)
rel_pos = (pos[..., :, None] - pos[..., None, :]).abs()
rel_pos = (rel_pos[0] * resolution[1]) + rel_pos[1]
self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, resolution[0] * resolution[1]))
self.register_buffer('attention_bias_idxs', rel_pos)
self.attention_bias_cache = {} # per-device attention_biases cache (data-parallel compat)
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and self.attention_bias_cache:
self.attention_bias_cache = {} # clear ab cache
def get_attention_biases(self, device: torch.device) -> torch.Tensor:
if torch.jit.is_tracing() or self.training:
return self.attention_biases[:, self.attention_bias_idxs]
else:
device_key = str(device)
if device_key not in self.attention_bias_cache:
self.attention_bias_cache[device_key] = self.attention_biases[:, self.attention_bias_idxs]
return self.attention_bias_cache[device_key]
def forward(self, x): # x (B,N,C)
B, N, C = x.shape
qkv = self.qkv(x)
qkv = qkv.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
q, k, v = qkv.split([self.key_dim, self.key_dim, self.val_dim], dim=3)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn + self.get_attention_biases(x.device)
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N, self.val_attn_dim)
x = self.proj(x)
return x
class Stem4(nn.Sequential):
def __init__(self, in_chs, out_chs, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
super().__init__()
self.stride = 4
self.add_module('conv1', nn.Conv2d(in_chs, out_chs // 2, kernel_size=3, stride=2, padding=1))
self.add_module('norm1', norm_layer(out_chs // 2))
self.add_module('act1', act_layer())
self.add_module('conv2', nn.Conv2d(out_chs // 2, out_chs, kernel_size=3, stride=2, padding=1))
self.add_module('norm2', norm_layer(out_chs))
self.add_module('act2', act_layer())
class Downsample(nn.Module):
"""
Downsampling via strided conv w/ norm
Input: tensor in shape [B, C, H, W]
Output: tensor in shape [B, C, H/stride, W/stride]
"""
def __init__(self, in_chs, out_chs, kernel_size=3, stride=2, padding=None, norm_layer=nn.BatchNorm2d):
super().__init__()
if padding is None:
padding = kernel_size // 2
self.conv = nn.Conv2d(in_chs, out_chs, kernel_size=kernel_size, stride=stride, padding=padding)
self.norm = norm_layer(out_chs)
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
return x
class Flat(nn.Module):
def __init__(self, ):
super().__init__()
def forward(self, x):
x = x.flatten(2).transpose(1, 2)
return x
class Pooling(nn.Module):
"""
Implementation of pooling for PoolFormer
--pool_size: pooling size
"""
def __init__(self, pool_size=3):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False)
def forward(self, x):
return self.pool(x) - x
class ConvMlpWithNorm(nn.Module):
"""
Implementation of MLP with 1*1 convolutions.
Input: tensor with shape [B, C, H, W]
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=nn.BatchNorm2d,
drop=0.
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
self.norm1 = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
self.norm2 = norm_layer(out_features) if norm_layer is not None else nn.Identity()
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.norm1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.norm2(x)
x = self.drop(x)
return x
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class MetaBlock1d(nn.Module):
def __init__(
self,
dim,
mlp_ratio=4.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
proj_drop=0.,
drop_path=0.,
layer_scale_init_value=1e-5
):
super().__init__()
self.norm1 = norm_layer(dim)
self.token_mixer = Attention(dim)
self.norm2 = norm_layer(dim)
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.ls1 = LayerScale(dim, layer_scale_init_value)
self.ls2 = LayerScale(dim, layer_scale_init_value)
def forward(self, x):
x = x + self.drop_path(self.ls1(self.token_mixer(self.norm1(x))))
x = x + self.drop_path(self.ls2(self.mlp(self.norm2(x))))
return x
class LayerScale2d(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
gamma = self.gamma.view(1, -1, 1, 1)
return x.mul_(gamma) if self.inplace else x * gamma
class MetaBlock2d(nn.Module):
def __init__(
self,
dim,
pool_size=3,
mlp_ratio=4.,
act_layer=nn.GELU,
norm_layer=nn.BatchNorm2d,
proj_drop=0.,
drop_path=0.,
layer_scale_init_value=1e-5
):
super().__init__()
self.token_mixer = Pooling(pool_size=pool_size)
self.ls1 = LayerScale2d(dim, layer_scale_init_value)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.mlp = ConvMlpWithNorm(
dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
norm_layer=norm_layer,
drop=proj_drop,
)
self.ls2 = LayerScale2d(dim, layer_scale_init_value)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
x = x + self.drop_path1(self.ls1(self.token_mixer(x)))
x = x + self.drop_path2(self.ls2(self.mlp(x)))
return x
class EfficientFormerStage(nn.Module):
def __init__(
self,
dim,
dim_out,
depth,
downsample=True,
num_vit=1,
pool_size=3,
mlp_ratio=4.,
act_layer=nn.GELU,
norm_layer=nn.BatchNorm2d,
norm_layer_cl=nn.LayerNorm,
proj_drop=.0,
drop_path=0.,
layer_scale_init_value=1e-5,
):
super().__init__()
self.grad_checkpointing = False
if downsample:
self.downsample = Downsample(in_chs=dim, out_chs=dim_out, norm_layer=norm_layer)
dim = dim_out
else:
assert dim == dim_out
self.downsample = nn.Identity()
blocks = []
if num_vit and num_vit >= depth:
blocks.append(Flat())
for block_idx in range(depth):
remain_idx = depth - block_idx - 1
if num_vit and num_vit > remain_idx:
blocks.append(
MetaBlock1d(
dim,
mlp_ratio=mlp_ratio,
act_layer=act_layer,
norm_layer=norm_layer_cl,
proj_drop=proj_drop,
drop_path=drop_path[block_idx],
layer_scale_init_value=layer_scale_init_value,
))
else:
blocks.append(
MetaBlock2d(
dim,
pool_size=pool_size,
mlp_ratio=mlp_ratio,
act_layer=act_layer,
norm_layer=norm_layer,
proj_drop=proj_drop,
drop_path=drop_path[block_idx],
layer_scale_init_value=layer_scale_init_value,
))
if num_vit and num_vit == remain_idx:
blocks.append(Flat())
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class EfficientFormer(nn.Module):
def __init__(
self,
depths,
embed_dims=None,
in_chans=3,
num_classes=1000,
global_pool='avg',
downsamples=None,
num_vit=0,
mlp_ratios=4,
pool_size=3,
layer_scale_init_value=1e-5,
act_layer=nn.GELU,
norm_layer=nn.BatchNorm2d,
norm_layer_cl=nn.LayerNorm,
drop_rate=0.,
proj_drop_rate=0.,
drop_path_rate=0.,
**kwargs
):
super().__init__()
self.num_classes = num_classes
self.global_pool = global_pool
self.stem = Stem4(in_chans, embed_dims[0], norm_layer=norm_layer)
prev_dim = embed_dims[0]
# stochastic depth decay rule
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
downsamples = downsamples or (False,) + (True,) * (len(depths) - 1)
stages = []
for i in range(len(depths)):
stage = EfficientFormerStage(
prev_dim,
embed_dims[i],
depths[i],
downsample=downsamples[i],
num_vit=num_vit if i == 3 else 0,
pool_size=pool_size,
mlp_ratio=mlp_ratios,
act_layer=act_layer,
norm_layer_cl=norm_layer_cl,
norm_layer=norm_layer,
proj_drop=proj_drop_rate,
drop_path=dpr[i],
layer_scale_init_value=layer_scale_init_value,
)
prev_dim = embed_dims[i]
stages.append(stage)
self.stages = nn.Sequential(*stages)
# Classifier head
self.num_features = embed_dims[-1]
self.norm = norm_layer_cl(self.num_features)
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
# assuming model is always distilled (valid for current checkpoints, will split def if that changes)
self.head_dist = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
self.distilled_training = False # must set this True to train w/ distillation token
self.apply(self._init_weights)
# init for classification
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def no_weight_decay(self):
return {k for k, _ in self.named_parameters() if 'attention_biases' in k}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem', # stem and embed
blocks=[(r'^stages\.(\d+)', None), (r'^norm', (99999,))]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head, self.head_dist
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
self.head_dist = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
@torch.jit.ignore
def set_distilled_training(self, enable=True):
self.distilled_training = enable
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool == 'avg':
x = x.mean(dim=1)
x = self.head_drop(x)
if pre_logits:
return x
x, x_dist = self.head(x), self.head_dist(x)
if self.distilled_training and self.training and not torch.jit.is_scripting():
# only return separate classification predictions when training in distilled mode
return x, x_dist
else:
# during standard train/finetune, inference average the classifier predictions
return (x + x_dist) / 2
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _checkpoint_filter_fn(state_dict, model):
""" Remap original checkpoints -> timm """
if 'stem.0.weight' in state_dict:
return state_dict # non-original checkpoint, no remapping needed
out_dict = {}
import re
stage_idx = 0
for k, v in state_dict.items():
if k.startswith('patch_embed'):
k = k.replace('patch_embed.0', 'stem.conv1')
k = k.replace('patch_embed.1', 'stem.norm1')
k = k.replace('patch_embed.3', 'stem.conv2')
k = k.replace('patch_embed.4', 'stem.norm2')
if re.match(r'network\.(\d+)\.proj\.weight', k):
stage_idx += 1
k = re.sub(r'network.(\d+).(\d+)', f'stages.{stage_idx}.blocks.\\2', k)
k = re.sub(r'network.(\d+).proj', f'stages.{stage_idx}.downsample.conv', k)
k = re.sub(r'network.(\d+).norm', f'stages.{stage_idx}.downsample.norm', k)
k = re.sub(r'layer_scale_([0-9])', r'ls\1.gamma', k)
k = k.replace('dist_head', 'head_dist')
out_dict[k] = v
return out_dict
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'fixed_input_size': True,
'crop_pct': .95, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1', 'classifier': ('head', 'head_dist'),
**kwargs
}
default_cfgs = generate_default_cfgs({
'efficientformer_l1.snap_dist_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientformer_l3.snap_dist_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientformer_l7.snap_dist_in1k': _cfg(
hf_hub_id='timm/',
),
})
def _create_efficientformer(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for EfficientFormer models.')
model = build_model_with_cfg(
EfficientFormer, variant, pretrained,
pretrained_filter_fn=_checkpoint_filter_fn,
**kwargs)
return model
@register_model
def efficientformer_l1(pretrained=False, **kwargs) -> EfficientFormer:
model_args = dict(
depths=EfficientFormer_depth['l1'],
embed_dims=EfficientFormer_width['l1'],
num_vit=1,
)
return _create_efficientformer('efficientformer_l1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientformer_l3(pretrained=False, **kwargs) -> EfficientFormer:
model_args = dict(
depths=EfficientFormer_depth['l3'],
embed_dims=EfficientFormer_width['l3'],
num_vit=4,
)
return _create_efficientformer('efficientformer_l3', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientformer_l7(pretrained=False, **kwargs) -> EfficientFormer:
model_args = dict(
depths=EfficientFormer_depth['l7'],
embed_dims=EfficientFormer_width['l7'],
num_vit=8,
)
return _create_efficientformer('efficientformer_l7', pretrained=pretrained, **dict(model_args, **kwargs))
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/fx_features.py
|
from ._features_fx import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/nasnet.py
|
""" NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_classifier
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['NASNetALarge']
class ActConvBn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=''):
super(ActConvBn, self).__init__()
self.act = nn.ReLU()
self.conv = create_conv2d(
in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1)
def forward(self, x):
x = self.act(x)
x = self.conv(x)
x = self.bn(x)
return x
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding=''):
super(SeparableConv2d, self).__init__()
self.depthwise_conv2d = create_conv2d(
in_channels, in_channels, kernel_size=kernel_size,
stride=stride, padding=padding, groups=in_channels)
self.pointwise_conv2d = create_conv2d(
in_channels, out_channels, kernel_size=1, padding=0)
def forward(self, x):
x = self.depthwise_conv2d(x)
x = self.pointwise_conv2d(x)
return x
class BranchSeparables(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, pad_type='', stem_cell=False):
super(BranchSeparables, self).__init__()
middle_channels = out_channels if stem_cell else in_channels
self.act_1 = nn.ReLU()
self.separable_1 = SeparableConv2d(
in_channels, middle_channels, kernel_size, stride=stride, padding=pad_type)
self.bn_sep_1 = nn.BatchNorm2d(middle_channels, eps=0.001, momentum=0.1)
self.act_2 = nn.ReLU(inplace=True)
self.separable_2 = SeparableConv2d(
middle_channels, out_channels, kernel_size, stride=1, padding=pad_type)
self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1)
def forward(self, x):
x = self.act_1(x)
x = self.separable_1(x)
x = self.bn_sep_1(x)
x = self.act_2(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
class CellStem0(nn.Module):
def __init__(self, stem_size, num_channels=42, pad_type=''):
super(CellStem0, self).__init__()
self.num_channels = num_channels
self.stem_size = stem_size
self.conv_1x1 = ActConvBn(self.stem_size, self.num_channels, 1, stride=1)
self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, pad_type)
self.comb_iter_0_right = BranchSeparables(self.stem_size, self.num_channels, 7, 2, pad_type, stem_cell=True)
self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type)
self.comb_iter_1_right = BranchSeparables(self.stem_size, self.num_channels, 7, 2, pad_type, stem_cell=True)
self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type)
self.comb_iter_2_right = BranchSeparables(self.stem_size, self.num_channels, 5, 2, pad_type, stem_cell=True)
self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_4_left = BranchSeparables(self.num_channels, self.num_channels, 3, 1, pad_type)
self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type)
def forward(self, x):
x1 = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x1)
x_comb_iter_0_right = self.comb_iter_0_right(x)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x1)
x_comb_iter_1_right = self.comb_iter_1_right(x)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x1)
x_comb_iter_2_right = self.comb_iter_2_right(x)
x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
x_comb_iter_4_right = self.comb_iter_4_right(x1)
x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
return x_out
class CellStem1(nn.Module):
def __init__(self, stem_size, num_channels, pad_type=''):
super(CellStem1, self).__init__()
self.num_channels = num_channels
self.stem_size = stem_size
self.conv_1x1 = ActConvBn(2 * self.num_channels, self.num_channels, 1, stride=1)
self.act = nn.ReLU()
self.path_1 = nn.Sequential()
self.path_1.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
self.path_1.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels // 2, 1, stride=1, bias=False))
self.path_2 = nn.Sequential()
self.path_2.add_module('pad', nn.ZeroPad2d((-1, 1, -1, 1)))
self.path_2.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
self.path_2.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels // 2, 1, stride=1, bias=False))
self.final_path_bn = nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1)
self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, pad_type)
self.comb_iter_0_right = BranchSeparables(self.num_channels, self.num_channels, 7, 2, pad_type)
self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type)
self.comb_iter_1_right = BranchSeparables(self.num_channels, self.num_channels, 7, 2, pad_type)
self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type)
self.comb_iter_2_right = BranchSeparables(self.num_channels, self.num_channels, 5, 2, pad_type)
self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_4_left = BranchSeparables(self.num_channels, self.num_channels, 3, 1, pad_type)
self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type)
def forward(self, x_conv0, x_stem_0):
x_left = self.conv_1x1(x_stem_0)
x_relu = self.act(x_conv0)
# path 1
x_path1 = self.path_1(x_relu)
# path 2
x_path2 = self.path_2(x_relu)
# final path
x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
x_comb_iter_0_left = self.comb_iter_0_left(x_left)
x_comb_iter_0_right = self.comb_iter_0_right(x_right)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_left)
x_comb_iter_1_right = self.comb_iter_1_right(x_right)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_left)
x_comb_iter_2_right = self.comb_iter_2_right(x_right)
x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
x_comb_iter_4_right = self.comb_iter_4_right(x_left)
x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
return x_out
class FirstCell(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(FirstCell, self).__init__()
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1)
self.act = nn.ReLU()
self.path_1 = nn.Sequential()
self.path_1.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
self.path_1.add_module('conv', nn.Conv2d(in_chs_left, out_chs_left, 1, stride=1, bias=False))
self.path_2 = nn.Sequential()
self.path_2.add_module('pad', nn.ZeroPad2d((-1, 1, -1, 1)))
self.path_2.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
self.path_2.add_module('conv', nn.Conv2d(in_chs_left, out_chs_left, 1, stride=1, bias=False))
self.final_path_bn = nn.BatchNorm2d(out_chs_left * 2, eps=0.001, momentum=0.1)
self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 1, pad_type)
self.comb_iter_0_right = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
self.comb_iter_1_left = BranchSeparables(out_chs_right, out_chs_right, 5, 1, pad_type)
self.comb_iter_1_right = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
self.comb_iter_2_left = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_3_left = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
def forward(self, x, x_prev):
x_relu = self.act(x_prev)
x_path1 = self.path_1(x_relu)
x_path2 = self.path_2(x_relu)
x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_left)
x_comb_iter_1_right = self.comb_iter_1_right(x_left)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_right)
x_comb_iter_2 = x_comb_iter_2_left + x_left
x_comb_iter_3_left = self.comb_iter_3_left(x_left)
x_comb_iter_3_right = self.comb_iter_3_right(x_left)
x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
x_comb_iter_4_left = self.comb_iter_4_left(x_right)
x_comb_iter_4 = x_comb_iter_4_left + x_right
x_out = torch.cat([x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
return x_out
class NormalCell(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(NormalCell, self).__init__()
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1, padding=pad_type)
self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 1, pad_type)
self.comb_iter_0_right = BranchSeparables(out_chs_left, out_chs_left, 3, 1, pad_type)
self.comb_iter_1_left = BranchSeparables(out_chs_left, out_chs_left, 5, 1, pad_type)
self.comb_iter_1_right = BranchSeparables(out_chs_left, out_chs_left, 3, 1, pad_type)
self.comb_iter_2_left = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_3_left = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
def forward(self, x, x_prev):
x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_left)
x_comb_iter_1_right = self.comb_iter_1_right(x_left)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_right)
x_comb_iter_2 = x_comb_iter_2_left + x_left
x_comb_iter_3_left = self.comb_iter_3_left(x_left)
x_comb_iter_3_right = self.comb_iter_3_right(x_left)
x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
x_comb_iter_4_left = self.comb_iter_4_left(x_right)
x_comb_iter_4 = x_comb_iter_4_left + x_right
x_out = torch.cat([x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
return x_out
class ReductionCell0(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(ReductionCell0, self).__init__()
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1, padding=pad_type)
self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type)
self.comb_iter_0_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type)
self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type)
self.comb_iter_1_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type)
self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type)
self.comb_iter_2_right = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type)
self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type)
def forward(self, x, x_prev):
x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_right)
x_comb_iter_1_right = self.comb_iter_1_right(x_left)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_right)
x_comb_iter_2_right = self.comb_iter_2_right(x_left)
x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
x_comb_iter_4_right = self.comb_iter_4_right(x_right)
x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
return x_out
class ReductionCell1(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(ReductionCell1, self).__init__()
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1, padding=pad_type)
self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type)
self.comb_iter_0_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type)
self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type)
self.comb_iter_1_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type)
self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type)
self.comb_iter_2_right = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type)
self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type)
def forward(self, x, x_prev):
x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_right)
x_comb_iter_1_right = self.comb_iter_1_right(x_left)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_right)
x_comb_iter_2_right = self.comb_iter_2_right(x_left)
x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
x_comb_iter_4_right = self.comb_iter_4_right(x_right)
x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
return x_out
class NASNetALarge(nn.Module):
"""NASNetALarge (6 @ 4032) """
def __init__(
self,
num_classes=1000,
in_chans=3,
stem_size=96,
channel_multiplier=2,
num_features=4032,
output_stride=32,
drop_rate=0.,
global_pool='avg',
pad_type='same',
):
super(NASNetALarge, self).__init__()
self.num_classes = num_classes
self.stem_size = stem_size
self.num_features = num_features
self.channel_multiplier = channel_multiplier
assert output_stride == 32
channels = self.num_features // 24
# 24 is default value for the architecture
self.conv0 = ConvNormAct(
in_channels=in_chans, out_channels=self.stem_size, kernel_size=3, padding=0, stride=2,
norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.1), apply_act=False)
self.cell_stem_0 = CellStem0(
self.stem_size, num_channels=channels // (channel_multiplier ** 2), pad_type=pad_type)
self.cell_stem_1 = CellStem1(
self.stem_size, num_channels=channels // channel_multiplier, pad_type=pad_type)
self.cell_0 = FirstCell(
in_chs_left=channels, out_chs_left=channels // 2,
in_chs_right=2 * channels, out_chs_right=channels, pad_type=pad_type)
self.cell_1 = NormalCell(
in_chs_left=2 * channels, out_chs_left=channels,
in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
self.cell_2 = NormalCell(
in_chs_left=6 * channels, out_chs_left=channels,
in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
self.cell_3 = NormalCell(
in_chs_left=6 * channels, out_chs_left=channels,
in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
self.cell_4 = NormalCell(
in_chs_left=6 * channels, out_chs_left=channels,
in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
self.cell_5 = NormalCell(
in_chs_left=6 * channels, out_chs_left=channels,
in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
self.reduction_cell_0 = ReductionCell0(
in_chs_left=6 * channels, out_chs_left=2 * channels,
in_chs_right=6 * channels, out_chs_right=2 * channels, pad_type=pad_type)
self.cell_6 = FirstCell(
in_chs_left=6 * channels, out_chs_left=channels,
in_chs_right=8 * channels, out_chs_right=2 * channels, pad_type=pad_type)
self.cell_7 = NormalCell(
in_chs_left=8 * channels, out_chs_left=2 * channels,
in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
self.cell_8 = NormalCell(
in_chs_left=12 * channels, out_chs_left=2 * channels,
in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
self.cell_9 = NormalCell(
in_chs_left=12 * channels, out_chs_left=2 * channels,
in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
self.cell_10 = NormalCell(
in_chs_left=12 * channels, out_chs_left=2 * channels,
in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
self.cell_11 = NormalCell(
in_chs_left=12 * channels, out_chs_left=2 * channels,
in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
self.reduction_cell_1 = ReductionCell1(
in_chs_left=12 * channels, out_chs_left=4 * channels,
in_chs_right=12 * channels, out_chs_right=4 * channels, pad_type=pad_type)
self.cell_12 = FirstCell(
in_chs_left=12 * channels, out_chs_left=2 * channels,
in_chs_right=16 * channels, out_chs_right=4 * channels, pad_type=pad_type)
self.cell_13 = NormalCell(
in_chs_left=16 * channels, out_chs_left=4 * channels,
in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
self.cell_14 = NormalCell(
in_chs_left=24 * channels, out_chs_left=4 * channels,
in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
self.cell_15 = NormalCell(
in_chs_left=24 * channels, out_chs_left=4 * channels,
in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
self.cell_16 = NormalCell(
in_chs_left=24 * channels, out_chs_left=4 * channels,
in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
self.cell_17 = NormalCell(
in_chs_left=24 * channels, out_chs_left=4 * channels,
in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
self.act = nn.ReLU(inplace=True)
self.feature_info = [
dict(num_chs=96, reduction=2, module='conv0'),
dict(num_chs=168, reduction=4, module='cell_stem_1.conv_1x1.act'),
dict(num_chs=1008, reduction=8, module='reduction_cell_0.conv_1x1.act'),
dict(num_chs=2016, reduction=16, module='reduction_cell_1.conv_1x1.act'),
dict(num_chs=4032, reduction=32, module='act'),
]
self.global_pool, self.head_drop, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, drop_rate=drop_rate)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^conv0|cell_stem_[01]',
blocks=[
(r'^cell_(\d+)', None),
(r'^reduction_cell_0', (6,)),
(r'^reduction_cell_1', (12,)),
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.last_linear
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x_conv0 = self.conv0(x)
x_stem_0 = self.cell_stem_0(x_conv0)
x_stem_1 = self.cell_stem_1(x_conv0, x_stem_0)
x_cell_0 = self.cell_0(x_stem_1, x_stem_0)
x_cell_1 = self.cell_1(x_cell_0, x_stem_1)
x_cell_2 = self.cell_2(x_cell_1, x_cell_0)
x_cell_3 = self.cell_3(x_cell_2, x_cell_1)
x_cell_4 = self.cell_4(x_cell_3, x_cell_2)
x_cell_5 = self.cell_5(x_cell_4, x_cell_3)
x_reduction_cell_0 = self.reduction_cell_0(x_cell_5, x_cell_4)
x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_4)
x_cell_7 = self.cell_7(x_cell_6, x_reduction_cell_0)
x_cell_8 = self.cell_8(x_cell_7, x_cell_6)
x_cell_9 = self.cell_9(x_cell_8, x_cell_7)
x_cell_10 = self.cell_10(x_cell_9, x_cell_8)
x_cell_11 = self.cell_11(x_cell_10, x_cell_9)
x_reduction_cell_1 = self.reduction_cell_1(x_cell_11, x_cell_10)
x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_10)
x_cell_13 = self.cell_13(x_cell_12, x_reduction_cell_1)
x_cell_14 = self.cell_14(x_cell_13, x_cell_12)
x_cell_15 = self.cell_15(x_cell_14, x_cell_13)
x_cell_16 = self.cell_16(x_cell_15, x_cell_14)
x_cell_17 = self.cell_17(x_cell_16, x_cell_15)
x = self.act(x_cell_17)
return x
def forward_head(self, x):
x = self.global_pool(x)
x = self.head_drop(x)
x = self.last_linear(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_nasnet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
NASNetALarge,
variant,
pretrained,
feature_cfg=dict(feature_cls='hook', no_rewrite=True), # not possible to re-write this model
**kwargs,
)
default_cfgs = generate_default_cfgs({
'nasnetalarge.tf_in1k': {
'hf_hub_id': 'timm/',
'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nasnetalarge-dc4a7b8b.pth',
'input_size': (3, 331, 331),
'pool_size': (11, 11),
'crop_pct': 0.911,
'interpolation': 'bicubic',
'mean': (0.5, 0.5, 0.5),
'std': (0.5, 0.5, 0.5),
'num_classes': 1000,
'first_conv': 'conv0.conv',
'classifier': 'last_linear',
},
})
@register_model
def nasnetalarge(pretrained=False, **kwargs) -> NASNetALarge:
"""NASNet-A large model architecture.
"""
model_kwargs = dict(pad_type='same', **kwargs)
return _create_nasnet('nasnetalarge', pretrained, **model_kwargs)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/helpers.py
|
from ._builder import *
from ._helpers import *
from ._manipulate import *
from ._prune import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/nest.py
|
""" Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been converted with convert/convert_nest_flax.py
Acknowledgments:
* The paper authors for sharing their research, code, and model weights
* Ross Wightman's existing code off which I based this
Copyright 2021 Alexander Soare
"""
import collections.abc
import logging
import math
from functools import partial
import torch
import torch.nn.functional as F
from torch import nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import PatchEmbed, Mlp, DropPath, create_classifier, trunc_normal_, _assert
from timm.layers import create_conv2d, create_pool2d, to_ntuple, use_fused_attn, LayerNorm
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._manipulate import checkpoint_seq, named_apply
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
__all__ = ['Nest'] # model_registry will add each entrypoint fn to this
_logger = logging.getLogger(__name__)
class Attention(nn.Module):
"""
This is much like `.vision_transformer.Attention` but uses *localised* self attention by accepting an input with
an extra "image block" dim
"""
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, 3*dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
"""
x is shape: B (batch_size), T (image blocks), N (seq length per image block), C (embed dim)
"""
B, T, N, C = x.shape
# result of next line is (qkv, B, num (H)eads, T, N, (C')hannels per head)
qkv = self.qkv(x).reshape(B, T, N, 3, self.num_heads, C // self.num_heads).permute(3, 0, 4, 1, 2, 5)
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
if self.fused_attn:
x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1) # (B, H, T, N, N)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
# (B, H, T, N, C'), permute -> (B, T, N, C', H)
x = x.permute(0, 2, 3, 4, 1).reshape(B, T, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x # (B, T, N, C)
class TransformerLayer(nn.Module):
"""
This is much like `.vision_transformer.Block` but:
- Called TransformerLayer here to allow for "block" as defined in the paper ("non-overlapping image blocks")
- Uses modified Attention layer that handles the "block" dimension
"""
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=proj_drop,
)
def forward(self, x):
y = self.norm1(x)
x = x + self.drop_path(self.attn(y))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class ConvPool(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer, pad_type=''):
super().__init__()
self.conv = create_conv2d(in_channels, out_channels, kernel_size=3, padding=pad_type, bias=True)
self.norm = norm_layer(out_channels)
self.pool = create_pool2d('max', kernel_size=3, stride=2, padding=pad_type)
def forward(self, x):
"""
x is expected to have shape (B, C, H, W)
"""
_assert(x.shape[-2] % 2 == 0, 'BlockAggregation requires even input spatial dims')
_assert(x.shape[-1] % 2 == 0, 'BlockAggregation requires even input spatial dims')
x = self.conv(x)
# Layer norm done over channel dim only
x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
x = self.pool(x)
return x # (B, C, H//2, W//2)
def blockify(x, block_size: int):
"""image to blocks
Args:
x (Tensor): with shape (B, H, W, C)
block_size (int): edge length of a single square block in units of H, W
"""
B, H, W, C = x.shape
_assert(H % block_size == 0, '`block_size` must divide input height evenly')
_assert(W % block_size == 0, '`block_size` must divide input width evenly')
grid_height = H // block_size
grid_width = W // block_size
x = x.reshape(B, grid_height, block_size, grid_width, block_size, C)
x = x.transpose(2, 3).reshape(B, grid_height * grid_width, -1, C)
return x # (B, T, N, C)
@register_notrace_function # reason: int receives Proxy
def deblockify(x, block_size: int):
"""blocks to image
Args:
x (Tensor): with shape (B, T, N, C) where T is number of blocks and N is sequence size per block
block_size (int): edge length of a single square block in units of desired H, W
"""
B, T, _, C = x.shape
grid_size = int(math.sqrt(T))
height = width = grid_size * block_size
x = x.reshape(B, grid_size, grid_size, block_size, block_size, C)
x = x.transpose(2, 3).reshape(B, height, width, C)
return x # (B, H, W, C)
class NestLevel(nn.Module):
""" Single hierarchical level of a Nested Transformer
"""
def __init__(
self,
num_blocks,
block_size,
seq_length,
num_heads,
depth,
embed_dim,
prev_embed_dim=None,
mlp_ratio=4.,
qkv_bias=True,
proj_drop=0.,
attn_drop=0.,
drop_path=[],
norm_layer=None,
act_layer=None,
pad_type='',
):
super().__init__()
self.block_size = block_size
self.grad_checkpointing = False
self.pos_embed = nn.Parameter(torch.zeros(1, num_blocks, seq_length, embed_dim))
if prev_embed_dim is not None:
self.pool = ConvPool(prev_embed_dim, embed_dim, norm_layer=norm_layer, pad_type=pad_type)
else:
self.pool = nn.Identity()
# Transformer encoder
if len(drop_path):
assert len(drop_path) == depth, 'Must provide as many drop path rates as there are transformer layers'
self.transformer_encoder = nn.Sequential(*[
TransformerLayer(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[i],
norm_layer=norm_layer,
act_layer=act_layer,
)
for i in range(depth)])
def forward(self, x):
"""
expects x as (B, C, H, W)
"""
x = self.pool(x)
x = x.permute(0, 2, 3, 1) # (B, H', W', C), switch to channels last for transformer
x = blockify(x, self.block_size) # (B, T, N, C')
x = x + self.pos_embed
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.transformer_encoder, x)
else:
x = self.transformer_encoder(x) # (B, T, N, C')
x = deblockify(x, self.block_size) # (B, H', W', C')
# Channel-first for block aggregation, and generally to replicate convnet feature map at each stage
return x.permute(0, 3, 1, 2) # (B, C, H', W')
class Nest(nn.Module):
""" Nested Transformer (NesT)
A PyTorch impl of : `Aggregating Nested Transformers`
- https://arxiv.org/abs/2105.12723
"""
def __init__(
self,
img_size=224,
in_chans=3,
patch_size=4,
num_levels=3,
embed_dims=(128, 256, 512),
num_heads=(4, 8, 16),
depths=(2, 2, 20),
num_classes=1000,
mlp_ratio=4.,
qkv_bias=True,
drop_rate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.5,
norm_layer=None,
act_layer=None,
pad_type='',
weight_init='',
global_pool='avg',
):
"""
Args:
img_size (int, tuple): input image size
in_chans (int): number of input channels
patch_size (int): patch size
num_levels (int): number of block hierarchies (T_d in the paper)
embed_dims (int, tuple): embedding dimensions of each level
num_heads (int, tuple): number of attention heads for each level
depths (int, tuple): number of transformer layers for each level
num_classes (int): number of classes for classification head
mlp_ratio (int): ratio of mlp hidden dim to embedding dim for MLP of transformer layers
qkv_bias (bool): enable bias for qkv if True
drop_rate (float): dropout rate for MLP of transformer layers, MSA final projection layer, and classifier
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
norm_layer: (nn.Module): normalization layer for transformer layers
act_layer: (nn.Module): activation layer in MLP of transformer layers
pad_type: str: Type of padding to use '' for PyTorch symmetric, 'same' for TF SAME
weight_init: (str): weight init scheme
global_pool: (str): type of pooling operation to apply to final feature map
Notes:
- Default values follow NesT-B from the original Jax code.
- `embed_dims`, `num_heads`, `depths` should be ints or tuples with length `num_levels`.
- For those following the paper, Table A1 may have errors!
- https://github.com/google-research/nested-transformer/issues/2
"""
super().__init__()
for param_name in ['embed_dims', 'num_heads', 'depths']:
param_value = locals()[param_name]
if isinstance(param_value, collections.abc.Sequence):
assert len(param_value) == num_levels, f'Require `len({param_name}) == num_levels`'
embed_dims = to_ntuple(num_levels)(embed_dims)
num_heads = to_ntuple(num_levels)(num_heads)
depths = to_ntuple(num_levels)(depths)
self.num_classes = num_classes
self.num_features = embed_dims[-1]
self.feature_info = []
norm_layer = norm_layer or LayerNorm
act_layer = act_layer or nn.GELU
self.drop_rate = drop_rate
self.num_levels = num_levels
if isinstance(img_size, collections.abc.Sequence):
assert img_size[0] == img_size[1], 'Model only handles square inputs'
img_size = img_size[0]
assert img_size % patch_size == 0, '`patch_size` must divide `img_size` evenly'
self.patch_size = patch_size
# Number of blocks at each level
self.num_blocks = (4 ** torch.arange(num_levels)).flip(0).tolist()
assert (img_size // patch_size) % math.sqrt(self.num_blocks[0]) == 0, \
'First level blocks don\'t fit evenly. Check `img_size`, `patch_size`, and `num_levels`'
# Block edge size in units of patches
# Hint: (img_size // patch_size) gives number of patches along edge of image. sqrt(self.num_blocks[0]) is the
# number of blocks along edge of image
self.block_size = int((img_size // patch_size) // math.sqrt(self.num_blocks[0]))
# Patch embedding
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dims[0],
flatten=False,
)
self.num_patches = self.patch_embed.num_patches
self.seq_length = self.num_patches // self.num_blocks[0]
# Build up each hierarchical level
levels = []
dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
prev_dim = None
curr_stride = 4
for i in range(len(self.num_blocks)):
dim = embed_dims[i]
levels.append(NestLevel(
self.num_blocks[i],
self.block_size,
self.seq_length,
num_heads[i],
depths[i],
dim,
prev_dim,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dp_rates[i],
norm_layer=norm_layer,
act_layer=act_layer,
pad_type=pad_type,
))
self.feature_info += [dict(num_chs=dim, reduction=curr_stride, module=f'levels.{i}')]
prev_dim = dim
curr_stride *= 2
self.levels = nn.Sequential(*levels)
# Final normalization layer
self.norm = norm_layer(embed_dims[-1])
# Classifier
global_pool, head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
self.global_pool = global_pool
self.head_drop = nn.Dropout(drop_rate)
self.head = head
self.init_weights(weight_init)
@torch.jit.ignore
def init_weights(self, mode=''):
assert mode in ('nlhb', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
for level in self.levels:
trunc_normal_(level.pos_embed, std=.02, a=-2, b=2)
named_apply(partial(_init_nest_weights, head_bias=head_bias), self)
@torch.jit.ignore
def no_weight_decay(self):
return {f'level.{i}.pos_embed' for i in range(len(self.levels))}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^patch_embed', # stem and embed
blocks=[
(r'^levels\.(\d+)' if coarse else r'^levels\.(\d+)\.transformer_encoder\.(\d+)', None),
(r'^levels\.(\d+)\.(?:pool|pos_embed)', (0,)),
(r'^norm', (99999,))
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for l in self.levels:
l.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.head = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.patch_embed(x)
x = self.levels(x)
# Layer norm done over channel dim only (to NHWC and back)
x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_nest_weights(module: nn.Module, name: str = '', head_bias: float = 0.):
""" NesT weight initialization
Can replicate Jax implementation. Otherwise follows vision_transformer.py
"""
if isinstance(module, nn.Linear):
if name.startswith('head'):
trunc_normal_(module.weight, std=.02, a=-2, b=2)
nn.init.constant_(module.bias, head_bias)
else:
trunc_normal_(module.weight, std=.02, a=-2, b=2)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
trunc_normal_(module.weight, std=.02, a=-2, b=2)
if module.bias is not None:
nn.init.zeros_(module.bias)
def resize_pos_embed(posemb, posemb_new):
"""
Rescale the grid of position embeddings when loading from state_dict
Expected shape of position embeddings is (1, T, N, C), and considers only square images
"""
_logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
seq_length_old = posemb.shape[2]
num_blocks_new, seq_length_new = posemb_new.shape[1:3]
size_new = int(math.sqrt(num_blocks_new*seq_length_new))
# First change to (1, C, H, W)
posemb = deblockify(posemb, int(math.sqrt(seq_length_old))).permute(0, 3, 1, 2)
posemb = F.interpolate(posemb, size=[size_new, size_new], mode='bicubic', align_corners=False)
# Now change to new (1, T, N, C)
posemb = blockify(posemb.permute(0, 2, 3, 1), int(math.sqrt(seq_length_new)))
return posemb
def checkpoint_filter_fn(state_dict, model):
""" resize positional embeddings of pretrained weights """
pos_embed_keys = [k for k in state_dict.keys() if k.startswith('pos_embed_')]
for k in pos_embed_keys:
if state_dict[k].shape != getattr(model, k).shape:
state_dict[k] = resize_pos_embed(state_dict[k], getattr(model, k))
return state_dict
def _create_nest(variant, pretrained=False, **kwargs):
model = build_model_with_cfg(
Nest,
variant,
pretrained,
feature_cfg=dict(out_indices=(0, 1, 2), flatten_sequential=True),
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs,
)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': [14, 14],
'crop_pct': .875, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = generate_default_cfgs({
'nest_base.untrained': _cfg(),
'nest_small.untrained': _cfg(),
'nest_tiny.untrained': _cfg(),
# (weights from official Google JAX impl, require 'SAME' padding)
'nest_base_jx.goog_in1k': _cfg(hf_hub_id='timm/'),
'nest_small_jx.goog_in1k': _cfg(hf_hub_id='timm/'),
'nest_tiny_jx.goog_in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def nest_base(pretrained=False, **kwargs) -> Nest:
""" Nest-B @ 224x224
"""
model_kwargs = dict(
embed_dims=(128, 256, 512), num_heads=(4, 8, 16), depths=(2, 2, 20), **kwargs)
model = _create_nest('nest_base', pretrained=pretrained, **model_kwargs)
return model
@register_model
def nest_small(pretrained=False, **kwargs) -> Nest:
""" Nest-S @ 224x224
"""
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 20), **kwargs)
model = _create_nest('nest_small', pretrained=pretrained, **model_kwargs)
return model
@register_model
def nest_tiny(pretrained=False, **kwargs) -> Nest:
""" Nest-T @ 224x224
"""
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 8), **kwargs)
model = _create_nest('nest_tiny', pretrained=pretrained, **model_kwargs)
return model
@register_model
def nest_base_jx(pretrained=False, **kwargs) -> Nest:
""" Nest-B @ 224x224
"""
kwargs.setdefault('pad_type', 'same')
model_kwargs = dict(
embed_dims=(128, 256, 512), num_heads=(4, 8, 16), depths=(2, 2, 20), **kwargs)
model = _create_nest('nest_base_jx', pretrained=pretrained, **model_kwargs)
return model
@register_model
def nest_small_jx(pretrained=False, **kwargs) -> Nest:
""" Nest-S @ 224x224
"""
kwargs.setdefault('pad_type', 'same')
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 20), **kwargs)
model = _create_nest('nest_small_jx', pretrained=pretrained, **model_kwargs)
return model
@register_model
def nest_tiny_jx(pretrained=False, **kwargs) -> Nest:
""" Nest-T @ 224x224
"""
kwargs.setdefault('pad_type', 'same')
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 8), **kwargs)
model = _create_nest('nest_tiny_jx', pretrained=pretrained, **model_kwargs)
return model
register_model_deprecations(__name__, {
'jx_nest_base': 'nest_base_jx',
'jx_nest_small': 'nest_small_jx',
'jx_nest_tiny': 'nest_tiny_jx',
})
| 0
|
hf_public_repos/pytorch-image-models/timm
|
hf_public_repos/pytorch-image-models/timm/models/davit.py
|
""" DaViT: Dual Attention Vision Transformers
As described in https://arxiv.org/abs/2204.03645
Input size invariant transformer architecture that combines channel and spacial
attention in each block. The attention mechanisms used are linear in complexity.
DaViT model defs and weights adapted from https://github.com/dingmyu/davit, original copyright below
"""
# Copyright (c) 2022 Mingyu Ding
# All rights reserved.
# This source code is licensed under the MIT license
from functools import partial
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, to_2tuple, trunc_normal_, Mlp, LayerNorm2d, get_norm_layer, use_fused_attn
from timm.layers import NormMlpClassifierHead, ClassifierHead
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._manipulate import checkpoint_seq
from ._registry import generate_default_cfgs, register_model
__all__ = ['DaVit']
class ConvPosEnc(nn.Module):
def __init__(self, dim: int, k: int = 3, act: bool = False):
super(ConvPosEnc, self).__init__()
self.proj = nn.Conv2d(dim, dim, k, 1, k // 2, groups=dim)
self.act = nn.GELU() if act else nn.Identity()
def forward(self, x: Tensor):
feat = self.proj(x)
x = x + self.act(feat)
return x
class Stem(nn.Module):
""" Size-agnostic implementation of 2D image to patch embedding,
allowing input size to be adjusted during model forward operation
"""
def __init__(
self,
in_chs=3,
out_chs=96,
stride=4,
norm_layer=LayerNorm2d,
):
super().__init__()
stride = to_2tuple(stride)
self.stride = stride
self.in_chs = in_chs
self.out_chs = out_chs
assert stride[0] == 4 # only setup for stride==4
self.conv = nn.Conv2d(
in_chs,
out_chs,
kernel_size=7,
stride=stride,
padding=3,
)
self.norm = norm_layer(out_chs)
def forward(self, x: Tensor):
B, C, H, W = x.shape
x = F.pad(x, (0, (self.stride[1] - W % self.stride[1]) % self.stride[1]))
x = F.pad(x, (0, 0, 0, (self.stride[0] - H % self.stride[0]) % self.stride[0]))
x = self.conv(x)
x = self.norm(x)
return x
class Downsample(nn.Module):
def __init__(
self,
in_chs,
out_chs,
norm_layer=LayerNorm2d,
):
super().__init__()
self.in_chs = in_chs
self.out_chs = out_chs
self.norm = norm_layer(in_chs)
self.conv = nn.Conv2d(
in_chs,
out_chs,
kernel_size=2,
stride=2,
padding=0,
)
def forward(self, x: Tensor):
B, C, H, W = x.shape
x = self.norm(x)
x = F.pad(x, (0, (2 - W % 2) % 2))
x = F.pad(x, (0, 0, 0, (2 - H % 2) % 2))
x = self.conv(x)
return x
class ChannelAttention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
def forward(self, x: Tensor):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
k = k * self.scale
attention = k.transpose(-1, -2) @ v
attention = attention.softmax(dim=-1)
x = (attention @ q.transpose(-1, -2)).transpose(-1, -2)
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
return x
class ChannelBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
ffn=True,
cpe_act=False,
):
super().__init__()
self.cpe1 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
self.ffn = ffn
self.norm1 = norm_layer(dim)
self.attn = ChannelAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.cpe2 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
if self.ffn:
self.norm2 = norm_layer(dim)
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
else:
self.norm2 = None
self.mlp = None
self.drop_path2 = None
def forward(self, x: Tensor):
B, C, H, W = x.shape
x = self.cpe1(x).flatten(2).transpose(1, 2)
cur = self.norm1(x)
cur = self.attn(cur)
x = x + self.drop_path1(cur)
x = self.cpe2(x.transpose(1, 2).view(B, C, H, W))
if self.mlp is not None:
x = x.flatten(2).transpose(1, 2)
x = x + self.drop_path2(self.mlp(self.norm2(x)))
x = x.transpose(1, 2).view(B, C, H, W)
return x
def window_partition(x: Tensor, window_size: Tuple[int, int]):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
return windows
@register_notrace_function # reason: int argument is a Proxy
def window_reverse(windows: Tensor, window_size: Tuple[int, int], H: int, W: int):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
C = windows.shape[-1]
x = windows.view(-1, H // window_size[0], W // window_size[1], window_size[0], window_size[1], C)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
return x
class WindowAttention(nn.Module):
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
"""
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, window_size, num_heads, qkv_bias=True):
super().__init__()
self.dim = dim
self.window_size = window_size
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x: Tensor):
B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
if self.fused_attn:
x = F.scaled_dot_product_attention(q, k, v)
else:
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
attn = self.softmax(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
return x
class SpatialBlock(nn.Module):
r""" Windows Block.
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
window_size (int): Window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(
self,
dim,
num_heads,
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
ffn=True,
cpe_act=False,
):
super().__init__()
self.dim = dim
self.ffn = ffn
self.num_heads = num_heads
self.window_size = to_2tuple(window_size)
self.mlp_ratio = mlp_ratio
self.cpe1 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
self.window_size,
num_heads=num_heads,
qkv_bias=qkv_bias,
)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.cpe2 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
if self.ffn:
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
else:
self.norm2 = None
self.mlp = None
self.drop_path1 = None
def forward(self, x: Tensor):
B, C, H, W = x.shape
shortcut = self.cpe1(x).flatten(2).transpose(1, 2)
x = self.norm1(shortcut)
x = x.view(B, H, W, C)
pad_l = pad_t = 0
pad_r = (self.window_size[1] - W % self.window_size[1]) % self.window_size[1]
pad_b = (self.window_size[0] - H % self.window_size[0]) % self.window_size[0]
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
_, Hp, Wp, _ = x.shape
x_windows = window_partition(x, self.window_size)
x_windows = x_windows.view(-1, self.window_size[0] * self.window_size[1], C)
# W-MSA/SW-MSA
attn_windows = self.attn(x_windows)
# merge windows
attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C)
x = window_reverse(attn_windows, self.window_size, Hp, Wp)
# if pad_r > 0 or pad_b > 0:
x = x[:, :H, :W, :].contiguous()
x = x.view(B, H * W, C)
x = shortcut + self.drop_path1(x)
x = self.cpe2(x.transpose(1, 2).view(B, C, H, W))
if self.mlp is not None:
x = x.flatten(2).transpose(1, 2)
x = x + self.drop_path2(self.mlp(self.norm2(x)))
x = x.transpose(1, 2).view(B, C, H, W)
return x
class DaVitStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
depth=1,
downsample=True,
attn_types=('spatial', 'channel'),
num_heads=3,
window_size=7,
mlp_ratio=4,
qkv_bias=True,
drop_path_rates=(0, 0),
norm_layer=LayerNorm2d,
norm_layer_cl=nn.LayerNorm,
ffn=True,
cpe_act=False
):
super().__init__()
self.grad_checkpointing = False
# downsample embedding layer at the beginning of each stage
if downsample:
self.downsample = Downsample(in_chs, out_chs, norm_layer=norm_layer)
else:
self.downsample = nn.Identity()
'''
repeating alternating attention blocks in each stage
default: (spatial -> channel) x depth
potential opportunity to integrate with a more general version of ByobNet/ByoaNet
since the logic is similar
'''
stage_blocks = []
for block_idx in range(depth):
dual_attention_block = []
for attn_idx, attn_type in enumerate(attn_types):
if attn_type == 'spatial':
dual_attention_block.append(SpatialBlock(
dim=out_chs,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop_path=drop_path_rates[block_idx],
norm_layer=norm_layer_cl,
ffn=ffn,
cpe_act=cpe_act,
window_size=window_size,
))
elif attn_type == 'channel':
dual_attention_block.append(ChannelBlock(
dim=out_chs,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop_path=drop_path_rates[block_idx],
norm_layer=norm_layer_cl,
ffn=ffn,
cpe_act=cpe_act
))
stage_blocks.append(nn.Sequential(*dual_attention_block))
self.blocks = nn.Sequential(*stage_blocks)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
def forward(self, x: Tensor):
x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class DaVit(nn.Module):
r""" DaViT
A PyTorch implementation of `DaViT: Dual Attention Vision Transformers` - https://arxiv.org/abs/2204.03645
Supports arbitrary input sizes and pyramid feature extraction
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
depths (tuple(int)): Number of blocks in each stage. Default: (1, 1, 3, 1)
embed_dims (tuple(int)): Patch embedding dimension. Default: (96, 192, 384, 768)
num_heads (tuple(int)): Number of attention heads in different layers. Default: (3, 6, 12, 24)
window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
"""
def __init__(
self,
in_chans=3,
depths=(1, 1, 3, 1),
embed_dims=(96, 192, 384, 768),
num_heads=(3, 6, 12, 24),
window_size=7,
mlp_ratio=4,
qkv_bias=True,
norm_layer='layernorm2d',
norm_layer_cl='layernorm',
norm_eps=1e-5,
attn_types=('spatial', 'channel'),
ffn=True,
cpe_act=False,
drop_rate=0.,
drop_path_rate=0.,
num_classes=1000,
global_pool='avg',
head_norm_first=False,
):
super().__init__()
num_stages = len(embed_dims)
assert num_stages == len(num_heads) == len(depths)
norm_layer = partial(get_norm_layer(norm_layer), eps=norm_eps)
norm_layer_cl = partial(get_norm_layer(norm_layer_cl), eps=norm_eps)
self.num_classes = num_classes
self.num_features = embed_dims[-1]
self.drop_rate = drop_rate
self.grad_checkpointing = False
self.feature_info = []
self.stem = Stem(in_chans, embed_dims[0], norm_layer=norm_layer)
in_chs = embed_dims[0]
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
stages = []
for stage_idx in range(num_stages):
out_chs = embed_dims[stage_idx]
stage = DaVitStage(
in_chs,
out_chs,
depth=depths[stage_idx],
downsample=stage_idx > 0,
attn_types=attn_types,
num_heads=num_heads[stage_idx],
window_size=window_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop_path_rates=dpr[stage_idx],
norm_layer=norm_layer,
norm_layer_cl=norm_layer_cl,
ffn=ffn,
cpe_act=cpe_act,
)
in_chs = out_chs
stages.append(stage)
self.feature_info += [dict(num_chs=out_chs, reduction=2, module=f'stages.{stage_idx}')]
self.stages = nn.Sequential(*stages)
# if head_norm_first == true, norm -> global pool -> fc ordering, like most other nets
# otherwise pool -> norm -> fc, the default DaViT order, similar to ConvNeXt
# FIXME generalize this structure to ClassifierHead
if head_norm_first:
self.norm_pre = norm_layer(self.num_features)
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=self.drop_rate,
)
else:
self.norm_pre = nn.Identity()
self.head = NormMlpClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=self.drop_rate,
norm_layer=norm_layer,
)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
for stage in self.stages:
stage.set_grad_checkpointing(enable=enable)
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.head.reset(num_classes, global_pool=global_pool)
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
x = self.norm_pre(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.head.global_pool(x)
x = self.head.norm(x)
x = self.head.flatten(x)
x = self.head.drop(x)
return x if pre_logits else self.head.fc(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
""" Remap MSFT checkpoints -> timm """
if 'head.fc.weight' in state_dict:
return state_dict # non-MSFT checkpoint
if 'state_dict' in state_dict:
state_dict = state_dict['state_dict']
import re
out_dict = {}
for k, v in state_dict.items():
k = re.sub(r'patch_embeds.([0-9]+)', r'stages.\1.downsample', k)
k = re.sub(r'main_blocks.([0-9]+)', r'stages.\1.blocks', k)
k = k.replace('downsample.proj', 'downsample.conv')
k = k.replace('stages.0.downsample', 'stem')
k = k.replace('head.', 'head.fc.')
k = k.replace('norms.', 'head.norm.')
k = k.replace('cpe.0', 'cpe1')
k = k.replace('cpe.1', 'cpe2')
out_dict[k] = v
return out_dict
def _create_davit(variant, pretrained=False, **kwargs):
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
DaVit,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.95, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
**kwargs
}
# TODO contact authors to get larger pretrained models
default_cfgs = generate_default_cfgs({
# official microsoft weights from https://github.com/dingmyu/davit
'davit_tiny.msft_in1k': _cfg(
hf_hub_id='timm/'),
'davit_small.msft_in1k': _cfg(
hf_hub_id='timm/'),
'davit_base.msft_in1k': _cfg(
hf_hub_id='timm/'),
'davit_large': _cfg(),
'davit_huge': _cfg(),
'davit_giant': _cfg(),
})
@register_model
def davit_tiny(pretrained=False, **kwargs) -> DaVit:
model_args = dict(depths=(1, 1, 3, 1), embed_dims=(96, 192, 384, 768), num_heads=(3, 6, 12, 24))
return _create_davit('davit_tiny', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def davit_small(pretrained=False, **kwargs) -> DaVit:
model_args = dict(depths=(1, 1, 9, 1), embed_dims=(96, 192, 384, 768), num_heads=(3, 6, 12, 24))
return _create_davit('davit_small', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def davit_base(pretrained=False, **kwargs) -> DaVit:
model_args = dict(depths=(1, 1, 9, 1), embed_dims=(128, 256, 512, 1024), num_heads=(4, 8, 16, 32))
return _create_davit('davit_base', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def davit_large(pretrained=False, **kwargs) -> DaVit:
model_args = dict(depths=(1, 1, 9, 1), embed_dims=(192, 384, 768, 1536), num_heads=(6, 12, 24, 48))
return _create_davit('davit_large', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def davit_huge(pretrained=False, **kwargs) -> DaVit:
model_args = dict(depths=(1, 1, 9, 1), embed_dims=(256, 512, 1024, 2048), num_heads=(8, 16, 32, 64))
return _create_davit('davit_huge', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def davit_giant(pretrained=False, **kwargs) -> DaVit:
model_args = dict(depths=(1, 1, 12, 3), embed_dims=(384, 768, 1536, 3072), num_heads=(12, 24, 48, 96))
return _create_davit('davit_giant', pretrained=pretrained, **dict(model_args, **kwargs))
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