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import torch.nn as nn
import numpy as np
from functools import partial
from dataclasses import dataclass
from typing import Callable, Dict, Optional
from enum import Enum, auto
from einops import rearrange
from omegaconf import II
from .modules import get_2d_sincos_pos_embed_flexible, PatchEmbed_new
from .base import (
D2vModalityConfig,
ModalitySpecificEncoder,
get_alibi_bias,
)
from .modules import (
BlockEncoder,
FixedPositionalEncoder,
)
class Modality(Enum):
AUDIO = auto()
IMAGE = auto()
TEXT = auto()
@dataclass
class D2vImageConfig(D2vModalityConfig):
type: Modality = Modality.IMAGE
in_chans: int = 1
patch_size: int = 16
embed_dim: int = II('model.embed_dim')
alibi_dims: int = 2
alibi_distance: str = "manhattan"
fixed_positions: bool = True
transformer_decoder: bool = False
enc_dec_transformer: bool = False
target_length: int = 1024
max_length: int = 128
max_freq: int = 50
flatten: str = 'freq' # 'time', 'freq'
class ImageEncoder(ModalitySpecificEncoder):
# forward() implemented in models.base.ModalitySpecificEncoder
modality_cfg: D2vImageConfig
def __init__(
self,
modality_cfg: D2vImageConfig,
embed_dim: int,
make_block: Callable[[float, Optional[int], Optional[int]], nn.ModuleList],
norm_layer: Callable[[int], nn.LayerNorm],
layer_norm_first: bool,
alibi_biases: Dict,
task=None,
):
self.patch_size = modality_cfg.patch_size
self.H = modality_cfg.target_length // self.patch_size # 64
# convert spec to patch embed, using conv1d
local_encoder = PatchEmbed_new(
patch_size=modality_cfg.patch_size, # 16
in_chans=modality_cfg.in_chans, # 1
embed_dim=modality_cfg.embed_dim, # 768
stride=modality_cfg.patch_size, # 16
flatten=modality_cfg.flatten
)
# CNN initialize
w = local_encoder.proj.weight.data
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
if modality_cfg.embed_dim != embed_dim:
local_encoder = nn.Sequential(
local_encoder,
nn.Linear(modality_cfg.embed_dim, embed_dim),
)
project_features = nn.Identity()
# note: max_length control the maximum time length of audio -> "64" for 10s, here we define it as 2min, you can change it yourself
max_length = modality_cfg.max_length
max_freq = modality_cfg.max_freq
# side_n = int(num_patches ** 0.5)
# note: we fix the variable length sequence problem here -> support up to 2min audio
emb = get_2d_sincos_pos_embed_flexible(
embed_dim,
(max_length, max_freq),
cls_token=False,
)
pos_embed = torch.from_numpy(emb[:max_length * max_freq, :]).float().unsqueeze(0)
fixed_positional_encoder = (
FixedPositionalEncoder(pos_embed) if modality_cfg.fixed_positions else None # True
)
dpr = np.linspace( # drop_path_rate
modality_cfg.start_drop_path_rate,
modality_cfg.end_drop_path_rate,
modality_cfg.prenet_depth, # actual: 0
)
# actual: only layer norm
context_encoder = BlockEncoder(
nn.ModuleList(make_block(dpr[i]) for i in range(modality_cfg.prenet_depth)),
norm_layer(embed_dim) if not layer_norm_first else None,
layer_norm_first,
modality_cfg.prenet_layerdrop,
modality_cfg.prenet_dropout,
)
alibi_bias_fn = partial(
get_alibi_bias,
alibi_biases=alibi_biases,
heads=modality_cfg.num_alibi_heads,
dims=modality_cfg.alibi_dims,
distance=modality_cfg.alibi_distance,
)
super().__init__(
modality_cfg=modality_cfg,
embed_dim=embed_dim,
local_encoder=local_encoder, # patch embed
project_features=project_features, # nn.Identity()
fixed_positional_encoder=fixed_positional_encoder,
relative_positional_encoder=None,
context_encoder=context_encoder, # apply mask
decoder=None,
get_alibi_bias=alibi_bias_fn,
)
def reset_parameters(self):
super().reset_parameters()
@torch.no_grad()
def patchify(self, imgs):
"""
imgs: (N, 3, H, W) audio: (N,1,H,W) 1024/16 = 64 128/16 = 8
x: (N, L, patch_size**2 *3)
"""
if self.modality_cfg.in_chans == 1: # actual: this one
p = self.modality_cfg.patch_size
h = imgs.shape[2] // p
w = imgs.shape[3] // p
# h,w = self.patch_embed.patch_hw
x = imgs.reshape(shape=(imgs.shape[0], 1, h, p, w, p))
x = torch.einsum('nchpwq->nhwpqc', x)
x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * 1))
else:
p = self.modality_cfg.patch_size
h = w = imgs.shape[2] // p
x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
x = torch.einsum("nchpwq->nhwpqc", x)
x = x.reshape(shape=(imgs.shape[0], h * w, p ** 2 * 3))
return x
@torch.no_grad()
def unpatchify(self, x):
"""
x: (N, L, patch_size**2 *C)
imgs: (N, C, H, W)
"""
p = self.modality_cfg.patch_size
h = w = int(x.shape[1] ** 0.5) # num patch along two axis
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, -1))
x = torch.einsum("nhwpqc->nchpwq", x)
imgs = x.reshape(shape=(x.shape[0], -1, h * p, h * p))
return imgs
def convert_padding_mask(
self,
x: torch.Tensor,
padding_mask: torch.Tensor
) -> torch.Tensor:
'''patchify and serialize padding_mask: [b,t,f] => [b,t_patch,f_patch] => [b,patch_seq]
Args:
x (torch.Tensor): input_features
padding_mask (torch.Tensor): [b,t_patch,f_patch], 1 for padded patch
Returns:
torch.Tensor: serialized padding mask. [b,patch_seq]
'''
B, T, F = x.shape
t_extra, f_extra = T % self.patch_size, F % self.patch_size
padding_mask = padding_mask[:, :-t_extra, :-f_extra]
padding_mask = rearrange(
padding_mask,
'b (tp p) (fp q) -> b tp fp (p q)',
p=self.patch_size, q=self.patch_size
)
padding_mask = padding_mask.all(-1)
if self.modality_cfg.flatten == 'time':
padding_mask = padding_mask.transpose(-2, -1).flatten(1)
else:
padding_mask = padding_mask.flatten(1)
return padding_mask
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