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import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, Union
from diffusers.models.attention_processor import AttentionProcessor
def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3,
stride=strd, padding=padding, bias=bias)
class ConvBlock(nn.Module):
def __init__(self, in_planes, out_planes):
super(ConvBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = conv3x3(in_planes, int(out_planes / 2))
self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
if in_planes != out_planes:
self.downsample = nn.Sequential(
nn.BatchNorm2d(in_planes, eps=1e-4),
nn.ReLU(True),
nn.Conv2d(in_planes, out_planes,
kernel_size=1, stride=1, bias=False),
)
else:
self.downsample = None
def forward(self, x):
residual = x
out1 = self.bn1(x)
out1 = F.relu(out1, True)
out1 = self.conv1(out1)
out2 = self.bn2(out1)
out2 = F.relu(out2, True)
out2 = self.conv2(out2)
out3 = self.bn3(out2)
out3 = F.relu(out3, True)
out3 = self.conv3(out3)
out3 = torch.cat((out1, out2, out3), 1)
if self.downsample is not None:
residual = self.downsample(residual)
out3 += residual
return out3
class HourGlass(nn.Module):
def __init__(self, num_modules, depth, num_features):
super(HourGlass, self).__init__()
self.num_modules = num_modules
self.depth = depth
self.features = num_features
self.dropout = nn.Dropout(0.5)
self._generate_network(self.depth)
def _generate_network(self, level):
self.add_module('b1_' + str(level), ConvBlock(256, 256))
self.add_module('b2_' + str(level), ConvBlock(256, 256))
if level > 1:
self._generate_network(level - 1)
else:
self.add_module('b2_plus_' + str(level), ConvBlock(256, 256))
self.add_module('b3_' + str(level), ConvBlock(256, 256))
def _forward(self, level, inp):
# Upper branch
up1 = inp
up1 = self._modules['b1_' + str(level)](up1)
up1 = self.dropout(up1)
# Lower branch
low1 = F.max_pool2d(inp, 2, stride=2)
low1 = self._modules['b2_' + str(level)](low1)
if level > 1:
low2 = self._forward(level - 1, low1)
else:
low2 = low1
low2 = self._modules['b2_plus_' + str(level)](low2)
low3 = low2
low3 = self._modules['b3_' + str(level)](low3)
up1size = up1.size()
rescale_size = (up1size[2], up1size[3])
up2 = F.upsample(low3, size=rescale_size, mode='bilinear')
return up1 + up2
def forward(self, x):
return self._forward(self.depth, x)
class FAN_use(nn.Module):
def __init__(self):
super(FAN_use, self).__init__()
self.num_modules = 1
# Base part
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = ConvBlock(64, 128)
self.conv3 = ConvBlock(128, 128)
self.conv4 = ConvBlock(128, 256)
# Stacking part
hg_module = 0
self.add_module('m' + str(hg_module), HourGlass(1, 4, 256))
self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))
self.add_module('conv_last' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
self.add_module('l' + str(hg_module), nn.Conv2d(256, 68, kernel_size=1, stride=1, padding=0))
self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
if hg_module < self.num_modules - 1:
self.add_module('bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
self.add_module('al' + str(hg_module), nn.Conv2d(68, 256, kernel_size=1, stride=1, padding=0))
self.avgpool = nn.MaxPool2d((2, 2), 2)
self.conv6 = nn.Conv2d(68, 1, 3, 2, 1)
self.fc = nn.Linear(28 * 28, 512)
self.bn5 = nn.BatchNorm2d(68)
self.relu = nn.ReLU(True)
def forward(self, x, return_featmap=False):
x = F.relu(self.bn1(self.conv1(x)), True) # 112
x = F.max_pool2d(self.conv2(x), 2) # 56 # [B, 128, 112, 112]
x = self.conv3(x)
x = self.conv4(x) # [B, 256, 56, 56]
previous = x
i = 0
hg = self._modules['m' + str(i)](previous)
ll = hg
ll = self._modules['top_m_' + str(i)](ll)
ll = self._modules['bn_end' + str(i)](self._modules['conv_last' + str(i)](ll)) # [B, 256, 56, 56]
if return_featmap:
return ll
tmp_out = self._modules['l' + str(i)](F.relu(ll))
net = self.relu(self.bn5(tmp_out)) # [B, 68, 56, 56]
net = self.conv6(net) # 28 # [B, 1, 28, 28]
net = net.view(-1, net.shape[-2] * net.shape[-1])
net = self.relu(net)
net = self.fc(net)
return net
from .FAN_temporal_feature_extractor import TemporalTransformer3DModel
from einops import rearrange
class FAN_SA(nn.Module):
def __init__(self):
super(FAN_SA, self).__init__()
self.num_modules = 1
# Base part
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = ConvBlock(64, 128)
self.conv3 = ConvBlock(128, 128)
self.conv4 = ConvBlock(128, 256)
# Stacking part
hg_module = 0
self.add_module('m' + str(hg_module), HourGlass(1, 4, 256))
self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))
self.add_module(
'conv_last' + str(hg_module),
nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0),
)
self.add_module(
'l' + str(hg_module), nn.Conv2d(256, 68, kernel_size=1, stride=1, padding=0)
)
self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
if hg_module < self.num_modules - 1:
self.add_module(
'bl' + str(hg_module),
nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0),
)
self.add_module(
'al' + str(hg_module),
nn.Conv2d(68, 256, kernel_size=1, stride=1, padding=0),
)
self.avgpool = nn.MaxPool2d((2, 2), 2)
self.conv6 = nn.Conv2d(68, 1, 3, 2, 1)
self.fc = nn.Linear(28 * 28, 512)
# self.conv6 = nn.Conv2d(68, 2, 3, 2, 1)
# self.fc = nn.Linear(28 * 28 * 2, 1024)
self.bn5 = nn.BatchNorm2d(68)
self.relu = nn.ReLU(True)
# Add by zxc
self.att_1 = TemporalTransformer3DModel(
in_channels=128,
sample_size=112,
patch_size=4,
attention_block_types=("Spatial_Self",),
zero_initialize=True,
)
self.att_2 = TemporalTransformer3DModel(
in_channels=256,
sample_size=56,
patch_size=2,
attention_block_types=("Spatial_Self",),
zero_initialize=True,
)
self.att_3 = TemporalTransformer3DModel(
in_channels=256,
sample_size=56,
patch_size=2,
attention_block_types=("Spatial_Self",),
zero_initialize=True,
)
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
def set_attn_processor(
self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)), True) # 112
x = self.conv2(x) # [B, 128, 112, 112]
# Temp Self-Att: [B*h*w, T, 128*p*p] [B*28*28, T, 1024]
x = rearrange(x, "(b f) c h w -> b c f h w", f=1)
x = self.att_1(x, skip=True)[:, :, 0]
x = F.max_pool2d(x, 2) # 56
x = self.conv3(x)
x = self.conv4(x) # [B, 256, 56, 56]
# Temp Self-Att: [B*h*w, T, 256*p*p] [B*28*28, T, 1024]
x = rearrange(x, "(b f) c h w -> b c f h w", f=1)
x = self.att_2(x, skip=True)[:, :, 0]
previous = x
i = 0
hg = self._modules['m' + str(i)](previous)
ll = hg
ll = self._modules['top_m_' + str(i)](ll)
ll = self._modules['bn_end' + str(i)](
self._modules['conv_last' + str(i)](ll)
) # [B, 256, 56, 56]
# Temp Cross-Att: [B*28*28, 1, 1024]*[B*28*28, T, 1024]
ll = rearrange(ll, "(b f) c h w -> b c f h w", f=1)
ll = self.att_3(ll, skip=True)[:, :, 0] # "b c 1 h w -> b c h w"
# print('att3', torch.abs(ll).mean().item())
tmp_out = self._modules['l' + str(i)](F.relu(ll))
net = self.relu(self.bn5(tmp_out)) # [B, 68, 56, 56]
net = self.conv6(net) # 28 # [B, 1, 28, 28]
net = net.view(-1, net.shape[-2] * net.shape[-1] * net.shape[1])
net = self.relu(net)
net = self.fc(net)
return net
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