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resnet50.py
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resnet50.py
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import torch.nn as nn
import torch
class FrozenBatchNorm2d(nn.Module):
def __init__(self, n):
super(FrozenBatchNorm2d, self).__init__()
self.register_buffer("weight", torch.ones(n))
self.register_buffer("bias", torch.zeros(n))
self.register_buffer("running_mean", torch.zeros(n))
self.register_buffer("running_var", torch.ones(n))
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
num_batches_tracked_key = prefix + 'num_batches_tracked'
if num_batches_tracked_key in state_dict:
del state_dict[num_batches_tracked_key]
super(FrozenBatchNorm2d, self)._load_from_state_dict(
state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs)
def forward(self, x):
scale = self.weight * self.running_var.rsqrt()
bias = self.bias - self.running_mean * scale
scale = scale.reshape(1, -1, 1, 1)
bias = bias.reshape(1, -1, 1, 1)
return x * scale + bias
class Bottleneck(nn.Module):
def __init__(self, in_channels, mid_channels, out_channels, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=1, stride=1, bias=False),
# nn.BatchNorm2d(mid_channels),
FrozenBatchNorm2d(mid_channels),
nn.ReLU(inplace=True)
)
self.conv2 = nn.Sequential(
nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=stride, padding=1, bias=False),
# nn.BatchNorm2d(mid_channels),
FrozenBatchNorm2d(mid_channels),
nn.ReLU(inplace=True)
)
self.conv3 = nn.Sequential(
nn.Conv2d(mid_channels, out_channels, kernel_size=1, stride=1, bias=False),
# nn.BatchNorm2d(out_channels)
FrozenBatchNorm2d(out_channels),
)
if in_channels == out_channels: # when dim not change, input_features could be added diectly to out
self.shortcut = nn.Sequential()
else: # when dim change, input_features should also change dim to be added to out
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
# nn.BatchNorm2d(out_channels)
FrozenBatchNorm2d(out_channels),
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
residual = x
residual = self.shortcut(residual)
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x += residual
return self.relu(x)
class ResNet(nn.Module):
def __init__(self, num_classes, num_block_lists=[3, 4, 6, 3]):
super(ResNet, self).__init__()
self.basic_conv = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
# nn.BatchNorm2d(64),
FrozenBatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
self.stage_1 = self._make_layer(64, 64, 256, nums_block=num_block_lists[0], stride=1)
self.stage_2 = self._make_layer(256, 128, 512, nums_block=num_block_lists[1], stride=2)
self.stage_3 = self._make_layer(512, 256, 1024, nums_block=num_block_lists[2], stride=2)
self.stage_4 = self._make_layer(1024, 512, 2048, nums_block=num_block_lists[3], stride=2)
self.gap = nn.AdaptiveAvgPool2d(1)
self.classifier = nn.Linear(2048, num_classes)
for m in self.modules():
if isinstance(m, (nn.Conv2d, nn.Linear)):
nn.init.kaiming_normal_(m.weight, mode='fan_in')
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)
def _make_layer(self, in_channels, mid_channels, out_channels, nums_block, stride=1):
layers = [Bottleneck(in_channels, mid_channels, out_channels, stride=stride)]
for _ in range(1, nums_block):
layers.append(Bottleneck(out_channels, mid_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x, classify=False):
x = self.basic_conv(x)
x = self.stage_1(x)
out3 = self.stage_2(x)
out4 = self.stage_3(out3)
out5 = self.stage_4(out4)
if classify:
x = self.gap(out5)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
else:
return (out3, out4, out5)
def freeze_stages(self, stage):
if stage >= 0:
self.basic_conv[1].eval()
for m in [self.basic_conv[0], self.basic_conv[1]]:
for param in m.parameters():
param.requires_grad = False
for i in range(1, stage + 1):
layer = getattr(self, 'stage_{}'.format(i))
layer.eval()
for param in layer.parameters():
param.requires_grad = False
def resnet50(pretrained=False):
model = ResNet(1000, [3, 4, 6, 3])
if pretrained:
# try:
# from torch.hub import load_state_dict_from_url
# except ImportError:
# from torch.utils.model_zoo import load_url as load_state_dict_from_url
checkpoint = torch.load("pretrained/resnet50.pt", map_location='cpu')
model.load_state_dict(checkpoint, strict=True)
return model