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import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(BasicBlockV1b, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, bias=False)
self.bn1 = norm_layer(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
padding=previous_dilation, dilation=previous_dilation, bias=False)
self.bn2 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu(out)
return out
class BottleneckV1b(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(BottleneckV1b, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = norm_layer(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, bias=False)
self.bn2 = norm_layer(planes)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = 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:
residual = self.downsample(x)
out = out + residual
out = self.relu(out)
return out
class ResNetV1b(nn.Module):
""" Pre-trained ResNetV1b Model, which produces the strides of 8 featuremaps at conv5.
Parameters
----------
block : Block
Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers : list of int
Numbers of layers in each block
classes : int, default 1000
Number of classification classes.
dilated : bool, default False
Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
typically used in Semantic Segmentation.
norm_layer : object
Normalization layer used (default: :class:`nn.BatchNorm2d`)
deep_stem : bool, default False
Whether to replace the 7x7 conv1 with 3 3x3 convolution layers.
avg_down : bool, default False
Whether to use average pooling for projection skip connection between stages/downsample.
final_drop : float, default 0.0
Dropout ratio before the final classification layer.
Reference:
- He, Kaiming, et al. "Deep residual learning for image recognition."
Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
"""
def __init__(self, block, layers, classes=1000, dilated=True, deep_stem=False, stem_width=32,
avg_down=False, final_drop=0.0, norm_layer=nn.BatchNorm2d):
self.inplanes = stem_width*2 if deep_stem else 64
super(ResNetV1b, self).__init__()
if not deep_stem:
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
else:
self.conv1 = nn.Sequential(
nn.Conv2d(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False),
norm_layer(stem_width),
nn.ReLU(True),
nn.Conv2d(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False),
norm_layer(stem_width),
nn.ReLU(True),
nn.Conv2d(stem_width, 2*stem_width, kernel_size=3, stride=1, padding=1, bias=False)
)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(True)
self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], avg_down=avg_down,
norm_layer=norm_layer)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, avg_down=avg_down,
norm_layer=norm_layer)
if dilated:
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2,
avg_down=avg_down, norm_layer=norm_layer)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4,
avg_down=avg_down, norm_layer=norm_layer)
else:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
avg_down=avg_down, norm_layer=norm_layer)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
avg_down=avg_down, norm_layer=norm_layer)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.drop = None
if final_drop > 0.0:
self.drop = nn.Dropout(final_drop)
self.fc = nn.Linear(512 * block.expansion, classes)
def _make_layer(self, block, planes, blocks, stride=1, dilation=1,
avg_down=False, norm_layer=nn.BatchNorm2d):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = []
if avg_down:
if dilation == 1:
downsample.append(
nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False)
)
else:
downsample.append(
nn.AvgPool2d(kernel_size=1, stride=1, ceil_mode=True, count_include_pad=False)
)
downsample.extend([
nn.Conv2d(self.inplanes, out_channels=planes * block.expansion,
kernel_size=1, stride=1, bias=False),
norm_layer(planes * block.expansion)
])
downsample = nn.Sequential(*downsample)
else:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, out_channels=planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
norm_layer(planes * block.expansion)
)
layers = []
if dilation in (1, 2):
layers.append(block(self.inplanes, planes, stride, dilation=1, downsample=downsample,
previous_dilation=dilation, norm_layer=norm_layer))
elif dilation == 4:
layers.append(block(self.inplanes, planes, stride, dilation=2, downsample=downsample,
previous_dilation=dilation, norm_layer=norm_layer))
else:
raise RuntimeError("=> unknown dilation size: {}".format(dilation))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilation,
previous_dilation=dilation, norm_layer=norm_layer))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
if self.drop is not None:
x = self.drop(x)
x = self.fc(x)
return x
def _safe_state_dict_filtering(orig_dict, model_dict_keys):
filtered_orig_dict = {}
for k, v in orig_dict.items():
if k in model_dict_keys:
filtered_orig_dict[k] = v
else:
print(f"[ERROR] Failed to load <{k}> in backbone")
return filtered_orig_dict
def resnet34_v1b(pretrained=False, **kwargs):
model = ResNetV1b(BasicBlockV1b, [3, 4, 6, 3], **kwargs)
if pretrained:
model_dict = model.state_dict()
filtered_orig_dict = _safe_state_dict_filtering(
torch.hub.load(GLUON_RESNET_TORCH_HUB, 'gluon_resnet34_v1b', pretrained=True).state_dict(),
model_dict.keys()
)
model_dict.update(filtered_orig_dict)
model.load_state_dict(model_dict)
return model
def resnet50_v1s(pretrained=False, **kwargs):
model = ResNetV1b(BottleneckV1b, [3, 4, 6, 3], deep_stem=True, stem_width=64, **kwargs)
if pretrained:
model_dict = model.state_dict()
filtered_orig_dict = _safe_state_dict_filtering(
torch.hub.load(GLUON_RESNET_TORCH_HUB, 'gluon_resnet50_v1s', pretrained=True).state_dict(),
model_dict.keys()
)
model_dict.update(filtered_orig_dict)
model.load_state_dict(model_dict)
return model
def resnet101_v1s(pretrained=False, **kwargs):
model = ResNetV1b(BottleneckV1b, [3, 4, 23, 3], deep_stem=True, stem_width=64, **kwargs)
if pretrained:
model_dict = model.state_dict()
filtered_orig_dict = _safe_state_dict_filtering(
torch.hub.load(GLUON_RESNET_TORCH_HUB, 'gluon_resnet101_v1s', pretrained=True).state_dict(),
model_dict.keys()
)
model_dict.update(filtered_orig_dict)
model.load_state_dict(model_dict)
return model
def resnet152_v1s(pretrained=False, **kwargs):
model = ResNetV1b(BottleneckV1b, [3, 8, 36, 3], deep_stem=True, stem_width=64, **kwargs)
if pretrained:
model_dict = model.state_dict()
filtered_orig_dict = _safe_state_dict_filtering(
torch.hub.load(GLUON_RESNET_TORCH_HUB, 'gluon_resnet152_v1s', pretrained=True).state_dict(),
model_dict.keys()
)
model_dict.update(filtered_orig_dict)
model.load_state_dict(model_dict)
return model
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