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# Original Author: Jonathan Donnellya ([email protected])
# Modified by Mohammad Reza Taesiri ([email protected])
import os
import torch
import torch.nn as nn
from collections import OrderedDict
model_dir = os.path.dirname(os.path.realpath(__file__))
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
# class attribute
expansion = 1
num_layers = 2
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
# only conv with possibly not 1 stride
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
# if stride is not 1 then self.downsample cannot be None
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = 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:
identity = self.downsample(x)
# the residual connection
out += identity
out = self.relu(out)
return out
def block_conv_info(self):
block_kernel_sizes = [3, 3]
block_strides = [self.stride, 1]
block_paddings = [1, 1]
return block_kernel_sizes, block_strides, block_paddings
class Bottleneck(nn.Module):
# class attribute
expansion = 4
num_layers = 3
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
# only conv with possibly not 1 stride
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
# if stride is not 1 then self.downsample cannot be None
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = 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:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
def block_conv_info(self):
block_kernel_sizes = [1, 3, 1]
block_strides = [1, self.stride, 1]
block_paddings = [0, 1, 0]
return block_kernel_sizes, block_strides, block_paddings
class ResNet_features(nn.Module):
"""
the convolutional layers of ResNet
the average pooling and final fully convolutional layer is removed
"""
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False):
super(ResNet_features, self).__init__()
self.inplanes = 64
# the first convolutional layer before the structured sequence of blocks
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# comes from the first conv and the following max pool
self.kernel_sizes = [7, 3]
self.strides = [2, 2]
self.paddings = [3, 1]
# the following layers, each layer is a sequence of blocks
self.block = block
self.layers = layers
self.layer1 = self._make_layer(
block=block, planes=64, num_blocks=self.layers[0]
)
self.layer2 = self._make_layer(
block=block, planes=128, num_blocks=self.layers[1], stride=2
)
self.layer3 = self._make_layer(
block=block, planes=256, num_blocks=self.layers[2], stride=2
)
self.layer4 = self._make_layer(
block=block, planes=512, num_blocks=self.layers[3], stride=2
)
# initialize the parameters
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):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, num_blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
# only the first block has downsample that is possibly not None
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, num_blocks):
layers.append(block(self.inplanes, planes))
# keep track of every block's conv size, stride size, and padding size
for each_block in layers:
(
block_kernel_sizes,
block_strides,
block_paddings,
) = each_block.block_conv_info()
self.kernel_sizes.extend(block_kernel_sizes)
self.strides.extend(block_strides)
self.paddings.extend(block_paddings)
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)
return x
def conv_info(self):
return self.kernel_sizes, self.strides, self.paddings
def num_layers(self):
"""
the number of conv layers in the network, not counting the number
of bypass layers
"""
return (
self.block.num_layers * self.layers[0]
+ self.block.num_layers * self.layers[1]
+ self.block.num_layers * self.layers[2]
+ self.block.num_layers * self.layers[3]
+ 1
)
def __repr__(self):
template = "resnet{}_features"
return template.format(self.num_layers() + 1)
def resnet50_features(pretrained=True, inat=True, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet or iNaturalist
pretrained (bool): If True, returns a model pre-trained on iNaturalst; else, ImageNet
"""
model = ResNet_features(Bottleneck, [3, 4, 6, 4], **kwargs)
if pretrained:
if inat:
# print('Loading iNat model')
model_dict = torch.load(
model_dir
+ "/../../weights/"
+ "BBN.iNaturalist2017.res50.90epoch.best_model.pth.pt"
)
else:
raise
if inat:
model_dict.pop("module.classifier.weight")
model_dict.pop("module.classifier.bias")
for key in list(model_dict.keys()):
model_dict[
key.replace("module.backbone.", "")
.replace("cb_block", "layer4.2")
.replace("rb_block", "layer4.3")
] = model_dict.pop(key)
else:
raise
model.load_state_dict(model_dict, strict=False)
return model
class ResNet_classifier(nn.Module):
"""
A classifier for Deformable ProtoPNet
"""
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False):
super(ResNet_classifier, self).__init__()
self.inplanes = 64
# the first convolutional layer before the structured sequence of blocks
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# comes from the first conv and the following max pool
self.kernel_sizes = [7, 3]
self.strides = [2, 2]
self.paddings = [3, 1]
# the following layers, each layer is a sequence of blocks
self.block = block
self.layers = layers
self.layer1 = self._make_layer(
block=block, planes=64, num_blocks=self.layers[0]
)
self.layer2 = self._make_layer(
block=block, planes=128, num_blocks=self.layers[1], stride=2
)
self.layer3 = self._make_layer(
block=block, planes=256, num_blocks=self.layers[2], stride=2
)
self.layer4 = self._make_layer(
block=block, planes=512, num_blocks=self.layers[3], stride=2
)
self.classifier = nn.Linear(2048 * 7 * 7, 200)
# initialize the parameters
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):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, num_blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
# only the first block has downsample that is possibly not None
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, num_blocks):
layers.append(block(self.inplanes, planes))
# keep track of every block's conv size, stride size, and padding size
for each_block in layers:
(
block_kernel_sizes,
block_strides,
block_paddings,
) = each_block.block_conv_info()
self.kernel_sizes.extend(block_kernel_sizes)
self.strides.extend(block_strides)
self.paddings.extend(block_paddings)
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.classifier(torch.flatten(x, start_dim=1))
return x
def conv_info(self):
return self.kernel_sizes, self.strides, self.paddings
def num_layers(self):
"""
the number of conv layers in the network, not counting the number
of bypass layers
"""
return (
self.block.num_layers * self.layers[0]
+ self.block.num_layers * self.layers[1]
+ self.block.num_layers * self.layers[2]
+ self.block.num_layers * self.layers[3]
+ 1
)
def __repr__(self):
template = "resnet{}_features"
return template.format(self.num_layers() + 1)
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