376 lines
12 KiB
Python
376 lines
12 KiB
Python
import math
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import paddle
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from paddle import nn
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BatchNorm2d = nn.BatchNorm2D
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__all__ = [
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'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
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'deformable_resnet18', 'deformable_resnet50', 'resnet152'
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]
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model_urls = {
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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}
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def constant_init(module, constant, bias=0):
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module.weight = paddle.create_parameter(
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shape=module.weight.shape,
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dtype='float32',
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default_initializer=paddle.nn.initializer.Constant(constant))
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if hasattr(module, 'bias'):
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module.bias = paddle.create_parameter(
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shape=module.bias.shape,
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dtype='float32',
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default_initializer=paddle.nn.initializer.Constant(bias))
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def conv3x3(in_planes, out_planes, stride=1):
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"""3x3 convolution with padding"""
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return nn.Conv2D(
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in_planes,
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out_planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias_attr=False)
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class BasicBlock(nn.Layer):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None, dcn=None):
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super(BasicBlock, self).__init__()
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self.with_dcn = dcn is not None
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = BatchNorm2d(planes, momentum=0.1)
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self.relu = nn.ReLU()
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self.with_modulated_dcn = False
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if not self.with_dcn:
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self.conv2 = nn.Conv2D(
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planes, planes, kernel_size=3, padding=1, bias_attr=False)
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else:
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from paddle.version.ops import DeformConv2D
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deformable_groups = dcn.get('deformable_groups', 1)
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offset_channels = 18
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self.conv2_offset = nn.Conv2D(
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planes,
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deformable_groups * offset_channels,
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kernel_size=3,
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padding=1)
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self.conv2 = DeformConv2D(
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planes, planes, kernel_size=3, padding=1, bias_attr=False)
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self.bn2 = BatchNorm2d(planes, momentum=0.1)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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# out = self.conv2(out)
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if not self.with_dcn:
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out = self.conv2(out)
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else:
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offset = self.conv2_offset(out)
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out = self.conv2(out, offset)
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out = self.bn2(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class Bottleneck(nn.Layer):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None, dcn=None):
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super(Bottleneck, self).__init__()
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self.with_dcn = dcn is not None
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self.conv1 = nn.Conv2D(inplanes, planes, kernel_size=1, bias_attr=False)
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self.bn1 = BatchNorm2d(planes, momentum=0.1)
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self.with_modulated_dcn = False
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if not self.with_dcn:
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self.conv2 = nn.Conv2D(
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planes,
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planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias_attr=False)
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else:
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deformable_groups = dcn.get('deformable_groups', 1)
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from paddle.vision.ops import DeformConv2D
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offset_channels = 18
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self.conv2_offset = nn.Conv2D(
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planes,
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deformable_groups * offset_channels,
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stride=stride,
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kernel_size=3,
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padding=1)
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self.conv2 = DeformConv2D(
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planes,
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planes,
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kernel_size=3,
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padding=1,
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stride=stride,
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bias_attr=False)
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self.bn2 = BatchNorm2d(planes, momentum=0.1)
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self.conv3 = nn.Conv2D(
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planes, planes * 4, kernel_size=1, bias_attr=False)
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self.bn3 = BatchNorm2d(planes * 4, momentum=0.1)
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self.relu = nn.ReLU()
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self.downsample = downsample
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self.stride = stride
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self.dcn = dcn
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self.with_dcn = dcn is not None
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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# out = self.conv2(out)
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if not self.with_dcn:
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out = self.conv2(out)
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else:
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offset = self.conv2_offset(out)
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out = self.conv2(out, offset)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNet(nn.Layer):
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def __init__(self, block, layers, in_channels=3, dcn=None):
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self.dcn = dcn
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self.inplanes = 64
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super(ResNet, self).__init__()
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self.out_channels = []
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self.conv1 = nn.Conv2D(
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in_channels,
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64,
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kernel_size=7,
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stride=2,
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padding=3,
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bias_attr=False)
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self.bn1 = BatchNorm2d(64, momentum=0.1)
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self.relu = nn.ReLU()
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self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dcn=dcn)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dcn=dcn)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dcn=dcn)
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if self.dcn is not None:
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for m in self.modules():
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if isinstance(m, Bottleneck) or isinstance(m, BasicBlock):
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if hasattr(m, 'conv2_offset'):
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constant_init(m.conv2_offset, 0)
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def _make_layer(self, block, planes, blocks, stride=1, dcn=None):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2D(
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self.inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=stride,
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bias_attr=False),
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BatchNorm2d(
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planes * block.expansion, momentum=0.1), )
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, dcn=dcn))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, dcn=dcn))
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self.out_channels.append(planes * block.expansion)
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x2 = self.layer1(x)
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x3 = self.layer2(x2)
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x4 = self.layer3(x3)
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x5 = self.layer4(x4)
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return x2, x3, x4, x5
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def load_torch_params(paddle_model, torch_patams):
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paddle_params = paddle_model.state_dict()
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fc_names = ['classifier']
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for key, torch_value in torch_patams.items():
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if 'num_batches_tracked' in key:
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continue
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key = key.replace("running_var", "_variance").replace(
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"running_mean", "_mean").replace("module.", "")
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torch_value = torch_value.detach().cpu().numpy()
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if key in paddle_params:
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flag = [i in key for i in fc_names]
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if any(flag) and "weight" in key: # ignore bias
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new_shape = [1, 0] + list(range(2, torch_value.ndim))
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print(
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f"name: {key}, ori shape: {torch_value.shape}, new shape: {torch_value.transpose(new_shape).shape}"
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)
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torch_value = torch_value.transpose(new_shape)
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paddle_params[key] = torch_value
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else:
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print(f'{key} not in paddle')
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paddle_model.set_state_dict(paddle_params)
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def load_models(model, model_name):
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import torch.utils.model_zoo as model_zoo
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torch_patams = model_zoo.load_url(model_urls[model_name])
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load_torch_params(model, torch_patams)
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def resnet18(pretrained=True, **kwargs):
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"""Constructs a ResNet-18 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
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if pretrained:
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assert kwargs.get(
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'in_channels',
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3) == 3, 'in_channels must be 3 whem pretrained is True'
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print('load from imagenet')
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load_models(model, 'resnet18')
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return model
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def deformable_resnet18(pretrained=True, **kwargs):
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"""Constructs a ResNet-18 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet(
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BasicBlock, [2, 2, 2, 2], dcn=dict(deformable_groups=1), **kwargs)
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if pretrained:
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assert kwargs.get(
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'in_channels',
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3) == 3, 'in_channels must be 3 whem pretrained is True'
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print('load from imagenet')
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model.load_state_dict(
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model_zoo.load_url(model_urls['resnet18']), strict=False)
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return model
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def resnet34(pretrained=True, **kwargs):
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"""Constructs a ResNet-34 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
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if pretrained:
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assert kwargs.get(
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'in_channels',
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3) == 3, 'in_channels must be 3 whem pretrained is True'
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model.load_state_dict(
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model_zoo.load_url(model_urls['resnet34']), strict=False)
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return model
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def resnet50(pretrained=True, **kwargs):
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"""Constructs a ResNet-50 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
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if pretrained:
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assert kwargs.get(
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'in_channels',
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3) == 3, 'in_channels must be 3 whem pretrained is True'
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load_models(model, 'resnet50')
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return model
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def deformable_resnet50(pretrained=True, **kwargs):
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"""Constructs a ResNet-50 model with deformable conv.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet(
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Bottleneck, [3, 4, 6, 3], dcn=dict(deformable_groups=1), **kwargs)
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if pretrained:
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assert kwargs.get(
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'in_channels',
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3) == 3, 'in_channels must be 3 whem pretrained is True'
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model.load_state_dict(
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model_zoo.load_url(model_urls['resnet50']), strict=False)
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return model
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def resnet101(pretrained=True, **kwargs):
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"""Constructs a ResNet-101 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
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if pretrained:
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assert kwargs.get(
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'in_channels',
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3) == 3, 'in_channels must be 3 whem pretrained is True'
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model.load_state_dict(
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model_zoo.load_url(model_urls['resnet101']), strict=False)
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return model
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def resnet152(pretrained=True, **kwargs):
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"""Constructs a ResNet-152 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
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if pretrained:
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assert kwargs.get(
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'in_channels',
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3) == 3, 'in_channels must be 3 whem pretrained is True'
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model.load_state_dict(
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model_zoo.load_url(model_urls['resnet152']), strict=False)
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return model
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if __name__ == '__main__':
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x = paddle.zeros([2, 3, 640, 640])
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net = resnet50(pretrained=True)
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y = net(x)
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for u in y:
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print(u.shape)
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print(net.out_channels)
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