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Running
on
Zero
| import torch.nn as nn | |
| import math | |
| import torch.utils.model_zoo as model_zoo | |
| from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d | |
| webroot = 'http://dl.yf.io/drn/' | |
| model_urls = { | |
| 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', | |
| 'drn-c-26': webroot + 'drn_c_26-ddedf421.pth', | |
| 'drn-c-42': webroot + 'drn_c_42-9d336e8c.pth', | |
| 'drn-c-58': webroot + 'drn_c_58-0a53a92c.pth', | |
| 'drn-d-22': webroot + 'drn_d_22-4bd2f8ea.pth', | |
| 'drn-d-38': webroot + 'drn_d_38-eebb45f0.pth', | |
| 'drn-d-54': webroot + 'drn_d_54-0e0534ff.pth', | |
| 'drn-d-105': webroot + 'drn_d_105-12b40979.pth' | |
| } | |
| def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1): | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=padding, bias=False, dilation=dilation) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, | |
| dilation=(1, 1), residual=True, BatchNorm=None): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3(inplanes, planes, stride, | |
| padding=dilation[0], dilation=dilation[0]) | |
| self.bn1 = BatchNorm(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes, | |
| padding=dilation[1], dilation=dilation[1]) | |
| self.bn2 = BatchNorm(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| self.residual = residual | |
| 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) | |
| if self.residual: | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, | |
| dilation=(1, 1), residual=True, BatchNorm=None): | |
| super(Bottleneck, self).__init__() | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = BatchNorm(planes) | |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, | |
| padding=dilation[1], bias=False, | |
| dilation=dilation[1]) | |
| self.bn2 = BatchNorm(planes) | |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = BatchNorm(planes * 4) | |
| 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 += residual | |
| out = self.relu(out) | |
| return out | |
| class DRN(nn.Module): | |
| def __init__(self, block, layers, arch='D', | |
| channels=(16, 32, 64, 128, 256, 512, 512, 512), | |
| BatchNorm=None): | |
| super(DRN, self).__init__() | |
| self.inplanes = channels[0] | |
| self.out_dim = channels[-1] | |
| self.arch = arch | |
| if arch == 'C': | |
| self.conv1 = nn.Conv2d(3, channels[0], kernel_size=7, stride=1, | |
| padding=3, bias=False) | |
| self.bn1 = BatchNorm(channels[0]) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.layer1 = self._make_layer( | |
| BasicBlock, channels[0], layers[0], stride=1, BatchNorm=BatchNorm) | |
| self.layer2 = self._make_layer( | |
| BasicBlock, channels[1], layers[1], stride=2, BatchNorm=BatchNorm) | |
| elif arch == 'D': | |
| self.layer0 = nn.Sequential( | |
| nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3, | |
| bias=False), | |
| BatchNorm(channels[0]), | |
| nn.ReLU(inplace=True) | |
| ) | |
| self.layer1 = self._make_conv_layers( | |
| channels[0], layers[0], stride=1, BatchNorm=BatchNorm) | |
| self.layer2 = self._make_conv_layers( | |
| channels[1], layers[1], stride=2, BatchNorm=BatchNorm) | |
| self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2, BatchNorm=BatchNorm) | |
| self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2, BatchNorm=BatchNorm) | |
| self.layer5 = self._make_layer(block, channels[4], layers[4], | |
| dilation=2, new_level=False, BatchNorm=BatchNorm) | |
| self.layer6 = None if layers[5] == 0 else \ | |
| self._make_layer(block, channels[5], layers[5], dilation=4, | |
| new_level=False, BatchNorm=BatchNorm) | |
| if arch == 'C': | |
| self.layer7 = None if layers[6] == 0 else \ | |
| self._make_layer(BasicBlock, channels[6], layers[6], dilation=2, | |
| new_level=False, residual=False, BatchNorm=BatchNorm) | |
| self.layer8 = None if layers[7] == 0 else \ | |
| self._make_layer(BasicBlock, channels[7], layers[7], dilation=1, | |
| new_level=False, residual=False, BatchNorm=BatchNorm) | |
| elif arch == 'D': | |
| self.layer7 = None if layers[6] == 0 else \ | |
| self._make_conv_layers(channels[6], layers[6], dilation=2, BatchNorm=BatchNorm) | |
| self.layer8 = None if layers[7] == 0 else \ | |
| self._make_conv_layers(channels[7], layers[7], dilation=1, BatchNorm=BatchNorm) | |
| self._init_weight() | |
| def _init_weight(self): | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| elif isinstance(m, SynchronizedBatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def _make_layer(self, block, planes, blocks, stride=1, dilation=1, | |
| new_level=True, residual=True, BatchNorm=None): | |
| assert dilation == 1 or dilation % 2 == 0 | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| nn.Conv2d(self.inplanes, planes * block.expansion, | |
| kernel_size=1, stride=stride, bias=False), | |
| BatchNorm(planes * block.expansion), | |
| ) | |
| layers = list() | |
| layers.append(block( | |
| self.inplanes, planes, stride, downsample, | |
| dilation=(1, 1) if dilation == 1 else ( | |
| dilation // 2 if new_level else dilation, dilation), | |
| residual=residual, BatchNorm=BatchNorm)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, residual=residual, | |
| dilation=(dilation, dilation), BatchNorm=BatchNorm)) | |
| return nn.Sequential(*layers) | |
| def _make_conv_layers(self, channels, convs, stride=1, dilation=1, BatchNorm=None): | |
| modules = [] | |
| for i in range(convs): | |
| modules.extend([ | |
| nn.Conv2d(self.inplanes, channels, kernel_size=3, | |
| stride=stride if i == 0 else 1, | |
| padding=dilation, bias=False, dilation=dilation), | |
| BatchNorm(channels), | |
| nn.ReLU(inplace=True)]) | |
| self.inplanes = channels | |
| return nn.Sequential(*modules) | |
| def forward(self, x): | |
| if self.arch == 'C': | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| elif self.arch == 'D': | |
| x = self.layer0(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| low_level_feat = x | |
| x = self.layer4(x) | |
| x = self.layer5(x) | |
| if self.layer6 is not None: | |
| x = self.layer6(x) | |
| if self.layer7 is not None: | |
| x = self.layer7(x) | |
| if self.layer8 is not None: | |
| x = self.layer8(x) | |
| return x, low_level_feat | |
| class DRN_A(nn.Module): | |
| def __init__(self, block, layers, BatchNorm=None): | |
| self.inplanes = 64 | |
| super(DRN_A, self).__init__() | |
| self.out_dim = 512 * block.expansion | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, | |
| bias=False) | |
| self.bn1 = BatchNorm(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, 64, layers[0], BatchNorm=BatchNorm) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2, BatchNorm=BatchNorm) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=1, | |
| dilation=2, BatchNorm=BatchNorm) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=1, | |
| dilation=4, BatchNorm=BatchNorm) | |
| self._init_weight() | |
| def _init_weight(self): | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| elif isinstance(m, SynchronizedBatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| nn.Conv2d(self.inplanes, planes * block.expansion, | |
| kernel_size=1, stride=stride, bias=False), | |
| BatchNorm(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample, BatchNorm=BatchNorm)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, | |
| dilation=(dilation, dilation, ), BatchNorm=BatchNorm)) | |
| 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 drn_a_50(BatchNorm, pretrained=True): | |
| model = DRN_A(Bottleneck, [3, 4, 6, 3], BatchNorm=BatchNorm) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) | |
| return model | |
| def drn_c_26(BatchNorm, pretrained=True): | |
| model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', BatchNorm=BatchNorm) | |
| if pretrained: | |
| pretrained = model_zoo.load_url(model_urls['drn-c-26']) | |
| del pretrained['fc.weight'] | |
| del pretrained['fc.bias'] | |
| model.load_state_dict(pretrained) | |
| return model | |
| def drn_c_42(BatchNorm, pretrained=True): | |
| model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', BatchNorm=BatchNorm) | |
| if pretrained: | |
| pretrained = model_zoo.load_url(model_urls['drn-c-42']) | |
| del pretrained['fc.weight'] | |
| del pretrained['fc.bias'] | |
| model.load_state_dict(pretrained) | |
| return model | |
| def drn_c_58(BatchNorm, pretrained=True): | |
| model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', BatchNorm=BatchNorm) | |
| if pretrained: | |
| pretrained = model_zoo.load_url(model_urls['drn-c-58']) | |
| del pretrained['fc.weight'] | |
| del pretrained['fc.bias'] | |
| model.load_state_dict(pretrained) | |
| return model | |
| def drn_d_22(BatchNorm, pretrained=True): | |
| model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='D', BatchNorm=BatchNorm) | |
| if pretrained: | |
| pretrained = model_zoo.load_url(model_urls['drn-d-22']) | |
| del pretrained['fc.weight'] | |
| del pretrained['fc.bias'] | |
| model.load_state_dict(pretrained) | |
| return model | |
| def drn_d_24(BatchNorm, pretrained=True): | |
| model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 2, 2], arch='D', BatchNorm=BatchNorm) | |
| if pretrained: | |
| pretrained = model_zoo.load_url(model_urls['drn-d-24']) | |
| del pretrained['fc.weight'] | |
| del pretrained['fc.bias'] | |
| model.load_state_dict(pretrained) | |
| return model | |
| def drn_d_38(BatchNorm, pretrained=True): | |
| model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', BatchNorm=BatchNorm) | |
| if pretrained: | |
| pretrained = model_zoo.load_url(model_urls['drn-d-38']) | |
| del pretrained['fc.weight'] | |
| del pretrained['fc.bias'] | |
| model.load_state_dict(pretrained) | |
| return model | |
| def drn_d_40(BatchNorm, pretrained=True): | |
| model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', BatchNorm=BatchNorm) | |
| if pretrained: | |
| pretrained = model_zoo.load_url(model_urls['drn-d-40']) | |
| del pretrained['fc.weight'] | |
| del pretrained['fc.bias'] | |
| model.load_state_dict(pretrained) | |
| return model | |
| def drn_d_54(BatchNorm, pretrained=True): | |
| model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', BatchNorm=BatchNorm) | |
| if pretrained: | |
| pretrained = model_zoo.load_url(model_urls['drn-d-54']) | |
| del pretrained['fc.weight'] | |
| del pretrained['fc.bias'] | |
| model.load_state_dict(pretrained) | |
| return model | |
| def drn_d_105(BatchNorm, pretrained=True): | |
| model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 1, 1], arch='D', BatchNorm=BatchNorm) | |
| if pretrained: | |
| pretrained = model_zoo.load_url(model_urls['drn-d-105']) | |
| del pretrained['fc.weight'] | |
| del pretrained['fc.bias'] | |
| model.load_state_dict(pretrained) | |
| return model | |
| if __name__ == "__main__": | |
| import torch | |
| model = drn_a_50(BatchNorm=nn.BatchNorm2d, pretrained=True) | |
| input = torch.rand(1, 3, 512, 512) | |
| output, low_level_feat = model(input) | |
| print(output.size()) | |
| print(low_level_feat.size()) | |