import math import torch.nn as nn import torch.nn.functional as F from .ozan_rep_fun import ozan_rep_function, trevor_rep_function, OzanRepFunction, TrevorRepFunction from easyfl.models.model import BaseModel __all__ = ['resnet18', 'resnet18_half', 'resnet18_tripple', 'resnet34', 'resnet50', 'resnet101', 'resnet152'] def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) 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): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(BasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) 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) out += identity out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(Bottleneck, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.)) * groups # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(inplace=True) 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 class ResNetEncoder(nn.Module): def __init__(self, block, layers, widths=[64, 128, 256, 512], num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None): super(ResNetEncoder, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, widths[0], layers[0]) self.layer2 = self._make_layer(block, widths[1], layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, widths[2], layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, widths[3], layers[3], stride=2, dilate=replace_stride_with_dilation[2]) 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.GroupNorm)): 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, blocks, stride=1, dilate=False): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.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) return x class Decoder(nn.Module): def __init__(self, output_channels=32, num_classes=None, base_match=512): super(Decoder, self).__init__() self.output_channels = output_channels self.num_classes = num_classes self.relu = nn.ReLU(inplace=True) if num_classes is not None: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) else: self.upconv0 = nn.ConvTranspose2d(base_match, 256, 2, 2) self.bn_upconv0 = nn.BatchNorm2d(256) self.conv_decode0 = nn.Conv2d(256, 256, 3, padding=1) self.bn_decode0 = nn.BatchNorm2d(256) self.upconv1 = nn.ConvTranspose2d(256, 128, 2, 2) self.bn_upconv1 = nn.BatchNorm2d(128) self.conv_decode1 = nn.Conv2d(128, 128, 3, padding=1) self.bn_decode1 = nn.BatchNorm2d(128) self.upconv2 = nn.ConvTranspose2d(128, 64, 2, 2) self.bn_upconv2 = nn.BatchNorm2d(64) self.conv_decode2 = nn.Conv2d(64, 64, 3, padding=1) self.bn_decode2 = nn.BatchNorm2d(64) self.upconv3 = nn.ConvTranspose2d(64, 48, 2, 2) self.bn_upconv3 = nn.BatchNorm2d(48) self.conv_decode3 = nn.Conv2d(48, 48, 3, padding=1) self.bn_decode3 = nn.BatchNorm2d(48) self.upconv4 = nn.ConvTranspose2d(48, 32, 2, 2) self.bn_upconv4 = nn.BatchNorm2d(32) self.conv_decode4 = nn.Conv2d(32, output_channels, 3, padding=1) def forward(self, representation): # batch_size=representation.shape[0] if self.num_classes is None: # x2 = self.conv_decode_res(representation) # x2 = self.bn_conv_decode_res(x2) # x2 = interpolate(x2,size=(256,256)) x = self.upconv0(representation) x = self.bn_upconv0(x) x = self.relu(x) x = self.conv_decode0(x) x = self.bn_decode0(x) x = self.relu(x) x = self.upconv1(x) x = self.bn_upconv1(x) x = self.relu(x) x = self.conv_decode1(x) x = self.bn_decode1(x) x = self.relu(x) x = self.upconv2(x) x = self.bn_upconv2(x) x = self.relu(x) x = self.conv_decode2(x) x = self.bn_decode2(x) x = self.relu(x) x = self.upconv3(x) x = self.bn_upconv3(x) x = self.relu(x) x = self.conv_decode3(x) x = self.bn_decode3(x) x = self.relu(x) x = self.upconv4(x) x = self.bn_upconv4(x) # x = torch.cat([x,x2],1) # print(x.shape,self.static.shape) # x = torch.cat([x,x2,input,self.static.expand(batch_size,-1,-1,-1)],1) x = self.relu(x) x = self.conv_decode4(x) # z = x[:,19:22,:,:].clone() # y = (z).norm(2,1,True).clamp(min=1e-12) # print(y.shape,x[:,21:24,:,:].shape) # x[:,19:22,:,:]=z/y else: x = F.adaptive_avg_pool2d(x, (1, 1)) x = x.view(x.size(0), -1) x = self.fc(x) return x class ResNet(BaseModel): def __init__(self, block, layers, tasks=None, num_classes=None, ozan=False, size=1, **kwargs): super(ResNet, self).__init__() if size == 1: self.encoder = ResNetEncoder(block, layers, **kwargs) elif size == 2: self.encoder = ResNetEncoder(block, layers, [96, 192, 384, 720], **kwargs) elif size == 3: self.encoder = ResNetEncoder(block, layers, [112, 224, 448, 880], **kwargs) elif size == 0.5: self.encoder = ResNetEncoder(block, layers, [48, 96, 192, 360], **kwargs) self.tasks = tasks self.ozan = ozan self.task_to_decoder = {} if tasks is not None: # self.final_conv = nn.Conv2d(728,512,3,1,1) # self.final_conv_bn = nn.BatchNorm2d(512) for task in tasks: if task == 'segment_semantic': output_channels = 18 if task == 'depth_zbuffer': output_channels = 1 if task == 'normal': output_channels = 3 if task == 'edge_occlusion': output_channels = 1 if task == 'reshading': output_channels = 3 if task == 'keypoints2d': output_channels = 1 if task == 'edge_texture': output_channels = 1 if size == 1: decoder = Decoder(output_channels) elif size == 2: decoder = Decoder(output_channels, base_match=720) elif size == 3: decoder = Decoder(output_channels, base_match=880) elif size == 0.5: decoder = Decoder(output_channels, base_match=360) self.task_to_decoder[task] = decoder else: self.task_to_decoder['classification'] = Decoder(output_channels=0, num_classes=1000) self.decoders = nn.ModuleList(self.task_to_decoder.values()) # ------- init weights -------- 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, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() # ----------------------------- def forward(self, input): rep = self.encoder(input) if self.tasks is None: return self.decoders[0](rep) # rep = self.final_conv(rep) # rep = self.final_conv_bn(rep) outputs = {'rep': rep} if self.ozan: OzanRepFunction.n = len(self.decoders) rep = ozan_rep_function(rep) for i, (task, decoder) in enumerate(zip(self.task_to_decoder.keys(), self.decoders)): outputs[task] = decoder(rep[i]) else: TrevorRepFunction.n = len(self.decoders) rep = trevor_rep_function(rep) for i, (task, decoder) in enumerate(zip(self.task_to_decoder.keys(), self.decoders)): outputs[task] = decoder(rep) return outputs def _resnet(arch, block, layers, pretrained, **kwargs): model = ResNet(block=block, layers=layers, **kwargs) # if pretrained: # state_dict = load_state_dict_from_url(model_urls[arch], # progress=progress) # model.load_state_dict(state_dict) return model def resnet18(pretrained=False, **kwargs): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, **kwargs) def resnet18_tripple(pretrained=False, **kwargs): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, size=3, **kwargs) def resnet18_half(pretrained=False, **kwargs): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, size=0.5, **kwargs) def resnet34(pretrained=False, **kwargs): """Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, **kwargs) def resnet50(pretrained=False, **kwargs): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, **kwargs) def resnet101(pretrained=False, **kwargs): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, **kwargs) def resnet152(pretrained=False, **kwargs): """Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, **kwargs)