Spaces:
Runtime error
Runtime error
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import cliport.utils.utils as utils | |
| from cliport.models.resnet import ConvBlock, IdentityBlock | |
| from torchvision.models import resnet18, resnet34, resnet50 | |
| class PretrainedResNet18(nn.Module): | |
| def __init__(self, input_shape, output_dim, cfg, device, preprocess): | |
| super(PretrainedResNet18, self).__init__() | |
| self.input_shape = input_shape | |
| self.input_dim = input_shape[-1] | |
| self.output_dim = output_dim | |
| self.cfg = cfg | |
| self.device = device | |
| self.batchnorm = self.cfg['train']['batchnorm'] | |
| self.preprocess = preprocess | |
| self.pretrained_model = resnet18(pretrained=True) | |
| self.pretrained_model.avgpool = nn.Identity() | |
| self.pretrained_model.fc = nn.Identity() | |
| # self.pretrained_model.eval() | |
| self.pretrained_model.conv1 = nn.Conv2d(self.input_dim, 64, kernel_size=2, stride=1, padding=3, bias=False) | |
| # import IPython; IPython.embed() | |
| for param in self.pretrained_model.parameters(): | |
| param.requires_grad = False | |
| self.pretrained_model.conv1.weight.requires_grad = True | |
| self._make_layers() | |
| def _make_layers(self): | |
| # conv1 | |
| # self.conv1 = nn.Sequential( | |
| # nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1), | |
| # nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(), | |
| # nn.ReLU(True), | |
| # ) | |
| # # fcn | |
| # self.layer1 = nn.Sequential( | |
| # ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| # IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| # ) | |
| # self.layer2 = nn.Sequential( | |
| # ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm), | |
| # IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| # ) | |
| # self.layer3 = nn.Sequential( | |
| # ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm), | |
| # IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| # ) | |
| # self.layer4 = nn.Sequential( | |
| # ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm), | |
| # IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| # ) | |
| # self.layer5 = nn.Sequential( | |
| # ConvBlock(512, [1024, 1024, 1024], kernel_size=3, stride=2, batchnorm=self.batchnorm), | |
| # IdentityBlock(1024, [1024, 1024, 1024], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| # ) | |
| # # head | |
| # self.layer6 = nn.Sequential( | |
| # ConvBlock(1024, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| # IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| # nn.UpsamplingBilinear2d(scale_factor=2), | |
| # ) | |
| self.layer7 = nn.Sequential( | |
| ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| nn.UpsamplingBilinear2d(scale_factor=2), | |
| ) | |
| self.layer8 = nn.Sequential( | |
| ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| nn.UpsamplingBilinear2d(scale_factor=2), | |
| ) | |
| self.layer9 = nn.Sequential( | |
| ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| nn.UpsamplingBilinear2d(scale_factor=2), | |
| ) | |
| self.layer10 = nn.Sequential( | |
| ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| nn.UpsamplingBilinear2d(scale_factor=2), | |
| ) | |
| # conv2 | |
| self.conv2 = nn.Sequential( | |
| ConvBlock(128, [16, 16, self.output_dim], kernel_size=3, stride=1, | |
| final_relu=False, batchnorm=self.batchnorm), | |
| IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1, | |
| final_relu=False, batchnorm=self.batchnorm) | |
| ) | |
| def forward(self, x): | |
| x = self.preprocess(x, dist='transporter') | |
| in_shape = x.shape | |
| # # encoder | |
| # for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4, self.layer5]: | |
| # x = layer(x) | |
| # # decoder | |
| # im = [] | |
| # for layer in [self.layer6, self.layer7, self.layer8, self.layer9, self.layer10, self.conv2]: | |
| # im.append(x) | |
| # x = layer(x) | |
| # encoder | |
| # for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4]: | |
| # x = layer(x) | |
| # x = x[:, :3, :, :] | |
| x = self.pretrained_model.conv1(x) | |
| for name, module in self.pretrained_model._modules.items(): | |
| if name == 'conv1': | |
| continue | |
| x = module(x) | |
| if name == 'layer4': | |
| break | |
| # with torch.no_grad(): | |
| # x = self.pretrained_model(x) | |
| # import ipdb;ipdb.set_trace() | |
| x = F.interpolate(x, size=(8, 8), mode='bilinear') | |
| # decoder | |
| im = [] | |
| for layer in [self.layer7, self.layer8, self.conv2]: | |
| im.append(x) | |
| x = layer(x) | |
| x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') | |
| return x, im |