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import torch.nn as nn |
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import torch.nn.functional as F |
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import cliport.utils.utils as utils |
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from cliport.models.resnet import IdentityBlock, ConvBlock |
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from cliport.models.clip_lingunet_lat import CLIPLingUNetLat |
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class CLIPWithoutSkipConnections(CLIPLingUNetLat): |
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""" CLIP RN50 with decoders (no skip connections) """ |
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def __init__(self, input_shape, output_dim, cfg, device, preprocess): |
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super().__init__(input_shape, output_dim, cfg, device, preprocess) |
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def _build_decoder(self): |
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self.layers = nn.Sequential( |
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nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), |
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nn.ReLU(True), |
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nn.UpsamplingBilinear2d(scale_factor=2), |
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ConvBlock(1024, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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ConvBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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nn.UpsamplingBilinear2d(scale_factor=2), |
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ConvBlock(512, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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ConvBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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nn.UpsamplingBilinear2d(scale_factor=2), |
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ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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nn.UpsamplingBilinear2d(scale_factor=2), |
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ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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ConvBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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nn.UpsamplingBilinear2d(scale_factor=2), |
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nn.Conv2d(32, self.output_dim, kernel_size=1) |
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) |
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def forward(self, x): |
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x = self.preprocess(x, dist='clip') |
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in_type = x.dtype |
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in_shape = x.shape |
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x = x[:,:3] |
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x, _ = self.encode_image(x) |
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x = x.to(in_type) |
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assert x.shape[1] == self.input_dim |
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x = self.layers(x) |
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x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') |
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return x |
