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import torch.nn as nn
import torch.nn.functional as F
import cliport.utils.utils as utils
from cliport.models.resnet import IdentityBlock, ConvBlock
from cliport.models.core.unet import Up
from cliport.models.core import fusion
from cliport.models.clip_lingunet_lat import CLIPLingUNetLat
class CLIPLing(CLIPLingUNetLat):
""" CLIP RN50 with U-Net skip connections """
def __init__(self, input_shape, output_dim, cfg, device, preprocess):
super().__init__(input_shape, output_dim, cfg, device, preprocess)
# def _build_decoder(self):
# # language
# self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2)
# self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4)
# self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8)
# self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024
# self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024)
# self.lang_proj2 = nn.Linear(self.proj_input_dim, 512)
# self.lang_proj3 = nn.Linear(self.proj_input_dim, 256)
# # vision
# self.conv1 = nn.Sequential(
# nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False),
# nn.ReLU(True)
# )
# self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear)
# self.up2 = Up(1024, 512 // self.up_factor, self.bilinear)
# self.up3 = Up(512, 256 // self.up_factor, self.bilinear)
# self.layer1 = 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.layer2 = 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),
# )
# self.layer3 = nn.Sequential(
# ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
# IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
# nn.UpsamplingBilinear2d(scale_factor=2),
# )
del self.lang_fuser2, self.lang_fuser1, self.lang_proj1, self.lang_proj2, self.layer2, self.layer1, self.layer3
self.conv2 = nn.Sequential(
nn.Conv2d(128, self.output_dim, kernel_size=1)
)
def forward(self, x, lat, l):
x = self.preprocess(x, dist='clip')
in_type = x.dtype
in_shape = x.shape
x = x[:,:3] # select RGB
x, im = self.encode_image(x)
x = x.to(in_type)
l_enc, l_emb, l_mask = self.encode_text(l)
l_input = l_emb if 'word' in self.lang_fusion_type else l_enc
l_input = l_input.to(dtype=x.dtype)
assert x.shape[1] == self.input_dim
x = self.conv1(x)
# x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1)
# x = self.up1(x, im[-2])
# x = self.lat_fusion1(x, lat[-6])
# x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2)
# x = self.up2(x, im[-3])
# x = self.lat_fusion2(x, lat[-5])
x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3)
x = self.up3(x, im[-4])
x = self.lat_fusion3(x, lat[1])
x = self.conv2(x)
x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear')
return x |