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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.clip_lingunet_lat import CLIPLingUNetLat
from cliport.models.core import fusion
from cliport.models.core.fusion import FusionConvLat
class CLIPFilmLingUNet(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_fusion_type = 'film'
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 = 1024
self.lang_gamma1 = nn.Linear(self.proj_input_dim, 1024)
self.lang_gamma2 = nn.Linear(self.proj_input_dim, 512)
self.lang_gamma3 = nn.Linear(self.proj_input_dim, 256)
self.lang_beta1 = nn.Linear(self.proj_input_dim, 1024)
self.lang_beta2 = nn.Linear(self.proj_input_dim, 512)
self.lang_beta3 = 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.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512)
self.up2 = Up(1024, 512 // self.up_factor, self.bilinear)
self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256)
self.up3 = Up(512, 256 // self.up_factor, self.bilinear)
self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128)
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.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64)
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.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32)
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),
)
self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16)
self.conv2 = nn.Sequential(
nn.Conv2d(16, 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_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, gamma=self.lang_gamma1, beta=self.lang_beta1)
x = self.up1(x, im[-2])
x = self.lat_fusion1(x, lat[-6])
x = self.lang_fuser2(x, l_input, gamma=self.lang_gamma2, beta=self.lang_beta2)
x = self.up2(x, im[-3])
x = self.lat_fusion2(x, lat[-5])
x = self.lang_fuser3(x, l_input, gamma=self.lang_gamma3, beta=self.lang_beta3)
x = self.up3(x, im[-4])
x = self.lat_fusion3(x, lat[-4])
x = self.layer1(x)
x = self.lat_fusion4(x, lat[-3])
x = self.layer2(x)
x = self.lat_fusion5(x, lat[-2])
x = self.layer3(x)
x = self.lat_fusion6(x, lat[-1])
x = self.conv2(x)
x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear')
return x |