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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
class ResNet45_10s_origin(nn.Module):
def __init__(self, input_shape, output_dim, cfg, device, preprocess):
super(ResNet45_10s_origin, 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._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(32, [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)
x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear')
return x, im |