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"""
Code adapted from https://github.com/shariqfarooq123/AdaBins/blob/main/models/unet_adaptive_bins.py
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
class UpSampleBN(nn.Module):
def __init__(self, skip_input, output_features):
super(UpSampleBN, self).__init__()
self._net = nn.Sequential(
nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(output_features),
nn.LeakyReLU(),
nn.Conv2d(
output_features, output_features, kernel_size=3, stride=1, padding=1
),
nn.BatchNorm2d(output_features),
nn.LeakyReLU(),
)
def forward(self, x, concat_with):
up_x = F.interpolate(
x,
size=(concat_with.shape[2], concat_with.shape[3]),
mode="bilinear",
align_corners=True,
)
f = torch.cat([up_x, concat_with], dim=1)
return self._net(f)
class DecoderBN(nn.Module):
def __init__(
self, num_features, bottleneck_features, out_feature, use_decoder=True
):
super(DecoderBN, self).__init__()
features = int(num_features)
self.use_decoder = use_decoder
self.conv2 = nn.Conv2d(
bottleneck_features, features, kernel_size=1, stride=1, padding=1
)
self.out_feature_1_1 = out_feature
self.out_feature_1_2 = out_feature
self.out_feature_1_4 = out_feature
self.out_feature_1_8 = out_feature
self.out_feature_1_16 = out_feature
self.feature_1_16 = features // 2
self.feature_1_8 = features // 4
self.feature_1_4 = features // 8
self.feature_1_2 = features // 16
self.feature_1_1 = features // 32
if self.use_decoder:
self.resize_output_1_1 = nn.Conv2d(
self.feature_1_1, self.out_feature_1_1, kernel_size=1
)
self.resize_output_1_2 = nn.Conv2d(
self.feature_1_2, self.out_feature_1_2, kernel_size=1
)
self.resize_output_1_4 = nn.Conv2d(
self.feature_1_4, self.out_feature_1_4, kernel_size=1
)
self.resize_output_1_8 = nn.Conv2d(
self.feature_1_8, self.out_feature_1_8, kernel_size=1
)
self.resize_output_1_16 = nn.Conv2d(
self.feature_1_16, self.out_feature_1_16, kernel_size=1
)
self.up16 = UpSampleBN(
skip_input=features + 224, output_features=self.feature_1_16
)
self.up8 = UpSampleBN(
skip_input=self.feature_1_16 + 80, output_features=self.feature_1_8
)
self.up4 = UpSampleBN(
skip_input=self.feature_1_8 + 48, output_features=self.feature_1_4
)
self.up2 = UpSampleBN(
skip_input=self.feature_1_4 + 32, output_features=self.feature_1_2
)
self.up1 = UpSampleBN(
skip_input=self.feature_1_2 + 3, output_features=self.feature_1_1
)
else:
self.resize_output_1_1 = nn.Conv2d(3, out_feature, kernel_size=1)
self.resize_output_1_2 = nn.Conv2d(32, out_feature * 2, kernel_size=1)
self.resize_output_1_4 = nn.Conv2d(48, out_feature * 4, kernel_size=1)
def forward(self, features):
x_block0, x_block1, x_block2, x_block3, x_block4 = (
features[4],
features[5],
features[6],
features[8],
features[11],
)
bs = x_block0.shape[0]
x_d0 = self.conv2(x_block4)
if self.use_decoder:
x_1_16 = self.up16(x_d0, x_block3)
x_1_8 = self.up8(x_1_16, x_block2)
x_1_4 = self.up4(x_1_8, x_block1)
x_1_2 = self.up2(x_1_4, x_block0)
x_1_1 = self.up1(x_1_2, features[0])
return {
"1_1": self.resize_output_1_1(x_1_1),
"1_2": self.resize_output_1_2(x_1_2),
"1_4": self.resize_output_1_4(x_1_4),
"1_8": self.resize_output_1_8(x_1_8),
"1_16": self.resize_output_1_16(x_1_16),
}
else:
x_1_1 = features[0]
x_1_2, x_1_4, x_1_8, x_1_16 = (
features[4],
features[5],
features[6],
features[8],
)
x_global = features[-1].reshape(bs, 2560, -1).mean(2)
return {
"1_1": self.resize_output_1_1(x_1_1),
"1_2": self.resize_output_1_2(x_1_2),
"1_4": self.resize_output_1_4(x_1_4),
"global": x_global,
}
class Encoder(nn.Module):
def __init__(self, backend):
super(Encoder, self).__init__()
self.original_model = backend
def forward(self, x):
features = [x]
for k, v in self.original_model._modules.items():
if k == "blocks":
for ki, vi in v._modules.items():
features.append(vi(features[-1]))
else:
features.append(v(features[-1]))
return features
class UNet2D(nn.Module):
def __init__(self, backend, num_features, out_feature, use_decoder=True):
super(UNet2D, self).__init__()
self.use_decoder = use_decoder
self.encoder = Encoder(backend)
self.decoder = DecoderBN(
out_feature=out_feature,
use_decoder=use_decoder,
bottleneck_features=num_features,
num_features=num_features,
)
def forward(self, x, **kwargs):
encoded_feats = self.encoder(x)
unet_out = self.decoder(encoded_feats, **kwargs)
return unet_out
def get_encoder_params(self): # lr/10 learning rate
return self.encoder.parameters()
def get_decoder_params(self): # lr learning rate
return self.decoder.parameters()
@classmethod
def build(cls, **kwargs):
basemodel_name = "tf_efficientnet_b7_ns"
num_features = 2560
print("Loading base model ()...".format(basemodel_name), end="")
basemodel = torch.hub.load(
"rwightman/gen-efficientnet-pytorch", basemodel_name, pretrained=True
)
print("Done.")
# Remove last layer
print("Removing last two layers (global_pool & classifier).")
basemodel.global_pool = nn.Identity()
basemodel.classifier = nn.Identity()
# Building Encoder-Decoder model
print("Building Encoder-Decoder model..", end="")
m = cls(basemodel, num_features=num_features, **kwargs)
print("Done.")
return m
if __name__ == '__main__':
model = UNet2D.build(out_feature=256, use_decoder=True)
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