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models.py
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169 lines (147 loc) · 6.41 KB
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import torch
import torch.nn as nn
from wcode.net.CNN.VNet.VNet import Encoder, Decoder
class DBUNet(nn.Module):
def __init__(self, params):
super(DBUNet, self).__init__()
self.need_features = params["need_features"]
self.deep_supervision = params["deep_supervision"]
self.encoder_params, self.decoder_params = self.get_EnDecoder_params(params)
self.encoder = Encoder(self.encoder_params)
self.decoder = Decoder(
self.decoder_params,
output_features=self.deep_supervision or self.need_features,
)
self.aux_decoder = Decoder(
self.decoder_params,
output_features=self.deep_supervision or self.need_features,
)
self.feature_dropout_rate = [0.5 for _ in range(len(params["dropout_p"]))]
if len(params["kernel_size"][0]) == 2:
Conv_layer = nn.Conv2d
self.dropout = nn.functional.dropout2d
elif len(params["kernel_size"][0]) == 3:
Conv_layer = nn.Conv3d
self.dropout = nn.functional.dropout3d
if self.deep_supervision:
self.prediction_head = nn.ModuleList()
# we will not do deep supervision on the prediction of bottleneck output feature
# the prediction_heads are from low to high resolution.
for i in range(1, len(self.encoder_params["num_conv_per_stage"])):
self.prediction_head.append(
Conv_layer(
self.decoder_params["features"][i],
params["out_channels"],
kernel_size=1,
)
)
self.aux_prediction_head = nn.ModuleList()
for i in range(1, len(self.encoder_params["num_conv_per_stage"])):
self.aux_prediction_head.append(
Conv_layer(
self.decoder_params["features"][i],
params["out_channels"],
kernel_size=1,
)
)
else:
self.prediction_head = Conv_layer(
self.decoder_params["features"][-1],
params["out_channels"],
kernel_size=1,
)
self.aux_prediction_head = Conv_layer(
self.decoder_params["features"][-1],
params["out_channels"],
kernel_size=1,
)
def forward(self, x):
main_features = self.encoder(x)
aux_features = [
self.dropout(main_features[i], p=self.feature_dropout_rate[i])
for i in range(len(main_features))
]
main_outfeatures = self.decoder(main_features)
aux_outfeatures = self.aux_decoder(aux_features)
if self.deep_supervision:
main_outputs = []
for i in range(len(main_outfeatures)):
main_outputs.append(self.prediction_head[i](main_outfeatures[i]))
aux_outputs = []
for i in range(len(aux_outfeatures)):
aux_outputs.append(self.aux_prediction_head[i](aux_outfeatures[i]))
# we assume that the multi-level prediction ranking ranges from high resolution to low resolution
if self.need_features:
net_out = {
"feature": [
main_features + main_outfeatures,
aux_features + aux_outfeatures,
],
"pred": main_outputs[::-1],
"pred_for_train": [
main_outputs[::-1],
aux_outputs[::-1],
],
}
else:
net_out = {
"pred": main_outputs[::-1],
"pred_for_train": [
main_outputs[::-1],
aux_outputs[::-1],
],
}
else:
if self.need_features:
main_outputs = self.prediction_head(main_outfeatures[-1])
aux_outputs = self.aux_prediction_head(aux_outfeatures[-1])
net_out = {
"feature": [
main_features + main_outfeatures,
aux_features + aux_outfeatures,
],
"pred": main_outputs,
"pred_for_train": [
main_outputs,
aux_outputs,
],
}
else:
main_outputs = self.prediction_head(main_outfeatures)
aux_outputs = self.aux_prediction_head(aux_outfeatures)
net_out = {
"pred": main_outputs,
"pred_for_train": [
main_outputs,
aux_outputs,
],
}
return net_out
def get_EnDecoder_params(self, params):
encoder_params = {}
decoder_params = {}
encoder_params["in_channels"] = params["in_channels"]
encoder_params["features"] = params["features"]
encoder_params["dropout_p"] = params["dropout_p"]
encoder_params["num_conv_per_stage"] = params["num_conv_per_stage"]
encoder_params["kernel_size"] = params["kernel_size"]
encoder_params["pool_kernel_size"] = params["pool_kernel_size"]
encoder_params["normalization"] = params["normalization"]
encoder_params["activate"] = params["activate"]
encoder_params["need_bias"] = params["need_bias"]
assert (
len(encoder_params["features"])
== len(encoder_params["dropout_p"])
== len(encoder_params["num_conv_per_stage"])
== len(encoder_params["kernel_size"])
== (len(encoder_params["pool_kernel_size"]) + 1)
)
decoder_params["features"] = params["features"][::-1]
decoder_params["kernel_size"] = params["kernel_size"][::-1]
decoder_params["pool_kernel_size"] = params["pool_kernel_size"][::-1]
decoder_params["dropout_p"] = [0.0 for _ in range(len(params["dropout_p"]))]
decoder_params["num_conv_per_stage"] = params["num_conv_per_stage"][::-1]
decoder_params["normalization"] = params["normalization"]
decoder_params["activate"] = params["activate"]
decoder_params["need_bias"] = params["need_bias"]
return encoder_params, decoder_params