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net_params.py 2.22 KB
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jhhuang96 提交于 5年前 . reconstruction
from collections import OrderedDict
from ConvRNN import CGRU_cell, CLSTM_cell
# build model
# in_channels=v[0], out_channels=v[1], kernel_size=v[2], stride=v[3], padding=v[4]
convlstm_encoder_params = [
[
OrderedDict({'conv1_leaky_1': [1, 16, 3, 1, 1]}),
OrderedDict({'conv2_leaky_1': [64, 64, 3, 2, 1]}),
OrderedDict({'conv3_leaky_1': [96, 96, 3, 2, 1]}),
],
[
CLSTM_cell(shape=(64,64), input_channels=16, filter_size=5, num_features=64),
CLSTM_cell(shape=(32,32), input_channels=64, filter_size=5, num_features=96),
CLSTM_cell(shape=(16,16), input_channels=96, filter_size=5, num_features=96)
]
]
convlstm_decoder_params = [
[
OrderedDict({'deconv1_leaky_1': [96, 96, 4, 2, 1]}),
OrderedDict({'deconv2_leaky_1': [96, 96, 4, 2, 1]}),
OrderedDict({
'conv3_leaky_1': [64, 16, 3, 1, 1],
'conv4_leaky_1': [16, 1, 1, 1, 0]
}),
],
[
CLSTM_cell(shape=(16,16), input_channels=96, filter_size=5, num_features=96),
CLSTM_cell(shape=(32,32), input_channels=96, filter_size=5, num_features=96),
CLSTM_cell(shape=(64,64), input_channels=96, filter_size=5, num_features=64),
]
]
convgru_encoder_params = [
[
OrderedDict({'conv1_leaky_1': [1, 16, 3, 1, 1]}),
OrderedDict({'conv2_leaky_1': [64, 64, 3, 2, 1]}),
OrderedDict({'conv3_leaky_1': [96, 96, 3, 2, 1]}),
],
[
CGRU_cell(shape=(64,64), input_channels=16, filter_size=5, num_features=64),
CGRU_cell(shape=(32,32), input_channels=64, filter_size=5, num_features=96),
CGRU_cell(shape=(16,16), input_channels=96, filter_size=5, num_features=96)
]
]
convgru_decoder_params = [
[
OrderedDict({'deconv1_leaky_1': [96, 96, 4, 2, 1]}),
OrderedDict({'deconv2_leaky_1': [96, 96, 4, 2, 1]}),
OrderedDict({
'conv3_leaky_1': [64, 16, 3, 1, 1],
'conv4_leaky_1': [16, 1, 1, 1, 0]
}),
],
[
CGRU_cell(shape=(16,16), input_channels=96, filter_size=5, num_features=96),
CGRU_cell(shape=(32,32), input_channels=96, filter_size=5, num_features=96),
CGRU_cell(shape=(64,64), input_channels=96, filter_size=5, num_features=64),
]
]
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ConvLSTM-PyTorch
ConvLSTM-PyTorch
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