code stringlengths 3 6.57k |
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Encoder(nn.Module) |
super(Encoder, self) |
__init__() |
nn.MaxPool2d(kernel_size=downsample_kernel) |
nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1) |
nn.BatchNorm2d(middle_channels) |
nn.ReLU(inplace=True) |
nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1) |
nn.BatchNorm2d(out_channels) |
nn.ReLU(inplace=True) |
layers.append(nn.Dropout2d(p=dropout) |
nn.Sequential(*layers) |
forward(self, x) |
self.encoder(x) |
Center(nn.Module) |
__init__(self, in_channels, middle_channels, out_channels, deconv_channels, dropout=False) |
super(Center, self) |
__init__() |
nn.MaxPool2d(kernel_size=2) |
nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1) |
nn.BatchNorm2d(middle_channels) |
nn.ReLU(inplace=True) |
nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1) |
nn.BatchNorm2d(out_channels) |
nn.ReLU(inplace=True) |
nn.ConvTranspose2d(out_channels, deconv_channels, kernel_size=2, stride=2) |
layers.append(nn.Dropout2d(p=dropout) |
nn.Sequential(*layers) |
forward(self, x) |
self.center(x) |
Decoder(nn.Module) |
__init__(self, in_channels, middle_channels, out_channels, deconv_channels, dropout=False) |
super(Decoder, self) |
__init__() |
nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1) |
nn.BatchNorm2d(middle_channels) |
nn.ReLU(inplace=True) |
nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1) |
nn.BatchNorm2d(out_channels) |
nn.ReLU(inplace=True) |
nn.ConvTranspose2d(out_channels, deconv_channels, kernel_size=2, stride=2) |
layers.append(nn.Dropout2d(p=dropout) |
nn.Sequential(*layers) |
forward(self, x) |
self.decoder(x) |
Last(nn.Module) |
__init__(self, in_channels, middle_channels, out_channels, softmax=False) |
super(Last, self) |
__init__() |
nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1) |
nn.BatchNorm2d(middle_channels) |
nn.ReLU(inplace=True) |
nn.Conv2d(middle_channels, middle_channels, kernel_size=3, padding=1) |
nn.BatchNorm2d(middle_channels) |
nn.ReLU(inplace=True) |
nn.Conv2d(middle_channels, out_channels, kernel_size=1) |
nn.Sigmoid() |
layers.append(nn.Softmax2d() |
nn.Sequential(*layers) |
forward(self, x) |
self.first(x) |
UNet(nn.Module) |
__init__(self, in_channels, out_channels, softmax=False) |
super(UNet, self) |
__init__() |
First(in_channels, 64, 64) |
Encoder(64, 128, 128) |
Encoder(128, 256, 256) |
Encoder(256, 512, 512) |
Center(512, 1024, 1024, 512) |
Decoder(1024, 512, 512, 256) |
Decoder(512, 256, 256, 128) |
Decoder(256, 128, 128, 64) |
Last(128, 64, out_channels, softmax=softmax) |
forward(self, x) |
self.first(x) |
self.encoder_1(x_first) |
self.encoder_2(x_enc_1) |
self.encoder_3(x_enc_2) |
self.center(x_enc_3) |
self.decoder_3(torch.cat([pad_to_shape(x_cent, x_enc_3.shape) |
self.decoder_2(torch.cat([pad_to_shape(x_dec_3, x_enc_2.shape) |
self.decoder_1(torch.cat([pad_to_shape(x_dec_2, x_enc_1.shape) |
self.last(torch.cat([pad_to_shape(x_dec_1, x_first.shape) |
BSD (3-clause) |
import (complete_behavior, get_events_interactions) |
op.join(path_data + 'results/' + subject + output_folder) |
os.path.exists(results_folder) |
os.makedirs(results_folder) |
op.join(path_data, subject, 'behavior_Target.hdf5') |
read_hdf5(fname) |
complete_behavior(events) |
get_events_interactions(events) |
op.join(path_data, subject, 'epochs_Target.fif') |
mne.read_epochs(fname) |
epochs_target.pick_types(meg=True, ref_meg=False) |
epochs_target.crop(-0.2, 0.9) |
op.join(path_data, subject, 'epochs_Cue.fif') |
mne.read_epochs(fname) |
epochs_cue.pick_types(meg=True, ref_meg=False) |
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