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Fix the unpacking various sequence LSTM #40

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Fix the unpacking various sequence LSTM #40

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f077d27
Fixed unpacking sequence output of LSTM
glmanhtu Dec 7, 2020
3a57edc
Simple fix to make sure it runnable for MacOS machines (for debugging…
glmanhtu Dec 7, 2020
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9 changes: 7 additions & 2 deletions ResNetCRNN_varylength/UCF101_ResNetCRNN_varlen.py
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Original file line number Diff line number Diff line change
Expand Up @@ -150,7 +150,10 @@ def validation(model, device, optimizer, test_loader):
device = torch.device("cuda" if use_cuda else "cpu") # use CPU or GPU

# Data loading parameters
params = {'batch_size': batch_size, 'shuffle': True, 'num_workers': 8, 'pin_memory': True} if use_cuda else {}
if use_cuda:
params = {'batch_size': batch_size, 'shuffle': True, 'num_workers': 8, 'pin_memory': True}
else:
params = {'batch_size': 2, 'shuffle': True} # Testing


# load UCF101 actions names
Expand Down Expand Up @@ -184,6 +187,8 @@ def validation(model, device, optimizer, test_loader):
all_names = []
all_length = [] # each video length
for f in fnames:
if not os.path.isdir(os.path.join(data_path, f)):
continue
loc1 = f.find('v_')
loc2 = f.find('_g')
actions.append(f[(loc1 + 2): loc2])
Expand Down Expand Up @@ -226,7 +231,7 @@ def validation(model, device, optimizer, test_loader):
list(cnn_encoder.module.fc2.parameters()) + list(cnn_encoder.module.bn2.parameters()) + \
list(cnn_encoder.module.fc3.parameters()) + list(rnn_decoder.parameters())

elif torch.cuda.device_count() == 1:
else:
crnn_params = list(cnn_encoder.fc1.parameters()) + list(cnn_encoder.bn1.parameters()) + \
list(cnn_encoder.fc2.parameters()) + list(cnn_encoder.bn2.parameters()) + \
list(cnn_encoder.fc3.parameters()) + list(rnn_decoder.parameters())
Expand Down
10 changes: 7 additions & 3 deletions ResNetCRNN_varylength/functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -264,16 +264,20 @@ def forward(self, x_RNN, x_lengths):
""" h_n shape (n_layers, batch, hidden_size), h_c shape (n_layers, batch, hidden_size) """
""" None represents zero initial hidden state. RNN_out has shape=(batch, time_step, output_size) """

RNN_out, _ = torch.nn.utils.rnn.pad_packed_sequence(packed_RNN_out, batch_first=True)
RNN_out, out_lengths = torch.nn.utils.rnn.pad_packed_sequence(packed_RNN_out, batch_first=True)
RNN_out = RNN_out.contiguous()
last_out = []
for i, length in enumerate(out_lengths):
last_out.append(RNN_out[i][length - 1])
last_out = torch.stack(last_out)
# RNN_out = RNN_out.view(-1, RNN_out.size(2))

# reverse back to original sequence order
_, unperm_idx = perm_idx.sort(0)
RNN_out = RNN_out[unperm_idx]
RNN_out = last_out[unperm_idx]

# FC layers
x = self.fc1(RNN_out[:, -1, :]) # choose RNN_out at the last time step
x = self.fc1(RNN_out) # choose RNN_out at the last time step
x = F.relu(x)
x = F.dropout(x, p=self.drop_p, training=self.training)
x = self.fc2(x)
Expand Down