Hello all, I don’t have this error before upgrading pytorch to 2.0.1.
Lib\site-packages\torch\utils\data\_utils\collate.py", line 138, in collate
raise RuntimeError('each element in list of batch should be of equal size')
RuntimeError: each element in list of batch should be of equal size
I tried to use collate_fn=lambda x: x in my dataloader like this:
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True, collate_fn=lambda x: x)
but I get an error:
for i, (inputs, target, _) in enumerate(train_loader):
^^^^^^^^^^^^^^^^^^^
ValueError: too many values to unpack (expected 3)
My task is MLP+GRU, and my model looks like this:
class MLP(nn.Module):
def __init__(self, num_classes, rnn_layers, hidden_size, fc_size):
super(MLP, self).__init__()
self.hidden_size = hidden_size
self.num_classes = num_classes
self.fc_size = fc_size
self.apply(self._init_weights)
self.fc_pre= nn.Sequential(
nn.Linear(201, fc_size),
nn.ReLU())
#Defines the number of features that define each element (time-stamp) of the input sequence
self.rnn = nn.GRU(input_size = fc_size,
hidden_size = hidden_size,
num_layers = rnn_layers,
batch_first = True)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, inputs, hidden=None, steps=0):
length = len(inputs)
# print('inputs size')
# print(inputs[3].size())
#Input data: RNN should have 3 dimensions. (Batch Size, Sequence Length and Input Dimension (the number of expected features which is 201)
fs = torch.zeros(inputs[0].size(0), length, self.rnn.input_size).cuda()
for i in range(length):
f = inputs[i]
#flattens the tensor
f = f.view(f.size(0), -1)
#print(f.size())
f = self.fc_pre(f)
# stores the tensor f in the sha pe [fs.size(0), fs.size(2)] to each “row” in fs to prepare for learning the sequence in RNN
fs[:, i, :] = f
# print('fs')
# print(fs.size())
#outputs : batch size, seq legnth, hidden size
outputs, hidden = self.rnn(fs, hidden)
#print(outputs.size())
#the training code applies crossentropyloss as criterion which also applies softmax to the output so I don't have to use softmax here
outputs = self.fc(outputs)
#print(outputs)
#print(outputs.size())
return outputs
Any suggestions/ideas for me? Thank you in advance