I’m constantly getting
RuntimeError: cuda runtime error (2) : out of memory at /opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/generic/THCTensorMathPairwise.cu:102
I’ve decreased batch_size to 2, lstm layers to 2 and still this error.
I’m trying to implement siamese model with a custom loss.
My model defined as follows
class Model(nn.Module):
def __init__(self):
super(SiameseNetwork, self).__init__()
self.embedding = nn.Embedding(ru.vectors.size(0), ru.vectors.size(1), padding_idx=2)
self.embedding.weight = nn.Parameter(ru.vectors)
self.embedding.weight.requires_grad = False
self.lstm = nn.LSTM(300, 300, 2, batch_first=True, bidirectional=True, dropout=.05)
self.dropout = nn.Dropout(p=.2)
#nn.LSTM(300, 300, 30, batch_first=True, bidirectional=True, dropout=.05),
self.fc1 = nn.Sequential(
nn.Linear(300*2*300, 1500),
nn.ReLU(inplace=True),
nn.Linear(1500, 1000),
nn.ReLU(inplace=True),
nn.Linear(1000, 500),
nn.ReLU(inplace=True),
nn.Linear(500, 5))
def forward_once(self, x):
output = self.embedding(x)
#print('embs', output)
output, _ = self.lstm(output)
#print('lstm', output)
output = self.dropout(output)
#print('drop', output)
output = output.view(output.size()[0], -1)
#print('view', output)
output = self.fc1(output)
return output
def forward(self, input1, input2):
output1 = self.forward_once(input1)
output2 = self.forward_once(input2)
return output1, output2
And loss:
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=2.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pairwise_distance(output1, output2)
loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance, 2) +
(label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))
return loss_contrastive
I’m using pretrained fasttext vectors as embeddings matrix, 1.5kk to 300.
Can anyone help me please 