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seathiefwang / MGN-pytorch

Reproduction of paper: Learning Discriminative Features with Multiple Granularities for Person Re-Identification
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new dataset with some problem #5

Open Jinjicheng opened 6 years ago

Jinjicheng commented 6 years ago

i run your code with new dataset(MSMT17),but it occured ： [INFO] Making model... [INFO] Making loss... 1.000 CrossEntropy 1.000 Triplet [INFO] Epoch: 1 Learning rate: 2.00e-04 [INFO] [1/160] 65/66 [CrossEntropy: 9.6161][Triplet: 3.0369][Total: 12.6529]Traceback (most recen t call last): File "main.py", line 20, in trainer.train() File "G:\code2\MGN-pytorch-master\trainer.py", line 50, in train loss.backward() File "D:\Anaconda3\lib\site-packages\torch\tensor.py", line 93, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "D:\Anaconda3\lib\site-packages\torch\autograd__init__.py", line 89, in backward allow_unreachable=True) # allow_unreachable flag RuntimeError: The expanded size of the tensor (1) must match the existing size (0) at non-singleton dimension 0 how to solve it.

lianfenghua commented 6 years ago

@Jinjicheng When I used another dataset (Mars) to train, the same error occured. The error would disappear if the parameter --batchid was setted as another value (e.g. --batchid 5), though I do not know the reason behind it.

Jinjicheng commented 6 years ago

i know it.For example,ids of train is 1041,batchid and batchimg is (16,4). Every batch has 16 id, but last batch has only 1 id,it is incompatible Triplet loss.

lianfenghua commented 6 years ago

So that explains it. If ids = 1041 and batchid = 16, then because 1041/16 = 65...1, the number of batches is 66, where the first 65 batches have 16 ids and the last batch has only one id. However calculatig triplet loss needs two examples with different labels (ids). Thanks.