Pytorch Model Error

[herleeyandi]

Hi I try to build ESPNet with class=2, p=2, q=8. I got error like this.

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-35-0e6836058e7e> in <module>()
      1 import module_ESPNet as net
----> 2 espnet = ESPNet(2, 2, 8)
      3 #espnet.to(GPU_ID)
      4 #enet.cpu()

<ipython-input-31-ee42dd8d8112> in __init__(self, classes, p, q, encoderFile)
    332 
    333         self.up_l3 = nn.Sequential(nn.ConvTranspose2d(classes, classes, 2, stride=2, padding=0, output_padding=0, bias=False))
--> 334         self.combine_l2_l3 = nn.Sequential(BR(2*classes), DilatedParllelResidualBlockB(2*classes , classes, add=False))
    335 
    336         self.up_l2 = nn.Sequential(nn.ConvTranspose2d(classes, classes, 2, stride=2, padding=0, output_padding=0, bias=False), BR(classes))

<ipython-input-31-ee42dd8d8112> in __init__(self, nIn, nOut, add)
    175         n = int(nOut/5)
    176         n1 = nOut - 4*n
--> 177         self.c1 = C(nIn, n, 1, 1)
    178         self.d1 = CDilated(n, n1, 3, 1, 1) # dilation rate of 2^0
    179         self.d2 = CDilated(n, n, 3, 1, 2) # dilation rate of 2^1

<ipython-input-31-ee42dd8d8112> in __init__(self, nIn, nOut, kSize, stride)
     92         super().__init__()
     93         padding = int((kSize - 1)/2)
---> 94         self.conv = nn.Conv2d(nIn, nOut, (kSize, kSize), stride=stride, padding=(padding, padding), bias=False)
     95 
     96     def forward(self, input):

~/.conda/envs/deep/lib/python3.6/site-packages/torch/nn/modules/conv.py in __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
    313         super(Conv2d, self).__init__(
    314             in_channels, out_channels, kernel_size, stride, padding, dilation,
--> 315             False, _pair(0), groups, bias)
    316 
    317     @weak_script_method

~/.conda/envs/deep/lib/python3.6/site-packages/torch/nn/modules/conv.py in __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias)
     41         else:
     42             self.register_parameter('bias', None)
---> 43         self.reset_parameters()
     44 
     45     def reset_parameters(self):

~/.conda/envs/deep/lib/python3.6/site-packages/torch/nn/modules/conv.py in reset_parameters(self)
     45     def reset_parameters(self):
     46         n = self.in_channels
---> 47         init.kaiming_uniform_(self.weight, a=math.sqrt(5))
     48         if self.bias is not None:
     49             fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)

~/.conda/envs/deep/lib/python3.6/site-packages/torch/nn/init.py in kaiming_uniform_(tensor, a, mode, nonlinearity)
    286         >>> nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu')
    287     """
--> 288     fan = _calculate_correct_fan(tensor, mode)
    289     gain = calculate_gain(nonlinearity, a)
    290     std = gain / math.sqrt(fan)

~/.conda/envs/deep/lib/python3.6/site-packages/torch/nn/init.py in _calculate_correct_fan(tensor, mode)
    255         raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
    256 
--> 257     fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
    258     return fan_in if mode == 'fan_in' else fan_out
    259 

~/.conda/envs/deep/lib/python3.6/site-packages/torch/nn/init.py in _calculate_fan_in_and_fan_out(tensor)
    189         receptive_field_size = 1
    190         if tensor.dim() > 2:
--> 191             receptive_field_size = tensor[0][0].numel()
    192         fan_in = num_input_fmaps * receptive_field_size
    193         fan_out = num_output_fmaps * receptive_field_size

IndexError: index 0 is out of bounds for dimension 0 with size 0

[sacmehta]

See #13

[zhangzhihanzzh]

hi! Have you solved this problem? I just meet the same error like you, would you like to tell me what did you do please?