I HAVE the model code as:
class RNN(nn.Module):
“”“RNN module(cell type lstm or gru)”""
def init(
self,
input_size,
hid_size,
num_rnn_layers=1,
dropout_p = 0.2,
bidirectional = False,
rnn_type = ‘lstm’,
):
super().init()
    if rnn_type == 'lstm':
        self.rnn_layer = nn.LSTM(
            input_size=input_size,
            hidden_size=hid_size,
            num_layers=num_rnn_layers,
            dropout=dropout_p if num_rnn_layers>1 else 0,
            bidirectional=bidirectional,
            batch_first=True,
        )
        
    else:
        self.rnn_layer = nn.GRU(
            input_size=input_size,
            hidden_size=hid_size,
            num_layers=num_rnn_layers,
            dropout=dropout_p if num_rnn_layers>1 else 0,
            bidirectional=bidirectional,
            batch_first=True,
        )
def forward(self, input):
    outputs, hidden_states = self.rnn_layer(input)
    return outputs, hidden_states


class RNNModel(nn.Module):
def init(
self,
input_size,
hid_size,
rnn_type,
bidirectional,
n_classes=5,
kernel_size=5,
):
super().init()
    self.rnn_layer = RNN(
        input_size=46,#hid_size * 2 if bidirectional else hid_size,
        hid_size=hid_size,
        rnn_type=rnn_type,
        bidirectional=bidirectional
    )
    self.conv1 = ConvNormPool(
        input_size=input_size,
        hidden_size=hid_size,
        kernel_size=kernel_size,
    )
    self.conv2 = ConvNormPool(
        input_size=hid_size,
        hidden_size=hid_size,
        kernel_size=kernel_size,
    )
    self.avgpool = nn.AdaptiveAvgPool1d((1))
    self.fc = nn.Linear(in_features=hid_size, out_features=n_classes)

def forward(self, input):
    print("shape")
    print(input.shape) # **shape isshape torch.Size([32, 1, 187])**
    x = self.conv1(input)
    x = self.conv2(x)
    x, _ = self.rnn_layer(x)
    x = self.avgpool(x)
    x = x.view(-1, x.size(1) * x.size(2))
    x = F.softmax(self.fc(x), dim=1)#.squeeze(1)
    return x


when I create 
m1=RNNModel(1, 64, ‘lstm’, True).to(device)
and run
#from torchsummary import summary
summary(m1,(1,187),32)
The following error raises:

AttributeError                            Traceback (most recent call last)
 in 
1 #from torchsummary import summary
----> 2 summary(m1,(1,187),32)
/opt/conda/lib/python3.7/site-packages/torchsummary/torchsummary.py in summary(model, input_size, batch_size, device)
70     # make a forward pass
71     # print(x.shape)
—> 72     model(*x)
73
74     # remove these hooks
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725             result = self._slow_forward(*input, **kwargs)
726         else:
 → 727             result = self.forward(*input, **kwargs)
728         for hook in itertools.chain(
729                 _global_forward_hooks.values(),
 in forward(self, input)
35         x = self.conv1(input)
36         x = self.conv2(x)
—> 37         x, _ = self.rnn_layer(x)
38         x = self.avgpool(x)
39         x = x.view(-1, x.size(1) * x.size(2))
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725             result = self._slow_forward(*input, **kwargs)
726         else:
 → 727             result = self.forward(*input, **kwargs)
728         for hook in itertools.chain(
729                 _global_forward_hooks.values(),
 in forward(self, input)
32             )
33     def forward(self, input):
—> 34         outputs, hidden_states = self.rnn_layer(input)
35         return outputs, hidden_states
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
729                 _global_forward_hooks.values(),
730                 self._forward_hooks.values()):
 → 731             hook_result = hook(self, input, result)
732             if hook_result is not None:
733                 result = hook_result
/opt/conda/lib/python3.7/site-packages/torchsummary/torchsummary.py in hook(module, input, output)
21             if isinstance(output, (list, tuple)):
22                 summary[m_key][“output_shape”] = [
—> 23                     [-1] + list(o.size())[1:] for o in output
24                 ]
25             else:
/opt/conda/lib/python3.7/site-packages/torchsummary/torchsummary.py in (.0)
21             if isinstance(output, (list, tuple)):
22                 summary[m_key][“output_shape”] = [
—> 23                     [-1] + list(o.size())[1:] for o in output
24                 ]
25             else:
AttributeError: ‘tuple’ object has no attribute ‘size’

image693×501 52.2 KB

in frwd layer i have commented i/p shape also shape isshape torch.Size([32, 1, 187])**
Batch size is 32
Why is this so??Pls help