Fix the multi-output, dict-input, parameter counting and calculation overflow problem.

[cainmagi]

Update report

1. Fix the bug of parameter number calculation when there are more than one output variables, including both sequence case and dict case (mentioned in #162).
2. Make multiple output variables split into multiple lines.
3. Remove the last line break of summary_string().
4. Enable argument device to accept both str and torch.device.
5. Fix a bug when the model requires batch_size to be a specific number.
6. Fix a bug caused by multiple input cases when dtypes=None.
7. Add text auto wrap when the layer name is too long.
8. Support counting all parameters instead of weight and bias (a different solution of #142, #148).
9. Drop the np.sum/prod to fix the overflow problem during calculating the total size (mentioned in #158).
10. Fix the bug caused by layers with dict input values (mentioned in #162).
11. Add docstring.

Example for verifying this update

The following code is not compatible with the base repository:

import torch
import torch.nn as nn
from torchsummary import summary

class VeryLongNameSimpleMultiConv(nn.Module):
    def __init__(self):
        super(VeryLongNameSimpleMultiConv, self).__init__()
        self.features_1 = nn.Sequential(
            nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
        )
        self.features_2 = nn.Sequential(
            nn.Conv2d(1, 2, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
        )

    def forward(self, x):
        x1 = self.features_1(x)
        x2 = self.features_2(x)
        return x1, x2

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = VeryLongNameSimpleMultiConv().to(device)

summary(model, (1, 16, 16))

Now the output is:

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1            [-1, 1, 16, 16]              10
              ReLU-2            [-1, 1, 16, 16]               0
            Conv2d-3            [-1, 2, 16, 16]              20
              ReLU-4            [-1, 2, 16, 16]               0
VeryLong...ltiConv-5            [-1, 1, 16, 16]               0
                                [-1, 2, 16, 16]
================================================================
Total params: 30
Trainable params: 30
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.02
Params size (MB): 0.00
Estimated Total Size (MB): 0.02
----------------------------------------------------------------