Metadata-Version: 2.1
Name: torch-summary
Version: 1.0.1
Summary: Model summary in PyTorch, based off of the original torchsummary
Home-page: https://github.com/tyleryep/torch-summary
Author: Tyler Yep @tyleryep
Author-email: tyep10@gmail.com
License: UNKNOWN
Description: ## torch-summary
        
        This is a rewritten version of the original torchsummary and torchsummaryX projects by @sksq96 and @nmhkahn.
        There are quite a few pull requests on the original project (which hasn't been updated in over a year), so I decided to take a stab at improving and consolidating some of its features.
        
        Specifically, this version has support for:
        - RNNs, LSTMs, and other recursive layers
        - Branching-tree output for Model layers to explore with specific depths
        - Verbose mode to show specific weights and bias layers
        - More comprehensive testing using pytest
        
        Keras has a neat API to view the visualization of the model which is very helpful while debugging your network. In this project, we attempt to do the same in PyTorch. The goal is to provide information complementary to what is provided by `print(your_model)` in PyTorch.
        
        ### Usage
        
        - `pip install torch-summary`
        or
        - `git clone https://github.com/tyleryep/torch-summary.git`
        
        Notice the dash in torch-summary!
        
        ```python
        from torchsummary import summary
        summary(your_model, input_size=(C, H, W))
        ```
        
        - Note that the `input_size` is required to make a forward pass through the network.
        
        ### Examples
        
        #### CNN for MNIST
        
        ```python
        import torch
        import torch.nn as nn
        import torch.nn.functional as F
        from torchsummary import summary
        
        class CNN(nn.Module):
            def __init__(self):
                super().__init__()
                self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
                self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
                self.conv2_drop = nn.Dropout2d(0.3)
                self.fc1 = nn.Linear(320, 50)
                self.fc2 = nn.Linear(50, 10)
        
            def forward(self, x):
                x = F.relu(F.max_pool2d(self.conv1(x), 2))
                x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
                x = x.view(-1, 320)
                x = F.relu(self.fc1(x))
                x = self.fc2(x)
                return F.log_softmax(x, dim=1)
        
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        model = CNN().to(device)
        
        summary(model, (1, 28, 28))
        ```
        
        
        ```
        ------------------------------------------------------------------------------------------
        Layer (type:depth-idx)                   Output Shape              Param #
        ==========================================================================================
        ├─Conv2d: 1-1                            [-1, 10, 24, 24]          260
        ├─Conv2d: 1-2                            [-1, 20, 8, 8]            5,020
        ├─Dropout2d: 1-3                         [-1, 20, 8, 8]            --
        ├─Linear: 1-4                            [-1, 50]                  16,050
        ├─Linear: 1-5                            [-1, 10]                  510
        ==========================================================================================
        Total params: 21,840
        Trainable params: 21,840
        Non-trainable params: 0
        ------------------------------------------------------------------------------------------
        Input size (MB): 0.00
        Forward/backward pass size (MB): 0.05
        Params size (MB): 0.08
        Estimated Total Size (MB): 0.14
        ------------------------------------------------------------------------------------------
        ```
        
        
        #### ResNet
        
        ```python
        import torch
        from torchvision import models
        from torchsummary import summary
        
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        model = torchvision.models.resnet50()
        
        summary(model, (3, 224, 224))
        ```
        
        
        ```
        ------------------------------------------------------------------------------------------
        Layer (type:depth-idx)                   Output Shape              Param #
        ==========================================================================================
        ├─Conv2d: 1-1                            [-1, 64, 112, 112]        9,408
        ├─BatchNorm2d: 1-2                       [-1, 64, 112, 112]        128
        ├─ReLU: 1-3                              [-1, 64, 112, 112]        --
        ├─MaxPool2d: 1-4                         [-1, 64, 56, 56]          --
        ├─Sequential: 1-5                        [-1, 256, 56, 56]         --
        |    └─Bottleneck: 2-1                   [-1, 256, 56, 56]         --
        |    |    └─Conv2d: 3-1                  [-1, 64, 56, 56]          4,096
        |    |    └─BatchNorm2d: 3-2             [-1, 64, 56, 56]          128
        |    |    └─ReLU: 3-3                    [-1, 64, 56, 56]          --
        |    |    └─Conv2d: 3-4                  [-1, 64, 56, 56]          36,864
        |    |    └─BatchNorm2d: 3-5             [-1, 64, 56, 56]          128
        |    |    └─ReLU: 3-6                    [-1, 64, 56, 56]          --
        |    |    └─Conv2d: 3-7                  [-1, 256, 56, 56]         16,384
        |    |    └─BatchNorm2d: 3-8             [-1, 256, 56, 56]         512
        |    |    └─Sequential: 3-9              [-1, 256, 56, 56]         --
        |    |    └─ReLU: 3-10                   [-1, 256, 56, 56]         --
        
          ...
          ...
          ...
        
        ├─AdaptiveAvgPool2d: 1-9                 [-1, 2048, 1, 1]          --
        ├─Linear: 1-10                           [-1, 1000]                2,049,000
        ==========================================================================================
        Total params: 60,192,808
        Trainable params: 60,192,808
        Non-trainable params: 0
        ------------------------------------------------------------------------------------------
        Input size (MB): 0.57
        Forward/backward pass size (MB): 344.16
        Params size (MB): 229.62
        Estimated Total Size (MB): 574.35
        ------------------------------------------------------------------------------------------
        
        
        ```
        
        
        #### Multiple Inputs
        
        
        ```python
        import torch
        import torch.nn as nn
        from torchsummary import summary
        
        class SimpleConv(nn.Module):
            def __init__(self):
                super(SimpleConv, self).__init__()
                self.features = nn.Sequential(
                    nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1),
                    nn.ReLU(),
                )
        
            def forward(self, x, y):
                x1 = self.features(x)
                x2 = self.features(y)
                return x1, x2
        
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        model = SimpleConv().to(device)
        
        summary(model, [(1, 16, 16), (1, 28, 28)])
        ```
        
        
        ```
        ----------------------------------------------------------------
                Layer (type)               Output Shape         Param #
        ================================================================
                    Conv2d-1            [-1, 1, 16, 16]              10
                      ReLU-2            [-1, 1, 16, 16]               0
                    Conv2d-3            [-1, 1, 28, 28]              10
                      ReLU-4            [-1, 1, 28, 28]               0
        ================================================================
        Total params: 20
        Trainable params: 20
        Non-trainable params: 0
        ----------------------------------------------------------------
        Input size (MB): 0.77
        Forward/backward pass size (MB): 0.02
        Params size (MB): 0.00
        Estimated Total Size (MB): 0.78
        ----------------------------------------------------------------
        ```
        
        ### References
        - Thanks to @sksq96, @nmhkahn, and @sangyx for providing the original code this project was based off of.
        - For Model Size Estimation @jacobkimmel ([details here](https://github.com/sksq96/pytorch-summary/pull/21))
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
