Metadata-Version: 2.1
Name: torch-summary
Version: 1.1.4
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
        
        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.
        
        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 the features.
        
        This version now supports:
        - RNNs, LSTMs, and other recursive layers
        - Branching output to explore model layers using specified depths
        - Returns ModelStatistics object to access summary data
        - Configurable columns of returned data
        
        Other features include:
        - Verbose mode to show specific weights and bias layers
        - Accepts either input data or simply the input shape to work!
        - Customizable widths and batch dimension.
        - More comprehensive testing using pytest
        
        
        # Usage
        `pip install torch-summary`
        
        or
        
        `git clone https://github.com/tyleryep/torch-summary.git`
        
        
        ```python
        from torchsummary import summary
        summary(your_model, input_data=(C, H, W))
        ```
        
        # Documentation
        ```python
        """
        Summarize the given PyTorch model. Summarized information includes:
            1) output shape,
            2) kernel shape,
            3) number of the parameters
            4) operations (Mult-Adds)
        Args:
            model (Module): Model to summarize
            input_data (Sequence of Sizes or Tensors):
                Example input tensor of the model (dtypes inferred from model input).
                - OR -
                Shape of input data as a List/Tuple/torch.Size (dtypes must match model input,
                default to FloatTensors). NOTE: For scalars, use torch.Size([]).
            use_branching (bool): Whether to use the branching layout for the printed output.
            max_depth (int): number of nested layers to traverse (e.g. Sequentials)
            verbose (int):
                0 (quiet): No output
                1 (default): Print model summary
                2 (verbose): Show weight and bias layers in full detail
            col_names (List): specify which columns to show in the output. Currently supported:
                ['output_size', 'num_params', 'kernel_size', 'mult_adds']
            col_width (int): width of each column
            dtypes (List or None): for multiple inputs or args, must specify the size of both inputs.
                You must also specify the types of each parameter here.
            batch_dim (int): batch_dimension of input data
            args, kwargs: Other arguments used in `model.forward` function
        Return:
            ModelStatistics object
                (see model_statistics.py for details on how to access the summary data)
        
        """
        ```
        
        
        # Examples
        ## Get Model Summary as String
        ```python
        model_stats = summary(your_model, input_data=(C, H, W), verbose=0)
        summary_str = str(model_stats)
        ```
        
        ## 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)
        
        
        model = CNN()
        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 torchvision
        from torchsummary import summary
        
        
        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().__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
        
        
        model = SimpleConv()
        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
