Metadata-Version: 2.1
Name: torch-summary
Version: 1.3.2
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
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        Torch-summary provides information complementary to what is provided by `print(your_model)` in PyTorch, similar to Tensorflow's `model.summary()` API to view the visualization of the model, which is  helpful while debugging your network. In this project, we implement a similar functionality in PyTorch and create a clean, simple interface to use in your projects.
        
        This is a completely 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 improve and consolidate all of the old features and the new feature requests.
        
        **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 new features:**
        - Verbose mode to show specific weights and bias layers
        - Accepts either input data or simply the input shape to work!
        - Customizable widths and custom 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)
        ```
        
        # 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)
        
        Arguments:
            model (nn.Module): PyTorch 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 is FloatTensors).
            batch_dim (int): batch_dimension of input data
            branching (bool): Whether to use the branching layout for the printed output.
            col_names (Sequence[str]): 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
            depth (int): number of nested layers to traverse (e.g. Sequentials)
            device (torch.Device): Uses this torch device for model and input_data.
                Defaults to torch.cuda.is_available().
            dtypes (List[torch.dtype]): for multiple inputs, specify the size of both inputs, and
                also specify the types of each parameter here.
            verbose (int):
                0 (quiet): No output
                1 (default): Print model summary
                2 (verbose): Show weight and bias layers in full detail
            args, kwargs: Other arguments used in `model.forward` function.
        """
        ```
        
        
        # Examples
        ## Get Model Summary as String
        ```python
        from torchsummary import summary
        
        model_stats = summary(your_model, input_data=(C, H, W), verbose=0)
        summary_str = str(model_stats)
        ```
        
        ## ConvNets
        
        ```python
        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
        ------------------------------------------------------------------------------------------
        ```
        
        
        ## Multiple Inputs w/ Different Data Types
        
        ```python
        class MultipleInputNetDifferentDtypes(nn.Module):
            def __init__(self):
                super().__init__()
                self.fc1a = nn.Linear(300, 50)
                self.fc1b = nn.Linear(50, 10)
        
                self.fc2a = nn.Linear(300, 50)
                self.fc2b = nn.Linear(50, 10)
        
            def forward(self, x1, x2):
                x1 = F.relu(self.fc1a(x1))
                x1 = self.fc1b(x1)
                x2 = x2.type(torch.float)
                x2 = F.relu(self.fc2a(x2))
                x2 = self.fc2b(x2)
                x = torch.cat((x1, x2), 0)
                return F.log_softmax(x, dim=1)
        
        
        summary(model, [(1, 300), (1, 300)], dtypes=[torch.float, torch.long])
        ```
        Alternatively, you can also pass in the input_data itself, and
        torchsummary will automatically infer the data types.
        
        ```python
        input_data = torch.randn(1, 300)
        other_input_data = torch.randn(1, 300).long()
        model = MultipleInputNetDifferentDtypes()
        
        summary(model, input_data, other_input_data, ...)
        ```
        
        ## Explore Different Configurations
        ```python
        class LSTMNet(nn.Module):
            def __init__(self, vocab_size=20, embed_dim=300, hidden_dim=512, num_layers=2):
                super().__init__()
                self.hidden_dim = hidden_dim
                self.embedding = nn.Embedding(vocab_size, embed_dim)
                self.encoder = nn.LSTM(embed_dim, hidden_dim, num_layers=num_layers)
                self.decoder = nn.Linear(hidden_dim, vocab_size)
        
            def forward(self, x):
                embed = self.embedding(x)
                out, hidden = self.encoder(embed)
                out = self.decoder(out)
                out = out.view(-1, out.size(2))
                return out, hidden
        
        summary(
            LSTMNet(),
            (100,),
            dtypes=[torch.long],
            branching=False,
            verbose=2,
            col_width=16,
            col_names=["kernel_size", "output_size", "num_params", "mult_adds"],
        )
        ```
        
        ```
        --------------------------------------------------------------------------------------------------------
        Layer (type:depth-idx)         Kernel Shape         Output Shape         Param #          Mult-Adds
        ========================================================================================================
        Embedding: 1-1                 [300, 20]            [-1, 100, 300]       6,000            6,000
        LSTM: 1-2                       --                  [2, 100, 512]        3,768,320        3,760,128
          weight_ih_l0                 [2048, 300]
          weight_hh_l0                 [2048, 512]
          weight_ih_l1                 [2048, 512]
          weight_hh_l1                 [2048, 512]
        Linear: 1-3                    [512, 20]            [-1, 100, 20]        10,260           10,240
        ========================================================================================================
        Total params: 3,784,580
        Trainable params: 3,784,580
        Non-trainable params: 0
        --------------------------------------------------------------------------------------------------------
        Input size (MB): 0.00
        Forward/backward pass size (MB): 1.03
        Params size (MB): 14.44
        Estimated Total Size (MB): 15.46
        --------------------------------------------------------------------------------------------------------
        ```
        
        
        ## ResNet
        
        ```python
        import torchvision
        
        model = torchvision.models.resnet50()
        summary(model, (3, 224, 224), depth=3)
        ```
        
        
        ```
        ------------------------------------------------------------------------------------------
        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
        ------------------------------------------------------------------------------------------
        
        
        ```
        
        # Other Examples
        ```
        ----------------------------------------------------------------
                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
        ----------------------------------------------------------------
        ```
        
        # Contributing
        All issues and pull requests are much appreciated! If you are wondering how to build the project:
        
        - torch-summary is actively developed using Python 3.7+.
            - It should be backward compatible with Python 3.5, but this is subject to change in the future.
            - To run all tests and other auto-formatting tools, check out `scripts/run-tests`.
            - To only run unit tests, run `pytest unit_test`.
        
        # 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))
        
Keywords: torch pytorch torchsummary torch-summary summary keras deep-learning ml
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
