Metadata-Version: 2.1
Name: thoplw
Version: 2024.1.25
Summary: Count the layer-wise MACs and number of parameters of your PyTorch model.
Project-URL: Repository, https://github.com/tiskw/pytorch-op-counter-layerwise
Project-URL: Issues, https://github.com/tiskw/pytorch-op-counter-layerwise/issues
Author-email: Tetsuya Ishikawa <tiskw111@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Tetsuya Ishikawa
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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        SOFTWARE.
License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown

pytorch-op-counter-layerwise
================================================================================

This repository provides `thoplw` that is a ython module to compute MACs
(multiply–accumulate operations) and number of parameters for each layer
of neural network models implemented by PyTorch.


Installation
--------------------------------------------------------------------------------

### Dependencies

The `thoplw` module requires:

* PyTorch >= 2.0.0 (will work on the older version, but not checked)

and the sample code require:

* Torchvision >= 0.15.0

### User installation

```console
pip install thoplw
```


Usage
--------------------------------------------------------------------------------

### Minimal example

```python
import torch, torchvision, thoplw

# Instanciate the target model.
model = torchvision.models.resnet18()

# Compute MACs, number of parameters, and details of each layer.
macs, params, layers_info = thoplw.profile(model, tensor=torch.randn(1, 3, 224, 224))

# Print the total MACs and number of parameters.
print("Total MACs and params:")
print("  - Macs   =", macs)
print("  - Params =", params)
print()

# Print layer details.
print(layers_info.summary())
```

Running the above code will give you the output below
(the table has been partially omitted because it is too long to show everything in this README).

```
Total MACs and params:
  - Macs   = 1824010216                                                                                                                                                       
  - Params = 11699112

| Name                  | Class             | Input shape    | Output shape   | MACs       | Params   |
+-----------------------+-------------------+----------------+----------------+------------+----------+
| conv1                 | Conv2d            | 3 x 224 x 224  | 64 x 112 x 112 | 118013952  | 9408     |
| bn1                   | BatchNorm2d       | 64 x 112 x 112 | 64 x 112 x 112 | 3211264    | 256      |
| relu                  | ReLU              | 64 x 112 x 112 | 64 x 112 x 112 | 0          | 0        |
| maxpool               | MaxPool2d         | 64 x 112 x 112 | 64 x 56 x 56   | 0          | 0        |
...
| layer4.1.conv2        | Conv2d            | 512 x 7 x 7    | 512 x 7 x 7    | 115605504  | 2359296  |
| layer4.1.bn2          | BatchNorm2d       | 512 x 7 x 7    | 512 x 7 x 7    | 100352     | 2048     |
| avgpool               | AdaptiveAvgPool2d | 512 x 7 x 7    | 512 x 1 x 1    | 1024       | 0        |
| fc                    | Linear            | 512            | 1000           | 513000     | 513000   |
+-----------------------+-------------------+----------------+----------------+------------+----------+
| Total                 | ResNet            | 3 x 224 x 224  | 1000           | 1824010216 | 11699112 |
```

### Clever formatting

The `thoplw` provides `clever_format` function that returns appropriate expressions
of the given numbers likewise `thop` package.

```python
macs, params = thoplw.clever_format([macs, params], "%.3f")
print("Total MACs and params:")
print("  - Macs   =", macs)
print("  - Params =", params)
```

The table of layers detail supports three types of formatting, raw number (default choice),
clever format like the `clever_format` function, and ratio.

```python
# Print the table with clever formatting.
print(layers_info.summary(kind="text", fmt="clever"))

# Print the table with ratio formatting.
print(layers_info.summary(kind="text", fmt="ratio"))
```

### Table type

The above example prints the NN model summary as a plain text, but you can dump the summary
as CSV and Markdown format too. The following is an example to save the table as CSV and
Markdown format respectively.

```python
# Save as CSV format.
with open("summary.csv") as ofp:
    ofp.write(layers_info.summary(kind="csv"))

# Save as Markdown format.
with open("summary.md") as ofp:
    ofp.write(layers_info.summary(kind="md"))
```

API reference
--------------------------------------------------------------------------------

### thoplw.profile

```python
macs, params, layers_info = thoplw.profile(model: torch.nn.Module,
                                           tensor: torch.Tensor,
                                           custom_ops: dict = {},
                                           verbose: bool = True)
```

Computes MADDs and the number of parameters.

* Args
  - `model`: the target NN model.
  - `tensor`: input tensor for the `model`.
  - `custom_ops`: optional custom operations.
  - `verbose`: shows extra message on your terminal if `True`.
* Returns
  - `macs`: total MADDs of the target model and the given input tensor.
  - `params`: number of parameters of the target model.
  - `layer_info`: `LayerInfo` class that store the details of each layer.

### thoplw.clever_format

```python
formatted_values = thoplw.clever_format(values: int or list, fmt: str = "%6.2f")
```
Returns formatted string of the given integer(s).

* Args
  - `values`: input value, or values.
  - `fmt`: format specifier.
* Returns
  - `formatted_values`: format result.

### LayerInfo class

```python
class LayerInfo:
    ...
    def summary(self,
                kind: str = "text",
                fmt: str = "raw") -> str:
    ...
```
A class to store layer details. Only the `summary` function is opened to users.

* Args
  - `kind`: table type to be returned (`"text"` means simple table, `"csv"` means CSV, and `"md"` means Markdown).
  - `fmt`: output format (`"raw"` means raw integer, `"clever"` means auto formatting, and `"ratio"` means ratio format).
* Returns
  - formatted string.


Results of Recent Models
--------------------------------------------------------------------------------

The following results can be obtained by running `tests/test_benchmarks.py`.
Click the model anem to see the layer details.

| Model name                                               | Params [M] | MACs [G] | | Model name                                                   | Params [M] | MACs [G] |
|:--------------------------------------------------------:|:----------:|:--------:|-|:------------------------------------------------------------:|:----------:|:--------:|
| [alexnet](tests/benchmarks/alexnet.md)                   |  714.22 M  |  61.10 M | | [resnext50_32x4d](tests/benchmarks/resnext50_32x4d.md)       |    4.29 G  |  25.10 M |
| [vgg11](tests/benchmarks/vgg11.md)                       |    7.61 G  | 132.86 M | | [resnext101_32x8d](tests/benchmarks/resnext101_32x8d.md)     |   16.54 G  |  88.99 M |
| [vgg11_bn](tests/benchmarks/vgg11_bn.md)                 |    7.64 G  | 132.87 M | | [densenet121](tests/benchmarks/densenet121.md)               |    2.90 G  |   8.06 M |
| [vgg13](tests/benchmarks/vgg13.md)                       |   11.31 G  | 133.05 M | | [densenet161](tests/benchmarks/densenet161.md)               |    7.85 G  |  28.90 M |
| [vgg13_bn](tests/benchmarks/vgg13_bn.md)                 |   11.36 G  | 133.06 M | | [densenet169](tests/benchmarks/densenet169.md)               |    3.44 G  |  14.31 M |
| [vgg16](tests/benchmarks/vgg16.md)                       |   15.47 G  | 138.36 M | | [densenet201](tests/benchmarks/densenet201.md)               |    4.39 G  |  20.24 M |
| [vgg16_bn](tests/benchmarks/vgg16_bn.md)                 |   15.52 G  | 138.37 M | | [googlenet](tests/benchmarks/googlenet.md)                   |    1.51 G  |   6.64 M |
| [vgg19](tests/benchmarks/vgg19.md)                       |   19.63 G  | 143.67 M | | [inception_v3](tests/benchmarks/inception_v3.md)             |    5.75 G  |  23.87 M |
| [vgg19_bn](tests/benchmarks/vgg19_bn.md)                 |   19.69 G  | 143.69 M | | [squeezenet1.0](tests/benchmarks/squeezenet1_0.md)           |  818.93 M  |   1.25 M |
| [resnet18](tests/benchmarks/resnet18.md)                 |    1.82 G  |  11.70 M | | [squeezenet1.1](tests/benchmarks/squeezenet1_1.md)           |  349.16 M  |   1.24 M |
| [resnet34](tests/benchmarks/resnet34.md)                 |    3.68 G  |  21.81 M | | [mobilenet_v2](tests/benchmarks/mobilenet_v2.md)             |  327.49 M  |   3.54 M |
| [resnet50](tests/benchmarks/resnet50.md)                 |    4.13 G  |  25.61 M | | [mobilenet_v3_small](tests/benchmarks/mobilenet_v3_small.md) |   62.17 M  |   2.55 M |
| [resnet101](tests/benchmarks/resnet101.md)               |    7.87 G  |  44.65 M | | [mobilenet_v3_large](tests/benchmarks/mobilenet_v3_large.md) |  234.21 M  |   5.51 M |
| [resnet152](tests/benchmarks/resnet152.md)               |   11.60 G  |  60.34 M | | [shufflenet_v2_x0.5](tests/benchmarks/shufflenet_v2_x0_5.md) |   44.57 M  |   1.37 M |
| [wide_resnet50_2](tests/benchmarks/wide_resnet50_2.md)   |   22.84 G  | 127.02 M | | [shufflenet_v2_x1.0](tests/benchmarks/shufflenet_v2_x1_0.md) |  152.71 M  |   2.29 M |
| [wide_resnet101_2](tests/benchmarks/wide_resnet101_2.md) |   11.46 G  |  68.95 M | | [mnasnet_0.5](tests/benchmarks/mnasnet_0_5.md)               |  116.72 M  |   2.24 M |
|                                                          |            |          | | [mnasnet_1.0](tests/benchmarks/mnasnet_1_0.md)               |  336.24 M  |   4.42 M |

<p align="center">
  <img src="tests/benchmarks/madds-vs-parameters.svg" width="80%">
</p>


Gratitude
--------------------------------------------------------------------------------

* Developers and maintainers of
  [pytorch-OpCounter](https://github.com/Lyken17/pytorch-OpCounter).
  The author learned a lot from the repository.

