Metadata-Version: 2.4 Name: ultralytics-thop Version: 2.0.18 Summary: Ultralytics THOP package for fast computation of PyTorch model FLOPs and parameters. Author-email: Ligeng Zhu Maintainer-email: Ultralytics License: AGPL-3.0 Project-URL: Homepage, https://ultralytics.com Project-URL: Source, https://github.com/ultralytics/thop Project-URL: Documentation, https://docs.ultralytics.com Project-URL: Bug Reports, https://github.com/ultralytics/thop/issues Project-URL: Changelog, https://github.com/ultralytics/thop/releases Keywords: FLOPs,PyTorch,Model Analysis Classifier: Development Status :: 4 - Beta Classifier: Intended Audience :: Developers Classifier: Intended Audience :: Education Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+) Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Programming Language :: Python :: 3.13 Classifier: Topic :: Software Development Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Classifier: Topic :: Scientific/Engineering :: Image Recognition Classifier: Operating System :: POSIX :: Linux Classifier: Operating System :: MacOS Classifier: Operating System :: Microsoft :: Windows Requires-Python: >=3.8 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: numpy Requires-Dist: torch Dynamic: license-file

# 🚀 THOP: PyTorch-OpCounter Welcome to the [THOP](https://github.com/ultralytics/thop) repository, your comprehensive solution for profiling [PyTorch](https://pytorch.org/) models by computing the number of Multiply-Accumulate Operations (MACs) and parameters. Developed by Ultralytics, this tool is essential for [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) practitioners aiming to evaluate model efficiency and performance, crucial aspects discussed in our [model training tips guide](https://docs.ultralytics.com/guides/model-training-tips/). [![Ultralytics Actions](https://github.com/ultralytics/thop/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/thop/actions/workflows/format.yml) [![Ultralytics Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://discord.com/invite/ultralytics) [![Ultralytics Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com/) [![Ultralytics Reddit](https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue)](https://reddit.com/r/ultralytics) ## 📄 Description THOP offers an intuitive API designed to profile PyTorch models by calculating the total number of MACs and parameters. This functionality is vital for assessing the computational efficiency and memory footprint of deep learning models, helping developers optimize performance for deployment, especially on [edge devices](https://www.ultralytics.com/glossary/edge-ai). Understanding these metrics is key to selecting the right model architecture, a topic explored in our [model comparison pages](https://docs.ultralytics.com/compare/). ## 📦 Installation Get started with THOP quickly by installing it via pip: [![PyPI - Version](https://img.shields.io/pypi/v/ultralytics-thop?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics-thop/) [![Downloads](https://static.pepy.tech/badge/ultralytics-thop)](https://clickpy.clickhouse.com/dashboard/ultralytics-thop) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics-thop?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics-thop/) ```bash pip install ultralytics-thop ``` Alternatively, for the latest features and updates, install directly from the GitHub repository: ```bash pip install --upgrade git+https://github.com/ultralytics/thop.git ``` This ensures you have the most recent version, incorporating the latest improvements and bug fixes. ## 🛠️ How to Use ### Basic Usage Profiling a standard PyTorch model like [ResNet50](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.resnet50.html) is straightforward. Import the necessary libraries, load your model and a sample input tensor, then use the `profile` function: ```python import torch from torchvision.models import resnet50 # Example model from thop import profile # Import the profile function from THOP # Load a pre-trained model (e.g., ResNet50) model = resnet50() # Create a dummy input tensor matching the model's expected input shape dummy_input = torch.randn(1, 3, 224, 224) # Profile the model macs, params = profile(model, inputs=(dummy_input,)) print(f"MACs: {macs}, Parameters: {params}") # Expected output: MACs: 4139975680.0, Parameters: 25557032.0 ``` ### Define Custom Rules for Third-Party Modules If your model includes custom or third-party modules not natively supported by THOP, you can define custom profiling rules using the `custom_ops` argument. This allows for accurate profiling even with complex or non-standard architectures, which is useful when working with models like those found in the [Ultralytics models section](https://docs.ultralytics.com/models/). ```python import torch import torch.nn as nn from thop import profile # Define your custom module class YourCustomModule(nn.Module): def __init__(self): super().__init__() # Define layers, e.g., a convolution self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) def forward(self, x): return self.conv(x) # Define a custom counting function for your module # This function should calculate and return the MACs for the module's operations def count_your_custom_module(module, x, y): # Example: Calculate MACs for the conv layer # Note: This is a simplified example. Real calculations depend on the module's specifics. # MACs = output_height * output_width * kernel_height * kernel_width * in_channels * out_channels # For simplicity, we'll just assign a placeholder value or use a helper if available # In a real scenario, you'd implement the precise MAC calculation here. # For nn.Conv2d, THOP usually handles it, but this demonstrates the concept. macs = 0 # Placeholder: Implement actual MAC calculation based on module logic # You might need access to module properties like kernel_size, stride, padding, channels etc. # Example for a Conv2d layer (simplified): if isinstance(module, nn.Conv2d): _, _, H, W = y.shape # Output shape k_h, k_w = module.kernel_size in_c = module.in_channels out_c = module.out_channels groups = module.groups macs = (k_h * k_w * in_c * out_c * H * W) / groups module.total_ops += torch.DoubleTensor([macs]) # Accumulate MACs # Instantiate a model containing your custom module model = YourCustomModule() # Or a larger model incorporating this module # Create a dummy input dummy_input = torch.randn(1, 3, 224, 224) # Profile the model, providing the custom operation mapping macs, params = profile(model, inputs=(dummy_input,), custom_ops={YourCustomModule: count_your_custom_module}) print(f"Custom MACs: {macs}, Parameters: {params}") # Expected output: Custom MACs: 87457792.0, Parameters: 1792.0 ``` ### Improve Output Readability For clearer and more interpretable results, use the `thop.clever_format` function. This formats the raw MACs and parameter counts into human-readable strings (e.g., GigaMACs, MegaParams). This formatting helps in quickly understanding the scale of computational resources required, similar to the metrics provided in our [Ultralytics YOLOv8 documentation](https://docs.ultralytics.com/models/yolov8/). ```python import torch from torchvision.models import resnet50 from thop import clever_format, profile model = resnet50() dummy_input = torch.randn(1, 3, 224, 224) macs, params = profile(model, inputs=(dummy_input,)) # Format the numbers into a readable format (e.g., 4.14 GMac, 25.56 MParams) macs_readable, params_readable = clever_format([macs, params], "%.3f") print(f"Formatted MACs: {macs_readable}, Formatted Parameters: {params_readable}") # Expected output: Formatted MACs: 4.140G, Formatted Parameters: 25.557M ``` ## 📊 Results of Recent Models The table below showcases the parameters and MACs for several popular [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models, profiled using THOP. These benchmarks provide a comparative overview of model complexity and computational cost. You can reproduce these results by running the script located at `benchmark/evaluate_famous_models.py` in this repository. Comparing these metrics is essential for tasks like selecting models for [object detection](https://www.ultralytics.com/glossary/object-detection) or [image classification](https://www.ultralytics.com/glossary/image-classification). For more comparisons, see our [model comparison section](https://docs.ultralytics.com/compare/).

| Model | Params(M) | MACs(G) | | ---------------- | --------- | ------- | | alexnet | 61.10 | 0.77 | | vgg11 | 132.86 | 7.74 | | vgg11_bn | 132.87 | 7.77 | | vgg13 | 133.05 | 11.44 | | vgg13_bn | 133.05 | 11.49 | | vgg16 | 138.36 | 15.61 | | vgg16_bn | 138.37 | 15.66 | | vgg19 | 143.67 | 19.77 | | vgg19_bn | 143.68 | 19.83 | | resnet18 | 11.69 | 1.82 | | resnet34 | 21.80 | 3.68 | | resnet50 | 25.56 | 4.14 | | resnet101 | 44.55 | 7.87 | | resnet152 | 60.19 | 11.61 | | wide_resnet101_2 | 126.89 | 22.84 | | wide_resnet50_2 | 68.88 | 11.46 |

| Model | Params(M) | MACs(G) | | ------------------ | --------- | ------- | | resnext50_32x4d | 25.03 | 4.29 | | resnext101_32x8d | 88.79 | 16.54 | | densenet121 | 7.98 | 2.90 | | densenet161 | 28.68 | 7.85 | | densenet169 | 14.15 | 3.44 | | densenet201 | 20.01 | 4.39 | | squeezenet1_0 | 1.25 | 0.82 | | squeezenet1_1 | 1.24 | 0.35 | | mnasnet0_5 | 2.22 | 0.14 | | mnasnet0_75 | 3.17 | 0.24 | | mnasnet1_0 | 4.38 | 0.34 | | mnasnet1_3 | 6.28 | 0.53 | | mobilenet_v2 | 3.50 | 0.33 | | shufflenet_v2_x0_5 | 1.37 | 0.05 | | shufflenet_v2_x1_0 | 2.28 | 0.15 | | shufflenet_v2_x1_5 | 3.50 | 0.31 | | shufflenet_v2_x2_0 | 7.39 | 0.60 | | inception_v3 | 27.16 | 5.75 |

## 🙌 Contribute We actively welcome and encourage community contributions to make THOP even better! Whether it's adding support for new [PyTorch layers](https://docs.pytorch.org/docs/stable/nn.html), improving existing calculations, enhancing documentation, or fixing bugs, your input is valuable. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for detailed instructions on how to participate. Together, we can ensure THOP remains a state-of-the-art tool for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) community. Don't hesitate to share your feedback and suggestions! ## 📜 License THOP is distributed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.en.html). This license promotes open collaboration and sharing of improvements. For complete details, please refer to the [LICENSE](https://github.com/ultralytics/thop/blob/main/LICENSE) file included in the repository. Understanding the license is important before integrating THOP into your projects, especially for commercial applications which may require an [Enterprise License](https://www.ultralytics.com/license). ## 📧 Contact Encountered a bug or have a feature request? Please submit an issue through our [GitHub Issues](https://github.com/ultralytics/thop/issues) page. For general discussions, questions, and community support, join the vibrant Ultralytics community on our [Discord server](https://discord.com/invite/ultralytics). We look forward to hearing from you and collaborating!