Python库 | ptflops-0.6.5.tar.gz

# Flops counter for convolutional networks in pytorch framework [](https://pypi.org/project/ptflops/) [](https://travis-ci.com/sovrasov/flops-counter.pytorch) This script is designed to compute the theoretical amount of multiply-add operations in convolutional neural networks. It also can compute the number of parameters and print per-layer computational cost of a given network. Supported layers: - Conv1d/2d/3d (including grouping) - ConvTranspose1d/2d/3d (including grouping) - BatchNorm1d/2d/3d - Activations (ReLU, PReLU, ELU, ReLU6, LeakyReLU) - Linear - Upsample - Poolings (AvgPool1d/2d/3d, MaxPool1d/2d/3d and adaptive ones) Experimental support: - RNN, LSTM, GRU (NLH layout is assumed). - RNNCell, LSTMCell, GRUCell. - MultiheadAttention. Requirements: Pytorch >= 1.1, torchvision >= 0.3 Thanks to @warmspringwinds for the initial version of script. ## Usage tips - This script doesn't take into account `torch.nn.functional.*` operations. For an instance, if one have a semantic segmentation model and use `torch.nn.functional.interpolate` to upscale features, these operations won't contribute to overall amount of flops. To avoid that one can use `torch.nn.Upsample` instead of `torch.nn.functional.interpolate`. - `ptflops` launches a given model on a random tensor and estimates amount of computations during inference. Complicated models can have several inputs, some of them could be optional. To construct non-trivial input one can use the `input_constructor` argument of the `get_model_complexity_info`. `input_constructor` is a function that takes the input spatial resolution as a tuple and returns a dict with named input arguments of the model. Next this dict would be passed to the model as keyworded arguments. - `verbose` parameter allows to get information about modules that don't contribute to the final numbers. - `ignore_modules` option forces `ptflops` to ignore the listed modules. This can be useful for research purposes. For an instance, one can drop all convolutuions from the counting process specifying `ignore_modules=[torch.nn.Conv2d]`. ## Install the latest version From PyPI: ```bash pip install ptflops ``` From this repository: ```bash pip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git ``` ## Example ```python import torchvision.models as models import torch from ptflops import get_model_complexity_info with torch.cuda.device(0): net = models.densenet161() macs, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True, print_per_layer_stat=True, verbose=True) print('{:<30} {:<8}'.format('Computational complexity: ', macs)) print('{:<30} {:<8}'.format('Number of parameters: ', params)) ``` ## Benchmark ### [torchvision](https://pytorch.org/docs/1.0.0/torchvision/models.html) Model | Input Resolution | Params(M) | MACs(G) | Top-1 error | Top-5 error --- |--- |--- |--- |--- |--- alexnet |224x224 | 61.1 | 0.72 | 43.45 | 20.91 vgg11 |224x224 | 132.86 | 7.63 | 30.98 | 11.37 vgg13 |224x224 | 133.05 | 11.34 | 30.07 | 10.75 vgg16 |224x224 | 138.36 | 15.5 | 28.41 | 9.62 vgg19 |224x224 | 143.67 | 19.67 | 27.62 | 9.12 vgg11_bn |224x224 | 132.87 | 7.64 | 29.62 | 10.19 vgg13_bn |224x224 | 133.05 | 11.36 | 28.45 | 9.63 vgg16_bn |224x224 | 138.37 | 15.53 | 26.63 | 8.50 vgg19_bn |224x224 | 143.68 | 19.7 | 25.76 | 8.15 resnet18 |224x224 | 11.69 | 1.82 | 30.24 | 10.92 resnet34 |224x224 | 21.8 | 3.68 | 26.70 | 8.58 resnet50 |224x224 | 25.56 | 4.12 | 23.85 | 7.13 resnet101 |224x224 | 44.55 | 7.85 | 22.63 | 6.44 resnet152 |224x224 | 60.19 | 11.58 | 21.69 | 5.94 squeezenet1_0 |224x224 | 1.25 | 0.83 | 41.90 | 19.58 squeezenet1_1 |224x224 | 1.24 | 0.36 | 41.81 | 19.38 densenet121 |224x224 | 7.98 | 2.88 | 25.35 | 7.83 densenet169 |224x224 | 14.15 | 3.42 | 24.00 | 7.00 densenet201 |224x224 | 20.01 | 4.37 | 22.80 | 6.43 densenet161 |224x224 | 28.68 | 7.82 | 22.35 | 6.20 inception_v3 |224x224 | 27.16 | 2.85 | 22.55 | 6.44 * Top-1 error - ImageNet single-crop top-1 error (224x224) * Top-5 error - ImageNet single-crop top-5 error (224x224) ### [Cadene/pretrained-models.pytorch](https://github.com/Cadene/pretrained-models.pytorch) Model | Input Resolution | Params(M) | MACs(G) | Acc@1 | Acc@5 --- |--- |--- |--- |--- |--- alexnet | 224x224 | 61.1 | 0.72 | 56.432 | 79.194 bninception | 224x224 | 11.3 | 2.05 | 73.524 | 91.562 cafferesnet101 | 224x224 | 44.55 | 7.62 | 76.2 | 92.766 densenet121 | 224x224 | 7.98 | 2.88 | 74.646 | 92.136 densenet161 | 224x224 | 28.68 | 7.82 | 77.56 | 93.798 densenet169 | 224x224 | 14.15 | 3.42 | 76.026 | 92.992 densenet201 | 224x224 | 20.01 | 4.37 | 77.152 | 93.548 dpn107 | 224x224 | 86.92 | 18.42 | 79.746 | 94.684 dpn131 | 224x224 | 79.25 | 16.13 | 79.432 | 94.574 dpn68 | 224x224 | 12.61 | 2.36 | 75.868 | 92.774 dpn68b | 224x224 | 12.61 | 2.36 | 77.034 | 93.59 dpn92 | 224x224 | 37.67 | 6.56 | 79.4 | 94.62 dpn98 | 224x224 | 61.57 | 11.76 | 79.224 | 94.488 fbresnet152 | 224x224 | 60.27 | 11.6 | 77.386 | 93.594 inceptionresnetv2 | 299x299 | 55.84 | 13.22 | 80.17 | 95.234 inceptionv3 | 299x299 | 27.16 | 5.73 | 77.294 | 93.454 inceptionv4 | 299x299 | 42.68 | 12.31 | 80.062 | 94.926 nasnetalarge | 331x331 | 88.75 | 24.04 | 82.566 | 96.086 nasnetamobile | 224x224 | 5.29 | 0.59 | 74.08 | 91.74 pnasnet5large | 331x331 | 86.06 | 25.21 | 82.736 | 95.992 polynet | 331x331 | 95.37 | 34.9 | 81.002 | 95.624 resnet101 | 224x224 | 44.55 | 7.85 | 77.438 | 93.672 resnet152 | 224x224 | 60.19 | 11.58 | 78.428 | 94.11 resnet18 | 224x224 | 11.69 | 1.82 | 70.142 | 89.274 resnet34 | 224x224 | 21.8 | 3.68 | 73.554 | 91.456 resnet50 | 224x224 | 25.56 | 4.12 | 76.002 | 92.98 resnext101_32x4d | 224x224 | 44.18 | 8.03 | 78.188 | 93.886 resnext101_64x4d | 224x224 | 83.46 | 15.55 | 78.956 | 94.252 se_resnet101 | 224x224 | 49.33 | 7.63 | 78.396 | 94.258 se_resnet152 | 224x224 | 66.82 | 11.37 | 78.658 | 94.374 se_resnet50 | 224x224 | 28.09 | 3.9 | 77.636 | 93.752 se_resnext101_32x4d | 224x224 | 48.96 | 8.05 | 80.236 | 95.028 se_resnext50_32x4d | 224x224 | 27.56 | 4.28