calflops

library0.3.2pypypiunverified

CalFlops is a PyTorch-based FLOPs, MACs, and parameter counter for neural networks including CNNs, RNNs, GCNs, and Transformers (e.g., BERT, LLaMA). Version 0.3.2 supports custom models. Released as needed on GitHub.

ai-ml

pip install calflops

github ↗package ↗

INSTALL

IMPORT

SIG · CALFLOPS

calflops

ai-mlpythonv0.3.2

Install

69.2s avg

Import

—

Disk

4890MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.3.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 80.6s

py 3.11

✕ build_error

✓ 73s

py 3.12

✕ build_error

✓ 63.5s

py 3.13

✕ build_error

✓ 59.7s

py 3.9

✕ build_error

✕ timeout

4890MB installed

● package 4890MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

calculate_flops

✓ from calflops import calculate_flops

✗ from calflops import calculate_flops

Compute FLOPs, MACs, and parameters for a ResNet-18 model.

import torch import torchvision.models as models from calflops import calculate_flops model = models.resnet18() batch_size = 1 input_shape = (batch_size, 3, 224, 224) flops, macs, params = calculate_flops( model=model, input_shape=input_shape, output_as_string=True, output_precision=4 ) print(f"FLOPs: {flops}, MACs: {macs}, Params: {params}")

Debug

Known issues

gotchaTorchvision models may include non-parameter operations (like softmax) that are counted differently. Ensure model is in eval mode.

fix

Set model.eval() before calling calculate_flops to disable dropout/batchnorm effects.

affects: all

gotchaIf model has multiple inputs (e.g., encoders), use input_constructor instead of input_shape.

fix

Pass a callable that returns a tuple of tensors: input_constructor=lambda: (x,).

affects: all

gotchaFLOPs counting for dynamic architectures (e.g., with torch.where) may be inaccurate due to static graph assumption.

fix

If using control flow, test with a simple forward pass first and verify counts are reasonable.

affects: all

Upgrade

Version history

0.3.2latest on PyPI · released Jun 7, 2024

Audit

Dependencies

torchrequiredCore dependency for model definitions and tensors.

Agent activity

23 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/MrYxJ/calculate-flops.pytorch ↗

packagehttps://pypi.org/project/calflops/ ↗