paras_flops.py

import argparse

import models
import torch
import torchvision
# import test_models as models
from thop import profile

model_names = sorted(
    name
    for name in models.__dict__
    if name.islower() and not name.startswith("__") and callable(models.__dict__[name])
)
parser = argparse.ArgumentParser(description="PyTorch ImageNet Training")
parser.add_argument(
    "--arch",
    "-a",
    metavar="ARCH",
    default="eca_resnet50",
    choices=model_names,
    help="model architecture: " + " | ".join(model_names) + " (default: eca_resnet50)",
)


def main():
    global args
    args = parser.parse_args()
    model = models.__dict__[args.arch]()
    print(model)
    input = torch.randn(1, 3, 224, 224)
    model.train()
    # model.eval()
    flops, params = profile(model, inputs=(input,))
    print("flops = ", flops)
    print("params = ", params)
    flops, params = clever_format([flops, params], "%.3f")
    print("flops = ", flops)
    print("params = ", params)


def clever_format(nums, format="%.2f"):
    clever_nums = []

    for num in nums:
        if num > 1e12:
            clever_nums.append(format % (num / 1024 ** 4) + "T")
        elif num > 1e9:
            clever_nums.append(format % (num / 1024 ** 3) + "G")
        elif num > 1e6:
            clever_nums.append(format % (num / 1024 ** 2) + "M")
        elif num > 1e3:
            clever_nums.append(format % (num / 1024) + "K")
        else:
            clever_nums.append(format % num + "B")

    clever_nums = clever_nums[0] if len(clever_nums) == 1 else (*clever_nums,)

    return clever_nums


if __name__ == "__main__":
    main()