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import argparse
import torch
import open_clip
import pandas as pd
from fvcore.nn import FlopCountAnalysis, flop_count_str, ActivationCountAnalysis
parser = argparse.ArgumentParser(description='OpenCLIP Profiler')
# benchmark specific args
parser.add_argument('--model', metavar='NAME', default='',
help='model(s) to profile')
parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
help='Output csv file for results')
def profile_fvcore(
model,
image_input_size=(3, 224, 224),
text_input_size=(77,),
batch_size=1,
detailed=False,
force_cpu=False
):
if force_cpu:
model = model.to('cpu')
device, dtype = next(model.parameters()).device, next(model.parameters()).dtype
example_image_input = torch.ones((batch_size,) + image_input_size, device=device, dtype=dtype)
example_text_input = torch.ones((batch_size,) + text_input_size, device=device, dtype=torch.int64)
fca = FlopCountAnalysis(model, (example_image_input, example_text_input))
aca = ActivationCountAnalysis(model, (example_image_input, example_text_input))
if detailed:
fcs = flop_count_str(fca)
print(fcs)
return fca.total(), aca.total()
def profile_fvcore_text(
model,
text_input_size=(77,),
batch_size=1,
detailed=False,
force_cpu=False
):
if force_cpu:
model = model.to('cpu')
device = next(model.parameters()).device
example_input = torch.ones((batch_size,) + text_input_size, device=device, dtype=torch.int64)
fca = FlopCountAnalysis(model, example_input)
aca = ActivationCountAnalysis(model, example_input)
if detailed:
fcs = flop_count_str(fca)
print(fcs)
return fca.total(), aca.total()
def profile_fvcore_image(
model,
image_input_size=(3, 224, 224),
batch_size=1,
detailed=False,
force_cpu=False
):
if force_cpu:
model = model.to('cpu')
device, dtype = next(model.parameters()).device, next(model.parameters()).dtype
example_input = torch.ones((batch_size,) + image_input_size, device=device, dtype=dtype)
fca = FlopCountAnalysis(model, example_input)
aca = ActivationCountAnalysis(model, example_input)
if detailed:
fcs = flop_count_str(fca)
print(fcs)
return fca.total(), aca.total()
def count_params(model):
return sum([m.numel() for m in model.parameters()])
def profile_model(model_name):
model = open_clip.create_model(model_name, force_custom_text=True, pretrained_hf=False)
model.eval()
if torch.cuda.is_available():
model = model.cuda()
if isinstance(model.visual.image_size, (tuple, list)):
image_input_size = (3,) + tuple(model.visual.image_size[-2:])
else:
image_input_size = (3, model.visual.image_size, model.visual.image_size)
text_input_size = (77,)
results = {}
results['model'] = model_name
results['image_size'] = image_input_size[1]
model_cfg = open_clip.get_model_config(model_name)
if model_cfg:
vision_cfg = open_clip.CLIPVisionCfg(**model_cfg['vision_cfg'])
text_cfg = open_clip.CLIPTextCfg(**model_cfg['text_cfg'])
results['image_width'] = int(vision_cfg.width)
results['text_width'] = int(text_cfg.width)
results['embed_dim'] = int(model_cfg['embed_dim'])
else:
results['image_width'] = 0
results['text_width'] = 0
results['embed_dim'] = 0
retries = 2
while retries:
retries -= 1
try:
macs, acts = profile_fvcore(
model, image_input_size=image_input_size, text_input_size=text_input_size, force_cpu=not retries)
image_macs, image_acts = profile_fvcore_image(
model.visual, image_input_size=image_input_size, force_cpu=not retries)
text_macs, text_acts = profile_fvcore_text(
model.text, text_input_size=text_input_size, force_cpu=not retries)
results['gmacs'] = round(macs / 1e9, 2)
results['macts'] = round(acts / 1e6, 2)
results['mparams'] = round(count_params(model) / 1e6, 2)
results['image_gmacs'] = round(image_macs / 1e9, 2)
results['image_macts'] = round(image_acts / 1e6, 2)
results['image_mparams'] = round(count_params(model.visual) / 1e6, 2)
results['text_gmacs'] = round(text_macs / 1e9, 2)
results['text_macts'] = round(text_acts / 1e6, 2)
results['text_mparams'] = round(count_params(model.text) / 1e6, 2)
except RuntimeError as e:
pass
return results
def main():
args = parser.parse_args()
# FIXME accept a text file name to allow lists of models in txt/csv
if args.model == 'all':
parsed_model = open_clip.list_models()
else:
parsed_model = args.model.split(',')
results = []
for m in parsed_model:
row = profile_model(m)
results.append(row)
df = pd.DataFrame(results, columns=results[0].keys())
df = df.sort_values('gmacs')
print(df)
if args.results_file:
df.to_csv(args.results_file, index=False)
if __name__ == '__main__':
main()
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