'DABNet': DABNet,
'FPENet': FPENet
}
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ptflops sample script')
parser.add_argument('--device',
type=int,
default=0,
help='Device to store the model.')
parser.add_argument('--model',
choices=list(pt_models.keys()),
type=str,
default='ENet')
parser.add_argument('--result', type=str, default=None)
args = parser.parse_args()
if args.result is None:
ost = sys.stdout
else:
ost = open(args.result, 'w')
with torch.cuda.device(args.device):
net = pt_models[args.model](classes=19).cuda()
flops, params = get_model_complexity_info(net, (3, 512, 1024),
as_strings=True,
print_per_layer_stat=True,
ost=ost)
print('Flops: ' + flops)
print('Params: ' + params)
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DF1SegX38(classes=19).to(device)
summary(model, (3, 352, 480))
x = torch.randn(2, 3, 512, 1024).to(device)
from fvcore.nn.jit_handles import batchnorm_flop_jit
from fvcore.nn.jit_handles import generic_activation_jit
supported_ops = {
"aten::batch_norm": batchnorm_flop_jit,
}
flop_dict, _ = flop_count(model, (x, ), supported_ops)
flops_count, params_count = get_model_complexity_info(
model, (3, 512, 1024), as_strings=False, print_per_layer_stat=True)
input = x
macs, params = profile(model, inputs=(input, ))
print(flop_dict)
print(flops_count, params_count)
print(macs, params)
'''
/home/ethan/anaconda3/envs/py36_cuda101/bin/python /home/ethan/codes/Efficient-Segmentation-Networks/model/DFSegX16.py
/home/ethan/anaconda3/envs/py36_cuda101/lib/python3.6/site-packages/torch/nn/functional.py:2941: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
/home/ethan/anaconda3/envs/py36_cuda101/lib/python3.6/site-packages/torch/nn/functional.py:3121: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 3, 353, 481] 36
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# for i in [1,2,4,6,8,10,12]:
for i in [16]:
img_h, img_w = 32 * i, 64 * i
for name, network in models.items():
print(name, network)
model = network(classes=11).to(device)
total_params, trainable_params, total_input_size, total_output_size, total_params_size, total_size = summary(
model, (3, img_h, img_w))
# time.sleep(1)
flops, params = get_model_complexity_info(
model, (3, img_h, img_w),
as_strings=False,
print_per_layer_stat=True,
ost=sys.stdout)
# print('Flops: ' + flops)
# print('Params: ' + params)
result = dict()
result['net'] = name
result['size'] = '{}x{}'.format(img_h, img_w)
result['Flops'] = flops / (10**9)
result['params'] = params / (10**6)
result['params_A'] = total_params.item()
result['params_T'] = trainable_params.item()
result['Input(MB)'] = total_input_size
result['F/B (MB)'] = total_output_size
result['Params(MB)'] = total_params_size
result['Total(MB)'] = total_size