def calculate_models_params_and_flops():
backbone='darknet53'
network = backbones.get_backbone(backbone, pretrained=False, feature_type='tri_stage_fpn', n_classes=1000)
input = torch.randn(1, 3, 448, 448)
bb_macs, _ = profile(network, inputs=(input, ),verbose=False)
bb_params = summary(network,(3,448,448),device='cpu')[0].item()
model_df = pd.DataFrame(columns=['model_name', 'params', 'mac/flops'])
for key in model_argmap:
model_cfg= create_model_cfg(key, model_argmap)
model_components = create_model_components(model_cfg['name'],
preprocess_args=model_cfg['preprocess_args'],
network_args=model_cfg['network_args'],
loss_args=model_cfg['loss_args'],
postprocess_args=model_cfg['postprocess_args'],
stage='validate')
network=model_components.network
model_macs, model_params = profile(network, inputs=(input, ),verbose=False)
if 'backbone' in model_cfg.network_args:
model_macs-=bb_macs
model_params-=bb_params
key+='_head'
model_macs_in_g=model_macs/(10**9)
model_params_in_m=model_params/(10**6)
print(key,model_params_in_m,model_macs_in_g)
model_df = model_df.append({'model_name': key,
'params': model_params_in_m,
'mac/flops': model_macs_in_g}, ignore_index=True)
model_df.to_csv('models_summary.txt',index=False)
def calculate_backbone_params_and_flops():
all_supported_backbones=backbones.all_models
backbone_df = pd.DataFrame(columns=['backbone_name', 'params', 'mac/flops'])
for backbone in all_supported_backbones:
network = backbones.get_backbone(backbone, pretrained=False, feature_type='tri_stage_fpn', n_classes=1000)
input = torch.randn(1, 3, 224, 224)
macs, _ = profile(network, inputs=(input, ),verbose=False)
params = summary(network,(3,224,224),device='cpu')[0].item()
macs_in_g=macs/(10**9)
params_in_m=params/(10**6)
print(backbone,params_in_m,macs_in_g)
backbone_df = backbone_df.append({'backbone_name': backbone,
'params': params_in_m,
'mac/flops': macs_in_g}, ignore_index=True)
backbone_df.to_csv('backbones_summary.txt',index=False)