print("Trainable params: {0:,}".format(trainable_params))
print("Non-trainable params: {0:,}".format(total_params -
trainable_params))
print(
"---------------------------------------------------------------------------------------------------------------------------"
)
print("Input size (MB): %0.2f" % total_input_size)
print("Forward/backward pass size (MB): %0.2f" % total_output_size)
print("Params size (MB): %0.2f" % total_params_size)
print("Estimated Total Size (MB): %0.2f" % total_size)
print(
"---------------------------------------------------------------------------------------------------------------------------"
)
# return summary
##############################################################################################################
if __name__ == '__main__':
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
model = resnet50(
) #Total params: 23,272,266 #Total FLOPS: 62,751,882 #Accuracy: 85.61_98.95
#model = torch.load("model_training").cpu()
model = torch.load("model_training_final").cpu()
#model = model.module
print("model:", model)
#summary(model, (1, 28, 28), device="cpu")
#summary(model, (3, 32, 32), device="cpu")
summary(model, (3, 224, 224), device="cpu")
type=str,
default=
'/data1/Datasets/ImageNet/ILSVRC2012/ILSVRC2012_img_val_subfolders/',
help='test dataset path')
parser.add_argument("--parallel", type=int, default=1)
parser.set_defaults(autoML=True)
parser.set_defaults(train=False)
args = parser.parse_args()
return args
##############################################################################################################
if __name__ == '__main__':
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
args = get_args()
print("args:", args)
model = resnet50(pretrained=False).cuda()
torch.save(model, "model")
print("model_training:", model)
if args.parallel == 1:
model = torch.nn.DataParallel(model).cuda()
fine_tuner = FineTuner_CNN(args.train_path, args.test_path, model)
fine_tuner.test()
if args.autoML:
fine_tuner.autoML()