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import argparse
import os
from utils.util import *
from models import create_model
from configs import parse_config
import torch
if __name__ == '__main__':
# parse arguments
parser = argparse.ArgumentParser(description='Style Master')
parser.add_argument('--cfg_file', type=str, default='./exp/cycle_gan_cfg.yaml')
parser.add_argument('--ckpt', type=str, default='')
parser.add_argument("--output_jit_file", type=str, default='./jit_models/cycle_gan.jit')
args = parser.parse_args()
# parse config
config = parse_config(args.cfg_file)
config['common']['phase'] = 'test'
model = create_model(config) # create a model given opt.model and other options
model.load_networks(0, ckpt=args.ckpt)
model.eval()
dummy_input = torch.rand(1, config['model']['input_nc'], config['testing']['load_size'], config['testing']['load_size'])
traced_script_module, dummy_output, dummy_output_traced = model.trace_jit(dummy_input)
if type(dummy_output) is list or type(dummy_output) is tuple:
diffs = []
for i in range(len(dummy_output)):
diffs.append(np.abs(dummy_output[i].detach().numpy() - dummy_output_traced[i].detach().numpy()))
else:
diffs = np.abs(dummy_output.detach().numpy() - dummy_output_traced.detach().numpy())
if not type(diffs) is list:
diffs = [ diffs ]
for i in range(len(diffs)):
avg_diff, max_diff, min_diff = np.mean(diffs[i]), np.max(diffs[i]), np.min(diffs[i])
print('Network output ', i + 1)
print("average difference between original and traced model: ", avg_diff)
print("max difference between original and traced model: ", max_diff)
print("min difference between original and traced model: ", min_diff)
traced_script_module.save(args.output_jit_file)
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