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test_edit_free.py
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test_edit_free.py
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### Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
# giving the edit label and where does each part come from
import os
from collections import OrderedDict
from torch.autograd import Variable
from options.test_options import TestOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
import util.util as util
from util.visualizer import Visualizer
from util import html
import torch
opt = TestOptions().parse(save=False)
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
visualizer = Visualizer(opt)
# create website
web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
def merge_image(no_bg_tensor, bg_tensor, label):
assert no_bg_tensor.dim() == 3
mask = (label == 0).type(torch.FloatTensor)
mask_f = (label != 0).type(torch.FloatTensor)
return no_bg_tensor.type(torch.FloatTensor)*mask_f + bg_tensor * mask
# test
if not opt.engine and not opt.onnx:
model = create_model(opt)
if opt.data_type == 16:
model.half()
elif opt.data_type == 8:
model.type(torch.uint8)
if opt.verbose:
print(model)
else:
from run_engine import run_trt_engine, run_onnx
for i, data in enumerate(dataset):
if i >= opt.how_many:
break
if opt.data_type == 16:
data['label'] = data['label'].half()
data['inst'] = data['inst'].half()
elif opt.data_type == 8:
data['label'] = data['label'].uint8()
data['inst'] = data['inst'].uint8()
if opt.export_onnx:
print ("Exporting to ONNX: ", opt.export_onnx)
assert opt.export_onnx.endswith("onnx"), "Export model file should end with .onnx"
torch.onnx.export(model, [data['label'], data['inst']],
opt.export_onnx, verbose=True)
exit(0)
minibatch = 1
img_path = data['path']
print('process image... %s' % img_path)
if opt.test_type == 'encode':
print(web_dir+"/encode_tensor")
if os.path.exists(web_dir+"/encode_tensor") == False:
os.mkdir(web_dir+"/encode_tensor")
generated = model.inference_encode(data['path'], data['bg_styleimage'], data['label'], data['mask'])
if opt.test_type == 'generate':
generated = model.inference_generate(data['path'], data['bg_styleimage'], data['label'], data['mask'])
visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)),
('style_image', util.tensor2im(data['bg_styleimage'][0])),
('reconstruct_style_image', util.tensor2im(generated.data[0])),
])
visualizer.save_images(webpage, visuals, img_path)
webpage.save()
# python test_twomask.py --name helen_mask/helen_mask_vae_debug36_6 --no_instance --dataroot ./datasets/helen_align/ --resize_or_crop none --label_nc 11 --n_downsample_global 2 --longSize 256 --norm batch --mask_model --debug_mask_part --phase test4 --phase2 test5 --return_bg