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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).
import os
from collections import OrderedDict
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
from run_engine import run_trt_engine, run_onnx
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))
# 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)
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
if opt.engine:
generated = run_trt_engine(opt.engine, minibatch, [data['label'], data['inst']])
elif opt.onnx:
generated = run_onnx(opt.onnx, opt.data_type, minibatch, [data['label'], data['inst']])
else:
generated = model.inference(data['label'], data['inst'])
visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)),
('synthesized_image', util.tensor2im(generated.data[0]))])
img_path = data['path']
print('process image... %s' % img_path)
visualizer.save_images(webpage, visuals, img_path)
webpage.save()
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