Nataniel Ruiz Gutierrez
68 lines
2.4 KiB
Python
Executable File
68 lines
2.4 KiB
Python
Executable File
import os
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from collections import OrderedDict
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from torch.autograd import Variable
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from options.test_options import TestOptions
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from data.data_loader import CreateDataLoader
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from models.models import create_model
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import util.util as util
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from util.visualizer import Visualizer
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from util import html
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import torch
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opt = TestOptions().parse(save=False)
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opt.nThreads = 1 # test code only supports nThreads = 1
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opt.batchSize = 1 # test code only supports batchSize = 1
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opt.serial_batches = True # no shuffle
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opt.no_flip = True # no flip
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data_loader = CreateDataLoader(opt)
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dataset = data_loader.load_data()
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visualizer = Visualizer(opt)
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# create website
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web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
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webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
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# test
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if not opt.engine and not opt.onnx:
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model = create_model(opt)
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if opt.data_type == 16:
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model.half()
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elif opt.data_type == 8:
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model.type(torch.uint8)
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if opt.verbose:
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print(model)
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else:
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from run_engine import run_trt_engine, run_onnx
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for i, data in enumerate(dataset):
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if i >= opt.how_many:
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break
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if opt.data_type == 16:
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data['label'] = data['label'].half()
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data['inst'] = data['inst'].half()
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elif opt.data_type == 8:
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data['label'] = data['label'].uint8()
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data['inst'] = data['inst'].uint8()
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if opt.export_onnx:
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print ("Exporting to ONNX: ", opt.export_onnx)
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assert opt.export_onnx.endswith("onnx"), "Export model file should end with .onnx"
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torch.onnx.export(model, [data['label'], data['inst']],
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opt.export_onnx, verbose=True)
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exit(0)
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minibatch = 1
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if opt.engine:
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generated = run_trt_engine(opt.engine, minibatch, [data['label'], data['inst']])
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elif opt.onnx:
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generated = run_onnx(opt.onnx, opt.data_type, minibatch, [data['label'], data['inst']])
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else:
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generated = model.inference(data['label'], data['inst'], data['image'])
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visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)),
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('synthesized_image', util.tensor2im(generated.data[0]))])
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img_path = data['path']
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print('process image... %s' % img_path)
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visualizer.save_images(webpage, visuals, img_path)
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webpage.save()
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