78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
import os,time
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import ntpath
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import numpy as np
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import scipy.io as sio
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from options.test_options import TestOptions
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from data import create_dataset
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from models import create_model
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from util import util
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if __name__ == '__main__':
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opt = TestOptions().parse() # get test options
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# hard-code some parameters for test
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opt.num_threads = 0 # test code only supports num_threads = 1
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opt.batch_size = 1 # test code only supports batch_size = 1
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opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
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opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
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opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
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dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
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model = create_model(opt) # create a model given opt.model and other options
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model.setup(opt) # regular setup: load and print networks; create schedulers
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if opt.save_image:
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curSaveFolder = os.path.join(opt.dataroot, opt.method_name)
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if not os.path.exists(curSaveFolder):
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os.makedirs(curSaveFolder, mode=0o777)
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# test with eval mode. This only affects layers like batchnorm and dropout.
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# For [pix2pix]: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode.
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# For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
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if opt.eval:
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model.eval()
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time_total = 0
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for i, data in enumerate(dataset):
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# if i <= 627:
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# continue
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img_path = data['paths']
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short_path = ntpath.basename(img_path[0])
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name = os.path.splitext(short_path)[0]
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print('%s [%d]'%(short_path, i+1))
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# print(data['B_paths'])
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if 'haze' in data.keys():
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minSize = min(data['haze'].shape[2:4])
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else:
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minSize = min(data['A'].shape[2:4])
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if minSize < 256:
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print(' skip because the minimum size is %s'%minSize)
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continue
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# if i >= opt.num_test: # only apply our model to opt.num_test images.
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# break
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t0 = time.time()
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model.set_input(data) # unpack data from data loader
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model.test() # run inference
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time_total += time.time() - t0
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visuals = model.get_current_visuals() # get image results
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rec_J = util.tensor2im(visuals['rec_J'], float)/255. # [0, 1]
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refine_J = util.tensor2im(visuals['refine_J'], float)/255. # [0, 1]
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real_I = util.tensor2im(data['haze'], float) # [0, 255], float
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result_J = util.fuse_images(real_I, rec_J*255., refine_J*255.)/255. # [0, 1], np.float
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# save result images
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if opt.save_image:
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dehzImg = (result_J*255).astype(np.uint8) #[0, 255], np.uint8
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util.save_image(dehzImg, os.path.join(curSaveFolder, '%s_dehz.png'%(name)))
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# refinedT = util.tensor2im(visuals['refine_T_vis'])
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# util.save_image(refinedT, os.path.join(curSaveFolder, '%s_ref_T.png'%(name)))
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print('num: %d'%len(dataset))
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print('average time: %f'%(time_total/len(dataset)))
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