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if '.png' in imgdir:
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#corrected image
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corrected = io.imread(os.path.join(result_path,imgdir))
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#reference image
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imgname = imgdir.split('corrected')[0]
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refdir = imgname+'.png'
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reference = io.imread(os.path.join(reference_path,refdir))
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psnr,ssim = rmetrics(corrected,reference)
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uiqm,uciqe = nmetrics(corrected)
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sumpsnr += psnr
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sumssim += ssim
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sumuiqm += uiqm
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sumuciqe += uciqe
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N +=1
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with open(os.path.join(result_path,'metrics.txt'), 'a') as f:
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f.write('{}: psnr={} ssim={} uiqm={} uciqe={}\n'.format(imgname,psnr,ssim,uiqm,uciqe))
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mpsnr = sumpsnr/N
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mssim = sumssim/N
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muiqm = sumuiqm/N
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muciqe = sumuciqe/N
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with open(os.path.join(result_path,'metrics.txt'), 'a') as f:
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f.write('Average: psnr={} ssim={} uiqm={} uciqe={}\n'.format(mpsnr, mssim, muiqm, muciqe))
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if __name__ == '__main__':
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main()
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# <FILESEP>
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import json
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class Settings(dict):
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"""Experiment configuration options.
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Wrapper around in-built dict class to access members through the dot operation.
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Experiment parameters:
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"expt_name": Name/description of experiment, used for logging.
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"gpu_id": Available GPU ID(s)
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"train_filepath": Training set path
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"val_filepath": Validation set path
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"test_filepath": Test set path
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"num_nodes": Number of nodes in TSP tours
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"num_neighbors": Number of neighbors in k-nearest neighbor input graph (-1 for fully connected)
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"node_dim": Number of dimensions for each node
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"voc_nodes_in": Input node signal vocabulary size
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"voc_nodes_out": Output node prediction vocabulary size
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"voc_edges_in": Input edge signal vocabulary size
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"voc_edges_out": Output edge prediction vocabulary size
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"beam_size": Beam size for beamsearch procedure (-1 for disabling beamsearch)
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"hidden_dim": Dimension of model's hidden state
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"num_layers": Number of GCN layers
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"mlp_layers": Number of MLP layers
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"aggregation": Node aggregation scheme in GCN (`mean` or `sum`)
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"max_epochs": Maximum training epochs
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"val_every": Interval (in epochs) at which validation is performed
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"test_every": Interval (in epochs) at which testing is performed
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"batch_size": Batch size
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"batches_per_epoch": Batches per epoch (-1 for using full training set)
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"accumulation_steps": Number of steps for gradient accumulation (DO NOT USE: BUGGY)
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"learning_rate": Initial learning rate
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"decay_rate": Learning rate decay parameter
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"""
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def __init__(self, config_dict):
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super().__init__()
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for key in config_dict:
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self[key] = config_dict[key]
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def __getattr__(self, attr):
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return self[attr]
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def __setitem__(self, key, value):
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return super().__setitem__(key, value)
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def __setattr__(self, key, value):
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return self.__setitem__(key, value)
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__delattr__ = dict.__delitem__
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def get_default_config():
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"""Returns default settings object.
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"""
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return Settings(json.load(open("./configs/default.json")))
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