70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
import numpy as np
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import torch
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from torch.utils.data import DataLoader
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from .data import FieldDataset
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from . import models
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from .models import narrow_like
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def test(args):
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print(args)
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test_dataset = FieldDataset(
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in_patterns=args.test_in_patterns,
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tgt_patterns=args.test_tgt_patterns,
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augment=False,
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**vars(args),
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)
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test_loader = DataLoader(
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test_dataset,
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batch_size=args.batches,
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shuffle=False,
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num_workers=args.loader_workers,
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)
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in_chan, out_chan = test_dataset.in_chan, test_dataset.tgt_chan
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model = getattr(models, args.model)
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model = model(sum(in_chan) + args.noise_chan, sum(out_chan))
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criterion = getattr(torch.nn, args.criterion)
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criterion = criterion()
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device = torch.device('cpu')
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state = torch.load(args.load_state, map_location=device)
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model.load_state_dict(state['model'])
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print('model state at epoch {} loaded from {}'.format(
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state['epoch'], args.load_state))
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del state
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model.eval()
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with torch.no_grad():
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for i, (input, target) in enumerate(test_loader):
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output = model(input)
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if args.pad > 0: # FIXME
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output = narrow_like(output, target)
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input = narrow_like(input, target)
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else:
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target = narrow_like(target, output)
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input = narrow_like(input, output)
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loss = criterion(output, target)
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print('sample {} loss: {}'.format(i, loss.item()))
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if args.in_norms is not None:
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start = 0
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for norm, stop in zip(test_dataset.in_norms, np.cumsum(in_chan)):
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norm(input[:, start:stop], undo=True)
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start = stop
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if args.tgt_norms is not None:
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start = 0
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for norm, stop in zip(test_dataset.tgt_norms, np.cumsum(out_chan)):
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norm(output[:, start:stop], undo=True)
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norm(target[:, start:stop], undo=True)
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start = stop
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np.savez('{}.npz'.format(i), input=input.numpy(),
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output=output.numpy(), target=target.numpy())
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