69 lines
2 KiB
Python
69 lines
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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cache=args.cache,
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crop=args.crop,
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pad=args.pad,
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augment=False,
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norms=args.norms,
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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_channels, out_channels = test_dataset.channels
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model = models.__dict__[args.model](in_channels, out_channels)
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criterion = torch.nn.__dict__[args.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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from collections import OrderedDict
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model_state = OrderedDict()
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for k, v in state['model'].items():
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model_k = k.replace('module.', '', 1) # FIXME
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model_state[model_k] = v
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model.load_state_dict(model_state)
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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.norms is not None:
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norm = test_dataset.norms[0] # FIXME
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norm(input, undo=True)
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norm(output, undo=True)
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norm(target, undo=True)
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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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