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87 lines
4.1 KiB
Python
87 lines
4.1 KiB
Python
from models.networks import get_generator_new
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# from aug import get_normalize
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import torch
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import numpy as np
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config = {'project': 'deblur_gan', 'warmup_num': 3, 'optimizer': {'lr': 0.0001, 'name': 'adam'},
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'val': {'preload': False, 'bounds': [0.9, 1], 'crop': 'center', 'files_b': '/datasets/my_dataset/**/*.jpg',
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'files_a': '/datasets/my_dataset/**/*.jpg', 'scope': 'geometric',
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'corrupt': [{'num_holes': 3, 'max_w_size': 25, 'max_h_size': 25, 'name': 'cutout', 'prob': 0.5},
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{'quality_lower': 70, 'name': 'jpeg', 'quality_upper': 90}, {'name': 'motion_blur'},
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{'name': 'median_blur'}, {'name': 'gamma'}, {'name': 'rgb_shift'}, {'name': 'hsv_shift'},
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{'name': 'sharpen'}], 'preload_size': 0, 'size': 256}, 'val_batches_per_epoch': 100,
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'num_epochs': 200, 'batch_size': 1, 'experiment_desc': 'fpn', 'train_batches_per_epoch': 1000,
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'train': {'preload': False, 'bounds': [0, 0.9], 'crop': 'random', 'files_b': '/datasets/my_dataset/**/*.jpg',
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'files_a': '/datasets/my_dataset/**/*.jpg', 'preload_size': 0,
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'corrupt': [{'num_holes': 3, 'max_w_size': 25, 'max_h_size': 25, 'name': 'cutout', 'prob': 0.5},
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{'quality_lower': 70, 'name': 'jpeg', 'quality_upper': 90}, {'name': 'motion_blur'},
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{'name': 'median_blur'}, {'name': 'gamma'}, {'name': 'rgb_shift'},
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{'name': 'hsv_shift'}, {'name': 'sharpen'}], 'scope': 'geometric', 'size': 256},
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'scheduler': {'min_lr': 1e-07, 'name': 'linear', 'start_epoch': 50}, 'image_size': [256, 256],
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'phase': 'train',
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'model': {'d_name': 'double_gan', 'disc_loss': 'wgan-gp', 'blocks': 9, 'content_loss': 'perceptual',
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'adv_lambda': 0.001, 'dropout': True, 'g_name': 'fpn_inception', 'd_layers': 3,
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'learn_residual': True, 'norm_layer': 'instance'}}
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class Predictor:
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def __init__(self, weights_path, model_name='',cf=False):
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# model = get_generator(model_name or config['model'])
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model = get_generator_new(weights_path[0:-11])
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model.load_state_dict(torch.load(weights_path, map_location=lambda storage, loc: storage)['model'])
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if torch.cuda.is_available() and not cf:
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self.model = model.cuda()
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else:
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self.model = model
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self.model.train(True)
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# GAN inference should be in train mode to use actual stats in norm layers,
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# it's not a bug
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# self.normalize_fn = get_normalize()
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@staticmethod
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def _array_to_batch(x):
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x = np.transpose(x, (2, 0, 1))
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x = np.expand_dims(x, 0)
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return torch.from_numpy(x)
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def _preprocess(self, x, mask):
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# x, _ = self.normalize_fn(x, x)
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x = ((x.astype(np.float32) / 255) - 0.5) / 0.5
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if mask is None:
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mask = np.ones_like(x, dtype=np.float32)
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else:
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mask = np.round(mask.astype('float32') / 255)
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h, w, _ = x.shape
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block_size = 32
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min_height = (h // block_size + 1) * block_size
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min_width = (w // block_size + 1) * block_size
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pad_params = {'mode': 'constant',
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'constant_values': 0,
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'pad_width': ((0, min_height - h), (0, min_width - w), (0, 0))
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}
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x = np.pad(x, **pad_params)
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mask = np.pad(mask, **pad_params)
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return map(self._array_to_batch, (x, mask)), h, w
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@staticmethod
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def _postprocess(x):
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x, = x
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x = x.detach().cpu().float().numpy()
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x = (np.transpose(x, (1, 2, 0)) + 1) / 2.0 * 255.0
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return x.astype('uint8')
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def __call__(self, img, mask, ignore_mask=True,cf=False):
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(img, mask), h, w = self._preprocess(img, mask)
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with torch.no_grad():
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if torch.cuda.is_available() and not cf:
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inputs = [img.cuda()]
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else:
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inputs = [img]
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if not ignore_mask:
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inputs += [mask]
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pred = self.model(*inputs)
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return self._postprocess(pred)[:h, :w, :]
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