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