def accimage_loader(path): try: import accimage return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def __getitem__(self, idx): if self.image_num_limit is not None and len(self.image_paths[idx]) > self.image_num_limit: image_path_list = sorted(sample(self.image_paths[idx], self.image_num_limit)) else: image_path_list = self.image_paths[idx] image_list = [accimage.Image(x) for x in image_path_list] if self.transform: image_list = [self.transform(img) for img in image_list] if self.channel_first: image_tensor = torch.stack(image_list, dim=1) else: image_tensor = torch.stack(image_list, dim=0) labels = self.labels_list[idx] if self.transform_3d: image_tensor = self.transform_3d(image_tensor) if self.channel_first: channel_num, image_num, image_height, image_width = image_tensor.size() if self.image_num_limit is not None: input_tensor = torch.zeros(channel_num, self.image_num_limit, image_height, image_width) else: input_tensor = torch.zeros(channel_num, self.max_image_num, image_height, image_width) input_tensor[:, :image_num, :, :] = image_tensor else: image_num, channel_num, image_height, image_width = image_tensor.size() if self.image_num_limit is not None: input_tensor = torch.zeros(self.image_num_limit, channel_num, image_height, image_width) else: input_tensor = torch.zeros(self.max_image_num, channel_num, image_height, image_width) input_tensor[:image_num, :, :, :] = image_tensor return input_tensor, labels
def __getitem__(self, idx): image_path_list = self.image_paths[idx] image_list = [accimage.Image(x) for x in image_path_list] if self.transform: image_list = [self.transform(img) for img in image_list] labels = self.labels_list[idx] return image_list, labels
def accimage_loader(self, path): """Accimage image loader.""" import accimage try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return self.pil_loader(path)
def accimage_loader(path, *, n_channel): import accimage try: img = accimage.Image(path) return img.convert('RGB') if n_channel == 3 else img.convert('L') except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path, n_channel=n_channel)
def __getitem__(self, idx): image = accimage.Image(self.image_path[idx]) labels = self.new_labels_list[idx] if self.transform: image = self.transform(image) return image, labels
def accimage_loader(path): torchvision.set_image_backend('accimage') import accimage try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def accimage_loader(path): # accimge:高性能图像加载和增强程序模拟的程序。 import accimage try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def test_accimage_to_tensor(self): trans = transforms.ToTensor() expected_output = trans(Image.open(GRACE_HOPPER).convert('RGB')) output = trans(accimage.Image(GRACE_HOPPER)) self.assertEqual(expected_output.size(), output.size()) assert np.allclose(output.numpy(), expected_output.numpy())
def accimage_loader(path): try: import accimage return accimage.Image(path) except ModuleNotFoundError: # Potentially a decoding problem, fall back to PIL.Image torchvision.set_image_backend('PIL') return pil_loader(path)
def accimage_loader(path: str) -> Any: import accimage try: return accimage.Image(path) except OSError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def accimage_loader(path): print("can't find acc image loader") import accimage try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def zip_loader(self, path: str) -> Image.Image: f = io.BytesIO(self.root_zip.read(path)) if get_image_backend() == 'accimage': try: import accimage # type: ignore return accimage.Image(f) except IOError: pass # fall through to PIL return Image.open(f).convert('RGB')
def accimage_loader(path, root_folder=None): import accimage if not (root_folder is None): path = osp.join(root_folder, path) try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def pil_loader(path): # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) with open(path, 'rb') as f: try: img = Image.open(f) return img.convert('RGB') except: import accimage return accimage.Image(path)
def tar_loader(self, path: str) -> Image.Image: f = self.root_data.extract(path) if get_image_backend() == 'accimage': try: import accimage # type: ignore return accimage.Image(f) except IOError: pass # fall through to PIL return Image.open(f).convert('RGB')
def test_cropping(): image = accimage.Image("chicago.jpg") image.crop(box=(50, 50, 150, 150)) if SAVE_IMAGES: save_image('test_cropping.jpg', image) assert image.width == 100 assert image.height == 100
def accimage_loader(path): """Accimage loader for accelebrating loading image. """ try: import accimage return accimage.Image(path) except IOError: # Potentionally a decoding problem, fall back to PIL.image return pil_loader(path)
def zip_loader(self, path) -> Any: f = self.root_zip[path] if get_image_backend() == 'accimage': try: import accimage # type: ignore return accimage.Image(f) except IOError: pass # fall through to PIL return Image.open(f).convert('RGB')
def test_resizing(): image = accimage.Image("chicago.jpg") image.resize(size=(200, 200)) if SAVE_IMAGES: save_image('test_resizing.jpg', image) assert image.width == 200 assert image.height == 200
def Sample_Image(imgs_path, sl): frams = [] for a in sl: img = transform( image_to_np( accimage.Image(os.path.join(imgs_path, "%06d.jpg" % a)))) frams.append( self.transform(img).view(3, sample_size, sample_size, 1)) return torch.cat(frams, dim=3).type(torch.FloatTensor)
def accimage_loader(path): import accimage try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image print('acc_loader', IOError) try: return pil_loader(path) except IOError: print('pil_loader', IOError)
def test_accimage_crop(self): trans = transforms.Compose([ transforms.CenterCrop(256), transforms.ToTensor(), ]) expected_output = trans(Image.open(GRACE_HOPPER).convert('RGB')) output = trans(accimage.Image(GRACE_HOPPER)) self.assertEqual(expected_output.size(), output.size()) assert np.allclose(output.numpy(), expected_output.numpy())
def accimage_loader(path): ''' --------------------------------------------------------------------------- code reference: https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py ''' import accimage try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def accimage_loader(path): """ compared with PIL, accimage loader eliminates useless function within class, so that it is faster than PIL :param path: image path :return: image data """ try: import accimage return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def pil_loader(path): # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) with open(path, 'rb') as f: img = Image.open(f) return img.convert('RGB') import accimage try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def accimage_loader(path): ''' Ref: https://pytorch.org/docs/stable/_modules/torchvision/datasets/folder.html#ImageFolder accimage is an accelerated Image loader and preprocessor leveraging Intel IPP. accimage is available on conda-forge. ''' import accimage try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)
def accimage_loader(path): # Tries to use accimage with PIL fallback. is_accimage_avail = importlib.find_loader('accimage') if is_accimage_avail: import accimage else: return pil_loader(path) try: return accimage.Image(path) except IOError as e: print("WARN: Exception in accimage_loader: {}".format(e)) return pil_loader(path)
def test_flipping(): image = accimage.Image("chicago.jpg") original_image_np = image_to_np(image) FLIP_LEFT_RIGHT = 0 image.transpose(FLIP_LEFT_RIGHT) if SAVE_IMAGES: save_image('test_flipping.jpg', image) new_image_np = image_to_np(image) assert image.width == 1920 assert image.height == 931 np.testing.assert_array_equal(new_image_np[:, ::-1, :], original_image_np)
def accimage_loader(path): import accimage try: start = time.time() img = accimage.Image(path) end = time.time() logger.debug('accimage decode {:.3f} ms'.format(1000 * (end - start))) return img except IOError: logger.warn('accimage failed to decode {}'.format(path)) # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path)