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Running
on
Zero
| import cv2 | |
| import torch | |
| from PIL import Image | |
| from torch import nn | |
| from torchvision import transforms | |
| from transformers import ProcessorMixin, BatchEncoding | |
| from transformers.image_processing_utils import BatchFeature | |
| OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) | |
| OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) | |
| def make_list_of_images(x): | |
| if not isinstance(x, list): | |
| return [x] | |
| return x | |
| def opencv_loader(path): | |
| return cv2.imread(path, cv2.IMREAD_UNCHANGED).astype('float32') | |
| class DepthNorm(nn.Module): | |
| def __init__( | |
| self, | |
| max_depth=0, | |
| min_depth=0.01, | |
| ): | |
| super().__init__() | |
| self.max_depth = max_depth | |
| self.min_depth = min_depth | |
| self.scale = 1000.0 # nyuv2 abs.depth | |
| def forward(self, image): | |
| # image = np.array(image) | |
| depth_img = image / self.scale # (H, W) in meters | |
| depth_img = depth_img.clip(min=self.min_depth) | |
| if self.max_depth != 0: | |
| depth_img = depth_img.clip(max=self.max_depth) | |
| depth_img /= self.max_depth # 0-1 | |
| else: | |
| depth_img /= depth_img.max() | |
| depth_img = torch.from_numpy(depth_img).unsqueeze(0).repeat(3, 1, 1) # assume image | |
| return depth_img.to(torch.get_default_dtype()) | |
| def get_depth_transform(config): | |
| config = config.vision_config | |
| transform = transforms.Compose( | |
| [ | |
| DepthNorm(max_depth=config.max_depth), | |
| transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC), | |
| transforms.CenterCrop(224), | |
| transforms.Normalize(OPENAI_DATASET_MEAN, OPENAI_DATASET_STD), # assume image | |
| # transforms.Normalize((0.5, ), (0.5, )) # 0-1 to norm distribution | |
| # transforms.Normalize((0.0418, ), (0.0295, )) # sun rgb-d imagebind | |
| # transforms.Normalize((0.02, ), (0.00295, )) # nyuv2 | |
| ] | |
| ) | |
| return transform | |
| def load_and_transform_depth(depth_path, transform): | |
| depth = opencv_loader(depth_path) | |
| depth_outputs = transform(depth) | |
| return depth_outputs | |
| class LanguageBindDepthProcessor(ProcessorMixin): | |
| attributes = [] | |
| tokenizer_class = ("LanguageBindDepthTokenizer") | |
| def __init__(self, config, tokenizer=None, **kwargs): | |
| super().__init__(**kwargs) | |
| self.config = config | |
| self.transform = get_depth_transform(config) | |
| self.image_processor = load_and_transform_depth | |
| self.tokenizer = tokenizer | |
| def __call__(self, images=None, text=None, context_length=77, return_tensors=None, **kwargs): | |
| if text is None and images is None: | |
| raise ValueError("You have to specify either text or images. Both cannot be none.") | |
| if text is not None: | |
| encoding = self.tokenizer(text, max_length=context_length, padding='max_length', | |
| truncation=True, return_tensors=return_tensors, **kwargs) | |
| if images is not None: | |
| images = make_list_of_images(images) | |
| image_features = [self.image_processor(image, self.transform) for image in images] | |
| image_features = torch.stack(image_features) | |
| if text is not None and images is not None: | |
| encoding["pixel_values"] = image_features | |
| return encoding | |
| elif text is not None: | |
| return encoding | |
| else: | |
| return {"pixel_values": image_features} | |
| def batch_decode(self, skip_special_tokens=True, *args, **kwargs): | |
| """ | |
| This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please | |
| refer to the docstring of this method for more information. | |
| """ | |
| return self.tokenizer.batch_decode(*args, skip_special_tokens=skip_special_tokens, **kwargs) | |
| def decode(self, skip_special_tokens=True, *args, **kwargs): | |
| """ | |
| This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to | |
| the docstring of this method for more information. | |
| """ | |
| return self.tokenizer.decode(*args, skip_special_tokens=skip_special_tokens, **kwargs) | |