processor(image, return_tensors='pt') ``` Should give you something like... @@ -235,7 +234,7 @@ Now that you know how to read images and transform them into inputs, let's write ```python def process_example(example): inputs = feature_extractor(example['image'], return_tensors...
text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length" ) Collaborator amyeroberts Aug 13, 2024 +1 on removing from the init. Checking the kwargs overrides is also a good idea - we should add a new test to isolate and check this behaviour ...
Built on Ampere architecture, the 3070 delivers impressive increased throughput in cores and tensor cores, dramatically outperforming cards from its two predecessors. The RTX 3070 also offers advanced AI rendering with impeccable image quality for an exceptional visual experience. It features real-time ...
Built on Ampere architecture, the 3070 delivers impressive increased throughput in cores and tensor cores, dramatically outperforming cards from its two predecessors. The RTX 3070 also offers advanced AI rendering with impeccable image quality for an exceptional visual experience. It features r...
PROMPT = "<s>[INST]Describe the images.\n[IMG][IMG][IMG][IMG][/INST]" # image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=PROMPT, images=IMG_URLS, return_tensors="pt").to("cuda") generate_ids = model.generate(**inputs, max_new_tokens=500)...
io import read_image images = torchvision.io.read_image(image_path) processor = DeformableDetrImageProcessorFast.from_pretrained("SenseTime/deformable-detr") images_processed = processor(images , return_tensors="pt", device="cuda") If device is not specified: If the input images are tensors,...
GeForce RTX™ 30 Series GPUs power the world’s fastest laptops for gamers and creators. They’re built with the award-winning Ampere—NVIDIA’s 2nd gen RTX architecture—with new RT Cores, Tensor Cores, and streaming multiprocessors to give you the most realistic ray-traced graphics and cu...
from PIL import Image @@ -51,7 +51,7 @@ IMG_URLS = [ ] PROMPT = "<s>[INST]Describe the images.\n[IMG][IMG][IMG][IMG][/INST]" inputs = processor(text=PROMPT, images=IMG_URLS, return_tensors="pt").to("cuda") inputs = processor(images=IMG_URLS, text=PROMPT, return_tenso...
Custom operations that return Tensors require meta-functions. Meta-functions should be implemented and registered in python so that dynamic dims can be handled automatically. See above documents for a description of meta-functions. Use torch.libary.opcheck() to test the function registration and met...
"images", "segmentation_maps", "do_resize", "size", "resample", "do_rescale", "rescale_factor", "do_normalize", "image_mean", "image_std", "do_reduce_labels", "return_tensors", "data_format", "input_data_format", ] @classmethod def from_dict(cls, image_processor_dict: Dict[...