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Fix owlv2 code snippet (#27698)
* Fix code snippet * Improve code snippet
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NielsRogge
GitHub
parent
334a6d18a1
commit
c832bcb812
1 changed files with 56 additions and 20 deletions
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@ -1544,19 +1544,38 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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>>> import requests
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>>> from PIL import Image
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>>> import torch
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>>> import numpy as np
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>>> from transformers import AutoProcessor, Owlv2ForObjectDetection
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>>> from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
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>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
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>>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
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>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> query_url = "http://images.cocodataset.org/val2017/000000001675.jpg"
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>>> query_image = Image.open(requests.get(query_url, stream=True).raw)
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>>> inputs = processor(images=image, query_images=query_image, return_tensors="pt")
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>>> # forward pass
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>>> with torch.no_grad():
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... outputs = model.image_guided_detection(**inputs)
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>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
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>>> target_sizes = torch.Tensor([image.size[::-1]])
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>>> # Note: boxes need to be visualized on the padded, unnormalized image
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>>> # hence we'll set the target image sizes (height, width) based on that
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>>> def get_preprocessed_image(pixel_values):
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... pixel_values = pixel_values.squeeze().numpy()
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... unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None]
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... unnormalized_image = (unnormalized_image * 255).astype(np.uint8)
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... unnormalized_image = np.moveaxis(unnormalized_image, 0, -1)
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... unnormalized_image = Image.fromarray(unnormalized_image)
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... return unnormalized_image
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>>> unnormalized_image = get_preprocessed_image(inputs.pixel_values)
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>>> target_sizes = torch.Tensor([unnormalized_image.size[::-1]])
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>>> # Convert outputs (bounding boxes and class logits) to COCO API
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>>> results = processor.post_process_image_guided_detection(
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... outputs=outputs, threshold=0.9, nms_threshold=0.3, target_sizes=target_sizes
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@ -1566,19 +1585,19 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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>>> for box, score in zip(boxes, scores):
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... box = [round(i, 2) for i in box.tolist()]
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... print(f"Detected similar object with confidence {round(score.item(), 3)} at location {box}")
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Detected similar object with confidence 0.938 at location [327.31, 54.94, 547.39, 268.06]
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Detected similar object with confidence 0.959 at location [5.78, 360.65, 619.12, 366.39]
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Detected similar object with confidence 0.902 at location [2.85, 360.01, 627.63, 380.79]
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Detected similar object with confidence 0.985 at location [176.97, -29.45, 672.69, 182.83]
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Detected similar object with confidence 1.0 at location [6.53, 14.35, 624.87, 470.82]
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Detected similar object with confidence 0.998 at location [579.98, 29.14, 615.49, 489.05]
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Detected similar object with confidence 0.985 at location [206.15, 10.53, 247.74, 466.01]
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Detected similar object with confidence 0.947 at location [18.62, 429.72, 646.5, 457.72]
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Detected similar object with confidence 0.996 at location [523.88, 20.69, 586.84, 483.18]
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Detected similar object with confidence 0.998 at location [3.39, 360.59, 617.29, 499.21]
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Detected similar object with confidence 0.969 at location [4.47, 449.05, 614.5, 474.76]
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Detected similar object with confidence 0.966 at location [31.44, 463.65, 654.66, 471.07]
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Detected similar object with confidence 0.924 at location [30.93, 468.07, 635.35, 475.39]
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Detected similar object with confidence 0.938 at location [490.96, 109.89, 821.09, 536.11]
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Detected similar object with confidence 0.959 at location [8.67, 721.29, 928.68, 732.78]
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Detected similar object with confidence 0.902 at location [4.27, 720.02, 941.45, 761.59]
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Detected similar object with confidence 0.985 at location [265.46, -58.9, 1009.04, 365.66]
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Detected similar object with confidence 1.0 at location [9.79, 28.69, 937.31, 941.64]
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Detected similar object with confidence 0.998 at location [869.97, 58.28, 923.23, 978.1]
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Detected similar object with confidence 0.985 at location [309.23, 21.07, 371.61, 932.02]
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Detected similar object with confidence 0.947 at location [27.93, 859.45, 969.75, 915.44]
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Detected similar object with confidence 0.996 at location [785.82, 41.38, 880.26, 966.37]
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Detected similar object with confidence 0.998 at location [5.08, 721.17, 925.93, 998.41]
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Detected similar object with confidence 0.969 at location [6.7, 898.1, 921.75, 949.51]
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Detected similar object with confidence 0.966 at location [47.16, 927.29, 981.99, 942.14]
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Detected similar object with confidence 0.924 at location [46.4, 936.13, 953.02, 950.78]
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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@ -1650,8 +1669,10 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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```python
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>>> import requests
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>>> from PIL import Image
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>>> import numpy as np
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>>> import torch
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>>> from transformers import AutoProcessor, Owlv2ForObjectDetection
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>>> from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
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>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
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>>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
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@ -1660,10 +1681,25 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> texts = [["a photo of a cat", "a photo of a dog"]]
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>>> inputs = processor(text=texts, images=image, return_tensors="pt")
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>>> outputs = model(**inputs)
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>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
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>>> target_sizes = torch.Tensor([image.size[::-1]])
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>>> # forward pass
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>>> with torch.no_grad():
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... outputs = model(**inputs)
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>>> # Note: boxes need to be visualized on the padded, unnormalized image
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>>> # hence we'll set the target image sizes (height, width) based on that
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>>> def get_preprocessed_image(pixel_values):
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... pixel_values = pixel_values.squeeze().numpy()
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... unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None]
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... unnormalized_image = (unnormalized_image * 255).astype(np.uint8)
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... unnormalized_image = np.moveaxis(unnormalized_image, 0, -1)
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... unnormalized_image = Image.fromarray(unnormalized_image)
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... return unnormalized_image
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>>> unnormalized_image = get_preprocessed_image(inputs.pixel_values)
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>>> target_sizes = torch.Tensor([unnormalized_image.size[::-1]])
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>>> # Convert outputs (bounding boxes and class logits) to final bounding boxes and scores
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>>> results = processor.post_process_object_detection(
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... outputs=outputs, threshold=0.2, target_sizes=target_sizes
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@ -1676,8 +1712,8 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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>>> for box, score, label in zip(boxes, scores, labels):
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... box = [round(i, 2) for i in box.tolist()]
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... print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
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Detected a photo of a cat with confidence 0.614 at location [341.67, 17.54, 642.32, 278.51]
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Detected a photo of a cat with confidence 0.665 at location [6.75, 38.97, 326.62, 354.85]
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Detected a photo of a cat with confidence 0.614 at location [512.5, 35.08, 963.48, 557.02]
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Detected a photo of a cat with confidence 0.665 at location [10.13, 77.94, 489.93, 709.69]
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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