Add segmentation + object detection image processors (#20160)
* Add transforms for object detection * DETR models + Yolos * Scrappy additions * Maskformer image processor * Fix up; MaskFormer tests * Update owlvit processor * Add to docs * OwlViT tests * Update pad logic * Remove changes to transforms * Import fn directly * Update to include pad transformation * Remove uninstended changes * Add new owlvit post processing function * Tidy up * Fix copies * Fix some copies * Include device fix * Fix scipy imports * Update _pad_image * Update padding functionality * Fix bug * Properly handle ignore index * Fix up * Remove defaults to None in docstrings * Fix docstrings & docs * Fix sizes bug * Resolve conflicts in init * Cast to float after resizing * Tidy & add size if missing * Allow kwards when processing for owlvit * Update test values
This commit is contained in:
amyeroberts
@@ -21,7 +21,7 @@ The abstract from the paper is the following:
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*The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.*
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/conditional_detr_curve.jpg"
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alt="drawing" width="600"/>
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alt="drawing" width="600"/>
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<small> Conditional DETR shows much faster convergence compared to the original DETR. Taken from the <a href="https://arxiv.org/abs/2108.06152">original paper</a>.</small>
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@@ -32,6 +32,16 @@ This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The o
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[[autodoc]] ConditionalDetrConfig
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## ConditionalDetrImageProcessor
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[[autodoc]] ConditionalDetrImageProcessor
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- preprocess
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- pad_and_create_pixel_mask
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- post_process_object_detection
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- post_process_instance_segmentation
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- post_process_semantic_segmentation
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- post_process_panoptic_segmentation
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## ConditionalDetrFeatureExtractor
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[[autodoc]] ConditionalDetrFeatureExtractor
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@@ -55,4 +65,4 @@ This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The o
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## ConditionalDetrForSegmentation
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[[autodoc]] ConditionalDetrForSegmentation
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- forward
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- forward
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@@ -27,12 +27,19 @@ Tips:
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- Training Deformable DETR is equivalent to training the original [DETR](detr) model. Demo notebooks can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR).
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png"
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alt="drawing" width="600"/>
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alt="drawing" width="600"/>
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<small> Deformable DETR architecture. Taken from the <a href="https://arxiv.org/abs/2010.04159">original paper</a>.</small>
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This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/fundamentalvision/Deformable-DETR).
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## DeformableDetrImageProcessor
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[[autodoc]] DeformableDetrImageProcessor
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- preprocess
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- pad_and_create_pixel_mask
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- post_process_object_detection
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## DeformableDetrFeatureExtractor
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[[autodoc]] DeformableDetrFeatureExtractor
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@@ -55,4 +62,4 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
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## DeformableDetrForObjectDetection
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[[autodoc]] DeformableDetrForObjectDetection
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- forward
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- forward
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@@ -114,7 +114,7 @@ Tips:
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It is advised to use a batch size of 2 per GPU. See [this Github thread](https://github.com/facebookresearch/detr/issues/150) for more info.
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There are three ways to instantiate a DETR model (depending on what you prefer):
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Option 1: Instantiate DETR with pre-trained weights for entire model
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```py
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>>> from transformers import DetrForObjectDetection
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@@ -166,6 +166,15 @@ mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are i
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[[autodoc]] DetrConfig
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## DetrImageProcessor
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[[autodoc]] DetrImageProcessor
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- preprocess
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- post_process_object_detection
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- post_process_semantic_segmentation
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- post_process_instance_segmentation
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- post_process_panoptic_segmentation
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## DetrFeatureExtractor
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[[autodoc]] DetrFeatureExtractor
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@@ -57,6 +57,15 @@ This model was contributed by [francesco](https://huggingface.co/francesco). The
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[[autodoc]] MaskFormerConfig
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## MaskFormerImageProcessor
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[[autodoc]] MaskFormerImageProcessor
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- preprocess
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- encode_inputs
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- post_process_semantic_segmentation
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- post_process_instance_segmentation
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- post_process_panoptic_segmentation
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## MaskFormerFeatureExtractor
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[[autodoc]] MaskFormerFeatureExtractor
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@@ -22,7 +22,7 @@ The abstract from the paper is the following:
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## Usage
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OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection.
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OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection.
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[`OwlViTFeatureExtractor`] can be used to resize (or rescale) and normalize images for the model and [`CLIPTokenizer`] is used to encode the text. [`OwlViTProcessor`] wraps [`OwlViTFeatureExtractor`] and [`CLIPTokenizer`] into a single instance to both encode the text and prepare the images. The following example shows how to perform object detection using [`OwlViTProcessor`] and [`OwlViTForObjectDetection`].
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@@ -76,6 +76,13 @@ This model was contributed by [adirik](https://huggingface.co/adirik). The origi
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[[autodoc]] OwlViTVisionConfig
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## OwlViTImageProcessor
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[[autodoc]] OwlViTImageProcessor
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- preprocess
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- post_process
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- post_process_image_guided_detection
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## OwlViTFeatureExtractor
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[[autodoc]] OwlViTFeatureExtractor
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@@ -27,7 +27,7 @@ Tips:
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- Demo notebooks (regarding inference and fine-tuning on custom data) can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS).
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yolos_architecture.png"
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alt="drawing" width="600"/>
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alt="drawing" width="600"/>
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<small> YOLOS architecture. Taken from the <a href="https://arxiv.org/abs/2106.00666">original paper</a>.</small>
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@@ -37,6 +37,12 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
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[[autodoc]] YolosConfig
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## YolosImageProcessor
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[[autodoc]] YolosImageProcessor
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- preprocess
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- pad
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- post_process_object_detection
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## YolosFeatureExtractor
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@@ -55,4 +61,4 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
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## YolosForObjectDetection
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[[autodoc]] YolosForObjectDetection
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- forward
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- forward
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@@ -736,11 +736,15 @@ else:
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_import_structure["image_utils"] = ["ImageFeatureExtractionMixin"]
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_import_structure["models.beit"].extend(["BeitFeatureExtractor", "BeitImageProcessor"])
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_import_structure["models.clip"].extend(["CLIPFeatureExtractor", "CLIPImageProcessor"])
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_import_structure["models.conditional_detr"].append("ConditionalDetrFeatureExtractor")
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_import_structure["models.conditional_detr"].extend(
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["ConditionalDetrFeatureExtractor", "ConditionalDetrImageProcessor"]
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)
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_import_structure["models.convnext"].extend(["ConvNextFeatureExtractor", "ConvNextImageProcessor"])
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_import_structure["models.deformable_detr"].append("DeformableDetrFeatureExtractor")
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_import_structure["models.deformable_detr"].extend(
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["DeformableDetrFeatureExtractor", "DeformableDetrImageProcessor"]
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)
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_import_structure["models.deit"].extend(["DeiTFeatureExtractor", "DeiTImageProcessor"])
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_import_structure["models.detr"].append("DetrFeatureExtractor")
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_import_structure["models.detr"].extend(["DetrFeatureExtractor", "DetrImageProcessor"])
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_import_structure["models.donut"].extend(["DonutFeatureExtractor", "DonutImageProcessor"])
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_import_structure["models.dpt"].extend(["DPTFeatureExtractor", "DPTImageProcessor"])
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_import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaImageProcessor", "FlavaProcessor"])
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@@ -749,18 +753,18 @@ else:
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_import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"])
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_import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"])
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_import_structure["models.levit"].extend(["LevitFeatureExtractor", "LevitImageProcessor"])
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_import_structure["models.maskformer"].append("MaskFormerFeatureExtractor")
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_import_structure["models.maskformer"].extend(["MaskFormerFeatureExtractor", "MaskFormerImageProcessor"])
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_import_structure["models.mobilenet_v1"].extend(["MobileNetV1FeatureExtractor", "MobileNetV1ImageProcessor"])
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_import_structure["models.mobilenet_v2"].extend(["MobileNetV2FeatureExtractor", "MobileNetV2ImageProcessor"])
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_import_structure["models.mobilevit"].extend(["MobileViTFeatureExtractor", "MobileViTImageProcessor"])
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_import_structure["models.owlvit"].append("OwlViTFeatureExtractor")
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_import_structure["models.owlvit"].extend(["OwlViTFeatureExtractor", "OwlViTImageProcessor"])
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_import_structure["models.perceiver"].extend(["PerceiverFeatureExtractor", "PerceiverImageProcessor"])
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_import_structure["models.poolformer"].extend(["PoolFormerFeatureExtractor", "PoolFormerImageProcessor"])
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_import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"])
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_import_structure["models.videomae"].extend(["VideoMAEFeatureExtractor", "VideoMAEImageProcessor"])
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_import_structure["models.vilt"].extend(["ViltFeatureExtractor", "ViltImageProcessor", "ViltProcessor"])
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_import_structure["models.vit"].extend(["ViTFeatureExtractor", "ViTImageProcessor"])
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_import_structure["models.yolos"].extend(["YolosFeatureExtractor"])
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_import_structure["models.yolos"].extend(["YolosFeatureExtractor", "YolosImageProcessor"])
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# Timm-backed objects
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try:
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@@ -3869,11 +3873,11 @@ if TYPE_CHECKING:
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from .image_utils import ImageFeatureExtractionMixin
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from .models.beit import BeitFeatureExtractor, BeitImageProcessor
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from .models.clip import CLIPFeatureExtractor, CLIPImageProcessor
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from .models.conditional_detr import ConditionalDetrFeatureExtractor
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from .models.conditional_detr import ConditionalDetrFeatureExtractor, ConditionalDetrImageProcessor
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from .models.convnext import ConvNextFeatureExtractor, ConvNextImageProcessor
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from .models.deformable_detr import DeformableDetrFeatureExtractor
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from .models.deformable_detr import DeformableDetrFeatureExtractor, DeformableDetrImageProcessor
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from .models.deit import DeiTFeatureExtractor, DeiTImageProcessor
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from .models.detr import DetrFeatureExtractor
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from .models.detr import DetrFeatureExtractor, DetrImageProcessor
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from .models.donut import DonutFeatureExtractor, DonutImageProcessor
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from .models.dpt import DPTFeatureExtractor, DPTImageProcessor
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from .models.flava import FlavaFeatureExtractor, FlavaImageProcessor, FlavaProcessor
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@@ -3882,18 +3886,18 @@ if TYPE_CHECKING:
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from .models.layoutlmv2 import LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor
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from .models.layoutlmv3 import LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor
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from .models.levit import LevitFeatureExtractor, LevitImageProcessor
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from .models.maskformer import MaskFormerFeatureExtractor
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from .models.maskformer import MaskFormerFeatureExtractor, MaskFormerImageProcessor
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from .models.mobilenet_v1 import MobileNetV1FeatureExtractor, MobileNetV1ImageProcessor
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from .models.mobilenet_v2 import MobileNetV2FeatureExtractor, MobileNetV2ImageProcessor
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from .models.mobilevit import MobileViTFeatureExtractor, MobileViTImageProcessor
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from .models.owlvit import OwlViTFeatureExtractor
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from .models.owlvit import OwlViTFeatureExtractor, OwlViTImageProcessor
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from .models.perceiver import PerceiverFeatureExtractor, PerceiverImageProcessor
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from .models.poolformer import PoolFormerFeatureExtractor, PoolFormerImageProcessor
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from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor
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from .models.videomae import VideoMAEFeatureExtractor, VideoMAEImageProcessor
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from .models.vilt import ViltFeatureExtractor, ViltImageProcessor, ViltProcessor
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from .models.vit import ViTFeatureExtractor, ViTImageProcessor
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from .models.yolos import YolosFeatureExtractor
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from .models.yolos import YolosFeatureExtractor, YolosImageProcessor
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# Modeling
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try:
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@@ -14,7 +14,7 @@
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# limitations under the License.
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import os
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from typing import TYPE_CHECKING, List, Tuple, Union
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from typing import TYPE_CHECKING, Dict, Iterable, List, Tuple, Union
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import numpy as np
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from packaging import version
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@@ -163,6 +163,47 @@ def get_image_size(image: np.ndarray, channel_dim: ChannelDimension = None) -> T
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raise ValueError(f"Unsupported data format: {channel_dim}")
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def is_valid_annotation_coco_detection(annotation: Dict[str, Union[List, Tuple]]) -> bool:
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if (
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isinstance(annotation, dict)
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and "image_id" in annotation
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and "annotations" in annotation
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and isinstance(annotation["annotations"], (list, tuple))
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and (
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# an image can have no annotations
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len(annotation["annotations"]) == 0
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or isinstance(annotation["annotations"][0], dict)
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)
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):
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return True
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return False
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def is_valid_annotation_coco_panoptic(annotation: Dict[str, Union[List, Tuple]]) -> bool:
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if (
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isinstance(annotation, dict)
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and "image_id" in annotation
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and "segments_info" in annotation
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and "file_name" in annotation
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and isinstance(annotation["segments_info"], (list, tuple))
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and (
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# an image can have no segments
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len(annotation["segments_info"]) == 0
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or isinstance(annotation["segments_info"][0], dict)
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)
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):
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return True
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return False
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def valid_coco_detection_annotations(annotations: Iterable[Dict[str, Union[List, Tuple]]]) -> bool:
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return all(is_valid_annotation_coco_detection(ann) for ann in annotations)
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def valid_coco_panoptic_annotations(annotations: Iterable[Dict[str, Union[List, Tuple]]]) -> bool:
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return all(is_valid_annotation_coco_panoptic(ann) for ann in annotations)
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def load_image(image: Union[str, "PIL.Image.Image"]) -> "PIL.Image.Image":
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"""
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Loads `image` to a PIL Image.
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@@ -39,10 +39,14 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
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[
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("beit", "BeitImageProcessor"),
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("clip", "CLIPImageProcessor"),
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("clipseg", "ViTImageProcessor"),
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("conditional_detr", "ConditionalDetrImageProcessor"),
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("convnext", "ConvNextImageProcessor"),
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("cvt", "ConvNextImageProcessor"),
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("data2vec-vision", "BeitImageProcessor"),
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("deformable_detr", "DeformableDetrImageProcessor"),
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("deit", "DeiTImageProcessor"),
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("detr", "DetrImageProcessor"),
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("dinat", "ViTImageProcessor"),
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("donut-swin", "DonutImageProcessor"),
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("dpt", "DPTImageProcessor"),
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@@ -53,10 +57,14 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
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("layoutlmv2", "LayoutLMv2ImageProcessor"),
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("layoutlmv3", "LayoutLMv3ImageProcessor"),
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("levit", "LevitImageProcessor"),
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("maskformer", "MaskFormerImageProcessor"),
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("mobilenet_v1", "MobileNetV1ImageProcessor"),
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("mobilenet_v2", "MobileNetV2ImageProcessor"),
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("mobilenet_v2", "MobileNetV2ImageProcessor"),
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("mobilevit", "MobileViTImageProcessor"),
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("mobilevit", "MobileViTImageProcessor"),
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("nat", "ViTImageProcessor"),
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("owlvit", "OwlViTImageProcessor"),
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("perceiver", "PerceiverImageProcessor"),
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("poolformer", "PoolFormerImageProcessor"),
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("regnet", "ConvNextImageProcessor"),
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@@ -64,6 +72,7 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
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("segformer", "SegformerImageProcessor"),
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("swin", "ViTImageProcessor"),
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("swinv2", "ViTImageProcessor"),
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("table-transformer", "DetrImageProcessor"),
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("van", "ConvNextImageProcessor"),
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("videomae", "VideoMAEImageProcessor"),
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("vilt", "ViltImageProcessor"),
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@@ -71,6 +80,7 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
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("vit_mae", "ViTImageProcessor"),
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("vit_msn", "ViTImageProcessor"),
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("xclip", "CLIPImageProcessor"),
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("yolos", "YolosImageProcessor"),
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]
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)
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@@ -113,7 +123,7 @@ def get_image_processor_config(
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**kwargs,
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):
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"""
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Loads the image processor configuration from a pretrained model imag processor configuration. # FIXME
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Loads the image processor configuration from a pretrained model image processor configuration.
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Args:
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pretrained_model_name_or_path (`str` or `os.PathLike`):
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@@ -36,6 +36,7 @@ except OptionalDependencyNotAvailable:
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pass
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else:
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_import_structure["feature_extraction_conditional_detr"] = ["ConditionalDetrFeatureExtractor"]
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_import_structure["image_processing_conditional_detr"] = ["ConditionalDetrImageProcessor"]
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try:
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if not is_timm_available():
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@@ -66,6 +67,7 @@ if TYPE_CHECKING:
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pass
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else:
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from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
|
||||
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
|
||||
|
||||
try:
|
||||
if not is_timm_available():
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -32,6 +32,7 @@ except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["feature_extraction_deformable_detr"] = ["DeformableDetrFeatureExtractor"]
|
||||
_import_structure["image_processing_deformable_detr"] = ["DeformableDetrImageProcessor"]
|
||||
|
||||
try:
|
||||
if not is_timm_available():
|
||||
@@ -57,6 +58,7 @@ if TYPE_CHECKING:
|
||||
pass
|
||||
else:
|
||||
from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
|
||||
from .image_processing_deformable_detr import DeformableDetrImageProcessor
|
||||
|
||||
try:
|
||||
if not is_timm_available():
|
||||
|
||||
@@ -14,729 +14,10 @@
|
||||
# limitations under the License.
|
||||
"""Feature extractor class for Deformable DETR."""
|
||||
|
||||
import pathlib
|
||||
import warnings
|
||||
from typing import Dict, List, Optional, Tuple, Union
|
||||
from ...utils import logging
|
||||
from .image_processing_deformable_detr import DeformableDetrImageProcessor
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
|
||||
from ...image_transforms import center_to_corners_format, corners_to_center_format, rgb_to_id
|
||||
from ...image_utils import ImageFeatureExtractionMixin
|
||||
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
ImageInput = Union[Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]]
|
||||
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.masks_to_boxes
|
||||
def masks_to_boxes(masks):
|
||||
"""
|
||||
Compute the bounding boxes around the provided panoptic segmentation masks.
|
||||
|
||||
The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
|
||||
|
||||
Returns a [N, 4] tensor, with the boxes in corner (xyxy) format.
|
||||
"""
|
||||
if masks.size == 0:
|
||||
return np.zeros((0, 4))
|
||||
|
||||
h, w = masks.shape[-2:]
|
||||
|
||||
y = np.arange(0, h, dtype=np.float32)
|
||||
x = np.arange(0, w, dtype=np.float32)
|
||||
# see https://github.com/pytorch/pytorch/issues/50276
|
||||
y, x = np.meshgrid(y, x, indexing="ij")
|
||||
|
||||
x_mask = masks * np.expand_dims(x, axis=0)
|
||||
x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
|
||||
x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
|
||||
x_min = x.filled(fill_value=1e8)
|
||||
x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
|
||||
|
||||
y_mask = masks * np.expand_dims(y, axis=0)
|
||||
y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
|
||||
y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
|
||||
y_min = y.filled(fill_value=1e8)
|
||||
y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
|
||||
|
||||
return np.stack([x_min, y_min, x_max, y_max], 1)
|
||||
|
||||
|
||||
class DeformableDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
|
||||
r"""
|
||||
Constructs a Deformable DETR feature extractor. Differs only in the postprocessing of object detection compared to
|
||||
DETR.
|
||||
|
||||
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
|
||||
should refer to this superclass for more information regarding those methods.
|
||||
|
||||
Args:
|
||||
format (`str`, *optional*, defaults to `"coco_detection"`):
|
||||
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
|
||||
do_resize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to resize the input to a certain `size`.
|
||||
size (`int`, *optional*, defaults to 800):
|
||||
Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
|
||||
sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
|
||||
the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
|
||||
height / width, size)`.
|
||||
max_size (`int`, *optional*, defaults to 1333):
|
||||
The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
|
||||
set to `True`.
|
||||
do_normalize (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to normalize the input with mean and standard deviation.
|
||||
image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
|
||||
The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
|
||||
image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
|
||||
The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
|
||||
ImageNet std.
|
||||
"""
|
||||
|
||||
model_input_names = ["pixel_values", "pixel_mask"]
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.__init__
|
||||
def __init__(
|
||||
self,
|
||||
format="coco_detection",
|
||||
do_resize=True,
|
||||
size=800,
|
||||
max_size=1333,
|
||||
do_normalize=True,
|
||||
image_mean=None,
|
||||
image_std=None,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.format = self._is_valid_format(format)
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.max_size = max_size
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean if image_mean is not None else [0.485, 0.456, 0.406] # ImageNet mean
|
||||
self.image_std = image_std if image_std is not None else [0.229, 0.224, 0.225] # ImageNet std
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._is_valid_format
|
||||
def _is_valid_format(self, format):
|
||||
if format not in ["coco_detection", "coco_panoptic"]:
|
||||
raise ValueError(f"Format {format} not supported")
|
||||
return format
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare
|
||||
def prepare(self, image, target, return_segmentation_masks=False, masks_path=None):
|
||||
if self.format == "coco_detection":
|
||||
image, target = self.prepare_coco_detection(image, target, return_segmentation_masks)
|
||||
return image, target
|
||||
elif self.format == "coco_panoptic":
|
||||
image, target = self.prepare_coco_panoptic(image, target, masks_path)
|
||||
return image, target
|
||||
else:
|
||||
raise ValueError(f"Format {self.format} not supported")
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.convert_coco_poly_to_mask
|
||||
def convert_coco_poly_to_mask(self, segmentations, height, width):
|
||||
|
||||
try:
|
||||
from pycocotools import mask as coco_mask
|
||||
except ImportError:
|
||||
raise ImportError("Pycocotools is not installed in your environment.")
|
||||
|
||||
masks = []
|
||||
for polygons in segmentations:
|
||||
rles = coco_mask.frPyObjects(polygons, height, width)
|
||||
mask = coco_mask.decode(rles)
|
||||
if len(mask.shape) < 3:
|
||||
mask = mask[..., None]
|
||||
mask = np.asarray(mask, dtype=np.uint8)
|
||||
mask = np.any(mask, axis=2)
|
||||
masks.append(mask)
|
||||
if masks:
|
||||
masks = np.stack(masks, axis=0)
|
||||
else:
|
||||
masks = np.zeros((0, height, width), dtype=np.uint8)
|
||||
|
||||
return masks
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare_coco_detection
|
||||
def prepare_coco_detection(self, image, target, return_segmentation_masks=False):
|
||||
"""
|
||||
Convert the target in COCO format into the format expected by DETR.
|
||||
"""
|
||||
w, h = image.size
|
||||
|
||||
image_id = target["image_id"]
|
||||
image_id = np.asarray([image_id], dtype=np.int64)
|
||||
|
||||
# get all COCO annotations for the given image
|
||||
anno = target["annotations"]
|
||||
|
||||
anno = [obj for obj in anno if "iscrowd" not in obj or obj["iscrowd"] == 0]
|
||||
|
||||
boxes = [obj["bbox"] for obj in anno]
|
||||
# guard against no boxes via resizing
|
||||
boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
|
||||
boxes[:, 2:] += boxes[:, :2]
|
||||
boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=w)
|
||||
boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=h)
|
||||
|
||||
classes = [obj["category_id"] for obj in anno]
|
||||
classes = np.asarray(classes, dtype=np.int64)
|
||||
|
||||
if return_segmentation_masks:
|
||||
segmentations = [obj["segmentation"] for obj in anno]
|
||||
masks = self.convert_coco_poly_to_mask(segmentations, h, w)
|
||||
|
||||
keypoints = None
|
||||
if anno and "keypoints" in anno[0]:
|
||||
keypoints = [obj["keypoints"] for obj in anno]
|
||||
keypoints = np.asarray(keypoints, dtype=np.float32)
|
||||
num_keypoints = keypoints.shape[0]
|
||||
if num_keypoints:
|
||||
keypoints = keypoints.reshape((-1, 3))
|
||||
|
||||
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
|
||||
boxes = boxes[keep]
|
||||
classes = classes[keep]
|
||||
if return_segmentation_masks:
|
||||
masks = masks[keep]
|
||||
if keypoints is not None:
|
||||
keypoints = keypoints[keep]
|
||||
|
||||
target = {}
|
||||
target["boxes"] = boxes
|
||||
target["class_labels"] = classes
|
||||
if return_segmentation_masks:
|
||||
target["masks"] = masks
|
||||
target["image_id"] = image_id
|
||||
if keypoints is not None:
|
||||
target["keypoints"] = keypoints
|
||||
|
||||
# for conversion to coco api
|
||||
area = np.asarray([obj["area"] for obj in anno], dtype=np.float32)
|
||||
iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno], dtype=np.int64)
|
||||
target["area"] = area[keep]
|
||||
target["iscrowd"] = iscrowd[keep]
|
||||
|
||||
target["orig_size"] = np.asarray([int(h), int(w)], dtype=np.int64)
|
||||
target["size"] = np.asarray([int(h), int(w)], dtype=np.int64)
|
||||
|
||||
return image, target
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare_coco_panoptic
|
||||
def prepare_coco_panoptic(self, image, target, masks_path, return_masks=True):
|
||||
w, h = image.size
|
||||
ann_info = target.copy()
|
||||
ann_path = pathlib.Path(masks_path) / ann_info["file_name"]
|
||||
|
||||
if "segments_info" in ann_info:
|
||||
masks = np.asarray(Image.open(ann_path), dtype=np.uint32)
|
||||
masks = rgb_to_id(masks)
|
||||
|
||||
ids = np.array([ann["id"] for ann in ann_info["segments_info"]])
|
||||
masks = masks == ids[:, None, None]
|
||||
masks = np.asarray(masks, dtype=np.uint8)
|
||||
|
||||
labels = np.asarray([ann["category_id"] for ann in ann_info["segments_info"]], dtype=np.int64)
|
||||
|
||||
target = {}
|
||||
target["image_id"] = np.asarray(
|
||||
[ann_info["image_id"] if "image_id" in ann_info else ann_info["id"]], dtype=np.int64
|
||||
)
|
||||
if return_masks:
|
||||
target["masks"] = masks
|
||||
target["class_labels"] = labels
|
||||
|
||||
target["boxes"] = masks_to_boxes(masks)
|
||||
|
||||
target["size"] = np.asarray([int(h), int(w)], dtype=np.int64)
|
||||
target["orig_size"] = np.asarray([int(h), int(w)], dtype=np.int64)
|
||||
if "segments_info" in ann_info:
|
||||
target["iscrowd"] = np.asarray([ann["iscrowd"] for ann in ann_info["segments_info"]], dtype=np.int64)
|
||||
target["area"] = np.asarray([ann["area"] for ann in ann_info["segments_info"]], dtype=np.float32)
|
||||
|
||||
return image, target
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._resize
|
||||
def _resize(self, image, size, target=None, max_size=None):
|
||||
"""
|
||||
Resize the image to the given size. Size can be min_size (scalar) or (w, h) tuple. If size is an int, smaller
|
||||
edge of the image will be matched to this number.
|
||||
|
||||
If given, also resize the target accordingly.
|
||||
"""
|
||||
if not isinstance(image, Image.Image):
|
||||
image = self.to_pil_image(image)
|
||||
|
||||
def get_size_with_aspect_ratio(image_size, size, max_size=None):
|
||||
w, h = image_size
|
||||
if max_size is not None:
|
||||
min_original_size = float(min((w, h)))
|
||||
max_original_size = float(max((w, h)))
|
||||
if max_original_size / min_original_size * size > max_size:
|
||||
size = int(round(max_size * min_original_size / max_original_size))
|
||||
|
||||
if (w <= h and w == size) or (h <= w and h == size):
|
||||
return (h, w)
|
||||
|
||||
if w < h:
|
||||
ow = size
|
||||
oh = int(size * h / w)
|
||||
else:
|
||||
oh = size
|
||||
ow = int(size * w / h)
|
||||
|
||||
return (oh, ow)
|
||||
|
||||
def get_size(image_size, size, max_size=None):
|
||||
if isinstance(size, (list, tuple)):
|
||||
return size
|
||||
else:
|
||||
# size returned must be (w, h) since we use PIL to resize images
|
||||
# so we revert the tuple
|
||||
return get_size_with_aspect_ratio(image_size, size, max_size)[::-1]
|
||||
|
||||
size = get_size(image.size, size, max_size)
|
||||
rescaled_image = self.resize(image, size=size)
|
||||
|
||||
if target is None:
|
||||
return rescaled_image, None
|
||||
|
||||
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
|
||||
ratio_width, ratio_height = ratios
|
||||
|
||||
target = target.copy()
|
||||
if "boxes" in target:
|
||||
boxes = target["boxes"]
|
||||
scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
|
||||
target["boxes"] = scaled_boxes
|
||||
|
||||
if "area" in target:
|
||||
area = target["area"]
|
||||
scaled_area = area * (ratio_width * ratio_height)
|
||||
target["area"] = scaled_area
|
||||
|
||||
w, h = size
|
||||
target["size"] = np.asarray([h, w], dtype=np.int64)
|
||||
|
||||
if "masks" in target:
|
||||
# use PyTorch as current workaround
|
||||
# TODO replace by self.resize
|
||||
masks = torch.from_numpy(target["masks"][:, None]).float()
|
||||
interpolated_masks = nn.functional.interpolate(masks, size=(h, w), mode="nearest")[:, 0] > 0.5
|
||||
target["masks"] = interpolated_masks.numpy()
|
||||
|
||||
return rescaled_image, target
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._normalize
|
||||
def _normalize(self, image, mean, std, target=None):
|
||||
"""
|
||||
Normalize the image with a certain mean and std.
|
||||
|
||||
If given, also normalize the target bounding boxes based on the size of the image.
|
||||
"""
|
||||
|
||||
image = self.normalize(image, mean=mean, std=std)
|
||||
if target is None:
|
||||
return image, None
|
||||
|
||||
target = target.copy()
|
||||
h, w = image.shape[-2:]
|
||||
|
||||
if "boxes" in target:
|
||||
boxes = target["boxes"]
|
||||
boxes = corners_to_center_format(boxes)
|
||||
boxes = boxes / np.asarray([w, h, w, h], dtype=np.float32)
|
||||
target["boxes"] = boxes
|
||||
|
||||
return image, target
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.__call__
|
||||
def __call__(
|
||||
self,
|
||||
images: ImageInput,
|
||||
annotations: Union[List[Dict], List[List[Dict]]] = None,
|
||||
return_segmentation_masks: Optional[bool] = False,
|
||||
masks_path: Optional[pathlib.Path] = None,
|
||||
pad_and_return_pixel_mask: Optional[bool] = True,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
**kwargs,
|
||||
) -> BatchFeature:
|
||||
"""
|
||||
Main method to prepare for the model one or several image(s) and optional annotations. Images are by default
|
||||
padded up to the largest image in a batch, and a pixel mask is created that indicates which pixels are
|
||||
real/which are padding.
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
|
||||
PIL images.
|
||||
|
||||
</Tip>
|
||||
|
||||
Args:
|
||||
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
||||
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
||||
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
|
||||
number of channels, H and W are image height and width.
|
||||
|
||||
annotations (`Dict`, `List[Dict]`, *optional*):
|
||||
The corresponding annotations in COCO format.
|
||||
|
||||
In case [`DetrFeatureExtractor`] was initialized with `format = "coco_detection"`, the annotations for
|
||||
each image should have the following format: {'image_id': int, 'annotations': [annotation]}, with the
|
||||
annotations being a list of COCO object annotations.
|
||||
|
||||
In case [`DetrFeatureExtractor`] was initialized with `format = "coco_panoptic"`, the annotations for
|
||||
each image should have the following format: {'image_id': int, 'file_name': str, 'segments_info':
|
||||
[segment_info]} with segments_info being a list of COCO panoptic annotations.
|
||||
|
||||
return_segmentation_masks (`Dict`, `List[Dict]`, *optional*, defaults to `False`):
|
||||
Whether to also include instance segmentation masks as part of the labels in case `format =
|
||||
"coco_detection"`.
|
||||
|
||||
masks_path (`pathlib.Path`, *optional*):
|
||||
Path to the directory containing the PNG files that store the class-agnostic image segmentations. Only
|
||||
relevant in case [`DetrFeatureExtractor`] was initialized with `format = "coco_panoptic"`.
|
||||
|
||||
pad_and_return_pixel_mask (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to pad images up to the largest image in a batch and create a pixel mask.
|
||||
|
||||
If left to the default, will return a pixel mask that is:
|
||||
|
||||
- 1 for pixels that are real (i.e. **not masked**),
|
||||
- 0 for pixels that are padding (i.e. **masked**).
|
||||
|
||||
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
||||
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
|
||||
objects.
|
||||
|
||||
Returns:
|
||||
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
||||
|
||||
- **pixel_values** -- Pixel values to be fed to a model.
|
||||
- **pixel_mask** -- Pixel mask to be fed to a model (when `pad_and_return_pixel_mask=True` or if
|
||||
*"pixel_mask"* is in `self.model_input_names`).
|
||||
- **labels** -- Optional labels to be fed to a model (when `annotations` are provided)
|
||||
"""
|
||||
# Input type checking for clearer error
|
||||
|
||||
valid_images = False
|
||||
valid_annotations = False
|
||||
valid_masks_path = False
|
||||
|
||||
# Check that images has a valid type
|
||||
if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
|
||||
valid_images = True
|
||||
elif isinstance(images, (list, tuple)):
|
||||
if len(images) == 0 or isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
|
||||
valid_images = True
|
||||
|
||||
if not valid_images:
|
||||
raise ValueError(
|
||||
"Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
|
||||
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
|
||||
)
|
||||
|
||||
is_batched = bool(
|
||||
isinstance(images, (list, tuple))
|
||||
and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
|
||||
)
|
||||
|
||||
# Check that annotations has a valid type
|
||||
if annotations is not None:
|
||||
if not is_batched:
|
||||
if self.format == "coco_detection":
|
||||
if isinstance(annotations, dict) and "image_id" in annotations and "annotations" in annotations:
|
||||
if isinstance(annotations["annotations"], (list, tuple)):
|
||||
# an image can have no annotations
|
||||
if len(annotations["annotations"]) == 0 or isinstance(annotations["annotations"][0], dict):
|
||||
valid_annotations = True
|
||||
elif self.format == "coco_panoptic":
|
||||
if isinstance(annotations, dict) and "image_id" in annotations and "segments_info" in annotations:
|
||||
if isinstance(annotations["segments_info"], (list, tuple)):
|
||||
# an image can have no segments (?)
|
||||
if len(annotations["segments_info"]) == 0 or isinstance(
|
||||
annotations["segments_info"][0], dict
|
||||
):
|
||||
valid_annotations = True
|
||||
else:
|
||||
if isinstance(annotations, (list, tuple)):
|
||||
if len(images) != len(annotations):
|
||||
raise ValueError("There must be as many annotations as there are images")
|
||||
if isinstance(annotations[0], Dict):
|
||||
if self.format == "coco_detection":
|
||||
if isinstance(annotations[0]["annotations"], (list, tuple)):
|
||||
valid_annotations = True
|
||||
elif self.format == "coco_panoptic":
|
||||
if isinstance(annotations[0]["segments_info"], (list, tuple)):
|
||||
valid_annotations = True
|
||||
|
||||
if not valid_annotations:
|
||||
raise ValueError(
|
||||
"""
|
||||
Annotations must of type `Dict` (single image) or `List[Dict]` (batch of images). In case of object
|
||||
detection, each dictionary should contain the keys 'image_id' and 'annotations', with the latter
|
||||
being a list of annotations in COCO format. In case of panoptic segmentation, each dictionary
|
||||
should contain the keys 'file_name', 'image_id' and 'segments_info', with the latter being a list
|
||||
of annotations in COCO format.
|
||||
"""
|
||||
)
|
||||
|
||||
# Check that masks_path has a valid type
|
||||
if masks_path is not None:
|
||||
if self.format == "coco_panoptic":
|
||||
if isinstance(masks_path, pathlib.Path):
|
||||
valid_masks_path = True
|
||||
if not valid_masks_path:
|
||||
raise ValueError(
|
||||
"The path to the directory containing the mask PNG files should be provided as a"
|
||||
" `pathlib.Path` object."
|
||||
)
|
||||
|
||||
if not is_batched:
|
||||
images = [images]
|
||||
if annotations is not None:
|
||||
annotations = [annotations]
|
||||
|
||||
# Create a copy of the list to avoid editing it in place
|
||||
images = [image for image in images]
|
||||
|
||||
if annotations is not None:
|
||||
annotations = [annotation for annotation in annotations]
|
||||
|
||||
# prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
|
||||
if annotations is not None:
|
||||
for idx, (image, target) in enumerate(zip(images, annotations)):
|
||||
if not isinstance(image, Image.Image):
|
||||
image = self.to_pil_image(image)
|
||||
image, target = self.prepare(image, target, return_segmentation_masks, masks_path)
|
||||
images[idx] = image
|
||||
annotations[idx] = target
|
||||
|
||||
# transformations (resizing + normalization)
|
||||
if self.do_resize and self.size is not None:
|
||||
if annotations is not None:
|
||||
for idx, (image, target) in enumerate(zip(images, annotations)):
|
||||
image, target = self._resize(image=image, target=target, size=self.size, max_size=self.max_size)
|
||||
images[idx] = image
|
||||
annotations[idx] = target
|
||||
else:
|
||||
for idx, image in enumerate(images):
|
||||
images[idx] = self._resize(image=image, target=None, size=self.size, max_size=self.max_size)[0]
|
||||
|
||||
if self.do_normalize:
|
||||
if annotations is not None:
|
||||
for idx, (image, target) in enumerate(zip(images, annotations)):
|
||||
image, target = self._normalize(
|
||||
image=image, mean=self.image_mean, std=self.image_std, target=target
|
||||
)
|
||||
images[idx] = image
|
||||
annotations[idx] = target
|
||||
else:
|
||||
images = [
|
||||
self._normalize(image=image, mean=self.image_mean, std=self.image_std)[0] for image in images
|
||||
]
|
||||
else:
|
||||
images = [np.array(image) for image in images]
|
||||
|
||||
if pad_and_return_pixel_mask:
|
||||
# pad images up to largest image in batch and create pixel_mask
|
||||
max_size = self._max_by_axis([list(image.shape) for image in images])
|
||||
c, h, w = max_size
|
||||
padded_images = []
|
||||
pixel_mask = []
|
||||
for image in images:
|
||||
# create padded image
|
||||
padded_image = np.zeros((c, h, w), dtype=np.float32)
|
||||
padded_image[: image.shape[0], : image.shape[1], : image.shape[2]] = np.copy(image)
|
||||
padded_images.append(padded_image)
|
||||
# create pixel mask
|
||||
mask = np.zeros((h, w), dtype=np.int64)
|
||||
mask[: image.shape[1], : image.shape[2]] = True
|
||||
pixel_mask.append(mask)
|
||||
images = padded_images
|
||||
|
||||
# return as BatchFeature
|
||||
data = {}
|
||||
data["pixel_values"] = images
|
||||
if pad_and_return_pixel_mask:
|
||||
data["pixel_mask"] = pixel_mask
|
||||
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
|
||||
|
||||
if annotations is not None:
|
||||
# Convert to TensorType
|
||||
tensor_type = return_tensors
|
||||
if not isinstance(tensor_type, TensorType):
|
||||
tensor_type = TensorType(tensor_type)
|
||||
|
||||
if not tensor_type == TensorType.PYTORCH:
|
||||
raise ValueError("Only PyTorch is supported for the moment.")
|
||||
else:
|
||||
if not is_torch_available():
|
||||
raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
|
||||
|
||||
encoded_inputs["labels"] = [
|
||||
{k: torch.from_numpy(v) for k, v in target.items()} for target in annotations
|
||||
]
|
||||
|
||||
return encoded_inputs
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._max_by_axis
|
||||
def _max_by_axis(self, the_list):
|
||||
# type: (List[List[int]]) -> List[int]
|
||||
maxes = the_list[0]
|
||||
for sublist in the_list[1:]:
|
||||
for index, item in enumerate(sublist):
|
||||
maxes[index] = max(maxes[index], item)
|
||||
return maxes
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.pad_and_create_pixel_mask
|
||||
def pad_and_create_pixel_mask(
|
||||
self, pixel_values_list: List["torch.Tensor"], return_tensors: Optional[Union[str, TensorType]] = None
|
||||
):
|
||||
"""
|
||||
Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
|
||||
|
||||
Args:
|
||||
pixel_values_list (`List[torch.Tensor]`):
|
||||
List of images (pixel values) to be padded. Each image should be a tensor of shape (C, H, W).
|
||||
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
||||
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
|
||||
objects.
|
||||
|
||||
Returns:
|
||||
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
||||
|
||||
- **pixel_values** -- Pixel values to be fed to a model.
|
||||
- **pixel_mask** -- Pixel mask to be fed to a model (when `pad_and_return_pixel_mask=True` or if
|
||||
*"pixel_mask"* is in `self.model_input_names`).
|
||||
|
||||
"""
|
||||
|
||||
max_size = self._max_by_axis([list(image.shape) for image in pixel_values_list])
|
||||
c, h, w = max_size
|
||||
padded_images = []
|
||||
pixel_mask = []
|
||||
for image in pixel_values_list:
|
||||
# create padded image
|
||||
padded_image = np.zeros((c, h, w), dtype=np.float32)
|
||||
padded_image[: image.shape[0], : image.shape[1], : image.shape[2]] = np.copy(image)
|
||||
padded_images.append(padded_image)
|
||||
# create pixel mask
|
||||
mask = np.zeros((h, w), dtype=np.int64)
|
||||
mask[: image.shape[1], : image.shape[2]] = True
|
||||
pixel_mask.append(mask)
|
||||
|
||||
# return as BatchFeature
|
||||
data = {"pixel_values": padded_images, "pixel_mask": pixel_mask}
|
||||
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
|
||||
|
||||
return encoded_inputs
|
||||
|
||||
def post_process(self, outputs, target_sizes):
|
||||
"""
|
||||
Converts the output of [`DeformableDetrForObjectDetection`] into the format expected by the COCO api. Only
|
||||
supports PyTorch.
|
||||
|
||||
Args:
|
||||
outputs ([`DeformableDetrObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
|
||||
Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the
|
||||
original image size (before any data augmentation). For visualization, this should be the image size
|
||||
after data augment, but before padding.
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model.
|
||||
"""
|
||||
warnings.warn(
|
||||
"`post_process` is deprecated and will be removed in v5 of Transformers, please use"
|
||||
" `post_process_object_detection`.",
|
||||
FutureWarning,
|
||||
)
|
||||
|
||||
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
|
||||
|
||||
if len(out_logits) != len(target_sizes):
|
||||
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
|
||||
if target_sizes.shape[1] != 2:
|
||||
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
|
||||
|
||||
prob = out_logits.sigmoid()
|
||||
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
|
||||
scores = topk_values
|
||||
topk_boxes = topk_indexes // out_logits.shape[2]
|
||||
labels = topk_indexes % out_logits.shape[2]
|
||||
boxes = center_to_corners_format(out_bbox)
|
||||
boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
|
||||
|
||||
# and from relative [0, 1] to absolute [0, height] coordinates
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
|
||||
boxes = boxes * scale_fct[:, None, :]
|
||||
|
||||
results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
|
||||
|
||||
return results
|
||||
|
||||
def post_process_object_detection(
|
||||
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
|
||||
):
|
||||
"""
|
||||
Converts the output of [`DeformableDetrForObjectDetection`] into the format expected by the COCO api. Only
|
||||
supports PyTorch.
|
||||
|
||||
Args:
|
||||
outputs ([`DetrObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
threshold (`float`, *optional*):
|
||||
Score threshold to keep object detection predictions.
|
||||
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*, defaults to `None`):
|
||||
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
|
||||
(height, width) of each image in the batch. If left to None, predictions will not be resized.
|
||||
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model.
|
||||
"""
|
||||
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
|
||||
|
||||
if target_sizes is not None:
|
||||
if len(out_logits) != len(target_sizes):
|
||||
raise ValueError(
|
||||
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
|
||||
)
|
||||
|
||||
prob = out_logits.sigmoid()
|
||||
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
|
||||
scores = topk_values
|
||||
topk_boxes = topk_indexes // out_logits.shape[2]
|
||||
labels = topk_indexes % out_logits.shape[2]
|
||||
boxes = center_to_corners_format(out_bbox)
|
||||
boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
|
||||
|
||||
# and from relative [0, 1] to absolute [0, height] coordinates
|
||||
if isinstance(target_sizes, List):
|
||||
img_h = torch.Tensor([i[0] for i in target_sizes])
|
||||
img_w = torch.Tensor([i[1] for i in target_sizes])
|
||||
else:
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
|
||||
boxes = boxes * scale_fct[:, None, :]
|
||||
|
||||
results = []
|
||||
for s, l, b in zip(scores, labels, boxes):
|
||||
score = s[s > threshold]
|
||||
label = l[s > threshold]
|
||||
box = b[s > threshold]
|
||||
results.append({"scores": score, "labels": label, "boxes": box})
|
||||
|
||||
return results
|
||||
DeformableDetrFeatureExtractor = DeformableDetrImageProcessor
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -30,6 +30,7 @@ except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["feature_extraction_detr"] = ["DetrFeatureExtractor"]
|
||||
_import_structure["image_processing_detr"] = ["DetrImageProcessor"]
|
||||
|
||||
try:
|
||||
if not is_timm_available():
|
||||
@@ -56,6 +57,7 @@ if TYPE_CHECKING:
|
||||
pass
|
||||
else:
|
||||
from .feature_extraction_detr import DetrFeatureExtractor
|
||||
from .image_processing_detr import DetrImageProcessor
|
||||
|
||||
try:
|
||||
if not is_timm_available():
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
1766
src/transformers/models/detr/image_processing_detr.py
Normal file
1766
src/transformers/models/detr/image_processing_detr.py
Normal file
File diff suppressed because it is too large
Load Diff
@@ -1589,7 +1589,7 @@ class DetrForSegmentation(DetrPreTrainedModel):
|
||||
>>> import numpy
|
||||
|
||||
>>> from transformers import DetrFeatureExtractor, DetrForSegmentation
|
||||
>>> from transformers.models.detr.feature_extraction_detr import rgb_to_id
|
||||
>>> from transformers.image_transforms import rgb_to_id
|
||||
|
||||
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
||||
>>> image = Image.open(requests.get(url, stream=True).raw)
|
||||
@@ -2289,8 +2289,6 @@ def generalized_box_iou(boxes1, boxes2):
|
||||
|
||||
|
||||
# below: taken from https://github.com/facebookresearch/detr/blob/master/util/misc.py#L306
|
||||
|
||||
|
||||
def _max_by_axis(the_list):
|
||||
# type: (List[List[int]]) -> List[int]
|
||||
maxes = the_list[0]
|
||||
|
||||
@@ -32,6 +32,7 @@ except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["feature_extraction_maskformer"] = ["MaskFormerFeatureExtractor"]
|
||||
_import_structure["image_processing_maskformer"] = ["MaskFormerImageProcessor"]
|
||||
|
||||
|
||||
try:
|
||||
@@ -63,6 +64,7 @@ if TYPE_CHECKING:
|
||||
pass
|
||||
else:
|
||||
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
|
||||
from .image_processing_maskformer import MaskFormerImageProcessor
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
|
||||
@@ -14,923 +14,11 @@
|
||||
# limitations under the License.
|
||||
"""Feature extractor class for MaskFormer."""
|
||||
|
||||
from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, Union
|
||||
from transformers.utils import logging
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from .image_processing_maskformer import MaskFormerImageProcessor
|
||||
|
||||
from transformers.image_utils import PILImageResampling
|
||||
|
||||
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
|
||||
from ...image_utils import ImageFeatureExtractionMixin, ImageInput, is_torch_tensor
|
||||
from ...utils import TensorType, is_torch_available, logging
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
from torch import Tensor, nn
|
||||
from torch.nn.functional import interpolate
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from transformers.models.maskformer.modeling_maskformer import MaskFormerForInstanceSegmentationOutput
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.binary_mask_to_rle
|
||||
def binary_mask_to_rle(mask):
|
||||
"""
|
||||
Args:
|
||||
Converts given binary mask of shape (height, width) to the run-length encoding (RLE) format.
|
||||
mask (`torch.Tensor` or `numpy.array`):
|
||||
A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
|
||||
segment_id or class_id.
|
||||
Returns:
|
||||
`List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
|
||||
format.
|
||||
"""
|
||||
if is_torch_tensor(mask):
|
||||
mask = mask.numpy()
|
||||
|
||||
pixels = mask.flatten()
|
||||
pixels = np.concatenate([[0], pixels, [0]])
|
||||
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
|
||||
runs[1::2] -= runs[::2]
|
||||
return [x for x in runs]
|
||||
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.convert_segmentation_to_rle
|
||||
def convert_segmentation_to_rle(segmentation):
|
||||
"""
|
||||
Converts given segmentation map of shape (height, width) to the run-length encoding (RLE) format.
|
||||
|
||||
Args:
|
||||
segmentation (`torch.Tensor` or `numpy.array`):
|
||||
A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
|
||||
Returns:
|
||||
`List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
|
||||
"""
|
||||
segment_ids = torch.unique(segmentation)
|
||||
|
||||
run_length_encodings = []
|
||||
for idx in segment_ids:
|
||||
mask = torch.where(segmentation == idx, 1, 0)
|
||||
rle = binary_mask_to_rle(mask)
|
||||
run_length_encodings.append(rle)
|
||||
|
||||
return run_length_encodings
|
||||
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.remove_low_and_no_objects
|
||||
def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
|
||||
"""
|
||||
Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
|
||||
`labels`.
|
||||
|
||||
Args:
|
||||
masks (`torch.Tensor`):
|
||||
A tensor of shape `(num_queries, height, width)`.
|
||||
scores (`torch.Tensor`):
|
||||
A tensor of shape `(num_queries)`.
|
||||
labels (`torch.Tensor`):
|
||||
A tensor of shape `(num_queries)`.
|
||||
object_mask_threshold (`float`):
|
||||
A number between 0 and 1 used to binarize the masks.
|
||||
Raises:
|
||||
`ValueError`: Raised when the first dimension doesn't match in all input tensors.
|
||||
Returns:
|
||||
`Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
|
||||
< `object_mask_threshold`.
|
||||
"""
|
||||
if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
|
||||
raise ValueError("mask, scores and labels must have the same shape!")
|
||||
|
||||
to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
|
||||
|
||||
return masks[to_keep], scores[to_keep], labels[to_keep]
|
||||
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.check_segment_validity
|
||||
def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
|
||||
# Get the mask associated with the k class
|
||||
mask_k = mask_labels == k
|
||||
mask_k_area = mask_k.sum()
|
||||
|
||||
# Compute the area of all the stuff in query k
|
||||
original_area = (mask_probs[k] >= mask_threshold).sum()
|
||||
mask_exists = mask_k_area > 0 and original_area > 0
|
||||
|
||||
# Eliminate disconnected tiny segments
|
||||
if mask_exists:
|
||||
area_ratio = mask_k_area / original_area
|
||||
if not area_ratio.item() > overlap_mask_area_threshold:
|
||||
mask_exists = False
|
||||
|
||||
return mask_exists, mask_k
|
||||
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.compute_segments
|
||||
def compute_segments(
|
||||
mask_probs,
|
||||
pred_scores,
|
||||
pred_labels,
|
||||
mask_threshold: float = 0.5,
|
||||
overlap_mask_area_threshold: float = 0.8,
|
||||
label_ids_to_fuse: Optional[Set[int]] = None,
|
||||
target_size: Tuple[int, int] = None,
|
||||
):
|
||||
height = mask_probs.shape[1] if target_size is None else target_size[0]
|
||||
width = mask_probs.shape[2] if target_size is None else target_size[1]
|
||||
|
||||
segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
|
||||
segments: List[Dict] = []
|
||||
|
||||
if target_size is not None:
|
||||
mask_probs = nn.functional.interpolate(
|
||||
mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
|
||||
)[0]
|
||||
|
||||
current_segment_id = 0
|
||||
|
||||
# Weigh each mask by its prediction score
|
||||
mask_probs *= pred_scores.view(-1, 1, 1)
|
||||
mask_labels = mask_probs.argmax(0) # [height, width]
|
||||
|
||||
# Keep track of instances of each class
|
||||
stuff_memory_list: Dict[str, int] = {}
|
||||
for k in range(pred_labels.shape[0]):
|
||||
pred_class = pred_labels[k].item()
|
||||
should_fuse = pred_class in label_ids_to_fuse
|
||||
|
||||
# Check if mask exists and large enough to be a segment
|
||||
mask_exists, mask_k = check_segment_validity(
|
||||
mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
|
||||
)
|
||||
|
||||
if mask_exists:
|
||||
if pred_class in stuff_memory_list:
|
||||
current_segment_id = stuff_memory_list[pred_class]
|
||||
else:
|
||||
current_segment_id += 1
|
||||
|
||||
# Add current object segment to final segmentation map
|
||||
segmentation[mask_k] = current_segment_id
|
||||
segment_score = round(pred_scores[k].item(), 6)
|
||||
segments.append(
|
||||
{
|
||||
"id": current_segment_id,
|
||||
"label_id": pred_class,
|
||||
"was_fused": should_fuse,
|
||||
"score": segment_score,
|
||||
}
|
||||
)
|
||||
if should_fuse:
|
||||
stuff_memory_list[pred_class] = current_segment_id
|
||||
|
||||
return segmentation, segments
|
||||
|
||||
|
||||
class MaskFormerFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
|
||||
r"""
|
||||
Constructs a MaskFormer feature extractor. The feature extractor can be used to prepare image(s) and optional
|
||||
targets for the model.
|
||||
|
||||
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
|
||||
should refer to this superclass for more information regarding those methods.
|
||||
|
||||
Args:
|
||||
do_resize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to resize the input to a certain `size`.
|
||||
size (`int`, *optional*, defaults to 800):
|
||||
Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
|
||||
sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
|
||||
the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
|
||||
height / width, size)`.
|
||||
max_size (`int`, *optional*, defaults to 1333):
|
||||
The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
|
||||
set to `True`.
|
||||
resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`):
|
||||
An optional resampling filter. This can be one of `PILImageResampling.NEAREST`, `PILImageResampling.BOX`,
|
||||
`PILImageResampling.BILINEAR`, `PILImageResampling.HAMMING`, `PILImageResampling.BICUBIC` or
|
||||
`PILImageResampling.LANCZOS`. Only has an effect if `do_resize` is set to `True`.
|
||||
size_divisibility (`int`, *optional*, defaults to 32):
|
||||
Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
|
||||
Swin Transformer.
|
||||
do_normalize (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to normalize the input with mean and standard deviation.
|
||||
image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
|
||||
The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
|
||||
image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
|
||||
The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
|
||||
ImageNet std.
|
||||
ignore_index (`int`, *optional*):
|
||||
Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
|
||||
denoted with 0 (background) will be replaced with `ignore_index`. The ignore index of the loss function of
|
||||
the model should then correspond to this ignore index.
|
||||
reduce_labels (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
|
||||
is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
|
||||
The background label will be replaced by `ignore_index`.
|
||||
|
||||
"""
|
||||
|
||||
model_input_names = ["pixel_values", "pixel_mask"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
do_resize=True,
|
||||
size=800,
|
||||
max_size=1333,
|
||||
resample=PILImageResampling.BILINEAR,
|
||||
size_divisibility=32,
|
||||
do_normalize=True,
|
||||
image_mean=None,
|
||||
image_std=None,
|
||||
ignore_index=None,
|
||||
reduce_labels=False,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.max_size = max_size
|
||||
self.resample = resample
|
||||
self.size_divisibility = size_divisibility
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean if image_mean is not None else [0.485, 0.456, 0.406] # ImageNet mean
|
||||
self.image_std = image_std if image_std is not None else [0.229, 0.224, 0.225] # ImageNet std
|
||||
self.ignore_index = ignore_index
|
||||
self.reduce_labels = reduce_labels
|
||||
|
||||
def _resize_with_size_divisibility(self, image, size, target=None, max_size=None):
|
||||
"""
|
||||
Resize the image to the given size. Size can be min_size (scalar) or (width, height) tuple. If size is an int,
|
||||
smaller edge of the image will be matched to this number.
|
||||
|
||||
If given, also resize the target accordingly.
|
||||
"""
|
||||
if not isinstance(image, Image.Image):
|
||||
image = self.to_pil_image(image)
|
||||
|
||||
def get_size_with_aspect_ratio(image_size, size, max_size=None):
|
||||
width, height = image_size
|
||||
if max_size is not None:
|
||||
min_original_size = float(min((width, height)))
|
||||
max_original_size = float(max((width, height)))
|
||||
if max_original_size / min_original_size * size > max_size:
|
||||
size = int(round(max_size * min_original_size / max_original_size))
|
||||
|
||||
if (width <= height and width == size) or (height <= width and height == size):
|
||||
return (height, width)
|
||||
|
||||
if width < height:
|
||||
output_width = size
|
||||
output_height = int(size * height / width)
|
||||
else:
|
||||
output_height = size
|
||||
output_width = int(size * width / height)
|
||||
|
||||
return (output_height, output_width)
|
||||
|
||||
def get_size(image_size, size, max_size=None):
|
||||
if isinstance(size, (list, tuple)):
|
||||
return size
|
||||
else:
|
||||
# size returned must be (width, height) since we use PIL to resize images
|
||||
# so we revert the tuple
|
||||
return get_size_with_aspect_ratio(image_size, size, max_size)[::-1]
|
||||
|
||||
width, height = get_size(image.size, size, max_size)
|
||||
|
||||
if self.size_divisibility > 0:
|
||||
height = int(np.ceil(height / self.size_divisibility)) * self.size_divisibility
|
||||
width = int(np.ceil(width / self.size_divisibility)) * self.size_divisibility
|
||||
|
||||
size = (width, height)
|
||||
image = self.resize(image, size=size, resample=self.resample)
|
||||
|
||||
if target is not None:
|
||||
target = self.resize(target, size=size, resample=Image.NEAREST)
|
||||
|
||||
return image, target
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
images: ImageInput,
|
||||
segmentation_maps: ImageInput = None,
|
||||
pad_and_return_pixel_mask: Optional[bool] = True,
|
||||
instance_id_to_semantic_id: Optional[Union[List[Dict[int, int]], Dict[int, int]]] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
**kwargs,
|
||||
) -> BatchFeature:
|
||||
"""
|
||||
Main method to prepare for the model one or several image(s) and optional annotations. Images are by default
|
||||
padded up to the largest image in a batch, and a pixel mask is created that indicates which pixels are
|
||||
real/which are padding.
|
||||
|
||||
Segmentation maps can be instance, semantic or panoptic segmentation maps. In case of instance and panoptic
|
||||
segmentation, one needs to provide `instance_id_to_semantic_id`, which is a mapping from instance/segment ids
|
||||
to semantic category ids.
|
||||
|
||||
MaskFormer addresses all 3 forms of segmentation (instance, semantic and panoptic) in the same way, namely by
|
||||
converting the segmentation maps to a set of binary masks with corresponding classes.
|
||||
|
||||
In case of instance segmentation, the segmentation maps contain the instance ids, and
|
||||
`instance_id_to_semantic_id` maps instance IDs to their corresponding semantic category.
|
||||
|
||||
In case of semantic segmentation, the segmentation maps contain the semantic category ids. Let's see an
|
||||
example, assuming `segmentation_maps = [[2,6,7,9]]`, the output will contain `mask_labels =
|
||||
[[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]` (four binary masks) and `class_labels = [2,6,7,9]`, the labels for
|
||||
each mask.
|
||||
|
||||
In case of panoptic segmentation, the segmentation maps contain the segment ids, and
|
||||
`instance_id_to_semantic_id` maps segment IDs to their corresponding semantic category.
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
|
||||
PIL images.
|
||||
|
||||
</Tip>
|
||||
|
||||
Args:
|
||||
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
||||
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
||||
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
|
||||
number of channels, H and W are image height and width.
|
||||
|
||||
segmentation_maps (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*):
|
||||
The corresponding segmentation maps with the pixel-wise instance id, semantic id or segment id
|
||||
annotations. Assumed to be semantic segmentation maps if no `instance_id_to_semantic_id map` is
|
||||
provided.
|
||||
|
||||
pad_and_return_pixel_mask (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to pad images up to the largest image in a batch and create a pixel mask.
|
||||
|
||||
If left to the default, will return a pixel mask that is:
|
||||
|
||||
- 1 for pixels that are real (i.e. **not masked**),
|
||||
- 0 for pixels that are padding (i.e. **masked**).
|
||||
|
||||
instance_id_to_semantic_id (`List[Dict[int, int]]` or `Dict[int, int]`, *optional*):
|
||||
A mapping between instance/segment ids and semantic category ids. If passed, `segmentation_maps` is
|
||||
treated as an instance or panoptic segmentation map where each pixel represents an instance or segment
|
||||
id. Can be provided as a single dictionary with a global / dataset-level mapping or as a list of
|
||||
dictionaries (one per image), to map instance ids in each image separately. Note that this assumes a
|
||||
mapping before reduction of labels.
|
||||
|
||||
return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
|
||||
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
|
||||
objects.
|
||||
|
||||
Returns:
|
||||
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
||||
|
||||
- **pixel_values** -- Pixel values to be fed to a model.
|
||||
- **pixel_mask** -- Pixel mask to be fed to a model (when `pad_and_return_pixel_mask=True` or if
|
||||
`pixel_mask` is in `self.model_input_names`).
|
||||
- **mask_labels** -- Optional list of mask labels of shape `(num_class_labels, height, width)` to be fed to
|
||||
a model (when `annotations` are provided).
|
||||
- **class_labels** -- Optional list of class labels of shape `(num_class_labels)` to be fed to a model
|
||||
(when `annotations` are provided). They identify the labels of `mask_labels`, e.g. the label of
|
||||
`mask_labels[i][j]` if `class_labels[i][j]`.
|
||||
"""
|
||||
# Input type checking for clearer error
|
||||
|
||||
valid_images = False
|
||||
valid_segmentation_maps = False
|
||||
|
||||
# Check that images has a valid type
|
||||
if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
|
||||
valid_images = True
|
||||
elif isinstance(images, (list, tuple)):
|
||||
if len(images) == 0 or isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
|
||||
valid_images = True
|
||||
|
||||
if not valid_images:
|
||||
raise ValueError(
|
||||
"Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
|
||||
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
|
||||
)
|
||||
# Check that segmentation maps has a valid type
|
||||
if segmentation_maps is not None:
|
||||
if isinstance(segmentation_maps, (Image.Image, np.ndarray)) or is_torch_tensor(segmentation_maps):
|
||||
valid_segmentation_maps = True
|
||||
elif isinstance(segmentation_maps, (list, tuple)):
|
||||
if (
|
||||
len(segmentation_maps) == 0
|
||||
or isinstance(segmentation_maps[0], (Image.Image, np.ndarray))
|
||||
or is_torch_tensor(segmentation_maps[0])
|
||||
):
|
||||
valid_segmentation_maps = True
|
||||
|
||||
if not valid_segmentation_maps:
|
||||
raise ValueError(
|
||||
"Segmentation maps must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single"
|
||||
" example),`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of"
|
||||
" examples)."
|
||||
)
|
||||
|
||||
is_batched = bool(
|
||||
isinstance(images, (list, tuple))
|
||||
and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
|
||||
)
|
||||
|
||||
if not is_batched:
|
||||
images = [images]
|
||||
if segmentation_maps is not None:
|
||||
segmentation_maps = [segmentation_maps]
|
||||
|
||||
# transformations (resizing + normalization)
|
||||
if self.do_resize and self.size is not None:
|
||||
if segmentation_maps is not None:
|
||||
for idx, (image, target) in enumerate(zip(images, segmentation_maps)):
|
||||
image, target = self._resize_with_size_divisibility(
|
||||
image=image, target=target, size=self.size, max_size=self.max_size
|
||||
)
|
||||
images[idx] = image
|
||||
segmentation_maps[idx] = target
|
||||
else:
|
||||
for idx, image in enumerate(images):
|
||||
images[idx] = self._resize_with_size_divisibility(
|
||||
image=image, target=None, size=self.size, max_size=self.max_size
|
||||
)[0]
|
||||
|
||||
if self.do_normalize:
|
||||
images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images]
|
||||
# NOTE I will be always forced to pad them them since they have to be stacked in the batch dim
|
||||
encoded_inputs = self.encode_inputs(
|
||||
images,
|
||||
segmentation_maps,
|
||||
pad_and_return_pixel_mask,
|
||||
instance_id_to_semantic_id=instance_id_to_semantic_id,
|
||||
return_tensors=return_tensors,
|
||||
)
|
||||
|
||||
# Convert to TensorType
|
||||
tensor_type = return_tensors
|
||||
if not isinstance(tensor_type, TensorType):
|
||||
tensor_type = TensorType(tensor_type)
|
||||
|
||||
if not tensor_type == TensorType.PYTORCH:
|
||||
raise ValueError("Only PyTorch is supported for the moment.")
|
||||
else:
|
||||
if not is_torch_available():
|
||||
raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
|
||||
|
||||
return encoded_inputs
|
||||
|
||||
def _max_by_axis(self, the_list: List[List[int]]) -> List[int]:
|
||||
maxes = the_list[0]
|
||||
for sublist in the_list[1:]:
|
||||
for index, item in enumerate(sublist):
|
||||
maxes[index] = max(maxes[index], item)
|
||||
return maxes
|
||||
|
||||
def convert_segmentation_map_to_binary_masks(
|
||||
self,
|
||||
segmentation_map: "np.ndarray",
|
||||
instance_id_to_semantic_id: Optional[Dict[int, int]] = None,
|
||||
):
|
||||
# Reduce labels, if requested
|
||||
if self.reduce_labels:
|
||||
if self.ignore_index is None:
|
||||
raise ValueError("`ignore_index` must be set when `reduce_labels` is `True`.")
|
||||
segmentation_map[segmentation_map == 0] = self.ignore_index
|
||||
segmentation_map -= 1
|
||||
segmentation_map[segmentation_map == self.ignore_index - 1] = self.ignore_index
|
||||
|
||||
# Get unique ids (instance, class ids or segment ids based on input)
|
||||
all_labels = np.unique(segmentation_map)
|
||||
|
||||
# Remove ignored label
|
||||
if self.ignore_index is not None:
|
||||
all_labels = all_labels[all_labels != self.ignore_index]
|
||||
|
||||
# Generate a binary mask for each object instance
|
||||
binary_masks = [(segmentation_map == i) for i in all_labels]
|
||||
binary_masks = np.stack(binary_masks, axis=0) # (num_labels, height, width)
|
||||
|
||||
# Convert instance/segment ids to class ids
|
||||
if instance_id_to_semantic_id is not None:
|
||||
labels = np.zeros(all_labels.shape[0])
|
||||
|
||||
for label in all_labels:
|
||||
class_id = instance_id_to_semantic_id[label + 1 if self.reduce_labels else label]
|
||||
labels[all_labels == label] = class_id - 1 if self.reduce_labels else class_id
|
||||
else:
|
||||
labels = all_labels
|
||||
|
||||
return binary_masks.astype(np.float32), labels.astype(np.int64)
|
||||
|
||||
def encode_inputs(
|
||||
self,
|
||||
pixel_values_list: Union[List["np.ndarray"], List["torch.Tensor"]],
|
||||
segmentation_maps: ImageInput = None,
|
||||
pad_and_return_pixel_mask: bool = True,
|
||||
instance_id_to_semantic_id: Optional[Union[List[Dict[int, int]], Dict[int, int]]] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
):
|
||||
"""
|
||||
Encode a list of pixel values and an optional list of corresponding segmentation maps.
|
||||
|
||||
This method is useful if you have resized and normalized your images and segmentation maps yourself, using a
|
||||
library like [torchvision](https://pytorch.org/vision/stable/transforms.html) or
|
||||
[albumentations](https://albumentations.ai/).
|
||||
|
||||
Images are padded up to the largest image in a batch, and a corresponding `pixel_mask` is created.
|
||||
|
||||
Segmentation maps can be instance, semantic or panoptic segmentation maps. In case of instance and panoptic
|
||||
segmentation, one needs to provide `instance_id_to_semantic_id`, which is a mapping from instance/segment ids
|
||||
to semantic category ids.
|
||||
|
||||
MaskFormer addresses all 3 forms of segmentation (instance, semantic and panoptic) in the same way, namely by
|
||||
converting the segmentation maps to a set of binary masks with corresponding classes.
|
||||
|
||||
In case of instance segmentation, the segmentation maps contain the instance ids, and
|
||||
`instance_id_to_semantic_id` maps instance IDs to their corresponding semantic category.
|
||||
|
||||
In case of semantic segmentation, the segmentation maps contain the semantic category ids. Let's see an
|
||||
example, assuming `segmentation_maps = [[2,6,7,9]]`, the output will contain `mask_labels =
|
||||
[[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]` (four binary masks) and `class_labels = [2,6,7,9]`, the labels for
|
||||
each mask.
|
||||
|
||||
In case of panoptic segmentation, the segmentation maps contain the segment ids, and
|
||||
`instance_id_to_semantic_id` maps segment IDs to their corresponding semantic category.
|
||||
|
||||
Args:
|
||||
pixel_values_list (`List[np.ndarray]` or `List[torch.Tensor]`):
|
||||
List of images (pixel values) to be padded. Each image should be a tensor of shape `(channels, height,
|
||||
width)`.
|
||||
|
||||
segmentation_maps (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*):
|
||||
The corresponding segmentation maps with the pixel-wise instance id, semantic id or segment id
|
||||
annotations. Assumed to be semantic segmentation maps if no `instance_id_to_semantic_id map` is
|
||||
provided.
|
||||
|
||||
pad_and_return_pixel_mask (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to pad images up to the largest image in a batch and create a pixel mask.
|
||||
|
||||
If left to the default, will return a pixel mask that is:
|
||||
|
||||
- 1 for pixels that are real (i.e. **not masked**),
|
||||
- 0 for pixels that are padding (i.e. **masked**).
|
||||
|
||||
instance_id_to_semantic_id (`List[Dict[int, int]]` or `Dict[int, int]`, *optional*):
|
||||
A mapping between instance/segment ids and semantic category ids. If passed, `segmentation_maps` is
|
||||
treated as an instance or panoptic segmentation map where each pixel represents an instance or segment
|
||||
id. Can be provided as a single dictionary with a global / dataset-level mapping or as a list of
|
||||
dictionaries (one per image), to map instance ids in each image separately. Note that this assumes a
|
||||
mapping before reduction of labels.
|
||||
|
||||
return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
|
||||
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
|
||||
objects.
|
||||
|
||||
Returns:
|
||||
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
||||
|
||||
- **pixel_values** -- Pixel values to be fed to a model.
|
||||
- **pixel_mask** -- Pixel mask to be fed to a model (when `pad_and_return_pixel_mask=True` or if
|
||||
`pixel_mask` is in `self.model_input_names`).
|
||||
- **mask_labels** -- Optional list of mask labels of shape `(labels, height, width)` to be fed to a model
|
||||
(when `annotations` are provided).
|
||||
- **class_labels** -- Optional list of class labels of shape `(labels)` to be fed to a model (when
|
||||
`annotations` are provided). They identify the labels of `mask_labels`, e.g. the label of
|
||||
`mask_labels[i][j]` if `class_labels[i][j]`.
|
||||
"""
|
||||
|
||||
max_size = self._max_by_axis([list(image.shape) for image in pixel_values_list])
|
||||
|
||||
annotations = None
|
||||
if segmentation_maps is not None:
|
||||
segmentation_maps = map(np.array, segmentation_maps)
|
||||
converted_segmentation_maps = []
|
||||
|
||||
for i, segmentation_map in enumerate(segmentation_maps):
|
||||
# Use instance2class_id mapping per image
|
||||
if isinstance(instance_id_to_semantic_id, List):
|
||||
converted_segmentation_map = self.convert_segmentation_map_to_binary_masks(
|
||||
segmentation_map, instance_id_to_semantic_id[i]
|
||||
)
|
||||
else:
|
||||
converted_segmentation_map = self.convert_segmentation_map_to_binary_masks(
|
||||
segmentation_map, instance_id_to_semantic_id
|
||||
)
|
||||
converted_segmentation_maps.append(converted_segmentation_map)
|
||||
|
||||
annotations = []
|
||||
for mask, classes in converted_segmentation_maps:
|
||||
annotations.append({"masks": mask, "classes": classes})
|
||||
|
||||
channels, height, width = max_size
|
||||
pixel_values = []
|
||||
pixel_mask = []
|
||||
mask_labels = []
|
||||
class_labels = []
|
||||
for idx, image in enumerate(pixel_values_list):
|
||||
# create padded image
|
||||
padded_image = np.zeros((channels, height, width), dtype=np.float32)
|
||||
padded_image[: image.shape[0], : image.shape[1], : image.shape[2]] = np.copy(image)
|
||||
image = padded_image
|
||||
pixel_values.append(image)
|
||||
# if we have a target, pad it
|
||||
if annotations:
|
||||
annotation = annotations[idx]
|
||||
masks = annotation["masks"]
|
||||
# pad mask with `ignore_index`
|
||||
masks = np.pad(
|
||||
masks,
|
||||
((0, 0), (0, height - masks.shape[1]), (0, width - masks.shape[2])),
|
||||
constant_values=self.ignore_index,
|
||||
)
|
||||
annotation["masks"] = masks
|
||||
# create pixel mask
|
||||
mask = np.zeros((height, width), dtype=np.int64)
|
||||
mask[: image.shape[1], : image.shape[2]] = True
|
||||
pixel_mask.append(mask)
|
||||
|
||||
# return as BatchFeature
|
||||
data = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
|
||||
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
|
||||
# we cannot batch them since they don't share a common class size
|
||||
if annotations:
|
||||
for label in annotations:
|
||||
mask_labels.append(torch.from_numpy(label["masks"]))
|
||||
class_labels.append(torch.from_numpy(label["classes"]))
|
||||
|
||||
encoded_inputs["mask_labels"] = mask_labels
|
||||
encoded_inputs["class_labels"] = class_labels
|
||||
|
||||
return encoded_inputs
|
||||
|
||||
def post_process_segmentation(
|
||||
self, outputs: "MaskFormerForInstanceSegmentationOutput", target_size: Tuple[int, int] = None
|
||||
) -> "torch.Tensor":
|
||||
"""
|
||||
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image segmentation predictions. Only
|
||||
supports PyTorch.
|
||||
|
||||
Args:
|
||||
outputs ([`MaskFormerForInstanceSegmentationOutput`]):
|
||||
The outputs from [`MaskFormerForInstanceSegmentation`].
|
||||
|
||||
target_size (`Tuple[int, int]`, *optional*):
|
||||
If set, the `masks_queries_logits` will be resized to `target_size`.
|
||||
|
||||
Returns:
|
||||
`torch.Tensor`:
|
||||
A tensor of shape (`batch_size, num_class_labels, height, width`).
|
||||
"""
|
||||
logger.warning(
|
||||
"`post_process_segmentation` is deprecated and will be removed in v5 of Transformers, please use"
|
||||
" `post_process_instance_segmentation`",
|
||||
FutureWarning,
|
||||
)
|
||||
|
||||
# class_queries_logits has shape [BATCH, QUERIES, CLASSES + 1]
|
||||
class_queries_logits = outputs.class_queries_logits
|
||||
# masks_queries_logits has shape [BATCH, QUERIES, HEIGHT, WIDTH]
|
||||
masks_queries_logits = outputs.masks_queries_logits
|
||||
if target_size is not None:
|
||||
masks_queries_logits = interpolate(
|
||||
masks_queries_logits,
|
||||
size=target_size,
|
||||
mode="bilinear",
|
||||
align_corners=False,
|
||||
)
|
||||
# remove the null class `[..., :-1]`
|
||||
masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
|
||||
# mask probs has shape [BATCH, QUERIES, HEIGHT, WIDTH]
|
||||
masks_probs = masks_queries_logits.sigmoid()
|
||||
# now we want to sum over the queries,
|
||||
# $ out_{c,h,w} = \sum_q p_{q,c} * m_{q,h,w} $
|
||||
# where $ softmax(p) \in R^{q, c} $ is the mask classes
|
||||
# and $ sigmoid(m) \in R^{q, h, w}$ is the mask probabilities
|
||||
# b(atch)q(uery)c(lasses), b(atch)q(uery)h(eight)w(idth)
|
||||
segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
|
||||
|
||||
return segmentation
|
||||
|
||||
def post_process_semantic_segmentation(
|
||||
self, outputs, target_sizes: Optional[List[Tuple[int, int]]] = None
|
||||
) -> "torch.Tensor":
|
||||
"""
|
||||
Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports
|
||||
PyTorch.
|
||||
|
||||
Args:
|
||||
outputs ([`MaskFormerForInstanceSegmentation`]):
|
||||
Raw outputs of the model.
|
||||
target_sizes (`List[Tuple[int, int]]`, *optional*, defaults to `None`):
|
||||
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
|
||||
final size (height, width) of each prediction. If left to None, predictions will not be resized.
|
||||
Returns:
|
||||
`List[torch.Tensor]`:
|
||||
A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
|
||||
corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
|
||||
`torch.Tensor` correspond to a semantic class id.
|
||||
"""
|
||||
class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
|
||||
masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
|
||||
|
||||
# Remove the null class `[..., :-1]`
|
||||
masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
|
||||
masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
|
||||
|
||||
# Semantic segmentation logits of shape (batch_size, num_classes, height, width)
|
||||
segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
|
||||
batch_size = class_queries_logits.shape[0]
|
||||
|
||||
# Resize logits and compute semantic segmentation maps
|
||||
if target_sizes is not None:
|
||||
if batch_size != len(target_sizes):
|
||||
raise ValueError(
|
||||
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
|
||||
)
|
||||
|
||||
semantic_segmentation = []
|
||||
for idx in range(batch_size):
|
||||
resized_logits = torch.nn.functional.interpolate(
|
||||
segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
|
||||
)
|
||||
semantic_map = resized_logits[0].argmax(dim=0)
|
||||
semantic_segmentation.append(semantic_map)
|
||||
else:
|
||||
semantic_segmentation = segmentation.argmax(dim=1)
|
||||
semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
|
||||
|
||||
return semantic_segmentation
|
||||
|
||||
def post_process_instance_segmentation(
|
||||
self,
|
||||
outputs,
|
||||
threshold: float = 0.5,
|
||||
mask_threshold: float = 0.5,
|
||||
overlap_mask_area_threshold: float = 0.8,
|
||||
target_sizes: Optional[List[Tuple[int, int]]] = None,
|
||||
return_coco_annotation: Optional[bool] = False,
|
||||
) -> List[Dict]:
|
||||
"""
|
||||
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into instance segmentation predictions. Only
|
||||
supports PyTorch.
|
||||
|
||||
Args:
|
||||
outputs ([`MaskFormerForInstanceSegmentation`]):
|
||||
Raw outputs of the model.
|
||||
threshold (`float`, *optional*, defaults to 0.5):
|
||||
The probability score threshold to keep predicted instance masks.
|
||||
mask_threshold (`float`, *optional*, defaults to 0.5):
|
||||
Threshold to use when turning the predicted masks into binary values.
|
||||
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
|
||||
The overlap mask area threshold to merge or discard small disconnected parts within each binary
|
||||
instance mask.
|
||||
target_sizes (`List[Tuple]`, *optional*):
|
||||
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
|
||||
final size (height, width) of each prediction. If left to None, predictions will not be resized.
|
||||
return_coco_annotation (`bool`, *optional*):
|
||||
Defaults to `False`. If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE)
|
||||
format.
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
|
||||
- **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id` or
|
||||
`List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
|
||||
`True`. Set to `None` if no mask if found above `threshold`.
|
||||
- **segments_info** -- A dictionary that contains additional information on each segment.
|
||||
- **id** -- An integer representing the `segment_id`.
|
||||
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
|
||||
- **score** -- Prediction score of segment with `segment_id`.
|
||||
"""
|
||||
class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
|
||||
masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
|
||||
|
||||
batch_size = class_queries_logits.shape[0]
|
||||
num_labels = class_queries_logits.shape[-1] - 1
|
||||
|
||||
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
|
||||
|
||||
# Predicted label and score of each query (batch_size, num_queries)
|
||||
pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
|
||||
|
||||
# Loop over items in batch size
|
||||
results: List[Dict[str, Tensor]] = []
|
||||
|
||||
for i in range(batch_size):
|
||||
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
|
||||
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
|
||||
)
|
||||
|
||||
# No mask found
|
||||
if mask_probs_item.shape[0] <= 0:
|
||||
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
|
||||
segmentation = torch.zeros((height, width)) - 1
|
||||
results.append({"segmentation": segmentation, "segments_info": []})
|
||||
continue
|
||||
|
||||
# Get segmentation map and segment information of batch item
|
||||
target_size = target_sizes[i] if target_sizes is not None else None
|
||||
segmentation, segments = compute_segments(
|
||||
mask_probs_item,
|
||||
pred_scores_item,
|
||||
pred_labels_item,
|
||||
mask_threshold,
|
||||
overlap_mask_area_threshold,
|
||||
target_size,
|
||||
)
|
||||
|
||||
# Return segmentation map in run-length encoding (RLE) format
|
||||
if return_coco_annotation:
|
||||
segmentation = convert_segmentation_to_rle(segmentation)
|
||||
|
||||
results.append({"segmentation": segmentation, "segments_info": segments})
|
||||
return results
|
||||
|
||||
def post_process_panoptic_segmentation(
|
||||
self,
|
||||
outputs,
|
||||
threshold: float = 0.5,
|
||||
mask_threshold: float = 0.5,
|
||||
overlap_mask_area_threshold: float = 0.8,
|
||||
label_ids_to_fuse: Optional[Set[int]] = None,
|
||||
target_sizes: Optional[List[Tuple[int, int]]] = None,
|
||||
) -> List[Dict]:
|
||||
"""
|
||||
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image panoptic segmentation
|
||||
predictions. Only supports PyTorch.
|
||||
|
||||
Args:
|
||||
outputs ([`MaskFormerForInstanceSegmentationOutput`]):
|
||||
The outputs from [`MaskFormerForInstanceSegmentation`].
|
||||
threshold (`float`, *optional*, defaults to 0.5):
|
||||
The probability score threshold to keep predicted instance masks.
|
||||
mask_threshold (`float`, *optional*, defaults to 0.5):
|
||||
Threshold to use when turning the predicted masks into binary values.
|
||||
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
|
||||
The overlap mask area threshold to merge or discard small disconnected parts within each binary
|
||||
instance mask.
|
||||
label_ids_to_fuse (`Set[int]`, *optional*):
|
||||
The labels in this state will have all their instances be fused together. For instance we could say
|
||||
there can only be one sky in an image, but several persons, so the label ID for sky would be in that
|
||||
set, but not the one for person.
|
||||
target_sizes (`List[Tuple]`, *optional*):
|
||||
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
|
||||
final size (height, width) of each prediction in batch. If left to None, predictions will not be
|
||||
resized.
|
||||
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
|
||||
- **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
|
||||
to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
|
||||
to the corresponding `target_sizes` entry.
|
||||
- **segments_info** -- A dictionary that contains additional information on each segment.
|
||||
- **id** -- an integer representing the `segment_id`.
|
||||
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
|
||||
- **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
|
||||
Multiple instances of the same class / label were fused and assigned a single `segment_id`.
|
||||
- **score** -- Prediction score of segment with `segment_id`.
|
||||
"""
|
||||
|
||||
if label_ids_to_fuse is None:
|
||||
logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
|
||||
label_ids_to_fuse = set()
|
||||
|
||||
class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
|
||||
masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
|
||||
|
||||
batch_size = class_queries_logits.shape[0]
|
||||
num_labels = class_queries_logits.shape[-1] - 1
|
||||
|
||||
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
|
||||
|
||||
# Predicted label and score of each query (batch_size, num_queries)
|
||||
pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
|
||||
|
||||
# Loop over items in batch size
|
||||
results: List[Dict[str, Tensor]] = []
|
||||
|
||||
for i in range(batch_size):
|
||||
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
|
||||
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
|
||||
)
|
||||
|
||||
# No mask found
|
||||
if mask_probs_item.shape[0] <= 0:
|
||||
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
|
||||
segmentation = torch.zeros((height, width)) - 1
|
||||
results.append({"segmentation": segmentation, "segments_info": []})
|
||||
continue
|
||||
|
||||
# Get segmentation map and segment information of batch item
|
||||
target_size = target_sizes[i] if target_sizes is not None else None
|
||||
segmentation, segments = compute_segments(
|
||||
mask_probs_item,
|
||||
pred_scores_item,
|
||||
pred_labels_item,
|
||||
mask_threshold,
|
||||
overlap_mask_area_threshold,
|
||||
label_ids_to_fuse,
|
||||
target_size,
|
||||
)
|
||||
|
||||
results.append({"segmentation": segmentation, "segments_info": segments})
|
||||
return results
|
||||
MaskFormerFeatureExtractor = MaskFormerImageProcessor
|
||||
|
||||
1143
src/transformers/models/maskformer/image_processing_maskformer.py
Normal file
1143
src/transformers/models/maskformer/image_processing_maskformer.py
Normal file
File diff suppressed because it is too large
Load Diff
@@ -47,6 +47,7 @@ except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["feature_extraction_owlvit"] = ["OwlViTFeatureExtractor"]
|
||||
_import_structure["image_processing_owlvit"] = ["OwlViTImageProcessor"]
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
@@ -80,6 +81,7 @@ if TYPE_CHECKING:
|
||||
pass
|
||||
else:
|
||||
from .feature_extraction_owlvit import OwlViTFeatureExtractor
|
||||
from .image_processing_owlvit import OwlViTImageProcessor
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
|
||||
@@ -14,317 +14,11 @@
|
||||
# limitations under the License.
|
||||
"""Feature extractor class for OwlViT."""
|
||||
|
||||
from typing import List, Optional, Union
|
||||
from ...utils import logging
|
||||
from .image_processing_owlvit import OwlViTImageProcessor
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
from transformers.image_utils import PILImageResampling
|
||||
|
||||
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
|
||||
from ...image_transforms import center_to_corners_format
|
||||
from ...image_utils import ImageFeatureExtractionMixin
|
||||
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
# Copied from transformers.models.detr.modeling_detr._upcast
|
||||
def _upcast(t):
|
||||
# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
|
||||
if t.is_floating_point():
|
||||
return t if t.dtype in (torch.float32, torch.float64) else t.float()
|
||||
else:
|
||||
return t if t.dtype in (torch.int32, torch.int64) else t.int()
|
||||
|
||||
|
||||
def box_area(boxes):
|
||||
"""
|
||||
Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
|
||||
|
||||
Args:
|
||||
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
|
||||
Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
|
||||
< x2` and `0 <= y1 < y2`.
|
||||
|
||||
Returns:
|
||||
`torch.FloatTensor`: a tensor containing the area for each box.
|
||||
"""
|
||||
boxes = _upcast(boxes)
|
||||
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
|
||||
|
||||
|
||||
def box_iou(boxes1, boxes2):
|
||||
area1 = box_area(boxes1)
|
||||
area2 = box_area(boxes2)
|
||||
|
||||
left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
|
||||
right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
|
||||
|
||||
width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2]
|
||||
inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M]
|
||||
|
||||
union = area1[:, None] + area2 - inter
|
||||
|
||||
iou = inter / union
|
||||
return iou, union
|
||||
|
||||
|
||||
class OwlViTFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
|
||||
r"""
|
||||
Constructs an OWL-ViT feature extractor.
|
||||
|
||||
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
|
||||
should refer to this superclass for more information regarding those methods.
|
||||
|
||||
Args:
|
||||
do_resize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to resize the shorter edge of the input to a certain `size`.
|
||||
size (`int` or `Tuple[int, int]`, *optional*, defaults to (768, 768)):
|
||||
The size to use for resizing the image. Only has an effect if `do_resize` is set to `True`. If `size` is a
|
||||
sequence like (h, w), output size will be matched to this. If `size` is an int, then image will be resized
|
||||
to (size, size).
|
||||
resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BICUBIC`):
|
||||
An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
|
||||
`PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
|
||||
`PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
|
||||
to `True`.
|
||||
do_center_crop (`bool`, *optional*, defaults to `False`):
|
||||
Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the
|
||||
image is padded with 0's and then center cropped.
|
||||
crop_size (`int`, *optional*, defaults to 768):
|
||||
do_normalize (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to normalize the input with `image_mean` and `image_std`. Desired output size when applying
|
||||
center-cropping. Only has an effect if `do_center_crop` is set to `True`.
|
||||
image_mean (`List[int]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
|
||||
The sequence of means for each channel, to be used when normalizing images.
|
||||
image_std (`List[int]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
|
||||
The sequence of standard deviations for each channel, to be used when normalizing images.
|
||||
"""
|
||||
|
||||
model_input_names = ["pixel_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
do_resize=True,
|
||||
size=(768, 768),
|
||||
resample=PILImageResampling.BICUBIC,
|
||||
crop_size=768,
|
||||
do_center_crop=False,
|
||||
do_normalize=True,
|
||||
image_mean=None,
|
||||
image_std=None,
|
||||
**kwargs
|
||||
):
|
||||
# Early versions of the OWL-ViT config on the hub had "rescale" as a flag. This clashes with the
|
||||
# vision feature extractor method `rescale` as it would be set as an attribute during the super().__init__
|
||||
# call. This is for backwards compatibility.
|
||||
if "rescale" in kwargs:
|
||||
rescale_val = kwargs.pop("rescale")
|
||||
kwargs["do_rescale"] = rescale_val
|
||||
|
||||
super().__init__(**kwargs)
|
||||
self.size = size
|
||||
self.resample = resample
|
||||
self.crop_size = crop_size
|
||||
self.do_resize = do_resize
|
||||
self.do_center_crop = do_center_crop
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean if image_mean is not None else [0.48145466, 0.4578275, 0.40821073]
|
||||
self.image_std = image_std if image_std is not None else [0.26862954, 0.26130258, 0.27577711]
|
||||
|
||||
def post_process(self, outputs, target_sizes):
|
||||
"""
|
||||
Converts the output of [`OwlViTForObjectDetection`] into the format expected by the COCO api.
|
||||
|
||||
Args:
|
||||
outputs ([`OwlViTObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
target_sizes (`torch.Tensor`, *optional*):
|
||||
Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in
|
||||
the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to
|
||||
None, predictions will not be unnormalized.
|
||||
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model.
|
||||
"""
|
||||
logits, boxes = outputs.logits, outputs.pred_boxes
|
||||
|
||||
if len(logits) != len(target_sizes):
|
||||
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
|
||||
if target_sizes.shape[1] != 2:
|
||||
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
|
||||
|
||||
probs = torch.max(logits, dim=-1)
|
||||
scores = torch.sigmoid(probs.values)
|
||||
labels = probs.indices
|
||||
|
||||
# Convert to [x0, y0, x1, y1] format
|
||||
boxes = center_to_corners_format(boxes)
|
||||
|
||||
# Convert from relative [0, 1] to absolute [0, height] coordinates
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
|
||||
boxes = boxes * scale_fct[:, None, :]
|
||||
|
||||
results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
|
||||
|
||||
return results
|
||||
|
||||
def post_process_image_guided_detection(self, outputs, threshold=0.6, nms_threshold=0.3, target_sizes=None):
|
||||
"""
|
||||
Converts the output of [`OwlViTForObjectDetection.image_guided_detection`] into the format expected by the COCO
|
||||
api.
|
||||
|
||||
Args:
|
||||
outputs ([`OwlViTImageGuidedObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
threshold (`float`, *optional*, defaults to 0.6):
|
||||
Minimum confidence threshold to use to filter out predicted boxes.
|
||||
nms_threshold (`float`, *optional*, defaults to 0.3):
|
||||
IoU threshold for non-maximum suppression of overlapping boxes.
|
||||
target_sizes (`torch.Tensor`, *optional*):
|
||||
Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in
|
||||
the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to
|
||||
None, predictions will not be unnormalized.
|
||||
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model. All labels are set to None as
|
||||
`OwlViTForObjectDetection.image_guided_detection` perform one-shot object detection.
|
||||
"""
|
||||
logits, target_boxes = outputs.logits, outputs.target_pred_boxes
|
||||
|
||||
if len(logits) != len(target_sizes):
|
||||
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
|
||||
if target_sizes.shape[1] != 2:
|
||||
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
|
||||
|
||||
probs = torch.max(logits, dim=-1)
|
||||
scores = torch.sigmoid(probs.values)
|
||||
|
||||
# Convert to [x0, y0, x1, y1] format
|
||||
target_boxes = center_to_corners_format(target_boxes)
|
||||
|
||||
# Apply non-maximum suppression (NMS)
|
||||
if nms_threshold < 1.0:
|
||||
for idx in range(target_boxes.shape[0]):
|
||||
for i in torch.argsort(-scores[idx]):
|
||||
if not scores[idx][i]:
|
||||
continue
|
||||
|
||||
ious = box_iou(target_boxes[idx][i, :].unsqueeze(0), target_boxes[idx])[0][0]
|
||||
ious[i] = -1.0 # Mask self-IoU.
|
||||
scores[idx][ious > nms_threshold] = 0.0
|
||||
|
||||
# Convert from relative [0, 1] to absolute [0, height] coordinates
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
|
||||
target_boxes = target_boxes * scale_fct[:, None, :]
|
||||
|
||||
# Compute box display alphas based on prediction scores
|
||||
results = []
|
||||
alphas = torch.zeros_like(scores)
|
||||
|
||||
for idx in range(target_boxes.shape[0]):
|
||||
# Select scores for boxes matching the current query:
|
||||
query_scores = scores[idx]
|
||||
if not query_scores.nonzero().numel():
|
||||
continue
|
||||
|
||||
# Scale box alpha such that the best box for each query has alpha 1.0 and the worst box has alpha 0.1.
|
||||
# All other boxes will either belong to a different query, or will not be shown.
|
||||
max_score = torch.max(query_scores) + 1e-6
|
||||
query_alphas = (query_scores - (max_score * 0.1)) / (max_score * 0.9)
|
||||
query_alphas[query_alphas < threshold] = 0.0
|
||||
query_alphas = torch.clip(query_alphas, 0.0, 1.0)
|
||||
alphas[idx] = query_alphas
|
||||
|
||||
mask = alphas[idx] > 0
|
||||
box_scores = alphas[idx][mask]
|
||||
boxes = target_boxes[idx][mask]
|
||||
results.append({"scores": box_scores, "labels": None, "boxes": boxes})
|
||||
|
||||
return results
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
images: Union[
|
||||
Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"] # noqa
|
||||
],
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
**kwargs
|
||||
) -> BatchFeature:
|
||||
"""
|
||||
Main method to prepare for the model one or several image(s).
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
|
||||
PIL images.
|
||||
|
||||
</Tip>
|
||||
|
||||
Args:
|
||||
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
||||
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
||||
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W) or (H, W, C),
|
||||
where C is a number of channels, H and W are image height and width.
|
||||
|
||||
return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
|
||||
If set, will return tensors of a particular framework. Acceptable values are:
|
||||
- `'tf'`: Return TensorFlow `tf.constant` objects.
|
||||
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
||||
- `'np'`: Return NumPy `np.ndarray` objects.
|
||||
- `'jax'`: Return JAX `jnp.ndarray` objects.
|
||||
|
||||
Returns:
|
||||
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
||||
|
||||
- **pixel_values** -- Pixel values to be fed to a model.
|
||||
"""
|
||||
# Input type checking for clearer error
|
||||
valid_images = False
|
||||
|
||||
# Check that images has a valid type
|
||||
if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
|
||||
valid_images = True
|
||||
elif isinstance(images, (list, tuple)):
|
||||
if isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
|
||||
valid_images = True
|
||||
|
||||
if not valid_images:
|
||||
raise ValueError(
|
||||
"Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
|
||||
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
|
||||
)
|
||||
|
||||
is_batched = bool(
|
||||
isinstance(images, (list, tuple))
|
||||
and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
|
||||
)
|
||||
|
||||
if not is_batched:
|
||||
images = [images]
|
||||
|
||||
# transformations (resizing + center cropping + normalization)
|
||||
if self.do_resize and self.size is not None and self.resample is not None:
|
||||
images = [
|
||||
self.resize(image=image, size=self.size, resample=self.resample, default_to_square=True)
|
||||
for image in images
|
||||
]
|
||||
if self.do_center_crop and self.crop_size is not None:
|
||||
images = [self.center_crop(image, self.crop_size) for image in images]
|
||||
if self.do_normalize:
|
||||
images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images]
|
||||
|
||||
# return as BatchFeature
|
||||
data = {"pixel_values": images}
|
||||
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
|
||||
|
||||
return encoded_inputs
|
||||
OwlViTFeatureExtractor = OwlViTImageProcessor
|
||||
|
||||
445
src/transformers/models/owlvit/image_processing_owlvit.py
Normal file
445
src/transformers/models/owlvit/image_processing_owlvit.py
Normal file
@@ -0,0 +1,445 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Image processor class for OwlViT"""
|
||||
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
|
||||
from transformers.image_transforms import (
|
||||
center_crop,
|
||||
center_to_corners_format,
|
||||
normalize,
|
||||
rescale,
|
||||
resize,
|
||||
to_channel_dimension_format,
|
||||
to_numpy_array,
|
||||
)
|
||||
from transformers.image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, valid_images
|
||||
from transformers.utils import TensorType, is_torch_available, logging
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
# Copied from transformers.models.detr.modeling_detr._upcast
|
||||
def _upcast(t):
|
||||
# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
|
||||
if t.is_floating_point():
|
||||
return t if t.dtype in (torch.float32, torch.float64) else t.float()
|
||||
else:
|
||||
return t if t.dtype in (torch.int32, torch.int64) else t.int()
|
||||
|
||||
|
||||
def box_area(boxes):
|
||||
"""
|
||||
Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
|
||||
|
||||
Args:
|
||||
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
|
||||
Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
|
||||
< x2` and `0 <= y1 < y2`.
|
||||
Returns:
|
||||
`torch.FloatTensor`: a tensor containing the area for each box.
|
||||
"""
|
||||
boxes = _upcast(boxes)
|
||||
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
|
||||
|
||||
|
||||
def box_iou(boxes1, boxes2):
|
||||
area1 = box_area(boxes1)
|
||||
area2 = box_area(boxes2)
|
||||
|
||||
left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
|
||||
right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
|
||||
|
||||
width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2]
|
||||
inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M]
|
||||
|
||||
union = area1[:, None] + area2 - inter
|
||||
|
||||
iou = inter / union
|
||||
return iou, union
|
||||
|
||||
|
||||
class OwlViTImageProcessor(BaseImageProcessor):
|
||||
r"""
|
||||
Constructs an OWL-ViT feature extractor.
|
||||
|
||||
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
|
||||
should refer to this superclass for more information regarding those methods.
|
||||
|
||||
Args:
|
||||
do_resize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to resize the shorter edge of the input to a certain `size`.
|
||||
size (`Dict[str, int]`, *optional*, defaults to {"height": 768, "width": 768}):
|
||||
The size to use for resizing the image. Only has an effect if `do_resize` is set to `True`. If `size` is a
|
||||
sequence like (h, w), output size will be matched to this. If `size` is an int, then image will be resized
|
||||
to (size, size).
|
||||
resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BICUBIC`):
|
||||
An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
|
||||
`PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
|
||||
`PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
|
||||
to `True`.
|
||||
do_center_crop (`bool`, *optional*, defaults to `False`):
|
||||
Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the
|
||||
image is padded with 0's and then center cropped.
|
||||
crop_size (`int`, *optional*, defaults to {"height": 768, "width": 768}):
|
||||
The size to use for center cropping the image. Only has an effect if `do_center_crop` is set to `True`.
|
||||
do_rescale (`bool`, *optional*, defaults to `True`):
|
||||
Whether to rescale the input by a certain factor.
|
||||
rescale_factor (`float`, *optional*, defaults to `1/255`):
|
||||
The factor to use for rescaling the image. Only has an effect if `do_rescale` is set to `True`.
|
||||
do_normalize (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to normalize the input with `image_mean` and `image_std`. Desired output size when applying
|
||||
center-cropping. Only has an effect if `do_center_crop` is set to `True`.
|
||||
image_mean (`List[int]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
|
||||
The sequence of means for each channel, to be used when normalizing images.
|
||||
image_std (`List[int]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
|
||||
The sequence of standard deviations for each channel, to be used when normalizing images.
|
||||
"""
|
||||
|
||||
model_input_names = ["pixel_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
do_resize=True,
|
||||
size=None,
|
||||
resample=PILImageResampling.BICUBIC,
|
||||
do_center_crop=False,
|
||||
crop_size=None,
|
||||
do_rescale=True,
|
||||
rescale_factor=1 / 255,
|
||||
do_normalize=True,
|
||||
image_mean=None,
|
||||
image_std=None,
|
||||
**kwargs
|
||||
):
|
||||
size = size if size is not None else {"height": 768, "width": 768}
|
||||
size = get_size_dict(size, default_to_square=True)
|
||||
|
||||
crop_size = crop_size if crop_size is not None else {"height": 768, "width": 768}
|
||||
crop_size = get_size_dict(crop_size, default_to_square=True)
|
||||
|
||||
# Early versions of the OWL-ViT config on the hub had "rescale" as a flag. This clashes with the
|
||||
# vision feature extractor method `rescale` as it would be set as an attribute during the super().__init__
|
||||
# call. This is for backwards compatibility.
|
||||
if "rescale" in kwargs:
|
||||
rescale_val = kwargs.pop("rescale")
|
||||
kwargs["do_rescale"] = rescale_val
|
||||
|
||||
super().__init__(**kwargs)
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.resample = resample
|
||||
self.do_center_crop = do_center_crop
|
||||
self.crop_size = crop_size
|
||||
self.do_rescale = do_rescale
|
||||
self.rescale_factor = rescale_factor
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean if image_mean is not None else [0.48145466, 0.4578275, 0.40821073]
|
||||
self.image_std = image_std if image_std is not None else [0.26862954, 0.26130258, 0.27577711]
|
||||
|
||||
def resize(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
size: Dict[str, int],
|
||||
resample: PILImageResampling.BICUBIC,
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
**kwargs
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Resize an image to a certain size.
|
||||
"""
|
||||
size = get_size_dict(size, default_to_square=True)
|
||||
if "height" not in size or "width" not in size:
|
||||
raise ValueError("size dictionary must contain height and width keys")
|
||||
|
||||
return resize(image, (size["height"], size["width"]), resample=resample, data_format=data_format, **kwargs)
|
||||
|
||||
def center_crop(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
crop_size: Dict[str, int],
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
**kwargs
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Center crop an image to a certain size.
|
||||
"""
|
||||
crop_size = get_size_dict(crop_size, default_to_square=True)
|
||||
if "height" not in crop_size or "width" not in crop_size:
|
||||
raise ValueError("crop_size dictionary must contain height and width keys")
|
||||
|
||||
return center_crop(image, (crop_size["height"], crop_size["width"]), data_format=data_format, **kwargs)
|
||||
|
||||
def rescale(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
rescale_factor: float,
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
**kwargs
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Rescale an image by a certain factor.
|
||||
"""
|
||||
return rescale(image, rescale_factor, data_format=data_format, **kwargs)
|
||||
|
||||
def normalize(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
mean: List[float],
|
||||
std: List[float],
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
**kwargs
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Normalize an image with a certain mean and standard deviation.
|
||||
"""
|
||||
return normalize(image, mean, std, data_format=data_format, **kwargs)
|
||||
|
||||
def preprocess(
|
||||
self,
|
||||
images: ImageInput,
|
||||
do_resize: Optional[bool] = None,
|
||||
size: Optional[Dict[str, int]] = None,
|
||||
resample: PILImageResampling = None,
|
||||
do_center_crop: Optional[bool] = None,
|
||||
crop_size: Optional[Dict[str, int]] = None,
|
||||
do_rescale: Optional[bool] = None,
|
||||
rescale_factor: Optional[float] = None,
|
||||
do_normalize: Optional[bool] = None,
|
||||
image_mean: Optional[Union[float, List[float]]] = None,
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
return_tensors: Optional[Union[TensorType, str]] = None,
|
||||
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
|
||||
**kwargs
|
||||
) -> BatchFeature:
|
||||
"""
|
||||
Prepares an image or batch of images for the model.
|
||||
|
||||
Args:
|
||||
images (`ImageInput`):
|
||||
The image or batch of images to be prepared.
|
||||
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
|
||||
Whether or not to resize the input. If `True`, will resize the input to the size specified by `size`.
|
||||
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
|
||||
The size to resize the input to. Only has an effect if `do_resize` is set to `True`.
|
||||
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
|
||||
The resampling filter to use when resizing the input. Only has an effect if `do_resize` is set to
|
||||
`True`.
|
||||
do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
|
||||
Whether or not to center crop the input. If `True`, will center crop the input to the size specified by
|
||||
`crop_size`.
|
||||
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
|
||||
The size to center crop the input to. Only has an effect if `do_center_crop` is set to `True`.
|
||||
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
|
||||
Whether or not to rescale the input. If `True`, will rescale the input by dividing it by
|
||||
`rescale_factor`.
|
||||
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
|
||||
The factor to rescale the input by. Only has an effect if `do_rescale` is set to `True`.
|
||||
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
|
||||
Whether or not to normalize the input. If `True`, will normalize the input by subtracting `image_mean`
|
||||
and dividing by `image_std`.
|
||||
image_mean (`Union[float, List[float]]`, *optional*, defaults to `self.image_mean`):
|
||||
The mean to subtract from the input when normalizing. Only has an effect if `do_normalize` is set to
|
||||
`True`.
|
||||
image_std (`Union[float, List[float]]`, *optional*, defaults to `self.image_std`):
|
||||
The standard deviation to divide the input by when normalizing. Only has an effect if `do_normalize` is
|
||||
set to `True`.
|
||||
return_tensors (`str` or `TensorType`, *optional*):
|
||||
The type of tensors to return. Can be one of:
|
||||
- Unset: Return a list of `np.ndarray`.
|
||||
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
|
||||
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
|
||||
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
|
||||
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
|
||||
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
|
||||
The channel dimension format for the output image. Can be one of:
|
||||
- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
||||
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
||||
- Unset: defaults to the channel dimension format of the input image.
|
||||
"""
|
||||
do_resize = do_resize if do_resize is not None else self.do_resize
|
||||
size = size if size is not None else self.size
|
||||
resample = resample if resample is not None else self.resample
|
||||
do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
|
||||
crop_size = crop_size if crop_size is not None else self.crop_size
|
||||
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
|
||||
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
|
||||
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
|
||||
image_mean = image_mean if image_mean is not None else self.image_mean
|
||||
image_std = image_std if image_std is not None else self.image_std
|
||||
|
||||
if do_resize is not None and size is None:
|
||||
raise ValueError("Size and max_size must be specified if do_resize is True.")
|
||||
|
||||
if do_center_crop is not None and crop_size is None:
|
||||
raise ValueError("Crop size must be specified if do_center_crop is True.")
|
||||
|
||||
if do_rescale is not None and rescale_factor is None:
|
||||
raise ValueError("Rescale factor must be specified if do_rescale is True.")
|
||||
|
||||
if do_normalize is not None and (image_mean is None or image_std is None):
|
||||
raise ValueError("Image mean and std must be specified if do_normalize is True.")
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
|
||||
"torch.Tensor, tf.Tensor or jax.ndarray."
|
||||
)
|
||||
|
||||
# All transformations expect numpy arrays
|
||||
images = [to_numpy_array(image) for image in images]
|
||||
|
||||
if do_resize:
|
||||
images = [self.resize(image, size=size, resample=resample) for image in images]
|
||||
|
||||
if do_center_crop:
|
||||
images = [self.center_crop(image, crop_size=crop_size) for image in images]
|
||||
|
||||
if do_rescale:
|
||||
images = [self.rescale(image, rescale_factor=rescale_factor) for image in images]
|
||||
|
||||
if do_normalize:
|
||||
images = [self.normalize(image, mean=image_mean, std=image_std) for image in images]
|
||||
|
||||
images = [to_channel_dimension_format(image, data_format) for image in images]
|
||||
encoded_inputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)
|
||||
return encoded_inputs
|
||||
|
||||
def post_process(self, outputs, target_sizes):
|
||||
"""
|
||||
Converts the output of [`OwlViTForObjectDetection`] into the format expected by the COCO api.
|
||||
|
||||
Args:
|
||||
outputs ([`OwlViTObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
|
||||
Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
|
||||
image size (before any data augmentation). For visualization, this should be the image size after data
|
||||
augment, but before padding.
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model.
|
||||
"""
|
||||
# TODO: (amy) add support for other frameworks
|
||||
logits, boxes = outputs.logits, outputs.pred_boxes
|
||||
|
||||
if len(logits) != len(target_sizes):
|
||||
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
|
||||
if target_sizes.shape[1] != 2:
|
||||
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
|
||||
|
||||
probs = torch.max(logits, dim=-1)
|
||||
scores = torch.sigmoid(probs.values)
|
||||
labels = probs.indices
|
||||
|
||||
# Convert to [x0, y0, x1, y1] format
|
||||
boxes = center_to_corners_format(boxes)
|
||||
|
||||
# Convert from relative [0, 1] to absolute [0, height] coordinates
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
|
||||
boxes = boxes * scale_fct[:, None, :]
|
||||
|
||||
results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
|
||||
|
||||
return results
|
||||
|
||||
# TODO: (Amy) Make compatible with other frameworks
|
||||
def post_process_image_guided_detection(self, outputs, threshold=0.6, nms_threshold=0.3, target_sizes=None):
|
||||
"""
|
||||
Converts the output of [`OwlViTForObjectDetection.image_guided_detection`] into the format expected by the COCO
|
||||
api.
|
||||
|
||||
Args:
|
||||
outputs ([`OwlViTImageGuidedObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
threshold (`float`, *optional*, defaults to 0.6):
|
||||
Minimum confidence threshold to use to filter out predicted boxes.
|
||||
nms_threshold (`float`, *optional*, defaults to 0.3):
|
||||
IoU threshold for non-maximum suppression of overlapping boxes.
|
||||
target_sizes (`torch.Tensor`, *optional*):
|
||||
Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in
|
||||
the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to
|
||||
None, predictions will not be unnormalized.
|
||||
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model. All labels are set to None as
|
||||
`OwlViTForObjectDetection.image_guided_detection` perform one-shot object detection.
|
||||
"""
|
||||
logits, target_boxes = outputs.logits, outputs.target_pred_boxes
|
||||
|
||||
if len(logits) != len(target_sizes):
|
||||
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
|
||||
if target_sizes.shape[1] != 2:
|
||||
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
|
||||
|
||||
probs = torch.max(logits, dim=-1)
|
||||
scores = torch.sigmoid(probs.values)
|
||||
|
||||
# Convert to [x0, y0, x1, y1] format
|
||||
target_boxes = center_to_corners_format(target_boxes)
|
||||
|
||||
# Apply non-maximum suppression (NMS)
|
||||
if nms_threshold < 1.0:
|
||||
for idx in range(target_boxes.shape[0]):
|
||||
for i in torch.argsort(-scores[idx]):
|
||||
if not scores[idx][i]:
|
||||
continue
|
||||
|
||||
ious = box_iou(target_boxes[idx][i, :].unsqueeze(0), target_boxes[idx])[0][0]
|
||||
ious[i] = -1.0 # Mask self-IoU.
|
||||
scores[idx][ious > nms_threshold] = 0.0
|
||||
|
||||
# Convert from relative [0, 1] to absolute [0, height] coordinates
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
|
||||
target_boxes = target_boxes * scale_fct[:, None, :]
|
||||
|
||||
# Compute box display alphas based on prediction scores
|
||||
results = []
|
||||
alphas = torch.zeros_like(scores)
|
||||
|
||||
for idx in range(target_boxes.shape[0]):
|
||||
# Select scores for boxes matching the current query:
|
||||
query_scores = scores[idx]
|
||||
if not query_scores.nonzero().numel():
|
||||
continue
|
||||
|
||||
# Scale box alpha such that the best box for each query has alpha 1.0 and the worst box has alpha 0.1.
|
||||
# All other boxes will either belong to a different query, or will not be shown.
|
||||
max_score = torch.max(query_scores) + 1e-6
|
||||
query_alphas = (query_scores - (max_score * 0.1)) / (max_score * 0.9)
|
||||
query_alphas[query_alphas < threshold] = 0.0
|
||||
query_alphas = torch.clip(query_alphas, 0.0, 1.0)
|
||||
alphas[idx] = query_alphas
|
||||
|
||||
mask = alphas[idx] > 0
|
||||
box_scores = alphas[idx][mask]
|
||||
boxes = target_boxes[idx][mask]
|
||||
results.append({"scores": box_scores, "labels": None, "boxes": boxes})
|
||||
|
||||
return results
|
||||
@@ -15,6 +15,7 @@
|
||||
"""
|
||||
Image/Text processor class for OWL-ViT
|
||||
"""
|
||||
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
@@ -33,15 +34,15 @@ class OwlViTProcessor(ProcessorMixin):
|
||||
|
||||
Args:
|
||||
feature_extractor ([`OwlViTFeatureExtractor`]):
|
||||
The feature extractor is a required input.
|
||||
The image processor is a required input.
|
||||
tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`]):
|
||||
The tokenizer is a required input.
|
||||
"""
|
||||
feature_extractor_class = "OwlViTFeatureExtractor"
|
||||
tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
|
||||
|
||||
def __init__(self, feature_extractor, tokenizer):
|
||||
super().__init__(feature_extractor, tokenizer)
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def __call__(self, text=None, images=None, query_images=None, padding="max_length", return_tensors="np", **kwargs):
|
||||
"""
|
||||
|
||||
@@ -287,7 +287,6 @@ class SegformerImageProcessor(BaseImageProcessor):
|
||||
do_reduce_labels: bool = None,
|
||||
do_resize: bool = None,
|
||||
size: Dict[str, int] = None,
|
||||
resample: PILImageResampling = None,
|
||||
) -> np.ndarray:
|
||||
"""Preprocesses a single mask."""
|
||||
segmentation_map = to_numpy_array(segmentation_map)
|
||||
@@ -301,7 +300,7 @@ class SegformerImageProcessor(BaseImageProcessor):
|
||||
image=segmentation_map,
|
||||
do_reduce_labels=do_reduce_labels,
|
||||
do_resize=do_resize,
|
||||
resample=PIL.Image.NEAREST,
|
||||
resample=PILImageResampling.NEAREST,
|
||||
size=size,
|
||||
do_rescale=False,
|
||||
do_normalize=False,
|
||||
@@ -438,7 +437,6 @@ class SegformerImageProcessor(BaseImageProcessor):
|
||||
segmentation_map=segmentation_map,
|
||||
do_reduce_labels=do_reduce_labels,
|
||||
do_resize=do_resize,
|
||||
resample=PIL.Image.NEAREST,
|
||||
size=size,
|
||||
)
|
||||
for segmentation_map in segmentation_maps
|
||||
|
||||
@@ -29,6 +29,7 @@ except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["feature_extraction_yolos"] = ["YolosFeatureExtractor"]
|
||||
_import_structure["image_processing_yolos"] = ["YolosImageProcessor"]
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
@@ -54,6 +55,7 @@ if TYPE_CHECKING:
|
||||
pass
|
||||
else:
|
||||
from .feature_extraction_yolos import YolosFeatureExtractor
|
||||
from .image_processing_yolos import YolosImageProcessor
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
|
||||
@@ -14,694 +14,11 @@
|
||||
# limitations under the License.
|
||||
"""Feature extractor class for YOLOS."""
|
||||
|
||||
import pathlib
|
||||
import warnings
|
||||
from typing import Dict, List, Optional, Tuple, Union
|
||||
from ...utils import logging
|
||||
from .image_processing_yolos import YolosImageProcessor
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
|
||||
from ...image_transforms import center_to_corners_format, corners_to_center_format, rgb_to_id
|
||||
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
|
||||
from ...utils import TensorType, is_torch_available, logging
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
ImageInput = Union[Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]]
|
||||
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.masks_to_boxes
|
||||
def masks_to_boxes(masks):
|
||||
"""
|
||||
Compute the bounding boxes around the provided panoptic segmentation masks.
|
||||
|
||||
The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
|
||||
|
||||
Returns a [N, 4] tensor, with the boxes in corner (xyxy) format.
|
||||
"""
|
||||
if masks.size == 0:
|
||||
return np.zeros((0, 4))
|
||||
|
||||
h, w = masks.shape[-2:]
|
||||
|
||||
y = np.arange(0, h, dtype=np.float32)
|
||||
x = np.arange(0, w, dtype=np.float32)
|
||||
# see https://github.com/pytorch/pytorch/issues/50276
|
||||
y, x = np.meshgrid(y, x, indexing="ij")
|
||||
|
||||
x_mask = masks * np.expand_dims(x, axis=0)
|
||||
x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
|
||||
x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
|
||||
x_min = x.filled(fill_value=1e8)
|
||||
x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
|
||||
|
||||
y_mask = masks * np.expand_dims(y, axis=0)
|
||||
y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
|
||||
y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
|
||||
y_min = y.filled(fill_value=1e8)
|
||||
y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
|
||||
|
||||
return np.stack([x_min, y_min, x_max, y_max], 1)
|
||||
|
||||
|
||||
class YolosFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
|
||||
r"""
|
||||
Constructs a YOLOS feature extractor.
|
||||
|
||||
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
|
||||
should refer to this superclass for more information regarding those methods.
|
||||
|
||||
|
||||
Args:
|
||||
format (`str`, *optional*, defaults to `"coco_detection"`):
|
||||
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
|
||||
do_resize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to resize the input to a certain `size`.
|
||||
size (`int`, *optional*, defaults to 800):
|
||||
Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
|
||||
sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
|
||||
the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
|
||||
height / width, size)`.
|
||||
max_size (`int`, *optional*, defaults to `1333`):
|
||||
The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
|
||||
set to `True`.
|
||||
do_normalize (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to normalize the input with mean and standard deviation.
|
||||
image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
|
||||
The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
|
||||
image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
|
||||
The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
|
||||
ImageNet std.
|
||||
"""
|
||||
|
||||
model_input_names = ["pixel_values"]
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.__init__
|
||||
def __init__(
|
||||
self,
|
||||
format="coco_detection",
|
||||
do_resize=True,
|
||||
size=800,
|
||||
max_size=1333,
|
||||
do_normalize=True,
|
||||
image_mean=None,
|
||||
image_std=None,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.format = self._is_valid_format(format)
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.max_size = max_size
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean if image_mean is not None else [0.485, 0.456, 0.406] # ImageNet mean
|
||||
self.image_std = image_std if image_std is not None else [0.229, 0.224, 0.225] # ImageNet std
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._is_valid_format
|
||||
def _is_valid_format(self, format):
|
||||
if format not in ["coco_detection", "coco_panoptic"]:
|
||||
raise ValueError(f"Format {format} not supported")
|
||||
return format
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare
|
||||
def prepare(self, image, target, return_segmentation_masks=False, masks_path=None):
|
||||
if self.format == "coco_detection":
|
||||
image, target = self.prepare_coco_detection(image, target, return_segmentation_masks)
|
||||
return image, target
|
||||
elif self.format == "coco_panoptic":
|
||||
image, target = self.prepare_coco_panoptic(image, target, masks_path)
|
||||
return image, target
|
||||
else:
|
||||
raise ValueError(f"Format {self.format} not supported")
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.convert_coco_poly_to_mask
|
||||
def convert_coco_poly_to_mask(self, segmentations, height, width):
|
||||
|
||||
try:
|
||||
from pycocotools import mask as coco_mask
|
||||
except ImportError:
|
||||
raise ImportError("Pycocotools is not installed in your environment.")
|
||||
|
||||
masks = []
|
||||
for polygons in segmentations:
|
||||
rles = coco_mask.frPyObjects(polygons, height, width)
|
||||
mask = coco_mask.decode(rles)
|
||||
if len(mask.shape) < 3:
|
||||
mask = mask[..., None]
|
||||
mask = np.asarray(mask, dtype=np.uint8)
|
||||
mask = np.any(mask, axis=2)
|
||||
masks.append(mask)
|
||||
if masks:
|
||||
masks = np.stack(masks, axis=0)
|
||||
else:
|
||||
masks = np.zeros((0, height, width), dtype=np.uint8)
|
||||
|
||||
return masks
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare_coco_detection
|
||||
def prepare_coco_detection(self, image, target, return_segmentation_masks=False):
|
||||
"""
|
||||
Convert the target in COCO format into the format expected by DETR.
|
||||
"""
|
||||
w, h = image.size
|
||||
|
||||
image_id = target["image_id"]
|
||||
image_id = np.asarray([image_id], dtype=np.int64)
|
||||
|
||||
# get all COCO annotations for the given image
|
||||
anno = target["annotations"]
|
||||
|
||||
anno = [obj for obj in anno if "iscrowd" not in obj or obj["iscrowd"] == 0]
|
||||
|
||||
boxes = [obj["bbox"] for obj in anno]
|
||||
# guard against no boxes via resizing
|
||||
boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
|
||||
boxes[:, 2:] += boxes[:, :2]
|
||||
boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=w)
|
||||
boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=h)
|
||||
|
||||
classes = [obj["category_id"] for obj in anno]
|
||||
classes = np.asarray(classes, dtype=np.int64)
|
||||
|
||||
if return_segmentation_masks:
|
||||
segmentations = [obj["segmentation"] for obj in anno]
|
||||
masks = self.convert_coco_poly_to_mask(segmentations, h, w)
|
||||
|
||||
keypoints = None
|
||||
if anno and "keypoints" in anno[0]:
|
||||
keypoints = [obj["keypoints"] for obj in anno]
|
||||
keypoints = np.asarray(keypoints, dtype=np.float32)
|
||||
num_keypoints = keypoints.shape[0]
|
||||
if num_keypoints:
|
||||
keypoints = keypoints.reshape((-1, 3))
|
||||
|
||||
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
|
||||
boxes = boxes[keep]
|
||||
classes = classes[keep]
|
||||
if return_segmentation_masks:
|
||||
masks = masks[keep]
|
||||
if keypoints is not None:
|
||||
keypoints = keypoints[keep]
|
||||
|
||||
target = {}
|
||||
target["boxes"] = boxes
|
||||
target["class_labels"] = classes
|
||||
if return_segmentation_masks:
|
||||
target["masks"] = masks
|
||||
target["image_id"] = image_id
|
||||
if keypoints is not None:
|
||||
target["keypoints"] = keypoints
|
||||
|
||||
# for conversion to coco api
|
||||
area = np.asarray([obj["area"] for obj in anno], dtype=np.float32)
|
||||
iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno], dtype=np.int64)
|
||||
target["area"] = area[keep]
|
||||
target["iscrowd"] = iscrowd[keep]
|
||||
|
||||
target["orig_size"] = np.asarray([int(h), int(w)], dtype=np.int64)
|
||||
target["size"] = np.asarray([int(h), int(w)], dtype=np.int64)
|
||||
|
||||
return image, target
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare_coco_panoptic
|
||||
def prepare_coco_panoptic(self, image, target, masks_path, return_masks=True):
|
||||
w, h = image.size
|
||||
ann_info = target.copy()
|
||||
ann_path = pathlib.Path(masks_path) / ann_info["file_name"]
|
||||
|
||||
if "segments_info" in ann_info:
|
||||
masks = np.asarray(Image.open(ann_path), dtype=np.uint32)
|
||||
masks = rgb_to_id(masks)
|
||||
|
||||
ids = np.array([ann["id"] for ann in ann_info["segments_info"]])
|
||||
masks = masks == ids[:, None, None]
|
||||
masks = np.asarray(masks, dtype=np.uint8)
|
||||
|
||||
labels = np.asarray([ann["category_id"] for ann in ann_info["segments_info"]], dtype=np.int64)
|
||||
|
||||
target = {}
|
||||
target["image_id"] = np.asarray(
|
||||
[ann_info["image_id"] if "image_id" in ann_info else ann_info["id"]], dtype=np.int64
|
||||
)
|
||||
if return_masks:
|
||||
target["masks"] = masks
|
||||
target["class_labels"] = labels
|
||||
|
||||
target["boxes"] = masks_to_boxes(masks)
|
||||
|
||||
target["size"] = np.asarray([int(h), int(w)], dtype=np.int64)
|
||||
target["orig_size"] = np.asarray([int(h), int(w)], dtype=np.int64)
|
||||
if "segments_info" in ann_info:
|
||||
target["iscrowd"] = np.asarray([ann["iscrowd"] for ann in ann_info["segments_info"]], dtype=np.int64)
|
||||
target["area"] = np.asarray([ann["area"] for ann in ann_info["segments_info"]], dtype=np.float32)
|
||||
|
||||
return image, target
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._resize
|
||||
def _resize(self, image, size, target=None, max_size=None):
|
||||
"""
|
||||
Resize the image to the given size. Size can be min_size (scalar) or (w, h) tuple. If size is an int, smaller
|
||||
edge of the image will be matched to this number.
|
||||
|
||||
If given, also resize the target accordingly.
|
||||
"""
|
||||
if not isinstance(image, Image.Image):
|
||||
image = self.to_pil_image(image)
|
||||
|
||||
def get_size_with_aspect_ratio(image_size, size, max_size=None):
|
||||
w, h = image_size
|
||||
if max_size is not None:
|
||||
min_original_size = float(min((w, h)))
|
||||
max_original_size = float(max((w, h)))
|
||||
if max_original_size / min_original_size * size > max_size:
|
||||
size = int(round(max_size * min_original_size / max_original_size))
|
||||
|
||||
if (w <= h and w == size) or (h <= w and h == size):
|
||||
return (h, w)
|
||||
|
||||
if w < h:
|
||||
ow = size
|
||||
oh = int(size * h / w)
|
||||
else:
|
||||
oh = size
|
||||
ow = int(size * w / h)
|
||||
|
||||
return (oh, ow)
|
||||
|
||||
def get_size(image_size, size, max_size=None):
|
||||
if isinstance(size, (list, tuple)):
|
||||
return size
|
||||
else:
|
||||
# size returned must be (w, h) since we use PIL to resize images
|
||||
# so we revert the tuple
|
||||
return get_size_with_aspect_ratio(image_size, size, max_size)[::-1]
|
||||
|
||||
size = get_size(image.size, size, max_size)
|
||||
rescaled_image = self.resize(image, size=size)
|
||||
|
||||
if target is None:
|
||||
return rescaled_image, None
|
||||
|
||||
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
|
||||
ratio_width, ratio_height = ratios
|
||||
|
||||
target = target.copy()
|
||||
if "boxes" in target:
|
||||
boxes = target["boxes"]
|
||||
scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
|
||||
target["boxes"] = scaled_boxes
|
||||
|
||||
if "area" in target:
|
||||
area = target["area"]
|
||||
scaled_area = area * (ratio_width * ratio_height)
|
||||
target["area"] = scaled_area
|
||||
|
||||
w, h = size
|
||||
target["size"] = np.asarray([h, w], dtype=np.int64)
|
||||
|
||||
if "masks" in target:
|
||||
# use PyTorch as current workaround
|
||||
# TODO replace by self.resize
|
||||
masks = torch.from_numpy(target["masks"][:, None]).float()
|
||||
interpolated_masks = nn.functional.interpolate(masks, size=(h, w), mode="nearest")[:, 0] > 0.5
|
||||
target["masks"] = interpolated_masks.numpy()
|
||||
|
||||
return rescaled_image, target
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._normalize
|
||||
def _normalize(self, image, mean, std, target=None):
|
||||
"""
|
||||
Normalize the image with a certain mean and std.
|
||||
|
||||
If given, also normalize the target bounding boxes based on the size of the image.
|
||||
"""
|
||||
|
||||
image = self.normalize(image, mean=mean, std=std)
|
||||
if target is None:
|
||||
return image, None
|
||||
|
||||
target = target.copy()
|
||||
h, w = image.shape[-2:]
|
||||
|
||||
if "boxes" in target:
|
||||
boxes = target["boxes"]
|
||||
boxes = corners_to_center_format(boxes)
|
||||
boxes = boxes / np.asarray([w, h, w, h], dtype=np.float32)
|
||||
target["boxes"] = boxes
|
||||
|
||||
return image, target
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
images: ImageInput,
|
||||
annotations: Union[List[Dict], List[List[Dict]]] = None,
|
||||
return_segmentation_masks: Optional[bool] = False,
|
||||
masks_path: Optional[pathlib.Path] = None,
|
||||
padding: Optional[bool] = True,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
**kwargs,
|
||||
) -> BatchFeature:
|
||||
"""
|
||||
Main method to prepare for the model one or several image(s) and optional annotations. Images are by default
|
||||
padded up to the largest image in a batch.
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
|
||||
PIL images.
|
||||
|
||||
</Tip>
|
||||
|
||||
Args:
|
||||
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
||||
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
||||
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
|
||||
number of channels, H and W are image height and width.
|
||||
|
||||
annotations (`Dict`, `List[Dict]`, *optional*):
|
||||
The corresponding annotations in COCO format.
|
||||
|
||||
In case [`DetrFeatureExtractor`] was initialized with `format = "coco_detection"`, the annotations for
|
||||
each image should have the following format: {'image_id': int, 'annotations': [annotation]}, with the
|
||||
annotations being a list of COCO object annotations.
|
||||
|
||||
In case [`DetrFeatureExtractor`] was initialized with `format = "coco_panoptic"`, the annotations for
|
||||
each image should have the following format: {'image_id': int, 'file_name': str, 'segments_info':
|
||||
[segment_info]} with segments_info being a list of COCO panoptic annotations.
|
||||
|
||||
return_segmentation_masks (`Dict`, `List[Dict]`, *optional*, defaults to `False`):
|
||||
Whether to also include instance segmentation masks as part of the labels in case `format =
|
||||
"coco_detection"`.
|
||||
|
||||
masks_path (`pathlib.Path`, *optional*):
|
||||
Path to the directory containing the PNG files that store the class-agnostic image segmentations. Only
|
||||
relevant in case [`DetrFeatureExtractor`] was initialized with `format = "coco_panoptic"`.
|
||||
|
||||
padding (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to pad images up to the largest image in a batch.
|
||||
|
||||
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
||||
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
|
||||
objects.
|
||||
|
||||
Returns:
|
||||
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
||||
|
||||
- **pixel_values** -- Pixel values to be fed to a model.
|
||||
- **labels** -- Optional labels to be fed to a model (when `annotations` are provided)
|
||||
"""
|
||||
# Input type checking for clearer error
|
||||
|
||||
valid_images = False
|
||||
valid_annotations = False
|
||||
valid_masks_path = False
|
||||
|
||||
# Check that images has a valid type
|
||||
if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
|
||||
valid_images = True
|
||||
elif isinstance(images, (list, tuple)):
|
||||
if len(images) == 0 or isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
|
||||
valid_images = True
|
||||
|
||||
if not valid_images:
|
||||
raise ValueError(
|
||||
"Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
|
||||
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
|
||||
)
|
||||
|
||||
is_batched = bool(
|
||||
isinstance(images, (list, tuple))
|
||||
and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
|
||||
)
|
||||
|
||||
# Check that annotations has a valid type
|
||||
if annotations is not None:
|
||||
if not is_batched:
|
||||
if self.format == "coco_detection":
|
||||
if isinstance(annotations, dict) and "image_id" in annotations and "annotations" in annotations:
|
||||
if isinstance(annotations["annotations"], (list, tuple)):
|
||||
# an image can have no annotations
|
||||
if len(annotations["annotations"]) == 0 or isinstance(annotations["annotations"][0], dict):
|
||||
valid_annotations = True
|
||||
elif self.format == "coco_panoptic":
|
||||
if isinstance(annotations, dict) and "image_id" in annotations and "segments_info" in annotations:
|
||||
if isinstance(annotations["segments_info"], (list, tuple)):
|
||||
# an image can have no segments (?)
|
||||
if len(annotations["segments_info"]) == 0 or isinstance(
|
||||
annotations["segments_info"][0], dict
|
||||
):
|
||||
valid_annotations = True
|
||||
else:
|
||||
if isinstance(annotations, (list, tuple)):
|
||||
if len(images) != len(annotations):
|
||||
raise ValueError("There must be as many annotations as there are images")
|
||||
if isinstance(annotations[0], Dict):
|
||||
if self.format == "coco_detection":
|
||||
if isinstance(annotations[0]["annotations"], (list, tuple)):
|
||||
valid_annotations = True
|
||||
elif self.format == "coco_panoptic":
|
||||
if isinstance(annotations[0]["segments_info"], (list, tuple)):
|
||||
valid_annotations = True
|
||||
|
||||
if not valid_annotations:
|
||||
raise ValueError(
|
||||
"""
|
||||
Annotations must of type `Dict` (single image) or `List[Dict]` (batch of images). In case of object
|
||||
detection, each dictionary should contain the keys 'image_id' and 'annotations', with the latter
|
||||
being a list of annotations in COCO format. In case of panoptic segmentation, each dictionary
|
||||
should contain the keys 'file_name', 'image_id' and 'segments_info', with the latter being a list
|
||||
of annotations in COCO format.
|
||||
"""
|
||||
)
|
||||
|
||||
# Check that masks_path has a valid type
|
||||
if masks_path is not None:
|
||||
if self.format == "coco_panoptic":
|
||||
if isinstance(masks_path, pathlib.Path):
|
||||
valid_masks_path = True
|
||||
if not valid_masks_path:
|
||||
raise ValueError(
|
||||
"The path to the directory containing the mask PNG files should be provided as a"
|
||||
" `pathlib.Path` object."
|
||||
)
|
||||
|
||||
if not is_batched:
|
||||
images = [images]
|
||||
if annotations is not None:
|
||||
annotations = [annotations]
|
||||
|
||||
# prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
|
||||
if annotations is not None:
|
||||
for idx, (image, target) in enumerate(zip(images, annotations)):
|
||||
if not isinstance(image, Image.Image):
|
||||
image = self.to_pil_image(image)
|
||||
image, target = self.prepare(image, target, return_segmentation_masks, masks_path)
|
||||
images[idx] = image
|
||||
annotations[idx] = target
|
||||
|
||||
# transformations (resizing + normalization)
|
||||
if self.do_resize and self.size is not None:
|
||||
if annotations is not None:
|
||||
for idx, (image, target) in enumerate(zip(images, annotations)):
|
||||
image, target = self._resize(image=image, target=target, size=self.size, max_size=self.max_size)
|
||||
images[idx] = image
|
||||
annotations[idx] = target
|
||||
else:
|
||||
for idx, image in enumerate(images):
|
||||
images[idx] = self._resize(image=image, target=None, size=self.size, max_size=self.max_size)[0]
|
||||
|
||||
if self.do_normalize:
|
||||
if annotations is not None:
|
||||
for idx, (image, target) in enumerate(zip(images, annotations)):
|
||||
image, target = self._normalize(
|
||||
image=image, mean=self.image_mean, std=self.image_std, target=target
|
||||
)
|
||||
images[idx] = image
|
||||
annotations[idx] = target
|
||||
else:
|
||||
images = [
|
||||
self._normalize(image=image, mean=self.image_mean, std=self.image_std)[0] for image in images
|
||||
]
|
||||
|
||||
if padding:
|
||||
# pad images up to largest image in batch
|
||||
max_size = self._max_by_axis([list(image.shape) for image in images])
|
||||
c, h, w = max_size
|
||||
padded_images = []
|
||||
for image in images:
|
||||
# create padded image
|
||||
padded_image = np.zeros((c, h, w), dtype=np.float32)
|
||||
padded_image[: image.shape[0], : image.shape[1], : image.shape[2]] = np.copy(image)
|
||||
padded_images.append(padded_image)
|
||||
images = padded_images
|
||||
|
||||
# return as BatchFeature
|
||||
data = {}
|
||||
data["pixel_values"] = images
|
||||
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
|
||||
|
||||
if annotations is not None:
|
||||
# Convert to TensorType
|
||||
tensor_type = return_tensors
|
||||
if not isinstance(tensor_type, TensorType):
|
||||
tensor_type = TensorType(tensor_type)
|
||||
|
||||
if not tensor_type == TensorType.PYTORCH:
|
||||
raise ValueError("Only PyTorch is supported for the moment.")
|
||||
else:
|
||||
if not is_torch_available():
|
||||
raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
|
||||
|
||||
encoded_inputs["labels"] = [
|
||||
{k: torch.from_numpy(v) for k, v in target.items()} for target in annotations
|
||||
]
|
||||
|
||||
return encoded_inputs
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._max_by_axis
|
||||
def _max_by_axis(self, the_list):
|
||||
# type: (List[List[int]]) -> List[int]
|
||||
maxes = the_list[0]
|
||||
for sublist in the_list[1:]:
|
||||
for index, item in enumerate(sublist):
|
||||
maxes[index] = max(maxes[index], item)
|
||||
return maxes
|
||||
|
||||
def pad(self, pixel_values_list: List["torch.Tensor"], return_tensors: Optional[Union[str, TensorType]] = None):
|
||||
"""
|
||||
Pad images up to the largest image in a batch.
|
||||
|
||||
Args:
|
||||
pixel_values_list (`List[torch.Tensor]`):
|
||||
List of images (pixel values) to be padded. Each image should be a tensor of shape (C, H, W).
|
||||
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
||||
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
|
||||
objects.
|
||||
|
||||
Returns:
|
||||
[`BatchFeature`]: A [`BatchFeature`] with the following field:
|
||||
|
||||
- **pixel_values** -- Pixel values to be fed to a model.
|
||||
|
||||
"""
|
||||
|
||||
max_size = self._max_by_axis([list(image.shape) for image in pixel_values_list])
|
||||
c, h, w = max_size
|
||||
padded_images = []
|
||||
for image in pixel_values_list:
|
||||
# create padded image
|
||||
padded_image = np.zeros((c, h, w), dtype=np.float32)
|
||||
padded_image[: image.shape[0], : image.shape[1], : image.shape[2]] = np.copy(image)
|
||||
padded_images.append(padded_image)
|
||||
|
||||
# return as BatchFeature
|
||||
data = {"pixel_values": padded_images}
|
||||
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
|
||||
|
||||
return encoded_inputs
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.post_process
|
||||
def post_process(self, outputs, target_sizes):
|
||||
"""
|
||||
Converts the output of [`DetrForObjectDetection`] into the format expected by the COCO api. Only supports
|
||||
PyTorch.
|
||||
|
||||
Args:
|
||||
outputs ([`DetrObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
|
||||
Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the
|
||||
original image size (before any data augmentation). For visualization, this should be the image size
|
||||
after data augment, but before padding.
|
||||
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model.
|
||||
"""
|
||||
warnings.warn(
|
||||
"`post_process` is deprecated and will be removed in v5 of Transformers, please use"
|
||||
" `post_process_object_detection`",
|
||||
FutureWarning,
|
||||
)
|
||||
|
||||
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
|
||||
|
||||
if len(out_logits) != len(target_sizes):
|
||||
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
|
||||
if target_sizes.shape[1] != 2:
|
||||
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
|
||||
|
||||
prob = nn.functional.softmax(out_logits, -1)
|
||||
scores, labels = prob[..., :-1].max(-1)
|
||||
|
||||
# convert to [x0, y0, x1, y1] format
|
||||
boxes = center_to_corners_format(out_bbox)
|
||||
# and from relative [0, 1] to absolute [0, height] coordinates
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
|
||||
boxes = boxes * scale_fct[:, None, :]
|
||||
|
||||
results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
|
||||
return results
|
||||
|
||||
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.post_process_object_detection with Detr->Yolos
|
||||
def post_process_object_detection(
|
||||
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
|
||||
):
|
||||
"""
|
||||
Converts the output of [`YolosForObjectDetection`] into the format expected by the COCO api. Only supports
|
||||
PyTorch.
|
||||
|
||||
Args:
|
||||
outputs ([`YolosObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
threshold (`float`, *optional*):
|
||||
Score threshold to keep object detection predictions.
|
||||
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*, defaults to `None`):
|
||||
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
|
||||
(height, width) of each image in the batch. If left to None, predictions will not be resized.
|
||||
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model.
|
||||
"""
|
||||
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
|
||||
|
||||
if target_sizes is not None:
|
||||
if len(out_logits) != len(target_sizes):
|
||||
raise ValueError(
|
||||
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
|
||||
)
|
||||
|
||||
prob = nn.functional.softmax(out_logits, -1)
|
||||
scores, labels = prob[..., :-1].max(-1)
|
||||
|
||||
# Convert to [x0, y0, x1, y1] format
|
||||
boxes = center_to_corners_format(out_bbox)
|
||||
|
||||
# Convert from relative [0, 1] to absolute [0, height] coordinates
|
||||
if target_sizes is not None:
|
||||
if isinstance(target_sizes, List):
|
||||
img_h = torch.Tensor([i[0] for i in target_sizes])
|
||||
img_w = torch.Tensor([i[1] for i in target_sizes])
|
||||
else:
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
|
||||
boxes = boxes * scale_fct[:, None, :]
|
||||
|
||||
results = []
|
||||
for s, l, b in zip(scores, labels, boxes):
|
||||
score = s[s > threshold]
|
||||
label = l[s > threshold]
|
||||
box = b[s > threshold]
|
||||
results.append({"scores": score, "labels": label, "boxes": box})
|
||||
|
||||
return results
|
||||
YolosFeatureExtractor = YolosImageProcessor
|
||||
|
||||
1214
src/transformers/models/yolos/image_processing_yolos.py
Normal file
1214
src/transformers/models/yolos/image_processing_yolos.py
Normal file
File diff suppressed because it is too large
Load Diff
@@ -64,6 +64,13 @@ class ConditionalDetrFeatureExtractor(metaclass=DummyObject):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class ConditionalDetrImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class ConvNextFeatureExtractor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
@@ -85,6 +92,13 @@ class DeformableDetrFeatureExtractor(metaclass=DummyObject):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class DeformableDetrImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class DeiTFeatureExtractor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
@@ -106,6 +120,13 @@ class DetrFeatureExtractor(metaclass=DummyObject):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class DetrImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class DonutFeatureExtractor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
@@ -232,6 +253,13 @@ class MaskFormerFeatureExtractor(metaclass=DummyObject):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class MaskFormerImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class MobileNetV1FeatureExtractor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
@@ -281,6 +309,13 @@ class OwlViTFeatureExtractor(metaclass=DummyObject):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class OwlViTImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class PerceiverFeatureExtractor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
@@ -377,3 +412,10 @@ class YolosFeatureExtractor(metaclass=DummyObject):
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class YolosImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
@@ -44,12 +44,16 @@ class ConditionalDetrFeatureExtractionTester(unittest.TestCase):
|
||||
min_resolution=30,
|
||||
max_resolution=400,
|
||||
do_resize=True,
|
||||
size=18,
|
||||
max_size=1333, # by setting max_size > max_resolution we're effectively not testing this :p
|
||||
size=None,
|
||||
do_normalize=True,
|
||||
image_mean=[0.5, 0.5, 0.5],
|
||||
image_std=[0.5, 0.5, 0.5],
|
||||
do_rescale=True,
|
||||
rescale_factor=1 / 255,
|
||||
do_pad=True,
|
||||
):
|
||||
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
|
||||
size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.num_channels = num_channels
|
||||
@@ -57,19 +61,23 @@ class ConditionalDetrFeatureExtractionTester(unittest.TestCase):
|
||||
self.max_resolution = max_resolution
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.max_size = max_size
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.do_rescale = do_rescale
|
||||
self.rescale_factor = rescale_factor
|
||||
self.do_pad = do_pad
|
||||
|
||||
def prepare_feat_extract_dict(self):
|
||||
return {
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
"max_size": self.max_size,
|
||||
"do_normalize": self.do_normalize,
|
||||
"image_mean": self.image_mean,
|
||||
"image_std": self.image_std,
|
||||
"do_rescale": self.do_rescale,
|
||||
"rescale_factor": self.rescale_factor,
|
||||
"do_pad": self.do_pad,
|
||||
}
|
||||
|
||||
def get_expected_values(self, image_inputs, batched=False):
|
||||
@@ -84,14 +92,14 @@ class ConditionalDetrFeatureExtractionTester(unittest.TestCase):
|
||||
else:
|
||||
h, w = image.shape[1], image.shape[2]
|
||||
if w < h:
|
||||
expected_height = int(self.size * h / w)
|
||||
expected_width = self.size
|
||||
expected_height = int(self.size["shortest_edge"] * h / w)
|
||||
expected_width = self.size["shortest_edge"]
|
||||
elif w > h:
|
||||
expected_height = self.size
|
||||
expected_width = int(self.size * w / h)
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = int(self.size["shortest_edge"] * w / h)
|
||||
else:
|
||||
expected_height = self.size
|
||||
expected_width = self.size
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = self.size["shortest_edge"]
|
||||
|
||||
else:
|
||||
expected_values = []
|
||||
@@ -124,7 +132,6 @@ class ConditionalDetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, uni
|
||||
self.assertTrue(hasattr(feature_extractor, "do_normalize"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_resize"))
|
||||
self.assertTrue(hasattr(feature_extractor, "size"))
|
||||
self.assertTrue(hasattr(feature_extractor, "max_size"))
|
||||
|
||||
def test_batch_feature(self):
|
||||
pass
|
||||
@@ -230,7 +237,7 @@ class ConditionalDetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, uni
|
||||
def test_equivalence_pad_and_create_pixel_mask(self):
|
||||
# Initialize feature_extractors
|
||||
feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
|
||||
feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False)
|
||||
feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False, do_rescale=False)
|
||||
# create random PyTorch tensors
|
||||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
|
||||
for image in image_inputs:
|
||||
@@ -331,7 +338,7 @@ class ConditionalDetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, uni
|
||||
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
|
||||
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
|
||||
# verify masks
|
||||
expected_masks_sum = 822338
|
||||
expected_masks_sum = 822873
|
||||
self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
|
||||
# verify orig_size
|
||||
expected_orig_size = torch.tensor([480, 640])
|
||||
|
||||
@@ -44,12 +44,16 @@ class DeformableDetrFeatureExtractionTester(unittest.TestCase):
|
||||
min_resolution=30,
|
||||
max_resolution=400,
|
||||
do_resize=True,
|
||||
size=18,
|
||||
max_size=1333, # by setting max_size > max_resolution we're effectively not testing this :p
|
||||
size=None,
|
||||
do_normalize=True,
|
||||
image_mean=[0.5, 0.5, 0.5],
|
||||
image_std=[0.5, 0.5, 0.5],
|
||||
do_rescale=True,
|
||||
rescale_factor=1 / 255,
|
||||
do_pad=True,
|
||||
):
|
||||
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
|
||||
size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.num_channels = num_channels
|
||||
@@ -57,19 +61,23 @@ class DeformableDetrFeatureExtractionTester(unittest.TestCase):
|
||||
self.max_resolution = max_resolution
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.max_size = max_size
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.do_rescale = do_rescale
|
||||
self.rescale_factor = rescale_factor
|
||||
self.do_pad = do_pad
|
||||
|
||||
def prepare_feat_extract_dict(self):
|
||||
return {
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
"max_size": self.max_size,
|
||||
"do_normalize": self.do_normalize,
|
||||
"image_mean": self.image_mean,
|
||||
"image_std": self.image_std,
|
||||
"do_rescale": self.do_rescale,
|
||||
"rescale_factor": self.rescale_factor,
|
||||
"do_pad": self.do_pad,
|
||||
}
|
||||
|
||||
def get_expected_values(self, image_inputs, batched=False):
|
||||
@@ -84,14 +92,14 @@ class DeformableDetrFeatureExtractionTester(unittest.TestCase):
|
||||
else:
|
||||
h, w = image.shape[1], image.shape[2]
|
||||
if w < h:
|
||||
expected_height = int(self.size * h / w)
|
||||
expected_width = self.size
|
||||
expected_height = int(self.size["shortest_edge"] * h / w)
|
||||
expected_width = self.size["shortest_edge"]
|
||||
elif w > h:
|
||||
expected_height = self.size
|
||||
expected_width = int(self.size * w / h)
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = int(self.size["shortest_edge"] * w / h)
|
||||
else:
|
||||
expected_height = self.size
|
||||
expected_width = self.size
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = self.size["shortest_edge"]
|
||||
|
||||
else:
|
||||
expected_values = []
|
||||
@@ -123,8 +131,9 @@ class DeformableDetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unit
|
||||
self.assertTrue(hasattr(feature_extractor, "image_std"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_normalize"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_resize"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_rescale"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_pad"))
|
||||
self.assertTrue(hasattr(feature_extractor, "size"))
|
||||
self.assertTrue(hasattr(feature_extractor, "max_size"))
|
||||
|
||||
def test_batch_feature(self):
|
||||
pass
|
||||
@@ -230,7 +239,8 @@ class DeformableDetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unit
|
||||
def test_equivalence_pad_and_create_pixel_mask(self):
|
||||
# Initialize feature_extractors
|
||||
feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
|
||||
feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False)
|
||||
feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False, do_rescale=False)
|
||||
|
||||
# create random PyTorch tensors
|
||||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
|
||||
for image in image_inputs:
|
||||
@@ -331,7 +341,7 @@ class DeformableDetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unit
|
||||
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
|
||||
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
|
||||
# verify masks
|
||||
expected_masks_sum = 822338
|
||||
expected_masks_sum = 822873
|
||||
self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
|
||||
# verify orig_size
|
||||
expected_orig_size = torch.tensor([480, 640])
|
||||
|
||||
@@ -44,12 +44,16 @@ class DetrFeatureExtractionTester(unittest.TestCase):
|
||||
min_resolution=30,
|
||||
max_resolution=400,
|
||||
do_resize=True,
|
||||
size=18,
|
||||
max_size=1333, # by setting max_size > max_resolution we're effectively not testing this :p
|
||||
size=None,
|
||||
do_rescale=True,
|
||||
rescale_factor=1 / 255,
|
||||
do_normalize=True,
|
||||
image_mean=[0.5, 0.5, 0.5],
|
||||
image_std=[0.5, 0.5, 0.5],
|
||||
do_pad=True,
|
||||
):
|
||||
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
|
||||
size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.num_channels = num_channels
|
||||
@@ -57,19 +61,23 @@ class DetrFeatureExtractionTester(unittest.TestCase):
|
||||
self.max_resolution = max_resolution
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.max_size = max_size
|
||||
self.do_rescale = do_rescale
|
||||
self.rescale_factor = rescale_factor
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.do_pad = do_pad
|
||||
|
||||
def prepare_feat_extract_dict(self):
|
||||
return {
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
"max_size": self.max_size,
|
||||
"do_rescale": self.do_rescale,
|
||||
"rescale_factor": self.rescale_factor,
|
||||
"do_normalize": self.do_normalize,
|
||||
"image_mean": self.image_mean,
|
||||
"image_std": self.image_std,
|
||||
"do_pad": self.do_pad,
|
||||
}
|
||||
|
||||
def get_expected_values(self, image_inputs, batched=False):
|
||||
@@ -84,14 +92,14 @@ class DetrFeatureExtractionTester(unittest.TestCase):
|
||||
else:
|
||||
h, w = image.shape[1], image.shape[2]
|
||||
if w < h:
|
||||
expected_height = int(self.size * h / w)
|
||||
expected_width = self.size
|
||||
expected_height = int(self.size["shortest_edge"] * h / w)
|
||||
expected_width = self.size["shortest_edge"]
|
||||
elif w > h:
|
||||
expected_height = self.size
|
||||
expected_width = int(self.size * w / h)
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = int(self.size["shortest_edge"] * w / h)
|
||||
else:
|
||||
expected_height = self.size
|
||||
expected_width = self.size
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = self.size["shortest_edge"]
|
||||
|
||||
else:
|
||||
expected_values = []
|
||||
@@ -122,9 +130,11 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
|
||||
self.assertTrue(hasattr(feature_extractor, "image_mean"))
|
||||
self.assertTrue(hasattr(feature_extractor, "image_std"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_normalize"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_rescale"))
|
||||
self.assertTrue(hasattr(feature_extractor, "rescale_factor"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_resize"))
|
||||
self.assertTrue(hasattr(feature_extractor, "size"))
|
||||
self.assertTrue(hasattr(feature_extractor, "max_size"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_pad"))
|
||||
|
||||
def test_batch_feature(self):
|
||||
pass
|
||||
@@ -230,7 +240,7 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
|
||||
def test_equivalence_pad_and_create_pixel_mask(self):
|
||||
# Initialize feature_extractors
|
||||
feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
|
||||
feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False)
|
||||
feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False, do_rescale=False)
|
||||
# create random PyTorch tensors
|
||||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
|
||||
for image in image_inputs:
|
||||
@@ -331,7 +341,7 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
|
||||
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
|
||||
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
|
||||
# verify masks
|
||||
expected_masks_sum = 822338
|
||||
expected_masks_sum = 822873
|
||||
self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
|
||||
# verify orig_size
|
||||
expected_orig_size = torch.tensor([480, 640])
|
||||
|
||||
@@ -31,7 +31,7 @@ if is_torch_available():
|
||||
|
||||
if is_vision_available():
|
||||
from transformers import MaskFormerFeatureExtractor
|
||||
from transformers.models.maskformer.feature_extraction_maskformer import binary_mask_to_rle
|
||||
from transformers.models.maskformer.image_processing_maskformer import binary_mask_to_rle
|
||||
from transformers.models.maskformer.modeling_maskformer import MaskFormerForInstanceSegmentationOutput
|
||||
|
||||
if is_vision_available():
|
||||
@@ -46,9 +46,8 @@ class MaskFormerFeatureExtractionTester(unittest.TestCase):
|
||||
num_channels=3,
|
||||
min_resolution=30,
|
||||
max_resolution=400,
|
||||
size=None,
|
||||
do_resize=True,
|
||||
size=32,
|
||||
max_size=1333, # by setting max_size > max_resolution we're effectively not testing this :p
|
||||
do_normalize=True,
|
||||
image_mean=[0.5, 0.5, 0.5],
|
||||
image_std=[0.5, 0.5, 0.5],
|
||||
@@ -62,12 +61,11 @@ class MaskFormerFeatureExtractionTester(unittest.TestCase):
|
||||
self.min_resolution = min_resolution
|
||||
self.max_resolution = max_resolution
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.max_size = max_size
|
||||
self.size = {"shortest_edge": 32, "longest_edge": 1333} if size is None else size
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.size_divisibility = 0
|
||||
self.size_divisor = 0
|
||||
# for the post_process_functions
|
||||
self.batch_size = 2
|
||||
self.num_queries = 3
|
||||
@@ -82,11 +80,10 @@ class MaskFormerFeatureExtractionTester(unittest.TestCase):
|
||||
return {
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
"max_size": self.max_size,
|
||||
"do_normalize": self.do_normalize,
|
||||
"image_mean": self.image_mean,
|
||||
"image_std": self.image_std,
|
||||
"size_divisibility": self.size_divisibility,
|
||||
"size_divisor": self.size_divisor,
|
||||
"num_labels": self.num_labels,
|
||||
"reduce_labels": self.reduce_labels,
|
||||
"ignore_index": self.ignore_index,
|
||||
@@ -104,14 +101,14 @@ class MaskFormerFeatureExtractionTester(unittest.TestCase):
|
||||
else:
|
||||
h, w = image.shape[1], image.shape[2]
|
||||
if w < h:
|
||||
expected_height = int(self.size * h / w)
|
||||
expected_width = self.size
|
||||
expected_height = int(self.size["shortest_edge"] * h / w)
|
||||
expected_width = self.size["shortest_edge"]
|
||||
elif w > h:
|
||||
expected_height = self.size
|
||||
expected_width = int(self.size * w / h)
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = int(self.size["shortest_edge"] * w / h)
|
||||
else:
|
||||
expected_height = self.size
|
||||
expected_width = self.size
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = self.size["shortest_edge"]
|
||||
|
||||
else:
|
||||
expected_values = []
|
||||
@@ -260,7 +257,7 @@ class MaskFormerFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest
|
||||
# Initialize feature_extractors
|
||||
feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
|
||||
feature_extractor_2 = self.feature_extraction_class(
|
||||
do_resize=False, do_normalize=False, num_labels=self.feature_extract_tester.num_classes
|
||||
do_resize=False, do_normalize=False, do_rescale=False, num_labels=self.feature_extract_tester.num_classes
|
||||
)
|
||||
# create random PyTorch tensors
|
||||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
|
||||
@@ -283,23 +280,23 @@ class MaskFormerFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest
|
||||
):
|
||||
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
|
||||
# prepare image and target
|
||||
batch_size = self.feature_extract_tester.batch_size
|
||||
num_labels = self.feature_extract_tester.num_labels
|
||||
annotations = None
|
||||
instance_id_to_semantic_id = None
|
||||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
|
||||
if with_segmentation_maps:
|
||||
high = num_labels
|
||||
if is_instance_map:
|
||||
high * 2
|
||||
labels_expanded = list(range(num_labels)) * 2
|
||||
instance_id_to_semantic_id = {
|
||||
instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
|
||||
}
|
||||
annotations = [np.random.randint(0, high, (384, 384)).astype(np.uint8) for _ in range(batch_size)]
|
||||
annotations = [
|
||||
np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
|
||||
]
|
||||
if segmentation_type == "pil":
|
||||
annotations = [Image.fromarray(annotation) for annotation in annotations]
|
||||
|
||||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
|
||||
inputs = feature_extractor(
|
||||
image_inputs,
|
||||
annotations,
|
||||
@@ -313,18 +310,18 @@ class MaskFormerFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest
|
||||
def test_init_without_params(self):
|
||||
pass
|
||||
|
||||
def test_with_size_divisibility(self):
|
||||
size_divisibilities = [8, 16, 32]
|
||||
def test_with_size_divisor(self):
|
||||
size_divisors = [8, 16, 32]
|
||||
weird_input_sizes = [(407, 802), (582, 1094)]
|
||||
for size_divisibility in size_divisibilities:
|
||||
feat_extract_dict = {**self.feat_extract_dict, **{"size_divisibility": size_divisibility}}
|
||||
for size_divisor in size_divisors:
|
||||
feat_extract_dict = {**self.feat_extract_dict, **{"size_divisor": size_divisor}}
|
||||
feature_extractor = self.feature_extraction_class(**feat_extract_dict)
|
||||
for weird_input_size in weird_input_sizes:
|
||||
inputs = feature_extractor([np.ones((3, *weird_input_size))], return_tensors="pt")
|
||||
pixel_values = inputs["pixel_values"]
|
||||
# check if divisible
|
||||
self.assertTrue((pixel_values.shape[-1] % size_divisibility) == 0)
|
||||
self.assertTrue((pixel_values.shape[-2] % size_divisibility) == 0)
|
||||
self.assertTrue((pixel_values.shape[-1] % size_divisor) == 0)
|
||||
self.assertTrue((pixel_values.shape[-2] % size_divisor) == 0)
|
||||
|
||||
def test_call_with_segmentation_maps(self):
|
||||
def common(is_instance_map=False, segmentation_type=None):
|
||||
|
||||
@@ -43,9 +43,9 @@ class OwlViTFeatureExtractionTester(unittest.TestCase):
|
||||
min_resolution=30,
|
||||
max_resolution=400,
|
||||
do_resize=True,
|
||||
size=20,
|
||||
size=None,
|
||||
do_center_crop=True,
|
||||
crop_size=18,
|
||||
crop_size=None,
|
||||
do_normalize=True,
|
||||
image_mean=[0.48145466, 0.4578275, 0.40821073],
|
||||
image_std=[0.26862954, 0.26130258, 0.27577711],
|
||||
@@ -58,9 +58,9 @@ class OwlViTFeatureExtractionTester(unittest.TestCase):
|
||||
self.min_resolution = min_resolution
|
||||
self.max_resolution = max_resolution
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.size = size if size is not None else {"height": 18, "width": 18}
|
||||
self.do_center_crop = do_center_crop
|
||||
self.crop_size = crop_size
|
||||
self.crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
@@ -119,8 +119,8 @@ class OwlViTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Tes
|
||||
(
|
||||
1,
|
||||
self.feature_extract_tester.num_channels,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size["height"],
|
||||
self.feature_extract_tester.crop_size["width"],
|
||||
),
|
||||
)
|
||||
|
||||
@@ -131,8 +131,8 @@ class OwlViTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Tes
|
||||
(
|
||||
self.feature_extract_tester.batch_size,
|
||||
self.feature_extract_tester.num_channels,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size["height"],
|
||||
self.feature_extract_tester.crop_size["width"],
|
||||
),
|
||||
)
|
||||
|
||||
@@ -151,8 +151,8 @@ class OwlViTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Tes
|
||||
(
|
||||
1,
|
||||
self.feature_extract_tester.num_channels,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size["height"],
|
||||
self.feature_extract_tester.crop_size["width"],
|
||||
),
|
||||
)
|
||||
|
||||
@@ -163,8 +163,8 @@ class OwlViTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Tes
|
||||
(
|
||||
self.feature_extract_tester.batch_size,
|
||||
self.feature_extract_tester.num_channels,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size["height"],
|
||||
self.feature_extract_tester.crop_size["width"],
|
||||
),
|
||||
)
|
||||
|
||||
@@ -183,8 +183,8 @@ class OwlViTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Tes
|
||||
(
|
||||
1,
|
||||
self.feature_extract_tester.num_channels,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size["height"],
|
||||
self.feature_extract_tester.crop_size["width"],
|
||||
),
|
||||
)
|
||||
|
||||
@@ -195,7 +195,7 @@ class OwlViTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Tes
|
||||
(
|
||||
self.feature_extract_tester.batch_size,
|
||||
self.feature_extract_tester.num_channels,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size,
|
||||
self.feature_extract_tester.crop_size["height"],
|
||||
self.feature_extract_tester.crop_size["width"],
|
||||
),
|
||||
)
|
||||
|
||||
@@ -44,12 +44,16 @@ class YolosFeatureExtractionTester(unittest.TestCase):
|
||||
min_resolution=30,
|
||||
max_resolution=400,
|
||||
do_resize=True,
|
||||
size=18,
|
||||
max_size=1333, # by setting max_size > max_resolution we're effectively not testing this :p
|
||||
size=None,
|
||||
do_normalize=True,
|
||||
image_mean=[0.5, 0.5, 0.5],
|
||||
image_std=[0.5, 0.5, 0.5],
|
||||
do_rescale=True,
|
||||
rescale_factor=1 / 255,
|
||||
do_pad=True,
|
||||
):
|
||||
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
|
||||
size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.num_channels = num_channels
|
||||
@@ -57,19 +61,23 @@ class YolosFeatureExtractionTester(unittest.TestCase):
|
||||
self.max_resolution = max_resolution
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.max_size = max_size
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.do_rescale = do_rescale
|
||||
self.rescale_factor = rescale_factor
|
||||
self.do_pad = do_pad
|
||||
|
||||
def prepare_feat_extract_dict(self):
|
||||
return {
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
"max_size": self.max_size,
|
||||
"do_normalize": self.do_normalize,
|
||||
"image_mean": self.image_mean,
|
||||
"image_std": self.image_std,
|
||||
"do_rescale": self.do_rescale,
|
||||
"rescale_factor": self.rescale_factor,
|
||||
"do_pad": self.do_pad,
|
||||
}
|
||||
|
||||
def get_expected_values(self, image_inputs, batched=False):
|
||||
@@ -84,14 +92,14 @@ class YolosFeatureExtractionTester(unittest.TestCase):
|
||||
else:
|
||||
h, w = image.shape[1], image.shape[2]
|
||||
if w < h:
|
||||
expected_height = int(self.size * h / w)
|
||||
expected_width = self.size
|
||||
expected_height = int(self.size["shortest_edge"] * h / w)
|
||||
expected_width = self.size["shortest_edge"]
|
||||
elif w > h:
|
||||
expected_height = self.size
|
||||
expected_width = int(self.size * w / h)
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = int(self.size["shortest_edge"] * w / h)
|
||||
else:
|
||||
expected_height = self.size
|
||||
expected_width = self.size
|
||||
expected_height = self.size["shortest_edge"]
|
||||
expected_width = self.size["shortest_edge"]
|
||||
|
||||
else:
|
||||
expected_values = []
|
||||
@@ -124,7 +132,6 @@ class YolosFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Test
|
||||
self.assertTrue(hasattr(feature_extractor, "do_normalize"))
|
||||
self.assertTrue(hasattr(feature_extractor, "do_resize"))
|
||||
self.assertTrue(hasattr(feature_extractor, "size"))
|
||||
self.assertTrue(hasattr(feature_extractor, "max_size"))
|
||||
|
||||
def test_batch_feature(self):
|
||||
pass
|
||||
@@ -230,7 +237,7 @@ class YolosFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Test
|
||||
def test_equivalence_padding(self):
|
||||
# Initialize feature_extractors
|
||||
feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
|
||||
feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False)
|
||||
feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False, do_rescale=False)
|
||||
# create random PyTorch tensors
|
||||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
|
||||
for image in image_inputs:
|
||||
@@ -328,7 +335,7 @@ class YolosFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Test
|
||||
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
|
||||
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
|
||||
# verify masks
|
||||
expected_masks_sum = 822338
|
||||
expected_masks_sum = 822873
|
||||
self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
|
||||
# verify orig_size
|
||||
expected_orig_size = torch.tensor([480, 640])
|
||||
|
||||
Reference in New Issue
Block a user