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import torchvision
class CocoDetection(torchvision.datasets.CocoDetection):
def init(self, img_folder, image_processor, ann_file):
super().init(img_folder, ann_file)
self.image_processor = image_processor
def getitem(self, idx):
# read in PIL image and target in COCO format
img, target = super(CocoDetection, self).getitem(idx)
# preprocess image and target: converting target to DETR format,
# resizing + normalization of both image and target)
image_id = self.ids[idx]
target = {"image_id": image_id, "annotations": target}
encoding = self.image_processor(images=img, annotations=target, return_tensors="pt")
pixel_values = encoding["pixel_values"].squeeze() # remove batch dimension
target = encoding["labels"][0] # remove batch dimension
return {"pixel_values": pixel_values, "labels": target}
im_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"])
test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno)
Finally, load the metrics and run the evaluation. |