| import evaluate | |
| from tqdm import tqdm | |
| model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") | |
| module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco) | |
| val_dataloader = torch.utils.data.DataLoader( | |
| test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn | |
| ) | |
| with torch.no_grad(): | |
| for idx, batch in enumerate(tqdm(val_dataloader)): | |
| pixel_values = batch["pixel_values"] | |
| pixel_mask = batch["pixel_mask"] | |
| labels = [ | |
| {k: v for k, v in t.items()} for t in batch["labels"] | |
| ] # these are in DETR format, resized + normalized | |
| # forward pass | |
| outputs = model(pixel_values=pixel_values, pixel_mask=pixel_mask) | |
| orig_target_sizes = torch.stack([target["orig_size"] for target in labels], dim=0) | |
| results = im_processor.post_process(outputs, orig_target_sizes) # convert outputs of model to Pascal VOC format (xmin, ymin, xmax, ymax) | |
| module.add(prediction=results, reference=labels) | |
| del batch | |
| results = module.compute() | |
| print(results) | |
| Accumulating evaluation results | |
| DONE (t=0.08s). |