{"record":{"id":"1ee86fb9c467e488","repo":"huggingface/pytorch-image-models","slug":"error-processing-sample-index-idx-error-e-s","errorCode":null,"errorMessage":"Error processing sample index {idx}. Error: {e}. Skipping sample.","messagePattern":"Error processing sample index (.+?)\\. Error: (.+?)\\. Skipping sample\\.","errorType":"console","errorClass":null,"httpStatus":null,"severity":"warning","filePath":"timm/data/naflex_dataset.py","lineNumber":555,"sourceCode":"                    # Get original image and label from map-style dataset\n                    img, label = self.base_dataset[idx]\n\n                    # Apply transform if available\n                    # Handle cases where transform might return None or fail\n                    processed_img = transform(img) if transform else img\n                    if processed_img is None:\n                        warnings.warn(f\"Transform returned None for index {idx}. Skipping sample.\")\n                        continue\n\n                    batch_imgs.append(processed_img)\n                    batch_targets.append(label)\n\n                except IndexError:\n                     warnings.warn(f\"IndexError encountered for index {idx} (possibly due to padding/repeated indices). Skipping sample.\")\n                     continue\n                except Exception as e:\n                    # Log other potential errors during data loading/processing\n                    warnings.warn(f\"Error processing sample index {idx}. Error: {e}. Skipping sample.\")\n                    continue # Skip problematic sample\n\n            if self.mixup_fn is not None:\n                batch_imgs, batch_targets = self.mixup_fn(batch_imgs, batch_targets)\n\n            batch_imgs = [batch_patchifier(img) for img in batch_imgs]\n            batch_samples = list(zip(batch_imgs, batch_targets))\n            if batch_samples: # Only yield if we successfully processed samples\n                # Collate the processed samples into a batch\n                yield self.collate_fns[seq_len](batch_samples)\n\n            # If batch_samples is empty after processing 'indices', an empty batch is skipped.\n","sourceCodeStart":537,"sourceCodeEnd":568,"githubUrl":"https://github.com/huggingface/pytorch-image-models/blob/9a5261e31b3b5128526eb2658333b4c0a54464ae/timm/data/naflex_dataset.py#L537-L568","documentation":"During NaFlexDataset iteration, per-sample loading and transform execution is wrapped in try/except; any non-IndexError exception (corrupt image, decode failure, transform error) triggers a warning and the sample is skipped so training continues.","triggerScenarios":"A dataset item whose image is corrupt/unreadable, or a transform (e.g. naflex patchify/resize) raising on an odd-sized or grayscale image; PIL decode errors; FileNotFoundError for missing files.","commonSituations":"Web-scraped datasets with broken files; mixed image modes (L/CMYK) hitting transforms expecting RGB; filesystem issues. Repeated warnings indicate real data corruption, not a bug in the loader.","solutions":["Inspect the warned indices and remove/repair those dataset entries (filter the underlying dataset)","Verify files exist and open with PIL before training; normalize images to RGB in the transform","If warnings are frequent, run a preprocessing pass to validate/clean the dataset"],"exampleFix":"# before\nds = NaFlexDataset(...)\n# after — pre-filter broken samples\nfrom PIL import Image\nclean = [i for i in range(len(base_ds)) if _opens_ok(base_ds, i)]\nbase_ds = torch.utils.data.Subset(base_ds, clean)\nds = NaFlexDataset(base_ds, ...)","handlingStrategy":"fallback","validationCode":"from PIL import Image\\ndef ok(i, ds):\\n    try:\\n        img, _ = ds[i]; Image.open if False else None\\n        return img is not None\\n    except Exception:\\n        return False","typeGuard":null,"tryCatchPattern":null,"preventionTips":["Pre-validate datasets once with a scan pass instead of relying on per-iteration skips","Run a canary epoch and count warnings to gauge corruption rate"],"tags":["dataset","robust-loading","naflex","timm"],"backgroundTag":"corrupt-dataset-sample","analyzedSha":"9a5261e31b3b5128526eb2658333b4c0a54464ae","analyzedAt":"2026-08-27T02:34:25.417Z","schemaVersion":2},"datasetVersion":"2026-08-27T03:17:27.898Z"}