bdcfbb9625
Import the official Hugging Face ecosystem skills and sync the\nexisting local coverage with upstream metadata and assets.\n\nRegenerate the canonical catalog, plugin mirrors, docs, and release\nnotes after the maintainer merge batch so main stays in sync.\n\nFixes #417
710 lines
27 KiB
Python
710 lines
27 KiB
Python
# /// script
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# dependencies = [
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# "transformers>=5.2.0",
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# "accelerate>=1.1.0",
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# "albumentations >= 1.4.16",
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# "timm",
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# "datasets>=4.0",
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# "torchmetrics",
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# "pycocotools",
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# "trackio",
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# "huggingface_hub",
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# ]
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# ///
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"""Finetuning any 🤗 Transformers model supported by AutoModelForObjectDetection for object detection leveraging the Trainer API."""
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import logging
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import math
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import os
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import sys
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from collections.abc import Mapping
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from dataclasses import dataclass, field
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from functools import partial
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from typing import Any
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import albumentations as A
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import numpy as np
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import torch
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from datasets import load_dataset
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from torchmetrics.detection.mean_ap import MeanAveragePrecision
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import trackio
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import transformers
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from transformers import (
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AutoConfig,
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AutoImageProcessor,
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AutoModelForObjectDetection,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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)
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from transformers.image_processing_utils import BatchFeature
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from transformers.image_transforms import center_to_corners_format
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from transformers.trainer import EvalPrediction
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from transformers.utils import check_min_version
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from transformers.utils.versions import require_version
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logger = logging.getLogger(__name__)
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.57.0.dev0")
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require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/object-detection/requirements.txt")
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@dataclass
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class ModelOutput:
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logits: torch.Tensor
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pred_boxes: torch.Tensor
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def format_image_annotations_as_coco(
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image_id: str, categories: list[int], areas: list[float], bboxes: list[tuple[float]]
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) -> dict:
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"""Format one set of image annotations to the COCO format
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Args:
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image_id (str): image id. e.g. "0001"
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categories (list[int]): list of categories/class labels corresponding to provided bounding boxes
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areas (list[float]): list of corresponding areas to provided bounding boxes
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bboxes (list[tuple[float]]): list of bounding boxes provided in COCO format
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([center_x, center_y, width, height] in absolute coordinates)
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Returns:
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dict: {
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"image_id": image id,
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"annotations": list of formatted annotations
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}
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"""
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annotations = []
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for category, area, bbox in zip(categories, areas, bboxes):
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formatted_annotation = {
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"image_id": image_id,
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"category_id": category,
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"iscrowd": 0,
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"area": area,
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"bbox": list(bbox),
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}
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annotations.append(formatted_annotation)
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return {
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"image_id": image_id,
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"annotations": annotations,
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}
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def detect_bbox_format_from_samples(dataset, image_col="image", objects_col="objects", num_samples=50):
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"""
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Detect whether bboxes are xyxy (Pascal VOC) or xywh (COCO) by checking
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bbox coordinates against image dimensions. The correct format interpretation
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should keep bboxes within image bounds.
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"""
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exceeds_if_xywh = 0
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exceeds_if_xyxy = 0
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total = 0
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for example in dataset.select(range(min(num_samples, len(dataset)))):
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img_w, img_h = example[image_col].size
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for bbox in example[objects_col]["bbox"]:
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if len(bbox) != 4:
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continue
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a, b, c, d = float(bbox[0]), float(bbox[1]), float(bbox[2]), float(bbox[3])
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total += 1
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# If 3rd < 1st or 4th < 2nd, can't be xyxy (x_max must exceed x_min)
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if c < a or d < b:
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return "xywh"
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# xywh: right/bottom edge = origin + size; exceeding image → wrong format
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if a + c > img_w * 1.05:
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exceeds_if_xywh += 1
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if b + d > img_h * 1.05:
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exceeds_if_xywh += 1
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# xyxy: right/bottom edge = coordinate itself
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if c > img_w * 1.05:
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exceeds_if_xyxy += 1
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if d > img_h * 1.05:
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exceeds_if_xyxy += 1
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if total == 0:
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return "xywh"
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fmt = "xyxy" if exceeds_if_xywh > exceeds_if_xyxy else "xywh"
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logger.info(
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f"Detected bbox format: {fmt} (checked {total} bboxes from {min(num_samples, len(dataset))} images)"
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)
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return fmt
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def sanitize_dataset(dataset, bbox_format="xywh", image_col="image", objects_col="objects"):
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"""
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Validate bboxes, convert xyxy→xywh if needed, clip to image bounds, and remove
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entries with non-finite values, non-positive dimensions, or degenerate area (<1 px).
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Drops images with no remaining valid bboxes.
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"""
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convert_xyxy = bbox_format == "xyxy"
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def _validate(example):
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img_w, img_h = example[image_col].size
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objects = example[objects_col]
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bboxes = objects["bbox"]
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n = len(bboxes)
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valid_indices = []
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converted_bboxes = []
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for i, bbox in enumerate(bboxes):
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if len(bbox) != 4:
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continue
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vals = [float(v) for v in bbox]
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if not all(math.isfinite(v) for v in vals):
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continue
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if convert_xyxy:
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x_min, y_min, x_max, y_max = vals
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w, h = x_max - x_min, y_max - y_min
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else:
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x_min, y_min, w, h = vals
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if w <= 0 or h <= 0:
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continue
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x_min, y_min = max(0.0, x_min), max(0.0, y_min)
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if x_min >= img_w or y_min >= img_h:
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continue
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w = min(w, img_w - x_min)
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h = min(h, img_h - y_min)
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if w * h < 1.0:
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continue
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valid_indices.append(i)
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converted_bboxes.append([x_min, y_min, w, h])
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# Rebuild objects dict, filtering all list-valued fields by valid_indices
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new_objects = {}
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for key, value in objects.items():
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if key == "bbox":
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new_objects["bbox"] = converted_bboxes
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elif isinstance(value, list) and len(value) == n:
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new_objects[key] = [value[j] for j in valid_indices]
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else:
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new_objects[key] = value
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if "area" not in new_objects or len(new_objects.get("area", [])) != len(converted_bboxes):
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new_objects["area"] = [b[2] * b[3] for b in converted_bboxes]
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example[objects_col] = new_objects
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return example
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before = len(dataset)
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dataset = dataset.map(_validate)
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dataset = dataset.filter(lambda ex: len(ex[objects_col]["bbox"]) > 0)
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after = len(dataset)
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if before != after:
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logger.warning(f"Dropped {before - after}/{before} images with no valid bboxes after sanitization")
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logger.info(f"Bbox sanitization complete: {after} images with valid bboxes remain")
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return dataset
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def convert_bbox_yolo_to_pascal(boxes: torch.Tensor, image_size: tuple[int, int]) -> torch.Tensor:
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"""
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Convert bounding boxes from YOLO format (x_center, y_center, width, height) in range [0, 1]
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to Pascal VOC format (x_min, y_min, x_max, y_max) in absolute coordinates.
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Args:
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boxes (torch.Tensor): Bounding boxes in YOLO format
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image_size (tuple[int, int]): Image size in format (height, width)
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Returns:
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torch.Tensor: Bounding boxes in Pascal VOC format (x_min, y_min, x_max, y_max)
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"""
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# convert center to corners format
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boxes = center_to_corners_format(boxes)
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if isinstance(image_size, torch.Tensor):
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image_size = image_size.tolist()
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elif isinstance(image_size, np.ndarray):
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image_size = image_size.tolist()
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height, width = image_size
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boxes = boxes * torch.tensor([[width, height, width, height]])
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return boxes
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def augment_and_transform_batch(
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examples: Mapping[str, Any],
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transform: A.Compose,
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image_processor: AutoImageProcessor,
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return_pixel_mask: bool = False,
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) -> BatchFeature:
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"""Apply augmentations and format annotations in COCO format for object detection task"""
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images = []
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annotations = []
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image_ids = examples["image_id"] if "image_id" in examples else range(len(examples["image"]))
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for image_id, image, objects in zip(image_ids, examples["image"], examples["objects"]):
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image = np.array(image.convert("RGB"))
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# Filter invalid bboxes before augmentation (safety net after sanitize_dataset)
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bboxes = objects["bbox"]
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categories = objects["category"]
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areas = objects["area"]
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valid = [
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(b, c, a)
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for b, c, a in zip(bboxes, categories, areas)
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if len(b) == 4 and b[2] > 0 and b[3] > 0 and b[0] >= 0 and b[1] >= 0
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]
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if valid:
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bboxes, categories, areas = zip(*valid)
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else:
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bboxes, categories, areas = [], [], []
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# apply augmentations
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output = transform(image=image, bboxes=list(bboxes), category=list(categories))
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images.append(output["image"])
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# format annotations in COCO format (recompute areas from post-augmentation bboxes)
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post_areas = [b[2] * b[3] for b in output["bboxes"]] if output["bboxes"] else []
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formatted_annotations = format_image_annotations_as_coco(
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image_id, output["category"], post_areas, output["bboxes"]
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)
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annotations.append(formatted_annotations)
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# Apply the image processor transformations: resizing, rescaling, normalization
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result = image_processor(images=images, annotations=annotations, return_tensors="pt")
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if not return_pixel_mask:
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result.pop("pixel_mask", None)
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return result
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def collate_fn(batch: list[BatchFeature]) -> Mapping[str, torch.Tensor | list[Any]]:
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data = {}
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data["pixel_values"] = torch.stack([x["pixel_values"] for x in batch])
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data["labels"] = [x["labels"] for x in batch]
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if "pixel_mask" in batch[0]:
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data["pixel_mask"] = torch.stack([x["pixel_mask"] for x in batch])
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return data
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@torch.no_grad()
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def compute_metrics(
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evaluation_results: EvalPrediction,
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image_processor: AutoImageProcessor,
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threshold: float = 0.0,
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id2label: Mapping[int, str] | None = None,
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) -> Mapping[str, float]:
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"""
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Compute mean average mAP, mAR and their variants for the object detection task.
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Args:
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evaluation_results (EvalPrediction): Predictions and targets from evaluation.
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threshold (float, optional): Threshold to filter predicted boxes by confidence. Defaults to 0.0.
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id2label (Optional[dict], optional): Mapping from class id to class name. Defaults to None.
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Returns:
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Mapping[str, float]: Metrics in a form of dictionary {<metric_name>: <metric_value>}
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"""
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predictions, targets = evaluation_results.predictions, evaluation_results.label_ids
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# For metric computation we need to provide:
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# - targets in a form of list of dictionaries with keys "boxes", "labels"
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# - predictions in a form of list of dictionaries with keys "boxes", "scores", "labels"
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image_sizes = []
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post_processed_targets = []
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post_processed_predictions = []
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# Collect targets in the required format for metric computation
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for batch in targets:
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# collect image sizes, we will need them for predictions post processing
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batch_image_sizes = torch.tensor([x["orig_size"] for x in batch])
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image_sizes.append(batch_image_sizes)
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# collect targets in the required format for metric computation
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# boxes were converted to YOLO format needed for model training
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# here we will convert them to Pascal VOC format (x_min, y_min, x_max, y_max)
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for image_target in batch:
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boxes = torch.tensor(image_target["boxes"])
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boxes = convert_bbox_yolo_to_pascal(boxes, image_target["orig_size"])
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labels = torch.tensor(image_target["class_labels"])
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post_processed_targets.append({"boxes": boxes, "labels": labels})
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# Collect predictions in the required format for metric computation,
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# model produce boxes in YOLO format, then image_processor convert them to Pascal VOC format
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for batch, target_sizes in zip(predictions, image_sizes):
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batch_logits, batch_boxes = batch[1], batch[2]
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output = ModelOutput(logits=torch.tensor(batch_logits), pred_boxes=torch.tensor(batch_boxes))
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post_processed_output = image_processor.post_process_object_detection(
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output, threshold=threshold, target_sizes=target_sizes
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)
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post_processed_predictions.extend(post_processed_output)
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# Compute metrics
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metric = MeanAveragePrecision(box_format="xyxy", class_metrics=True)
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metric.update(post_processed_predictions, post_processed_targets)
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metrics = metric.compute()
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# Replace list of per class metrics with separate metric for each class
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classes = metrics.pop("classes")
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map_per_class = metrics.pop("map_per_class")
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mar_100_per_class = metrics.pop("mar_100_per_class")
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# Single-class datasets return 0-d scalar tensors; make them iterable
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if classes.dim() == 0:
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classes = classes.unsqueeze(0)
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map_per_class = map_per_class.unsqueeze(0)
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mar_100_per_class = mar_100_per_class.unsqueeze(0)
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for class_id, class_map, class_mar in zip(classes, map_per_class, mar_100_per_class):
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class_name = id2label[class_id.item()] if id2label is not None else class_id.item()
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metrics[f"map_{class_name}"] = class_map
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metrics[f"mar_100_{class_name}"] = class_mar
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metrics = {k: round(v.item(), 4) for k, v in metrics.items()}
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return metrics
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify
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them on the command line.
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"""
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dataset_name: str = field(
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default="cppe-5",
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metadata={
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"help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
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},
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)
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dataset_config_name: str | None = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_val_split: float | None = field(
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default=0.15, metadata={"help": "Percent to split off of train for validation."}
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)
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image_square_size: int | None = field(
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default=600,
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metadata={"help": "Image longest size will be resized to this value, then image will be padded to square."},
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)
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max_train_samples: int | None = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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)
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},
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)
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max_eval_samples: int | None = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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)
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},
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)
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use_fast: bool | None = field(
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default=True,
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metadata={"help": "Use a fast torchvision-base image processor if it is supported for a given model."},
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)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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default="facebook/detr-resnet-50",
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},
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)
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config_name: str | None = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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cache_dir: str | None = field(
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default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
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ignore_mismatched_sizes: bool = field(
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default=True,
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metadata={
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"help": "Whether or not to raise an error if some of the weights from the checkpoint do not have the same size as the weights of the model (if for instance, you are instantiating a model with 10 labels from a checkpoint with 3 labels)."
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},
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)
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token: str = field(
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default=None,
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metadata={
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"help": (
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"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
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"generated when running `hf auth login` (stored in `~/.huggingface`)."
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)
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},
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)
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trust_remote_code: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether to trust the execution of code from datasets/models defined on the Hub."
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" This option should only be set to `True` for repositories you trust and in which you have read the"
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" code, as it will execute code present on the Hub on your local machine."
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)
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},
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)
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def main():
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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from huggingface_hub import login
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hf_token = os.environ.get("HF_TOKEN") or os.environ.get("hfjob")
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if hf_token:
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login(token=hf_token)
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training_args.hub_token = hf_token
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logger.info("Logged in to Hugging Face Hub")
|
|
elif training_args.push_to_hub:
|
|
logger.warning("HF_TOKEN not found in environment. Hub push will likely fail.")
|
|
|
|
# Initialize Trackio for real-time experiment tracking
|
|
trackio.init(project=training_args.output_dir, name=training_args.run_name)
|
|
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
handlers=[logging.StreamHandler(sys.stdout)],
|
|
)
|
|
|
|
if training_args.should_log:
|
|
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
|
|
transformers.utils.logging.set_verbosity_info()
|
|
|
|
log_level = training_args.get_process_log_level()
|
|
logger.setLevel(log_level)
|
|
transformers.utils.logging.set_verbosity(log_level)
|
|
transformers.utils.logging.enable_default_handler()
|
|
transformers.utils.logging.enable_explicit_format()
|
|
|
|
# Log on each process the small summary:
|
|
logger.warning(
|
|
f"Process rank: {training_args.local_process_index}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
|
|
+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
|
|
)
|
|
logger.info(f"Training/evaluation parameters {training_args}")
|
|
|
|
dataset = load_dataset(
|
|
data_args.dataset_name, cache_dir=model_args.cache_dir, trust_remote_code=model_args.trust_remote_code
|
|
)
|
|
|
|
bbox_format = detect_bbox_format_from_samples(dataset["train"])
|
|
if bbox_format == "xyxy":
|
|
logger.info("Converting bboxes from xyxy (Pascal VOC) → xywh (COCO) format across all splits")
|
|
for split_name in list(dataset.keys()):
|
|
dataset[split_name] = sanitize_dataset(dataset[split_name], bbox_format=bbox_format)
|
|
|
|
for split_name in list(dataset.keys()):
|
|
if "image_id" not in dataset[split_name].column_names:
|
|
dataset[split_name] = dataset[split_name].add_column(
|
|
"image_id", list(range(len(dataset[split_name])))
|
|
)
|
|
|
|
dataset["train"] = dataset["train"].shuffle(seed=training_args.seed)
|
|
|
|
data_args.train_val_split = None if "validation" in dataset else data_args.train_val_split
|
|
if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
|
|
split = dataset["train"].train_test_split(data_args.train_val_split, seed=training_args.seed)
|
|
dataset["train"] = split["train"]
|
|
dataset["validation"] = split["test"]
|
|
|
|
categories = None
|
|
try:
|
|
if isinstance(dataset["train"].features["objects"], dict):
|
|
cat_feature = dataset["train"].features["objects"]["category"].feature
|
|
else:
|
|
cat_feature = dataset["train"].features["objects"].feature["category"]
|
|
|
|
if hasattr(cat_feature, "names"):
|
|
categories = cat_feature.names
|
|
except (AttributeError, KeyError):
|
|
pass
|
|
|
|
if categories is None:
|
|
# Category is a Value type (not ClassLabel) — scan dataset to discover labels
|
|
logger.info("Category feature is not ClassLabel — scanning dataset to discover category labels...")
|
|
unique_cats = set()
|
|
for example in dataset["train"]:
|
|
cats = example["objects"]["category"]
|
|
if isinstance(cats, list):
|
|
unique_cats.update(cats)
|
|
else:
|
|
unique_cats.add(cats)
|
|
|
|
if all(isinstance(c, int) for c in unique_cats):
|
|
max_cat = max(unique_cats)
|
|
categories = [f"class_{i}" for i in range(max_cat + 1)]
|
|
elif all(isinstance(c, str) for c in unique_cats):
|
|
categories = sorted(unique_cats)
|
|
else:
|
|
categories = [str(c) for c in sorted(unique_cats, key=str)]
|
|
logger.info(f"Discovered {len(categories)} categories: {categories}")
|
|
|
|
id2label = dict(enumerate(categories))
|
|
label2id = {v: k for k, v in id2label.items()}
|
|
|
|
# Remap string categories to integer IDs if needed
|
|
sample_cats = dataset["train"][0]["objects"]["category"]
|
|
if sample_cats and isinstance(sample_cats[0], str):
|
|
logger.info(f"Remapping string categories to integer IDs: {label2id}")
|
|
|
|
def _remap_categories(example):
|
|
objects = example["objects"]
|
|
objects["category"] = [label2id[c] for c in objects["category"]]
|
|
example["objects"] = objects
|
|
return example
|
|
|
|
for split_name in list(dataset.keys()):
|
|
dataset[split_name] = dataset[split_name].map(_remap_categories)
|
|
logger.info("Category remapping complete")
|
|
|
|
if data_args.max_train_samples is not None:
|
|
max_train = min(data_args.max_train_samples, len(dataset["train"]))
|
|
dataset["train"] = dataset["train"].select(range(max_train))
|
|
logger.info(f"Truncated training set to {max_train} samples")
|
|
if data_args.max_eval_samples is not None and "validation" in dataset:
|
|
max_eval = min(data_args.max_eval_samples, len(dataset["validation"]))
|
|
dataset["validation"] = dataset["validation"].select(range(max_eval))
|
|
logger.info(f"Truncated validation set to {max_eval} samples")
|
|
|
|
common_pretrained_args = {
|
|
"cache_dir": model_args.cache_dir,
|
|
"revision": model_args.model_revision,
|
|
"token": model_args.token,
|
|
"trust_remote_code": model_args.trust_remote_code,
|
|
}
|
|
config = AutoConfig.from_pretrained(
|
|
model_args.config_name or model_args.model_name_or_path,
|
|
label2id=label2id,
|
|
id2label=id2label,
|
|
**common_pretrained_args,
|
|
)
|
|
model = AutoModelForObjectDetection.from_pretrained(
|
|
model_args.model_name_or_path,
|
|
config=config,
|
|
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
|
|
**common_pretrained_args,
|
|
)
|
|
image_processor = AutoImageProcessor.from_pretrained(
|
|
model_args.image_processor_name or model_args.model_name_or_path,
|
|
do_resize=True,
|
|
size={"max_height": data_args.image_square_size, "max_width": data_args.image_square_size},
|
|
do_pad=True,
|
|
pad_size={"height": data_args.image_square_size, "width": data_args.image_square_size},
|
|
use_fast=data_args.use_fast,
|
|
**common_pretrained_args,
|
|
)
|
|
|
|
max_size = data_args.image_square_size
|
|
train_augment_and_transform = A.Compose(
|
|
[
|
|
A.Compose(
|
|
[
|
|
A.SmallestMaxSize(max_size=max_size, p=1.0),
|
|
A.RandomSizedBBoxSafeCrop(height=max_size, width=max_size, p=1.0),
|
|
],
|
|
p=0.2,
|
|
),
|
|
A.OneOf(
|
|
[
|
|
A.Blur(blur_limit=7, p=0.5),
|
|
A.MotionBlur(blur_limit=7, p=0.5),
|
|
A.Defocus(radius=(1, 5), alias_blur=(0.1, 0.25), p=0.1),
|
|
],
|
|
p=0.1,
|
|
),
|
|
A.Perspective(p=0.1),
|
|
A.HorizontalFlip(p=0.5),
|
|
A.RandomBrightnessContrast(p=0.5),
|
|
A.HueSaturationValue(p=0.1),
|
|
],
|
|
bbox_params=A.BboxParams(format="coco", label_fields=["category"], clip=True, min_area=25),
|
|
)
|
|
validation_transform = A.Compose(
|
|
[A.NoOp()],
|
|
bbox_params=A.BboxParams(format="coco", label_fields=["category"], clip=True),
|
|
)
|
|
|
|
train_transform_batch = partial(
|
|
augment_and_transform_batch, transform=train_augment_and_transform, image_processor=image_processor
|
|
)
|
|
validation_transform_batch = partial(
|
|
augment_and_transform_batch, transform=validation_transform, image_processor=image_processor
|
|
)
|
|
|
|
dataset["train"] = dataset["train"].with_transform(train_transform_batch)
|
|
dataset["validation"] = dataset["validation"].with_transform(validation_transform_batch)
|
|
if "test" in dataset:
|
|
dataset["test"] = dataset["test"].with_transform(validation_transform_batch)
|
|
|
|
|
|
eval_compute_metrics_fn = partial(
|
|
compute_metrics, image_processor=image_processor, id2label=id2label, threshold=0.0
|
|
)
|
|
|
|
trainer = Trainer(
|
|
model=model,
|
|
args=training_args,
|
|
train_dataset=dataset["train"] if training_args.do_train else None,
|
|
eval_dataset=dataset["validation"] if training_args.do_eval else None,
|
|
processing_class=image_processor,
|
|
data_collator=collate_fn,
|
|
compute_metrics=eval_compute_metrics_fn,
|
|
)
|
|
|
|
# Training
|
|
if training_args.do_train:
|
|
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
|
|
trainer.save_model()
|
|
trainer.log_metrics("train", train_result.metrics)
|
|
trainer.save_metrics("train", train_result.metrics)
|
|
trainer.save_state()
|
|
|
|
if training_args.do_eval:
|
|
test_dataset = dataset["test"] if "test" in dataset else dataset["validation"]
|
|
test_prefix = "test" if "test" in dataset else "eval"
|
|
metrics = trainer.evaluate(eval_dataset=test_dataset, metric_key_prefix=test_prefix)
|
|
trainer.log_metrics(test_prefix, metrics)
|
|
trainer.save_metrics(test_prefix, metrics)
|
|
|
|
trackio.finish()
|
|
|
|
kwargs = {
|
|
"finetuned_from": model_args.model_name_or_path,
|
|
"dataset": data_args.dataset_name,
|
|
"tags": ["object-detection", "vision"],
|
|
}
|
|
if training_args.push_to_hub:
|
|
trainer.push_to_hub(**kwargs)
|
|
else:
|
|
trainer.create_model_card(**kwargs)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |