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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import contextlib |
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import json |
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from collections import defaultdict |
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from itertools import repeat |
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from multiprocessing.pool import ThreadPool |
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from pathlib import Path |
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import cv2 |
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import numpy as np |
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import torch |
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from PIL import Image |
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from torch.utils.data import ConcatDataset |
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from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr |
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from ultralytics.utils.ops import resample_segments |
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from .augment import ( |
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Compose, |
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Format, |
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Instances, |
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LetterBox, |
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RandomLoadText, |
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classify_augmentations, |
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classify_transforms, |
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v8_transforms, |
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) |
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from .base import BaseDataset |
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from .utils import ( |
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HELP_URL, |
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LOGGER, |
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get_hash, |
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img2label_paths, |
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load_dataset_cache_file, |
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save_dataset_cache_file, |
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verify_image, |
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verify_image_label, |
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) |
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# Ultralytics dataset *.cache version, >= 1.0.0 for YOLOv8 |
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DATASET_CACHE_VERSION = "1.0.3" |
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class YOLODataset(BaseDataset): |
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""" |
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Dataset class for loading object detection and/or segmentation labels in YOLO format. |
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Args: |
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data (dict, optional): A dataset YAML dictionary. Defaults to None. |
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task (str): An explicit arg to point current task, Defaults to 'detect'. |
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Returns: |
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(torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model. |
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""" |
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def __init__(self, *args, data=None, task="detect", **kwargs): |
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"""Initializes the YOLODataset with optional configurations for segments and keypoints.""" |
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self.use_segments = task == "segment" |
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self.use_keypoints = task == "pose" |
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self.use_obb = task == "obb" |
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self.data = data |
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assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints." |
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super().__init__(*args, **kwargs) |
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def cache_labels(self, path=Path("./labels.cache")): |
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""" |
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Cache dataset labels, check images and read shapes. |
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Args: |
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path (Path): Path where to save the cache file. Default is Path('./labels.cache'). |
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Returns: |
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(dict): labels. |
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""" |
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x = {"labels": []} |
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nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages |
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desc = f"{self.prefix}Scanning {path.parent / path.stem}..." |
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total = len(self.im_files) |
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nkpt, ndim = self.data.get("kpt_shape", (0, 0)) |
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if self.use_keypoints and (nkpt <= 0 or ndim not in {2, 3}): |
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raise ValueError( |
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"'kpt_shape' in data.yaml missing or incorrect. Should be a list with [number of " |
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"keypoints, number of dims (2 for x,y or 3 for x,y,visible)], i.e. 'kpt_shape: [17, 3]'" |
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) |
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with ThreadPool(NUM_THREADS) as pool: |
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results = pool.imap( |
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func=verify_image_label, |
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iterable=zip( |
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self.im_files, |
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self.label_files, |
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repeat(self.prefix), |
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repeat(self.use_keypoints), |
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repeat(len(self.data["names"])), |
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repeat(nkpt), |
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repeat(ndim), |
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), |
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) |
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pbar = TQDM(results, desc=desc, total=total) |
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for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar: |
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nm += nm_f |
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nf += nf_f |
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ne += ne_f |
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nc += nc_f |
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if im_file: |
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x["labels"].append( |
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{ |
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"im_file": im_file, |
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"shape": shape, |
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"cls": lb[:, 0:1], # n, 1 |
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"bboxes": lb[:, 1:], # n, 4 |
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"segments": segments, |
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"keypoints": keypoint, |
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"normalized": True, |
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"bbox_format": "xywh", |
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} |
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) |
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if msg: |
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msgs.append(msg) |
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pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt" |
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pbar.close() |
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if msgs: |
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LOGGER.info("\n".join(msgs)) |
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if nf == 0: |
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LOGGER.warning(f"{self.prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}") |
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x["hash"] = get_hash(self.label_files + self.im_files) |
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x["results"] = nf, nm, ne, nc, len(self.im_files) |
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x["msgs"] = msgs # warnings |
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save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION) |
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return x |
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def get_labels(self): |
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"""Returns dictionary of labels for YOLO training.""" |
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self.label_files = img2label_paths(self.im_files) |
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cache_path = Path(self.label_files[0]).parent.with_suffix(".cache") |
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try: |
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cache, exists = load_dataset_cache_file(cache_path), True # attempt to load a *.cache file |
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assert cache["version"] == DATASET_CACHE_VERSION # matches current version |
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assert cache["hash"] == get_hash(self.label_files + self.im_files) # identical hash |
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except (FileNotFoundError, AssertionError, AttributeError): |
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cache, exists = self.cache_labels(cache_path), False # run cache ops |
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# Display cache |
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nf, nm, ne, nc, n = cache.pop("results") # found, missing, empty, corrupt, total |
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if exists and LOCAL_RANK in {-1, 0}: |
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d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt" |
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TQDM(None, desc=self.prefix + d, total=n, initial=n) # display results |
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if cache["msgs"]: |
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LOGGER.info("\n".join(cache["msgs"])) # display warnings |
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# Read cache |
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[cache.pop(k) for k in ("hash", "version", "msgs")] # remove items |
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labels = cache["labels"] |
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if not labels: |
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LOGGER.warning(f"WARNING ⚠️ No images found in {cache_path}, training may not work correctly. {HELP_URL}") |
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self.im_files = [lb["im_file"] for lb in labels] # update im_files |
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# Check if the dataset is all boxes or all segments |
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lengths = ((len(lb["cls"]), len(lb["bboxes"]), len(lb["segments"])) for lb in labels) |
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len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths)) |
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if len_segments and len_boxes != len_segments: |
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LOGGER.warning( |
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f"WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, " |
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f"len(boxes) = {len_boxes}. To resolve this only boxes will be used and all segments will be removed. " |
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"To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset." |
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) |
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for lb in labels: |
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lb["segments"] = [] |
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if len_cls == 0: |
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LOGGER.warning(f"WARNING ⚠️ No labels found in {cache_path}, training may not work correctly. {HELP_URL}") |
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return labels |
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def build_transforms(self, hyp=None): |
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"""Builds and appends transforms to the list.""" |
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if self.augment: |
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hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0 |
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hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0 |
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transforms = v8_transforms(self, self.imgsz, hyp) |
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else: |
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transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False)]) |
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transforms.append( |
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Format( |
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bbox_format="xywh", |
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normalize=True, |
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return_mask=self.use_segments, |
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return_keypoint=self.use_keypoints, |
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return_obb=self.use_obb, |
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batch_idx=True, |
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mask_ratio=hyp.mask_ratio, |
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mask_overlap=hyp.overlap_mask, |
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bgr=hyp.bgr if self.augment else 0.0, # only affect training. |
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) |
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) |
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return transforms |
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def close_mosaic(self, hyp): |
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"""Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations.""" |
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hyp.mosaic = 0.0 # set mosaic ratio=0.0 |
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hyp.copy_paste = 0.0 # keep the same behavior as previous v8 close-mosaic |
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hyp.mixup = 0.0 # keep the same behavior as previous v8 close-mosaic |
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self.transforms = self.build_transforms(hyp) |
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def update_labels_info(self, label): |
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""" |
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Custom your label format here. |
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Note: |
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cls is not with bboxes now, classification and semantic segmentation need an independent cls label |
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Can also support classification and semantic segmentation by adding or removing dict keys there. |
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""" |
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bboxes = label.pop("bboxes") |
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segments = label.pop("segments", []) |
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keypoints = label.pop("keypoints", None) |
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bbox_format = label.pop("bbox_format") |
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normalized = label.pop("normalized") |
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# NOTE: do NOT resample oriented boxes |
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segment_resamples = 100 if self.use_obb else 1000 |
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if len(segments) > 0: |
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# list[np.array(1000, 2)] * num_samples |
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# (N, 1000, 2) |
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segments = np.stack(resample_segments(segments, n=segment_resamples), axis=0) |
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else: |
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segments = np.zeros((0, segment_resamples, 2), dtype=np.float32) |
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label["instances"] = Instances(bboxes, segments, keypoints, bbox_format=bbox_format, normalized=normalized) |
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return label |
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@staticmethod |
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def collate_fn(batch): |
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"""Collates data samples into batches.""" |
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new_batch = {} |
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keys = batch[0].keys() |
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values = list(zip(*[list(b.values()) for b in batch])) |
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for i, k in enumerate(keys): |
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value = values[i] |
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if k == "img": |
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value = torch.stack(value, 0) |
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if k in {"masks", "keypoints", "bboxes", "cls", "segments", "obb"}: |
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value = torch.cat(value, 0) |
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new_batch[k] = value |
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new_batch["batch_idx"] = list(new_batch["batch_idx"]) |
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for i in range(len(new_batch["batch_idx"])): |
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new_batch["batch_idx"][i] += i # add target image index for build_targets() |
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new_batch["batch_idx"] = torch.cat(new_batch["batch_idx"], 0) |
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return new_batch |
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class YOLOMultiModalDataset(YOLODataset): |
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""" |
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Dataset class for loading object detection and/or segmentation labels in YOLO format. |
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Args: |
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data (dict, optional): A dataset YAML dictionary. Defaults to None. |
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task (str): An explicit arg to point current task, Defaults to 'detect'. |
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Returns: |
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(torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model. |
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""" |
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def __init__(self, *args, data=None, task="detect", **kwargs): |
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"""Initializes a dataset object for object detection tasks with optional specifications.""" |
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super().__init__(*args, data=data, task=task, **kwargs) |
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def update_labels_info(self, label): |
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"""Add texts information for multi modal model training.""" |
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labels = super().update_labels_info(label) |
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# NOTE: some categories are concatenated with its synonyms by `/`. |
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labels["texts"] = [v.split("/") for _, v in self.data["names"].items()] |
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return labels |
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def build_transforms(self, hyp=None): |
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"""Enhances data transformations with optional text augmentation for multi-modal training.""" |
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transforms = super().build_transforms(hyp) |
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if self.augment: |
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# NOTE: hard-coded the args for now. |
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transforms.insert(-1, RandomLoadText(max_samples=min(self.data["nc"], 80), padding=True)) |
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return transforms |
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class GroundingDataset(YOLODataset): |
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def __init__(self, *args, task="detect", json_file, **kwargs): |
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"""Initializes a GroundingDataset for object detection, loading annotations from a specified JSON file.""" |
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assert task == "detect", "`GroundingDataset` only support `detect` task for now!" |
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self.json_file = json_file |
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super().__init__(*args, task=task, data={}, **kwargs) |
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def get_img_files(self, img_path): |
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"""The image files would be read in `get_labels` function, return empty list here.""" |
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return [] |
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def get_labels(self): |
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"""Loads annotations from a JSON file, filters, and normalizes bounding boxes for each image.""" |
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labels = [] |
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LOGGER.info("Loading annotation file...") |
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with open(self.json_file, "r") as f: |
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annotations = json.load(f) |
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images = {f'{x["id"]:d}': x for x in annotations["images"]} |
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imgToAnns = defaultdict(list) |
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for ann in annotations["annotations"]: |
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imgToAnns[ann["image_id"]].append(ann) |
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for img_id, anns in TQDM(imgToAnns.items(), desc=f"Reading annotations {self.json_file}"): |
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img = images[f"{img_id:d}"] |
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h, w, f = img["height"], img["width"], img["file_name"] |
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im_file = Path(self.img_path) / f |
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if not im_file.exists(): |
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continue |
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self.im_files.append(str(im_file)) |
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bboxes = [] |
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cat2id = {} |
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texts = [] |
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for ann in anns: |
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if ann["iscrowd"]: |
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continue |
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box = np.array(ann["bbox"], dtype=np.float32) |
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box[:2] += box[2:] / 2 |
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box[[0, 2]] /= float(w) |
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box[[1, 3]] /= float(h) |
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if box[2] <= 0 or box[3] <= 0: |
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continue |
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cat_name = " ".join([img["caption"][t[0] : t[1]] for t in ann["tokens_positive"]]) |
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if cat_name not in cat2id: |
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cat2id[cat_name] = len(cat2id) |
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texts.append([cat_name]) |
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cls = cat2id[cat_name] # class |
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box = [cls] + box.tolist() |
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if box not in bboxes: |
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bboxes.append(box) |
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lb = np.array(bboxes, dtype=np.float32) if len(bboxes) else np.zeros((0, 5), dtype=np.float32) |
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labels.append( |
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{ |
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"im_file": im_file, |
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"shape": (h, w), |
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"cls": lb[:, 0:1], # n, 1 |
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"bboxes": lb[:, 1:], # n, 4 |
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"normalized": True, |
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"bbox_format": "xywh", |
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"texts": texts, |
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} |
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) |
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return labels |
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def build_transforms(self, hyp=None): |
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"""Configures augmentations for training with optional text loading; `hyp` adjusts augmentation intensity.""" |
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transforms = super().build_transforms(hyp) |
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if self.augment: |
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# NOTE: hard-coded the args for now. |
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transforms.insert(-1, RandomLoadText(max_samples=80, padding=True)) |
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return transforms |
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class YOLOConcatDataset(ConcatDataset): |
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""" |
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Dataset as a concatenation of multiple datasets. |
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This class is useful to assemble different existing datasets. |
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""" |
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@staticmethod |
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def collate_fn(batch): |
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"""Collates data samples into batches.""" |
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return YOLODataset.collate_fn(batch) |
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# TODO: support semantic segmentation |
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class SemanticDataset(BaseDataset): |
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""" |
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Semantic Segmentation Dataset. |
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This class is responsible for handling datasets used for semantic segmentation tasks. It inherits functionalities |
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from the BaseDataset class. |
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Note: |
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This class is currently a placeholder and needs to be populated with methods and attributes for supporting |
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semantic segmentation tasks. |
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""" |
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def __init__(self): |
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"""Initialize a SemanticDataset object.""" |
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super().__init__() |
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class ClassificationDataset: |
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""" |
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Extends torchvision ImageFolder to support YOLO classification tasks, offering functionalities like image |
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augmentation, caching, and verification. It's designed to efficiently handle large datasets for training deep |
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learning models, with optional image transformations and caching mechanisms to speed up training. |
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This class allows for augmentations using both torchvision and Albumentations libraries, and supports caching images |
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in RAM or on disk to reduce IO overhead during training. Additionally, it implements a robust verification process |
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to ensure data integrity and consistency. |
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Attributes: |
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cache_ram (bool): Indicates if caching in RAM is enabled. |
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cache_disk (bool): Indicates if caching on disk is enabled. |
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samples (list): A list of tuples, each containing the path to an image, its class index, path to its .npy cache |
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file (if caching on disk), and optionally the loaded image array (if caching in RAM). |
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torch_transforms (callable): PyTorch transforms to be applied to the images. |
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""" |
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def __init__(self, root, args, augment=False, prefix=""): |
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""" |
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Initialize YOLO object with root, image size, augmentations, and cache settings. |
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Args: |
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root (str): Path to the dataset directory where images are stored in a class-specific folder structure. |
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args (Namespace): Configuration containing dataset-related settings such as image size, augmentation |
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parameters, and cache settings. It includes attributes like `imgsz` (image size), `fraction` (fraction |
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of data to use), `scale`, `fliplr`, `flipud`, `cache` (disk or RAM caching for faster training), |
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`auto_augment`, `hsv_h`, `hsv_s`, `hsv_v`, and `crop_fraction`. |
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augment (bool, optional): Whether to apply augmentations to the dataset. Default is False. |
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prefix (str, optional): Prefix for logging and cache filenames, aiding in dataset identification and |
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debugging. Default is an empty string. |
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""" |
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import torchvision # scope for faster 'import ultralytics' |
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# Base class assigned as attribute rather than used as base class to allow for scoping slow torchvision import |
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self.base = torchvision.datasets.ImageFolder(root=root) |
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self.samples = self.base.samples |
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self.root = self.base.root |
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# Initialize attributes |
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if augment and args.fraction < 1.0: # reduce training fraction |
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self.samples = self.samples[: round(len(self.samples) * args.fraction)] |
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self.prefix = colorstr(f"{prefix}: ") if prefix else "" |
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self.cache_ram = args.cache is True or str(args.cache).lower() == "ram" # cache images into RAM |
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self.cache_disk = str(args.cache).lower() == "disk" # cache images on hard drive as uncompressed *.npy files |
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self.samples = self.verify_images() # filter out bad images |
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self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im |
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scale = (1.0 - args.scale, 1.0) # (0.08, 1.0) |
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self.torch_transforms = ( |
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|
classify_augmentations( |
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size=args.imgsz, |
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scale=scale, |
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hflip=args.fliplr, |
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|
vflip=args.flipud, |
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|
erasing=args.erasing, |
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|
auto_augment=args.auto_augment, |
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|
hsv_h=args.hsv_h, |
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|
hsv_s=args.hsv_s, |
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|
hsv_v=args.hsv_v, |
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|
) |
|
|
if augment |
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|
else classify_transforms(size=args.imgsz, crop_fraction=args.crop_fraction) |
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) |
|
|
|
|
|
def __getitem__(self, i): |
|
|
"""Returns subset of data and targets corresponding to given indices.""" |
|
|
f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image |
|
|
if self.cache_ram: |
|
|
if im is None: # Warning: two separate if statements required here, do not combine this with previous line |
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|
im = self.samples[i][3] = cv2.imread(f) |
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|
elif self.cache_disk: |
|
|
if not fn.exists(): # load npy |
|
|
np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False) |
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|
im = np.load(fn) |
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|
else: # read image |
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|
im = cv2.imread(f) # BGR |
|
|
# Convert NumPy array to PIL image |
|
|
im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB)) |
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|
sample = self.torch_transforms(im) |
|
|
return {"img": sample, "cls": j} |
|
|
|
|
|
def __len__(self) -> int: |
|
|
"""Return the total number of samples in the dataset.""" |
|
|
return len(self.samples) |
|
|
|
|
|
def verify_images(self): |
|
|
"""Verify all images in dataset.""" |
|
|
desc = f"{self.prefix}Scanning {self.root}..." |
|
|
path = Path(self.root).with_suffix(".cache") # *.cache file path |
|
|
|
|
|
with contextlib.suppress(FileNotFoundError, AssertionError, AttributeError): |
|
|
cache = load_dataset_cache_file(path) # attempt to load a *.cache file |
|
|
assert cache["version"] == DATASET_CACHE_VERSION # matches current version |
|
|
assert cache["hash"] == get_hash([x[0] for x in self.samples]) # identical hash |
|
|
nf, nc, n, samples = cache.pop("results") # found, missing, empty, corrupt, total |
|
|
if LOCAL_RANK in {-1, 0}: |
|
|
d = f"{desc} {nf} images, {nc} corrupt" |
|
|
TQDM(None, desc=d, total=n, initial=n) |
|
|
if cache["msgs"]: |
|
|
LOGGER.info("\n".join(cache["msgs"])) # display warnings |
|
|
return samples |
|
|
|
|
|
# Run scan if *.cache retrieval failed |
|
|
nf, nc, msgs, samples, x = 0, 0, [], [], {} |
|
|
with ThreadPool(NUM_THREADS) as pool: |
|
|
results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix))) |
|
|
pbar = TQDM(results, desc=desc, total=len(self.samples)) |
|
|
for sample, nf_f, nc_f, msg in pbar: |
|
|
if nf_f: |
|
|
samples.append(sample) |
|
|
if msg: |
|
|
msgs.append(msg) |
|
|
nf += nf_f |
|
|
nc += nc_f |
|
|
pbar.desc = f"{desc} {nf} images, {nc} corrupt" |
|
|
pbar.close() |
|
|
if msgs: |
|
|
LOGGER.info("\n".join(msgs)) |
|
|
x["hash"] = get_hash([x[0] for x in self.samples]) |
|
|
x["results"] = nf, nc, len(samples), samples |
|
|
x["msgs"] = msgs # warnings |
|
|
save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION) |
|
|
return samples
|
|
|
|
