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# Ultralytics YOLO 🚀, GPL-3.0 license |
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from itertools import repeat |
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from multiprocessing.pool import Pool |
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from pathlib import Path |
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import torchvision |
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from tqdm import tqdm |
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from ..utils import NUM_THREADS, TQDM_BAR_FORMAT |
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from .augment import * |
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from .base import BaseDataset |
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from .utils import HELP_URL, LOCAL_RANK, get_hash, img2label_paths, verify_image_label |
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class YOLODataset(BaseDataset): |
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cache_version = 1.0 # dataset labels *.cache version, >= 1.0 for YOLOv8 |
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rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] |
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"""YOLO Dataset. |
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Args: |
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img_path (str): image path. |
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prefix (str): prefix. |
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""" |
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def __init__( |
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self, |
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img_path, |
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imgsz=640, |
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label_path=None, |
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cache=False, |
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augment=True, |
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hyp=None, |
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prefix="", |
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rect=False, |
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batch_size=None, |
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stride=32, |
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pad=0.0, |
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single_cls=False, |
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use_segments=False, |
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use_keypoints=False, |
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): |
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self.use_segments = use_segments |
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self.use_keypoints = use_keypoints |
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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__(img_path, imgsz, label_path, cache, augment, hyp, prefix, rect, batch_size, stride, pad, |
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single_cls) |
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def cache_labels(self, path=Path("./labels.cache")): |
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# Cache dataset labels, check images and read shapes |
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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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with Pool(NUM_THREADS) as pool: |
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pbar = tqdm( |
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pool.imap(verify_image_label, |
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zip(self.im_files, self.label_files, repeat(self.prefix), repeat(self.use_keypoints))), |
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desc=desc, |
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total=len(self.im_files), |
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bar_format=TQDM_BAR_FORMAT, |
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) |
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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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dict( |
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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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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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x["version"] = self.cache_version # cache version |
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try: |
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np.save(path, x) # save cache for next time |
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path.with_suffix(".cache.npy").rename(path) # remove .npy suffix |
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LOGGER.info(f"{self.prefix}New cache created: {path}") |
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except Exception as e: |
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LOGGER.warning( |
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f"{self.prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}") # not writeable |
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return x |
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def get_labels(self): |
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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 = np.load(str(cache_path), allow_pickle=True).item(), True # load dict |
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assert cache["version"] == self.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 Exception: |
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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, bar_format=TQDM_BAR_FORMAT) # display cache results |
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if cache["msgs"]: |
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LOGGER.info("\n".join(cache["msgs"])) # display warnings |
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assert nf > 0, f"{self.prefix}No labels found in {cache_path}, can not start training. {HELP_URL}" |
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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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nl = len(np.concatenate([label["cls"] for label in labels], 0)) # number of labels |
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assert nl > 0, f"{self.prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}" |
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return labels |
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# TODO: use hyp config to set all these augmentations |
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def build_transforms(self, hyp=None): |
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if self.augment: |
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mosaic = self.augment and not self.rect |
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transforms = mosaic_transforms(self, self.imgsz, hyp) if mosaic else affine_transforms(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(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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batch_idx=True)) |
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return transforms |
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def close_mosaic(self, hyp): |
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self.transforms = affine_transforms(self.imgsz, hyp) |
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self.transforms.append( |
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Format(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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batch_idx=True)) |
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def update_labels_info(self, label): |
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"""custom your label format here""" |
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# NOTE: cls is not with bboxes now, classification and semantic segmentation need an independent cls label |
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# we can make it also support classification and semantic segmentation by add or remove some dict keys there. |
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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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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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# TODO: returning a dict can make thing easier and cleaner when using dataset in training |
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# but I don't know if this will slow down a little bit. |
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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"]: |
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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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# Classification dataloaders ------------------------------------------------------------------------------------------- |
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class ClassificationDataset(torchvision.datasets.ImageFolder): |
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""" |
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YOLOv5 Classification Dataset. |
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Arguments |
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root: Dataset path |
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transform: torchvision transforms, used by default |
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album_transform: Albumentations transforms, used if installed |
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""" |
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def __init__(self, root, augment, imgsz, cache=False): |
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super().__init__(root=root) |
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self.torch_transforms = classify_transforms(imgsz) |
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self.album_transforms = classify_albumentations(augment, imgsz) if augment else None |
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self.cache_ram = cache is True or cache == "ram" |
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self.cache_disk = cache == "disk" |
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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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def __getitem__(self, i): |
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f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image |
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if self.cache_ram and im is None: |
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im = self.samples[i][3] = cv2.imread(f) |
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elif self.cache_disk: |
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if not fn.exists(): # load npy |
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np.save(fn.as_posix(), cv2.imread(f)) |
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im = np.load(fn) |
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else: # read image |
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im = cv2.imread(f) # BGR |
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if self.album_transforms: |
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sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"] |
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else: |
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sample = self.torch_transforms(im) |
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return {'img': sample, 'cls': j} |
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def __len__(self) -> int: |
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return len(self.samples) |
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# TODO: support semantic segmentation |
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class SemanticDataset(BaseDataset): |
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def __init__(self): |
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pass
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