Fix Windows non-UTF source filenames (#4524)
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
a7419617a6
commit
1db9afc2e5
16 changed files with 129 additions and 95 deletions
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@ -634,7 +634,7 @@ class CopyPaste:
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result = cv2.flip(im, 1) # augment segments (flip left-right)
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i = cv2.flip(im_new, 1).astype(bool)
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im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
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im[i] = result[i]
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labels['img'] = im
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labels['cls'] = cls
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@ -9,8 +9,6 @@ import cv2
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import numpy as np
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from tqdm import tqdm
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from ultralytics.utils.checks import check_requirements
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def coco91_to_coco80_class():
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"""Converts 91-index COCO class IDs to 80-index COCO class IDs.
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@ -18,7 +16,6 @@ def coco91_to_coco80_class():
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Returns:
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(list): A list of 91 class IDs where the index represents the 80-index class ID and the value is the
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corresponding 91-index class ID.
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"""
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return [
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
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@ -119,9 +116,7 @@ def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keyp
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if len(ann['segmentation']) == 0:
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segments.append([])
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continue
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if isinstance(ann['segmentation'], dict):
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ann['segmentation'] = rle2polygon(ann['segmentation'])
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if len(ann['segmentation']) > 1:
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elif len(ann['segmentation']) > 1:
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s = merge_multi_segment(ann['segmentation'])
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s = (np.concatenate(s, axis=0) / np.array([w, h])).reshape(-1).tolist()
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else:
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@ -131,9 +126,8 @@ def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keyp
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if s not in segments:
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segments.append(s)
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if use_keypoints and ann.get('keypoints') is not None:
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k = (np.array(ann['keypoints']).reshape(-1, 3) / np.array([w, h, 1])).reshape(-1).tolist()
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k = box + k
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keypoints.append(k)
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keypoints.append(box + (np.array(ann['keypoints']).reshape(-1, 3) /
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np.array([w, h, 1])).reshape(-1).tolist())
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# Write
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with open((fn / f).with_suffix('.txt'), 'a') as file:
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@ -237,34 +231,6 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
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convert_label(image_name_without_ext, w, h, orig_label_dir, save_dir)
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def rle2polygon(segmentation):
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"""
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Convert Run-Length Encoding (RLE) mask to polygon coordinates.
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Args:
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segmentation (dict, list): RLE mask representation of the object segmentation.
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Returns:
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(list): A list of lists representing the polygon coordinates for each contour.
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Note:
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Requires the 'pycocotools' package to be installed.
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"""
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check_requirements('pycocotools')
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from pycocotools import mask
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m = mask.decode(segmentation)
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m[m > 0] = 255
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contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)
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polygons = []
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for contour in contours:
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epsilon = 0.001 * cv2.arcLength(contour, True)
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contour_approx = cv2.approxPolyDP(contour, epsilon, True)
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polygon = contour_approx.flatten().tolist()
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polygons.append(polygon)
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return polygons
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def min_index(arr1, arr2):
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"""
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Find a pair of indexes with the shortest distance between two arrays of 2D points.
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@ -144,9 +144,7 @@ def verify_image_label(args):
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if keypoint:
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keypoints = lb[:, 5:].reshape(-1, nkpt, ndim)
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if ndim == 2:
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kpt_mask = np.ones(keypoints.shape[:2], dtype=np.float32)
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kpt_mask = np.where(keypoints[..., 0] < 0, 0.0, kpt_mask)
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kpt_mask = np.where(keypoints[..., 1] < 0, 0.0, kpt_mask)
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kpt_mask = np.where((keypoints[..., 0] < 0) | (keypoints[..., 1] < 0), 0.0, 1.0).astype(np.float32)
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keypoints = np.concatenate([keypoints, kpt_mask[..., None]], axis=-1) # (nl, nkpt, 3)
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lb = lb[:, :5]
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return im_file, lb, shape, segments, keypoints, nm, nf, ne, nc, msg
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@ -127,7 +127,7 @@ class Exporter:
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Attributes:
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args (SimpleNamespace): Configuration for the exporter.
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save_dir (Path): Directory to save results.
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callbacks (list, optional): List of callback functions. Defaults to None.
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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@ -189,7 +189,7 @@ class Exporter:
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model.eval()
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model.float()
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model = model.fuse()
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for k, m in model.named_modules():
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for m in model.modules():
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if isinstance(m, (Detect, RTDETRDecoder)): # Segment and Pose use Detect base class
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m.dynamic = self.args.dynamic
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m.export = True
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@ -427,7 +427,7 @@ class Exporter:
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system = 'macos' if MACOS else 'ubuntu' if LINUX else 'windows' # operating system
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asset = [x for x in assets if system in x][0] if assets else \
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f'https://github.com/pnnx/pnnx/releases/download/20230816/pnnx-20230816-{system}.zip' # fallback
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attempt_download_asset(asset, repo='pnnx/pnnx', release='latest')
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asset = attempt_download_asset(asset, repo='pnnx/pnnx', release='latest')
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unzip_dir = Path(asset).with_suffix('')
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pnnx = ROOT / pnnx_filename # new location
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(unzip_dir / pnnx_filename).rename(pnnx) # move binary to ROOT
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@ -502,9 +502,9 @@ class Exporter:
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check_requirements('scikit-learn') # scikit-learn package required for k-means quantization
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if mlmodel:
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ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
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else:
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elif bits == 8: # mlprogram already quantized to FP16
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import coremltools.optimize.coreml as cto
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op_config = cto.OpPalettizerConfig(mode=mode, nbits=bits, weight_threshold=512)
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op_config = cto.OpPalettizerConfig(mode='kmeans', nbits=bits, weight_threshold=512)
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config = cto.OptimizationConfig(global_config=op_config)
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ct_model = cto.palettize_weights(ct_model, config=config)
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if self.args.nms and self.model.task == 'detect':
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@ -839,7 +839,7 @@ class Exporter:
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import coremltools as ct # noqa
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LOGGER.info(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
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batch_size, ch, h, w = list(self.im.shape) # BCHW
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_, _, h, w = list(self.im.shape) # BCHW
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# Output shapes
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spec = model.get_spec()
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@ -857,8 +857,8 @@ class Exporter:
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# Checks
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names = self.metadata['names']
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nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
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na, nc = out0_shape
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# na, nc = out0.type.multiArrayType.shape # number anchors, classes
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_, nc = out0_shape # number of anchors, number of classes
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# _, nc = out0.type.multiArrayType.shape
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assert len(names) == nc, f'{len(names)} names found for nc={nc}' # check
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# Define output shapes (missing)
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@ -968,7 +968,7 @@ class IOSDetectModel(torch.nn.Module):
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def __init__(self, model, im):
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"""Initialize the IOSDetectModel class with a YOLO model and example image."""
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super().__init__()
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b, c, h, w = im.shape # batch, channel, height, width
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_, _, h, w = im.shape # batch, channel, height, width
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self.model = model
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self.nc = len(model.names) # number of classes
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if w == h:
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@ -343,8 +343,7 @@ class BasePredictor:
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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suffix = '.mp4' if MACOS else '.avi' if WINDOWS else '.avi'
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fourcc = 'avc1' if MACOS else 'WMV2' if WINDOWS else 'MJPG'
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suffix, fourcc = ('.mp4', 'avc1') if MACOS else ('.avi', 'WMV2') if WINDOWS else ('.avi', 'MJPG')
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save_path = str(Path(save_path).with_suffix(suffix))
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self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
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self.vid_writer[idx].write(im0)
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@ -261,7 +261,6 @@ class DetectionModel(BaseModel):
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for si, fi in zip(s, f):
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xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
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yi = super().predict(xi)[0] # forward
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# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
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yi = self._descale_pred(yi, fi, si, img_size)
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y.append(yi)
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y = self._clip_augmented(y) # clip augmented tails
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@ -852,9 +852,10 @@ ENVIRONMENT = 'Colab' if is_colab() else 'Kaggle' if is_kaggle() else 'Jupyter'
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TESTS_RUNNING = is_pytest_running() or is_github_actions_ci()
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set_sentry()
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# Apply monkey patches if the script is being run from within the parent directory of the script's location
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from .patches import imread, imshow, imwrite
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# Apply monkey patches
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from .patches import imread, imshow, imwrite, torch_save
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# torch.save = torch_save
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if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:
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torch.save = torch_save
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if WINDOWS:
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# Apply cv2 patches for non-ASCII and non-UTF characters in image paths
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cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow
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@ -240,7 +240,7 @@ class ProfileModels:
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if path.is_dir():
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extensions = ['*.pt', '*.onnx', '*.yaml']
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files.extend([file for ext in extensions for file in glob.glob(str(path / ext))])
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elif path.suffix in ('.pt', '.yaml', '.yml'): # add non-existing
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elif path.suffix in {'.pt', '.yaml', '.yml'}: # add non-existing
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files.append(str(path))
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else:
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files.extend(glob.glob(str(path)))
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@ -262,7 +262,7 @@ class ProfileModels:
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data = clipped_data
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return data
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def profile_tensorrt_model(self, engine_file: str):
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def profile_tensorrt_model(self, engine_file: str, eps: float = 1e-7):
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if not self.trt or not Path(engine_file).is_file():
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return 0.0, 0.0
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@ -279,7 +279,7 @@ class ProfileModels:
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elapsed = time.time() - start_time
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# Compute number of runs as higher of min_time or num_timed_runs
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num_runs = max(round(self.min_time / elapsed * self.num_warmup_runs), self.num_timed_runs * 50)
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num_runs = max(round(self.min_time / (elapsed + eps) * self.num_warmup_runs), self.num_timed_runs * 50)
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# Timed runs
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run_times = []
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@ -290,7 +290,7 @@ class ProfileModels:
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run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3) # sigma clipping
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return np.mean(run_times), np.std(run_times)
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def profile_onnx_model(self, onnx_file: str):
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def profile_onnx_model(self, onnx_file: str, eps: float = 1e-7):
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check_requirements('onnxruntime')
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import onnxruntime as ort
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@ -330,7 +330,7 @@ class ProfileModels:
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elapsed = time.time() - start_time
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# Compute number of runs as higher of min_time or num_timed_runs
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num_runs = max(round(self.min_time / elapsed * self.num_warmup_runs), self.num_timed_runs)
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num_runs = max(round(self.min_time / (elapsed + eps) * self.num_warmup_runs), self.num_timed_runs)
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# Timed runs
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run_times = []
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@ -101,7 +101,11 @@ def zip_directory(directory, compress=True, exclude=('.DS_Store', '__MACOSX'), p
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zip_file = directory.with_suffix('.zip')
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compression = ZIP_DEFLATED if compress else ZIP_STORED
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with ZipFile(zip_file, 'w', compression) as f:
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for file in tqdm(files_to_zip, desc=f'Zipping {directory} to {zip_file}...', unit='file', disable=not progress):
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for file in tqdm(files_to_zip,
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desc=f'Zipping {directory} to {zip_file}...',
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unit='file',
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disable=not progress,
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bar_format=TQDM_BAR_FORMAT):
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f.write(file, file.relative_to(directory))
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return zip_file # return path to zip file
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@ -159,7 +163,11 @@ def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX'), exist_ok=Fals
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LOGGER.info(f'Skipping {file} unzip (already unzipped)')
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return path
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for f in tqdm(files, desc=f'Unzipping {file} to {Path(path).resolve()}...', unit='file', disable=not progress):
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for f in tqdm(files,
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desc=f'Unzipping {file} to {Path(path).resolve()}...',
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unit='file',
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disable=not progress,
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bar_format=TQDM_BAR_FORMAT):
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zipObj.extract(f, path=extract_path)
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return path # return unzip dir
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@ -13,20 +13,45 @@ import torch
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_imshow = cv2.imshow # copy to avoid recursion errors
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def imread(filename, flags=cv2.IMREAD_COLOR):
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def imread(filename: str, flags: int = cv2.IMREAD_COLOR):
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"""Read an image from a file.
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Args:
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filename (str): Path to the file to read.
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flags (int, optional): Flag that can take values of cv2.IMREAD_*. Defaults to cv2.IMREAD_COLOR.
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Returns:
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(np.ndarray): The read image.
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"""
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return cv2.imdecode(np.fromfile(filename, np.uint8), flags)
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def imwrite(filename, img):
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def imwrite(filename: str, img: np.ndarray, params=None):
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"""Write an image to a file.
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Args:
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filename (str): Path to the file to write.
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img (np.ndarray): Image to write.
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params (list of ints, optional): Additional parameters. See OpenCV documentation.
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Returns:
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(bool): True if the file was written, False otherwise.
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"""
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try:
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cv2.imencode(Path(filename).suffix, img)[1].tofile(filename)
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cv2.imencode(Path(filename).suffix, img, params)[1].tofile(filename)
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return True
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except Exception:
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return False
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def imshow(path, im):
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_imshow(path.encode('unicode_escape').decode(), im)
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def imshow(winname: str, mat: np.ndarray):
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"""Displays an image in the specified window.
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Args:
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winname (str): Name of the window.
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mat (np.ndarray): Image to be shown.
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"""
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_imshow(winname.encode('unicode_escape').decode(), mat)
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# PyTorch functions ----------------------------------------------------------------------------------------------------
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@ -34,12 +59,17 @@ _torch_save = torch.save # copy to avoid recursion errors
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def torch_save(*args, **kwargs):
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"""Use dill (if exists) to serialize the lambda functions where pickle does not do this."""
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"""Use dill (if exists) to serialize the lambda functions where pickle does not do this.
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Args:
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*args (tuple): Positional arguments to pass to torch.save.
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**kwargs (dict): Keyword arguments to pass to torch.save.
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"""
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try:
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import dill as pickle
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import dill as pickle # noqa
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except ImportError:
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import pickle
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if 'pickle_module' not in kwargs:
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kwargs['pickle_module'] = pickle
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kwargs['pickle_module'] = pickle # noqa
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return _torch_save(*args, **kwargs)
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