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ultralytics 8.0.74 Pose labels, fp64 labels, Ensemble fixes (#1956)
Co-authored-by: jjlira <63210717+jjlira@users.noreply.github.com> Co-authored-by: Jose Lira <jose.lira@georgebrown.ca> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: wilsonlmh <lu6ni4z-forum@Yahoo.com.hk> Co-authored-by: HaeJin Lee <seareale@gmail.com>
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Glenn Jocher
GitHub
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@ -26,14 +26,14 @@
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"<img width=\"1024\", src=\"https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png\"></a>\n",
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"\n",
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"<div align=\"center\">\n",
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" <a href=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml\">\n",
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" <img src=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg\" alt=\"CI CPU\"></a>\n",
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" <a href=\"https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb\">\n",
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" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\">\n",
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" <img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"CI CPU\"></a>\n",
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" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/hub.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
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"\n",
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"Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook! \n",
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"\n",
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"This notebook allows you to train [YOLOv5](https://github.com/ultralytics/yolov5) and [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀 models using [HUB](https://hub.ultralytics.com/). Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/hub/issues/new/choose\">GitHub</a> for support, and join our <a href=\"https://discord.gg/n6cFeSPZdD\">Discord</a> community for questions and discussions!\n",
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"This notebook allows you to train [YOLOv5](https://github.com/ultralytics/yolov5) and [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀 models using [HUB](https://hub.ultralytics.com/). Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics/issues/new/choose\">GitHub</a> for support, and join our <a href=\"https://discord.gg/n6cFeSPZdD\">Discord</a> community for questions and discussions!\n",
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"</div>"
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]
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},
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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__version__ = '8.0.73'
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__version__ = '8.0.74'
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from ultralytics.hub import start
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from ultralytics.yolo.engine.model import YOLO
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@ -112,17 +112,21 @@ class HUBTrainingSession:
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raise ValueError('Dataset may still be processing. Please wait a minute and try again.') # RF fix
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self.model_id = data['id']
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self.train_args = {
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'batch': data['batch' if ('batch' in data) else 'batch_size'], # TODO: deprecate 'batch_size' in 3Q23
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'epochs': data['epochs'],
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'imgsz': data['imgsz'],
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'patience': data['patience'],
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'device': data['device'],
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'cache': data['cache'],
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'data': data['data']}
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self.model_file = data.get('cfg', data['weights'])
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self.model_file = checks.check_yolov5u_filename(self.model_file, verbose=False) # YOLOv5->YOLOv5u
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if data['status'] == 'new': # new model to start training
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self.train_args = {
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# TODO: deprecate 'batch_size' key for 'batch' in 3Q23
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'batch': data['batch' if ('batch' in data) else 'batch_size'],
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'epochs': data['epochs'],
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'imgsz': data['imgsz'],
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'patience': data['patience'],
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'device': data['device'],
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'cache': data['cache'],
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'data': data['data']}
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self.model_file = data.get('cfg', data['weights'])
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self.model_file = checks.check_yolov5u_filename(self.model_file, verbose=False) # YOLOv5->YOLOv5u
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elif data['status'] == 'training': # existing model to resume training
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self.train_args = {'data': data['data'], 'resume': True}
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self.model_file = data['resume']
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return data
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except requests.exceptions.ConnectionError as e:
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@ -374,7 +374,7 @@ class Ensemble(nn.ModuleList):
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y = [module(x, augment, profile, visualize)[0] for module in self]
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# y = torch.stack(y).max(0)[0] # max ensemble
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# y = torch.stack(y).mean(0) # mean ensemble
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y = torch.cat(y, 1) # nms ensemble
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y = torch.cat(y, 2) # nms ensemble, y shape(B, HW, C)
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return y, None # inference, train output
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@ -127,7 +127,7 @@ class Mosaic(BaseMixTransform):
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s = self.imgsz
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yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border) # mosaic center x, y
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for i in range(4):
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labels_patch = (labels if i == 0 else labels['mix_labels'][i - 1]).copy()
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labels_patch = labels if i == 0 else labels['mix_labels'][i - 1]
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# Load image
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img = labels_patch['img']
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h, w = labels_patch.pop('resized_shape')
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@ -223,18 +223,18 @@ class RandomPerspective:
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def affine_transform(self, img, border):
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# Center
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C = np.eye(3)
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C = np.eye(3, dtype=np.float32)
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C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
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C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
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# Perspective
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P = np.eye(3)
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P = np.eye(3, dtype=np.float32)
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P[2, 0] = random.uniform(-self.perspective, self.perspective) # x perspective (about y)
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P[2, 1] = random.uniform(-self.perspective, self.perspective) # y perspective (about x)
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# Rotation and Scale
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R = np.eye(3)
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R = np.eye(3, dtype=np.float32)
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a = random.uniform(-self.degrees, self.degrees)
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# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
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s = random.uniform(1 - self.scale, 1 + self.scale)
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@ -242,12 +242,12 @@ class RandomPerspective:
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R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
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# Shear
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S = np.eye(3)
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S = np.eye(3, dtype=np.float32)
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S[0, 1] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180) # x shear (deg)
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S[1, 0] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180) # y shear (deg)
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# Translation
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T = np.eye(3)
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T = np.eye(3, dtype=np.float32)
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T[0, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[0] # x translation (pixels)
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T[1, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[1] # y translation (pixels)
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@ -274,7 +274,7 @@ class RandomPerspective:
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if n == 0:
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return bboxes
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xy = np.ones((n * 4, 3))
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xy = np.ones((n * 4, 3), dtype=bboxes.dtype)
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xy[:, :2] = bboxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
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xy = xy @ M.T # transform
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xy = (xy[:, :2] / xy[:, 2:3] if self.perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
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@ -282,7 +282,7 @@ class RandomPerspective:
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# create new boxes
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x = xy[:, [0, 2, 4, 6]]
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y = xy[:, [1, 3, 5, 7]]
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return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
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return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1)), dtype=bboxes.dtype).reshape(4, n).T
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def apply_segments(self, segments, M):
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"""apply affine to segments and generate new bboxes from segments.
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@ -298,7 +298,7 @@ class RandomPerspective:
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if n == 0:
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return [], segments
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xy = np.ones((n * num, 3))
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xy = np.ones((n * num, 3), dtype=segments.dtype)
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segments = segments.reshape(-1, 2)
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xy[:, :2] = segments
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xy = xy @ M.T # transform
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@ -319,7 +319,7 @@ class RandomPerspective:
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n, nkpt = keypoints.shape[:2]
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if n == 0:
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return keypoints
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xy = np.ones((n * nkpt, 3))
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xy = np.ones((n * nkpt, 3), dtype=keypoints.dtype)
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visible = keypoints[..., 2].reshape(n * nkpt, 1)
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xy[:, :2] = keypoints[..., :2].reshape(n * nkpt, 2)
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xy = xy @ M.T # transform
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@ -3,6 +3,7 @@
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import glob
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import math
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import os
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from copy import deepcopy
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from multiprocessing.pool import ThreadPool
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from pathlib import Path
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from typing import Optional
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@ -177,7 +178,7 @@ class BaseDataset(Dataset):
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return self.transforms(self.get_label_info(index))
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def get_label_info(self, index):
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label = self.labels[index].copy()
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label = deepcopy(self.labels[index]) # requires deepcopy() https://github.com/ultralytics/ultralytics/pull/1948
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label.pop('shape', None) # shape is for rect, remove it
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label['img'], label['ori_shape'], label['resized_shape'] = self.load_image(index)
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label['ratio_pad'] = (label['resized_shape'][0] / label['ori_shape'][0],
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@ -166,7 +166,9 @@ class YOLO:
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"""
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Raises TypeError is model is not a PyTorch model
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"""
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if not isinstance(self.model, nn.Module):
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pt_str = isinstance(self.model, (str, Path)) and Path(self.model).suffix == '.pt'
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pt_module = isinstance(self.model, nn.Module)
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if not (pt_module or pt_str):
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raise TypeError(f"model='{self.model}' must be a *.pt PyTorch model, but is a different type. "
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f'PyTorch models can be used to train, val, predict and export, i.e. '
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f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
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@ -40,7 +40,7 @@ def on_model_save(trainer):
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# Upload checkpoints with rate limiting
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is_best = trainer.best_fitness == trainer.fitness
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if time() - session.timers['ckpt'] > session.rate_limits['ckpt']:
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LOGGER.info(f'{PREFIX}Uploading checkpoint {session.model_id}')
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LOGGER.info(f'{PREFIX}Uploading checkpoint https://hub.ultralytics.com/models/{session.model_id}')
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session.upload_model(trainer.epoch, trainer.last, is_best)
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session.timers['ckpt'] = time() # reset timer
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@ -81,7 +81,8 @@ def segment2box(segment, width=640, height=640):
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x, y = segment.T # segment xy
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inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
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x, y, = x[inside], y[inside]
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return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros(4) # xyxy
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return np.array([x.min(), y.min(), x.max(), y.max()], dtype=segment.dtype) if any(x) else np.zeros(
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4, dtype=segment.dtype) # xyxy
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def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
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@ -529,7 +530,8 @@ def resample_segments(segments, n=1000):
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s = np.concatenate((s, s[0:1, :]), axis=0)
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x = np.linspace(0, len(s) - 1, n)
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xp = np.arange(len(s))
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segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
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segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)],
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dtype=np.float32).reshape(2, -1).T # segment xy
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return segments
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