`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>

examples/hub.ipynb

@@ -26,14 +26,14 @@
         "<img width=\"1024\", src=\"https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png\"></a>\n",
         "\n",
         "<div align=\"center\">\n",
-        "  <a href=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml\">\n",
-        "    <img src=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg\" alt=\"CI CPU\"></a>\n",
-        "  <a href=\"https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb\">\n",
+        "  <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\">\n",
+        "    <img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"CI CPU\"></a>\n",
+        "  <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/hub.ipynb\">\n",
         "    <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
         "\n",
         "Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook! \n",
         "\n",
-        "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",
+        "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",
         "</div>"
       ]
     },

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = '8.0.73'
+__version__ = '8.0.74'
 
 from ultralytics.hub import start
 from ultralytics.yolo.engine.model import YOLO

ultralytics/hub/session.py

@@ -112,17 +112,21 @@ class HUBTrainingSession:
                 raise ValueError('Dataset may still be processing. Please wait a minute and try again.')  # RF fix
             self.model_id = data['id']
 
-            self.train_args = {
-                'batch': data['batch' if ('batch' in data) else 'batch_size'],  # TODO: deprecate 'batch_size' in 3Q23
-                'epochs': data['epochs'],
-                'imgsz': data['imgsz'],
-                'patience': data['patience'],
-                'device': data['device'],
-                'cache': data['cache'],
-                'data': data['data']}
-
-            self.model_file = data.get('cfg', data['weights'])
-            self.model_file = checks.check_yolov5u_filename(self.model_file, verbose=False)  # YOLOv5->YOLOv5u
+            if data['status'] == 'new':  # new model to start training
+                self.train_args = {
+                    # TODO: deprecate 'batch_size' key for 'batch' in 3Q23
+                    'batch': data['batch' if ('batch' in data) else 'batch_size'],
+                    'epochs': data['epochs'],
+                    'imgsz': data['imgsz'],
+                    'patience': data['patience'],
+                    'device': data['device'],
+                    'cache': data['cache'],
+                    'data': data['data']}
+                self.model_file = data.get('cfg', data['weights'])
+                self.model_file = checks.check_yolov5u_filename(self.model_file, verbose=False)  # YOLOv5->YOLOv5u
+            elif data['status'] == 'training':  # existing model to resume training
+                self.train_args = {'data': data['data'], 'resume': True}
+                self.model_file = data['resume']
 
             return data
         except requests.exceptions.ConnectionError as e:

ultralytics/nn/modules.py

@@ -374,7 +374,7 @@ class Ensemble(nn.ModuleList):
         y = [module(x, augment, profile, visualize)[0] for module in self]
         # y = torch.stack(y).max(0)[0]  # max ensemble
         # y = torch.stack(y).mean(0)  # mean ensemble
-        y = torch.cat(y, 1)  # nms ensemble
+        y = torch.cat(y, 2)  # nms ensemble, y shape(B, HW, C)
         return y, None  # inference, train output
 
 

ultralytics/yolo/data/augment.py

@@ -127,7 +127,7 @@ class Mosaic(BaseMixTransform):
         s = self.imgsz
         yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border)  # mosaic center x, y
         for i in range(4):
-            labels_patch = (labels if i == 0 else labels['mix_labels'][i - 1]).copy()
+            labels_patch = labels if i == 0 else labels['mix_labels'][i - 1]
             # Load image
             img = labels_patch['img']
             h, w = labels_patch.pop('resized_shape')
@@ -223,18 +223,18 @@ class RandomPerspective:
 
     def affine_transform(self, img, border):
         # Center
-        C = np.eye(3)
+        C = np.eye(3, dtype=np.float32)
 
         C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
         C[1, 2] = -img.shape[0] / 2  # y translation (pixels)
 
         # Perspective
-        P = np.eye(3)
+        P = np.eye(3, dtype=np.float32)
         P[2, 0] = random.uniform(-self.perspective, self.perspective)  # x perspective (about y)
         P[2, 1] = random.uniform(-self.perspective, self.perspective)  # y perspective (about x)
 
         # Rotation and Scale
-        R = np.eye(3)
+        R = np.eye(3, dtype=np.float32)
         a = random.uniform(-self.degrees, self.degrees)
         # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
         s = random.uniform(1 - self.scale, 1 + self.scale)
@@ -242,12 +242,12 @@ class RandomPerspective:
         R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
 
         # Shear
-        S = np.eye(3)
+        S = np.eye(3, dtype=np.float32)
         S[0, 1] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180)  # x shear (deg)
         S[1, 0] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180)  # y shear (deg)
 
         # Translation
-        T = np.eye(3)
+        T = np.eye(3, dtype=np.float32)
         T[0, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[0]  # x translation (pixels)
         T[1, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[1]  # y translation (pixels)
 
@@ -274,7 +274,7 @@ class RandomPerspective:
         if n == 0:
             return bboxes
 
-        xy = np.ones((n * 4, 3))
+        xy = np.ones((n * 4, 3), dtype=bboxes.dtype)
         xy[:, :2] = bboxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
         xy = xy @ M.T  # transform
         xy = (xy[:, :2] / xy[:, 2:3] if self.perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine
@@ -282,7 +282,7 @@ class RandomPerspective:
         # create new boxes
         x = xy[:, [0, 2, 4, 6]]
         y = xy[:, [1, 3, 5, 7]]
-        return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
+        return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1)), dtype=bboxes.dtype).reshape(4, n).T
 
     def apply_segments(self, segments, M):
         """apply affine to segments and generate new bboxes from segments.
@@ -298,7 +298,7 @@ class RandomPerspective:
         if n == 0:
             return [], segments
 
-        xy = np.ones((n * num, 3))
+        xy = np.ones((n * num, 3), dtype=segments.dtype)
         segments = segments.reshape(-1, 2)
         xy[:, :2] = segments
         xy = xy @ M.T  # transform
@@ -319,7 +319,7 @@ class RandomPerspective:
         n, nkpt = keypoints.shape[:2]
         if n == 0:
             return keypoints
-        xy = np.ones((n * nkpt, 3))
+        xy = np.ones((n * nkpt, 3), dtype=keypoints.dtype)
         visible = keypoints[..., 2].reshape(n * nkpt, 1)
         xy[:, :2] = keypoints[..., :2].reshape(n * nkpt, 2)
         xy = xy @ M.T  # transform

ultralytics/yolo/data/base.py

@@ -3,6 +3,7 @@
 import glob
 import math
 import os
+from copy import deepcopy
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
 from typing import Optional
@@ -177,7 +178,7 @@ class BaseDataset(Dataset):
         return self.transforms(self.get_label_info(index))
 
     def get_label_info(self, index):
-        label = self.labels[index].copy()
+        label = deepcopy(self.labels[index])  # requires deepcopy() https://github.com/ultralytics/ultralytics/pull/1948
         label.pop('shape', None)  # shape is for rect, remove it
         label['img'], label['ori_shape'], label['resized_shape'] = self.load_image(index)
         label['ratio_pad'] = (label['resized_shape'][0] / label['ori_shape'][0],

ultralytics/yolo/engine/model.py

@@ -166,7 +166,9 @@ class YOLO:
         """
         Raises TypeError is model is not a PyTorch model
         """
-        if not isinstance(self.model, nn.Module):
+        pt_str = isinstance(self.model, (str, Path)) and Path(self.model).suffix == '.pt'
+        pt_module = isinstance(self.model, nn.Module)
+        if not (pt_module or pt_str):
             raise TypeError(f"model='{self.model}' must be a *.pt PyTorch model, but is a different type. "
                             f'PyTorch models can be used to train, val, predict and export, i.e. '
                             f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "

ultralytics/yolo/utils/callbacks/hub.py

@@ -40,7 +40,7 @@ def on_model_save(trainer):
         # Upload checkpoints with rate limiting
         is_best = trainer.best_fitness == trainer.fitness
         if time() - session.timers['ckpt'] > session.rate_limits['ckpt']:
-            LOGGER.info(f'{PREFIX}Uploading checkpoint {session.model_id}')
+            LOGGER.info(f'{PREFIX}Uploading checkpoint https://hub.ultralytics.com/models/{session.model_id}')
             session.upload_model(trainer.epoch, trainer.last, is_best)
             session.timers['ckpt'] = time()  # reset timer
 

ultralytics/yolo/utils/ops.py

@@ -81,7 +81,8 @@ def segment2box(segment, width=640, height=640):
     x, y = segment.T  # segment xy
     inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
     x, y, = x[inside], y[inside]
-    return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros(4)  # xyxy
+    return np.array([x.min(), y.min(), x.max(), y.max()], dtype=segment.dtype) if any(x) else np.zeros(
+        4, dtype=segment.dtype)  # xyxy
 
 
 def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
@@ -529,7 +530,8 @@ def resample_segments(segments, n=1000):
         s = np.concatenate((s, s[0:1, :]), axis=0)
         x = np.linspace(0, len(s) - 1, n)
         xp = np.arange(len(s))
-        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T  # segment xy
+        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)],
+                                     dtype=np.float32).reshape(2, -1).T  # segment xy
     return segments