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ultralytics 8.0.74 Pose labels, fp64 labels, Ensemble fixes (#1956)

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Co-authored-by: jjlira <63210717+jjlira@users.noreply.github.com>
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Co-authored-by: HaeJin Lee <seareale@gmail.com>
This commit is contained in:
Glenn Jocher
2023-04-11 15:30:01 +02:00
committed by GitHub
parent 5629ed0bb7
commit 24363236f2
9 changed files with 41 additions and 32 deletions
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20
ultralytics/yolo/data/augment.py
View File

@ -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

3
ultralytics/yolo/data/base.py
View File

@ -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],

4
ultralytics/yolo/engine/model.py
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@ -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 "

2
ultralytics/yolo/utils/callbacks/hub.py
View File

@ -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

6
ultralytics/yolo/utils/ops.py
View File

@ -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