Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-04-21 20:35:11 +02:00
parent 4a4668224b
commit 2bfea0c980
4 changed files with 55 additions and 26 deletions
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2
test.py
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@ -93,7 +93,7 @@ def test(
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
image_id = int(Path(paths[si]).stem.split('_')[-1])
box = pred[:, :4].clone() # xyxy
scale_coords(img_size, box, shapes[si]) # to original shape
scale_coords(imgs[si].shape, box, shapes[si]) # to original shape
box = xyxy2xywh(box) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for di, d in enumerate(pred):

47
train.py
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@ -11,20 +11,34 @@ from utils.datasets import *
from utils.utils import *
# Hyperparameters
# 0.852 0.94 0.924 0.883 1.33 8.52 0.06833 0.01524 0.01509 0.9013 0.1003 0.001325 -3.853 0.8948 0.0004053 # hyp
hyp = {'k': 8.52, # loss multiple
'xy': 0.06833, # xy loss fraction
'wh': 0.01524, # wh loss fraction
'cls': 0.01509, # cls loss fraction
'conf': 0.9013, # conf loss fraction
'iou_t': 0.1003, # iou target-anchor training threshold
'lr0': 0.001325, # initial learning rate
'lrf': -3.853, # final learning rate = lr0 * (10 ** lrf)
'momentum': 0.8948, # SGD momentum
'weight_decay': 0.0004053, # optimizer weight decay
# 0.861 0.956 0.936 0.897 1.51 10.39 0.1367 0.01057 0.01181 0.8409 0.1287 0.001028 -3.441 0.9127 0.0004841
hyp = {'k': 10.39, # loss multiple
'xy': 0.1367, # xy loss fraction
'wh': 0.01057, # wh loss fraction
'cls': 0.01181, # cls loss fraction
'conf': 0.8409, # conf loss fraction
'iou_t': 0.1287, # iou target-anchor training threshold
'lr0': 0.001028, # initial learning rate
'lrf': -3.441, # final learning rate = lr0 * (10 ** lrf)
'momentum': 0.9127, # SGD momentum
'weight_decay': 0.0004841, # optimizer weight decay
}
# 0.856 0.95 0.935 0.887 1.3 8.488 0.1081 0.01351 0.01351 0.8649 0.1 0.001 -3 0.9 0.0005
# hyp = {'k': 8.4875, # loss multiple
# 'xy': 0.108108, # xy loss fraction
# 'wh': 0.013514, # wh loss fraction
# 'cls': 0.013514, # cls loss fraction
# 'conf': 0.86486, # conf loss fraction
# 'iou_t': 0.1, # iou target-anchor training threshold
# 'lr0': 0.001, # initial learning rate
# 'lrf': -3., # final learning rate = lr0 * (10 ** lrf)
# 'momentum': 0.9, # SGD momentum
# 'weight_decay': 0.0005, # optimizer weight decay
# }
def train(
cfg,
data_cfg,
@ -89,12 +103,9 @@ def train(
# Scheduler (reduce lr at epochs 218, 245, i.e. batches 400k, 450k)
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (-2 * x / epochs) # exp ramp to lr0 * 1e-2
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inv exp ramp to lr0 * 1e-2
# scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[218, 245],
gamma=0.1,
last_epoch=start_epoch - 1)
lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inv exp ramp to lr0 * 1e-2
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
# scheduler = optim.lr_scheduler.MultiStepLR(optimizer,milestones=[218, 245],gamma=0.1,last_epoch=start_epoch - 1)
# Plot lr schedule
# y = []
@ -311,7 +322,7 @@ if __name__ == '__main__':
# Mutate hyperparameters
old_hyp = hyp.copy()
init_seeds(seed=int(time.time()))
s = [.2, .2, .2, .2, .2, .2, .2, .2, .02, .2]
s = [.2, .2, .2, .2, .2, .3, .2, .2, .02, .3]
for i, k in enumerate(hyp.keys()):
x = (np.random.randn(1) * s[i] + 1) ** 1.1 # plt.hist(x.ravel(), 100)
hyp[k] = hyp[k] * float(x) # vary by about 30% 1sigma

22
utils/datasets.py
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@ -74,7 +74,7 @@ class LoadImages: # for inference
print('image %g/%g %s: ' % (self.count, self.nF, path), end='')
# Padded resize
img, _, _, _ = letterbox(img0, height=self.height)
img, _, _, _ = letterbox_rect(img0, height=self.height)
# Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
@ -239,9 +239,11 @@ def letterbox(img, height=416, color=(127.5, 127.5, 127.5)):
# Resize a rectangular image to a padded square
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio))
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
dw = (height - new_shape[0]) / 2 # width padding
dh = (height - new_shape[1]) / 2 # height padding
top, bottom = round(dh - 0.1), round(dh + 0.1)
left, right = round(dw - 0.1), round(dw + 0.1)
img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
@ -249,6 +251,22 @@ def letterbox(img, height=416, color=(127.5, 127.5, 127.5)):
return img, ratio, dw, dh
def letterbox_rect(img, height=416, color=(127.5, 127.5, 127.5)):
# Resize a rectangular image to a 32 pixel multiple rectangle
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
dw = np.mod(height - new_shape[0], 32) / 2 # width padding
dh = np.mod(height - new_shape[1], 32) / 2 # height padding
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded square
return img, ratio, dw, dh
def random_affine(img, targets=(), degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
borderValue=(127.5, 127.5, 127.5)):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))

10
utils/utils.py
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@ -100,11 +100,11 @@ def xywh2xyxy(x):
return y
def scale_coords(img_size, coords, img0_shape):
# Rescale x1, y1, x2, y2 from 416 to image size
gain = float(img_size) / max(img0_shape) # gain = old / new
pad_x = (img_size - img0_shape[1] * gain) / 2 # width padding
pad_y = (img_size - img0_shape[0] * gain) / 2 # height padding
def scale_coords(img1_shape, coords, img0_shape):
# Rescale coords1 (xyxy) from img1_shape to img0_shape
gain = max(img1_shape[1:3]) / max(img0_shape[:2]) # gain = old / new
pad_x = (img1_shape[2] - img0_shape[1] * gain) / 2 # width padding
pad_y = (img1_shape[1] - img0_shape[0] * gain) / 2 # height padding
coords[:, [0, 2]] -= pad_x
coords[:, [1, 3]] -= pad_y
coords[:, :4] /= gain