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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-03-31 19:57:44 +02:00
parent 8901e96a38
commit 09b02d2029
2 changed files with 19 additions and 17 deletions
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18
utils/datasets.py
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@ -100,7 +100,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
assert len(self.img_files) > 0, 'No images found in %s' % path assert len(self.img_files) > 0, 'No images found in %s' % path
self.img_size = img_size self.img_size = img_size
self.augment = augment self.augment = augment
self.label_files = [x.replace('images', 'labels').replace('.bmp', '.txt').replace('.jpg', '.txt').replace('.png', '.txt') self.label_files = [
x.replace('images', 'labels').replace('.bmp', '.txt').replace('.jpg', '.txt').replace('.png', '.txt')
for x in self.img_files] for x in self.img_files]
def __len__(self): def __len__(self):
@ -116,7 +117,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
augment_hsv = True augment_hsv = True
if self.augment and augment_hsv: if self.augment and augment_hsv:
# SV augmentation by 50% # SV augmentation by 50%
fraction = 0.50 fraction = 0.50 # must be < 1.0
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
S = img_hsv[:, :, 1].astype(np.float32) S = img_hsv[:, :, 1].astype(np.float32)
V = img_hsv[:, :, 2].astype(np.float32) V = img_hsv[:, :, 2].astype(np.float32)
@ -124,15 +125,15 @@ class LoadImagesAndLabels(Dataset): # for training/testing
a = (random.random() * 2 - 1) * fraction + 1 a = (random.random() * 2 - 1) * fraction + 1
S *= a S *= a
if a > 1: if a > 1:
np.clip(S, a_min=0, a_max=255, out=S) np.clip(S, None, 255, out=S)
a = (random.random() * 2 - 1) * fraction + 1 a = (random.random() * 2 - 1) * fraction + 1
V *= a V *= a
if a > 1: if a > 1:
np.clip(V, a_min=0, a_max=255, out=V) np.clip(V, None, 255, out=V)
img_hsv[:, :, 1] = S.astype(np.uint8) img_hsv[:, :, 1] = S # .astype(np.uint8)
img_hsv[:, :, 2] = V.astype(np.uint8) img_hsv[:, :, 2] = V # .astype(np.uint8)
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
h, w, _ = img.shape h, w, _ = img.shape
@ -196,7 +197,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
return torch.stack(img, 0), torch.cat(label, 0), path, hw return torch.stack(img, 0), torch.cat(label, 0), path, hw
def letterbox(img, height=416, color=(127.5, 127.5, 127.5)): # resize a rectangular image to a padded square 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] shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new 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))
@ -256,7 +258,7 @@ def random_affine(img, targets=(), degrees=(-10, 10), translate=(.1, .1), scale=
y = xy[:, [1, 3, 5, 7]] y = xy[:, [1, 3, 5, 7]]
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
# apply angle-based reduction # apply angle-based reduction of bounding boxes
radians = a * math.pi / 180 radians = a * math.pi / 180
reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
x = (xy[:, 2] + xy[:, 0]) / 2 x = (xy[:, 2] + xy[:, 0]) / 2

14
utils/utils.py
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@ -249,7 +249,7 @@ def wh_iou(box1, box2):
def compute_loss(p, targets): # predictions, targets def compute_loss(p, targets): # predictions, targets
FT = torch.cuda.FloatTensor if p[0].is_cuda else torch.FloatTensor FT = torch.cuda.FloatTensor if p[0].is_cuda else torch.FloatTensor
loss, lxy, lwh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]) lxy, lwh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0])
txy, twh, tcls, indices = targets txy, twh, tcls, indices = targets
MSE = nn.MSELoss() MSE = nn.MSELoss()
CE = nn.CrossEntropyLoss() CE = nn.CrossEntropyLoss()
@ -267,13 +267,13 @@ def compute_loss(p, targets): # predictions, targets
pi = pi0[b, a, gj, gi] # predictions closest to anchors pi = pi0[b, a, gj, gi] # predictions closest to anchors
tconf[b, a, gj, gi] = 1 # conf tconf[b, a, gj, gi] = 1 # conf
lxy += k * MSE(torch.sigmoid(pi[..., 0:2]), txy[i]) # xy lxy += k * MSE(torch.sigmoid(pi[..., 0:2]), txy[i]) # xy loss
lwh += k * MSE(pi[..., 2:4], twh[i]) # wh lwh += k * MSE(pi[..., 2:4], twh[i]) # wh loss
lcls += (k / 4) * CE(pi[..., 5:], tcls[i]) lcls += (k / 4) * CE(pi[..., 5:], tcls[i]) # class_conf loss
# pos_weight = FT([gp[i] / min(gp) * 4.]) # pos_weight = FT([gp[i] / min(gp) * 4.])
# BCE = nn.BCEWithLogitsLoss(pos_weight=pos_weight) # BCE = nn.BCEWithLogitsLoss(pos_weight=pos_weight)
lconf += (k * 64) * BCE(pi0[..., 4], tconf) lconf += (k * 64) * BCE(pi0[..., 4], tconf) # obj_conf loss
loss = lxy + lwh + lconf + lcls loss = lxy + lwh + lconf + lcls
# Add to dictionary # Add to dictionary
@ -300,7 +300,7 @@ def build_targets(model, targets):
iou = [wh_iou(x, gwh) for x in anchor_vec] iou = [wh_iou(x, gwh) for x in anchor_vec]
iou, a = torch.stack(iou, 0).max(0) # best iou and anchor iou, a = torch.stack(iou, 0).max(0) # best iou and anchor
# reject below threshold ious (OPTIONAL) # reject below threshold ious (OPTIONAL, increases P, lowers R)
reject = True reject = True
if reject: if reject:
j = iou > 0.01 j = iou > 0.01
@ -309,7 +309,7 @@ def build_targets(model, targets):
t = targets t = targets
# Indices # Indices
b, c = t[:, 0:2].long().t() # target image, class b, c = t[:, :2].long().t() # target image, class
gxy = t[:, 2:4] * nG gxy = t[:, 2:4] * nG
gi, gj = gxy.long().t() # grid_i, grid_j gi, gj = gxy.long().t() # grid_i, grid_j
indices.append((b, a, gj, gi)) indices.append((b, a, gj, gi))