Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-11-20 13:36:15 -08:00
parent 8e327e3bd0
commit bb209111c4
2 changed files with 5 additions and 4 deletions
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3
train.py
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@ -204,8 +204,7 @@ def train():
model.nc = nc # attach number of classes to model model.nc = nc # attach number of classes to model
model.arc = opt.arc # attach yolo architecture model.arc = opt.arc # attach yolo architecture
model.hyp = hyp # attach hyperparameters to model model.hyp = hyp # attach hyperparameters to model
if hasattr(dataset, 'labels'): model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
torch_utils.model_info(model, report='summary') # 'full' or 'summary' torch_utils.model_info(model, report='summary') # 'full' or 'summary'
nb = len(dataloader) nb = len(dataloader)
maps = np.zeros(nc) # mAP per class maps = np.zeros(nc) # mAP per class

6
utils/utils.py
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@ -43,14 +43,16 @@ def load_classes(path):
def labels_to_class_weights(labels, nc=80): def labels_to_class_weights(labels, nc=80):
# Get class weights (inverse frequency) from training labels # Get class weights (inverse frequency) from training labels
ni = len(labels) # number of images if labels[0] is None: # no labels loaded
return None
labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
classes = labels[:, 0].astype(np.int) # labels = [class xywh] classes = labels[:, 0].astype(np.int) # labels = [class xywh]
weights = np.bincount(classes, minlength=nc) # occurences per class weights = np.bincount(classes, minlength=nc) # occurences per class
# Prepend gridpoint count (for uCE trianing) # Prepend gridpoint count (for uCE trianing)
# gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
# weights = np.hstack([gpi * ni - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
weights[weights == 0] = 1 # replace empty bins with 1 weights[weights == 0] = 1 # replace empty bins with 1
weights = 1 / weights # number of targets per class weights = 1 / weights # number of targets per class