yolo_v3改focal loss
retinanet中的损失函数定义如下：
def _focal(y_true, y_pred):
""" Compute the focal loss given the target tensor and the predicted tensor.
As defined in https://arxiv.org/abs/1708.02002
Args
y_true: Tensor of target data from the generator with shape (B, N, num_classes).
y_pred: Tensor of predicted data from the network with shape (B, N, num_classes).
Returns
The focal loss of y_pred w.r.t. y_true.
"""
labels = y_true[:, :, :-1]##这行的意思是：The slices [1:] and [:-1] mean all but the first and
### all but the last elements of the array。更多，请看下面第三行的解释.
anchor_state = y_true[:, :, -1] # -1 for ignore, 0 for background, 1 for object
classification = y_pred
#### 解释：
####请看anchors.py中的函数anchor_targets_bbox，就知道
#### Returns
#### labels_batch: batch that contains labels & anchor states (np.array of shape (batch_size, N, num_classes + 1),
#### where N is the number of anchors for an image and the last column defines the anchor state (-1 for ignore, 0 for bg, 1 for fg).
#### regression_batch: batch that contains bounding-box regression targets for an image & anchor states (np.array of shape (batch_size, N, 4 + 1),
#### where N is the number of anchors for an image, the first 4 columns define regression targets for (x1, y1, x2, y2) and the
#### last column defines anchor states (-1 for ignore, 0 for bg, 1 for fg).
#### annotations_batch: annotations per anchor (np.array of shape (batch_size, N, annotations.shape[1]), where N is the number of anchors for an image)
## y_true就是labels_batch
# filter out "ignore" anchors
indices = backend.where(keras.backend.not_equal(anchor_state, -1))
labels = backend.gather_nd(labels, indices)
classification = backend.gather_nd(classification, indices)
# compute the focal loss
alpha_factor = keras.backend.ones_like(labels) * alpha
alpha_factor = backend.where(keras.backend.equal(labels, 1), alpha_factor, 1 - alpha_factor)
focal_weight = backend.where(keras.backend.equal(labels, 1), 1 - classification, classification)
focal_weight = alpha_factor * focal_weight ** gamma
cls_loss = focal_weight * keras.backend.binary_crossentropy(labels, classification)
# compute the normalizer: the number of positive anchors
normalizer = backend.where(keras.backend.equal(anchor_state, 1))
normalizer = keras.backend.cast(keras.backend.shape(normalizer)[0], keras.backend.floatx())
normalizer = keras.backend.maximum(1.0, normalizer)
return keras.backend.sum(cls_loss) / normalizer
return _focal
--------------------------------------------------------
def smooth_l1(sigma=3.0):
""" Create a smooth L1 loss functor.
Args
sigma: This argument defines the point where the loss changes from L2 to L1.
Returns
A functor for computing the smooth L1 loss given target data and predicted data.
"""
sigma_squared = sigma ** 2
def _smooth_l1(y_true, y_pred):
""" Compute the smooth L1 loss of y_pred w.r.t. y_true.
Args
y_true: Tensor from the generator of shape (B, N, 5). The last value for each box is the state of the anchor (ignore, negative, positive).
y_pred: Tensor from the network of shape (B, N, 4).
Returns
The smooth L1 loss of y_pred w.r.t. y_true.
"""
# separate target and state
regression = y_pred
regression_target = y_true[:, :, :4]
anchor_state = y_true[:, :, 4]
# filter out "ignore" anchors
indices = backend.where(keras.backend.equal(anchor_state, 1))
regression = backend.gather_nd(regression, indices)
regression_target = backend.gather_nd(regression_target, indices)
# compute smooth L1 loss
# f(x) = 0.5 * (sigma * x)^2 if |x| < 1 / sigma / sigma
# |x| - 0.5 / sigma / sigma otherwise
regression_diff = regression - regression_target
regression_diff = keras.backend.abs(regression_diff)
regression_loss = backend.where(
keras.backend.less(regression_diff, 1.0 / sigma_squared),
0.5 * sigma_squared * keras.backend.pow(regression_diff, 2),
regression_diff - 0.5 / sigma_squared
)
# compute the normalizer: the number of positive anchors
normalizer = keras.backend.maximum(1, keras.backend.shape(indices)[0])
normalizer = keras.backend.cast(normalizer, dtype=keras.backend.floatx())
return keras.backend.sum(regression_loss) / normalizer
return _smooth_l1
在yolo_v3中，修改这句：
confidence_loss = object_mask * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True)+ \
(1-object_mask) * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True) * ignore_mask
改为：
alpha=0.25
gamma=2
alpha_factor = keras.backend.ones_like(object_mask) * alpha
alpha_factor =tf.where(keras.backend.equal(object_mask, 1), alpha_factor, 1 - alpha_factor)
focal_weight = tf.where(keras.backend.equal(object_mask, 1), 1 - raw_pred[...,4:5], raw_pred[...,4:5])
focal_weight = alpha_factor * focal_weight ** gamma
confidence_loss = focal_weight * keras.backend.binary_crossentropy(object_mask, raw_pred[...,4:5],from_logits=True)
ignore_mask呢？？？？？？
