def gradient_cost(input,
label,
weight,
height,
width,
num_channel,
scales=[1, 2]):
grad_diff_input = pd.gradient_diff(input=input, scales=scales)
grad_diff_label = pd.gradient_diff(input=label, scales=scales)
weight = image_resize_func['nearest'](input=weight,
out_size_x=width,
out_size_y=height)
weight_diff = pd.gradient_diff(input=weight, scales=scales)
weight_diff = util_layers.reduce(input=weight_diff,
shape=[len(scales) * 2, height, width],
op='sum')
weight_diff = util_layers.math_op(input=weight_diff,
act=pd.activation.IsZero())
weight_diff = util_layers.math_op(input=[weight_diff, weight], op='dot')
out_ch = num_channel * len(scales) * 2
cost = ns_ele_l2_cost(grad_diff_input,
grad_diff_label,
weight_diff,
height,
width,
num_channel=out_ch)
return cost
def ns_ele_l2_cost(input,
label,
weight,
height,
width,
num_channel=None,
interp='nearest'):
assert interp in image_resize_func.keys()
# make sure all the input label and weight have the same size
input = pd.bilinear_interp(input=input,
out_size_x=width,
out_size_y=height)
label = image_resize_func[interp](input=label,
out_size_x=width,
out_size_y=height)
weight = image_resize_func[interp](input=weight,
out_size_x=width,
out_size_y=height)
# reshape the orignal layer
# input has shape c x h x w change to h x w x c
input_ts = pd.transpose(input=input,
trans_order=[1, 2, 0],
height=height,
width=width)
input_rs = pd.resize(input=input_ts, size=num_channel, height=1, width=1)
label_ts = pd.transpose(input=label,
trans_order=[1, 2, 0],
height=height,
width=width)
label_rs = pd.resize(input=label_ts, size=num_channel, height=1, width=1)
weight_rs = pd.resize(input=weight, size=1, height=1, width=1)
cost_rs = pd.mse_cost(input=input_rs, label=label_rs)
sqrt_l2_cost = util_layers.math_op(input=cost_rs, act=pd.activation.Sqrt())
sqrt_l2_cost = pd.mixed(
size=1,
input=[pd.dotmul_operator(a=sqrt_l2_cost, b=weight_rs, scale=1.0)])
sqrt_l2_cost = pd.resize(input=sqrt_l2_cost,
size=height * width,
height=height,
width=width)
weight_fac = pd.sum_cost(input=weight)
weight_fac = util_layers.math_op(input=weight_fac, act=pd.activation.Inv())
sqrt_l2_cost = pd.scaling(input=sqrt_l2_cost, weight=weight_fac)
cost = pd.sum_cost(input=sqrt_l2_cost)
return cost
def iou_score(input, label, weight, height, width, class_num, is_cost=True):
""" class num is semantic classes plus background,
this score can also serve as iou cost for training
"""
# input = pd.resize(input=input, size=height * width)
# label = pd.resize(input=label, size=height * width)
weight = pd.nearest_interp(input=weight,
out_size_x=width,
out_size_y=height)
if not is_cost:
# if not is cost, then it is eval, we can do
# one hot for label. Otherwise
input = util_layers.math_op(input=[input, weight], op='dot')
input_one_hot = util_layers.ele_one_hot(input, class_num, height,
width)
else:
input_one_hot = input
input_one_hot = pd.bilinear_interp(input=input_one_hot,
out_size_x=width,
out_size_y=height)
label = pd.nearest_interp(input=label, out_size_x=width, out_size_y=height)
label = util_layers.math_op(input=[label, weight], op='dot')
label_one_hot = util_layers.ele_one_hot(label, class_num, height, width)
inter = util_layers.math_op(input=[input_one_hot, label_one_hot], op='dot')
union = pd.addto(input=[input_one_hot, label_one_hot],
act=pd.activation.Linear(),
bias_attr=False)
inter_neg = pd.slope_intercept(input=inter, slope=-1)
union = pd.addto(input=[union, inter_neg],
act=pd.activation.Linear(),
bias_attr=False)
inter = pd.resize(input=inter, size=height * width)
inter = pd.sum_cost(input=inter)
union = pd.resize(input=union, size=height * width)
union = pd.sum_cost(input=union)
union_inv = util_layers.math_op(input=union, act=pd.activation.Inv())
iou = pd.mixed(size=1,
input=[pd.dotmul_operator(a=inter, b=union_inv, scale=1.0)])
iou = pd.resize(input=iou, size=class_num)
if is_cost:
iou = pd.sum_cost(iou)
return iou
def inner_product_cost(input,
label,
weight,
height,
width,
num_channel,
interp='nearest',
is_angle=False):
"""If is_angle, we can not back propagate through the angle, only back
through the inner product, the loss is not consistent with the evaluation.
"""
# make sure all the input label and weight have the same size
if height > 1 and width > 1:
input = pd.bilinear_interp(input=input,
out_size_x=width,
out_size_y=height)
label = pd.bilinear_interp(input=label,
out_size_x=width,
out_size_y=height)
if weight:
weight = image_resize_func[interp](input=weight,
out_size_x=width,
out_size_y=height)
size = height * width * num_channel
input = util_layers.norm(input,
height,
width,
num_channel,
trans_back=False)
label = util_layers.norm(label,
height,
width,
num_channel,
trans_back=False)
inner = pd.mixed(size=size,
input=[pd.dotmul_operator(a=input, b=label, scale=1.0)])
inner = pd.resize(input=pd.sum_cost(input=inner),
size=height * width,
height=height,
width=width)
if is_angle:
inner = util_layers.math_op(input=inner, act=pd.activation.Acos())
else:
inner = pd.slope_intercept(input=inner, slope=-1, intercept=1.0)
if weight:
inner_error = sum_weighted_loss(inner, weight, size=height * width)
else:
fac = 1.0 / float(height * width)
inner = pd.slope_intercept(input=inner, slope=fac, intercept=0.0)
inner_error = pd.sum_cost(input=inner)
return inner_error
def pixel_accuracy(input, label, weight, height, width):
label = pd.nearest_interp(input=label, out_size_x=width, out_size_y=height)
weight = pd.nearest_interp(input=weight,
out_size_x=width,
out_size_y=height)
label_neg = pd.slope_intercept(input=label, slope=-1)
diff = pd.addto(input=[input, label_neg],
act=pd.activation.Linear(),
bias_attr=False)
correct = util_layers.math_op(input=diff, act=pd.activation.IsZero())
correct = util_layers.math_op(input=[correct, weight], op='dot')
correct = pd.sum_cost(input=correct)
eval_pixels_num = pd.sum_cost(input=weight)
divider = util_layers.math_op(input=eval_pixels_num,
act=pd.activation.Inv())
acc = util_layers.math_op(input=[correct, divider], op='dot')
return acc
def sum_weighted_loss(loss, weight, size=1):
"""Loss has input batch_size x image_size, weight has input batch_size x weight
( i * w ) / sum(W)
The output is normalized weighted loss
"""
weighted_loss = pd.mixed(
size=size, input=[pd.dotmul_operator(a=loss, b=weight, scale=1.0)])
weight_fac = pd.sum_cost(input=weight)
weight_fac = util_layers.math_op(input=weight_fac, act=pd.activation.Inv())
weighted_loss = pd.scaling(input=loss, weight=weight_fac)
weighted_loss = pd.sum_cost(input=weighted_loss)
return weighted_loss
def relative_l1(input,
label,
weight,
height,
width,
interp='nearest',
is_inverse=False):
"""Relative l1 loss for depth
"""
assert interp in image_resize_func.keys()
# make sure all the input label and weight have the same size
if height > 1 and width > 1:
input = pd.bilinear_interp(input=input,
out_size_x=width,
out_size_y=height)
label = pd.bilinear_interp(input=label,
out_size_x=width,
out_size_y=height)
if weight:
weight = image_resize_func[interp](input=weight,
out_size_x=width,
out_size_y=height)
label_inv = util_layers.math_op(input=label, act=pd.activation.Inv())
label_neg = pd.slope_intercept(input=label, slope=-1)
diff = pd.addto(input=[input, label_neg],
act=pd.activation.Abs(),
bias_attr=False)
rel_error = pd.mixed(
size=1, input=[pd.dotmul_operator(a=diff, b=label_inv, scale=1.0)])
if weight:
rel_error = sum_weighted_loss(rel_error, weight, size=height * width)
else:
fac = 1.0 / float(height * width)
inner = pd.slope_intercept(input=inner, slope=fac, intercept=0.0)
inner_error = pd.sum_cost(input=inner)
return rel_error
def ele_norm_cost(input,
label,
weight,
height=None,
width=None,
num_channel=None,
cost_type='l1'):
if height > 1 and width > 1:
input = pd.bilinear_interp(input=input,
out_size_x=width,
out_size_y=height)
label = pd.bilinear_interp(input=label,
out_size_x=width,
out_size_y=height)
if weight:
weight = pd.nearest_interp(input=weight,
out_size_x=width,
out_size_y=height)
size = height * width * num_channel
if weight:
input = pd.mixed(
size=size,
input=[pd.dotmul_operator(a=input, b=weight, scale=1.0)])
label = pd.mixed(
size=size,
input=[pd.dotmul_operator(a=label, b=weight, scale=1.0)])
cost = cost_func[cost_type](input=input, label=label)
fac = pd.sum_cost(input=weight)
fac = util_layers.math_op(input=fac, act=pd.activation.Inv())
cost = pd.scaling(input=cost, weight=fac)
cost = pd.sum_cost(input=cost)
else:
cost = cost_func[cost_type](input=input, label=label)
fac = 1.0 / float(height * width)
cost = pd.slope_intercept(input=cost, slope=fac, intercept=0.0)
cost = pd.sum_cost(input=cost)
return cost