def binary_cross_entropy_with_logits(g, input, target, weight, pos_weight,
reduction):
from torch.onnx.symbolic_opset9 import sigmoid, log, sub, neg, mul, add
p = g.op("Constant", value_t=torch.tensor([1]))
sig_x = sigmoid(g, input)
log_sig_x = log(g, sig_x)
sub_1_x = sub(g, p, sig_x)
sub_1_y = sub(g, p, target)
log_1_x = log(g, sub_1_x)
if pos_weight is None or sym_help._is_none(pos_weight):
output = neg(
g, add(g, mul(g, target, log_sig_x), mul(g, sub_1_y, log_1_x)))
else:
output = neg(
g,
add(g, mul(g, mul(g, target, log_sig_x), pos_weight),
mul(g, sub_1_y, log_1_x)))
if weight is not None and not sym_help._is_none(weight):
output = mul(g, weight, output)
reduction = sym_help._maybe_get_const(reduction, 'i')
if reduction == 0:
return output
elif reduction == 1:
return g.op("ReduceMean", output)
elif reduction == 2:
return g.op("ReduceSum", output)
else:
return sym_help._onnx_unsupported(
"binary_cross_entropy_with_logits with reduction other than none, mean, or sum"
)
def sigmoid(g, x, op_scale, op_zero_point):
x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
output = opset9.sigmoid(g, x)
return symbolic_helper.quantize_helper(g, output, op_scale,
op_zero_point)
def sigmoid(g, input):
if input not in symbolic_helper._quantized_ops:
return opset9.sigmoid(g, input)
# Caffe2 expects the output scale to be 1/2^8
# and output zero_point to be 0 (quint8 type)
out_scale = 1.0 / 256
zero_point = 0
kwargs = {
"Y_scale_f": out_scale,
"Y_zero_point_i": zero_point,
}
output = g.op("_caffe2::Int8Sigmoid", input, **kwargs)
symbolic_helper._quantized_ops.add(output)
return output
def binary_cross_entropy_with_logits(g, input, target, weight, pos_weight,
reduction):
p = g.op("Constant", value_t=torch.tensor([1]))
sig_x = opset9.sigmoid(g, input)
log_sig_x = opset9.log(g, sig_x)
sub_1_x = opset9.sub(g, p, sig_x)
sub_1_y = opset9.sub(g, p, target)
log_1_x = opset9.log(g, sub_1_x)
if pos_weight is None or symbolic_helper._is_none(pos_weight):
output = opset9.neg(
g,
opset9.add(g, opset9.mul(g, target, log_sig_x),
opset9.mul(g, sub_1_y, log_1_x)),
)
else:
output = opset9.neg(
g,
opset9.add(
g,
opset9.mul(g, opset9.mul(g, target, log_sig_x), pos_weight),
opset9.mul(g, sub_1_y, log_1_x),
),
)
if weight is not None and not symbolic_helper._is_none(weight):
output = opset9.mul(g, weight, output)
reduction = symbolic_helper._maybe_get_const(reduction, "i")
if reduction == 0:
return output
elif reduction == 1:
return g.op("ReduceMean", output, keepdims_i=0)
elif reduction == 2:
return g.op("ReduceSum", output, keepdims_i=0)
else:
return symbolic_helper._onnx_unsupported(
"binary_cross_entropy_with_logits with reduction other than none, mean, or sum",
input,
)
