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 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, )
def logdet(g, input): from torch.onnx.symbolic_opset9 import log return log(g, linalg_det(g, input))
def logdet(g, input): return opset9.log(g, linalg_det(g, input))