def conv2d(
g,
q_input,
q_weight,
bias,
stride,
padding,
dilation,
groups,
op_scale,
op_zero_point,
):
input, input_scale, _, _ = symbolic_helper.dequantize_helper(
g, q_input)
weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(
g, q_weight)
q_bias = symbolic_helper.requantize_bias_helper(
g, bias, input_scale, weight_scale)
bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
output = opset9.conv2d(g, input, weight, bias, stride, padding,
dilation, groups)
return symbolic_helper.quantize_helper(g, output, op_scale,
op_zero_point)
def linear(g, q_input, q_weight, bias, op_scale, op_zero_point):
input, input_scale, _ = sym_help.dequantize_helper(g, q_input)
weight, weight_scale, _ = sym_help.dequantize_helper(g, q_weight)
q_bias = sym_help.requantize_bias_helper(g, bias, input_scale, weight_scale)
bias, _, _ = sym_help.dequantize_helper(g, q_bias)
output = linear(g, input, weight, bias)
return sym_help.quantize_helper(g, output, op_scale, op_zero_point)
def conv2d_relu(g, q_input, q_weight, bias, stride, padding, dilation, groups, op_scale, op_zero_point):
input, input_scale, _ = sym_help.dequantize_helper(g, q_input)
weight, weight_scale, _ = sym_help.dequantize_helper(g, q_weight)
q_bias = sym_help.requantize_bias_helper(g, bias, input_scale, weight_scale)
bias, _, _ = sym_help.dequantize_helper(g, q_bias)
output = conv2d(g, input, weight, bias, stride, padding, dilation, groups)
output = relu(g, output)
return sym_help.quantize_helper(g, output, op_scale, op_zero_point)
def linear(g, q_input, q_weight, bias, op_scale, op_zero_point):
input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
q_bias = symbolic_helper.requantize_bias_helper(
g, bias, input_scale, weight_scale, axis
)
bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
output = opset9.linear(g, input, weight, bias)
return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)