86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
import numpy as np
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import onnx
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import sys
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from tools.other import topological_sort
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from tools import helper
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def fuse_bias_in_consecutive_1x1_conv(g):
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for second in g.node:
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# Find two conv
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if second.op_type != "Conv":
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continue
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first = helper.find_node_by_output_name(g, second.input[0])
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if first is None or first.op_type != "Conv":
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continue
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# Check if the first one has only one folloing node
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if (
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len(
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helper.find_following_nodes_by_input_value_name(
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g, first.output[0]
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)
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)
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!= 1
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):
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continue
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# If first node has no bias, continue
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if len(first.input) == 2:
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continue
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# Check their kernel size
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first_kernel_shape = helper.get_list_attribute_by_name(
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first, "kernel_shape", "int"
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)
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second_kernel_shape = helper.get_list_attribute_by_name(
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second, "kernel_shape", "int"
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)
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prod = (
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first_kernel_shape[0]
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* first_kernel_shape[1]
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* second_kernel_shape[0]
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* second_kernel_shape[1]
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)
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if prod != 1:
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continue
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print("Found: ", first.name, " ", second.name)
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# Get bias of the nodes
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first_bias_node = helper.find_node_by_output_name(g, first.input[2])
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second_weight_node = helper.find_node_by_output_name(
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g, second.input[1]
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)
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second_bias_node = helper.find_node_by_output_name(g, second.input[2])
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first_bias = helper.constant_to_numpy(first_bias_node)
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second_weight = helper.constant_to_numpy(second_weight_node)
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second_bias = helper.constant_to_numpy(second_bias_node)
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# Calculate the weight for second node
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first_bias = np.reshape(first_bias, (1, first_bias.size))
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second_weight = np.reshape(
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second_weight, (second_weight.shape[0], second_weight.shape[1])
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)
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second_weight = np.transpose(second_weight)
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new_second_bias = second_bias + np.matmul(first_bias, second_weight)
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new_second_bias = np.reshape(new_second_bias, (new_second_bias.size,))
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# Generate new weight
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new_first_bias = np.reshape(first_bias, (first_bias.size,))
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for i in range(new_first_bias.shape[0]):
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new_first_bias[i] = 0.0
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new_first_bias_node = helper.numpy_to_constant(
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first_bias_node.output[0], new_first_bias
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)
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new_second_bias_node = helper.numpy_to_constant(
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second_bias_node.output[0], new_second_bias
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)
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# Delete old weight and add new weights
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g.node.remove(first_bias_node)
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g.node.remove(second_bias_node)
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g.node.extend([new_first_bias_node, new_second_bias_node])
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topological_sort(g)
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if __name__ == "__main__":
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if len(sys.argv) != 3:
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exit(1)
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m = onnx.load(sys.argv[1])
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fuse_bias_in_consecutive_1x1_conv(m.graph)
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onnx.save(m, sys.argv[2])
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