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https://github.com/PaddlePaddle/FastDeploy.git
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f2619b0546
* Refactor js submodule * Remove change-log * Update ocr module * Update ocr-detection module * Update ocr-detection module * Remove change-log
274 lines
9.0 KiB
Python
274 lines
9.0 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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def splice_rnn_op(model_info, rnn_index):
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global input_shape
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global weight_0_shape
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global weight_1_shape
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global rnn_input_name
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ops = model_info['ops']
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vars = model_info['vars']
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op = ops[rnn_index]
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rnn_input_name = op['inputs']['Input'][0]
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rnn_output_name = op['outputs']['Out'][0]
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is_bidirec = 2 if op['attrs']['is_bidirec'] else 1
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num_layers = op['attrs']['num_layers']
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hidden_size = op['attrs']['hidden_size']
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layer_num = num_layers * is_bidirec
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# concat input最大值
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max_concat_num = 15
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def concat_mul(index, list, num):
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global rnn_input_name
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end = len(list)
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if end < max_concat_num:
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concat_output_name = 'lstm_' + str(index - 1) + '_' + str(num) + '.tmp_concat'
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# 非最后一层遍历，将concat作为下一层输入
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if index < is_bidirec * num_layers - 1:
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rnn_input_name = concat_output_name
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# 最后一层遍历，将rnn_output_name赋给最后一个concat
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else:
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concat_output_name = rnn_output_name
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concat_op = {
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'attrs': {
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'axis': 0
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},
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'inputs': {
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'X': []
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},
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'outputs': {'Out': [concat_output_name]},
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'type': 'concat'
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}
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concat_output_shape = 0
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for x in range(0, end):
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x_input_name = 'lstm_' + str(index - 1) + '_' + str(list[x]) + '.tmp_concat'
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concat_op['inputs']['X'].append(x_input_name)
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concat_output_shape += vars[x_input_name]['shape'][0]
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concat_var = {
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'name': concat_output_name,
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'persistable': False,
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'shape': [concat_output_shape, 1, weight_1_shape[1] * 2]
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}
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ops.append(concat_op)
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if index < is_bidirec * num_layers - 1:
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vars[concat_output_name] = concat_var
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return
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# concat新列表
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new_list = []
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for i in range(0, end, max_concat_num):
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if i + max_concat_num > end:
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for n in range(i, end):
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new_list.append(list[n])
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break
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concat_output_name = 'lstm_' + str(index - 1) + '_' + str(num) + '.tmp_concat'
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# concat_list长度为max_concat_num && 最后一层遍历，将rnn_output_name赋给最后一个concat
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if end == max_concat_num and index == is_bidirec * num_layers - 1:
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concat_output_name = rnn_output_name
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concat_op = {
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'attrs': {
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'axis': 0
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},
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'inputs': {
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'X': []
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},
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'outputs': {'Out': [concat_output_name]},
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'type': 'concat'
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}
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concat_output_shape = 0
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for x in range(0, max_concat_num):
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x_input_name = 'lstm_' + str(index - 1) + '_' + str(list[i + x]) + '.tmp_concat'
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concat_op['inputs']['X'].append(x_input_name)
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concat_output_shape += vars[x_input_name]['shape'][0]
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concat_var = {
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'name': concat_output_name,
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'persistable': False,
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'shape': [concat_output_shape, 1, weight_1_shape[1] * 2]
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}
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ops.append(concat_op)
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vars[concat_output_name] = concat_var
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new_list.append(num)
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# 若concat_list长度为max_concat_num，在下一次递归时直接修改rnn_input_name，结束递归，num无需+1
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if end != max_concat_num:
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num += 1
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concat_mul(index, new_list, num)
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for index in range(layer_num):
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last_hidden = op['inputs']['PreState'][0]
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last_cell = op['inputs']['PreState'][1]
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weight_list_0 = op['inputs']['WeightList'][index * 2]
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weight_list_1 = op['inputs']['WeightList'][index * 2 + 1]
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weight_list_2 = op['inputs']['WeightList'][(index + num_layers * is_bidirec) * 2]
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weight_list_3 = op['inputs']['WeightList'][(index + num_layers * is_bidirec) * 2 + 1]
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output_name = 'rnn_origin_' + str(index)
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input_shape = vars[rnn_input_name]['shape']
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batch = input_shape[0]
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if vars[weight_list_0]:
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weight_0_shape = vars[weight_list_0]['shape']
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if vars[weight_list_1]:
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weight_1_shape = vars[weight_list_1]['shape']
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if batch == 0:
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continue
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origin_op = {
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'attrs': {
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'state_axis': index
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},
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'inputs': {
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'Input': [rnn_input_name],
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'PreState': [last_hidden],
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'WeightList': [
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weight_list_0,
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weight_list_1,
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weight_list_2,
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weight_list_3
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]
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},
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'outputs': {'Out': [output_name]},
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'type': 'rnn_origin'
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}
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origin_var = {
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'name': output_name,
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'persistable': False,
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'shape': [input_shape[0], input_shape[1], weight_0_shape[0]]
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}
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ops.append(origin_op)
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vars[output_name] = origin_var
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for bat in range(batch):
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matmul_output_name = 'lstm_' + str(index) + '_' + str(bat) + '.tmp_matmul'
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cell_output_name = 'lstm_' + str(index) + '_' + str(bat) + '.tmp_c'
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hidden_output_name = 'lstm_' + str(index) + '_' + str(bat) + '.tmp_h'
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matmul_op = {
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'attrs': {
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'input_axis': bat,
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'state_axis': index if bat == 0 else 0,
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'batch': batch,
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'reverse': False if index % 2 == 0 else True
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},
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'inputs': {
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'Input': [output_name],
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'PreState': [last_hidden],
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'WeightList': [weight_list_1]
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},
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'outputs': {'Out': [matmul_output_name]},
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'type': 'rnn_matmul'
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}
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matmul_var = {
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'name': matmul_output_name,
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'persistable': False,
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'shape': [1, 1, weight_0_shape[0]]
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}
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ops.append(matmul_op)
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vars[matmul_output_name] = matmul_var
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cell_op = {
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'attrs': {
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'state_axis': index if bat == 0 else 0,
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'hidden_size': hidden_size
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},
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'inputs': {
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'X': [matmul_output_name],
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'Y': [last_cell]
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},
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'outputs': {'Out': [cell_output_name]},
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'type': 'rnn_cell'
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}
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cell_var = {
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'name': cell_output_name,
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'persistable': False,
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'shape': [1, 1, weight_1_shape[1]]
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}
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ops.append(cell_op)
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vars[cell_output_name] = cell_var
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hidden_op = {
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'attrs': {
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'state_axis': index if bat == 0 else 0,
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'hidden_size': hidden_size
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},
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'inputs': {
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'X': [matmul_output_name],
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'Y': [last_cell]
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},
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'outputs': {'Out': [hidden_output_name]},
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'type': 'rnn_hidden'
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}
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hidden_var = {
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'name': hidden_output_name,
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'persistable': False,
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'shape': [1, 1, weight_1_shape[1]]
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}
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ops.append(hidden_op)
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vars[hidden_output_name] = hidden_var
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last_hidden = hidden_output_name
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last_cell = cell_output_name
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# concat
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if index % 2 == 1:
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concat_list = []
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concat_num = 0
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# concat forword and backword
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for bat in range(batch):
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x_input_name_0 = 'lstm_' + str(index - 1) + '_' + str(bat) + '.tmp_h'
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x_input_name_1 = 'lstm_' + str(index) + '_' + str(batch - bat - 1) + '.tmp_h'
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concat_output_name = 'lstm_' + str(index - 1) + '_' + str(bat) + '.tmp_concat'
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concat_op = {
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'attrs': {
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'axis': 2
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},
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'inputs': {
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'X': [x_input_name_0, x_input_name_1]
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},
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'outputs': {'Out': [concat_output_name]},
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'type': 'concat'
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}
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concat_var = {
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'name': concat_output_name,
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'persistable': False,
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'shape': [1, 1, weight_1_shape[1] * 2]
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}
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ops.append(concat_op)
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vars[concat_output_name] = concat_var
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concat_list.append(bat)
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concat_num += 1
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concat_mul(index, concat_list, concat_num)
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# 删除rnn op
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del ops[rnn_index]
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