wehub-resource-sync
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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def convert_params_for_cell(np_cell, paddle_cell):
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state = np_cell.parameters
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for k, v in paddle_cell.named_parameters():
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v.set_value(state[k])
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def convert_params_for_cell_static(np_cell, paddle_cell, place):
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state = np_cell.parameters
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for k, v in paddle_cell.named_parameters():
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scope = paddle.static.global_scope()
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tensor = scope.find_var(v.name).get_tensor()
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tensor.set(state[k], place)
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def convert_params_for_net(np_net, paddle_net):
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for np_layer, paddle_layer in zip(np_net, paddle_net):
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if hasattr(np_layer, "cell"):
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convert_params_for_cell(np_layer.cell, paddle_layer.cell)
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else:
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convert_params_for_cell(np_layer.cell_fw, paddle_layer.cell_fw)
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convert_params_for_cell(np_layer.cell_bw, paddle_layer.cell_bw)
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def convert_params_for_net_static(np_net, paddle_net, place):
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for np_layer, paddle_layer in zip(np_net, paddle_net):
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if hasattr(np_layer, "cell"):
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convert_params_for_cell_static(
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np_layer.cell, paddle_layer.cell, place
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)
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else:
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convert_params_for_cell_static(
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np_layer.cell_fw, paddle_layer.cell_fw, place
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)
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convert_params_for_cell_static(
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np_layer.cell_bw, paddle_layer.cell_bw, place
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)
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def get_params_for_cell(np_cell, num_layers, idx):
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state = np_cell.parameters
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weight_list = [
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(f'{num_layers}.weight_{idx}', state['weight_ih']),
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(f'{num_layers}.weight_{idx + 1}', state['weight_hh']),
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]
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bias_list = [
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(f'{num_layers}.bias_{idx}', state['bias_ih']),
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(f'{num_layers}.bias_{idx + 1}', state['bias_hh']),
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]
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return weight_list, bias_list
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def get_params_for_net(np_net):
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weight_list = []
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bias_list = []
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for layer_idx, np_layer in enumerate(np_net):
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if hasattr(np_layer, "cell"):
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weight, bias = get_params_for_cell(np_layer.cell, layer_idx, 0)
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for w, b in zip(weight, bias):
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weight_list.append(w)
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bias_list.append(b)
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else:
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for count, cell in enumerate([np_layer.cell_fw, np_layer.cell_bw]):
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weight, bias = get_params_for_cell(cell, layer_idx, count * 2)
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for w, b in zip(weight, bias):
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weight_list.append(w)
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bias_list.append(b)
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weight_list.extend(bias_list)
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return weight_list
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