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103 lines
3.1 KiB
103 lines
3.1 KiB
# 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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import numpy as np
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quant_input_layers_map = {
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'Conv2D': paddle.nn.Conv2D,
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'Linear': paddle.nn.Linear,
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'AdaptiveAvgPool2D': paddle.nn.AdaptiveAvgPool2D,
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'AdaptiveMaxPool2D': paddle.nn.AdaptiveMaxPool2D,
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'AvgPool2D': paddle.nn.AvgPool2D,
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'MaxPool2D': paddle.nn.MaxPool2D,
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'Hardswish': paddle.nn.Hardswish,
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'LeakyReLU': paddle.nn.LeakyReLU,
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'PReLU': paddle.nn.PReLU,
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'ReLU': paddle.nn.ReLU,
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'ReLU6': paddle.nn.ReLU6,
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'Sigmoid': paddle.nn.Sigmoid,
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'Softmax': paddle.nn.Softmax,
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'Swish': paddle.nn.Swish,
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'Tanh': paddle.nn.Tanh,
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'Hardswish': paddle.nn.Hardswish,
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'BatchNorm': paddle.nn.BatchNorm,
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'GroupNorm': paddle.nn.GroupNorm,
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'LayerNorm': paddle.nn.LayerNorm,
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}
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fake_quantize_dequantize_types = [
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"fake_quantize_dequantize_abs_max",
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"fake_quantize_dequantize_channel_wise_abs_max",
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"fake_quantize_dequantize_moving_average_abs_max"
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]
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quant_output_layers_map = {
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'Conv2D': paddle.nn.Conv2D,
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'Conv2DTranspose': paddle.nn.Conv2DTranspose,
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'Linear': paddle.nn.Linear,
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'AdaptiveAvgPool2D': paddle.nn.AdaptiveAvgPool2D,
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'AdaptiveMaxPool2D': paddle.nn.AdaptiveMaxPool2D,
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'AvgPool2D': paddle.nn.AvgPool2D,
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'MaxPool2D': paddle.nn.MaxPool2D,
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'BatchNorm': paddle.nn.BatchNorm,
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'BatchNorm2D': paddle.nn.BatchNorm2D,
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'SyncBatchNorm': paddle.nn.SyncBatchNorm,
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'ELU': paddle.nn.ELU,
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'GELU': paddle.nn.GELU,
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'LeakyReLU': paddle.nn.LeakyReLU,
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'PReLU': paddle.nn.PReLU,
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'ReLU': paddle.nn.ReLU,
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'ReLU6': paddle.nn.ReLU6,
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'Sigmoid': paddle.nn.Sigmoid,
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'Softmax': paddle.nn.Softmax,
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'Tanh': paddle.nn.Tanh,
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'Swish': paddle.nn.Swish,
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}
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weight_op_types = [
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"conv2d", "depthwise_conv2d", "matmul", "conv2d_transpose",
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"depthwise_conv2d_transpose"
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]
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def load_variable_data(scope, var_name):
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'''
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Load variable value from scope
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'''
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var_node = scope.find_var(var_name)
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assert var_node is not None, \
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"Can not find " + var_name + " in the scope."
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return np.array(var_node.get_tensor())
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def find_previous_op(block, var_name):
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"""
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Find the previous op for the input variable.
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"""
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for op in block.ops:
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if var_name in op.output_arg_names:
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return op
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def find_next_ops(block, var_name):
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"""
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Find all followed ops for the input variable.
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"""
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res_ops = []
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for op in block.ops:
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if var_name in op.input_arg_names:
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res_ops.append(op)
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return res_ops
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