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# 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
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sys.path.append(
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os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
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import yaml
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import paddle
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import paddle.distributed as dist
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paddle.seed(2)
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from ppocr.data import build_dataloader
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from ppocr.modeling.architectures import build_model
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from ppocr.losses import build_loss
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from ppocr.optimizer import build_optimizer
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from ppocr.postprocess import build_post_process
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from ppocr.metrics import build_metric
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from ppocr.utils.save_load import init_model
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import tools.program as program
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import paddleslim
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from paddleslim.dygraph.quant import QAT
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import numpy as np
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dist.get_world_size()
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class PACT(paddle.nn.Layer):
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def __init__(self):
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super(PACT, self).__init__()
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alpha_attr = paddle.ParamAttr(
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name=self.full_name() + ".pact",
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initializer=paddle.nn.initializer.Constant(value=20),
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learning_rate=1.0,
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regularizer=paddle.regularizer.L2Decay(2e-5))
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self.alpha = self.create_parameter(
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shape=[1], attr=alpha_attr, dtype='float32')
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def forward(self, x):
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out_left = paddle.nn.functional.relu(x - self.alpha)
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out_right = paddle.nn.functional.relu(-self.alpha - x)
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x = x - out_left + out_right
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return x
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quant_config = {
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# weight preprocess type, default is None and no preprocessing is performed.
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'weight_preprocess_type': None,
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# activation preprocess type, default is None and no preprocessing is performed.
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'activation_preprocess_type': None,
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# weight quantize type, default is 'channel_wise_abs_max'
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'weight_quantize_type': 'channel_wise_abs_max',
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# activation quantize type, default is 'moving_average_abs_max'
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'activation_quantize_type': 'moving_average_abs_max',
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# weight quantize bit num, default is 8
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'weight_bits': 8,
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# activation quantize bit num, default is 8
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'activation_bits': 8,
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# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
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'dtype': 'int8',
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# window size for 'range_abs_max' quantization. default is 10000
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'window_size': 10000,
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# The decay coefficient of moving average, default is 0.9
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'moving_rate': 0.9,
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# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
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'quantizable_layer_type': ['Conv2D', 'Linear'],
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}
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def sample_generator(loader):
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def __reader__():
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for indx, data in enumerate(loader):
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images = np.array(data[0])
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yield images
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return __reader__
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def main(config, device, logger, vdl_writer):
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# init dist environment
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if config['Global']['distributed']:
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dist.init_parallel_env()
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global_config = config['Global']
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# build dataloader
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config['Train']['loader']['num_workers'] = 0
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train_dataloader = build_dataloader(config, 'Train', device, logger)
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if config['Eval']:
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config['Eval']['loader']['num_workers'] = 0
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valid_dataloader = build_dataloader(config, 'Eval', device, logger)
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else:
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valid_dataloader = None
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paddle.enable_static()
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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if 'inference_model' in global_config.keys(): # , 'inference_model'):
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inference_model_dir = global_config['inference_model']
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else:
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inference_model_dir = os.path.dirname(global_config['pretrained_model'])
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if not (os.path.exists(os.path.join(inference_model_dir, "inference.pdmodel")) and \
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os.path.exists(os.path.join(inference_model_dir, "inference.pdiparams")) ):
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raise ValueError(
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"Please set inference model dir in Global.inference_model or Global.pretrained_model for post-quantazition"
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)
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paddleslim.quant.quant_post_static(
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executor=exe,
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model_dir=inference_model_dir,
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model_filename='inference.pdmodel',
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params_filename='inference.pdiparams',
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quantize_model_path=global_config['save_inference_dir'],
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sample_generator=sample_generator(train_dataloader),
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save_model_filename='inference.pdmodel',
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save_params_filename='inference.pdiparams',
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batch_nums=10)
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if __name__ == '__main__':
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config, device, logger, vdl_writer = program.preprocess(is_train=True)
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main(config, device, logger, vdl_writer)
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