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8207382 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, "..", "..", "..")))
sys.path.append(os.path.abspath(os.path.join(__dir__, "..", "..", "..", "tools")))
import yaml
import paddle
import paddle.distributed as dist
paddle.seed(2)
from ppocr.data import build_dataloader, set_signal_handlers
from ppocr.modeling.architectures import build_model
from ppocr.losses import build_loss
from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import load_model
import tools.program as program
import paddleslim
from paddleslim.dygraph.quant import QAT
import numpy as np
dist.get_world_size()
class PACT(paddle.nn.Layer):
def __init__(self):
super(PACT, self).__init__()
alpha_attr = paddle.ParamAttr(
name=self.full_name() + ".pact",
initializer=paddle.nn.initializer.Constant(value=20),
learning_rate=1.0,
regularizer=paddle.regularizer.L2Decay(2e-5),
)
self.alpha = self.create_parameter(shape=[1], attr=alpha_attr, dtype="float32")
def forward(self, x):
out_left = paddle.nn.functional.relu(x - self.alpha)
out_right = paddle.nn.functional.relu(-self.alpha - x)
x = x - out_left + out_right
return x
quant_config = {
# weight preprocess type, default is None and no preprocessing is performed.
"weight_preprocess_type": None,
# activation preprocess type, default is None and no preprocessing is performed.
"activation_preprocess_type": None,
# weight quantize type, default is 'channel_wise_abs_max'
"weight_quantize_type": "channel_wise_abs_max",
# activation quantize type, default is 'moving_average_abs_max'
"activation_quantize_type": "moving_average_abs_max",
# weight quantize bit num, default is 8
"weight_bits": 8,
# activation quantize bit num, default is 8
"activation_bits": 8,
# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
"dtype": "int8",
# window size for 'range_abs_max' quantization. default is 10000
"window_size": 10000,
# The decay coefficient of moving average, default is 0.9
"moving_rate": 0.9,
# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
"quantizable_layer_type": ["Conv2D", "Linear"],
}
def sample_generator(loader):
def __reader__():
for _, data in enumerate(loader):
images = np.array(data[0])
yield images
return __reader__
def sample_generator_layoutxlm_ser(loader):
def __reader__():
for _, data in enumerate(loader):
input_ids = np.array(data[0])
bbox = np.array(data[1])
attention_mask = np.array(data[2])
token_type_ids = np.array(data[3])
images = np.array(data[4])
yield [input_ids, bbox, attention_mask, token_type_ids, images]
return __reader__
def main(config, device, logger, vdl_writer):
# init dist environment
if config["Global"]["distributed"]:
dist.init_parallel_env()
global_config = config["Global"]
# build dataloader
set_signal_handlers()
config["Train"]["loader"]["num_workers"] = 0
is_layoutxlm_ser = (
config["Architecture"]["model_type"] == "kie"
and config["Architecture"]["Backbone"]["name"] == "LayoutXLMForSer"
)
train_dataloader = build_dataloader(config, "Train", device, logger)
if config["Eval"]:
config["Eval"]["loader"]["num_workers"] = 0
valid_dataloader = build_dataloader(config, "Eval", device, logger)
if is_layoutxlm_ser:
train_dataloader = valid_dataloader
else:
valid_dataloader = None
paddle.enable_static()
exe = paddle.static.Executor(device)
if "inference_model" in global_config.keys(): # , 'inference_model'):
inference_model_dir = global_config["inference_model"]
else:
inference_model_dir = os.path.dirname(global_config["pretrained_model"])
if not (
os.path.exists(os.path.join(inference_model_dir, "inference.pdmodel"))
and os.path.exists(os.path.join(inference_model_dir, "inference.pdiparams"))
):
raise ValueError(
"Please set inference model dir in Global.inference_model or Global.pretrained_model for post-quantization"
)
if is_layoutxlm_ser:
generator = sample_generator_layoutxlm_ser(train_dataloader)
else:
generator = sample_generator(train_dataloader)
paddleslim.quant.quant_post_static(
executor=exe,
model_dir=inference_model_dir,
model_filename="inference.pdmodel",
params_filename="inference.pdiparams",
quantize_model_path=global_config["save_inference_dir"],
sample_generator=generator,
save_model_filename="inference.pdmodel",
save_params_filename="inference.pdiparams",
batch_size=1,
batch_nums=None,
)
if __name__ == "__main__":
config, device, logger, vdl_writer = program.preprocess(is_train=True)
main(config, device, logger, vdl_writer)
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