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#
# 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
from paddleslim.dygraph.quant import QAT
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 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()
train_dataloader = build_dataloader(config, "Train", device, logger)
if config["Eval"]:
valid_dataloader = build_dataloader(config, "Eval", device, logger)
else:
valid_dataloader = None
# build post process
post_process_class = build_post_process(config["PostProcess"], global_config)
# build model
# for rec algorithm
if hasattr(post_process_class, "character"):
char_num = len(getattr(post_process_class, "character"))
if config["Architecture"]["algorithm"] in [
"Distillation",
]: # distillation model
for key in config["Architecture"]["Models"]:
if (
config["Architecture"]["Models"][key]["Head"]["name"] == "MultiHead"
): # for multi head
if config["PostProcess"]["name"] == "DistillationSARLabelDecode":
char_num = char_num - 2
# update SARLoss params
assert (
list(config["Loss"]["loss_config_list"][-1].keys())[0]
== "DistillationSARLoss"
)
config["Loss"]["loss_config_list"][-1]["DistillationSARLoss"][
"ignore_index"
] = (char_num + 1)
out_channels_list = {}
out_channels_list["CTCLabelDecode"] = char_num
out_channels_list["SARLabelDecode"] = char_num + 2
config["Architecture"]["Models"][key]["Head"][
"out_channels_list"
] = out_channels_list
else:
config["Architecture"]["Models"][key]["Head"][
"out_channels"
] = char_num
elif config["Architecture"]["Head"]["name"] == "MultiHead": # for multi head
if config["PostProcess"]["name"] == "SARLabelDecode":
char_num = char_num - 2
# update SARLoss params
assert list(config["Loss"]["loss_config_list"][1].keys())[0] == "SARLoss"
if config["Loss"]["loss_config_list"][1]["SARLoss"] is None:
config["Loss"]["loss_config_list"][1]["SARLoss"] = {
"ignore_index": char_num + 1
}
else:
config["Loss"]["loss_config_list"][1]["SARLoss"]["ignore_index"] = (
char_num + 1
)
out_channels_list = {}
out_channels_list["CTCLabelDecode"] = char_num
out_channels_list["SARLabelDecode"] = char_num + 2
config["Architecture"]["Head"]["out_channels_list"] = out_channels_list
else: # base rec model
config["Architecture"]["Head"]["out_channels"] = char_num
if config["PostProcess"]["name"] == "SARLabelDecode": # for SAR model
config["Loss"]["ignore_index"] = char_num - 1
model = build_model(config["Architecture"])
pre_best_model_dict = dict()
# load fp32 model to begin quantization
pre_best_model_dict = load_model(
config, model, None, config["Architecture"]["model_type"]
)
freeze_params = False
if config["Architecture"]["algorithm"] in ["Distillation"]:
for key in config["Architecture"]["Models"]:
freeze_params = freeze_params or config["Architecture"]["Models"][key].get(
"freeze_params", False
)
act = None if freeze_params else PACT
quanter = QAT(config=quant_config, act_preprocess=act)
quanter.quantize(model)
if config["Global"]["distributed"]:
model = paddle.DataParallel(model)
# build loss
loss_class = build_loss(config["Loss"])
# build optim
optimizer, lr_scheduler, _wd_scheduler = build_optimizer(
config["Optimizer"],
epochs=config["Global"]["epoch_num"],
step_each_epoch=len(train_dataloader),
model=model,
)
# resume PACT training process
pre_best_model_dict = load_model(
config, model, optimizer, config["Architecture"]["model_type"]
)
# build metric
eval_class = build_metric(config["Metric"])
logger.info(
"train dataloader has {} iters, valid dataloader has {} iters".format(
len(train_dataloader), len(valid_dataloader)
)
)
# start train
program.train(
config,
train_dataloader,
valid_dataloader,
device,
model,
loss_class,
optimizer,
lr_scheduler,
post_process_class,
eval_class,
pre_best_model_dict,
logger,
vdl_writer,
)
if __name__ == "__main__":
config, device, logger, vdl_writer = program.preprocess(is_train=True)
main(config, device, logger, vdl_writer)
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