mirror of
https://github.com/PaddlePaddle/PaddleClas.git
synced 2025-06-03 21:55:06 +08:00
update slim
This commit is contained in:
dongshuilong
parent
50f2547005
commit
8e85ef4775
@ -51,7 +51,7 @@ PaddleClas提供了一系列训练好的[模型](../../docs/zh_CN/models/models_
|
|||||||
cd PaddleClas
|
cd PaddleClas
|
||||||
```
|
```
|
||||||
|
|
||||||
`slim`相关代码都位于`deploy/slim`目录下
|
`slim`训练相关代码已经集成到`ppcls/engine/`下，离线量化代码位于`deploy/slim/quant_post_static.py`。
|
||||||
|
|
||||||
#### 3.1 模型量化
|
#### 3.1 模型量化
|
||||||
|
|
||||||
|
|||||||
@ -49,7 +49,7 @@ Go to the root directory of PaddleClas
|
|||||||
cd PaddleClase
|
cd PaddleClase
|
||||||
```
|
```
|
||||||
|
|
||||||
The code of slim is located in `deploy/slim`
|
The training related codes have been integrated into `ppcls/engine/`. The offline quantization code is located in `deploy/slim/quant_post_static.py`
|
||||||
|
|
||||||
#### 3.1 Model Quantization
|
#### 3.1 Model Quantization
|
||||||
|
|
||||||
|
|||||||
@ -1,330 +0,0 @@
|
|||||||
# Copyright (c) 2021 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, division, print_function
|
|
||||||
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
|
|
||||||
import paddle
|
|
||||||
from paddle import nn
|
|
||||||
import numpy as np
|
|
||||||
import paddleslim
|
|
||||||
from paddle.jit import to_static
|
|
||||||
from paddleslim.analysis import dygraph_flops as flops
|
|
||||||
import argparse
|
|
||||||
import paddle.distributed as dist
|
|
||||||
from visualdl import LogWriter
|
|
||||||
|
|
||||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
|
||||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
|
|
||||||
from paddleslim.dygraph.quant import QAT
|
|
||||||
|
|
||||||
from ppcls.engine.trainer import Trainer
|
|
||||||
from ppcls.utils import config, logger
|
|
||||||
from ppcls.utils.logger import init_logger
|
|
||||||
from ppcls.utils.config import print_config
|
|
||||||
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
|
|
||||||
from ppcls.data import build_dataloader
|
|
||||||
from ppcls.arch import apply_to_static
|
|
||||||
from ppcls.arch import build_model, RecModel, DistillationModel
|
|
||||||
from ppcls.arch.gears.identity_head import IdentityHead
|
|
||||||
|
|
||||||
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'],
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
class ExportModel(nn.Layer):
|
|
||||||
"""
|
|
||||||
ExportModel: add softmax onto the model
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, config, model):
|
|
||||||
super().__init__()
|
|
||||||
self.base_model = model
|
|
||||||
|
|
||||||
# we should choose a final model to export
|
|
||||||
if isinstance(self.base_model, DistillationModel):
|
|
||||||
self.infer_model_name = config["infer_model_name"]
|
|
||||||
else:
|
|
||||||
self.infer_model_name = None
|
|
||||||
|
|
||||||
self.infer_output_key = config.get("infer_output_key", None)
|
|
||||||
if self.infer_output_key == "features" and isinstance(self.base_model,
|
|
||||||
RecModel):
|
|
||||||
self.base_model.head = IdentityHead()
|
|
||||||
if config.get("infer_add_softmax", True):
|
|
||||||
self.softmax = nn.Softmax(axis=-1)
|
|
||||||
else:
|
|
||||||
self.softmax = None
|
|
||||||
|
|
||||||
def eval(self):
|
|
||||||
self.training = False
|
|
||||||
for layer in self.sublayers():
|
|
||||||
layer.training = False
|
|
||||||
layer.eval()
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
x = self.base_model(x)
|
|
||||||
if isinstance(x, list):
|
|
||||||
x = x[0]
|
|
||||||
if self.infer_model_name is not None:
|
|
||||||
x = x[self.infer_model_name]
|
|
||||||
if self.infer_output_key is not None:
|
|
||||||
x = x[self.infer_output_key]
|
|
||||||
if self.softmax is not None:
|
|
||||||
x = self.softmax(x)
|
|
||||||
return x
|
|
||||||
|
|
||||||
|
|
||||||
class Trainer_slim(Trainer):
|
|
||||||
def __init__(self, config, mode="train"):
|
|
||||||
|
|
||||||
self.mode = mode
|
|
||||||
self.config = config
|
|
||||||
self.output_dir = self.config['Global']['output_dir']
|
|
||||||
|
|
||||||
log_file = os.path.join(self.output_dir, self.config["Arch"]["name"],
|
|
||||||
f"{mode}.log")
|
|
||||||
init_logger(name='root', log_file=log_file)
|
|
||||||
print_config(config)
|
|
||||||
# set device
|
|
||||||
assert self.config["Global"]["device"] in ["cpu", "gpu", "xpu"]
|
|
||||||
self.device = paddle.set_device(self.config["Global"]["device"])
|
|
||||||
# set dist
|
|
||||||
self.config["Global"][
|
|
||||||
"distributed"] = paddle.distributed.get_world_size() != 1
|
|
||||||
|
|
||||||
if "Head" in self.config["Arch"]:
|
|
||||||
self.is_rec = True
|
|
||||||
else:
|
|
||||||
self.is_rec = False
|
|
||||||
|
|
||||||
self.model = build_model(self.config["Arch"])
|
|
||||||
# set @to_static for benchmark, skip this by default.
|
|
||||||
apply_to_static(self.config, self.model)
|
|
||||||
|
|
||||||
if self.config["Global"]["pretrained_model"] is not None:
|
|
||||||
if self.config["Global"]["pretrained_model"].startswith("http"):
|
|
||||||
load_dygraph_pretrain_from_url(
|
|
||||||
self.model, self.config["Global"]["pretrained_model"])
|
|
||||||
else:
|
|
||||||
load_dygraph_pretrain(
|
|
||||||
self.model, self.config["Global"]["pretrained_model"])
|
|
||||||
|
|
||||||
self.vdl_writer = None
|
|
||||||
if self.config['Global']['use_visualdl'] and mode == "train":
|
|
||||||
vdl_writer_path = os.path.join(self.output_dir, "vdl")
|
|
||||||
if not os.path.exists(vdl_writer_path):
|
|
||||||
os.makedirs(vdl_writer_path)
|
|
||||||
self.vdl_writer = LogWriter(logdir=vdl_writer_path)
|
|
||||||
logger.info('train with paddle {} and device {}'.format(
|
|
||||||
paddle.__version__, self.device))
|
|
||||||
# init members
|
|
||||||
self.train_dataloader = None
|
|
||||||
self.eval_dataloader = None
|
|
||||||
self.gallery_dataloader = None
|
|
||||||
self.query_dataloader = None
|
|
||||||
self.eval_mode = self.config["Global"].get("eval_mode",
|
|
||||||
"classification")
|
|
||||||
self.amp = True if "AMP" in self.config else False
|
|
||||||
if self.amp and self.config["AMP"] is not None:
|
|
||||||
self.scale_loss = self.config["AMP"].get("scale_loss", 1.0)
|
|
||||||
self.use_dynamic_loss_scaling = self.config["AMP"].get(
|
|
||||||
"use_dynamic_loss_scaling", False)
|
|
||||||
else:
|
|
||||||
self.scale_loss = 1.0
|
|
||||||
self.use_dynamic_loss_scaling = False
|
|
||||||
if self.amp:
|
|
||||||
AMP_RELATED_FLAGS_SETTING = {
|
|
||||||
'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
|
|
||||||
'FLAGS_max_inplace_grad_add': 8,
|
|
||||||
}
|
|
||||||
paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
|
|
||||||
self.train_loss_func = None
|
|
||||||
self.eval_loss_func = None
|
|
||||||
self.train_metric_func = None
|
|
||||||
self.eval_metric_func = None
|
|
||||||
self.use_dali = self.config['Global'].get("use_dali", False)
|
|
||||||
|
|
||||||
# for slim
|
|
||||||
pact = self.config["Slim"].get("quant", False)
|
|
||||||
self.pact = pact.get("name", False) if pact else pact
|
|
||||||
|
|
||||||
if self.pact and str(self.pact.lower()) != 'pact':
|
|
||||||
raise RuntimeError("The quantization only support 'PACT'!")
|
|
||||||
if pact:
|
|
||||||
quant_config["activation_preprocess_type"] = "PACT"
|
|
||||||
self.quanter = QAT(config=quant_config)
|
|
||||||
self.quanter.quantize(self.model)
|
|
||||||
logger.info("QAT model summary:")
|
|
||||||
paddle.summary(self.model, (1, 3, 224, 224))
|
|
||||||
else:
|
|
||||||
self.quanter = None
|
|
||||||
|
|
||||||
prune_config = self.config["Slim"].get("prune", False)
|
|
||||||
if prune_config:
|
|
||||||
if prune_config["name"].lower() not in ["fpgm", "l1_norm"]:
|
|
||||||
raise RuntimeError(
|
|
||||||
"The prune methods only support 'fpgm' and 'l1_norm'")
|
|
||||||
else:
|
|
||||||
logger.info("FLOPs before pruning: {}GFLOPs".format(
|
|
||||||
flops(self.model, [1] + self.config["Global"][
|
|
||||||
"image_shape"]) / 1e9))
|
|
||||||
self.model.eval()
|
|
||||||
|
|
||||||
if prune_config["name"].lower() == "fpgm":
|
|
||||||
self.model.eval()
|
|
||||||
self.pruner = paddleslim.dygraph.FPGMFilterPruner(
|
|
||||||
self.model, [1] + self.config["Global"]["image_shape"])
|
|
||||||
else:
|
|
||||||
self.pruner = paddleslim.dygraph.L1NormFilterPruner(
|
|
||||||
self.model, [1] + self.config["Global"]["image_shape"])
|
|
||||||
self.prune_model()
|
|
||||||
else:
|
|
||||||
self.pruner = None
|
|
||||||
|
|
||||||
if self.quanter is None and self.pruner is None:
|
|
||||||
logger.info("Training without slim")
|
|
||||||
|
|
||||||
# for distributed training
|
|
||||||
if self.config["Global"]["distributed"]:
|
|
||||||
dist.init_parallel_env()
|
|
||||||
self.model = paddle.DataParallel(self.model)
|
|
||||||
|
|
||||||
def export_inference_model(self):
|
|
||||||
if os.path.exists(
|
|
||||||
os.path.join(self.output_dir, self.config["Arch"]["name"],
|
|
||||||
"best_model.pdparams")):
|
|
||||||
load_dygraph_pretrain(self.model,
|
|
||||||
os.path.join(self.output_dir,
|
|
||||||
self.config["Arch"]["name"],
|
|
||||||
"best_model"))
|
|
||||||
elif self.config["Global"].get(
|
|
||||||
"pretrained_model", False) and os.path.exists(self.config[
|
|
||||||
"Global"]["pretrained_model"] + ".pdparams"):
|
|
||||||
load_dygraph_pretrain(self.model,
|
|
||||||
self.config["Global"]["pretrained_model"])
|
|
||||||
else:
|
|
||||||
raise RuntimeError(
|
|
||||||
"The best_model or pretraine_model should exist to generate inference model"
|
|
||||||
)
|
|
||||||
model = ExportModel(self.config["Arch"], self.model)
|
|
||||||
save_path = os.path.join(self.config["Global"]["save_inference_dir"],
|
|
||||||
"inference")
|
|
||||||
if self.quanter:
|
|
||||||
self.quanter.save_quantized_model(
|
|
||||||
model,
|
|
||||||
save_path,
|
|
||||||
input_spec=[
|
|
||||||
paddle.static.InputSpec(
|
|
||||||
shape=[None] + config["Global"]["image_shape"],
|
|
||||||
dtype='float32')
|
|
||||||
])
|
|
||||||
else:
|
|
||||||
model = to_static(
|
|
||||||
model,
|
|
||||||
input_spec=[
|
|
||||||
paddle.static.InputSpec(
|
|
||||||
shape=[None] + self.config["Global"]["image_shape"],
|
|
||||||
dtype='float32',
|
|
||||||
name="image")
|
|
||||||
])
|
|
||||||
paddle.jit.save(model, save_path)
|
|
||||||
|
|
||||||
def prune_model(self):
|
|
||||||
params = []
|
|
||||||
for sublayer in self.model.sublayers():
|
|
||||||
for param in sublayer.parameters(include_sublayers=False):
|
|
||||||
if isinstance(sublayer, paddle.nn.Conv2D):
|
|
||||||
params.append(param.name)
|
|
||||||
ratios = {}
|
|
||||||
for param in params:
|
|
||||||
ratios[param] = self.config["Slim"]["prune"]["pruned_ratio"]
|
|
||||||
plan = self.pruner.prune_vars(ratios, [0])
|
|
||||||
|
|
||||||
logger.info("FLOPs after pruning: {}GFLOPs; pruned ratio: {}".format(
|
|
||||||
flops(self.model, [1] + self.config["Global"]["image_shape"]) /
|
|
||||||
1e9, plan.pruned_flops))
|
|
||||||
|
|
||||||
for param in self.model.parameters():
|
|
||||||
if "conv2d" in param.name:
|
|
||||||
logger.info("{}\t{}".format(param.name, param.shape))
|
|
||||||
|
|
||||||
self.model.train()
|
|
||||||
|
|
||||||
|
|
||||||
def parse_args():
|
|
||||||
parser = argparse.ArgumentParser(
|
|
||||||
"generic-image-rec slim script, for train, eval and export inference model"
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-c',
|
|
||||||
'--config',
|
|
||||||
type=str,
|
|
||||||
default='configs/config.yaml',
|
|
||||||
help='config file path')
|
|
||||||
parser.add_argument(
|
|
||||||
'-o',
|
|
||||||
'--override',
|
|
||||||
action='append',
|
|
||||||
default=[],
|
|
||||||
help='config options to be overridden')
|
|
||||||
parser.add_argument(
|
|
||||||
'-m',
|
|
||||||
'--mode',
|
|
||||||
type=str,
|
|
||||||
default='train',
|
|
||||||
choices=['train', 'eval', 'infer', 'export'],
|
|
||||||
help='the different function')
|
|
||||||
args = parser.parse_args()
|
|
||||||
return args
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
args = parse_args()
|
|
||||||
config = config.get_config(
|
|
||||||
args.config, overrides=args.override, show=False)
|
|
||||||
if args.mode == 'train':
|
|
||||||
trainer = Trainer_slim(config, mode="train")
|
|
||||||
trainer.train()
|
|
||||||
elif args.mode == 'eval':
|
|
||||||
trainer = Trainer_slim(config, mode="eval")
|
|
||||||
trainer.eval()
|
|
||||||
elif args.mode == 'infer':
|
|
||||||
trainer = Trainer_slim(config, mode="infer")
|
|
||||||
trainer.infer()
|
|
||||||
else:
|
|
||||||
trainer = Trainer_slim(config, mode="train")
|
|
||||||
trainer.export_inference_model()
|
|
||||||
@ -333,8 +333,6 @@ class SEModule(TheseusLayer):
|
|||||||
stride=1,
|
stride=1,
|
||||||
padding=0)
|
padding=0)
|
||||||
self.hardsigmoid = Hardsigmoid(slope=0.2, offset=0.5)
|
self.hardsigmoid = Hardsigmoid(slope=0.2, offset=0.5)
|
||||||
self.conv1.skip_quant = True
|
|
||||||
self.conv2.skip_quant = True
|
|
||||||
|
|
||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
identity = x
|
identity = x
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user