From 8e85ef4775f45537113c302a11747d5daaf887c2 Mon Sep 17 00:00:00 2001 From: dongshuilong Date: Fri, 10 Sep 2021 03:09:03 +0000 Subject: [PATCH] update slim --- deploy/slim/README.md | 2 +- deploy/slim/README_en.md | 2 +- deploy/slim/slim.py | 330 ------------------ .../backbone/legendary_models/mobilenet_v3.py | 2 - 4 files changed, 2 insertions(+), 334 deletions(-) delete mode 100644 deploy/slim/slim.py diff --git a/deploy/slim/README.md b/deploy/slim/README.md index 5f1156cc4..eed8aa3b2 100644 --- a/deploy/slim/README.md +++ b/deploy/slim/README.md @@ -51,7 +51,7 @@ PaddleClas提供了一系列训练好的[模型](../../docs/zh_CN/models/models_ 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 diff --git a/deploy/slim/README_en.md b/deploy/slim/README_en.md index 2eb2a49fe..d7a978f46 100644 --- a/deploy/slim/README_en.md +++ b/deploy/slim/README_en.md @@ -49,7 +49,7 @@ Go to the root directory of PaddleClas 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 diff --git a/deploy/slim/slim.py b/deploy/slim/slim.py deleted file mode 100644 index f6940851c..000000000 --- a/deploy/slim/slim.py +++ /dev/null @@ -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() diff --git a/ppcls/arch/backbone/legendary_models/mobilenet_v3.py b/ppcls/arch/backbone/legendary_models/mobilenet_v3.py index 4e330ffcd..0cb550d44 100644 --- a/ppcls/arch/backbone/legendary_models/mobilenet_v3.py +++ b/ppcls/arch/backbone/legendary_models/mobilenet_v3.py @@ -333,8 +333,6 @@ class SEModule(TheseusLayer): stride=1, padding=0) self.hardsigmoid = Hardsigmoid(slope=0.2, offset=0.5) - self.conv1.skip_quant = True - self.conv2.skip_quant = True def forward(self, x): identity = x