434 lines
17 KiB
Python
434 lines
17 KiB
Python
# 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
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import os
|
|
import platform
|
|
import paddle
|
|
import paddle.distributed as dist
|
|
from visualdl import LogWriter
|
|
from paddle import nn
|
|
import numpy as np
|
|
import random
|
|
|
|
from ppcls.utils.check import check_gpu
|
|
from ppcls.utils.misc import AverageMeter
|
|
from ppcls.utils import logger
|
|
from ppcls.utils.logger import init_logger
|
|
from ppcls.utils.config import print_config
|
|
from ppcls.data import build_dataloader
|
|
from ppcls.arch import build_model, RecModel, DistillationModel
|
|
from ppcls.arch import apply_to_static
|
|
from ppcls.loss import build_loss
|
|
from ppcls.metric import build_metrics
|
|
from ppcls.optimizer import build_optimizer
|
|
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
|
|
from ppcls.utils.save_load import init_model
|
|
from ppcls.utils import save_load
|
|
|
|
from ppcls.data.utils.get_image_list import get_image_list
|
|
from ppcls.data.postprocess import build_postprocess
|
|
from ppcls.data import create_operators
|
|
from ppcls.engine.train import train_epoch
|
|
from ppcls.engine import evaluation
|
|
from ppcls.arch.gears.identity_head import IdentityHead
|
|
from ppcls.engine.slim import get_pruner, get_quaner
|
|
|
|
|
|
class Engine(object):
|
|
def __init__(self, config, mode="train"):
|
|
assert mode in ["train", "eval", "infer", "export"]
|
|
self.mode = mode
|
|
self.config = config
|
|
self.eval_mode = self.config["Global"].get("eval_mode",
|
|
"classification")
|
|
if "Head" in self.config["Arch"]:
|
|
self.is_rec = True
|
|
else:
|
|
self.is_rec = False
|
|
|
|
# set seed
|
|
seed = self.config["Global"].get("seed", False)
|
|
if seed:
|
|
assert isinstance(seed, int), "The 'seed' must be a integer!"
|
|
paddle.seed(seed)
|
|
np.random.seed(seed)
|
|
random.seed(seed)
|
|
|
|
# init logger
|
|
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)
|
|
|
|
# init train_func and eval_func
|
|
assert self.eval_mode in ["classification", "retrieval"], logger.error(
|
|
"Invalid eval mode: {}".format(self.eval_mode))
|
|
self.train_epoch_func = train_epoch
|
|
self.eval_func = getattr(evaluation, self.eval_mode + "_eval")
|
|
|
|
self.use_dali = self.config['Global'].get("use_dali", False)
|
|
|
|
# for visualdl
|
|
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)
|
|
|
|
# set device
|
|
assert self.config["Global"]["device"] in ["cpu", "gpu", "xpu", "npu"]
|
|
self.device = paddle.set_device(self.config["Global"]["device"])
|
|
logger.info('train with paddle {} and device {}'.format(
|
|
paddle.__version__, self.device))
|
|
|
|
# AMP training
|
|
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)
|
|
|
|
# build dataloader
|
|
if self.mode == 'train':
|
|
self.train_dataloader = build_dataloader(
|
|
self.config["DataLoader"], "Train", self.device, self.use_dali)
|
|
if self.mode == "eval" or (self.mode == "train" and
|
|
self.config["Global"]["eval_during_train"]):
|
|
if self.eval_mode == "classification":
|
|
self.eval_dataloader = build_dataloader(
|
|
self.config["DataLoader"], "Eval", self.device,
|
|
self.use_dali)
|
|
elif self.eval_mode == "retrieval":
|
|
self.gallery_query_dataloader = None
|
|
if len(self.config["DataLoader"]["Eval"].keys()) == 1:
|
|
key = list(self.config["DataLoader"]["Eval"].keys())[0]
|
|
self.gallery_query_dataloader = build_dataloader(
|
|
self.config["DataLoader"]["Eval"], key, self.device,
|
|
self.use_dali)
|
|
else:
|
|
self.gallery_dataloader = build_dataloader(
|
|
self.config["DataLoader"]["Eval"], "Gallery",
|
|
self.device, self.use_dali)
|
|
self.query_dataloader = build_dataloader(
|
|
self.config["DataLoader"]["Eval"], "Query",
|
|
self.device, self.use_dali)
|
|
|
|
# build loss
|
|
if self.mode == "train":
|
|
loss_info = self.config["Loss"]["Train"]
|
|
self.train_loss_func = build_loss(loss_info)
|
|
if self.mode == "eval" or (self.mode == "train" and
|
|
self.config["Global"]["eval_during_train"]):
|
|
loss_config = self.config.get("Loss", None)
|
|
if loss_config is not None:
|
|
loss_config = loss_config.get("Eval")
|
|
if loss_config is not None:
|
|
self.eval_loss_func = build_loss(loss_config)
|
|
else:
|
|
self.eval_loss_func = None
|
|
else:
|
|
self.eval_loss_func = None
|
|
|
|
# build metric
|
|
if self.mode == 'train':
|
|
metric_config = self.config.get("Metric")
|
|
if metric_config is not None:
|
|
metric_config = metric_config.get("Train")
|
|
if metric_config is not None:
|
|
self.train_metric_func = build_metrics(metric_config)
|
|
else:
|
|
self.train_metric_func = None
|
|
else:
|
|
self.train_metric_func = None
|
|
|
|
if self.mode == "eval" or (self.mode == "train" and
|
|
self.config["Global"]["eval_during_train"]):
|
|
metric_config = self.config.get("Metric")
|
|
if self.eval_mode == "classification":
|
|
if metric_config is not None:
|
|
metric_config = metric_config.get("Eval")
|
|
if metric_config is not None:
|
|
self.eval_metric_func = build_metrics(metric_config)
|
|
elif self.eval_mode == "retrieval":
|
|
if metric_config is None:
|
|
metric_config = [{"name": "Recallk", "topk": (1, 5)}]
|
|
else:
|
|
metric_config = metric_config["Eval"]
|
|
self.eval_metric_func = build_metrics(metric_config)
|
|
else:
|
|
self.eval_metric_func = None
|
|
|
|
# build model
|
|
self.model = build_model(self.config["Arch"])
|
|
# set @to_static for benchmark, skip this by default.
|
|
apply_to_static(self.config, self.model)
|
|
|
|
# for slim
|
|
self.pruner = get_pruner(self.config, self.model)
|
|
self.quanter = get_quaner(self.config, self.model)
|
|
|
|
# load_pretrain
|
|
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"])
|
|
|
|
# build optimizer
|
|
if self.mode == 'train':
|
|
self.optimizer, self.lr_sch = build_optimizer(
|
|
self.config["Optimizer"], self.config["Global"]["epochs"],
|
|
len(self.train_dataloader), [self.model])
|
|
|
|
# for distributed
|
|
self.config["Global"][
|
|
"distributed"] = paddle.distributed.get_world_size() != 1
|
|
if self.config["Global"]["distributed"]:
|
|
dist.init_parallel_env()
|
|
if self.config["Global"]["distributed"]:
|
|
self.model = paddle.DataParallel(self.model)
|
|
|
|
# build postprocess for infer
|
|
if self.mode == 'infer':
|
|
self.preprocess_func = create_operators(self.config["Infer"][
|
|
"transforms"])
|
|
self.postprocess_func = build_postprocess(self.config["Infer"][
|
|
"PostProcess"])
|
|
|
|
def train(self):
|
|
assert self.mode == "train"
|
|
print_batch_step = self.config['Global']['print_batch_step']
|
|
save_interval = self.config["Global"]["save_interval"]
|
|
best_metric = {
|
|
"metric": 0.0,
|
|
"epoch": 0,
|
|
}
|
|
# key:
|
|
# val: metrics list word
|
|
self.output_info = dict()
|
|
self.time_info = {
|
|
"batch_cost": AverageMeter(
|
|
"batch_cost", '.5f', postfix=" s,"),
|
|
"reader_cost": AverageMeter(
|
|
"reader_cost", ".5f", postfix=" s,"),
|
|
}
|
|
# global iter counter
|
|
self.global_step = 0
|
|
|
|
if self.config["Global"]["checkpoints"] is not None:
|
|
metric_info = init_model(self.config["Global"], self.model,
|
|
self.optimizer)
|
|
if metric_info is not None:
|
|
best_metric.update(metric_info)
|
|
|
|
# for amp training
|
|
if self.amp:
|
|
self.scaler = paddle.amp.GradScaler(
|
|
init_loss_scaling=self.scale_loss,
|
|
use_dynamic_loss_scaling=self.use_dynamic_loss_scaling)
|
|
|
|
self.max_iter = len(self.train_dataloader) - 1 if platform.system(
|
|
) == "Windows" else len(self.train_dataloader)
|
|
for epoch_id in range(best_metric["epoch"] + 1,
|
|
self.config["Global"]["epochs"] + 1):
|
|
acc = 0.0
|
|
# for one epoch train
|
|
self.train_epoch_func(self, epoch_id, print_batch_step)
|
|
|
|
if self.use_dali:
|
|
self.train_dataloader.reset()
|
|
metric_msg = ", ".join([
|
|
"{}: {:.5f}".format(key, self.output_info[key].avg)
|
|
for key in self.output_info
|
|
])
|
|
logger.info("[Train][Epoch {}/{}][Avg]{}".format(
|
|
epoch_id, self.config["Global"]["epochs"], metric_msg))
|
|
self.output_info.clear()
|
|
|
|
# eval model and save model if possible
|
|
if self.config["Global"][
|
|
"eval_during_train"] and epoch_id % self.config["Global"][
|
|
"eval_interval"] == 0:
|
|
acc = self.eval(epoch_id)
|
|
if acc > best_metric["metric"]:
|
|
best_metric["metric"] = acc
|
|
best_metric["epoch"] = epoch_id
|
|
save_load.save_model(
|
|
self.model,
|
|
self.optimizer,
|
|
best_metric,
|
|
self.output_dir,
|
|
model_name=self.config["Arch"]["name"],
|
|
prefix="best_model")
|
|
logger.info("[Eval][Epoch {}][best metric: {}]".format(
|
|
epoch_id, best_metric["metric"]))
|
|
logger.scaler(
|
|
name="eval_acc",
|
|
value=acc,
|
|
step=epoch_id,
|
|
writer=self.vdl_writer)
|
|
|
|
self.model.train()
|
|
|
|
# save model
|
|
if epoch_id % save_interval == 0:
|
|
save_load.save_model(
|
|
self.model,
|
|
self.optimizer, {"metric": acc,
|
|
"epoch": epoch_id},
|
|
self.output_dir,
|
|
model_name=self.config["Arch"]["name"],
|
|
prefix="epoch_{}".format(epoch_id))
|
|
# save the latest model
|
|
save_load.save_model(
|
|
self.model,
|
|
self.optimizer, {"metric": acc,
|
|
"epoch": epoch_id},
|
|
self.output_dir,
|
|
model_name=self.config["Arch"]["name"],
|
|
prefix="latest")
|
|
|
|
if self.vdl_writer is not None:
|
|
self.vdl_writer.close()
|
|
|
|
@paddle.no_grad()
|
|
def eval(self, epoch_id=0):
|
|
assert self.mode in ["train", "eval"]
|
|
self.model.eval()
|
|
eval_result = self.eval_func(self, epoch_id)
|
|
self.model.train()
|
|
return eval_result
|
|
|
|
@paddle.no_grad()
|
|
def infer(self):
|
|
assert self.mode == "infer" and self.eval_mode == "classification"
|
|
total_trainer = paddle.distributed.get_world_size()
|
|
local_rank = paddle.distributed.get_rank()
|
|
image_list = get_image_list(self.config["Infer"]["infer_imgs"])
|
|
# data split
|
|
image_list = image_list[local_rank::total_trainer]
|
|
|
|
batch_size = self.config["Infer"]["batch_size"]
|
|
self.model.eval()
|
|
batch_data = []
|
|
image_file_list = []
|
|
for idx, image_file in enumerate(image_list):
|
|
with open(image_file, 'rb') as f:
|
|
x = f.read()
|
|
for process in self.preprocess_func:
|
|
x = process(x)
|
|
batch_data.append(x)
|
|
image_file_list.append(image_file)
|
|
if len(batch_data) >= batch_size or idx == len(image_list) - 1:
|
|
batch_tensor = paddle.to_tensor(batch_data)
|
|
out = self.model(batch_tensor)
|
|
if isinstance(out, list):
|
|
out = out[0]
|
|
if isinstance(out, dict):
|
|
out = out["output"]
|
|
result = self.postprocess_func(out, image_file_list)
|
|
print(result)
|
|
batch_data.clear()
|
|
image_file_list.clear()
|
|
|
|
def export(self):
|
|
assert self.mode == "export"
|
|
use_multilabel = self.config["Global"].get("use_multilabel", False)
|
|
model = ExportModel(self.config["Arch"], self.model, use_multilabel)
|
|
if self.config["Global"]["pretrained_model"] is not None:
|
|
load_dygraph_pretrain(model.base_model,
|
|
self.config["Global"]["pretrained_model"])
|
|
|
|
model.eval()
|
|
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] + self.config["Global"]["image_shape"],
|
|
dtype='float32')
|
|
])
|
|
else:
|
|
model = paddle.jit.to_static(
|
|
model,
|
|
input_spec=[
|
|
paddle.static.InputSpec(
|
|
shape=[None] + self.config["Global"]["image_shape"],
|
|
dtype='float32')
|
|
])
|
|
paddle.jit.save(model, save_path)
|
|
|
|
|
|
class ExportModel(nn.Layer):
|
|
"""
|
|
ExportModel: add softmax onto the model
|
|
"""
|
|
|
|
def __init__(self, config, model, use_multilabel):
|
|
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 use_multilabel:
|
|
self.out_act = nn.Sigmoid()
|
|
else:
|
|
if config.get("infer_add_softmax", True):
|
|
self.out_act = nn.Softmax(axis=-1)
|
|
else:
|
|
self.out_act = 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.out_act is not None:
|
|
x = self.out_act(x)
|
|
return x
|