# 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, TheseusLayer
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
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"] or self.config["Arch"].get("is_rec",
False):
self.is_rec = True
else:
self.is_rec = False
# set seed
seed = self.config["Global"].get("seed", False)
if seed or seed == 0:
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" and dist.get_rank() == 0:
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 and self.mode == "train" 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)
if "class_num" in config["Global"]:
global_class_num = config["Global"]["class_num"]
if "class_num" not in config["Arch"]:
config["Arch"]["class_num"] = global_class_num
msg = f"The Global.class_num will be deprecated. Please use Arch.class_num instead. Arch.class_num has been set to {global_class_num}."
else:
msg = "The Global.class_num will be deprecated. Please use Arch.class_num instead. The Global.class_num has been ignored."
logger.warning(msg)
#TODO(gaotingquan): support rec
class_num = config["Arch"].get("class_num", None)
self.config["DataLoader"].update({"class_num": class_num})
# 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:
if hasattr(self.train_dataloader, "collate_fn"):
for m_idx, m in enumerate(metric_config):
if "TopkAcc" in m:
msg = f"'TopkAcc' metric can not be used when setting 'batch_transform_ops' in config. The 'TopkAcc' metric has been removed."
logger.warning(msg)
break
metric_config.pop(m_idx)
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)
# set @to_static for benchmark, skip this by default.
apply_to_static(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 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)
amp_level = self.config['AMP'].get("level", "O1")
if amp_level not in ["O1", "O2"]:
msg = "[Parameter Error]: The optimize level of AMP only support 'O1' and 'O2'. The level has been set 'O1'."
logger.warning(msg)
self.config['AMP']["level"] = "O1"
amp_level = "O1"
self.model, self.optimizer = paddle.amp.decorate(
models=self.model,
optimizers=self.optimizer,
level=amp_level,
save_dtype='float32')
# for distributed
world_size = dist.get_world_size()
self.config["Global"]["distributed"] = world_size != 1
if world_size != 4 and self.mode == "train":
msg = f"The training strategy in config files provided by PaddleClas is based on 4 gpus. But the number of gpus is {world_size} in current training. Please modify the stategy (learning rate, batch size and so on) if use config files in PaddleClas to train."
logger.warning(msg)
if self.config["Global"]["distributed"]:
dist.init_parallel_env()
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)
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 = dist.get_world_size()
local_rank = dist.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) and "logits" in out:
out = out["logits"]
if isinstance(out, dict) and "output" in out:
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 model.quanter:
model.quanter.save_quantized_model(
model.base_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(TheseusLayer):
"""
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:
if isinstance(x, dict):
x = x["logits"]
x = self.out_act(x)
return x