refactor trainer v2
parent
ebde0e13cb
commit
15f6f58139
ppcls/engine
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@ -47,19 +47,18 @@ from ppcls.utils import save_load
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from ppcls.data.utils.get_image_list import get_image_list
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from ppcls.data.postprocess import build_postprocess
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from ppcls.data import create_operators
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from ppcls.engine.train import classification_train, retrieval_train
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from ppcls.engine.eval import classification_eval, retrieval_eval
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from ppcls.engine.train import train_epoch
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from ppcls.engine import evaluation
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from ppcls.arch.gears.identity_head import IdentityHead
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class Core(object):
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class Engine(object):
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def __init__(self, config, mode="train"):
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assert mode in ['train', 'eval', 'infer', 'export']
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assert mode in ["train", "eval", "infer", "export"]
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self.mode = mode
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self.config = config
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self.eval_mode = self.config["Global"].get("eval_mode",
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"classification")
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# init logger
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self.output_dir = self.config['Global']['output_dir']
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log_file = os.path.join(self.output_dir, self.config["Arch"]["name"],
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@ -68,14 +67,10 @@ class Core(object):
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print_config(config)
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# init train_func and eval_func
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if self.eval_mode == "classification":
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self.evaler = classification_eval
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self.trainer = classification_train
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elif self.eval_mode == "retrieval":
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self.trainer = retrieval_train
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self.evaler = retrieval_eval
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else:
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logger.warning("Invalid eval mode: {}".format(self.eval_mode))
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assert self.eval_mode in ["classification", "retrieval"], logger.error("Invalid eval mode: {}".format(self.eval_mode))
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self.train_epoch_func = train_epoch
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self.eval_func = getattr(evaluation, self.eval_mode + "_eval")
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self.use_dali = self.config['Global'].get("use_dali", False)
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# for visualdl
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@ -242,7 +237,7 @@ class Core(object):
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self.config["Global"]["epochs"] + 1):
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acc = 0.0
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# for one epoch train
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self.trainer(self, epoch_id, print_batch_step)
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self.train_epoch_func(self, epoch_id, print_batch_step)
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if self.use_dali:
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self.train_dataloader.reset()
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@ -304,7 +299,7 @@ class Core(object):
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def eval(self, epoch_id=0):
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assert self.mode in ["train", "eval"]
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self.model.eval()
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eval_result = self.evaler(self, epoch_id)
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eval_result = self.eval_func(self, epoch_id)
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self.model.train()
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return eval_result
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@ -12,5 +12,5 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ppcls.engine.eval.classification import classification_eval
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from ppcls.engine.eval.retrieval import retrieval_eval
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from ppcls.engine.evaluation.classification import classification_eval
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from ppcls.engine.evaluation.retrieval import retrieval_eval
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@ -11,5 +11,4 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ppcls.engine.train.classification import classification_train
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from ppcls.engine.train.retrieval import retrieval_train
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from ppcls.engine.train.train import train_epoch
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@ -1,89 +0,0 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import, division, print_function
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import datetime
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import os
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import platform
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import sys
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import time
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import numpy as np
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import paddle
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))
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from ppcls.utils import logger
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from ppcls.utils.misc import AverageMeter
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from ppcls.engine.train.utils import update_loss, update_metric, log_info
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def classification_train(trainer, epoch_id, print_batch_step):
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tic = time.time()
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train_dataloader = trainer.train_dataloader if trainer.use_dali else trainer.train_dataloader(
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)
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for iter_id, batch in enumerate(train_dataloader):
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if iter_id >= trainer.max_iter:
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break
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if iter_id == 5:
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for key in trainer.time_info:
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trainer.time_info[key].reset()
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trainer.time_info["reader_cost"].update(time.time() - tic)
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if trainer.use_dali:
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batch = [
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paddle.to_tensor(batch[0]['data']),
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paddle.to_tensor(batch[0]['label'])
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]
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batch_size = batch[0].shape[0]
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batch[1] = batch[1].reshape([-1, 1]).astype("int64")
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trainer.global_step += 1
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# image input
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if trainer.amp:
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with paddle.amp.auto_cast(custom_black_list={
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"flatten_contiguous_range", "greater_than"
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}):
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out = trainer.model(batch[0])
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loss_dict = trainer.train_loss_func(out, batch[1])
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else:
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out = trainer.model(batch[0])
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# calc loss
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if trainer.config["DataLoader"]["Train"]["dataset"].get(
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"batch_transform_ops", None):
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loss_dict = trainer.train_loss_func(out, batch[1:])
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else:
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loss_dict = trainer.train_loss_func(out, batch[1])
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# step opt and lr
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if trainer.amp:
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scaled = trainer.scaler.scale(loss_dict["loss"])
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scaled.backward()
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trainer.scaler.minimize(trainer.optimizer, scaled)
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else:
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loss_dict["loss"].backward()
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trainer.optimizer.step()
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trainer.optimizer.clear_grad()
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trainer.lr_sch.step()
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# below code just for logging
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# update metric_for_logger
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update_metric(trainer, out, batch, batch_size)
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# update_loss_for_logger
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update_loss(trainer, loss_dict, batch_size)
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trainer.time_info["batch_cost"].update(time.time() - tic)
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if iter_id % print_batch_step == 0:
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log_info(trainer, batch_size, epoch_id, iter_id)
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tic = time.time()
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@ -29,7 +29,7 @@ from ppcls.utils.misc import AverageMeter
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from ppcls.engine.train.utils import update_loss, update_metric, log_info
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def retrieval_train(trainer, epoch_id, print_batch_step):
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def train_epoch(trainer, epoch_id, print_batch_step):
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tic = time.time()
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train_dataloader = trainer.train_dataloader if trainer.use_dali else trainer.train_dataloader(
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@ -55,10 +55,10 @@ def retrieval_train(trainer, epoch_id, print_batch_step):
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with paddle.amp.auto_cast(custom_black_list={
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"flatten_contiguous_range", "greater_than"
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}):
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out = trainer.model(batch[0], batch[1])
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out = forward(trainer, batch)
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loss_dict = trainer.train_loss_func(out, batch[1])
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else:
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out = trainer.model(batch[0], batch[1])
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out = forward(trainer, batch)
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# calc loss
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if trainer.config["DataLoader"]["Train"]["dataset"].get(
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@ -81,10 +81,15 @@ def retrieval_train(trainer, epoch_id, print_batch_step):
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# below code just for logging
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# update metric_for_logger
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update_metric(trainer, out, batch, batch_size)
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# update_loss_for_logger
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update_loss(trainer, loss_dict, batch_size)
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trainer.time_info["batch_cost"].update(time.time() - tic)
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if iter_id % print_batch_step == 0:
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log_info(trainer, batch_size, epoch_id, iter_id)
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tic = time.time()
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def forward(trainer, batch):
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if trainer.eval_mode == "classification":
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return trainer.model(batch[0])
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else:
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return trainer.model(batch[0], batch[1])
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@ -21,11 +21,11 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
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from ppcls.utils import config
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from ppcls.engine.core import Core
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from ppcls.engine.engine import Engine
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if __name__ == "__main__":
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args = config.parse_args()
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config = config.get_config(
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args.config, overrides=args.override, show=False)
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evaler = Core(config, mode="eval")
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evaler.eval()
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engine = Engine(config, mode="eval")
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engine.eval()
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@ -24,11 +24,11 @@ import paddle
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import paddle.nn as nn
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from ppcls.utils import config
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from ppcls.engine.core import Core
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from ppcls.engine.engine import Engine
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if __name__ == "__main__":
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args = config.parse_args()
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config = config.get_config(
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args.config, overrides=args.override, show=False)
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exporter = Core(config, mode="export")
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exporter.export()
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engine = Engine(config, mode="export")
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engine.export()
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@ -21,11 +21,11 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
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from ppcls.utils import config
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from ppcls.engine.core import Core
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from ppcls.engine.engine import Engine
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if __name__ == "__main__":
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args = config.parse_args()
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config = config.get_config(
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args.config, overrides=args.override, show=False)
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inferer = Core(config, mode="infer")
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inferer.infer()
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engine = Engine(config, mode="infer")
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engine.infer()
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@ -21,11 +21,11 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
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from ppcls.utils import config
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from ppcls.engine.core import Core
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from ppcls.engine.engine import Engine
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if __name__ == "__main__":
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args = config.parse_args()
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config = config.get_config(
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args.config, overrides=args.override, show=False)
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trainer = Core(config, mode="train")
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trainer.train()
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engine = Engine(config, mode="train")
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engine.train()
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