271 lines
9.7 KiB
Python
271 lines
9.7 KiB
Python
# Copyright (c) 2020 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
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "..")))
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import yaml
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import paddle
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import paddle.distributed as dist
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from ppocr.data import build_dataloader, set_signal_handlers
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from ppocr.modeling.architectures import build_model
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from ppocr.losses import build_loss
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from ppocr.optimizer import build_optimizer
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from ppocr.postprocess import build_post_process
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from ppocr.metrics import build_metric
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from ppocr.utils.save_load import load_model
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from ppocr.utils.utility import set_seed
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from ppocr.modeling.architectures import apply_to_static
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import tools.program as program
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import tools.naive_sync_bn as naive_sync_bn
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dist.get_world_size()
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def main(config, device, logger, vdl_writer, seed):
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# init dist environment
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if config["Global"]["distributed"]:
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dist.init_parallel_env()
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global_config = config["Global"]
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# build dataloader
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set_signal_handlers()
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train_dataloader = build_dataloader(config, "Train", device, logger, seed)
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if len(train_dataloader) == 0:
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logger.error(
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"No Images in train dataset, please ensure\n"
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+ "\t1. The images num in the train label_file_list should be larger than or equal with batch size.\n"
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+ "\t2. The annotation file and path in the configuration file are provided normally."
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)
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return
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if config["Eval"]:
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valid_dataloader = build_dataloader(config, "Eval", device, logger, seed)
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else:
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valid_dataloader = None
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step_pre_epoch = len(train_dataloader)
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# build post process
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post_process_class = build_post_process(config["PostProcess"], global_config)
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# build model
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# for rec algorithm
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if hasattr(post_process_class, "character"):
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char_num = len(getattr(post_process_class, "character"))
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if config["Architecture"]["algorithm"] in [
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"Distillation",
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]: # distillation model
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for key in config["Architecture"]["Models"]:
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if (
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config["Architecture"]["Models"][key]["Head"]["name"] == "MultiHead"
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): # for multi head
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if config["PostProcess"]["name"] == "DistillationSARLabelDecode":
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char_num = char_num - 2
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if config["PostProcess"]["name"] == "DistillationNRTRLabelDecode":
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char_num = char_num - 3
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out_channels_list = {}
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out_channels_list["CTCLabelDecode"] = char_num
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# update SARLoss params
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if (
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list(config["Loss"]["loss_config_list"][-1].keys())[0]
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== "DistillationSARLoss"
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):
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config["Loss"]["loss_config_list"][-1]["DistillationSARLoss"][
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"ignore_index"
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] = (char_num + 1)
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out_channels_list["SARLabelDecode"] = char_num + 2
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elif any(
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"DistillationNRTRLoss" in d
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for d in config["Loss"]["loss_config_list"]
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):
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out_channels_list["NRTRLabelDecode"] = char_num + 3
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config["Architecture"]["Models"][key]["Head"][
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"out_channels_list"
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] = out_channels_list
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else:
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config["Architecture"]["Models"][key]["Head"][
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"out_channels"
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] = char_num
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elif config["Architecture"]["Head"]["name"] == "MultiHead": # for multi head
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if config["PostProcess"]["name"] == "SARLabelDecode":
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char_num = char_num - 2
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if config["PostProcess"]["name"] == "NRTRLabelDecode":
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char_num = char_num - 3
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out_channels_list = {}
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out_channels_list["CTCLabelDecode"] = char_num
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# update SARLoss params
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if list(config["Loss"]["loss_config_list"][1].keys())[0] == "SARLoss":
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if config["Loss"]["loss_config_list"][1]["SARLoss"] is None:
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config["Loss"]["loss_config_list"][1]["SARLoss"] = {
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"ignore_index": char_num + 1
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}
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else:
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config["Loss"]["loss_config_list"][1]["SARLoss"]["ignore_index"] = (
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char_num + 1
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)
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out_channels_list["SARLabelDecode"] = char_num + 2
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elif list(config["Loss"]["loss_config_list"][1].keys())[0] == "NRTRLoss":
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out_channels_list["NRTRLabelDecode"] = char_num + 3
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config["Architecture"]["Head"]["out_channels_list"] = out_channels_list
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else: # base rec model
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config["Architecture"]["Head"]["out_channels"] = char_num
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if config["PostProcess"]["name"] == "SARLabelDecode": # for SAR model
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config["Loss"]["ignore_index"] = char_num - 1
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model = build_model(config["Architecture"])
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use_sync_bn = config["Global"].get("use_sync_bn", False)
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if use_sync_bn:
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if config["Global"].get("use_npu", False):
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naive_sync_bn.convert_syncbn(model)
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else:
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model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(model)
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logger.info("convert_sync_batchnorm")
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model = apply_to_static(model, config, logger)
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# build loss
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loss_class = build_loss(config["Loss"])
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# build optim
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optimizer, lr_scheduler = build_optimizer(
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config["Optimizer"],
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epochs=config["Global"]["epoch_num"],
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step_each_epoch=len(train_dataloader),
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model=model,
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)
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# build metric
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eval_class = build_metric(config["Metric"])
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logger.info("train dataloader has {} iters".format(len(train_dataloader)))
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if valid_dataloader is not None:
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logger.info("valid dataloader has {} iters".format(len(valid_dataloader)))
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use_amp = config["Global"].get("use_amp", False)
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amp_level = config["Global"].get("amp_level", "O2")
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amp_dtype = config["Global"].get("amp_dtype", "float16")
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amp_custom_black_list = config["Global"].get("amp_custom_black_list", [])
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amp_custom_white_list = config["Global"].get("amp_custom_white_list", [])
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if os.path.exists(
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os.path.join(config["Global"]["save_model_dir"], "train_result.json")
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):
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try:
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os.remove(
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os.path.join(config["Global"]["save_model_dir"], "train_result.json")
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)
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except:
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pass
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if use_amp:
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AMP_RELATED_FLAGS_SETTING = {
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"FLAGS_max_inplace_grad_add": 8,
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}
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if paddle.is_compiled_with_cuda():
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AMP_RELATED_FLAGS_SETTING.update(
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{
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"FLAGS_cudnn_batchnorm_spatial_persistent": 1,
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"FLAGS_gemm_use_half_precision_compute_type": 0,
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}
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)
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paddle.set_flags(AMP_RELATED_FLAGS_SETTING)
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scale_loss = config["Global"].get("scale_loss", 1.0)
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use_dynamic_loss_scaling = config["Global"].get(
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"use_dynamic_loss_scaling", False
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)
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scaler = paddle.amp.GradScaler(
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init_loss_scaling=scale_loss,
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use_dynamic_loss_scaling=use_dynamic_loss_scaling,
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)
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if amp_level == "O2":
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model, optimizer = paddle.amp.decorate(
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models=model,
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optimizers=optimizer,
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level=amp_level,
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master_weight=True,
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dtype=amp_dtype,
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)
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else:
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scaler = None
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# load pretrain model
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pre_best_model_dict = load_model(
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config, model, optimizer, config["Architecture"]["model_type"]
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)
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if config["Global"]["distributed"]:
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model = paddle.DataParallel(model)
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# start train
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program.train(
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config,
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train_dataloader,
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valid_dataloader,
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device,
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model,
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loss_class,
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optimizer,
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lr_scheduler,
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post_process_class,
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eval_class,
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pre_best_model_dict,
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logger,
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step_pre_epoch,
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vdl_writer,
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scaler,
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amp_level,
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amp_custom_black_list,
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amp_custom_white_list,
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amp_dtype,
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)
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def test_reader(config, device, logger):
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loader = build_dataloader(config, "Train", device, logger)
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import time
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starttime = time.time()
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count = 0
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try:
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for data in loader():
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count += 1
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if count % 1 == 0:
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batch_time = time.time() - starttime
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starttime = time.time()
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logger.info(
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"reader: {}, {}, {}".format(count, len(data[0]), batch_time)
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)
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except Exception as e:
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logger.info(e)
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logger.info("finish reader: {}, Success!".format(count))
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if __name__ == "__main__":
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config, device, logger, vdl_writer = program.preprocess(is_train=True)
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seed = config["Global"]["seed"] if "seed" in config["Global"] else 1024
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set_seed(seed)
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main(config, device, logger, vdl_writer, seed)
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# test_reader(config, device, logger)
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