309 lines
12 KiB
Python
309 lines
12 KiB
Python
# 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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# 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 paddle
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import paddle.distributed as dist
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from visualdl import LogWriter
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from paddle import nn
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import numpy as np
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import random
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from ..utils.amp import AMPForwardDecorator
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from ppcls.utils import logger
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from ppcls.utils.logger import init_logger
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from ppcls.utils.config import print_config
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from ppcls.arch import build_model, RecModel, DistillationModel, TheseusLayer
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from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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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 .train import build_train_func
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from .evaluation import build_eval_func
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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 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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self.mode = mode
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self.config = config
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# init logger
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log_file = os.path.join(self.config['Global']['output_dir'],
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self.config["Arch"]["name"], f"{mode}.log")
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init_logger(log_file=log_file)
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# set seed
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self._init_seed()
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# set device
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self._init_device()
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# build model
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self.model = build_model(self.config, self.mode)
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# load_pretrain
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self._init_pretrained()
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self._init_amp()
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# init train_func and eval_func
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self.eval = build_eval_func(
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self.config, mode=self.mode, model=self.model)
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self.train = build_train_func(
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self.config, mode=self.mode, model=self.model, eval_func=self.eval)
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# for distributed
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self._init_dist()
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print_config(self.config)
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@paddle.no_grad()
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def infer(self):
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assert self.mode == "infer" and self.eval_mode == "classification"
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self.preprocess_func = create_operators(self.config["Infer"][
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"transforms"])
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self.postprocess_func = build_postprocess(self.config["Infer"][
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"PostProcess"])
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total_trainer = dist.get_world_size()
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local_rank = dist.get_rank()
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image_list = get_image_list(self.config["Infer"]["infer_imgs"])
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# data split
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image_list = image_list[local_rank::total_trainer]
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batch_size = self.config["Infer"]["batch_size"]
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self.model.eval()
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batch_data = []
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image_file_list = []
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for idx, image_file in enumerate(image_list):
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with open(image_file, 'rb') as f:
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x = f.read()
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for process in self.preprocess_func:
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x = process(x)
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batch_data.append(x)
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image_file_list.append(image_file)
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if len(batch_data) >= batch_size or idx == len(image_list) - 1:
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batch_tensor = paddle.to_tensor(batch_data)
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out = self.model(batch_tensor)
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if isinstance(out, list):
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out = out[0]
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if isinstance(out, dict) and "Student" in out:
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out = out["Student"]
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if isinstance(out, dict) and "logits" in out:
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out = out["logits"]
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if isinstance(out, dict) and "output" in out:
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out = out["output"]
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result = self.postprocess_func(out, image_file_list)
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print(result)
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batch_data.clear()
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image_file_list.clear()
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def export(self):
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assert self.mode == "export"
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use_multilabel = self.config["Global"].get(
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"use_multilabel",
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False) or "ATTRMetric" in self.config["Metric"]["Eval"][0]
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model = ExportModel(self.config["Arch"], self.model, use_multilabel)
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if self.config["Global"]["pretrained_model"] is not None:
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if self.config["Global"]["pretrained_model"].startswith("http"):
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load_dygraph_pretrain_from_url(
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model.base_model,
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self.config["Global"]["pretrained_model"])
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else:
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load_dygraph_pretrain(
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model.base_model,
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self.config["Global"]["pretrained_model"])
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model.eval()
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# for re-parameterization nets
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for layer in self.model.sublayers():
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if hasattr(layer, "re_parameterize") and not getattr(layer,
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"is_repped"):
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layer.re_parameterize()
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save_path = os.path.join(self.config["Global"]["save_inference_dir"],
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"inference")
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model = paddle.jit.to_static(
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model,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None] + self.config["Global"]["image_shape"],
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dtype='float32')
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])
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if hasattr(model.base_model,
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"quanter") and model.base_model.quanter is not None:
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model.base_model.quanter.save_quantized_model(model,
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save_path + "_int8")
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else:
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paddle.jit.save(model, save_path)
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logger.info(
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f"Export succeeded! The inference model exported has been saved in \"{self.config['Global']['save_inference_dir']}\"."
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)
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def _init_seed(self):
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seed = self.config["Global"].get("seed", False)
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if dist.get_world_size() != 1:
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# if self.config["Global"]["distributed"]:
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# set different seed in different GPU manually in distributed environment
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if not seed:
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logger.warning(
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"The random seed cannot be None in a distributed environment. Global.seed has been set to 42 by default"
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)
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self.config["Global"]["seed"] = seed = 42
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logger.info(
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f"Set random seed to ({int(seed)} + $PADDLE_TRAINER_ID) for different trainer"
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)
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dist_seed = int(seed) + dist.get_rank()
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paddle.seed(dist_seed)
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np.random.seed(dist_seed)
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random.seed(dist_seed)
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elif seed or seed == 0:
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assert isinstance(seed, int), "The 'seed' must be a integer!"
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paddle.seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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def _init_device(self):
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device = self.config["Global"]["device"]
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assert device in ["cpu", "gpu", "xpu", "npu", "mlu", "ascend"]
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logger.info('train with paddle {} and device {}'.format(
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paddle.__version__, device))
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paddle.set_device(device)
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def _init_pretrained(self):
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if self.config["Global"]["pretrained_model"] is not None:
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if self.config["Global"]["pretrained_model"].startswith("http"):
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load_dygraph_pretrain_from_url(
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[self.model, getattr(self, 'train_loss_func', None)],
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self.config["Global"]["pretrained_model"])
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else:
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load_dygraph_pretrain(
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[self.model, getattr(self, 'train_loss_func', None)],
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self.config["Global"]["pretrained_model"])
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def _init_amp(self):
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if "AMP" in self.config and self.config["AMP"] is not None:
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paddle_version = paddle.__version__[:3]
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# paddle version < 2.3.0 and not develop
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if paddle_version not in ["2.3", "2.4", "0.0"]:
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msg = "When using AMP, PaddleClas release/2.6 and later version only support PaddlePaddle version >= 2.3.0."
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logger.error(msg)
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raise Exception(msg)
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AMP_RELATED_FLAGS_SETTING = {'FLAGS_max_inplace_grad_add': 8, }
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if paddle.is_compiled_with_cuda():
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AMP_RELATED_FLAGS_SETTING.update({
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'FLAGS_cudnn_batchnorm_spatial_persistent': 1
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})
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paddle.set_flags(AMP_RELATED_FLAGS_SETTING)
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amp_level = self.config['AMP'].get("level", "O1").upper()
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if amp_level not in ["O1", "O2"]:
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msg = "[Parameter Error]: The optimize level of AMP only support 'O1' and 'O2'. The level has been set 'O1'."
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logger.warning(msg)
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self.config['AMP']["level"] = "O1"
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amp_level = "O1"
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amp_eval = self.config["AMP"].get("use_fp16_test", False)
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# TODO(gaotingquan): Paddle not yet support FP32 evaluation when training with AMPO2
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if self.mode == "train" and self.config["Global"].get(
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"eval_during_train",
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True) and amp_level == "O2" and amp_eval == False:
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msg = "PaddlePaddle only support FP16 evaluation when training with AMP O2 now. "
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logger.warning(msg)
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self.config["AMP"]["use_fp16_test"] = True
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amp_eval = True
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if self.mode == "train" or amp_eval:
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AMPForwardDecorator.amp_level = amp_level
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AMPForwardDecorator.amp_eval = amp_eval
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def _init_dist(self):
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# check the gpu num
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world_size = dist.get_world_size()
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self.config["Global"]["distributed"] = world_size != 1
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# TODO(gaotingquan):
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if self.mode == "train":
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std_gpu_num = 8 if isinstance(
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self.config["Optimizer"],
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dict) and self.config["Optimizer"]["name"] == "AdamW" else 4
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if world_size != std_gpu_num:
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msg = f"The training strategy provided by PaddleClas is based on {std_gpu_num} gpus. But the number of gpu is {world_size} in current training. Please modify the stategy (learning rate, batch size and so on) if use this config to train."
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logger.warning(msg)
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if self.config["Global"]["distributed"]:
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dist.init_parallel_env()
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self.model = paddle.DataParallel(self.model)
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if self.mode == 'train' and len(self.train_loss_func.parameters(
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)) > 0:
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self.train_loss_func = paddle.DataParallel(
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self.train_loss_func)
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class ExportModel(TheseusLayer):
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"""
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ExportModel: add softmax onto the model
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"""
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def __init__(self, config, model, use_multilabel):
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super().__init__()
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self.base_model = model
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# we should choose a final model to export
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if isinstance(self.base_model, DistillationModel):
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self.infer_model_name = config["infer_model_name"]
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else:
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self.infer_model_name = None
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self.infer_output_key = config.get("infer_output_key", None)
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if self.infer_output_key == "features" and isinstance(self.base_model,
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RecModel):
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self.base_model.head = IdentityHead()
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if use_multilabel:
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self.out_act = nn.Sigmoid()
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else:
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if config.get("infer_add_softmax", True):
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self.out_act = nn.Softmax(axis=-1)
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else:
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self.out_act = None
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def eval(self):
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self.training = False
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for layer in self.sublayers():
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layer.training = False
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layer.eval()
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def forward(self, x):
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x = self.base_model(x)
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if isinstance(x, list):
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x = x[0]
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if self.infer_model_name is not None:
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x = x[self.infer_model_name]
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if self.infer_output_key is not None:
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x = x[self.infer_output_key]
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if self.out_act is not None:
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if isinstance(x, dict):
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x = x["logits"]
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x = self.out_act(x)
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return x
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