mirror of
https://github.com/PaddlePaddle/PaddleClas.git
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123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
# Copyright (c) 2023 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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import os
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import json
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import yaml
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import paddle
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from packaging import version
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from . import logger
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# just to determine the inference model file format
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def get_FLAGS_json_format_model():
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# json format by default
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return os.environ.get("FLAGS_json_format_model", "1").lower() in (
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"1", "true", "t")
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FLAGS_json_format_model = get_FLAGS_json_format_model()
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def save_predict_result(save_path, result):
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if os.path.splitext(save_path)[-1] == '':
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if save_path[-1] == "/":
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save_path = save_path[:-1]
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save_path = save_path + '.json'
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elif os.path.splitext(save_path)[-1] == '.json':
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save_path = save_path
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else:
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raise Exception(
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f"{save_path} is invalid input path, only files in json format are supported."
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)
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if os.path.exists(save_path):
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logger.warning(f"The file {save_path} will be overwritten.")
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with open(save_path, 'w', encoding='utf-8') as f:
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json.dump(result, f)
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def update_train_results(config,
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prefix,
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metric_info,
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done_flag=False,
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last_num=5,
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ema=False):
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if paddle.distributed.get_rank() != 0:
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return
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assert last_num >= 1
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train_results_path = os.path.join(config["Global"]["output_dir"],
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"train_result.json")
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save_model_tag = ["pdparams", "pdopt", "pdstates"]
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paddle_version = version.parse(paddle.__version__)
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if FLAGS_json_format_model or paddle_version >= version.parse("3.0.0"):
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save_inference_files = {
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"inference_config": "inference.yml",
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"pdmodel": "inference.json",
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"pdiparams": "inference.pdiparams",
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}
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else:
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save_inference_files = {
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"inference_config": "inference.yml",
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"pdmodel": "inference.pdmodel",
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"pdiparams": "inference.pdiparams",
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"pdiparams.info": "inference.pdiparams.info"
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}
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if ema:
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save_model_tag.append("pdema")
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if os.path.exists(train_results_path):
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with open(train_results_path, "r") as fp:
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train_results = json.load(fp)
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else:
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train_results = {}
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train_results["model_name"] = config["Global"].get("pdx_model_name",
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None)
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if config.get("infer", None):
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train_results["label_dict"] = config["Infer"]["PostProcess"].get(
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"class_id_map_file", "")
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else:
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train_results["label_dict"] = ""
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train_results["train_log"] = "train.log"
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train_results["visualdl_log"] = ""
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train_results["config"] = "config.yaml"
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train_results["models"] = {}
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for i in range(1, last_num + 1):
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train_results["models"][f"last_{i}"] = {}
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train_results["models"]["best"] = {}
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train_results["done_flag"] = done_flag
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if prefix == "best_model":
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train_results["models"]["best"]["score"] = metric_info["metric"]
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for tag in save_model_tag:
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train_results["models"]["best"][tag] = os.path.join(
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prefix, f"{prefix}.{tag}")
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for key in save_inference_files:
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train_results["models"]["best"][key] = os.path.join(
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prefix, "inference", save_inference_files[key])
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else:
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for i in range(last_num - 1, 0, -1):
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train_results["models"][f"last_{i + 1}"] = train_results["models"][
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f"last_{i}"].copy()
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train_results["models"][f"last_{1}"]["score"] = metric_info["metric"]
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for tag in save_model_tag:
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train_results["models"][f"last_{1}"][tag] = os.path.join(
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prefix, f"{prefix}.{tag}")
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for key in save_inference_files:
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train_results["models"][f"last_{1}"][key] = os.path.join(
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prefix, "inference", save_inference_files[key])
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with open(train_results_path, "w") as fp:
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json.dump(train_results, fp)
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