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deploy/slim/auto_compression/test_ocr.py
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292
deploy/slim/auto_compression/test_ocr.py
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# 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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#
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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 argparse
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import time
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import os
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import sys
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import cv2
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import numpy as np
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import paddle
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import logging
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import numpy as np
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import argparse
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from tqdm import tqdm
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import paddle
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from paddleslim.common import load_config as load_slim_config
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from paddleslim.common import get_logger
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import sys
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sys.path.append("../../../")
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from ppocr.data import build_dataloader
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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 paddle.inference import create_predictor, PrecisionType
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from paddle.inference import Config as PredictConfig
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logger = get_logger(__name__, level=logging.INFO)
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def find_images_with_bounding_size(dataset: paddle.io.Dataset):
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max_length_index = -1
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max_width_index = -1
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min_length_index = -1
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min_width_index = -1
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max_length = float("-inf")
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max_width = float("-inf")
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min_length = float("inf")
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min_width = float("inf")
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for idx, data in enumerate(dataset):
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image = np.array(data[0])
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h, w = image.shape[-2:]
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if h > max_length:
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max_length = h
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max_length_index = idx
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if w > max_width:
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max_width = w
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max_width_index = idx
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if h < min_length:
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min_length = h
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min_length_index = idx
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if w < min_width:
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min_width = w
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min_width_index = idx
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print(f"Found max image length: {max_length}, index: {max_length_index}")
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print(f"Found max image width: {max_width}, index: {max_width_index}")
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print(f"Found min image length: {min_length}, index: {min_length_index}")
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print(f"Found min image width: {min_width}, index: {min_width_index}")
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return paddle.io.Subset(
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dataset, [max_width_index, max_length_index, min_width_index, min_length_index]
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)
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def load_predictor(args):
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"""
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load predictor func
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"""
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rerun_flag = False
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model_file = os.path.join(args.model_path, args.model_filename)
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params_file = os.path.join(args.model_path, args.params_filename)
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pred_cfg = PredictConfig(model_file, params_file)
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pred_cfg.enable_memory_optim()
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pred_cfg.switch_ir_optim(True)
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if args.device == "GPU":
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pred_cfg.enable_use_gpu(100, 0)
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else:
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pred_cfg.disable_gpu()
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pred_cfg.set_cpu_math_library_num_threads(args.cpu_threads)
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if args.use_mkldnn:
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pred_cfg.enable_mkldnn()
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if args.precision == "int8":
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pred_cfg.enable_mkldnn_int8({"conv2d"})
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if global_config["model_type"] == "rec":
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# delete pass which influence the accuracy, please refer to https://github.com/PaddlePaddle/Paddle/issues/55290
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pred_cfg.delete_pass("fc_mkldnn_pass")
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pred_cfg.delete_pass("fc_act_mkldnn_fuse_pass")
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if args.use_trt:
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# To collect the dynamic shapes of inputs for TensorRT engine
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dynamic_shape_file = os.path.join(args.model_path, "dynamic_shape.txt")
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if os.path.exists(dynamic_shape_file):
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pred_cfg.enable_tuned_tensorrt_dynamic_shape(dynamic_shape_file, True)
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print("trt set dynamic shape done!")
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precision_map = {
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"fp16": PrecisionType.Half,
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"fp32": PrecisionType.Float32,
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"int8": PrecisionType.Int8,
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}
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if (
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args.precision == "int8"
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and "ppocrv4_det_server_qat_dist.yaml" in args.config_path
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):
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# Use the following settings only when the hardware is a Tesla V100. If you are using
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# a RTX 3090, use the settings in the else branch.
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pred_cfg.enable_tensorrt_engine(
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workspace_size=1 << 30,
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max_batch_size=1,
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min_subgraph_size=30,
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precision_mode=precision_map[args.precision],
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use_static=True,
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use_calib_mode=False,
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)
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pred_cfg.exp_disable_tensorrt_ops(["elementwise_add"])
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else:
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pred_cfg.enable_tensorrt_engine(
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workspace_size=1 << 30,
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max_batch_size=1,
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min_subgraph_size=4,
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precision_mode=precision_map[args.precision],
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use_static=True,
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use_calib_mode=False,
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)
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else:
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# pred_cfg.disable_gpu()
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# pred_cfg.set_cpu_math_library_num_threads(24)
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pred_cfg.collect_shape_range_info(dynamic_shape_file)
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print("Start collect dynamic shape...")
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rerun_flag = True
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predictor = create_predictor(pred_cfg)
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return predictor, rerun_flag
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def eval(args):
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"""
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eval mIoU func
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"""
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# DataLoader need run on cpu
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paddle.set_device("cpu")
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devices = paddle.device.get_device().split(":")[0]
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val_loader = build_dataloader(all_config, "Eval", devices, logger)
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post_process_class = build_post_process(all_config["PostProcess"], global_config)
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eval_class = build_metric(all_config["Metric"])
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model_type = global_config["model_type"]
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predictor, rerun_flag = load_predictor(args)
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if rerun_flag:
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eval_dataset = find_images_with_bounding_size(val_loader.dataset)
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batch_sampler = paddle.io.BatchSampler(
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eval_dataset, batch_size=1, shuffle=False, drop_last=False
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)
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val_loader = paddle.io.DataLoader(
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eval_dataset, batch_sampler=batch_sampler, num_workers=4, return_list=True
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)
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input_names = predictor.get_input_names()
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input_handle = predictor.get_input_handle(input_names[0])
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output_names = predictor.get_output_names()
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output_handle = predictor.get_output_handle(output_names[0])
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sample_nums = len(val_loader)
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predict_time = 0.0
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time_min = float("inf")
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time_max = float("-inf")
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print("Start evaluating ( total_iters: {}).".format(sample_nums))
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for batch_id, batch in enumerate(val_loader):
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images = np.array(batch[0])
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batch_numpy = []
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for item in batch:
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batch_numpy.append(np.array(item))
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# ori_shape = np.array(batch_numpy).shape[-2:]
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input_handle.reshape(images.shape)
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input_handle.copy_from_cpu(images)
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start_time = time.time()
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predictor.run()
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preds = output_handle.copy_to_cpu()
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end_time = time.time()
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timed = end_time - start_time
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time_min = min(time_min, timed)
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time_max = max(time_max, timed)
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predict_time += timed
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if model_type == "det":
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preds_map = {"maps": preds}
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post_result = post_process_class(preds_map, batch_numpy[1])
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eval_class(post_result, batch_numpy)
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elif model_type == "rec":
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post_result = post_process_class(preds, batch_numpy[1])
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eval_class(post_result, batch_numpy)
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if rerun_flag:
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if batch_id == 3:
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print(
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"***** Collect dynamic shape done, Please rerun the program to get correct results. *****"
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)
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return
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if batch_id % 100 == 0:
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print("Eval iter:", batch_id)
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sys.stdout.flush()
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metric = eval_class.get_metric()
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time_avg = predict_time / sample_nums
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print(
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"[Benchmark] Inference time(ms): min={}, max={}, avg={}".format(
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round(time_min * 1000, 2),
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round(time_max * 1000, 1),
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round(time_avg * 1000, 1),
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)
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)
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for k, v in metric.items():
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print("{}:{}".format(k, v))
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sys.stdout.flush()
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def main():
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global all_config, global_config
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all_config = load_slim_config(args.config_path)
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global_config = all_config["Global"]
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eval(args)
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if __name__ == "__main__":
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paddle.enable_static()
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_path", type=str, help="inference model filepath")
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parser.add_argument(
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"--config_path",
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type=str,
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default="./configs/ppocrv3_det_qat_dist.yaml",
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help="path of compression strategy config.",
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)
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parser.add_argument(
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"--model_filename",
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type=str,
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default="inference.pdmodel",
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help="model file name",
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)
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parser.add_argument(
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"--params_filename",
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type=str,
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default="inference.pdiparams",
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help="params file name",
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)
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parser.add_argument(
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"--device",
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type=str,
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default="GPU",
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choices=["CPU", "GPU"],
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help="Choose the device you want to run, it can be: CPU/GPU, default is GPU",
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)
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parser.add_argument(
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"--precision",
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type=str,
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default="fp32",
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choices=["fp32", "fp16", "int8"],
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help="The precision of inference. It can be 'fp32', 'fp16' or 'int8'. Default is 'fp16'.",
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)
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parser.add_argument(
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"--use_trt",
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type=bool,
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default=False,
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help="Whether to use tensorrt engine or not.",
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)
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parser.add_argument(
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"--use_mkldnn", type=bool, default=False, help="Whether use mkldnn or not."
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)
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parser.add_argument(
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"--cpu_threads", type=int, default=10, help="Num of cpu threads."
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)
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args = parser.parse_args()
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main()
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