379 lines
13 KiB
Python
379 lines
13 KiB
Python
# Copyright (c) 2022 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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import six
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import glob
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import time
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import yaml
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import argparse
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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 paddle.version as paddle_version
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from paddle.inference import Config, create_predictor, PrecisionType, get_trt_runtime_version
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TUNED_TRT_DYNAMIC_MODELS = {'DETR'}
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def check_version(version='2.2'):
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err = "PaddlePaddle version {} or higher is required, " \
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"or a suitable develop version is satisfied as well. \n" \
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"Please make sure the version is good with your code.".format(version)
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version_installed = [
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paddle_version.major, paddle_version.minor, paddle_version.patch,
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paddle_version.rc
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]
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if version_installed == ['0', '0', '0', '0']:
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return
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if version == 'develop':
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raise Exception("PaddlePaddle develop version is required!")
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version_split = version.split('.')
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length = min(len(version_installed), len(version_split))
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for i in six.moves.range(length):
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if version_installed[i] > version_split[i]:
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return
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if version_installed[i] < version_split[i]:
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raise Exception(err)
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def check_trt_version(version='8.2'):
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err = "TensorRT version {} or higher is required," \
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"Please make sure the version is good with your code.".format(version)
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version_split = list(map(int, version.split('.')))
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version_installed = get_trt_runtime_version()
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length = min(len(version_installed), len(version_split))
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for i in six.moves.range(length):
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if version_installed[i] > version_split[i]:
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return
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if version_installed[i] < version_split[i]:
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raise Exception(err)
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# preprocess ops
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def decode_image(im_file, im_info):
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if isinstance(im_file, str):
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with open(im_file, 'rb') as f:
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im_read = f.read()
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data = np.frombuffer(im_read, dtype='uint8')
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im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode
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im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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else:
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im = im_file
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im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
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im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
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return im, im_info
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class Resize(object):
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def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR):
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if isinstance(target_size, int):
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target_size = [target_size, target_size]
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self.target_size = target_size
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self.keep_ratio = keep_ratio
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self.interp = interp
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def __call__(self, im, im_info):
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assert len(self.target_size) == 2
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assert self.target_size[0] > 0 and self.target_size[1] > 0
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im_channel = im.shape[2]
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im_scale_y, im_scale_x = self.generate_scale(im)
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im = cv2.resize(
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im,
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None,
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None,
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fx=im_scale_x,
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fy=im_scale_y,
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interpolation=self.interp)
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im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
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im_info['scale_factor'] = np.array(
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[im_scale_y, im_scale_x]).astype('float32')
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return im, im_info
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def generate_scale(self, im):
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origin_shape = im.shape[:2]
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im_c = im.shape[2]
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if self.keep_ratio:
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im_size_min = np.min(origin_shape)
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im_size_max = np.max(origin_shape)
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target_size_min = np.min(self.target_size)
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target_size_max = np.max(self.target_size)
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im_scale = float(target_size_min) / float(im_size_min)
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if np.round(im_scale * im_size_max) > target_size_max:
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im_scale = float(target_size_max) / float(im_size_max)
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im_scale_x = im_scale
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im_scale_y = im_scale
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else:
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resize_h, resize_w = self.target_size
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im_scale_y = resize_h / float(origin_shape[0])
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im_scale_x = resize_w / float(origin_shape[1])
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return im_scale_y, im_scale_x
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class Permute(object):
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def __init__(self, ):
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super(Permute, self).__init__()
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def __call__(self, im, im_info):
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im = im.transpose((2, 0, 1))
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return im, im_info
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class NormalizeImage(object):
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def __init__(self, mean, std, is_scale=True, norm_type='mean_std'):
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self.mean = mean
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self.std = std
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self.is_scale = is_scale
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self.norm_type = norm_type
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def __call__(self, im, im_info):
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im = im.astype(np.float32, copy=False)
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if self.is_scale:
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scale = 1.0 / 255.0
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im *= scale
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if self.norm_type == 'mean_std':
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mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
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std = np.array(self.std)[np.newaxis, np.newaxis, :]
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im -= mean
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im /= std
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return im, im_info
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class PadStride(object):
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def __init__(self, stride=0):
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self.coarsest_stride = stride
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def __call__(self, im, im_info):
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coarsest_stride = self.coarsest_stride
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if coarsest_stride <= 0:
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return im, im_info
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im_c, im_h, im_w = im.shape
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pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
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pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
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padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
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padding_im[:, :im_h, :im_w] = im
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return padding_im, im_info
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def preprocess(im, preprocess_ops):
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# process image by preprocess_ops
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im_info = {
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'scale_factor': np.array(
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[1., 1.], dtype=np.float32),
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'im_shape': None,
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}
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im, im_info = decode_image(im, im_info)
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for operator in preprocess_ops:
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im, im_info = operator(im, im_info)
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return im, im_info
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--model_dir', type=str, help='directory of inference model')
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parser.add_argument(
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'--run_mode', type=str, default='paddle', help='running mode')
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parser.add_argument('--batch_size', type=int, default=1, help='batch size')
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parser.add_argument(
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'--image_dir',
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type=str,
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default='/paddle/data/DOTA_1024_ss/test1024/images',
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help='directory of test images')
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parser.add_argument(
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'--warmup_iter', type=int, default=5, help='num of warmup iters')
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parser.add_argument(
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'--total_iter', type=int, default=2000, help='num of total iters')
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parser.add_argument(
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'--log_iter', type=int, default=50, help='num of log interval')
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parser.add_argument(
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'--tuned_trt_shape_file',
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type=str,
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default='shape_range_info.pbtxt',
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help='dynamic shape range info')
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args = parser.parse_args()
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return args
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def init_predictor(FLAGS):
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model_dir, run_mode, batch_size = FLAGS.model_dir, FLAGS.run_mode, FLAGS.batch_size
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yaml_file = os.path.join(model_dir, 'infer_cfg.yml')
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with open(yaml_file) as f:
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yml_conf = yaml.safe_load(f)
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config = Config(
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os.path.join(model_dir, 'model.pdmodel'),
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os.path.join(model_dir, 'model.pdiparams'))
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# initial GPU memory(M), device ID
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config.enable_use_gpu(200, 0)
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# optimize graph and fuse op
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config.switch_ir_optim(True)
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precision_map = {
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'trt_int8': Config.Precision.Int8,
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'trt_fp32': Config.Precision.Float32,
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'trt_fp16': Config.Precision.Half
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}
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arch = yml_conf['arch']
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tuned_trt_shape_file = os.path.join(model_dir, FLAGS.tuned_trt_shape_file)
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if run_mode in precision_map.keys():
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if arch in TUNED_TRT_DYNAMIC_MODELS and not os.path.exists(
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tuned_trt_shape_file):
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print(
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'dynamic shape range info is saved in {}. After that, rerun the code'.
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format(tuned_trt_shape_file))
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config.collect_shape_range_info(tuned_trt_shape_file)
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config.enable_tensorrt_engine(
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workspace_size=(1 << 25) * batch_size,
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max_batch_size=batch_size,
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min_subgraph_size=yml_conf['min_subgraph_size'],
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precision_mode=precision_map[run_mode],
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use_static=True,
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use_calib_mode=False)
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if yml_conf['use_dynamic_shape']:
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if arch in TUNED_TRT_DYNAMIC_MODELS and os.path.exists(
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tuned_trt_shape_file):
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config.enable_tuned_tensorrt_dynamic_shape(tuned_trt_shape_file,
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True)
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else:
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min_input_shape = {
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'image': [batch_size, 3, 640, 640],
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'scale_factor': [batch_size, 2]
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}
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max_input_shape = {
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'image': [batch_size, 3, 1280, 1280],
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'scale_factor': [batch_size, 2]
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}
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opt_input_shape = {
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'image': [batch_size, 3, 1024, 1024],
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'scale_factor': [batch_size, 2]
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}
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config.set_trt_dynamic_shape_info(
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min_input_shape, max_input_shape, opt_input_shape)
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# disable print log when predict
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config.disable_glog_info()
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# enable shared memory
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config.enable_memory_optim()
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# disable feed, fetch OP, needed by zero_copy_run
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config.switch_use_feed_fetch_ops(False)
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predictor = create_predictor(config)
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return predictor, yml_conf
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def create_preprocess_ops(yml_conf):
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preprocess_ops = []
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for op_info in yml_conf['Preprocess']:
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new_op_info = op_info.copy()
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op_type = new_op_info.pop('type')
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preprocess_ops.append(eval(op_type)(**new_op_info))
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return preprocess_ops
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def get_test_images(image_dir):
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images = set()
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infer_dir = os.path.abspath(image_dir)
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exts = ['jpg', 'jpeg', 'png', 'bmp']
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exts += [ext.upper() for ext in exts]
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for ext in exts:
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images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
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images = list(images)
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return images
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def create_inputs(image_files, preprocess_ops):
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inputs = dict()
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im_list, im_info_list = [], []
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for im_path in image_files:
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im, im_info = preprocess(im_path, preprocess_ops)
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im_list.append(im)
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im_info_list.append(im_info)
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inputs['im_shape'] = np.stack(
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[e['im_shape'] for e in im_info_list], axis=0).astype('float32')
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inputs['scale_factor'] = np.stack(
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[e['scale_factor'] for e in im_info_list], axis=0).astype('float32')
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inputs['image'] = np.stack(im_list, axis=0).astype('float32')
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return inputs
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def measure_speed(FLAGS):
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predictor, yml_conf = init_predictor(FLAGS)
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input_names = predictor.get_input_names()
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preprocess_ops = create_preprocess_ops(yml_conf)
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image_files = get_test_images(FLAGS.image_dir)
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batch_size = FLAGS.batch_size
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warmup_iter, log_iter, total_iter = FLAGS.warmup_iter, FLAGS.log_iter, FLAGS.total_iter
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total_time = 0
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fps = 0
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for i in range(0, total_iter, batch_size):
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# make data ready
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inputs = create_inputs(image_files[i:i + batch_size], preprocess_ops)
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for name in input_names:
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input_tensor = predictor.get_input_handle(name)
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input_tensor.copy_from_cpu(inputs[name])
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paddle.device.cuda.synchronize()
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# start running
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start_time = time.perf_counter()
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predictor.run()
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paddle.device.cuda.synchronize()
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if i >= warmup_iter:
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total_time += time.perf_counter() - start_time
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if (i + 1) % log_iter == 0:
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fps = (i + 1 - warmup_iter) / total_time
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print(
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f'Done image [{i + 1:<3}/ {total_iter}], '
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f'fps: {fps:.1f} img / s, '
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f'times per image: {1000 / fps:.1f} ms / img',
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flush=True)
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if (i + 1) == total_iter:
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fps = (i + 1 - warmup_iter) / total_time
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print(
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f'Overall fps: {fps:.1f} img / s, '
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f'times per image: {1000 / fps:.1f} ms / img',
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flush=True)
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break
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if __name__ == '__main__':
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FLAGS = parse_args()
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if 'trt' in FLAGS.run_mode:
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check_version('develop')
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check_trt_version('8.2')
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else:
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check_version('2.4')
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measure_speed(FLAGS)
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