更换文档检测模型
This commit is contained in:
378
paddle_detection/configs/rotate/tools/inference_benchmark.py
Normal file
378
paddle_detection/configs/rotate/tools/inference_benchmark.py
Normal file
@@ -0,0 +1,378 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import sys
|
||||
import six
|
||||
import glob
|
||||
import time
|
||||
import yaml
|
||||
import argparse
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
import paddle
|
||||
import paddle.version as paddle_version
|
||||
from paddle.inference import Config, create_predictor, PrecisionType, get_trt_runtime_version
|
||||
|
||||
TUNED_TRT_DYNAMIC_MODELS = {'DETR'}
|
||||
|
||||
|
||||
def check_version(version='2.2'):
|
||||
err = "PaddlePaddle version {} or higher is required, " \
|
||||
"or a suitable develop version is satisfied as well. \n" \
|
||||
"Please make sure the version is good with your code.".format(version)
|
||||
|
||||
version_installed = [
|
||||
paddle_version.major, paddle_version.minor, paddle_version.patch,
|
||||
paddle_version.rc
|
||||
]
|
||||
|
||||
if version_installed == ['0', '0', '0', '0']:
|
||||
return
|
||||
|
||||
if version == 'develop':
|
||||
raise Exception("PaddlePaddle develop version is required!")
|
||||
|
||||
version_split = version.split('.')
|
||||
|
||||
length = min(len(version_installed), len(version_split))
|
||||
for i in six.moves.range(length):
|
||||
if version_installed[i] > version_split[i]:
|
||||
return
|
||||
if version_installed[i] < version_split[i]:
|
||||
raise Exception(err)
|
||||
|
||||
|
||||
def check_trt_version(version='8.2'):
|
||||
err = "TensorRT version {} or higher is required," \
|
||||
"Please make sure the version is good with your code.".format(version)
|
||||
version_split = list(map(int, version.split('.')))
|
||||
version_installed = get_trt_runtime_version()
|
||||
length = min(len(version_installed), len(version_split))
|
||||
for i in six.moves.range(length):
|
||||
if version_installed[i] > version_split[i]:
|
||||
return
|
||||
if version_installed[i] < version_split[i]:
|
||||
raise Exception(err)
|
||||
|
||||
|
||||
# preprocess ops
|
||||
def decode_image(im_file, im_info):
|
||||
if isinstance(im_file, str):
|
||||
with open(im_file, 'rb') as f:
|
||||
im_read = f.read()
|
||||
data = np.frombuffer(im_read, dtype='uint8')
|
||||
im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode
|
||||
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
|
||||
else:
|
||||
im = im_file
|
||||
im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
|
||||
im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
|
||||
return im, im_info
|
||||
|
||||
|
||||
class Resize(object):
|
||||
def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR):
|
||||
if isinstance(target_size, int):
|
||||
target_size = [target_size, target_size]
|
||||
self.target_size = target_size
|
||||
self.keep_ratio = keep_ratio
|
||||
self.interp = interp
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
assert len(self.target_size) == 2
|
||||
assert self.target_size[0] > 0 and self.target_size[1] > 0
|
||||
im_channel = im.shape[2]
|
||||
im_scale_y, im_scale_x = self.generate_scale(im)
|
||||
im = cv2.resize(
|
||||
im,
|
||||
None,
|
||||
None,
|
||||
fx=im_scale_x,
|
||||
fy=im_scale_y,
|
||||
interpolation=self.interp)
|
||||
im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
|
||||
im_info['scale_factor'] = np.array(
|
||||
[im_scale_y, im_scale_x]).astype('float32')
|
||||
return im, im_info
|
||||
|
||||
def generate_scale(self, im):
|
||||
origin_shape = im.shape[:2]
|
||||
im_c = im.shape[2]
|
||||
if self.keep_ratio:
|
||||
im_size_min = np.min(origin_shape)
|
||||
im_size_max = np.max(origin_shape)
|
||||
target_size_min = np.min(self.target_size)
|
||||
target_size_max = np.max(self.target_size)
|
||||
im_scale = float(target_size_min) / float(im_size_min)
|
||||
if np.round(im_scale * im_size_max) > target_size_max:
|
||||
im_scale = float(target_size_max) / float(im_size_max)
|
||||
im_scale_x = im_scale
|
||||
im_scale_y = im_scale
|
||||
else:
|
||||
resize_h, resize_w = self.target_size
|
||||
im_scale_y = resize_h / float(origin_shape[0])
|
||||
im_scale_x = resize_w / float(origin_shape[1])
|
||||
return im_scale_y, im_scale_x
|
||||
|
||||
|
||||
class Permute(object):
|
||||
def __init__(self, ):
|
||||
super(Permute, self).__init__()
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
im = im.transpose((2, 0, 1))
|
||||
return im, im_info
|
||||
|
||||
|
||||
class NormalizeImage(object):
|
||||
def __init__(self, mean, std, is_scale=True, norm_type='mean_std'):
|
||||
self.mean = mean
|
||||
self.std = std
|
||||
self.is_scale = is_scale
|
||||
self.norm_type = norm_type
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
im = im.astype(np.float32, copy=False)
|
||||
if self.is_scale:
|
||||
scale = 1.0 / 255.0
|
||||
im *= scale
|
||||
|
||||
if self.norm_type == 'mean_std':
|
||||
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
|
||||
std = np.array(self.std)[np.newaxis, np.newaxis, :]
|
||||
im -= mean
|
||||
im /= std
|
||||
return im, im_info
|
||||
|
||||
|
||||
class PadStride(object):
|
||||
def __init__(self, stride=0):
|
||||
self.coarsest_stride = stride
|
||||
|
||||
def __call__(self, im, im_info):
|
||||
coarsest_stride = self.coarsest_stride
|
||||
if coarsest_stride <= 0:
|
||||
return im, im_info
|
||||
im_c, im_h, im_w = im.shape
|
||||
pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
|
||||
pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
|
||||
padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
|
||||
padding_im[:, :im_h, :im_w] = im
|
||||
return padding_im, im_info
|
||||
|
||||
|
||||
def preprocess(im, preprocess_ops):
|
||||
# process image by preprocess_ops
|
||||
im_info = {
|
||||
'scale_factor': np.array(
|
||||
[1., 1.], dtype=np.float32),
|
||||
'im_shape': None,
|
||||
}
|
||||
im, im_info = decode_image(im, im_info)
|
||||
for operator in preprocess_ops:
|
||||
im, im_info = operator(im, im_info)
|
||||
return im, im_info
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
'--model_dir', type=str, help='directory of inference model')
|
||||
parser.add_argument(
|
||||
'--run_mode', type=str, default='paddle', help='running mode')
|
||||
parser.add_argument('--batch_size', type=int, default=1, help='batch size')
|
||||
parser.add_argument(
|
||||
'--image_dir',
|
||||
type=str,
|
||||
default='/paddle/data/DOTA_1024_ss/test1024/images',
|
||||
help='directory of test images')
|
||||
parser.add_argument(
|
||||
'--warmup_iter', type=int, default=5, help='num of warmup iters')
|
||||
parser.add_argument(
|
||||
'--total_iter', type=int, default=2000, help='num of total iters')
|
||||
parser.add_argument(
|
||||
'--log_iter', type=int, default=50, help='num of log interval')
|
||||
parser.add_argument(
|
||||
'--tuned_trt_shape_file',
|
||||
type=str,
|
||||
default='shape_range_info.pbtxt',
|
||||
help='dynamic shape range info')
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def init_predictor(FLAGS):
|
||||
model_dir, run_mode, batch_size = FLAGS.model_dir, FLAGS.run_mode, FLAGS.batch_size
|
||||
yaml_file = os.path.join(model_dir, 'infer_cfg.yml')
|
||||
with open(yaml_file) as f:
|
||||
yml_conf = yaml.safe_load(f)
|
||||
|
||||
config = Config(
|
||||
os.path.join(model_dir, 'model.pdmodel'),
|
||||
os.path.join(model_dir, 'model.pdiparams'))
|
||||
|
||||
# initial GPU memory(M), device ID
|
||||
config.enable_use_gpu(200, 0)
|
||||
# optimize graph and fuse op
|
||||
config.switch_ir_optim(True)
|
||||
|
||||
precision_map = {
|
||||
'trt_int8': Config.Precision.Int8,
|
||||
'trt_fp32': Config.Precision.Float32,
|
||||
'trt_fp16': Config.Precision.Half
|
||||
}
|
||||
|
||||
arch = yml_conf['arch']
|
||||
tuned_trt_shape_file = os.path.join(model_dir, FLAGS.tuned_trt_shape_file)
|
||||
|
||||
if run_mode in precision_map.keys():
|
||||
if arch in TUNED_TRT_DYNAMIC_MODELS and not os.path.exists(
|
||||
tuned_trt_shape_file):
|
||||
print(
|
||||
'dynamic shape range info is saved in {}. After that, rerun the code'.
|
||||
format(tuned_trt_shape_file))
|
||||
config.collect_shape_range_info(tuned_trt_shape_file)
|
||||
config.enable_tensorrt_engine(
|
||||
workspace_size=(1 << 25) * batch_size,
|
||||
max_batch_size=batch_size,
|
||||
min_subgraph_size=yml_conf['min_subgraph_size'],
|
||||
precision_mode=precision_map[run_mode],
|
||||
use_static=True,
|
||||
use_calib_mode=False)
|
||||
|
||||
if yml_conf['use_dynamic_shape']:
|
||||
if arch in TUNED_TRT_DYNAMIC_MODELS and os.path.exists(
|
||||
tuned_trt_shape_file):
|
||||
config.enable_tuned_tensorrt_dynamic_shape(tuned_trt_shape_file,
|
||||
True)
|
||||
else:
|
||||
min_input_shape = {
|
||||
'image': [batch_size, 3, 640, 640],
|
||||
'scale_factor': [batch_size, 2]
|
||||
}
|
||||
max_input_shape = {
|
||||
'image': [batch_size, 3, 1280, 1280],
|
||||
'scale_factor': [batch_size, 2]
|
||||
}
|
||||
opt_input_shape = {
|
||||
'image': [batch_size, 3, 1024, 1024],
|
||||
'scale_factor': [batch_size, 2]
|
||||
}
|
||||
config.set_trt_dynamic_shape_info(
|
||||
min_input_shape, max_input_shape, opt_input_shape)
|
||||
|
||||
# disable print log when predict
|
||||
config.disable_glog_info()
|
||||
# enable shared memory
|
||||
config.enable_memory_optim()
|
||||
# disable feed, fetch OP, needed by zero_copy_run
|
||||
config.switch_use_feed_fetch_ops(False)
|
||||
predictor = create_predictor(config)
|
||||
return predictor, yml_conf
|
||||
|
||||
|
||||
def create_preprocess_ops(yml_conf):
|
||||
preprocess_ops = []
|
||||
for op_info in yml_conf['Preprocess']:
|
||||
new_op_info = op_info.copy()
|
||||
op_type = new_op_info.pop('type')
|
||||
preprocess_ops.append(eval(op_type)(**new_op_info))
|
||||
return preprocess_ops
|
||||
|
||||
|
||||
def get_test_images(image_dir):
|
||||
images = set()
|
||||
infer_dir = os.path.abspath(image_dir)
|
||||
exts = ['jpg', 'jpeg', 'png', 'bmp']
|
||||
exts += [ext.upper() for ext in exts]
|
||||
for ext in exts:
|
||||
images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
|
||||
images = list(images)
|
||||
return images
|
||||
|
||||
|
||||
def create_inputs(image_files, preprocess_ops):
|
||||
inputs = dict()
|
||||
im_list, im_info_list = [], []
|
||||
for im_path in image_files:
|
||||
im, im_info = preprocess(im_path, preprocess_ops)
|
||||
im_list.append(im)
|
||||
im_info_list.append(im_info)
|
||||
|
||||
inputs['im_shape'] = np.stack(
|
||||
[e['im_shape'] for e in im_info_list], axis=0).astype('float32')
|
||||
inputs['scale_factor'] = np.stack(
|
||||
[e['scale_factor'] for e in im_info_list], axis=0).astype('float32')
|
||||
inputs['image'] = np.stack(im_list, axis=0).astype('float32')
|
||||
return inputs
|
||||
|
||||
|
||||
def measure_speed(FLAGS):
|
||||
predictor, yml_conf = init_predictor(FLAGS)
|
||||
input_names = predictor.get_input_names()
|
||||
preprocess_ops = create_preprocess_ops(yml_conf)
|
||||
|
||||
image_files = get_test_images(FLAGS.image_dir)
|
||||
|
||||
batch_size = FLAGS.batch_size
|
||||
warmup_iter, log_iter, total_iter = FLAGS.warmup_iter, FLAGS.log_iter, FLAGS.total_iter
|
||||
|
||||
total_time = 0
|
||||
fps = 0
|
||||
for i in range(0, total_iter, batch_size):
|
||||
# make data ready
|
||||
inputs = create_inputs(image_files[i:i + batch_size], preprocess_ops)
|
||||
for name in input_names:
|
||||
input_tensor = predictor.get_input_handle(name)
|
||||
input_tensor.copy_from_cpu(inputs[name])
|
||||
|
||||
paddle.device.cuda.synchronize()
|
||||
# start running
|
||||
start_time = time.perf_counter()
|
||||
predictor.run()
|
||||
paddle.device.cuda.synchronize()
|
||||
|
||||
if i >= warmup_iter:
|
||||
total_time += time.perf_counter() - start_time
|
||||
if (i + 1) % log_iter == 0:
|
||||
fps = (i + 1 - warmup_iter) / total_time
|
||||
print(
|
||||
f'Done image [{i + 1:<3}/ {total_iter}], '
|
||||
f'fps: {fps:.1f} img / s, '
|
||||
f'times per image: {1000 / fps:.1f} ms / img',
|
||||
flush=True)
|
||||
|
||||
if (i + 1) == total_iter:
|
||||
fps = (i + 1 - warmup_iter) / total_time
|
||||
print(
|
||||
f'Overall fps: {fps:.1f} img / s, '
|
||||
f'times per image: {1000 / fps:.1f} ms / img',
|
||||
flush=True)
|
||||
break
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
FLAGS = parse_args()
|
||||
if 'trt' in FLAGS.run_mode:
|
||||
check_version('develop')
|
||||
check_trt_version('8.2')
|
||||
else:
|
||||
check_version('2.4')
|
||||
measure_speed(FLAGS)
|
||||
Reference in New Issue
Block a user