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infer.py
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# coding: utf8
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import os
import sys
import ast
import time
import gflags
import yaml
import cv2
import numpy as np
import paddle.fluid as fluid
from concurrent.futures import ThreadPoolExecutor, as_completed
gflags.DEFINE_string("conf", default="", help="Configuration File Path")
gflags.DEFINE_string("input_dir", default="", help="Directory of Input Images")
gflags.DEFINE_string("trt_mode", default="", help="Use optimized model")
gflags.DEFINE_string(
"ext", default=".jpeg|.jpg", help="Input Image File Extensions")
gflags.FLAGS = gflags.FLAGS
# Generate ColorMap for visualization
def generate_colormap(num_classes):
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
return color_map
# Paddle-TRT Precision Map
trt_precision_map = {
"int8": fluid.core.AnalysisConfig.Precision.Int8,
"fp32": fluid.core.AnalysisConfig.Precision.Float32,
"fp16": fluid.core.AnalysisConfig.Precision.Half
}
# scan a directory and get all images with support extensions
def get_images_from_dir(img_dir, support_ext=".jpg|.jpeg"):
if (not os.path.exists(img_dir) or not os.path.isdir(img_dir)):
raise Exception("Image Directory [%s] invalid" % img_dir)
imgs = []
for item in os.listdir(img_dir):
ext = os.path.splitext(item)[1][1:].strip().lower()
if (len(ext) > 0 and ext in support_ext):
item_path = os.path.join(img_dir, item)
imgs.append(item_path)
return imgs
# Deploy Configuration File Parser
class DeployConfig:
def __init__(self, conf_file):
if not os.path.exists(conf_file):
raise Exception('Config file path [%s] invalid!' % conf_file)
with open(conf_file) as fp:
configs = yaml.load(fp, Loader=yaml.FullLoader)
deploy_conf = configs["DEPLOY"]
# 1. get eval_crop_size
self.eval_crop_size = ast.literal_eval(
deploy_conf["EVAL_CROP_SIZE"])
# 2. get mean
self.mean = deploy_conf["MEAN"]
# 3. get std
self.std = deploy_conf["STD"]
# 4. get class_num
self.class_num = deploy_conf["NUM_CLASSES"]
# 5. get paddle model and params file path
self.model_file = os.path.join(deploy_conf["MODEL_PATH"],
deploy_conf["MODEL_FILENAME"])
self.param_file = os.path.join(deploy_conf["MODEL_PATH"],
deploy_conf["PARAMS_FILENAME"])
# 6. use_gpu
self.use_gpu = deploy_conf["USE_GPU"]
# 7. predictor_mode
self.predictor_mode = deploy_conf["PREDICTOR_MODE"]
# 8. batch_size
self.batch_size = deploy_conf["BATCH_SIZE"]
# 9. channels
self.channels = deploy_conf["CHANNELS"]
# 10. use_pr
self.use_pr = deploy_conf["USE_PR"]
class ImageReader:
def __init__(self, configs):
self.config = configs
self.threads_pool = ThreadPoolExecutor(configs.batch_size)
# image processing thread worker
def process_worker(self, imgs, idx, use_pr=False):
image_path = imgs[idx]
cv2_imread_flag = cv2.IMREAD_COLOR
if self.config.channels == 4:
cv2_imread_flag = cv2.IMREAD_UNCHANGED
im = cv2.imread(image_path, cv2_imread_flag)
channels = im.shape[2]
if channels != 3 and channels != 4:
print("Only support rgb(gray) or rgba image.")
return -1
ori_h = im.shape[0]
ori_w = im.shape[1]
# resize to eval_crop_size
eval_crop_size = self.config.eval_crop_size
if (ori_h != eval_crop_size[1] or ori_w != eval_crop_size[0]):
im = cv2.resize(
im, eval_crop_size, fx=0, fy=0, interpolation=cv2.INTER_LINEAR)
# if use models with no pre-processing/post-processing op optimizations
if not use_pr:
im_mean = np.array(self.config.mean).reshape((self.config.channels,
1, 1))
im_std = np.array(self.config.std).reshape((self.config.channels, 1,
1))
# HWC -> CHW, don't use transpose((2, 0, 1))
im = im.swapaxes(1, 2)
im = im.swapaxes(0, 1)
im = im[:, :, :].astype('float32') / 255.0
im -= im_mean
im /= im_std
im = im[np.newaxis, :, :, :]
info = [image_path, im, (ori_w, ori_h)]
return info
# process multiple images with multithreading
def process(self, imgs, use_pr=False):
imgs_data = []
with ThreadPoolExecutor(max_workers=self.config.batch_size) as exe_pool:
tasks = [
exe_pool.submit(self.process_worker, imgs, idx, use_pr)
for idx in range(len(imgs))
]
for task in as_completed(tasks):
imgs_data.append(task.result())
return imgs_data
class Predictor:
def __init__(self, conf_file):
self.config = DeployConfig(conf_file)
self.image_reader = ImageReader(self.config)
if self.config.predictor_mode == "NATIVE":
predictor_config = fluid.core.NativeConfig()
predictor_config.prog_file = self.config.model_file
predictor_config.param_file = self.config.param_file
predictor_config.use_gpu = self.config.use_gpu
predictor_config.device = 0
predictor_config.fraction_of_gpu_memory = 0
elif self.config.predictor_mode == "ANALYSIS":
predictor_config = fluid.core.AnalysisConfig(
self.config.model_file, self.config.param_file)
if self.config.use_gpu:
predictor_config.enable_use_gpu(100, 0)
predictor_config.switch_ir_optim(True)
if gflags.FLAGS.trt_mode != "":
precision_type = trt_precision_map[gflags.FLAGS.trt_mode]
use_calib = (gflags.FLAGS.trt_mode == "int8")
predictor_config.enable_tensorrt_engine(
workspace_size=1 << 30,
max_batch_size=self.config.batch_size,
min_subgraph_size=40,
precision_mode=precision_type,
use_static=False,
use_calib_mode=use_calib)
else:
predictor_config.disable_gpu()
predictor_config.switch_specify_input_names(True)
predictor_config.enable_memory_optim()
self.predictor = fluid.core.create_paddle_predictor(predictor_config)
def create_tensor(self, inputs, batch_size, use_pr=False):
im_tensor = fluid.core.PaddleTensor()
im_tensor.name = "image"
if not use_pr:
im_tensor.shape = [
batch_size, self.config.channels, self.config.eval_crop_size[1],
self.config.eval_crop_size[0]
]
else:
im_tensor.shape = [
batch_size, self.config.eval_crop_size[1],
self.config.eval_crop_size[0], self.config.channels
]
im_tensor.dtype = fluid.core.PaddleDType.FLOAT32
im_tensor.data = fluid.core.PaddleBuf(inputs.ravel().astype("float32"))
return [im_tensor]
# save prediction results and visualization them
def output_result(self, imgs_data, infer_out, use_pr=False):
for idx in range(len(imgs_data)):
img_name = imgs_data[idx][0]
ori_shape = imgs_data[idx][2]
mask = infer_out[idx]
if not use_pr:
mask = np.argmax(mask, axis=0)
mask = mask.astype('uint8')
mask_png = mask
score_png = mask_png[:, :, np.newaxis]
score_png = np.concatenate([score_png] * 3, axis=2)
# visualization score png
color_map = generate_colormap(self.config.class_num)
for i in range(score_png.shape[0]):
for j in range(score_png.shape[1]):
score_png[i, j] = color_map[score_png[i, j, 0]]
# save the mask
# mask of xxx.jpeg will be saved as xxx_jpeg_mask.png
ext_pos = img_name.rfind(".")
img_name_fix = img_name[:ext_pos] + "_" + img_name[ext_pos + 1:]
mask_save_name = img_name_fix + "_mask.png"
cv2.imwrite(mask_save_name, mask_png, [cv2.CV_8UC1])
# save the visualized result
# result of xxx.jpeg will be saved as xxx_jpeg_result.png
vis_result_name = img_name_fix + "_result.png"
result_png = score_png
# if not use_pr:
result_png = cv2.resize(
result_png,
ori_shape,
fx=0,
fy=0,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(vis_result_name, result_png, [cv2.CV_8UC1])
print("save result of [" + img_name + "] done.")
def predict(self, images):
# image reader preprocessing time cost
reader_time = 0
# inference time cost
infer_time = 0
# post_processing: generate mask and visualize it
post_time = 0
# total time cost: preprocessing + inference + postprocessing
total_runtime = 0
# record starting time point
total_start = time.time()
batch_size = self.config.batch_size
use_pr = self.config.use_pr
for i in range(0, len(images), batch_size):
real_batch_size = batch_size
if i + batch_size >= len(images):
real_batch_size = len(images) - i
reader_start = time.time()
img_datas = self.image_reader.process(images[i:i + real_batch_size],
use_pr)
input_data = np.concatenate([item[1] for item in img_datas])
input_data = self.create_tensor(
input_data, real_batch_size, use_pr=use_pr)
reader_end = time.time()
infer_start = time.time()
output_data = self.predictor.run(input_data)[0]
infer_end = time.time()
output_data = output_data.as_ndarray()
post_start = time.time()
self.output_result(img_datas, output_data, use_pr)
post_end = time.time()
reader_time += (reader_end - reader_start)
infer_time += (infer_end - infer_start)
post_time += (post_end - post_start)
# finishing process all images
total_end = time.time()
# compute whole processing time
total_runtime = (total_end - total_start)
print(
"images_num=[%d],preprocessing_time=[%f],infer_time=[%f],postprocessing_time=[%f],total_runtime=[%f]"
% (len(images), reader_time, infer_time, post_time, total_runtime))
def run(deploy_conf, imgs_dir, support_extensions=".jpg|.jpeg"):
# 1. scan and get all images with valid extensions in directory imgs_dir
imgs = get_images_from_dir(imgs_dir, support_extensions)
if len(imgs) == 0:
print("No Image (with extensions : %s) found in [%s]" %
(support_extensions, imgs_dir))
return -1
# 2. create a predictor
seg_predictor = Predictor(deploy_conf)
# 3. do a inference on images
seg_predictor.predict(imgs)
return 0
if __name__ == "__main__":
# 0. parse the arguments
gflags.FLAGS(sys.argv)
if (gflags.FLAGS.conf == "" or gflags.FLAGS.input_dir == ""):
print("Usage: python infer.py --conf=/config/path/to/your/model " +
"--input_dir=/directory/of/your/input/images [--use_pr=True]")
exit(-1)
# set empty to turn off as default
trt_mode = gflags.FLAGS.trt_mode
if (trt_mode != "" and trt_mode not in trt_precision_map):
print(
"Invalid trt_mode [%s], only support[int8, fp16, fp32]" % trt_mode)
exit(-1)
# run inference
run(gflags.FLAGS.conf, gflags.FLAGS.input_dir, gflags.FLAGS.ext)