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predict_cls.py
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predict_cls.py
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# Copyright (c) 2020 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.
import os
import cv2
import numpy as np
from paddleclas.deploy.utils import logger, config
from paddleclas.deploy.utils.predictor import Predictor
from paddleclas.deploy.utils.get_image_list import get_image_list
from paddleclas.deploy.python.preprocess import create_operators
from paddleclas.deploy.python.postprocess import build_postprocess
class ClsPredictor(Predictor):
def __init__(self, config):
super().__init__(config["Global"])
self.preprocess_ops = []
self.postprocess = None
if "PreProcess" in config:
if "transform_ops" in config["PreProcess"]:
self.preprocess_ops = create_operators(config["PreProcess"][
"transform_ops"])
if "PostProcess" in config:
self.postprocess = build_postprocess(config["PostProcess"])
# for whole_chain project to test each repo of paddle
self.benchmark = config["Global"].get("benchmark", False)
if self.benchmark:
import auto_log
import os
pid = os.getpid()
size = config["PreProcess"]["transform_ops"][1]["CropImage"][
"size"]
if config["Global"].get("use_int8", False):
precision = "int8"
elif config["Global"].get("use_fp16", False):
precision = "fp16"
else:
precision = "fp32"
self.auto_logger = auto_log.AutoLogger(
model_name=config["Global"].get("model_name", "cls"),
model_precision=precision,
batch_size=config["Global"].get("batch_size", 1),
data_shape=[3, size, size],
save_path=config["Global"].get("save_log_path",
"./auto_log.log"),
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],
warmup=2)
def predict(self, images):
use_onnx = self.args.get("use_onnx", False)
if not use_onnx:
input_names = self.predictor.get_input_names()
input_tensor = self.predictor.get_input_handle(input_names[0])
output_names = self.predictor.get_output_names()
output_tensor = self.predictor.get_output_handle(output_names[0])
else:
input_names = self.predictor.get_inputs()[0].name
output_names = self.predictor.get_outputs()[0].name
if self.benchmark:
self.auto_logger.times.start()
if not isinstance(images, (list, )):
images = [images]
for idx in range(len(images)):
for ops in self.preprocess_ops:
images[idx] = ops(images[idx])
image = np.array(images)
if self.benchmark:
self.auto_logger.times.stamp()
if not use_onnx:
input_tensor.copy_from_cpu(image)
self.predictor.run()
batch_output = output_tensor.copy_to_cpu()
else:
batch_output = self.predictor.run(
output_names=[output_names],
input_feed={input_names: image})[0]
if self.benchmark:
self.auto_logger.times.stamp()
if self.postprocess is not None:
batch_output = self.postprocess(batch_output)
if self.benchmark:
self.auto_logger.times.end(stamp=True)
return batch_output
def main(config):
cls_predictor = ClsPredictor(config)
image_list = get_image_list(config["Global"]["infer_imgs"])
batch_imgs = []
batch_names = []
cnt = 0
for idx, img_path in enumerate(image_list):
img = cv2.imread(img_path)
if img is None:
logger.warning(
"Image file failed to read and has been skipped. The path: {}".
format(img_path))
else:
img = img[:, :, ::-1]
batch_imgs.append(img)
img_name = os.path.basename(img_path)
batch_names.append(img_name)
cnt += 1
if cnt % config["Global"]["batch_size"] == 0 or (idx + 1
) == len(image_list):
if len(batch_imgs) == 0:
continue
batch_results = cls_predictor.predict(batch_imgs)
for number, result_dict in enumerate(batch_results):
if "PersonAttribute" in config[
"PostProcess"] or "VehicleAttribute" in config[
"PostProcess"] or "TableAttribute" in config[
"PostProcess"] or "FaceAttribute" in config[
"PostProcess"]:
filename = batch_names[number]
print("{}:\t {}".format(filename, result_dict))
else:
filename = batch_names[number]
scores_str = "[{}]".format(", ".join("{:.2f}".format(
r) for r in result_dict["scores"]))
if "class_ids" in result_dict and "label_names" in result_dict:
clas_ids = result_dict["class_ids"]
label_names = result_dict["label_names"]
print(
"{}:\tclass id(s): {}, score(s): {}, label_name(s): {}".
format(filename, clas_ids, scores_str,
label_names))
else:
print("{}:\tscore(s): {}".format(filename, scores_str))
batch_imgs = []
batch_names = []
if cls_predictor.benchmark:
cls_predictor.auto_logger.report()
return
if __name__ == "__main__":
args = config.parse_args()
config = config.get_config(args.config, overrides=args.override, show=True)
main(config)