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test_ppyolo.py
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# 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.
import fastdeploy as fd
import cv2
import os
import pickle
import numpy as np
import runtime_config as rc
def test_detection_ppyolo():
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/ppyolov2_r101vd_dcn_365e_coco.tgz"
input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
result_url = "https://bj.bcebos.com/fastdeploy/tests/data/ppyolo_baseline.pkl"
fd.download_and_decompress(model_url, "resources")
fd.download(input_url1, "resources")
fd.download(result_url, "resources")
model_path = "resources/ppyolov2_r101vd_dcn_365e_coco"
model_file = os.path.join(model_path, "model.pdmodel")
params_file = os.path.join(model_path, "model.pdiparams")
config_file = os.path.join(model_path, "infer_cfg.yml")
rc.test_option.use_paddle_backend()
model = fd.vision.detection.PPYOLO(
model_file, params_file, config_file, runtime_option=rc.test_option)
# compare diff
im1 = cv2.imread("./resources/000000014439.jpg")
for i in range(2):
result = model.predict(im1)
with open("resources/ppyolo_baseline.pkl", "rb") as f:
boxes, scores, label_ids = pickle.load(f)
pred_boxes = np.array(result.boxes)
pred_scores = np.array(result.scores)
pred_label_ids = np.array(result.label_ids)
diff_boxes = np.fabs(boxes - pred_boxes)
diff_scores = np.fabs(scores - pred_scores)
diff_label_ids = np.fabs(label_ids - pred_label_ids)
print(diff_boxes.max(), diff_scores.max(), diff_label_ids.max())
score_threshold = 0.0
assert diff_boxes[scores > score_threshold].max(
) < 1e-04, "There's diff in boxes."
assert diff_scores[scores > score_threshold].max(
) < 1e-04, "There's diff in scores."
assert diff_label_ids[scores > score_threshold].max(
) < 1e-04, "There's diff in label_ids."
# result = model.predict(im1)
# with open("ppyolo_baseline.pkl", "wb") as f:
# pickle.dump([np.array(result.boxes), np.array(result.scores), np.array(result.label_ids)], f)
def test_detection_ppyolo1():
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/ppyolov2_r101vd_dcn_365e_coco.tgz"
input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
result_url = "https://bj.bcebos.com/fastdeploy/tests/data/ppyolo_baseline.pkl"
fd.download_and_decompress(model_url, "resources")
fd.download(input_url1, "resources")
fd.download(result_url, "resources")
model_path = "resources/ppyolov2_r101vd_dcn_365e_coco"
model_file = os.path.join(model_path, "model.pdmodel")
params_file = os.path.join(model_path, "model.pdiparams")
config_file = os.path.join(model_path, "infer_cfg.yml")
preprocessor = fd.vision.detection.PaddleDetPreprocessor(config_file)
postprocessor = fd.vision.detection.PaddleDetPostprocessor()
option = rc.test_option
option.use_paddle_backend()
option.set_model_path(model_file, params_file)
runtime = fd.Runtime(option);
# compare diff
im1 = cv2.imread("./resources/000000014439.jpg")
for i in range(2):
input_tensors = preprocessor.run([im1])
output_tensors = runtime.infer({"image": input_tensors[0], "scale_factor": input_tensors[1], "im_shape": input_tensors[2]})
results = postprocessor.run(output_tensors)
result = results[0]
with open("resources/ppyolo_baseline.pkl", "rb") as f:
boxes, scores, label_ids = pickle.load(f)
pred_boxes = np.array(result.boxes)
pred_scores = np.array(result.scores)
pred_label_ids = np.array(result.label_ids)
diff_boxes = np.fabs(boxes - pred_boxes)
diff_scores = np.fabs(scores - pred_scores)
diff_label_ids = np.fabs(label_ids - pred_label_ids)
print(diff_boxes.max(), diff_scores.max(), diff_label_ids.max())
with open("resources/dump_result.pkl", "wb") as f:
pickle.dump([pred_boxes, pred_scores, pred_label_ids], f)
score_threshold = 0.0
assert diff_boxes[scores > score_threshold].max(
) < 1e-01, "There's diff in boxes."
assert diff_scores[scores > score_threshold].max(
) < 1e-03, "There's diff in scores."
assert diff_label_ids[scores > score_threshold].max(
) < 1e-04, "There's diff in label_ids."
if __name__ == "__main__":
test_detection_ppyolo()
test_detection_ppyolo1()