mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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185 lines
7.6 KiB
Python
Executable File
185 lines
7.6 KiB
Python
Executable File
# 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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import fastdeploy as fd
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print(fd.__path__)
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import cv2
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import os
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import pickle
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import numpy as np
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import runtime_config as rc
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def test_detection_faster_rcnn():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/faster_rcnn_r50_vd_fpn_2x_coco.tgz"
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input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
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result_url = "https://bj.bcebos.com/fastdeploy/tests/data/faster_rcnn_baseline.pkl"
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fd.download_and_decompress(model_url, "resources")
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fd.download(input_url1, "resources")
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fd.download(result_url, "resources")
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model_path = "resources/faster_rcnn_r50_vd_fpn_2x_coco"
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model_file = os.path.join(model_path, "model.pdmodel")
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params_file = os.path.join(model_path, "model.pdiparams")
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config_file = os.path.join(model_path, "infer_cfg.yml")
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rc.test_option.use_paddle_backend()
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model = fd.vision.detection.FasterRCNN(
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model_file, params_file, config_file, runtime_option=rc.test_option)
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# compare diff
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im1 = cv2.imread("./resources/000000014439.jpg")
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for i in range(2):
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result = model.predict(im1)
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with open("resources/faster_rcnn_baseline.pkl", "rb") as f:
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boxes, scores, label_ids = pickle.load(f)
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pred_boxes = np.array(result.boxes)
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pred_scores = np.array(result.scores)
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pred_label_ids = np.array(result.label_ids)
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diff_boxes = np.fabs(boxes - pred_boxes)
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diff_scores = np.fabs(scores - pred_scores)
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diff_label_ids = np.fabs(label_ids - pred_label_ids)
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print(diff_boxes.max(), diff_scores.max(), diff_label_ids.max())
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score_threshold = 0.0
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assert diff_boxes[scores > score_threshold].max(
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) < 1e-04, "There's diff in boxes."
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assert diff_scores[scores > score_threshold].max(
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) < 1e-04, "There's diff in scores."
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assert diff_label_ids[scores > score_threshold].max(
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) < 1e-04, "There's diff in label_ids."
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# result = model.predict(im1)
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# with open("faster_rcnn_baseline.pkl", "wb") as f:
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# pickle.dump([np.array(result.boxes), np.array(result.scores), np.array(result.label_ids)], f)
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def test_detection_faster_rcnn1():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/faster_rcnn_r50_vd_fpn_2x_coco.tgz"
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input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
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result_url = "https://bj.bcebos.com/fastdeploy/tests/data/faster_rcnn_baseline.pkl"
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fd.download_and_decompress(model_url, "resources")
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fd.download(input_url1, "resources")
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fd.download(result_url, "resources")
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model_path = "resources/faster_rcnn_r50_vd_fpn_2x_coco"
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model_file = os.path.join(model_path, "model.pdmodel")
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params_file = os.path.join(model_path, "model.pdiparams")
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config_file = os.path.join(model_path, "infer_cfg.yml")
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preprocessor = fd.vision.detection.PaddleDetPreprocessor(config_file)
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postprocessor = fd.vision.detection.PaddleDetPostprocessor()
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option = rc.test_option
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option.set_model_path(model_file, params_file)
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option.use_paddle_infer_backend()
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runtime = fd.Runtime(option)
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# compare diff
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for i in range(2):
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im1 = cv2.imread("./resources/000000014439.jpg")
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input_tensors = preprocessor.run([im1])
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output_tensors = runtime.infer({
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"image": input_tensors[0],
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"scale_factor": input_tensors[1],
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"im_shape": input_tensors[2]
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})
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results = postprocessor.run(output_tensors)
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result = results[0]
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with open("resources/faster_rcnn_baseline.pkl", "rb") as f:
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boxes, scores, label_ids = pickle.load(f)
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pred_boxes = np.array(result.boxes)
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pred_scores = np.array(result.scores)
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pred_label_ids = np.array(result.label_ids)
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diff_boxes = np.fabs(boxes - pred_boxes)
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diff_scores = np.fabs(scores - pred_scores)
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diff_label_ids = np.fabs(label_ids - pred_label_ids)
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print(diff_boxes.max(), diff_scores.max(), diff_label_ids.max())
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score_threshold = 0.0
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assert diff_boxes[scores > score_threshold].max(
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) < 1e-04, "There's diff in boxes."
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assert diff_scores[scores > score_threshold].max(
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) < 1e-04, "There's diff in scores."
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assert diff_label_ids[scores > score_threshold].max(
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) < 1e-04, "There's diff in label_ids."
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# test runtime.zero_copy_infer and bind_input_tensor get_output_tensor
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def test_detection_faster_rcnn2():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/faster_rcnn_r50_vd_fpn_2x_coco.tgz"
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input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
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result_url = "https://bj.bcebos.com/fastdeploy/tests/data/faster_rcnn_baseline.pkl"
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fd.download_and_decompress(model_url, "resources")
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fd.download(input_url1, "resources")
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fd.download(result_url, "resources")
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model_path = "resources/faster_rcnn_r50_vd_fpn_2x_coco"
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model_file = os.path.join(model_path, "model.pdmodel")
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params_file = os.path.join(model_path, "model.pdiparams")
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config_file = os.path.join(model_path, "infer_cfg.yml")
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preprocessor = fd.vision.detection.PaddleDetPreprocessor(config_file)
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postprocessor = fd.vision.detection.PaddleDetPostprocessor()
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option = rc.test_option
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option.set_model_path(model_file, params_file)
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option.use_paddle_infer_backend()
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runtime = fd.Runtime(option)
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# compare diff
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input_names = ["image", "scale_factor", "im_shape"]
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output_names = ["concat_12.tmp_0", "concat_8.tmp_0"]
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for i in range(2):
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im1 = cv2.imread("./resources/000000014439.jpg")
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input_tensors = preprocessor.run([im1.copy(), ])
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for i, input_tensor in enumerate(input_tensors):
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runtime.bind_input_tensor(input_names[i], input_tensor)
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runtime.zero_copy_infer()
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output_tensors = []
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for name in output_names:
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output_tensor = runtime.get_output_tensor(name)
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output_tensors.append(output_tensor)
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results = postprocessor.run(output_tensors)
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result = results[0]
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with open("resources/faster_rcnn_baseline.pkl", "rb") as f:
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boxes, scores, label_ids = pickle.load(f)
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pred_boxes = np.array(result.boxes)
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pred_scores = np.array(result.scores)
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pred_label_ids = np.array(result.label_ids)
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diff_boxes = np.fabs(boxes - pred_boxes)
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diff_scores = np.fabs(scores - pred_scores)
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diff_label_ids = np.fabs(label_ids - pred_label_ids)
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print(diff_boxes.max(), diff_scores.max(), diff_label_ids.max())
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score_threshold = 0.0
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assert diff_boxes[scores > score_threshold].max(
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) < 1e-04, "There's diff in boxes."
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assert diff_scores[scores > score_threshold].max(
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) < 1e-04, "There's diff in scores."
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assert diff_label_ids[scores > score_threshold].max(
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) < 1e-04, "There's diff in label_ids."
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if __name__ == "__main__":
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test_detection_faster_rcnn()
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test_detection_faster_rcnn1()
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test_detection_faster_rcnn2()
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