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Update model tests
This commit is contained in:
jiangjiajun
@@ -1,42 +0,0 @@
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# 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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import cv2
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import os
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import numpy as np
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def test_facealignment_pfld():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/pfld-106-lite.onnx"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png"
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output_url = "https://bj.bcebos.com/paddlehub/fastdeploy/result_landmarks.npy"
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fd.download(model_url, ".")
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fd.download(input_url, ".")
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fd.download(output_url, ".")
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model_path = "pfld-106-lite.onnx"
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# use ORT
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runtime_option = fd.RuntimeOption()
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runtime_option.use_ort_backend()
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model = fd.vision.facealign.PFLD(model_path, runtime_option=runtime_option)
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# compare diff
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im = cv2.imread("./facealign_input.png")
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result = model.predict(im.copy())
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expect = np.load("./result_landmarks.npy")
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diff = np.fabs(np.array(result.landmarks) - expect)
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thres = 1e-04
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assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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@@ -1,109 +0,0 @@
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# 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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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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def test_matting_ppmatting():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "./PP-Matting-512"
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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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, "deploy.yaml")
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model = fd.vision.matting.PPMatting(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片抠图结果
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im = cv2.imread("./matting_input.jpg")
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result = model.predict(im.copy())
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pkl_url = "https://bj.bcebos.com/fastdeploy/tests/ppmatting_result.pkl"
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if pkl_url:
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fd.download(pkl_url, ".")
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with open("./ppmatting_result.pkl", "rb") as f:
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baseline = pickle.load(f)
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diff = np.fabs(np.array(result.alpha) - np.array(baseline))
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thres = 1e-05
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assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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def test_matting_ppmodnet():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_MobileNetV2.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "./PPModnet_MobileNetV2"
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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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, "deploy.yaml")
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model = fd.vision.matting.PPMatting(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片抠图结果
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im = cv2.imread("./matting_input.jpg")
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result = model.predict(im.copy())
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pkl_url = "https://bj.bcebos.com/fastdeploy/tests/ppmodnet_result.pkl"
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if pkl_url:
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fd.download(pkl_url, ".")
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with open("./ppmodnet_result.pkl", "rb") as f:
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baseline = pickle.load(f)
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diff = np.fabs(np.array(result.alpha) - np.array(baseline))
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thres = 1e-05
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assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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def test_matting_pphumanmatting():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/PPHumanMatting.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "./PPHumanMatting"
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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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, "deploy.yaml")
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model = fd.vision.matting.PPMatting(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片抠图结果
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im = cv2.imread("./matting_input.jpg")
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result = model.predict(im.copy())
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pkl_url = "https://bj.bcebos.com/fastdeploy/tests/pphumanmatting_result.pkl"
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if pkl_url:
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fd.download(pkl_url, ".")
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with open("./pphumanmatting_result.pkl", "rb") as f:
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baseline = pickle.load(f)
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diff = np.fabs(np.array(result.alpha) - np.array(baseline))
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thres = 1e-05
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assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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@@ -1,100 +0,0 @@
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# 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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import cv2
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import os
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import numpy as np
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def test_keypointdetection_pptinypose():
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pp_tinypose_model_url = "https://bj.bcebos.com/fastdeploy/tests/PP_TinyPose_256x192_test.tgz"
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fd.download_and_decompress(pp_tinypose_model_url, ".")
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model_path = "./PP_TinyPose_256x192_test"
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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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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image_file = os.path.join(model_path, "hrnet_demo.jpg")
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baseline_file = os.path.join(model_path, "baseline.npy")
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model = fd.vision.keypointdetection.PPTinyPose(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片关键点
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im = cv2.imread(image_file)
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result = model.predict(im)
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result = np.concatenate(
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(np.array(result.keypoints), np.array(result.scores)[:, np.newaxis]),
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axis=1)
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baseline = np.load(baseline_file)
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diff = np.fabs(result - np.array(baseline))
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thres = 1e-05
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assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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print("No diff")
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def test_keypointdetection_det_keypoint_unite():
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det_keypoint_unite_model_url = "https://bj.bcebos.com/fastdeploy/tests/PicoDet_320x320_TinyPose_256x192_test.tgz"
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fd.download_and_decompress(det_keypoint_unite_model_url, ".")
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model_path = "./PicoDet_320x320_TinyPose_256x192_test"
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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tinypose_model_file = os.path.join(
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model_path, "PP_TinyPose_256x192_infer/model.pdmodel")
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tinypose_params_file = os.path.join(
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model_path, "PP_TinyPose_256x192_infer/model.pdiparams")
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tinypose_config_file = os.path.join(
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model_path, "PP_TinyPose_256x192_infer/infer_cfg.yml")
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picodet_model_file = os.path.join(
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model_path, "PP_PicoDet_V2_S_Pedestrian_320x320_infer/model.pdmodel")
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picodet_params_file = os.path.join(
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model_path, "PP_PicoDet_V2_S_Pedestrian_320x320_infer/model.pdiparams")
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picodet_config_file = os.path.join(
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model_path, "PP_PicoDet_V2_S_Pedestrian_320x320_infer/infer_cfg.yml")
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image_file = os.path.join(model_path, "000000018491.jpg")
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# image_file = os.path.join(model_path, "hrnet_demo.jpg")
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baseline_file = os.path.join(model_path, "baseline.npy")
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tinypose_model = fd.vision.keypointdetection.PPTinyPose(
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tinypose_model_file,
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tinypose_params_file,
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tinypose_config_file,
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runtime_option=runtime_option)
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det_model = fd.vision.detection.PicoDet(
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picodet_model_file,
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picodet_params_file,
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picodet_config_file,
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runtime_option=runtime_option)
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# 预测图片关键点
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im = cv2.imread(image_file)
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pipeline = fd.pipeline.PPTinyPose(det_model, tinypose_model)
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pipeline.detection_model_score_threshold = 0.5
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result = pipeline.predict(im)
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print(result)
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result = np.concatenate(
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(np.array(result.keypoints), np.array(result.scores)[:, np.newaxis]),
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axis=1)
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print(result)
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np.save("baseline.npy", result)
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baseline = np.load(baseline_file)
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diff = np.fabs(result - np.array(baseline))
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thres = 1e-05
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assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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print("No diff")
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@@ -1,89 +0,0 @@
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# 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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import cv2
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import os
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import numpy as np
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import pickle
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def test_pptracking_cpu():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/pptracking.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "pptracking/fairmot_hrnetv2_w18_dlafpn_30e_576x320"
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# use default backend
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runtime_option = fd.RuntimeOption()
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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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model = fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=runtime_option)
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cap = cv2.VideoCapture("./person.mp4")
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frame_id = 0
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while True:
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_, frame = cap.read()
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if frame is None:
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break
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result = model.predict(frame)
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# compare diff
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expect = pickle.load(open("pptracking/frame" + str(frame_id) + ".pkl", "rb"))
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diff_boxes = np.fabs(np.array(expect["boxes"]) - np.array(result.boxes))
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diff_scores = np.fabs(np.array(expect["scores"]) - np.array(result.scores))
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diff = max(diff_boxes.max(), diff_scores.max())
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thres = 1e-05
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assert diff < thres, "The label diff is %f, which is bigger than %f" % (diff, thres)
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frame_id = frame_id + 1
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cv2.waitKey(30)
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if frame_id >= 10:
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cap.release()
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cv2.destroyAllWindows()
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break
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def test_pptracking_gpu():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/pptracking.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "pptracking/fairmot_hrnetv2_w18_dlafpn_30e_576x320"
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runtime_option = fd.RuntimeOption()
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runtime_option.use_gpu()
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# Not supported trt backend, up to now
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# runtime_option.use_trt_backend()
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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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model = fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=runtime_option)
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cap = cv2.VideoCapture("./person.mp4")
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frame_id = 0
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while True:
|
|
||||||
_, frame = cap.read()
|
|
||||||
if frame is None:
|
|
||||||
break
|
|
||||||
result = model.predict(frame)
|
|
||||||
# compare diff
|
|
||||||
expect = pickle.load(open("pptracking/frame" + str(frame_id) + ".pkl", "rb"))
|
|
||||||
diff_boxes = np.fabs(np.array(expect["boxes"]) - np.array(result.boxes))
|
|
||||||
diff_scores = np.fabs(np.array(expect["scores"]) - np.array(result.scores))
|
|
||||||
diff = max(diff_boxes.max(), diff_scores.max())
|
|
||||||
thres = 1e-05
|
|
||||||
assert diff < thres, "The label diff is %f, which is bigger than %f" % (diff, thres)
|
|
||||||
frame_id = frame_id + 1
|
|
||||||
cv2.waitKey(30)
|
|
||||||
if frame_id >= 10:
|
|
||||||
cap.release()
|
|
||||||
cv2.destroyAllWindows()
|
|
||||||
break
|
|
||||||
@@ -1,96 +0,0 @@
|
|||||||
# 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
|
|
||||||
|
|
||||||
model_url = "https://bj.bcebos.com/fastdeploy/tests/yolov6_quant.tgz"
|
|
||||||
fd.download_and_decompress(model_url, ".")
|
|
||||||
|
|
||||||
|
|
||||||
def test_quant_mkldnn():
|
|
||||||
model_path = "./yolov6_quant"
|
|
||||||
model_file = os.path.join(model_path, "model.pdmodel")
|
|
||||||
params_file = os.path.join(model_path, "model.pdiparams")
|
|
||||||
|
|
||||||
input_file = os.path.join(model_path, "input.npy")
|
|
||||||
output_file = os.path.join(model_path, "mkldnn_output.npy")
|
|
||||||
|
|
||||||
option = fd.RuntimeOption()
|
|
||||||
option.use_paddle_backend()
|
|
||||||
option.use_cpu()
|
|
||||||
|
|
||||||
option.set_model_path(model_file, params_file)
|
|
||||||
runtime = fd.Runtime(option)
|
|
||||||
input_name = runtime.get_input_info(0).name
|
|
||||||
data = np.load(input_file)
|
|
||||||
outs = runtime.infer({input_name: data})
|
|
||||||
expected = np.load(output_file)
|
|
||||||
diff = np.fabs(outs[0] - expected)
|
|
||||||
thres = 1e-05
|
|
||||||
assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
|
|
||||||
diff.max(), thres)
|
|
||||||
|
|
||||||
|
|
||||||
def test_quant_ort():
|
|
||||||
model_path = "./yolov6_quant"
|
|
||||||
model_file = os.path.join(model_path, "model.pdmodel")
|
|
||||||
params_file = os.path.join(model_path, "model.pdiparams")
|
|
||||||
|
|
||||||
input_file = os.path.join(model_path, "input.npy")
|
|
||||||
output_file = os.path.join(model_path, "ort_output.npy")
|
|
||||||
|
|
||||||
option = fd.RuntimeOption()
|
|
||||||
option.use_ort_backend()
|
|
||||||
option.use_cpu()
|
|
||||||
|
|
||||||
option.set_ort_graph_opt_level(1)
|
|
||||||
|
|
||||||
option.set_model_path(model_file, params_file)
|
|
||||||
runtime = fd.Runtime(option)
|
|
||||||
input_name = runtime.get_input_info(0).name
|
|
||||||
data = np.load(input_file)
|
|
||||||
outs = runtime.infer({input_name: data})
|
|
||||||
expected = np.load(output_file)
|
|
||||||
diff = np.fabs(outs[0] - expected)
|
|
||||||
thres = 1e-05
|
|
||||||
assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
|
|
||||||
diff.max(), thres)
|
|
||||||
|
|
||||||
|
|
||||||
def test_quant_trt():
|
|
||||||
model_path = "./yolov6_quant"
|
|
||||||
model_file = os.path.join(model_path, "model.pdmodel")
|
|
||||||
params_file = os.path.join(model_path, "model.pdiparams")
|
|
||||||
|
|
||||||
input_file = os.path.join(model_path, "input.npy")
|
|
||||||
output_file = os.path.join(model_path, "trt_output.npy")
|
|
||||||
|
|
||||||
option = fd.RuntimeOption()
|
|
||||||
option.use_trt_backend()
|
|
||||||
option.use_gpu()
|
|
||||||
|
|
||||||
option.set_model_path(model_file, params_file)
|
|
||||||
runtime = fd.Runtime(option)
|
|
||||||
input_name = runtime.get_input_info(0).name
|
|
||||||
data = np.load(input_file)
|
|
||||||
outs = runtime.infer({input_name: data})
|
|
||||||
expected = np.load(output_file)
|
|
||||||
diff = np.fabs(outs[0] - expected)
|
|
||||||
thres = 1e-05
|
|
||||||
assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
|
|
||||||
diff.max(), thres)
|
|
||||||
@@ -1,101 +0,0 @@
|
|||||||
# 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
|
|
||||||
|
|
||||||
|
|
||||||
def test_matting_rvm_cpu():
|
|
||||||
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/rvm.tgz"
|
|
||||||
input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/video.mp4"
|
|
||||||
fd.download_and_decompress(model_url, ".")
|
|
||||||
fd.download(input_url, ".")
|
|
||||||
model_path = "rvm/rvm_mobilenetv3_fp32.onnx"
|
|
||||||
# use ORT
|
|
||||||
runtime_option = fd.RuntimeOption()
|
|
||||||
runtime_option.use_ort_backend()
|
|
||||||
model = fd.vision.matting.RobustVideoMatting(
|
|
||||||
model_path, runtime_option=runtime_option)
|
|
||||||
|
|
||||||
cap = cv2.VideoCapture(input_url)
|
|
||||||
|
|
||||||
frame_id = 0
|
|
||||||
while True:
|
|
||||||
_, frame = cap.read()
|
|
||||||
if frame is None:
|
|
||||||
break
|
|
||||||
result = model.predict(frame)
|
|
||||||
# compare diff
|
|
||||||
expect_alpha = np.load("rvm/result_alpha_" + str(frame_id) + ".npy")
|
|
||||||
result_alpha = np.array(result.alpha).reshape(1920, 1080)
|
|
||||||
diff = np.fabs(expect_alpha - result_alpha)
|
|
||||||
thres = 1e-05
|
|
||||||
assert diff.max(
|
|
||||||
) < thres, "The label diff is %f, which is bigger than %f" % (
|
|
||||||
diff.max(), thres)
|
|
||||||
frame_id = frame_id + 1
|
|
||||||
cv2.waitKey(30)
|
|
||||||
if frame_id >= 10:
|
|
||||||
cap.release()
|
|
||||||
cv2.destroyAllWindows()
|
|
||||||
break
|
|
||||||
|
|
||||||
|
|
||||||
def test_matting_rvm_gpu_trt():
|
|
||||||
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/rvm.tgz"
|
|
||||||
input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/video.mp4"
|
|
||||||
fd.download_and_decompress(model_url, ".")
|
|
||||||
fd.download(input_url, ".")
|
|
||||||
model_path = "rvm/rvm_mobilenetv3_trt.onnx"
|
|
||||||
# use TRT
|
|
||||||
runtime_option = fd.RuntimeOption()
|
|
||||||
runtime_option.use_gpu()
|
|
||||||
runtime_option.use_trt_backend()
|
|
||||||
runtime_option.set_trt_input_shape("src", [1, 3, 1920, 1080])
|
|
||||||
runtime_option.set_trt_input_shape("r1i", [1, 1, 1, 1], [1, 16, 240, 135],
|
|
||||||
[1, 16, 240, 135])
|
|
||||||
runtime_option.set_trt_input_shape("r2i", [1, 1, 1, 1], [1, 20, 120, 68],
|
|
||||||
[1, 20, 120, 68])
|
|
||||||
runtime_option.set_trt_input_shape("r3i", [1, 1, 1, 1], [1, 40, 60, 34],
|
|
||||||
[1, 40, 60, 34])
|
|
||||||
runtime_option.set_trt_input_shape("r4i", [1, 1, 1, 1], [1, 64, 30, 17],
|
|
||||||
[1, 64, 30, 17])
|
|
||||||
model = fd.vision.matting.RobustVideoMatting(
|
|
||||||
model_path, runtime_option=runtime_option)
|
|
||||||
|
|
||||||
cap = cv2.VideoCapture("./video.mp4")
|
|
||||||
|
|
||||||
frame_id = 0
|
|
||||||
while True:
|
|
||||||
_, frame = cap.read()
|
|
||||||
if frame is None:
|
|
||||||
break
|
|
||||||
result = model.predict(frame)
|
|
||||||
# compare diff
|
|
||||||
expect_alpha = np.load("rvm/result_alpha_" + str(frame_id) + ".npy")
|
|
||||||
result_alpha = np.array(result.alpha).reshape(1920, 1080)
|
|
||||||
diff = np.fabs(expect_alpha - result_alpha)
|
|
||||||
thres = 1e-04
|
|
||||||
assert diff.max(
|
|
||||||
) < thres, "The label diff is %f, which is bigger than %f" % (
|
|
||||||
diff.max(), thres)
|
|
||||||
frame_id = frame_id + 1
|
|
||||||
cv2.waitKey(30)
|
|
||||||
if frame_id >= 10:
|
|
||||||
cap.release()
|
|
||||||
cv2.destroyAllWindows()
|
|
||||||
break
|
|
||||||
@@ -1,49 +0,0 @@
|
|||||||
# 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
|
|
||||||
|
|
||||||
|
|
||||||
def test_classification_yolov5cls():
|
|
||||||
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n-cls.tgz"
|
|
||||||
input_url = "https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg"
|
|
||||||
fd.download_and_decompress(model_url, ".")
|
|
||||||
fd.download(input_url, ".")
|
|
||||||
model_path = "yolov5n-cls/yolov5n-cls.onnx"
|
|
||||||
# use ORT
|
|
||||||
runtime_option = fd.RuntimeOption()
|
|
||||||
runtime_option.use_ort_backend()
|
|
||||||
model = fd.vision.classification.YOLOv5Cls(
|
|
||||||
model_path, runtime_option=runtime_option)
|
|
||||||
|
|
||||||
# compare diff
|
|
||||||
im = cv2.imread("./ILSVRC2012_val_00000010.jpeg")
|
|
||||||
result = model.predict(im.copy(), topk=5)
|
|
||||||
with open("yolov5n-cls/result.pkl", "rb") as f:
|
|
||||||
expect = pickle.load(f)
|
|
||||||
|
|
||||||
diff_label = np.fabs(
|
|
||||||
np.array(result.label_ids) - np.array(expect["labels"]))
|
|
||||||
diff_score = np.fabs(np.array(result.scores) - np.array(expect["scores"]))
|
|
||||||
thres = 1e-05
|
|
||||||
assert diff_label.max(
|
|
||||||
) < thres, "The label diff is %f, which is bigger than %f" % (
|
|
||||||
diff_label.max(), thres)
|
|
||||||
assert diff_score.max(
|
|
||||||
) < thres, "The score diff is %f, which is bigger than %f" % (
|
|
||||||
diff_score.max(), thres)
|
|
||||||
Reference in New Issue
Block a user