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[Model] Refactor PaddleClas module (#505)

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* Refactor the PaddleClas module

* fix bug

* remove debug code

* clean unused code

* support pybind

* Update fd_tensor.h

* Update fd_tensor.cc

* temporary revert python api

* fix ci error

* fix code style problem
This commit is contained in:
Jason
2022-11-07 19:33:47 +08:00
committed by GitHub
parent a0a8ace174
commit 3589c0fa94
15 changed files with 527 additions and 142 deletions
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79
tests/models/test_mobilenetv2.py
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@@ -22,9 +22,11 @@ import runtime_config as rc
def test_classification_mobilenetv2():
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz"
input_url = "https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg"
input_url1 = "https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg"
input_url2 = "https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00030010.jpeg"
fd.download_and_decompress(model_url, "resources")
fd.download(input_url, "resources")
fd.download(input_url1, "resources")
fd.download(input_url2, "resources")
model_path = "resources/MobileNetV1_x0_25_infer"
model_file = "resources/MobileNetV1_x0_25_infer/inference.pdmodel"
@@ -33,18 +35,67 @@ def test_classification_mobilenetv2():
model = fd.vision.classification.PaddleClasModel(
model_file, params_file, config_file, runtime_option=rc.test_option)
expected_label_ids = [153, 333, 259, 338, 265, 154]
expected_scores = [
expected_label_ids_1 = [153, 333, 259, 338, 265, 154]
expected_scores_1 = [
0.221088, 0.109457, 0.078668, 0.076814, 0.052401, 0.048206
]
expected_label_ids_2 = [80, 23, 93, 99, 143, 7]
expected_scores_2 = [
0.975599, 0.014083, 0.003821, 0.001571, 0.001233, 0.000924
]
# compare diff
im = cv2.imread("./resources/ILSVRC2012_val_00000010.jpeg")
for i in range(2):
result = model.predict(im, topk=6)
diff_label = np.fabs(
np.array(result.label_ids) - np.array(expected_label_ids))
diff_scores = np.fabs(
np.array(result.scores) - np.array(expected_scores))
assert diff_label.max() < 1e-06, "There's difference in classify label."
assert diff_scores.max(
) < 1e-05, "There's difference in classify score."
im1 = cv2.imread("./resources/ILSVRC2012_val_00000010.jpeg")
im2 = cv2.imread("./resources/ILSVRC2012_val_00030010.jpeg")
# for i in range(3000000):
while True:
# test single predict
model.postprocessor.topk = 6
result1 = model.predict(im1)
result2 = model.predict(im2)
diff_label_1 = np.fabs(
np.array(result1.label_ids) - np.array(expected_label_ids_1))
diff_label_2 = np.fabs(
np.array(result2.label_ids) - np.array(expected_label_ids_2))
diff_scores_1 = np.fabs(
np.array(result1.scores) - np.array(expected_scores_1))
diff_scores_2 = np.fabs(
np.array(result2.scores) - np.array(expected_scores_2))
assert diff_label_1.max(
) < 1e-06, "There's difference in classify label 1."
assert diff_scores_1.max(
) < 1e-05, "There's difference in classify score 1."
assert diff_label_2.max(
) < 1e-06, "There's difference in classify label 2."
assert diff_scores_2.max(
) < 1e-05, "There's difference in classify score 2."
# test batch predict
results = model.batch_predict([im1, im2])
result1 = results[0]
result2 = results[1]
diff_label_1 = np.fabs(
np.array(result1.label_ids) - np.array(expected_label_ids_1))
diff_label_2 = np.fabs(
np.array(result2.label_ids) - np.array(expected_label_ids_2))
diff_scores_1 = np.fabs(
np.array(result1.scores) - np.array(expected_scores_1))
diff_scores_2 = np.fabs(
np.array(result2.scores) - np.array(expected_scores_2))
assert diff_label_1.max(
) < 1e-06, "There's difference in classify label 1."
assert diff_scores_1.max(
) < 1e-05, "There's difference in classify score 1."
assert diff_label_2.max(
) < 1e-06, "There's difference in classify label 2."
assert diff_scores_2.max(
) < 1e-05, "There's difference in classify score 2."
if __name__ == "__main__":
test_classification_mobilenetv2()