[Model] add RobustVideoMatting model (#400)

* add yolov5cls * fixed bugs * fixed bugs * fixed preprocess bug * add yolov5cls readme * deal with comments * Add YOLOv5Cls Note * add yolov5cls test * add rvm support * support rvm model * add rvm demo * fixed bugs * add rvm readme * add TRT support * add trt support * add rvm test * add EXPORT.md * rename export.md * rm poros doxyen * deal with comments * deal with comments * add rvm video_mode note Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
2026-05-08 00:14:27 +08:00 · 2022-10-26 14:30:04 +08:00
parent ba501fd963
commit 718698a32a
22 changed files with 1080 additions and 16 deletions
@@ -0,0 +1,101 @@
+# 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