StridedSlice from TensorFlow

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -820,6 +820,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
       *                  * `*.t7` | `*.net` (Torch, http://torch.ch/)
       *                  * `*.weights` (Darknet, https://pjreddie.com/darknet/)
       *                  * `*.bin` (DLDT, https://software.intel.com/openvino-toolkit)
+      *                  * `*.onnx` (ONNX, https://onnx.ai/)
       * @param[in] config Text file contains network configuration. It could be a
       *                   file with the following extensions:
       *                  * `*.prototxt` (Caffe, http://caffe.berkeleyvision.org/)

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -1423,6 +1423,43 @@ void TFImporter::populateNet(Net dstNet)
 
             connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
         }
+        else if (type == "StridedSlice")
+        {
+            CV_Assert(layer.input_size() == 4);
+            Mat begins = getTensorContent(getConstBlob(layer, value_id, 1));
+            Mat ends = getTensorContent(getConstBlob(layer, value_id, 2));
+            Mat strides = getTensorContent(getConstBlob(layer, value_id, 3));
+            CV_CheckTypeEQ(begins.type(), CV_32SC1, "");
+            CV_CheckTypeEQ(ends.type(), CV_32SC1, "");
+            CV_CheckTypeEQ(strides.type(), CV_32SC1, "");
+            const int num = begins.total();
+            CV_Assert_N(num == ends.total(), num == strides.total());
+
+            int end_mask = getLayerAttr(layer, "end_mask").i();
+            for (int i = 0; i < num; ++i)
+            {
+                if (end_mask & (1 << i))
+                    ends.at<int>(i) = -1;
+                if (strides.at<int>(i) != 1)
+                    CV_Error(Error::StsNotImplemented,
+                             format("StridedSlice with stride %d", strides.at<int>(i)));
+            }
+            if (begins.total() == 4 && getDataLayout(name, data_layouts) == DATA_LAYOUT_NHWC)
+            {
+                // Swap NHWC parameters' order to NCHW.
+                std::swap(begins.at<int>(2), begins.at<int>(3));
+                std::swap(begins.at<int>(1), begins.at<int>(2));
+                std::swap(ends.at<int>(2), ends.at<int>(3));
+                std::swap(ends.at<int>(1), ends.at<int>(2));
+            }
+            layerParams.set("begin", DictValue::arrayInt((int*)begins.data, begins.total()));
+            layerParams.set("end", DictValue::arrayInt((int*)ends.data, ends.total()));
+
+            int id = dstNet.addLayer(name, "Slice", layerParams);
+            layer_id[name] = id;
+
+            connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
+        }
         else if (type == "Mul")
         {
             bool haveConst = false;

modules/dnn/test/test_onnx_importer.cpp

@@ -248,7 +248,7 @@ TEST_P(Test_ONNX_layers, Reshape)
 TEST_P(Test_ONNX_layers, Softmax)
 {
     testONNXModels("softmax");
-    testONNXModels("log_softmax");
+    testONNXModels("log_softmax", npy, 0, 0, false, false);
 }
 
 INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());

modules/dnn/test/test_tf_importer.cpp

@@ -663,6 +663,7 @@ TEST_P(Test_TensorFlow_layers, slice)
         (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
         throw SkipTestException("");
     runTensorFlowNet("slice_4d");
+    runTensorFlowNet("strided_slice");
 }
 
 TEST_P(Test_TensorFlow_layers, softmax)

samples/dnn/tf_text_graph_faster_rcnn.py

@@ -31,7 +31,13 @@ def createFasterRCNNGraph(modelPath, configPath, outputPath):
     aspect_ratios = [float(ar) for ar in grid_anchor_generator['aspect_ratios']]
     width_stride = float(grid_anchor_generator['width_stride'][0])
     height_stride = float(grid_anchor_generator['height_stride'][0])
-    features_stride = float(config['feature_extractor'][0]['first_stage_features_stride'][0])
+
+    feature_extractor = config['feature_extractor'][0]
+    if 'type' in feature_extractor and feature_extractor['type'][0] == 'faster_rcnn_nas':
+        features_stride = 16.0
+    else:
+        features_stride = float(feature_extractor['first_stage_features_stride'][0])
+
     first_stage_nms_iou_threshold = float(config['first_stage_nms_iou_threshold'][0])
     first_stage_max_proposals = int(config['first_stage_max_proposals'][0])