Modifications for Object Detection SSD Python sample (#3976)

* Modifications for Object Detection SSD Sample Python

* Fixes for tests

* Changing the way output checks are processed

inference-engine/ie_bridges/python/sample/object_detection_sample_ssd/object_detection_sample_ssd.py

@@ -73,13 +73,7 @@ def main():
     # -----------------------------------------------------------------------------------------------------
 
     # --------------------------- 3. Read and preprocess input --------------------------------------------
-
-    print("inputs number: " + str(len(net.input_info.keys())))
-    assert len(net.input_info.keys()) == 1, 'Sample supports networks with one input'
-
     for input_key in net.input_info:
-        print("input shape: " + str(net.input_info[input_key].input_data.shape))
-        print("input key: " + input_key)
         if len(net.input_info[input_key].input_data.layout) == 4:
             n, c, h, w = net.input_info[input_key].input_data.shape
 
@@ -96,7 +90,6 @@ def main():
             image = cv2.resize(image, (w, h))
         image = image.transpose((2, 0, 1))  # Change data layout from HWC to CHW
         images[i] = image
-
     # -----------------------------------------------------------------------------------------------------
 
     # --------------------------- 4. Configure input & output ---------------------------------------------
@@ -122,23 +115,30 @@ def main():
     data[input_name] = images
 
     if input_info_name != "":
-        infos = np.ndarray(shape=(n, c), dtype=float)
+        detection_size = net.input_info[input_info_name].input_data.shape[1]
+        infos = np.ndarray(shape=(n, detection_size), dtype=float)
         for i in range(n):
             infos[i, 0] = h
             infos[i, 1] = w
-            infos[i, 2] = 1.0
+            for j in range(2, detection_size):
+                infos[i, j] = 1.0
         data[input_info_name] = infos
 
     # --------------------------- Prepare output blobs ----------------------------------------------------
     log.info('Preparing output blobs')
 
+    output_name, output_info = "", None
     func = ng.function_from_cnn(net)
-    ops = func.get_ordered_ops()
-    output_name, output_info = "", net.outputs[next(iter(net.outputs.keys()))]
-    output_ops = {op.friendly_name : op for op in ops \
-                  if op.friendly_name in net.outputs and op.get_type_name() == "DetectionOutput"}
-    if len(output_ops) != 0:
-        output_name, output_info = output_ops.popitem()
+    if func:
+        ops = func.get_ordered_ops()
+        for op in ops:
+            if op.friendly_name in net.outputs and op.get_type_name() == "DetectionOutput":
+                output_name = op.friendly_name
+                output_info = net.outputs[output_name]
+                break
+    else:
+        output_name = list(net.outputs.keys())[0]
+        output_info = net.outputs[output_name]
 
     if output_name == "":
         log.error("Can't find a DetectionOutput layer in the topology")
@@ -189,12 +189,12 @@ def main():
             else:
                 print()
 
+    tmp_image = cv2.imread(args.input)
     for imid in classes:
-        tmp_image = cv2.imread(args.input)
         for box in boxes[imid]:
             cv2.rectangle(tmp_image, (box[0], box[1]), (box[2], box[3]), (232, 35, 244), 2)
-        cv2.imwrite("out.bmp", tmp_image)
-        log.info("Image out.bmp created!")
+    cv2.imwrite("out.bmp", tmp_image)
+    log.info("Image out.bmp created!")
     # -----------------------------------------------------------------------------------------------------
 
     log.info("Execution successful\n")