Added support for Resizing op

README.md

@@ -43,6 +43,7 @@ python3 -m keras2ncnn -i SOME_H5DF_FILE.h5 -o DIR_TO_SAVE_NCNN_PARAM --plot_grap
 - Maximum
 - TensorFlowOpLayer (Mul with constant)
 - Permute (Need more testing)
+- Resizing (Requires tensorflow/keras for conversion)
 
 ## Ops that will be dropped by converter
 - Dropout

keras2ncnn/h5df_parser.py

@@ -39,6 +39,24 @@ def __init__(self, h5_file):
         self.model_config = json.loads(self.__decode(model_config_raw))
         self.keras_version = self.get_keras_version()
 
+        try:
+            import tensorflow.keras as keras
+        except ModuleNotFoundError:
+            try:
+                import keras
+            except ModuleNotFoundError:
+                pass
+
+        try:
+            model = keras.models.load_model(h5_file)
+            self.layer_input_shapes = {layer.name: layer.input_shape for layer in model.layers}
+        except NameError:
+            self.layer_input_shapes = None
+        except Exception as e:
+            print(f"Exception {e} occurred when attempting to load model in keras. Will attempt conversion without "
+                  f"additional information.")
+            self.layer_input_shapes = None
+
         if self.keras_version != '1':
             weight_layers = self.f['model_weights']
         else:
@@ -124,10 +142,16 @@ def parse_graph(self, graph_helper):
                     inbound_nodes = graph_helper.get_graph_tail()
 
                 graph_helper.node(layer_name, inbound_nodes)
-                graph_helper.set_node_attr(
-                    layer_name, {
-                        'layer': layers, 'weight': self.find_weights_root(
-                            layer_name)})
+                if self.layer_input_shapes is None:
+                    graph_helper.set_node_attr(
+                        layer_name, {
+                            'layer': layers, 'weight': self.find_weights_root(
+                                layer_name)})
+                else:
+                    graph_helper.set_node_attr(
+                        layer_name, {
+                            'layer': layers, 'weight': self.find_weights_root(
+                                layer_name), 'input_shape': self.layer_input_shapes[layer_name]})
 
     def parse_model_graph(self, model_layers, graph_helper):
         for layer in model_layers:
@@ -136,7 +160,13 @@ def parse_model_graph(self, model_layers, graph_helper):
                 inbound_nodes = graph_helper.get_graph_tail()
 
             graph_helper.node(layer['name'], inbound_nodes)
-            graph_helper.set_node_attr(
-                layer['name'], {
-                    'layer': layer, 'weight': self.find_weights_root(
-                        layer['name'])})
+            if self.layer_input_shapes is None:
+                graph_helper.set_node_attr(
+                    layer['name'], {
+                        'layer': layer, 'weight': self.find_weights_root(
+                            layer['name'])})
+            else:
+                graph_helper.set_node_attr(
+                    layer['name'], {
+                        'layer': layer, 'weight': self.find_weights_root(
+                            layer['name']), 'input_shape': self.layer_input_shapes[layer['name']]})

keras2ncnn/keras_converter.py

@@ -504,6 +504,55 @@ def BatchNormalization_helper(
                     bn_params['bn_moving_variance'],
                     bn_params['bn_beta']]})
 
+    def Resizing_helper(
+            self,
+            layer,
+            keras_graph_helper,
+            ncnn_graph_helper,
+            ncnn_helper):
+        if "crop_to_aspect_ratio" in layer["layer"]["config"] and \
+                layer["layer"]["config"]["crop_to_aspect_ratio"]:
+            print(f'[ERROR] Crop to aspect ratio is currently not supported for Resizing layers.')
+            frameinfo = inspect.getframeinfo(inspect.currentframe())
+            print('Failed to convert at %s:%d %s()' %
+                  (frameinfo.filename, frameinfo.lineno, frameinfo.function))
+            sys.exit(-1)
+        # Set the interpolation type and make sure it is supported.
+        if layer["layer"]["config"]["interpolation"].lower() == "bilinear":
+            resize_type = 2
+        elif layer["layer"]["config"]["interpolation"].lower() == "nearest":
+            resize_type = 1
+        else:
+            print(f'[ERROR] Activation type {layer.interpolation} is is not supported yet. '
+                  f'Currently, only bilinear and nearest are supported as Resizing layer interpolation types.')
+            frameinfo = inspect.getframeinfo(inspect.currentframe())
+            print('Failed to convert at %s:%d %s()' %
+                  (frameinfo.filename, frameinfo.lineno, frameinfo.function))
+            sys.exit(-1)
+        if "input_shape" in layer:
+            height_scale = layer["layer"]["config"]["height"] / layer["input_shape"][1]
+            width_scale = layer["layer"]["config"]["width"] / layer["input_shape"][2]
+            output_height = layer["layer"]["config"]["height"]
+            output_width = layer["layer"]["config"]["width"]
+        else:
+            print(f'[ERROR] Could not find input shape for Resizing layer. Please make sure you have either tensorflow '
+                  f'or keras installed.')
+            frameinfo = inspect.getframeinfo(inspect.currentframe())
+            print('Failed to convert at %s:%d %s()' %
+                  (frameinfo.filename, frameinfo.lineno, frameinfo.function))
+            sys.exit(-1)
+        ncnn_graph_attr = ncnn_helper.dump_args(
+            'Interp', resize_type=resize_type, height_scale=height_scale, width_scale=width_scale,
+            output_height=output_height, output_width=output_width
+        )
+        ncnn_graph_helper.node(
+            layer['layer']['name'],
+            keras_graph_helper.get_node_inbounds(
+                layer['layer']['name']))
+        ncnn_graph_helper.set_node_attr(
+            layer['layer']['name'], {
+                'type': 'Interp', 'param': ncnn_graph_attr, 'binary': []})
+
     def Add_helper(
             self,
             layer,