Feature: Support caffe Interp layer.

mace/python/tools/converter_tool/caffe_converter.py

@@ -183,6 +183,7 @@ class CaffeConverter(base_converter.ConverterInterface):
             'Slice': self.convert_slice,
             'Softmax': self.convert_softmax,
             'InnerProduct': self.convert_fully_connected,
+            'Interp': self.convert_interp,
             'BatchNorm': self.convert_folded_batchnorm,
             'Crop': self.convert_crop,
             'Scale': self.convert_scale,
@@ -555,7 +556,7 @@ class CaffeConverter(base_converter.ConverterInterface):
         axis_arg = op.arg.add()
         axis_arg.name = MaceKeyword.mace_axis_str
         axis_arg.i = 2
-        if param.HasField('axis'):
+        if param.HasField(MaceKeyword.mace_axis_str):
             axis_arg.i = param.axis
         axis_arg.i = 4 + axis_arg.i if axis_arg.i < 0 else axis_arg.i
         offset_arg = op.arg.add()
@@ -573,7 +574,7 @@ class CaffeConverter(base_converter.ConverterInterface):
         axis_arg = op.arg.add()
         axis_arg.name = MaceKeyword.mace_axis_str
         axis_arg.i = 1
-        if param.HasField('axis'):
+        if param.HasField(MaceKeyword.mace_axis_str):
             axis_arg.i = param.axis
         elif param.HasField('concat_dim'):
             axis_arg.i = param.concat_dim
@@ -593,6 +594,18 @@ class CaffeConverter(base_converter.ConverterInterface):
         axis_arg.name = MaceKeyword.mace_axis_str
         axis_arg.i = 1
 
+    def convert_interp(self, caffe_op):
+        op = self.convert_general_op(caffe_op)
+        param = caffe_op.layer.interp_param
+        mace_check(param.HasField("height") and param.HasField("width"),
+                   'Only support bilinear interp with height and width')
+        op.type = MaceOp.ResizeBilinear.name
+
+        size_arg = op.arg.add()
+        size_arg.name = MaceKeyword.mace_resize_size_str
+        size_value = np.array([param.height, param.width], dtype=np.int32)
+        size_arg.ints.extend(size_value)
+
     def convert_fully_connected(self, caffe_op):
         op = self.convert_general_op(caffe_op)
         param = caffe_op.layer.inner_product_param

mace/python/tools/converter_tool/shape_inference.py

@@ -52,6 +52,7 @@ class ShapeInference(object):
             MaceOp.Transpose.name: self.infer_shape_permute,
             MaceOp.PriorBox.name: self.infer_shape_prior_box,
             MaceOp.Reshape.name: self.infer_shape_reshape,
+            MaceOp.ResizeBilinear.name: self.infer_shape_resize_bilinear,
         }
 
         self._net = net
@@ -289,3 +290,17 @@ class ShapeInference(object):
                 output_shape.append(self._output_shape_cache[op.input[0]][i])
             output_shape[axis] = dim
             self.add_output_shape(op, [output_shape])
+
+    def infer_shape_resize_bilinear(self, op):
+        input_shape = self._output_shape_cache[op.input[0]]
+        size = ConverterUtil.get_arg(
+            op, MaceKeyword.mace_resize_size_str).ints
+        if ConverterUtil.data_format(op) == DataFormat.NCHW:
+            output_shape = [input_shape[0], input_shape[1], size[0], size[1]]
+        elif ConverterUtil.data_format(op) == DataFormat.NHWC:
+            output_shape = [input_shape[0], size[0], size[1], input_shape[3]]
+        else:
+            output_shape = []
+            mace_check(False, "format %s is not supported"
+                       % ConverterUtil.data_format(op))
+        self.add_output_shape(op, [output_shape])

third_party/caffe/caffe.proto

@@ -515,6 +515,7 @@ message LayerParameter {
   optional InfogainLossParameter infogain_loss_param = 116;
   optional InnerProductParameter inner_product_param = 117;
   optional InputParameter input_param = 143;
+  optional InterpParameter interp_param = 147;
   optional LogParameter log_param = 134;
   optional LRNParameter lrn_param = 118;
   optional MemoryDataParameter memory_data_param = 119;
@@ -1207,6 +1208,15 @@ message InputParameter {
   repeated BlobShape shape = 1;
 }
 
+message InterpParameter {
+  optional int32 height = 1 [default = 0]; // Height of output
+  optional int32 width = 2 [default = 0]; // Width of output
+  optional int32 zoom_factor = 3 [default = 1]; // zoom factor
+  optional int32 shrink_factor = 4 [default = 1]; // shrink factor
+  optional int32 pad_beg = 5 [default = 0]; // padding at begin of input
+  optional int32 pad_end = 6 [default = 0]; // padding at end of input
+}
+
 // Message that stores parameters used by LogLayer
 message LogParameter {
   // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.