Merge branch 'feature_wuch' into 'master'

fix dsp converter

See merge request !492

mace/python/tools/tf_dsp_converter_lib.py

@@ -95,12 +95,19 @@ def add_shape_const_node(net_def, op, values, name):
 
 
 def convert_op_outputs(mace_op_def, tf_op):
+    mace_op_def.out_max_byte_size.extend(
+        [max_elem_size(output) for output in tf_op.outputs])
     mace_op_def.output_type.extend(
         [tf_dtype_2_mace_dtype(output.dtype) for output in tf_op.outputs])
     output_shapes = []
     for output in tf_op.outputs:
         output_shape = mace_pb2.OutputShape()
-        output_shape.dims.extend(output.shape.as_list())
+        shape_list = output.shape.as_list()
+        if not shape_list:
+            shape_list = [1]
+        elif len(shape_list) == 2:
+            shape_list = [1, 1, shape_list[0], shape_list[1]]
+        output_shape.dims.extend(shape_list)
         output_shapes.append(output_shape)
     mace_op_def.output_shape.extend(output_shapes)
 
@@ -159,8 +166,6 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
             op_def.input.append(input_tensor.name)
             op_def.input.extend([t.name for t in s2b_op.inputs[1:]])
             op_def.input.extend([min_tensor.name, max_tensor.name])
-            op_def.out_max_byte_size.extend(
-                [max_elem_size(out) for out in quantize_op.outputs])
             convert_op_outputs(op_def, quantize_op)
         elif len(first_op.outputs) > 0 and \
             first_op.type == 'QuantizedReshape' and \
@@ -193,9 +198,71 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
             op_def.type = dsp_ops.map_nn_op('QuantizedSoftmax')
             op_def.input.extend(
                 [input_tensor.name, min_tensor.name, max_tensor.name])
-            op_def.out_max_byte_size.extend(
-                [max_elem_size(out) for out in quantize_reshape_op.outputs])
             convert_op_outputs(op_def, quantize_reshape_op)
+        # remove Squeeze
+        elif len(first_op.outputs) > 0 and \
+            first_op.type == 'Requantize' and \
+            len(first_op.outputs[0].consumers()) > 0 and \
+            first_op.outputs[0].consumers()[0].type == 'Dequantize' and \
+            len(first_op.outputs[0].consumers()[0].outputs[0].consumers()) \
+            > 0 and \
+            first_op.outputs[0].consumers()[0].outputs[0].consumers()[0].type \
+                == 'Squeeze':
+            dequantize_op = first_op.outputs[0].consumers()[0]
+            squeeze_op = dequantize_op.outputs[0].consumers()[0]
+            reshape_op = squeeze_op.outputs[0].consumers()[0]
+            min_op = reshape_op.outputs[0].consumers()[0]
+            max_op = reshape_op.outputs[0].consumers()[1]
+            quantize_op = min_op.outputs[0].consumers()[0]
+
+            resolved_ops.add(dequantize_op.name)
+            resolved_ops.add(squeeze_op.name)
+            resolved_ops.add(reshape_op.name)
+            resolved_ops.add(min_op.name)
+            resolved_ops.add(max_op.name)
+            resolved_ops.add(quantize_op.name)
+
+            op_def.name = quantize_op.name
+            op_def.input.extend([t.name for t in first_op.inputs])
+            convert_op_outputs(op_def, quantize_op)
+
+            # Squeeze -> Softmax
+            next_op = quantize_op.outputs[0].consumers()[0] \
+                if len(quantize_op.outputs) > 0 else None
+            dequantize_op = next_op.outputs[0].consumers()[0] \
+                if next_op and len(next_op.outputs) > 0 and \
+                next_op.type == 'QuantizedReshape' and \
+                len(next_op.outputs[0].consumers()) > 0 else None
+            softmax_op = dequantize_op.outputs[0].consumers()[0]\
+                if dequantize_op and len(dequantize_op.outputs) > 0 and \
+                dequantize_op.type == 'Dequantize' and \
+                len(dequantize_op.outputs[0].consumers()) > 0 else None
+            if softmax_op and softmax_op.type == 'Softmax':
+                reshape_op = softmax_op.outputs[0].consumers()[0]
+                min_op = reshape_op.outputs[0].consumers()[0]
+                max_op = reshape_op.outputs[0].consumers()[1]
+                quantize_op = min_op.outputs[0].consumers()[0]
+                quantize_reshape_op = quantize_op.outputs[0].consumers()[0]
+
+                resolved_ops.add(next_op.name)
+                resolved_ops.add(dequantize_op.name)
+                resolved_ops.add(softmax_op.name)
+                resolved_ops.add(reshape_op.name)
+                resolved_ops.add(min_op.name)
+                resolved_ops.add(max_op.name)
+                resolved_ops.add(quantize_op.name)
+                resolved_ops.add(quantize_reshape_op.name)
+
+                softmax_op_def = net_def.op.add()
+                softmax_op_def.padding = padding_mode['NA']
+                softmax_op_def.name = quantize_reshape_op.name
+                softmax_op_def.type = dsp_ops.map_nn_op('QuantizedSoftmax')
+                softmax_op_def.input.extend([
+                    get_tensor_name_from_op(op_def.name, 0),
+                    get_tensor_name_from_op(op_def.name, 1),
+                    get_tensor_name_from_op(op_def.name, 2)])
+                convert_op_outputs(softmax_op_def, quantize_reshape_op)
+
         elif len(first_op.outputs) > 0 and first_op.type == 'Dequantize' and \
             len(first_op.outputs[0].consumers()) > 0 and \
                 first_op.outputs[0].consumers()[0].type == 'Tanh':
@@ -220,8 +287,6 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
                 op_def.type = dsp_ops.map_nn_op('Quantized' + tanh_op.type)
                 op_def.input.extend(
                     [input_tensor.name, min_tensor.name, max_tensor.name])
-                op_def.out_max_byte_size.extend(
-                    [max_elem_size(out) for out in quantize_op.outputs])
                 convert_op_outputs(op_def, quantize_op)
             # tanh is last op
             else:
@@ -251,8 +316,6 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
                     get_tensor_name_from_op(op_def.name, 1),
                     get_tensor_name_from_op(op_def.name, 2)
                 ])
-                new_tanh_op_def.out_max_byte_size.extend(
-                    [max_elem_size(tanh_op.outputs[0])])
                 convert_op_outputs(new_tanh_op_def, tanh_op)
         elif has_padding_and_strides(first_op):
             op_def.padding = padding_mode[first_op.get_attr('padding')]
@@ -266,19 +329,13 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
             strides_tensor = add_shape_const_node(net_def, first_op, strides,
                                                   'strides')
             op_def.input.extend([strides_tensor])
-            op_def.out_max_byte_size.extend(
-                [max_elem_size(out) for out in first_op.outputs])
             convert_op_outputs(op_def, first_op)
         elif is_node_flatten_reshape(first_op):
             op_def.type = 'Flatten'
-            op_def.input.extend([t.name for t in first_op.inputs])
-            op_def.out_max_byte_size.extend(
-                [max_elem_size(out) for out in first_op.outputs])
+            op_def.input.extend([first_op.inputs[0].name])
             convert_op_outputs(op_def, first_op)
         elif dsp_ops.has_op(first_op.type):
             op_def.input.extend([t.name for t in first_op.inputs])
-            op_def.out_max_byte_size.extend(
-                [max_elem_size(out) for out in first_op.outputs])
             convert_op_outputs(op_def, first_op)
         else:
             raise Exception('Unsupported op: ', first_op)
@@ -478,7 +535,8 @@ def fuse_quantize(net_def, input_node, output_node):
                 skip_ops = skip_ops.union(
                     [flatten_op.name, minf_op.name, maxf_op.name])
                 skip_tensors = skip_tensors.union(
-                    [flatten_op.input[1], minf_op.input[1], maxf_op.input[1]])
+                    [minf_op.input[0], maxf_op.input[0],
+                     quantize_op.input[1], quantize_op.input[2]])
                 quantize_op.type = 'AutoQuantize'
                 del quantize_op.input[1:]