[TFLite] Convert TFLite NCHW to NHWC (#3141)

* Convert TFLite NCHW to NHWC

* Minor comment fix

python/tvm/relay/frontend/tflite.py

@@ -209,44 +209,10 @@ class OperatorConverter(object):
         reshape_options = ReshapeOptions()
         reshape_options.Init(op_options.Bytes, op_options.Pos)
         target_shape = reshape_options.NewShapeAsNumpy()
-        input_shape_length = len(input_tensor.tensor.ShapeAsNumpy())
 
         in_expr = self.get_expr(input_tensor_idx)
-
-        if input_shape_length in (1, 2):
-            # The rule is channel first (after N but before H, W).
-            # length of 1 means N*H*W*C, do nothing.
-            # length of 2 means N*H*W, C, do nothing.
-            pass
-        elif input_shape_length == 3:
-            # convert N C H*W to N H*W C
-            in_expr = _op.transpose(in_expr, axes=(0, 2, 1))
-        elif input_shape_length == 4:
-            # convert input to N H W C, then reshape to target shape,
-            # finally convert back if necessary
-            in_expr = _op.transpose(in_expr, axes=(0, 2, 3, 1))
-        else:
-            msg = 'Input shape length {} for operator Reshape is not valid.'
-            raise tvm.error.OpAttributeInvalid(msg.format(input_shape_length))
-
         out = _op.reshape(in_expr, newshape=tuple(target_shape))
 
-        # The rule is channel first.
-        # 1: N*H*W*C
-        # 2: N*H*W, C
-        # 3: N H W C, reshape to N H*W C, transpose to N C H*W
-        # 4: N H W C, transpose to N C H W
-        # add more if we need target shapes in future
-        if len(target_shape) == 1 or len(target_shape) == 2:
-            pass
-        elif len(target_shape) == 3:
-            out = _op.transpose(out, axes=(0, 2, 1))
-        elif len(target_shape) == 4:
-            out = _op.transpose(out, axes=(0, 3, 1, 2))
-        else:
-            raise tvm.error.OpAttributeInvalid(
-                'Length of target shape must be between 1 and 5 for operator Reshape.')
-
         return out
 
     def convert_softmax(self, op):
@@ -269,7 +235,7 @@ class OperatorConverter(object):
         return out
 
     def convert_concatenation(self, op):
-        """ convert TFLite concatenation"""
+        """Convert TFLite concatenation"""
         try:
             from tflite.Operator import Operator
             from tflite.ConcatenationOptions import ConcatenationOptions
@@ -292,15 +258,6 @@ class OperatorConverter(object):
         concatenation_options.Init(op_options.Bytes, op_options.Pos)
         concatenation_axis = concatenation_options.Axis()
         fused_activation_fn = concatenation_options.FusedActivationFunction()
-        input_shape_length = len(input_tensors[0].tensor.ShapeAsNumpy())
-
-        # TFLite is N H W C, our layout is N C H W
-        if input_shape_length <= 4:
-            axis_convert_map = [0] + list(range(2, input_shape_length)) + [1]
-            concatenation_axis = axis_convert_map[concatenation_axis]
-        else:
-            raise NotImplementedError("Not support input shape length {} of concatenatio : "
-                                      .format(str(input_shape_length)))
 
         # with axis in N H W C
         out = _op.concatenate(in_exprs, axis=concatenation_axis)
@@ -336,20 +293,6 @@ class OperatorConverter(object):
             rhs_expr = self.exp_tab.new_const(self.get_tensor_value(rhs_tensor),
                                               dtype=rhs_type_str)
 
-            # In this case, we have to be careful about formatting.
-            input_shape_length = len(rhs_tensor.tensor.ShapeAsNumpy())
-            if input_shape_length in (1, 2):
-                pass
-            elif input_shape_length == 3:
-                # N H*W C to N C H*W
-                rhs_expr = _op.transpose(rhs_expr, axes=(0, 2, 1))
-            elif input_shape_length == 4:
-                # N H W C to N C H W
-                rhs_expr = _op.transpose(rhs_expr, axes=(0, 3, 1, 2))
-            else:
-                msg = 'Input shape length {} for operator ADD is not valid.'
-                raise tvm.error.OpAttributeInvalid(msg.format(input_shape_length))
-
         out = _op.add(lhs_expr, rhs_expr)
         return out
 
@@ -440,46 +383,10 @@ class OperatorConverter(object):
         squeeze_options = SqueezeOptions()
         squeeze_options.Init(op_options.Bytes, op_options.Pos)
         squeeze_axis = squeeze_options.SqueezeDimsAsNumpy()
-        input_shape_length = len(input_tensor.tensor.ShapeAsNumpy())
-        output_shape_length = len(output_tensors[0].tensor.ShapeAsNumpy())
 
         in_expr = self.get_expr(input_tensor_idx)
-
-        # TFLite is N H W C, our layout is N C H W
-        if input_shape_length in (1, 2):
-            # The rule is channel first (after N but before H, W).
-            # length of 1 means N*H*W*C, do nothing.
-            # length of 2 means N*H*W, C, do nothing.
-            pass
-        elif input_shape_length == 3:
-            # convert N C H*W to N H*W C
-            in_expr = _op.transpose(in_expr, axes=(0, 2, 1))
-        elif input_shape_length == 4:
-            # convert input to N H W C, then reshape to target shape,
-            # finally convert back if necessary
-            in_expr = _op.transpose(in_expr, axes=(0, 2, 3, 1))
-        else:
-            msg = 'Input shape length {} for operator Squeeze is not valid.'
-            raise tvm.error.OpAttributeInvalid(msg.format(input_shape_length))
-
         out = _op.squeeze(in_expr, axis=tuple(squeeze_axis))
 
-        # The rule is channel first.
-        # 1: N*H*W*C
-        # 2: N*H*W, C
-        # 3: N H W C, reshape to N H*W C, transpose to N C H*W
-        # 4: N H W C, transpose to N C H W
-        # add more if we need target shapes in future
-        if output_shape_length in (1, 2):
-            pass
-        elif output_shape_length == 3:
-            out = _op.transpose(out, axes=(0, 2, 1))
-        elif output_shape_length == 4:
-            out = _op.transpose(out, axes=(0, 3, 1, 2))
-        else:
-            msg = 'Output shape length {} for operator Squeeze is not valid.'
-            raise tvm.error.OpAttributeInvalid(msg.format(output_shape_length))
-
         return out
 
     def convert_fused_activation_function(self, in_expr, fused_activation_fn):
@@ -562,13 +469,16 @@ class OperatorConverter(object):
         params = {'kernel_size': [kernel_h, kernel_w],
                   'strides': [stride_h, stride_w],
                   'dilation': [dilation_h, dilation_w],
-                  'padding': [0, 0]}
+                  'padding': [0, 0],
+                  'data_layout': 'NHWC'}
 
         if is_depthwise_conv:
             params['channels'] = int(in_channels * multiplier)
             params['groups'] = int(in_channels)
+            params['kernel_layout'] = 'HWOI'
         else:
             params['channels'] = int(output_channels)
+            params['kernel_layout'] = 'HWIO'
 
         # weight tensor type should be UINT8 (quantization) or FLOAT32
         weight_tensor_type = weight_tensor.tensor.Type()
@@ -578,12 +488,9 @@ class OperatorConverter(object):
         in_expr = self.get_expr(input_tensor_idx)
         weight_value = self.get_tensor_value(weight_tensor)
 
-        if is_depthwise_conv:
-            # TFLite is M KH KW IC, we require IC M KH KW
-            weight_value = weight_value.transpose((3, 0, 1, 2))
-        else:
-            # TFLite is OC KH KW IC, we require OC IC KH kW
-            weight_value = weight_value.transpose((0, 3, 1, 2))
+        # TFLite is OC/M KH KW IC, we require KH KW IC OC/M
+        # M means multiplier in depthwise convolution
+        weight_value = weight_value.transpose((1, 2, 3, 0))
 
         weight_expr = self.exp_tab.new_const(weight_value, dtype=weight_tensor_type_str)
 
@@ -592,9 +499,10 @@ class OperatorConverter(object):
         elif padding == Padding.SAME:
             pad_top, pad_bottom = get_pad_value(input_h, dilated_kernel_h, stride_h)
             pad_left, pad_right = get_pad_value(input_w, dilated_kernel_w, stride_w)
-            in_expr = _op.nn.pad(data=in_expr, pad_width=((0, 0), (0, 0),
+            in_expr = _op.nn.pad(data=in_expr, pad_width=((0, 0),
                                                           (pad_top, pad_bottom),
-                                                          (pad_left, pad_right)))
+                                                          (pad_left, pad_right),
+                                                          (0, 0)))
         else:
             raise tvm.error.OpAttributeUnimplemented(
                 'Padding format {} is not supported for operator Conv.'.format(padding))
@@ -610,7 +518,8 @@ class OperatorConverter(object):
             bias_tensor_type_str = self.get_tensor_type_str(bias_tensor_type)
             bias_expr = self.exp_tab.new_const(self.get_tensor_value(bias_tensor),
                                                dtype=bias_tensor_type_str)
-            out = _op.nn.bias_add(out, bias_expr)
+            channel_axis = 3
+            out = _op.nn.bias_add(out, bias_expr, axis=channel_axis)
 
         # If we have fused activations
         if fused_activation_fn != ActivationFunctionType.NONE:
@@ -648,7 +557,8 @@ class OperatorConverter(object):
 
         params = {'pool_size': (filter_h, filter_w),
                   'strides': (stride_h, stride_w),
-                  'padding': [0, 0]}
+                  'padding': [0, 0],
+                  'layout': 'NHWC'}
 
         in_expr = self.get_expr(input_tensor_idx)
 

tutorials/frontend/from_tflite.py

@@ -117,32 +117,23 @@ plt.imshow(resized_image)
 plt.show()
 image_data = np.asarray(resized_image).astype("float32")
 
-# convert HWC to CHW
-image_data = image_data.transpose((2, 0, 1))
-
-# after expand_dims, we have format NCHW
+# after expand_dims, we have format NHWC
 image_data = np.expand_dims(image_data, axis=0)
 
 # preprocess image as described here:
 # https://github.com/tensorflow/models/blob/edb6ed22a801665946c63d650ab9a0b23d98e1b1/research/slim/preprocessing/inception_preprocessing.py#L243
-image_data[:, 0, :, :] = 2.0 / 255.0 * image_data[:, 0, :, :] - 1
-image_data[:, 1, :, :] = 2.0 / 255.0 * image_data[:, 1, :, :] - 1
-image_data[:, 2, :, :] = 2.0 / 255.0 * image_data[:, 2, :, :] - 1
+image_data[:, :, :, 0] = 2.0 / 255.0 * image_data[:, :, :, 0] - 1
+image_data[:, :, :, 1] = 2.0 / 255.0 * image_data[:, :, :, 1] - 1
+image_data[:, :, :, 2] = 2.0 / 255.0 * image_data[:, :, :, 2] - 1
 print('input', image_data.shape)
 
-####################################################################
-#
-# .. note:: Input layout:
-#
-#   Currently, TVM TFLite frontend accepts ``NCHW`` as input layout.
-
 ######################################################################
 # Compile the model with relay
 # ---------------------------------------------
 
 # TFLite input tensor name, shape and type
 input_tensor = "input"
-input_shape = (1, 3, 224, 224)
+input_shape = (1, 224, 224, 3)
 input_dtype = "float32"
 
 # parse TFLite model and convert into Relay computation graph