[TFLite] Support depthwise convolution multiplier greater than 1 (#3922)

python/tvm/relay/frontend/tflite.py

@@ -623,8 +623,6 @@ class OperatorConverter(object):
             conv_options = DepthwiseConv2DOptions()
             conv_options.Init(op_options.Bytes, op_options.Pos)
             depth_multiplier = conv_options.DepthMultiplier()
-            assert depth_multiplier == 1, "TF frontend transforms it to be 1 regardless of what " \
-                                          "original value is set to 0.25, 0.5 or anything else"
         else:
             raise tvm.error.OpNotImplemented(
                 'Operator {} is not supported for frontend TFLite.'.format(conv_type))
@@ -636,11 +634,13 @@ class OperatorConverter(object):
         padding = conv_options.Padding()
         fused_activation_fn = conv_options.FusedActivationFunction()
 
-        _, input_h, input_w, _ = input_tensor.tensor.ShapeAsNumpy()
+        _, input_h, input_w, input_c = input_tensor.tensor.ShapeAsNumpy()
 
         if is_depthwise_conv:
-            multiplier, kernel_h, kernel_w, in_channels = weight_tensor.tensor.ShapeAsNumpy()
-            assert multiplier == depth_multiplier
+            # TFLite depthwise convolution kernel layout is:
+            # 1 KH KW C(input_c * depth_multiplier)
+            _, kernel_h, kernel_w, in_channels = weight_tensor.tensor.ShapeAsNumpy()
+            assert in_channels == input_c * depth_multiplier
         else:
             output_channels, kernel_h, kernel_w, _ = weight_tensor.tensor.ShapeAsNumpy()
 
@@ -654,7 +654,7 @@ class OperatorConverter(object):
                   'data_layout': 'NHWC'}
 
         if is_depthwise_conv:
-            params['channels'] = int(in_channels * multiplier)
+            params['channels'] = int(in_channels)
             params['groups'] = int(in_channels)
             params['kernel_layout'] = 'HWOI'
         else:
@@ -669,9 +669,16 @@ class OperatorConverter(object):
         in_expr = self.get_expr(input_tensor_idx)
         weight_value = self.get_tensor_value(weight_tensor)
 
-        # 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))
+        # TFLite kernel layout:
+        # convolution:
+        # OC KH KW IC, we require KH KW IC OC (HWIO)
+        # depthwise convolution:
+        # 1 KH KW C(input_c * depth_multiplier), we require
+        # KH KW IC M (depth_multiplier) (HWOI)
+        if is_depthwise_conv:
+            weight_value = weight_value.reshape(kernel_h, kernel_w, input_c, depth_multiplier)
+        else:
+            weight_value = weight_value.transpose((1, 2, 3, 0))
 
         weight_expr = self.exp_tab.new_const(weight_value, dtype=weight_tensor_type_str)
 

tests/python/frontend/tflite/test_forward.py

@@ -356,6 +356,7 @@ def test_forward_convolution():
     _test_convolution([4, 17, 17, 19], [3, 3, 19, 1], [1, 1], [2, 2], 'VALID', 'NHWC', True)
     _test_convolution([4, 17, 17, 124], [1, 1, 124, 1], [1, 1], [1, 1], 'SAME', 'NHWC', True)
     _test_convolution([4, 17, 17, 12], [3, 3, 12, 1], [1, 1], [2, 2], 'VALID', 'NHWC', True)
+    _test_convolution([4, 17, 17, 12], [3, 3, 12, 2], [1, 1], [2, 2], 'VALID', 'NHWC', True)
 
 
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