FrozenGene commented on a change in pull request #4440: [TFLite] Add
transpose_conv to TFLite parser
URL: https://github.com/apache/incubator-tvm/pull/4440#discussion_r351583281
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File path: python/tvm/relay/frontend/tflite.py
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@@ -1370,6 +1371,85 @@ def convert_prelu(self, op):
return out
+ def convert_transpose_conv(self, op):
+ """Convert TFLite TRANSPOSE_CONV"""
+ try:
+ from tflite.BuiltinOptions import BuiltinOptions
+ from tflite.TensorType import TensorType
+ from tflite.Operator import Operator
+ from tflite.TransposeConvOptions import TransposeConvOptions
+ from tflite.Padding import Padding
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ assert isinstance(op, Operator)
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) == 3, "input tensors length should be 3"
+
+ # Input (data) Tensor. NHWC layout
+ input_tensor = input_tensors[2]
+ _, _, _, input_c = input_tensor.tensor.ShapeAsNumpy()
+ # Weights tensor. TFLite uses OHWI layout
+ weights_tensor = input_tensors[1]
+ out_channels, kernel_h, kernel_w, in_channels =
weights_tensor.tensor.ShapeAsNumpy()
+ assert input_c == in_channels, \
+ "Input channel in the filter should match to channel in the input"
+ # output_shape Tensor. NHWC layout
+ output_shape_tensor = input_tensors[0]
+
+ output_tensors = self.get_output_tensors(op)
+ assert len(output_tensors) == 1, "output tensors length should be 1"
+ output_tensor = output_tensors[0]
+ output_tensor_type = output_tensor.tensor.Type()
+ output_tensor_type_str = self.get_tensor_type_str(output_tensor_type)
+
+ assert op.BuiltinOptionsType() == BuiltinOptions.TransposeConvOptions
+ op_options = op.BuiltinOptions()
+ deconv_options = TransposeConvOptions()
+ deconv_options.Init(op_options.Bytes, op_options.Pos)
+
+ padding = deconv_options.Padding()
+ stride_h = deconv_options.StrideH()
+ stride_w = deconv_options.StrideW()
+ assert padding in (Padding.VALID, Padding.SAME), \
+ 'Padding format {} is not supported for operator
TRANSPOSE_CONV'.format(padding)
+
+ # Data
+ in_expr = self.get_expr(input_tensor.tensor_idx)
+
+ # Weights
+ weights_tensor_type = weights_tensor.tensor.Type()
+ # weights tensor type should be UINT8 (quantization) or FLOAT32
+ assert weights_tensor_type in (TensorType.UINT8, TensorType.FLOAT32)
+ weight_tensor_type_str = self.get_tensor_type_str(weights_tensor_type)
+ weight_value_ohwi = self.get_tensor_value(weights_tensor)
+ # Relay kernel_layout should be OIHW
+ # Relay weights layout should be different from kernel_layout - it
should be IOHW
+ weight_value_iohw = np.transpose(weight_value_ohwi, (3, 0, 1, 2))
+ weight_expr_iohw = self.exp_tab.new_const(weight_value_iohw,
dtype=weight_tensor_type_str)
+
+ # Output shape value
+ output_shape_value = self.get_tensor_value(output_shape_tensor)
+ # Relay expects filter output channel to match to output tensor
channel.
+ assert out_channels == output_shape_value[3], \
+ "Output channel in the filter should match to channel in the
output_shape"
+
+ # TF frontend supports 'SAME' padding for kernel 1x1 only. Lets do the
same here
+ if padding == Padding.SAME:
+ assert (kernel_h, kernel_w) == (1, 1), \
+ "SAME padding is supported for kernel (1,1) only"
Review comment:
if kh kw is 3x3, what is the current error msg?
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