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[TFLite] Added ability to infer shapes for arguments #7293

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Jan 20, 2021
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[TFLite] Added ability to infer shapes for arguments #7293

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35 changes: 23 additions & 12 deletions python/tvm/relay/frontend/tflite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -353,7 +353,7 @@ def get_tensor_value(self, tensor_wrapper, is_sparse=False):
data = tensor_wrapper.buffer.DataAsNumpy()

if tensor_wrapper.tensor.ShapeLength() != 0:
shape = to_int_list(tensor_wrapper.tensor.ShapeAsNumpy())
shape = to_int_list(self.get_tensor_shape(tensor_wrapper))
else:
shape = []

Expand Down Expand Up @@ -1417,7 +1417,7 @@ def convert_gather(self, op):
axis = gather_options.Axis()

# Check the indices are with in bounds.
data_shape = to_int_list(input_tensors[0].tensor.ShapeAsNumpy())
data_shape = to_int_list(self.get_tensor_shape(input_tensors[0]))
data_dim = len(data_shape)

axis = data_dim + axis if axis < 0 else axis
Expand Down Expand Up @@ -1535,7 +1535,7 @@ def convert_strided_slice(self, op):
new_axis_mask = options.NewAxisMask()
shrink_axis_mask = options.ShrinkAxisMask()

data_shape = to_int_list(input_tensors[0].tensor.ShapeAsNumpy())
data_shape = to_int_list(self.get_tensor_shape(input_tensors[0]))
data_dim = len(data_shape)
stride_dim = len(stride)

Expand Down Expand Up @@ -1792,7 +1792,7 @@ def convert_fully_connected(self, op):
output_tensor_type = output_tensor.tensor.Type()
output_tensor_type_str = self.get_tensor_type_str(output_tensor_type)

weight_tensor_shape = to_int_list(weight_tensor.tensor.ShapeAsNumpy())
weight_tensor_shape = to_int_list(self.get_tensor_shape(weight_tensor))

# Weight should have only 2 dimensions(TFLite convention)
assert len(weight_tensor_shape) == 2, "Weight should be only 2-dim"
Expand Down Expand Up @@ -1987,16 +1987,16 @@ def convert_conv(self, op, conv_type):
padding = conv_options.Padding()
fused_activation_fn = conv_options.FusedActivationFunction()

_, input_h, input_w, input_c = to_int_list(input_tensor.tensor.ShapeAsNumpy())
_, input_h, input_w, input_c = to_int_list(self.get_tensor_shape(input_tensor))

if is_depthwise_conv:
# TFLite depthwise convolution kernel layout is:
# 1 KH KW C(input_c * depth_multiplier)
_, kernel_h, kernel_w, in_channels = to_int_list(weight_tensor.tensor.ShapeAsNumpy())
_, kernel_h, kernel_w, in_channels = to_int_list(self.get_tensor_shape(weight_tensor))
assert in_channels == input_c * depth_multiplier
else:
output_channels, kernel_h, kernel_w, _ = to_int_list(
weight_tensor.tensor.ShapeAsNumpy()
self.get_tensor_shape(weight_tensor)
)

dilated_kernel_h = dilation_h * (kernel_h - 1) + 1
Expand Down Expand Up @@ -2219,7 +2219,7 @@ def convert_slice(self, op):
size = list(self.get_tensor_value(input_tensors[2]))
# strided_slice(Relay) needs the slice's end indices, not the size
end = size
input_tensor_shape = to_int_list(input_tensor.tensor.ShapeAsNumpy())
input_tensor_shape = to_int_list(self.get_tensor_shape(input_tensor))
input_tensor_rank = len(input_tensor_shape)
for i in range(input_tensor_rank):
if size[i] == -1:
Expand Down Expand Up @@ -2381,7 +2381,8 @@ def convert_pool2d(self, op, pool_type):

in_expr = self.get_expr(input_tensor_idx)

_, input_h, input_w, _ = to_int_list(input_tensor.tensor.ShapeAsNumpy())
_, input_h, input_w, _ = to_int_list(self.get_tensor_shape(input_tensor))

if padding == Padding.VALID:
pass
elif padding == Padding.SAME:
Expand Down Expand Up @@ -2771,12 +2772,13 @@ def convert_transpose_conv(self, op):

# Input (data) Tensor. NHWC layout
input_tensor = input_tensors[2]
_, input_h, input_w, input_c = to_int_list(input_tensor.tensor.ShapeAsNumpy())
_, input_h, input_w, input_c = to_int_list(self.get_tensor_shape(input_tensor))
# Weights tensor. TFLite uses OHWI layout
weights_tensor = input_tensors[1]
out_channels, kernel_h, kernel_w, in_channels = to_int_list(
weights_tensor.tensor.ShapeAsNumpy()
self.get_tensor_shape(weights_tensor)
)

assert (
input_c == in_channels
), "Input channel in the filter should match to channel in the input"
Expand Down Expand Up @@ -3204,7 +3206,7 @@ def convert_matrix_diag(self, op):
), "TFLite MATRIX_DIAG requires diagonal and output tensors' \
scale and zero points to be equal"

shape = to_int_list(diagonal.tensor.ShapeAsNumpy())
shape = to_int_list(self.get_tensor_shape(diagonal))
shape = np.append(shape, shape[-1])
dtype = self.get_tensor_type_str(diagonal.tensor.Type())

Expand Down Expand Up @@ -3265,6 +3267,15 @@ def get_tensor_expr(self, tensor, is_sparse=False):
expr = self.exp_tab.new_const(self.get_tensor_value(tensor, is_sparse), dtype=type_str)
return expr

def get_tensor_shape(self, tensor_wrapper):
""" Returns tensor shape. Infers shape if the shape is empty. """
assert isinstance(tensor_wrapper, TensorWrapper), "Expecting TensorWrapper here"
return (
tensor_wrapper.tensor.ShapeAsNumpy()
if tensor_wrapper.tensor.ShapeLength() > 0
else _infer_shape(self.get_tensor_expr(tensor_wrapper))
)


# pylint: disable=no-else-return
def prepare_dense_matrix_from_sparse(sparse_tensor, sparse_tensor_value, sparse_tensor_type):
Expand Down