u99127 commented on a change in pull request #5848:
URL: https://github.com/apache/incubator-tvm/pull/5848#discussion_r448548518
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -651,12 +701,43 @@ def convert_shape(self, op):
def convert_relu(self, op):
"""Convert TFLite ReLU"""
+ try:
+ from tflite.ActivationFunctionType import ActivationFunctionType
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
Review comment:
I think this is unnecessary given the import of ActivationFunctionType
in the constructor
[here](https://github.com/apache/incubator-tvm/blob/b979bf6a7630ada055fff2d65e1cd0f8d55bb6a0/python/tvm/relay/frontend/tflite.py#L54)
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -692,6 +773,11 @@ def _hard_swish(data):
def convert_relu6(self, op):
"""Convert TFLite ReLU6"""
+ try:
+ from tflite.ActivationFunctionType import ActivationFunctionType
Review comment:
Same as relu, I think this is unnecessary given the import of
ActivationFunctionType in the constructor
[here](https://github.com/apache/incubator-tvm/blob/b979bf6a7630ada055fff2d65e1cd0f8d55bb6a0/python/tvm/relay/frontend/tflite.py#L54)
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -263,21 +305,29 @@ def get_tensor_value(self, tensor_wrapper):
except ImportError:
raise ImportError("The tflite package must be installed")
+ # Read the data from the buffer. Also extract the shape.
+ # The shape is used later to reshape the data.
+ data = tensor_wrapper.buffer.DataAsNumpy()
+ shape = tensor_wrapper.tensor.ShapeAsNumpy()
+
+ # When TFLite buffer is of size 1 (scalar), then TFLite tensor shape
is set to 0.
+ # Therefore, we set the shape to 1 for numpy reshape to work. Set
shape to 1 if the data is
+ # a scalar type
+ if data.size == 1 and isinstance(shape, int) and shape == 0:
+ shape = (1,)
+
+ if tensor_wrapper.tensor.Type() == TensorType.INT8:
Review comment:
Minor nit and this should really be credited to Dmitriy Smirnov.
https://github.com/d-smirnov
the condition here could well be pulled out into a helper function that has
a dictionary to help us map from TensorType to numpy type ?
Would make the code much cleaner and reduce duplication.
i.e. something like
def get_tensor_type_as_numpy(self, tensor_wrapper):
"""Returns np.dtype out of TensorType"""
"""Returns np.dtype out of TensorType"""
assert isinstance(tensor_wrapper, TensorWrapper)
try:
from tflite.TensorType import TensorType
return { TensorType.UINT8: np.uint8,
TensorType.INT8: np.int8,
TensorType.FLOAT32: np.float32,
TensorType.INT32: np.int32,
TensorType.INT64: np.int64,
TensorType.BOOL: np.bool_ }[
tensor_wrapper.tensor.Type() ]
except ImportError:
raise ImportError("The tflite package must be installed")
except KeyError:
raise NotImplementedError("Tensor type '{}' currently not
supported"
.format(tensor_wrapper.tensor.Type()))
def get_tensor_value(self, tensor_wrapper):
"""Get tensor buffer value from given tensor wrapper"""
assert isinstance(tensor_wrapper, TensorWrapper)
value_type = self.get_tensor_type_as_numpy( tensor_wrapper )
return np.frombuffer( tensor_wrapper.buffer.DataAsNumpy(),
dtype=value_type ).reshape(
tensor_wrapper.tensor.ShapeAsNumpy() \
if 0 != tensor_wrapper.tensor.ShapeLength() \
else [] )
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -787,14 +897,30 @@ def test_forward_convolution():
_test_convolution([4, 17, 17, 124], [1, 1, 124, 19], [1, 1], [1, 1],
'SAME', 'NHWC', quantized=quantized)
_test_convolution([4, 17, 17, 12], [3, 3, 12, 32], [1, 1], [2, 2],
'VALID', 'NHWC', quantized=quantized)
- # depthwise convolution
- _test_convolution([4, 8, 8, 176], [1, 1, 176, 1], [1, 1], [1, 1], 'SAME',
'NHWC', True)
- _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)
- # dephtwise convolution with single input channel
- _test_convolution([1, 76, 64, 1], [9, 5, 1, 96], [1, 1], [1, 1], 'SAME',
'NHWC', True)
+ # depthwise convolution
+ _test_convolution([4, 8, 8, 176], [1, 1, 176, 1], [1, 1], [1, 1],
'SAME', 'NHWC', True, quantized=quantized)
+ _test_convolution([4, 17, 17, 19], [3, 3, 19, 1], [1, 1], [2, 2],
'VALID', 'NHWC', True, quantized=quantized)
+ _test_convolution([4, 17, 17, 124], [1, 1, 124, 1], [1, 1], [1, 1],
'SAME', 'NHWC', True, quantized=quantized)
+ _test_convolution([4, 17, 17, 12], [3, 3, 12, 1], [1, 1], [2, 2],
'VALID', 'NHWC', True, quantized=quantized)
+ _test_convolution([4, 17, 17, 12], [3, 3, 12, 2], [1, 1], [2, 2],
'VALID', 'NHWC', True, quantized=quantized)
+ # dephtwise convolution with single input channel
Review comment:
dephtwise / depthwise.
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