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new b3730e5 [FRONTEND][TFLite] Fully connected op conversion made in
sync with TFLite (#5510)
b3730e5 is described below
commit b3730e575ac0ab4a54fe6984bde35408e93b5e6f
Author: ANSHUMAN TRIPATHY <anshuma...@huawei.com>
AuthorDate: Thu May 7 16:06:20 2020 +0530
[FRONTEND][TFLite] Fully connected op conversion made in sync with TFLite
(#5510)
* [FRONTEND][TFLite] Fully connected op conversion made in sync with TFLite
* [1] Test case added
* [2] Review comments handled
* [3] Prints removed
---
python/tvm/relay/frontend/tflite.py | 33 ++++++++++++++++++++--------
tests/python/frontend/tflite/test_forward.py | 26 ++++++++++++++++++++++
2 files changed, 50 insertions(+), 9 deletions(-)
diff --git a/python/tvm/relay/frontend/tflite.py
b/python/tvm/relay/frontend/tflite.py
index a55a57f..bb456f1 100644
--- a/python/tvm/relay/frontend/tflite.py
+++ b/python/tvm/relay/frontend/tflite.py
@@ -1331,16 +1331,28 @@ class OperatorConverter(object):
input_tensor_shape = input_tensor.tensor.ShapeAsNumpy()
weight_tensor_shape = weight_tensor.tensor.ShapeAsNumpy()
- # reshape input tensor from N H W C to N H*W*C
- input_size_per_batch = 1
- for s in range(1, len(input_tensor_shape)):
- input_size_per_batch *= input_tensor_shape[s]
- assert input_size_per_batch == weight_tensor_shape[1], \
- "input size and weight size are mismatched"
- target_shape = tuple((input_tensor_shape[0], input_size_per_batch))
+ # Weight should have only 2 dimensions(TFLite convention)
+ assert len(weight_tensor_shape) == 2, "Weight should be only 2-dim"
+
+ # Input shape: [i_batch_size, ..., n_inputs]
+ # Filter shape: [n_inputs, n_units]
+ #
+ # As we will transform Fully_Connected Input to Dense Op inputs as
below
+ # Dense expected Input shape: [batch_size, n_units]
+ # Dense expected Weight shape: [out_dim, n_units]
+ # Dense output shape: [batch_size, out_dim]
+ # So it is evident that input shape: [batch_size = input_size /
n_units, n_units]
+ input_size = 1
+ for _, shape in enumerate(input_tensor_shape):
+ input_size *= shape
+
+ # First get the batch size
+ batch_size = int(input_size / weight_tensor_shape[1])
+ target_shape = tuple((batch_size, weight_tensor_shape[1]))
in_expr = self.get_expr(input_tensor_idx)
in_expr = _op.reshape(in_expr, target_shape)
+ #TODO: Change the output shape calculation based on keep_dim option
assert op.BuiltinOptionsType() == BuiltinOptions.FullyConnectedOptions
op_options = op.BuiltinOptions()
fully_connected_options = FullyConnectedOptions()
@@ -1352,8 +1364,11 @@ class OperatorConverter(object):
assert weight_tensor_type in (TensorType.UINT8, TensorType.FLOAT32)
weight_tensor_type_str = self.get_tensor_type_str(weight_tensor_type)
- weight_value = self.get_tensor_value(weight_tensor)
- weight_expr = self.exp_tab.new_const(weight_value,
dtype=weight_tensor_type_str)
+ if self.has_expr(weight_tensor.tensor_idx):
+ weight_expr = self.get_expr(weight_tensor.tensor_idx)
+ else:
+ weight_value = self.get_tensor_value(weight_tensor)
+ weight_expr = self.exp_tab.new_const(weight_value,
dtype=weight_tensor_type_str)
weight_shape = _infer_shape(weight_expr)
if input_tensor.qnn_params:
diff --git a/tests/python/frontend/tflite/test_forward.py
b/tests/python/frontend/tflite/test_forward.py
index 7257748..20a077f 100644
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -420,6 +420,29 @@ def test_forward_cast():
_test_cast(np.arange(6.0, dtype=np.int32).reshape((1, 6)),
cast_dtype=tf.int64)
#######################################################################
+# Batch Mat Mul
+# ----
+def _test_batch_matmul(A_shape, B_shape, dtype, adjoint_a=False,
adjoint_b=False):
+ with tf.Graph().as_default():
+ A = array_ops.placeholder(shape=A_shape, dtype=dtype, name='A')
+ B = array_ops.placeholder(shape=B_shape, dtype=dtype, name='B')
+ result = math_ops.matmul(A, B, adjoint_a=adjoint_a,
+ adjoint_b=adjoint_b, name='batchmatmul')
+
+ A_np = np.random.uniform(high=5.0, size=A_shape).astype(dtype)
+ B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
+ compare_tflite_with_tvm([A_np, B_np], [A.name, B.name], [A, B],
[result])
+
+
+def test_forward_batch_matmul():
+ """ BATCH_MAT_MUL """
+ _test_batch_matmul((3, 5, 4), (3, 4, 5), 'float32')
+ _test_batch_matmul((3, 5, 4), (3, 4, 5), 'float32', True, True)
+ _test_batch_matmul((3, 5, 4), (3, 5, 4), 'float32', True, False)
+ _test_batch_matmul((3, 5, 4), (3, 5, 4), 'float32', False, True)
+ _test_batch_matmul((2, 3, 4, 5, 6), (2, 3, 4, 6, 5), 'float32')
+
+#######################################################################
# Tile
# ----
@@ -2001,6 +2024,9 @@ if __name__ == '__main__':
# Cast
test_forward_cast()
+ # BatchMatMul
+ test_forward_batch_matmul()
+
# Tile
test_forward_tile()
