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new ab83993 [TFLITE]Activation functions support (#4978)
ab83993 is described below
commit ab839933093b37e4ee455e49a31e6a57b6156c5d
Author: Samuel <siju.sam...@huawei.com>
AuthorDate: Wed Mar 11 08:38:48 2020 +0530
[TFLITE]Activation functions support (#4978)
* [TFLITE]elu, leaky_relu, lrn, log_softmax activation functions
* removed ops present in pr 4805
* review_comments updated
---
python/tvm/relay/frontend/tflite.py | 162 ++++++++++++++++++---------
tests/python/frontend/tflite/test_forward.py | 54 +++++++--
2 files changed, 154 insertions(+), 62 deletions(-)
diff --git a/python/tvm/relay/frontend/tflite.py
b/python/tvm/relay/frontend/tflite.py
index c2ec4d4..c820713 100644
--- a/python/tvm/relay/frontend/tflite.py
+++ b/python/tvm/relay/frontend/tflite.py
@@ -62,70 +62,72 @@ class OperatorConverter(object):
# Add more operators
self.convert_map = {
'ABS': self.convert_abs,
+ 'ADD': self.convert_add,
+ 'AVERAGE_POOL_2D': self.convert_average_pool2d,
+ 'BATCH_TO_SPACE_ND': self.convert_batch_to_space_nd,
+ 'CAST': self.convert_cast,
+ 'CEIL': self.convert_ceil,
+ 'CONCATENATION': self.convert_concatenation,
+ 'CONV_2D': self.convert_conv2d,
+ 'COS': self.convert_cos,
+ 'DEPTHWISE_CONV_2D': self.convert_depthwise_conv2d,
+ 'DETECTION_POSTPROCESS': self.convert_detection_postprocess,
+ 'DIV': self.convert_div,
+ 'ELU': self.convert_elu,
+ 'EQUAL': self.convert_equal,
'EXP': self.convert_exp,
+ 'FLOOR_DIV': self.convert_floor_div,
+ 'FLOOR_MOD': self.convert_floor_mod,
'FLOOR': self.convert_floor,
- 'CEIL': self.convert_ceil,
+ 'FULLY_CONNECTED': self.convert_fully_connected,
+ 'GREATER_EQUAL': self.convert_greater_equal,
+ 'GREATER': self.convert_greater,
+ 'L2_NORMALIZATION': self.convert_l2_normalization,
+ 'LESS_EQUAL': self.convert_less_equal,
+ 'LESS': self.convert_less,
+ 'LOCAL_RESPONSE_NORMALIZATION': self.convert_lrn,
'LOG': self.convert_log,
- 'SIN': self.convert_sin,
- 'COS': self.convert_cos,
- 'TAN': self.convert_tan,
- 'SQRT': self.convert_sqrt,
- 'RSQRT': self.convert_rsqrt,
+ 'LOGICAL_AND': self.convert_logical_and,
+ 'LOGICAL_OR': self.convert_logical_or,
+ 'LOGISTIC': self.convert_logistic,
+ 'MAX_POOL_2D': self.convert_max_pool2d,
+ 'MAXIMUM': self.convert_maximum,
+ 'MEAN': self._convert_reduce_mean,
+ 'MINIMUM': self.convert_minimum,
+ 'MIRROR_PAD': self.convert_mirror_pad,
+ 'MUL': self.convert_mul,
'NEG': self.convert_neg,
- 'CONV_2D': self.convert_conv2d,
- 'DEPTHWISE_CONV_2D': self.convert_depthwise_conv2d,
- 'AVERAGE_POOL_2D': self.convert_average_pool2d,
+ 'NOT_EQUAL': self.convert_not_equal,
+ 'PACK': self.convert_pack,
+ 'PAD': self.convert_pad,
+ 'POW': self.convert_pow,
+ 'PRELU': self.convert_prelu,
+ 'REDUCE_MAX': self._convert_reduce_max,
+ 'REDUCE_MIN': self._convert_reduce_min,
+ 'REDUCE_PROD': self._convert_reduce_prod,
+ 'RELU':self.convert_relu,
'RESHAPE': self.convert_reshape,
'RESIZE_BILINEAR': self.convert_resize_bilinear,
'RESIZE_NEAREST_NEIGHBOR': self.convert_resize_nearest_neighbor,
+ 'RSQRT': self.convert_rsqrt,
+ 'SIN': self.convert_sin,
+ 'SLICE': self.convert_slice,
'SOFTMAX': self.convert_softmax,
+ 'SPACE_TO_BATCH_ND': self.convert_space_to_batch_nd,
+ 'SPLIT': self.convert_split,
+ 'SQRT': self.convert_sqrt,
+ 'SQUARE': self.convert_square,
+ 'SQUARED_DIFFERENCE': self.convert_squared_difference,
'SQUEEZE': self.convert_squeeze,
- 'MAX_POOL_2D': self.convert_max_pool2d,
- 'CONCATENATION': self.convert_concatenation,
- 'ADD': self.convert_add,
'SUB': self.convert_sub,
- 'MUL': self.convert_mul,
- 'DIV': self.convert_div,
- 'POW': self.convert_pow,
- 'MAXIMUM': self.convert_maximum,
- 'MINIMUM': self.convert_minimum,
- 'GREATER': self.convert_greater,
- 'GREATER_EQUAL': self.convert_greater_equal,
- 'LESS': self.convert_less,
- 'LESS_EQUAL': self.convert_less_equal,
- 'EQUAL': self.convert_equal,
- 'NOT_EQUAL': self.convert_not_equal,
- 'ZEROS_LIKE': self.convert_zeros_like,
- 'REDUCE_MIN': self._convert_reduce_min,
- 'REDUCE_MAX': self._convert_reduce_max,
- 'MEAN': self._convert_reduce_mean,
- 'REDUCE_PROD': self._convert_reduce_prod,
'SUM': self._convert_reduce_sum,
- 'FULLY_CONNECTED': self.convert_fully_connected,
- 'PAD': self.convert_pad,
- 'MIRROR_PAD': self.convert_mirror_pad,
- 'PACK': self.convert_pack,
- 'UNPACK': self.convert_unpack,
- 'LOGISTIC': self.convert_logistic,
+ 'TAN': self.convert_tan,
'TANH':self.convert_tanh,
- 'RELU':self.convert_relu,
- 'SPLIT': self.convert_split,
- 'SLICE': self.convert_slice,
- 'TRANSPOSE': self.convert_transpose,
- 'CAST': self.convert_cast,
'TILE': self.convert_tile,
- 'BATCH_TO_SPACE_ND': self.convert_batch_to_space_nd,
- 'SPACE_TO_BATCH_ND': self.convert_space_to_batch_nd,
- 'PRELU': self.convert_prelu,
'TRANSPOSE_CONV': self.convert_transpose_conv,
- 'SQUARED_DIFFERENCE': self.convert_squared_difference,
- 'LOGICAL_AND': self.convert_logical_and,
- 'LOGICAL_OR': self.convert_logical_or,
- 'DETECTION_POSTPROCESS': self.convert_detection_postprocess,
- 'SQUARE': self.convert_square,
- 'L2_NORMALIZATION': self.convert_l2_normalization,
- 'FLOOR_DIV': self.convert_floor_div,
- 'FLOOR_MOD': self.convert_floor_mod,
+ 'TRANSPOSE': self.convert_transpose,
+ 'UNPACK': self.convert_unpack,
+ 'ZEROS_LIKE': self.convert_zeros_like,
}
def check_unsupported_ops(self):
@@ -455,6 +457,43 @@ class OperatorConverter(object):
return out
+ def convert_lrn(self, op):
+ """Convert TFLite LOCAL_RESPONSE_NORMALIZATION """
+ try:
+ from tflite.Operator import Operator
+ from tflite.BuiltinOptions import BuiltinOptions
+ from tflite.LocalResponseNormalizationOptions import
LocalResponseNormalizationOptions
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ assert isinstance(op, Operator)
+ if self.is_quantized(op):
+ raise tvm.error.OpNotImplemented(
+ 'TFlite quantized LRN operator is not supported yet.')
+
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) == 1, "input tensors length should be 1"
+ input_tensor = input_tensors[0]
+ in_expr = self.get_expr(input_tensor.tensor_idx)
+
+ output_tensors = self.get_output_tensors(op)
+ assert len(output_tensors) == 1, "output tensors length should be 1"
+
+ assert op.BuiltinOptionsType() ==
BuiltinOptions.LocalResponseNormalizationOptions
+ op_options = op.BuiltinOptions()
+ lrn_options = LocalResponseNormalizationOptions()
+ lrn_options.Init(op_options.Bytes, op_options.Pos)
+ radius = lrn_options.Radius()
+ bias = lrn_options.Bias()
+ alpha = lrn_options.Alpha()
+ beta = lrn_options.Beta()
+ size = (radius * 2) + 1
+ alpha = alpha * size
+ axis = 3 # NHWC format
+ out = _op.nn.lrn(in_expr, size=size, axis=axis, bias=bias,
alpha=alpha, beta=beta)
+
+ return out
+
def convert_logistic(self, op):
"""Convert TFLite LOGISTIC"""
try:
@@ -693,6 +732,29 @@ class OperatorConverter(object):
'TFlite quantized NEG operator is not supported yet.')
return self._convert_unary_elemwise(_op.negative, op)
+ def convert_elu(self, op):
+ """Convert TFLite ELU"""
+ try:
+ from tflite.Operator import Operator
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+ assert isinstance(op, Operator)
+
+ if self.is_quantized(op):
+ raise tvm.error.OpNotImplemented(
+ 'TFlite quantized ELU operator is not supported yet.')
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) == 1, "input tensors length should be 1"
+
+ input_tensor = input_tensors[0]
+ in_expr = self.get_expr(input_tensor.tensor_idx)
+ exp_type = self.get_tensor_type_str(input_tensor.tensor.Type())
+ out = relay.const(-1.0, exp_type) * \
+ _op.nn.relu(relay.const(1., exp_type) - _op.exp(in_expr)) + \
+ _op.nn.relu(in_expr)
+
+ return out
+
def convert_square(self, op):
"""Convert TFLite SQUARE"""
try:
diff --git a/tests/python/frontend/tflite/test_forward.py
b/tests/python/frontend/tflite/test_forward.py
index 28216fc..78d6c3e 100644
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -57,8 +57,8 @@ def convert_to_list(x):
#######################################################################
-# Get a real image for e2e testing.
-# --------------------------------------
+# Get a real image for e2e testing
+# --------------------------------
def get_real_image(im_height, im_width):
repo_base =
'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/'
img_name = 'elephant-299.jpg'
@@ -299,7 +299,7 @@ def test_forward_transpose():
#######################################################################
# Cast
-# --------
+# ----
def _test_cast(data, cast_dtype):
""" One iteration of CAST """
@@ -316,8 +316,8 @@ def test_forward_cast():
_test_cast(np.arange(6.0, dtype=np.int32).reshape((1, 6)),
cast_dtype=tf.int64)
#######################################################################
-# tile
-# ---------
+# Tile
+# ----
def _test_forward_tile(in_shape, reps, dtype):
@@ -758,6 +758,14 @@ def _test_square(data):
""" One iteration of square """
return _test_unary_elemwise(math_ops.square, data)
+#######################################################################
+# Elu
+# ---
+
+def _test_elu(data):
+ """ One iteration of elu """
+ return _test_unary_elemwise(nn_ops.elu, data)
+
def _test_forward_unary_elemwise(test_op):
# functions that need positive input
if test_op.__name__ in {'_test_log', '_test_sqrt', '_test_rsqrt'}:
@@ -780,10 +788,11 @@ def test_all_unary_elemwise():
_test_forward_unary_elemwise(_test_ceil)
_test_forward_unary_elemwise(_test_cos)
_test_forward_unary_elemwise(_test_tan)
+ _test_forward_unary_elemwise(_test_elu)
#######################################################################
# Element-wise
-# ---
+# ------------
def _test_elemwise(math_op, data, fused_activation_function=None,
quantized=False, qnn_op=None):
""" One iteration of elemwise """
@@ -1049,7 +1058,7 @@ def test_all_logical():
#######################################################################
# Zeros like
-# --------
+# ----------
def _test_zeros_like(data):
""" One iteration of ZEROS LIKE """
@@ -1237,7 +1246,7 @@ def test_forward_pad():
#######################################################################
# Pack
-# -------------
+# ----
def _test_pack(data, axis):
""" One iteration of pack """
@@ -1291,6 +1300,26 @@ def test_forward_unpack():
_test_unpack(np.array(np.random.uniform(0, 5, (3, 6)),
dtype=np.int32), axis=-2, num_unpacks=3)
_test_unpack(np.array(np.random.uniform(0, 5, (2, 3, 4)),
dtype=np.int32), axis=-3, num_unpacks=2)
+
+#######################################################################
+# Local response normalization
+# ----------------------------
+
+def _test_local_response_normalization(data, depth_radius, bias, alpha, beta):
+ """ One iteration of LOCAL_RESPONSE_NORMALIZATION """
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype='float32',
name='in_0')
+ out = nn_ops.local_response_normalization(in_data,
depth_radius=depth_radius, bias=bias, alpha=alpha, beta=beta)
+ compare_tflite_with_tvm(data, 'in_0:0', [in_data], [out])
+
+def test_forward_local_response_normalization():
+ """ LOCAL_RESPONSE_NORMALIZATION """
+ data = np.random.uniform(size=(1, 6, 4, 3)).astype('float32')
+ # LOCAL_RESPONSE_NORMALIZATION come with TFLite >= 1.14.0 fbs schema
+ if package_version.parse(tf.VERSION) >= package_version.parse('1.14.0'):
+ _test_local_response_normalization(data, depth_radius=5, bias=1,
alpha=1, beta=0.5)
+
+
#######################################################################
# L2 normalization
# ----------------
@@ -1350,7 +1379,7 @@ def test_forward_softmax():
#######################################################################
# Tanh
-# --------
+# ----
def _test_tanh(data):
""" One iteration of TANH """
@@ -1365,7 +1394,7 @@ def test_forward_tanh():
#######################################################################
# ReLu
-# --------
+# ----
def _test_relu(data):
""" One iteration of ReLU """
@@ -1393,7 +1422,7 @@ def test_forward_prelu():
#######################################################################
# Fully Connected
-# -------
+# ---------------
def _test_fully_connected(tensor_in_sizes, filter_in_sizes, bias_in_size=None):
""" One iteration of fully connected """
@@ -1518,7 +1547,7 @@ def test_forward_mobilenet_v2():
#######################################################################
# Inception
-# ------------
+# ---------
def test_forward_inception_v3_net():
"""Test the Inception V3 TF Lite model."""
@@ -1696,6 +1725,7 @@ if __name__ == '__main__':
test_forward_prelu()
test_forward_fully_connected()
test_forward_l2_normalization()
+ test_forward_local_response_normalization()
# Elemwise
test_all_elemwise()
