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Unverified Commit f8b56a66 authored by kahmed10's avatar kahmed10 Committed by GitHub
Browse files

Extra tf ops (#679) 

* initial testing

* add new ops

* remove comment

* remove extra changes

* remove extra changes

* add tests

* formatting

* add tests
parent 2466dd6f
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Showing with 308 additions and 112 deletions
src/tf/tf.cpp
View file @ f8b56a66
...@@ -180,12 +180,12 @@ struct tf_parser ...@@ -180,12 +180,12 @@ struct tf_parser
add_generic_op("Identity", op::identity{}); add_generic_op("Identity", op::identity{});
add_generic_op("LessEqual", op::identity{}); add_generic_op("LessEqual", op::identity{});
add_generic_op("Relu", op::relu{}); add_generic_op("Relu", op::relu{});
// add_generic_op("Relu6", op::clip{6.0, 0.0});
add_generic_op("Rsqrt", op::rsqrt{}); add_generic_op("Rsqrt", op::rsqrt{});
add_generic_op("Tanh", op::tanh{}); add_generic_op("Tanh", op::tanh{});
add_generic_op("StopGradient", op::identity{}); add_generic_op("StopGradient", op::identity{});
add_binary_op("Add", op::add{}); add_binary_op("Add", op::add{});
add_binary_op("AddV2", op::add{});
add_binary_op("Mul", op::mul{}); add_binary_op("Mul", op::mul{});
add_binary_op("Pow", op::pow{}); add_binary_op("Pow", op::pow{});
add_binary_op("SquaredDifference", op::sqdiff{}); add_binary_op("SquaredDifference", op::sqdiff{});
...@@ -204,6 +204,7 @@ struct tf_parser ...@@ -204,6 +204,7 @@ struct tf_parser
add_mem_op("DepthwiseConv2dNative", &tf_parser::parse_depthwiseconv); add_mem_op("DepthwiseConv2dNative", &tf_parser::parse_depthwiseconv);
add_mem_op("ExpandDims", &tf_parser::parse_expanddims, false); add_mem_op("ExpandDims", &tf_parser::parse_expanddims, false);
add_mem_op("FusedBatchNorm", &tf_parser::parse_batchnorm); add_mem_op("FusedBatchNorm", &tf_parser::parse_batchnorm);
add_mem_op("FusedBatchNormV3", &tf_parser::parse_batchnorm);
add_mem_op("GatherV2", &tf_parser::parse_gather, false); add_mem_op("GatherV2", &tf_parser::parse_gather, false);
add_mem_op("MatMul", &tf_parser::parse_matmul, false); add_mem_op("MatMul", &tf_parser::parse_matmul, false);
add_mem_op("MaxPool", &tf_parser::parse_pooling); add_mem_op("MaxPool", &tf_parser::parse_pooling);
...@@ -1061,6 +1062,9 @@ struct tf_parser ...@@ -1061,6 +1062,9 @@ struct tf_parser
// assert ops ignored // assert ops ignored
if(node.op() == "Assert" or contains(name, "Assert")) if(node.op() == "Assert" or contains(name, "Assert"))
return; return;
// noOps ignored
if(node.op() == "NoOp" or contains(name, "NoOp"))
return;
std::vector<instruction_ref> args; std::vector<instruction_ref> args;
for(auto&& input : node.input()) for(auto&& input : node.input())
......
test/tf/add_bcast_test.pb
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2 2
0 Placeholder* 0 Placeholder*
dtype0*
shape shape
:* :
dtype0
2 2
1 Placeholder* 1 Placeholder*
dtype0* dtype0*
...@@ -12,4 +12,4 @@ ...@@ -12,4 +12,4 @@
add_bcast1Add01* add_bcast1Add01*
T0" T0"
\ No newline at end of file \ No newline at end of file
test/tf/add_test.pb
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: :
0 Placeholder* 0 Placeholder*
shape:* dtype0*
dtype0 shape:
: :
1 Placeholder* 1 Placeholder*
dtype0* dtype0*
shape: shape:
 
add1Add01* add1Add01*
T0" T0"
\ No newline at end of file \ No newline at end of file
test/tf/addv2_test.pb 0 → 100644
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:
0 Placeholder*
dtype0*
shape:
:
1 Placeholder*
dtype0*
shape:

add1AddV201*
T0"
\ No newline at end of file
test/tf/argmax_test.pb
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test/tf/argmin_test.pb
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test/tf/batchmatmul_test.pb
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: :
0 Placeholder* 0 Placeholder*
shape:* dtype0*
dtype0 shape:
: :
1 Placeholder* 1 Placeholder*
dtype0* dtype0*
shape: shape:
D F
batchmatmul1 BatchMatMul01* batchmatmul1 BatchMatMulV201*
T0*
adj_x(* adj_x(*
adj_y(* adj_y("
T0" \ No newline at end of file
\ No newline at end of file
test/tf/biasadd_test.pb
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; ;
0 Placeholder* 0 Placeholder*
shape:* dtype0*
dtype0 shape:
/ /
1 Placeholder* 1 Placeholder*
dtype0* dtype0*
...@@ -10,4 +10,4 @@ ...@@ -10,4 +10,4 @@
: :
bias_add1BiasAdd01* bias_add1BiasAdd01*
T0* T0*
data_formatNHWC" data_formatNHWC"
\ No newline at end of file \ No newline at end of file
test/tf/cast_test.pb
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test/tf/concat_test.pb
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test/tf/conv_test.pb
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test/tf/gather_test.pb
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test/tf/gen_tf_pb.py
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...@@ -18,48 +18,72 @@ def tf_test(op_test): ...@@ -18,48 +18,72 @@ def tf_test(op_test):
@tf_test @tf_test
def add_test(g1): def add_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1') shape=(1, 2, 2, 3),
name='0')
g2_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 2, 2, 3),
name='1')
tf.add(g1_input, g2_input, name='add1') tf.add(g1_input, g2_input, name='add1')
@tf_test
def addv2_test(g1):
with g1.as_default():
g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 2, 2, 3),
name='0')
g2_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 2, 2, 3),
name='1')
tf.raw_ops.AddV2(x=g1_input, y=g2_input, name='add1')
@tf_test @tf_test
def add_bcast_test(g1): def add_bcast_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32, shape=(2, 3), name='0')
g2_input = tf.placeholder(tf.float32, shape=(2, 1), name='1') g2_input = tf.compat.v1.placeholder(tf.float32, shape=(2, 1), name='1')
tf.math.add(g1_input, g2_input, name='add_bcast1') tf.math.add(g1_input, g2_input, name='add_bcast1')
@tf_test @tf_test
def argmax_test(g1): def argmax_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(3, 4, 5, 6), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(3, 4, 5, 6),
name='0')
tf.argmax(g1_input, axis=2, name='argmax1') tf.argmax(g1_input, axis=2, name='argmax1')
@tf_test @tf_test
def argmin_test(g1): def argmin_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(3, 4, 5, 6), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(3, 4, 5, 6),
name='0')
tf.argmin(g1_input, axis=2, name='argmin1') tf.argmin(g1_input, axis=2, name='argmin1')
@tf_test @tf_test
def assert_less_equal_test(g1): def assert_less_equal_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32, shape=(2, 3), name='0')
g2_input = tf.placeholder(tf.float32, shape=(2, 3), name='1') g2_input = tf.compat.v1.placeholder(tf.float32, shape=(2, 3), name='1')
with tf.control_dependencies( with tf.control_dependencies(
[tf.assert_less_equal(g1_input, g2_input)]): [tf.compat.v1.assert_less_equal(g1_input, g2_input)]):
tf.add(g1_input, g2_input, name='add1') tf.add(g1_input, g2_input, name='add1')
@tf_test @tf_test
def batchmatmul_test(g1): def batchmatmul_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 8, 4), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 4, 8), name='1') shape=(1, 2, 8, 4),
name='0')
g2_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 2, 4, 8),
name='1')
tf.matmul(g1_input, tf.matmul(g1_input,
g2_input, g2_input,
transpose_a=True, transpose_a=True,
...@@ -70,41 +94,75 @@ def batchmatmul_test(g1): ...@@ -70,41 +94,75 @@ def batchmatmul_test(g1):
@tf_test @tf_test
def batchnorm_test(g1): def batchnorm_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 32), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 16, 16, 32),
name='0')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='1')
g1_offset = tf.compat.v1.placeholder(tf.float32, shape=(32), name='2')
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='3')
g1_variance = tf.compat.v1.placeholder(tf.float32,
shape=(32),
name='4')
tf.compat.v1.nn.fused_batch_norm(x=g1_input,
scale=g1_scale,
offset=g1_offset,
mean=g1_mean,
variance=g1_variance,
epsilon=0.00001,
is_training=False,
name='batchnorm1')
@tf_test
def batchnormv3_test(g1):
with g1.as_default():
g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 16, 16, 32),
name='0')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='1') g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='1')
g1_offset = tf.placeholder(tf.float32, shape=(32), name='2') g1_offset = tf.compat.v1.placeholder(tf.float32, shape=(32), name='2')
g1_mean = tf.placeholder(tf.float32, shape=(32), name='3') g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='3')
g1_variance = tf.placeholder(tf.float32, shape=(32), name='4') g1_variance = tf.compat.v1.placeholder(tf.float32,
tf.nn.fused_batch_norm(g1_input, shape=(32),
g1_scale, name='4')
g1_offset, tf.raw_ops.FusedBatchNormV3(x=g1_input,
g1_mean, scale=g1_scale,
g1_variance, offset=g1_offset,
epsilon=0.00001, mean=g1_mean,
is_training=False, variance=g1_variance,
name='batchnorm1') epsilon=0.00001,
is_training=False,
name='batchnorm1')
@tf_test @tf_test
def biasadd_test(g1): def biasadd_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 500), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
g2_input = tf.placeholder(tf.float32, shape=(500), name='1') shape=(1, 1, 1, 500),
name='0')
g2_input = tf.compat.v1.placeholder(tf.float32, shape=(500), name='1')
tf.nn.bias_add(g1_input, g2_input, name='bias_add1') tf.nn.bias_add(g1_input, g2_input, name='bias_add1')
@tf_test @tf_test
def cast_test(g1): def cast_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
tf.cast(g1_input, dtype=tf.int32, name='cast1') tf.cast(g1_input, dtype=tf.int32, name='cast1')
@tf_test @tf_test
def concat_test(g1): def concat_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(4, 7, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
g2_input = tf.placeholder(tf.float32, shape=(4, 2, 3), name='1') shape=(4, 7, 3),
name='0')
g2_input = tf.compat.v1.placeholder(tf.float32,
shape=(4, 2, 3),
name='1')
tf.concat([g1_input, g2_input], axis=1, name='concat1') tf.concat([g1_input, g2_input], axis=1, name='concat1')
...@@ -117,7 +175,9 @@ def const_test(g1): ...@@ -117,7 +175,9 @@ def const_test(g1):
@tf_test @tf_test
def conv_test(g1): def conv_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 16, 16, 3),
name='0')
g1_weights = tf.constant(value=1.0, g1_weights = tf.constant(value=1.0,
dtype=tf.float32, dtype=tf.float32,
shape=(3, 3, 3, 32), shape=(3, 3, 3, 32),
...@@ -128,43 +188,49 @@ def conv_test(g1): ...@@ -128,43 +188,49 @@ def conv_test(g1):
@tf_test @tf_test
def depthwiseconv_test(g1): def depthwiseconv_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 16, 16, 3),
name='0')
g1_weights = tf.constant(value=1.0, g1_weights = tf.constant(value=1.0,
dtype=tf.float32, dtype=tf.float32,
shape=(3, 3, 3, 1), shape=(3, 3, 3, 1),
name='1') name='1')
tf.nn.depthwise_conv2d_native(g1_input, tf.compat.v1.nn.depthwise_conv2d_native(g1_input,
g1_weights, [1, 1, 1, 1], g1_weights, [1, 1, 1, 1],
"SAME", "SAME",
name='depthwiseconv1') name='depthwiseconv1')
@tf_test @tf_test
def expanddims_test(g1): def expanddims_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2, 3, 4), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
tf.expand_dims(g1_input, axis=-1, name='expanddims_neg') shape=(2, 3, 4),
name='0')
tf.expand_dims(g1_input, axis=0, name='expanddims_neg')
@tf_test @tf_test
def gather_test(g1): def gather_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2, 4), name='0') g1_input = tf.compat.v1.placeholder(tf.float32, shape=(2, 4), name='0')
tf.gather(g1_input, [1, 1], axis=1, name='gather1') tf.gather(g1_input, [1, 1], axis=1, name='gather1')
@tf_test @tf_test
def identity_test(g1): def identity_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
tf.identity(g1_input, 'identity') tf.identity(g1_input, 'identity')
@tf_test @tf_test
def matmul_test(g1): def matmul_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(8, 4), name='0') g1_input = tf.compat.v1.placeholder(tf.float32, shape=(8, 4), name='0')
g2_input = tf.placeholder(tf.float32, shape=(4, 8), name='1') g2_input = tf.compat.v1.placeholder(tf.float32, shape=(4, 8), name='1')
tf.matmul(g1_input, tf.matmul(g1_input,
g2_input, g2_input,
transpose_a=True, transpose_a=True,
...@@ -175,7 +241,9 @@ def matmul_test(g1): ...@@ -175,7 +241,9 @@ def matmul_test(g1):
@tf_test @tf_test
def mean_test(g1): def mean_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
tf.math.reduce_mean(g1_input, axis=(2, 3), keepdims=True, name='mean1') tf.math.reduce_mean(g1_input, axis=(2, 3), keepdims=True, name='mean1')
tf.math.reduce_mean(g1_input, tf.math.reduce_mean(g1_input,
axis=(2, 3), axis=(2, 3),
...@@ -186,7 +254,9 @@ def mean_test(g1): ...@@ -186,7 +254,9 @@ def mean_test(g1):
@tf_test @tf_test
def mean_test_nhwc(g1): def mean_test_nhwc(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 16, 16, 3),
name='0')
tf.math.reduce_mean(g1_input, axis=(1, 2), keepdims=True, name='mean1') tf.math.reduce_mean(g1_input, axis=(1, 2), keepdims=True, name='mean1')
tf.math.reduce_mean(g1_input, tf.math.reduce_mean(g1_input,
axis=(1, 2), axis=(1, 2),
...@@ -197,11 +267,21 @@ def mean_test_nhwc(g1): ...@@ -197,11 +267,21 @@ def mean_test_nhwc(g1):
@tf_test @tf_test
def mul_test(g1): def mul_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
g2_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 16), name='1') shape=(1, 1, 1, 16),
name='0')
g2_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 1, 1, 16),
name='1')
tf.multiply(g1_input, g2_input, name='mul1') tf.multiply(g1_input, g2_input, name='mul1')
@tf_test
def noop_test(g1):
with g1.as_default():
tf.raw_ops.NoOp(name='noop1')
@tf_test @tf_test
def onehot_test(g1): def onehot_test(g1):
with g1.as_default(): with g1.as_default():
...@@ -212,100 +292,124 @@ def onehot_test(g1): ...@@ -212,100 +292,124 @@ def onehot_test(g1):
@tf_test @tf_test
def pack_test(g1): def pack_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2), name='0') g1_input = tf.compat.v1.placeholder(tf.float32, shape=(2), name='0')
g2_input = tf.placeholder(tf.float32, shape=(2), name='1') g2_input = tf.compat.v1.placeholder(tf.float32, shape=(2), name='1')
g3_input = tf.placeholder(tf.float32, shape=(2), name='2') g3_input = tf.compat.v1.placeholder(tf.float32, shape=(2), name='2')
tf.stack([g1_input, g2_input, g3_input], axis=1, name='pack1') tf.stack([g1_input, g2_input, g3_input], axis=1, name='pack1')
@tf_test @tf_test
def pack_test_nhwc(g1): def pack_test_nhwc(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 2), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
g2_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 2), name='1') shape=(1, 1, 1, 2),
g3_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 2), name='2') name='0')
g2_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 1, 1, 2),
name='1')
g3_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 1, 1, 2),
name='2')
tf.stack([g1_input, g2_input, g3_input], axis=3, name='pack1') tf.stack([g1_input, g2_input, g3_input], axis=3, name='pack1')
@tf_test @tf_test
def pooling_test(g1): def pooling_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
tf.nn.avg_pool(value=g1_input, shape=(1, 16, 16, 3),
ksize=(1, 2, 2, 1), name='0')
strides=(1, 2, 2, 1), tf.compat.v1.nn.avg_pool(value=g1_input,
padding='VALID', ksize=(1, 2, 2, 1),
data_format='NHWC', strides=(1, 2, 2, 1),
name='avg_pooling') padding='VALID',
tf.nn.max_pool(value=g1_input, data_format='NHWC',
ksize=(1, 2, 2, 1), name='avg_pooling')
strides=(1, 2, 2, 1), tf.compat.v1.nn.max_pool(value=g1_input,
padding='VALID', ksize=(1, 2, 2, 1),
data_format='NHWC', strides=(1, 2, 2, 1),
name='max_pooling') padding='VALID',
data_format='NHWC',
name='max_pooling')
@tf_test @tf_test
def pow_test(g1): def pow_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1') shape=(1, 2, 2, 3),
name='0')
g2_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 2, 2, 3),
name='1')
tf.pow(g1_input, g2_input, name='pow1') tf.pow(g1_input, g2_input, name='pow1')
@tf_test @tf_test
def relu_test(g1): def relu_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
tf.nn.relu(g1_input, 'relu') tf.nn.relu(g1_input, 'relu')
@tf_test @tf_test
def relu6_test(g1): def relu6_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
tf.nn.relu6(g1_input, 'relu6') tf.nn.relu6(g1_input, 'relu6')
@tf_test @tf_test
def reshape_test(g1): def reshape_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32, shape=(16), name='0')
tf.reshape(g1_input, (1, 1, 1, 16), 'reshape') tf.reshape(g1_input, (1, 1, 1, 16), 'reshape')
@tf_test @tf_test
def rsqrt_test(g1): def rsqrt_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
tf.math.rsqrt(g1_input, 'rsqrt') tf.math.rsqrt(g1_input, 'rsqrt')
@tf_test @tf_test
def shape_test(g1): def shape_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
g1.create_op(op_type='Shape', inputs=[g1_input]) g1.create_op(op_type='Shape', inputs=[g1_input])
@tf_test @tf_test
def slice_test(g1): def slice_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(5, 10), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(5, 10),
name='0')
tf.slice(g1_input, [1, 0], [2, -1], name='slice1') tf.slice(g1_input, [1, 0], [2, -1], name='slice1')
@tf_test @tf_test
def softmax_test(g1): def softmax_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32, shape=(1, 3), name='0')
tf.nn.softmax(g1_input, name='softmax') tf.nn.softmax(g1_input, name='softmax')
@tf_test @tf_test
def split_test(g1): def split_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(5, 30), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(5, 30),
name='0')
split0, split1, split2 = tf.split(g1_input, 3, 1, name='split') split0, split1, split2 = tf.split(g1_input, 3, 1, name='split')
tf.concat([split0, split1], axis=1, name='concat1') tf.concat([split0, split1], axis=1, name='concat1')
tf.concat([split1, split2], axis=1, name='concat2') tf.concat([split1, split2], axis=1, name='concat2')
...@@ -314,14 +418,18 @@ def split_test(g1): ...@@ -314,14 +418,18 @@ def split_test(g1):
@tf_test @tf_test
def split_test_one_output(g1): def split_test_one_output(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(5, 30), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(5, 30),
name='0')
tf.split(g1_input, 1, 1, name='split') tf.split(g1_input, 1, 1, name='split')
@tf_test @tf_test
def split_test_vector_as_input(g1): def split_test_vector_as_input(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(5, 30), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(5, 30),
name='0')
split0, split1, split2 = tf.split(g1_input, [4, 15, 11], split0, split1, split2 = tf.split(g1_input, [4, 15, 11],
1, 1,
name='split') name='split')
...@@ -332,29 +440,39 @@ def split_test_vector_as_input(g1): ...@@ -332,29 +440,39 @@ def split_test_vector_as_input(g1):
@tf_test @tf_test
def sqdiff_test(g1): def sqdiff_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1') shape=(1, 2, 2, 3),
tf.squared_difference(g1_input, g2_input, name='sqdiff') name='0')
g2_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 2, 2, 3),
name='1')
tf.compat.v1.squared_difference(g1_input, g2_input, name='sqdiff')
@tf_test @tf_test
def squeeze_test(g1): def squeeze_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 3, 1), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 2, 3, 1),
name='0')
tf.squeeze(g1_input, name='squeeze') tf.squeeze(g1_input, name='squeeze')
@tf_test @tf_test
def stopgradient_test(g1): def stopgradient_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
tf.stop_gradient(g1_input, 'stopgradient') tf.stop_gradient(g1_input, 'stopgradient')
@tf_test @tf_test
def stridedslice_test(g1): def stridedslice_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 10), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 1, 1, 10),
name='0')
tf.strided_slice(g1_input, [0, 0, 0, 0], [1, 1, 1, 5], [1, 1, 1, 1], tf.strided_slice(g1_input, [0, 0, 0, 0], [1, 1, 1, 5], [1, 1, 1, 1],
shrink_axis_mask=2, shrink_axis_mask=2,
name='stridedslice1') name='stridedslice1')
...@@ -363,7 +481,9 @@ def stridedslice_test(g1): ...@@ -363,7 +481,9 @@ def stridedslice_test(g1):
@tf_test @tf_test
def stridedslice_masks_test(g1): def stridedslice_masks_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 3, 10), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 3, 10),
name='0')
tf.strided_slice(g1_input, [0, 1, 1, 0], [0, 0, 0, 0], [1, 1, 1, 1], tf.strided_slice(g1_input, [0, 1, 1, 0], [0, 0, 0, 0], [1, 1, 1, 1],
begin_mask=9, begin_mask=9,
end_mask=15, end_mask=15,
...@@ -373,27 +493,87 @@ def stridedslice_masks_test(g1): ...@@ -373,27 +493,87 @@ def stridedslice_masks_test(g1):
@tf_test @tf_test
def sub_test(g1): def sub_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1') shape=(1, 2, 2, 3),
name='0')
g2_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 2, 2, 3),
name='1')
tf.subtract(g1_input, g2_input, name='sub1') tf.subtract(g1_input, g2_input, name='sub1')
@tf_test @tf_test
def tanh_test(g1): def tanh_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
tf.tanh(g1_input, 'tanh') tf.tanh(g1_input, 'tanh')
@tf_test @tf_test
def transpose_test(g1): def transpose_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 3, 16, 16),
name='0')
tf.transpose(g1_input, perm=[0, 2, 3, 1], name='transpose') tf.transpose(g1_input, perm=[0, 2, 3, 1], name='transpose')
@tf_test @tf_test
def variable_batch_test(g1): def variable_batch_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(0, 3, 16, 16), name='0') g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(0, 3, 16, 16),
name='0')
tf.identity(g1_input, name='identity') tf.identity(g1_input, name='identity')
if __name__ == '__main__':
add_test()
addv2_test()
add_bcast_test()
argmax_test()
argmin_test()
assert_less_equal_test()
batchmatmul_test()
batchnorm_test()
batchnormv3_test()
biasadd_test()
cast_test()
concat_test()
const_test()
conv_test()
depthwiseconv_test()
expanddims_test()
gather_test()
identity_test()
matmul_test()
mean_test()
mean_test_nhwc()
mul_test()
onehot_test()
noop_test()
pack_test()
pack_test_nhwc()
pooling_test()
pow_test()
relu_test()
relu6_test()
reshape_test()
rsqrt_test()
shape_test()
slice_test()
softmax_test()
split_test()
split_test_one_output()
split_test_vector_as_input()
sqdiff_test()
squeeze_test()
stopgradient_test()
stridedslice_test()
stridedslice_masks_test()
sub_test()
tanh_test()
transpose_test()
variable_batch_test()
test/tf/identity_test.pb
View file @ f8b56a66
: :
0 Placeholder* 0 Placeholder*
shape:* dtype0*
dtype0 shape:
identityIdentity0* identityIdentity0*
T0" T0"
\ No newline at end of file \ No newline at end of file
test/tf/matmul_test.pb
View file @ f8b56a66
...@@ -13,4 +13,4 @@ F ...@@ -13,4 +13,4 @@ F
matmul1MatMul01* matmul1MatMul01*
T0* T0*
transpose_a(* transpose_a(*
transpose_b(" transpose_b("
\ No newline at end of file \ No newline at end of file
test/tf/mean_test.pb
View file @ f8b56a66
No preview for this file type
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