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Commit 4295961c authored by Khalique's avatar Khalique
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test/onnx/gen_onnx.py
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test/tf/gen_tf_pb.py
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import numpy as np
import tensorflow as tf
def tf_test(op_test):
def run_test():
g1 = tf.Graph()
op_test(g1)
tf.io.write_graph(g1, '.', '{}.pb'.format(op_test.__name__), as_text=False)
tf.io.write_graph(g1,
'.',
'{}.pb'.format(op_test.__name__),
as_text=False)
return run_test
@tf_test
def add_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.add(g1_input, g2_input, name = 'add1')
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0')
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1')
tf.add(g1_input, g2_input, name='add1')
@tf_test
def add_bcast_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(2,1), name = '1')
tf.math.add(g1_input, g2_input, name = 'add_bcast1')
g1_input = tf.placeholder(tf.float32, shape=(2, 3), name='0')
g2_input = tf.placeholder(tf.float32, shape=(2, 1), name='1')
tf.math.add(g1_input, g2_input, name='add_bcast1')
@tf_test
def assert_less_equal_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(2,3), name = '1')
with tf.control_dependencies([tf.assert_less_equal(g1_input, g2_input)]):
tf.add(g1_input, g2_input, name = 'add1')
g1_input = tf.placeholder(tf.float32, shape=(2, 3), name='0')
g2_input = tf.placeholder(tf.float32, shape=(2, 3), name='1')
with tf.control_dependencies(
[tf.assert_less_equal(g1_input, g2_input)]):
tf.add(g1_input, g2_input, name='add1')
@tf_test
def batchmatmul_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,8,4), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,4,8), name = '1')
tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='batchmatmul1')
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 8, 4), name='0')
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 4, 8), name='1')
tf.matmul(g1_input,
g2_input,
transpose_a=True,
transpose_b=True,
name='batchmatmul1')
@tf_test
def batchnorm_test(g1):
with g1.as_default():
g1_input = tf.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.placeholder(tf.float32, shape=(32), name = '2')
g1_mean = tf.placeholder(tf.float32, shape=(32), name = '3')
g1_variance = tf.placeholder(tf.float32, shape=(32), name = '4')
tf.nn.fused_batch_norm(
g1_input, g1_scale, g1_offset, g1_mean, g1_variance,
epsilon=0.00001, is_training=False, name='batchnorm1'
)
g1_input = tf.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.placeholder(tf.float32, shape=(32), name='2')
g1_mean = tf.placeholder(tf.float32, shape=(32), name='3')
g1_variance = tf.placeholder(tf.float32, shape=(32), name='4')
tf.nn.fused_batch_norm(g1_input,
g1_scale,
g1_offset,
g1_mean,
g1_variance,
epsilon=0.00001,
is_training=False,
name='batchnorm1')
@tf_test
def biasadd_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,500), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(500), name = '1')
tf.nn.bias_add(g1_input, g2_input, name = 'bias_add1')
g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 500), name='0')
g2_input = tf.placeholder(tf.float32, shape=(500), name='1')
tf.nn.bias_add(g1_input, g2_input, name='bias_add1')
@tf_test
def cast_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
tf.cast(g1_input, dtype=tf.int32, name='cast1')
@tf_test
def concat_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(4,7,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(4,2,3), name = '1')
tf.concat([g1_input, g2_input], axis=1, name = 'concat1')
g1_input = tf.placeholder(tf.float32, shape=(4, 7, 3), name='0')
g2_input = tf.placeholder(tf.float32, shape=(4, 2, 3), name='1')
tf.concat([g1_input, g2_input], axis=1, name='concat1')
@tf_test
def const_test(g1):
with g1.as_default():
tf.constant(1.0, dtype=tf.float32 ,name='constant1')
tf.constant(1.0, dtype=tf.float32, name='constant1')
@tf_test
def conv_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,32), name = '1')
tf.nn.conv2d(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'conv1')
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0')
g1_weights = tf.constant(value=1.0,
dtype=tf.float32,
shape=(3, 3, 3, 32),
name='1')
tf.nn.conv2d(g1_input, g1_weights, [1, 1, 1, 1], "SAME", name='conv1')
@tf_test
def depthwiseconv_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,1), name = '1')
tf.nn.depthwise_conv2d_native(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'depthwiseconv1')
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0')
g1_weights = tf.constant(value=1.0,
dtype=tf.float32,
shape=(3, 3, 3, 1),
name='1')
tf.nn.depthwise_conv2d_native(g1_input,
g1_weights, [1, 1, 1, 1],
"SAME",
name='depthwiseconv1')
@tf_test
def expanddims_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,3,4), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(2, 3, 4), name='0')
tf.expand_dims(g1_input, axis=-1, name='expanddims_neg')
@tf_test
def gather_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,4), name = '0')
tf.gather(g1_input, [1,1], axis=1, name='gather1')
g1_input = tf.placeholder(tf.float32, shape=(2, 4), name='0')
tf.gather(g1_input, [1, 1], axis=1, name='gather1')
@tf_test
def identity_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
tf.identity(g1_input, 'identity')
@tf_test
def matmul_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(8,4), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(4,8), name = '1')
tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='matmul1')
g1_input = tf.placeholder(tf.float32, shape=(8, 4), name='0')
g2_input = tf.placeholder(tf.float32, shape=(4, 8), name='1')
tf.matmul(g1_input,
g2_input,
transpose_a=True,
transpose_b=True,
name='matmul1')
@tf_test
def mean_test(g1):
with g1.as_default():
g1_input = tf.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=False,
name='mean2'
)
g1_input = tf.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=False,
name='mean2')
@tf_test
def mean_test_nhwc(g1):
with g1.as_default():
g1_input = tf.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=False,
name='mean2'
)
g1_input = tf.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=False,
name='mean2')
@tf_test
def mul_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '1')
g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 16), name='0')
g2_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 16), name='1')
tf.multiply(g1_input, g2_input, name='mul1')
@tf_test
def pack_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(2), name = '1')
g3_input = tf.placeholder(tf.float32, shape=(2), name = '2')
tf.stack([g1_input, g2_input, g3_input], axis=1, name = 'pack1')
g1_input = tf.placeholder(tf.float32, shape=(2), name='0')
g2_input = tf.placeholder(tf.float32, shape=(2), name='1')
g3_input = tf.placeholder(tf.float32, shape=(2), name='2')
tf.stack([g1_input, g2_input, g3_input], axis=1, name='pack1')
@tf_test
def pack_test_nhwc(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '1')
g3_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '2')
tf.stack([g1_input, g2_input, g3_input], axis=3, name = 'pack1')
g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 2), name='0')
g2_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 2), name='1')
g3_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 2), name='2')
tf.stack([g1_input, g2_input, g3_input], axis=3, name='pack1')
@tf_test
def pooling_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
tf.nn.avg_pool(
value=g1_input,
ksize=(1,2,2,1),
strides=(1,2,2,1),
padding='VALID',
data_format='NHWC',
name='avg_pooling'
)
tf.nn.max_pool(
value=g1_input,
ksize=(1,2,2,1),
strides=(1,2,2,1),
padding='VALID',
data_format='NHWC',
name='max_pooling'
)
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0')
tf.nn.avg_pool(value=g1_input,
ksize=(1, 2, 2, 1),
strides=(1, 2, 2, 1),
padding='VALID',
data_format='NHWC',
name='avg_pooling')
tf.nn.max_pool(value=g1_input,
ksize=(1, 2, 2, 1),
strides=(1, 2, 2, 1),
padding='VALID',
data_format='NHWC',
name='max_pooling')
@tf_test
def pow_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.pow(g1_input, g2_input, name = 'pow1')
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0')
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1')
tf.pow(g1_input, g2_input, name='pow1')
@tf_test
def relu_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
tf.nn.relu(g1_input, 'relu')
@tf_test
def relu6_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
tf.nn.relu6(g1_input, 'relu6')
@tf_test
def reshape_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(16), name = '0')
tf.reshape(g1_input, (1,1,1,16), 'reshape')
g1_input = tf.placeholder(tf.float32, shape=(16), name='0')
tf.reshape(g1_input, (1, 1, 1, 16), 'reshape')
@tf_test
def rsqrt_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
tf.math.rsqrt(g1_input, 'rsqrt')
@tf_test
def slice_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(5,10), name = '0')
tf.slice(g1_input, [1, 0], [2, -1], name = 'slice1')
g1_input = tf.placeholder(tf.float32, shape=(5, 10), name='0')
tf.slice(g1_input, [1, 0], [2, -1], name='slice1')
@tf_test
def softmax_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(1, 3), name='0')
tf.nn.softmax(g1_input, name='softmax')
@tf_test
def sqdiff_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.squared_difference(g1_input, g2_input, name = 'sqdiff')
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0')
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1')
tf.squared_difference(g1_input, g2_input, name='sqdiff')
@tf_test
def squeeze_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,3,1), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 3, 1), name='0')
tf.squeeze(g1_input, name='squeeze')
@tf_test
def stopgradient_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
tf.stop_gradient(g1_input, 'stopgradient')
@tf_test
def stridedslice_test(g1):
with g1.as_default():
g1_input = tf.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], shrink_axis_mask=2, name = 'stridedslice1')
g1_input = tf.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],
shrink_axis_mask=2,
name='stridedslice1')
@tf_test
def stridedslice_masks_test(g1):
with g1.as_default():
g1_input = tf.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], begin_mask=9, end_mask=15, name = 'stridedslice1')
g1_input = tf.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],
begin_mask=9,
end_mask=15,
name='stridedslice1')
@tf_test
def sub_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.subtract(g1_input, g2_input, name = 'sub1')
g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0')
g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1')
tf.subtract(g1_input, g2_input, name='sub1')
@tf_test
def tanh_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
tf.tanh(g1_input, 'tanh')
@tf_test
def transpose_test(g1):
with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.transpose(g1_input, perm=[0,2,3,1], name = 'transpose')
g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
tf.transpose(g1_input, perm=[0, 2, 3, 1], name='transpose')
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