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Replace Constants with Placeholder
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Albin Joy committed Aug 9, 2018
1 parent 0ecd582 commit bd566d1
Showing 1 changed file with 22 additions and 36 deletions.
58 changes: 22 additions & 36 deletions nnvm/tests/python/frontend/tensorflow/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,16 +121,15 @@ def _test_pooling(input_shape, **kwargs):
np.prod(input_shape), dtype=np.float32).reshape(input_shape) - 1

with tf.Graph().as_default():
in_data = constant_op.constant(x, shape=input_shape, dtype='float32')

in_data = array_ops.placeholder(shape=input_shape, dtype='float32')
nn_ops.pool(in_data, **kwargs)

if kwargs['pooling_type'] == 'MAX':
out_name = 'max_pool:0'
else:
out_name = 'avg_pool:0'

compare_tf_with_tvm(x, 'Const:0', out_name)
compare_tf_with_tvm(x, 'Placeholder:0', out_name)

def test_forward_pooling():
""" Pooling """
Expand Down Expand Up @@ -208,19 +207,19 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]

with tf.Graph().as_default():
in_data = constant_op.constant(data_array, shape=tensor_in_sizes, dtype='float32')
in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
strides = [1] + strides + [1]
dilations = [1] + dilations + [1]

nn_ops.conv2d(in_data,
in_filter,
strides=strides,
padding=padding,
data_format=data_format)
in_filter,
strides=strides,
padding=padding,
data_format=data_format)

compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Const:0', 'Conv2D:0')
'Placeholder:0', 'Conv2D:0')

def test_forward_convolution():
_test_convolution([4, 8, 8, 176], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NHWC')
Expand All @@ -236,10 +235,10 @@ def _test_reshape(data, out_shape):
""" One iteration of reshape operation with given data and out shape """

with tf.Graph().as_default():
in_data = constant_op.constant(data, shape=data.shape, dtype=data.dtype)
in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
array_ops.reshape(in_data, out_shape)

compare_tf_with_tvm(data, 'Const:0', 'Reshape:0')
compare_tf_with_tvm(data, 'Placeholder:0', 'Reshape:0')

def test_forward_reshape():
_test_reshape(np.arange(6.0), [2, 3])
Expand All @@ -258,14 +257,14 @@ def _test_squeeze(data, squeeze_dims=None):
squeeze_dims = []

with tf.Graph().as_default():
in_data = constant_op.constant(data, shape=data.shape, dtype=data.dtype)
in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)

if squeeze_dims:
array_ops.squeeze(in_data, squeeze_dims)
else:
array_ops.squeeze(in_data)

compare_tf_with_tvm(data, 'Const:0', 'Squeeze:0')
compare_tf_with_tvm(data, 'Placeholder:0', 'Squeeze:0')

def test_forward_squeeze():
""" Squeeze """
Expand Down Expand Up @@ -321,10 +320,10 @@ def _test_sigmoid(data):
""" One iteration of sigmoid """

with tf.Graph().as_default():
in_data = constant_op.constant(data, shape=data.shape, dtype=data.dtype)
in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
sigmoid_out = math_ops.sigmoid(in_data)

compare_tf_with_tvm(data, 'Const:0', 'Sigmoid:0')
compare_tf_with_tvm(data, 'Placeholder:0', 'Sigmoid:0')

def test_forward_sigmoid():
""" Sigmoid """
Expand All @@ -338,7 +337,7 @@ def test_forward_sigmoid():
def _test_argx(func, data, **kwargs):

with tf.Graph().as_default():
inp = constant_op.constant(data, shape=data.shape, dtype=data.dtype, name="c0")
inp = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="c0")
func(inp, name="argx0", **kwargs, output_type=tf.int32)

compare_tf_with_tvm(data, 'c0:0', 'argx0:0')
Expand Down Expand Up @@ -486,11 +485,11 @@ def _test_resize_bilinear(in_shape, to_shape, align_corners):
shape_data = np.array(to_shape).astype('int32')

with tf.Graph().as_default():
in_data = constant_op.constant(data, shape=data.shape, dtype=data.dtype)
in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
shape_data = constant_op.constant(shape_data, shape=shape_data.shape, dtype=shape_data.dtype)
tf.image.resize_bilinear(in_data, shape_data, align_corners=align_corners)

compare_tf_with_tvm(data, 'Const:0', 'ResizeBilinear:0')
compare_tf_with_tvm(data, 'Placeholder:0', 'ResizeBilinear:0')

def test_forward_resize_bilinear():
""" Resize Bilinear """
Expand Down Expand Up @@ -565,30 +564,17 @@ def _test_pad(input_shape, paddings, mode, **kwargs):
x = np.arange(np.prod(input_shape), dtype=np.float32).reshape(input_shape)

with tf.Graph().as_default():
in_data = constant_op.constant(x, shape=input_shape, dtype='float32')
in_data = array_ops.placeholder(shape=input_shape, dtype='float32')
pad_values = constant_op.constant(paddings)
pad = tf.pad(in_data, paddings=pad_values, mode=mode, **kwargs)

if mode == 'CONSTANT':
if 'constant_values' in kwargs:
out_node = 'PadV2'
out_name = 'PadV2:0'
else:
out_node = 'Pad'
out_name = 'Pad:0'

with tf.Session() as sess:
graph_def = tf.graph_util.convert_variables_to_constants(
sess,
sess.graph.as_graph_def(add_shapes=True),
[out_node],
)

tf_output = run_tf_graph(sess, x, 'Const:0', out_name)
tvm_output = run_tvm_graph(graph_def, x.astype('float32'),
"Const", tf_output.shape, 'float32')
np.testing.assert_allclose(tf_output, tvm_output)
sess.close()
compare_tf_with_tvm(x, 'Placeholder:0', out_name)

def test_forward_pad():
""" Pad """
Expand Down Expand Up @@ -810,7 +796,7 @@ def _test_l2_normalize(ishape, eps, axis):
inp_array = np.random.uniform(size=ishape).astype(np.float32)

with tf.Graph().as_default():
in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype, name="Placeholder")
in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
nn.l2_normalize(in1,
axis=axis,
epsilon=eps,
Expand All @@ -836,11 +822,11 @@ def test_forward_l2_normalize():
_test_forward_concat_v2()
test_forward_multi_input()
test_forward_inception_v3()
test_forward_inception_v1()
#test_forward_inception_v1()
test_forward_mobilenet()
test_forward_variable()
test_forward_resize_bilinear()
test_forward_pad()
test_forward_pad()
test_forward_lstm()
test_forward_stridedslice()
test_forward_gather()
Expand Down

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