def test_write_and_gather(self, use_cpu_only, backend, size_dynamic_shape):
size, dynamic_size, element_shape = size_dynamic_shape
@make_tf_graph([(1, ), (1, )])
def build_model(x, y):
ta = tf.TensorArray(
tf.float32,
size=size,
dynamic_size=dynamic_size,
element_shape=element_shape,
)
ta = ta.write(0, x)
ta = ta.write(1, y)
return ta.gather(indices=[0, 1])
model, inputs, outputs = build_model
input_values = [
np.array([3.14], dtype=np.float32),
np.array([6.17], dtype=np.float32),
]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(model,
input_dict,
outputs,
use_cpu_only=use_cpu_only,
backend=backend)
def test_fused_batch_norm_v3(self, use_cpu_only, backend, epsilon):
input_shape = np.random.randint(low=1, high=4, size=4)
attr_shape = [list(input_shape)[-1]]
m = random_gen(shape=attr_shape, rand_min=-1.0, rand_max=1.0)
v = random_gen(shape=attr_shape, rand_min=0.0, rand_max=10.0)
o = random_gen(shape=attr_shape, rand_min=1.0, rand_max=10.0)
s = random_gen(shape=attr_shape, rand_min=-1.0, rand_max=1.0)
@make_tf_graph([input_shape])
def build_model(x):
return tf.raw_ops.FusedBatchNormV3(
x=x,
scale=s,
offset=o,
mean=m,
variance=v,
epsilon=epsilon,
is_training=False,
)[0]
model, inputs, outputs = build_model
input_values = [random_gen(shape=input_shape)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model,
input_dict,
outputs,
use_cpu_only=use_cpu_only,
backend=backend,
atol=1e-2,
rtol=1e-3,
)
def test_add_v2(self, use_cpu_only, backend, rank):
x_shape = list(np.random.randint(low=2, high=5, size=rank))
y_shape = x_shape[:]
for i in range(rank):
if np.random.randint(4) == 0:
y_shape[i] = 1
if np.random.randint(2) == 0:
y_shape = [1] + y_shape
if use_cpu_only:
dtype = np.float32
else:
dtype = np.float16
@make_tf_graph([x_shape, y_shape])
def build_model(x, y):
return tf.raw_ops.AddV2(x=x, y=y)
model, inputs, outputs = build_model
input_values = [
np.random.randint(low=-1000, high=1000,
size=x_shape).astype(dtype),
np.random.randint(low=-1000, high=1000,
size=y_shape).astype(dtype),
]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(model,
input_dict,
outputs,
use_cpu_only=use_cpu_only,
backend=backend)
def test_while_loop_power(self, use_cpu_only, backend):
@make_tf_graph([(1,)])
def build_model(x):
i = 0
while i < 3:
x *= x
i += 1
return x
model, inputs, outputs = build_model
input_values = [np.array([2.0], dtype=np.float32)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model, input_dict, outputs, use_cpu_only=use_cpu_only, backend=backend
)
def test_if_unary_double_if_positive_else_square(self, use_cpu_only, backend):
@make_tf_graph([(1,)])
def build_model(x):
if x >= 0:
out = x + x
else:
out = x * x
return out
model, inputs, outputs = build_model
input_values = [np.array([2], dtype=np.float32)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model, input_dict, outputs, use_cpu_only=use_cpu_only, backend=backend
)
def test_if_unary_const(self, use_cpu_only, backend):
@make_tf_graph([(1,)])
def build_model(x):
if x > 0.5:
y = x - 0.5
else:
y = x + 0.5
return y
model, inputs, outputs = build_model
input_values = [np.array([0.7], dtype=np.float32)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model, input_dict, outputs, use_cpu_only=use_cpu_only, backend=backend
)
def test_while_loop_nested_body(self, use_cpu_only, backend):
@make_tf_graph([(1,)])
def build_model(x):
i, j = 0, 10
while i < j:
while 2 * i < i + 2:
i += 1
x -= 1
i += 2
x *= 2
return x
model, inputs, outputs = build_model
input_values = [np.array([9.0], dtype=np.float32)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model, input_dict, outputs, use_cpu_only=use_cpu_only, backend=backend
)
def test_if_binary_add_if_else_mul(self, use_cpu_only, backend):
@make_tf_graph([(1,), (1,)])
def build_model(x, y):
if x > y:
out = x + x
else:
out = x * x
return out
model, inputs, outputs = build_model
input_values = [
np.array([3], dtype=np.float32),
np.array([7], dtype=np.float32),
]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model, input_dict, outputs, use_cpu_only=use_cpu_only, backend=backend
)
def test_partial_element_shape(self, use_cpu_only, backend, size_dynamic_shape):
size, dynamic_size, element_shape = size_dynamic_shape
@make_tf_graph([(3, 1, 8)])
def build_model(x):
ta = tf.TensorArray(
tf.float32,
size=size,
dynamic_size=dynamic_size,
element_shape=element_shape,
)
ta = ta.scatter(indices=[0, 1, 2], value=x)
return ta.read(0), ta.read(1), ta.read(2)
model, inputs, outputs = build_model
input_values = [np.random.rand(3, 1, 8).astype(np.float32)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model, input_dict, outputs, use_cpu_only=use_cpu_only, backend=backend
)
def test_unstack_and_read(self, use_cpu_only, backend, size_dynamic_shape):
size, dynamic_size, element_shape = size_dynamic_shape
@make_tf_graph([(3, 1)])
def build_model(x):
ta = tf.TensorArray(
tf.float32,
size=size,
dynamic_size=dynamic_size,
element_shape=element_shape,
)
ta = ta.unstack(x)
return ta.read(0), ta.read(1), ta.read(2)
model, inputs, outputs = build_model
input_values = [np.array([[3.14], [6.17], [12.14]], dtype=np.float32)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model, input_dict, outputs, use_cpu_only=use_cpu_only, backend=backend
)
