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test_backend.py
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test_backend.py
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# SPDX-License-Identifier: Apache-2.0
"""Unit tests using onnx backends."""
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import unittest
from distutils.version import LooseVersion
from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal
import tensorflow as tf
from tensorflow.python.ops import lookup_ops
from backend_test_base import Tf2OnnxBackendTestBase
# pylint reports unused-wildcard-import which is false positive, __all__ is defined in common
from common import * # pylint: disable=wildcard-import,unused-wildcard-import
from tf2onnx import constants, utils
from tf2onnx.graph_matcher import OpTypePattern, GraphMatcher
from tf2onnx.tf_loader import is_tf2, tf_placeholder_with_default, tf_placeholder
from tf2onnx.onnx_opset.signal import make_dft_constant
# pylint: disable=missing-docstring,invalid-name,unused-argument,function-redefined,cell-var-from-loop
NCHW_TO_NHWC = [0, 2, 3, 1]
NHWC_TO_NCHW = [0, 3, 1, 2]
HWCN_TO_NCHW = [3, 2, 0, 1]
_STRIDE1x1 = [1, 1, 1, 1]
_KERNEL3x3 = [3, 3, 1, 1]
_DILATIONS1x1 = [1, 1, 1, 1]
# names for input and outputs for tests
_TFINPUT = "input"
_INPUT = "input:0"
_TFINPUT1 = "input1"
_INPUT1 = "input1:0"
_TFINPUT2 = "input2"
_INPUT2 = "input2:0"
_TFINPUT3 = "input3"
_INPUT3 = "input3:0"
_TFOUTPUT = "output"
_OUTPUT = "output:0"
_TFOUTPUT1 = "output1"
_OUTPUT1 = "output1:0"
_TFOUTPUT2 = "output2"
_OUTPUT2 = "output2:0"
_TFOUTPUT3 = "output3"
_OUTPUT3 = "output3:0"
if is_tf2():
conv2d_backprop_input = tf.compat.v1.nn.conv2d_backprop_input
conv3d_transpose = tf.compat.v1.nn.conv3d_transpose
multinomial = tf.compat.v1.random.multinomial
space_to_batch_nd = tf.compat.v1.space_to_batch_nd
batch_to_space_nd = tf.compat.v1.batch_to_space_nd
reverse_v2 = tf.compat.v1.reverse_v2
random_normal = tf.compat.v1.random_normal
random_uniform = tf.compat.v1.random_uniform
fused_batch_norm = tf.compat.v1.nn.fused_batch_norm
dropout = tf.compat.v1.nn.dropout
resize_nearest_neighbor = tf.compat.v1.image.resize_nearest_neighbor
quantize_and_dequantize = tf.quantization.quantize_and_dequantize
resize_bilinear = tf.compat.v1.image.resize_bilinear
resize_bilinear_v2 = tf.compat.v2.image.resize
is_nan = tf.math.is_nan
is_inf = tf.math.is_inf
floormod = tf.math.floormod
matrix_diag_part = tf.compat.v1.matrix_diag_part
fake_quant_with_min_max_args = tf.quantization.fake_quant_with_min_max_args
fake_quant_with_min_max_vars = tf.quantization.fake_quant_with_min_max_vars
elif LooseVersion(tf.__version__) >= "1.13":
conv2d_backprop_input = tf.compat.v1.nn.conv2d_backprop_input
conv3d_transpose = tf.compat.v1.nn.conv3d_transpose
multinomial = tf.compat.v1.random.multinomial
space_to_batch_nd = tf.compat.v1.space_to_batch_nd
batch_to_space_nd = tf.compat.v1.batch_to_space_nd
reverse_v2 = tf.compat.v1.reverse_v2
random_normal = tf.compat.v1.random_normal
random_uniform = tf.compat.v1.random_uniform
fused_batch_norm = tf.compat.v1.nn.fused_batch_norm
dropout = tf.compat.v1.nn.dropout
quantize_and_dequantize = tf.compat.v1.quantization.quantize_and_dequantize
resize_nearest_neighbor = tf.compat.v1.image.resize_nearest_neighbor
resize_bilinear = tf.compat.v1.image.resize_bilinear
if LooseVersion(tf.__version__) >= "1.14":
resize_bilinear_v2 = tf.compat.v2.image.resize
is_nan = tf.math.is_nan
is_inf = tf.math.is_inf
floormod = tf.floormod
matrix_diag_part = tf.compat.v1.matrix_diag_part
fake_quant_with_min_max_args = tf.compat.v1.quantization.fake_quant_with_min_max_args
fake_quant_with_min_max_vars = tf.compat.v1.quantization.fake_quant_with_min_max_vars
else:
conv2d_backprop_input = tf.nn.conv2d_backprop_input
conv3d_transpose = tf.nn.conv3d_transpose
multinomial = tf.multinomial
space_to_batch_nd = tf.space_to_batch_nd
batch_to_space_nd = tf.batch_to_space_nd
reverse_v2 = tf.reverse_v2
random_normal = tf.random_normal
random_uniform = tf.random_uniform
fused_batch_norm = tf.nn.fused_batch_norm
dropout = tf.nn.dropout
resize_nearest_neighbor = tf.image.resize_nearest_neighbor
resize_bilinear = tf.image.resize_bilinear
is_nan = tf.is_nan
is_inf = tf.is_inf
floormod = tf.floormod
matrix_diag_part = tf.matrix_diag_part
def make_xval(shape):
x_val = np.arange(np.prod(shape)).astype("float32").reshape(shape)
return x_val
def get_conv_getdata(kind=1):
if kind == 0:
# generate all combinations (costly)
dims = [
("padding", ["SAME", "VALID"]),
("input_sizes", [[32, 35, 35, 3], [32, 17, 17, 3], [1, 28, 28, 3], [32, 8, 8, 3]]),
("filter_sizes", [[1, 3, 3, 1], [1, 2, 2, 1], [1, 5, 5, 1], [1, 1, 1, 1], [1, 5, 2, 1], [1, 2, 5, 1]]),
("strides", [[1, 2, 2, 1], [1, 1, 1, 1]]),
]
values = [key_values[1] for key_values in dims]
for idx, v in enumerate(product(*values)):
if True or idx == 30:
yield (idx,) + v
elif kind == 1:
# some combination to that give decent padding coverage
data = [
('SAME', [32, 35, 35, 3], [1, 3, 3, 1], [1, 2, 2, 1]),
('SAME', [32, 35, 35, 3], [1, 2, 2, 1], [1, 2, 2, 1]),
('SAME', [32, 35, 35, 3], [1, 1, 1, 1], [1, 1, 1, 1]),
('SAME', [32, 35, 35, 3], [1, 5, 2, 1], [1, 2, 2, 1]),
('SAME', [32, 35, 35, 3], [1, 2, 5, 1], [1, 2, 2, 1]),
('SAME', [32, 35, 35, 3], [1, 2, 5, 1], [1, 1, 1, 1]),
('SAME', [1, 28, 28, 3], [1, 3, 3, 1], [1, 2, 2, 1]),
('SAME', [1, 28, 28, 3], [1, 3, 3, 1], [1, 1, 1, 1]),
('SAME', [1, 28, 28, 3], [1, 2, 2, 1], [1, 2, 2, 1]),
('SAME', [1, 28, 28, 3], [1, 2, 2, 1], [1, 1, 1, 1]),
('SAME', [1, 28, 28, 3], [1, 5, 5, 1], [1, 2, 2, 1]),
('SAME', [1, 28, 28, 3], [1, 5, 5, 1], [1, 1, 1, 1]),
('SAME', [1, 28, 28, 3], [1, 5, 2, 1], [1, 2, 2, 1]),
('SAME', [32, 8, 8, 3], [1, 3, 3, 1], [1, 2, 2, 1]),
('SAME', [32, 8, 8, 3], [1, 3, 3, 1], [1, 1, 1, 1]),
('VALID', [32, 35, 35, 3], [1, 3, 3, 1], [1, 1, 1, 1]),
('VALID', [32, 35, 35, 3], [1, 2, 2, 1], [1, 2, 2, 1]),
]
for idx, v in enumerate(data):
yield (idx,) + v
else:
raise ValueError("kind not known")
def get_maxpoolwithargmax_getdata():
data = [
('SAME', [1, 3, 3, 2], [1, 3, 3, 1], [1, 2, 2, 1]),
('SAME', [2, 5, 5, 3], [1, 4, 4, 1], [1, 2, 2, 1]),
('SAME', [2, 10, 5, 1], [1, 2, 2, 1], [1, 2, 2, 1]),
('SAME', [2, 10, 5, 3], [1, 4, 4, 1], [1, 1, 1, 1]),
('VALID', [2, 3, 3, 3], [1, 3, 3, 1], [1, 2, 2, 1]),
('VALID', [2, 5, 5, 3], [1, 4, 4, 1], [1, 2, 2, 1]),
]
for idx, v in enumerate(data):
yield (idx,) + v
class BackendTests(Tf2OnnxBackendTestBase):
def _run_test_case(self, func, output_names_with_port, feed_dict, **kwargs):
kwargs["convert_var_to_const"] = False
kwargs["constant_fold"] = False
return self.run_test_case(func, feed_dict, [], output_names_with_port, **kwargs)
def _test_expand_dims_known_rank(self, idx):
x_val = make_xval([3, 4])
def func(x):
op = tf.expand_dims(x, idx)
return tf.identity(op, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def test_expand_dims_known_rank(self):
for i in [-1, 0, 1, -2]:
self._test_expand_dims_known_rank(i)
def test_expand_dims_one_unknown_rank(self):
x_val = make_xval([3, 4])
def func(x):
op = tf.expand_dims(x, 0)
return tf.identity(op, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def test_expand_dims_with_list(self):
x_val = make_xval([3, 4])
def func(x):
op = tf.expand_dims(x, [[0]])
return tf.identity(op, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def _test_expand_dims_more_unknown_rank(self, idx):
x_val = make_xval([3, 4])
def func(x):
op = tf.expand_dims(x, idx)
return tf.identity(op, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def test_expand_dims_more_unknown_rank(self):
for i in [-1, 0, 1, -2]:
self._test_expand_dims_more_unknown_rank(i)
@check_opset_min_version(13, "Unsqueeze")
def test_expand_dims_nonconst_dims(self):
x_val = make_xval([3, 4])
y_val = np.array([-1], dtype=np.int32)
def func(x, y):
op = tf.expand_dims(x, y)
return tf.identity(op, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: y_val})
@check_opset_min_version(9, "ConstantOfShape")
def test_layer_normalization(self):
x_val = make_xval([3, 4, 5])
scale_val = make_xval([3, 4, 5]) * 0.2
bias_val = make_xval([3, 4, 5]) * 0.1
def func(x):
mean = tf.reduce_mean(x, axis=[2], keepdims=True)
centered = tf.subtract(x, mean)
variance = tf.add(tf.reduce_mean(tf.square(centered), axis=[2], keepdims=True), 0.001)
inv_std_dev = tf.math.rsqrt(variance)
normalized = tf.multiply(centered, inv_std_dev)
scaled = tf.multiply(normalized, scale_val)
biased = tf.add(scaled, bias_val)
return tf.identity(biased, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-05,
graph_validator=lambda g: (check_op_count(g, "InstanceNormalization", 1)))
@check_opset_min_version(9, "ConstantOfShape")
def test_eye_non_const1(self):
# tf.eye(num_rows), num_rows is not const here
x_val = np.array(5, dtype=np.int32)
def func(x):
y = tf.eye(x, dtype=tf.int32)
y1 = tf.eye(x, dtype=tf.int64)
y2 = tf.eye(x, dtype=tf.float32)
return tf.identity(y, name=_TFOUTPUT), tf.identity(y1, name=_TFOUTPUT1), tf.identity(y2, name=_TFOUTPUT2)
self._run_test_case(func, [_OUTPUT, _OUTPUT1, _OUTPUT2], {_INPUT: x_val}, rtol=0)
# tf.eye(num_rows, num_columns), both num_rows and num_columns are not const here
x_val = np.array([5, 10], dtype=np.int32)
def func(x):
y = tf.eye(x[0], x[1], dtype=tf.int32)
y1 = tf.eye(x[0], x[1], dtype=tf.int64)
y2 = tf.eye(x[0], x[1], dtype=tf.float32)
return tf.identity(y, name=_TFOUTPUT), tf.identity(y1, name=_TFOUTPUT1), tf.identity(y2, name=_TFOUTPUT2)
self._run_test_case(func, [_OUTPUT, _OUTPUT1, _OUTPUT2], {_INPUT: x_val}, rtol=0)
@check_tf_min_version("1.11", "eye has bug when version is below 1.11")
@check_opset_min_version(9, "ConstantOfShape")
def test_eye_non_const2(self):
# tf.eye(num_rows), num_rows is not const here
for np_dtype in [np.int32, np.int64, np.float32, np.float64]:
x_val = np.array(5, dtype=np_dtype)
def func(x):
y = tf.eye(x, dtype=tf.int32)
y1 = tf.eye(x, dtype=tf.float32)
return tf.identity(y, name=_TFOUTPUT),\
tf.identity(y1, name=_TFOUTPUT1)
self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: x_val}, rtol=0)
# tf.eye(num_rows, num_columns), both num_rows and num_columns are not const here
for np_dtype in [np.int32, np.int64, np.float32, np.float64]:
x_val = np.array([5, 10], dtype=np_dtype)
def func(x):
y = tf.eye(x[0], x[1], dtype=tf.int32)
y1 = tf.eye(x[0], x[1], dtype=tf.float32)
return tf.identity(y, name=_TFOUTPUT), \
tf.identity(y1, name=_TFOUTPUT1)
self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: x_val}, rtol=0)
@check_opset_min_version(7, "trig")
def test_trig_ops(self):
for op in [tf.sin, tf.cos, tf.tan, tf.asin, tf.acos, tf.atan]:
x_val = make_xval([3, 4])
def func(x):
op_ = op(x)
return tf.identity(op_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-06)
@check_opset_min_version(9, "trigh")
def test_atrig_ops(self):
for op in [tf.sinh, tf.cosh, tf.atanh, tf.asinh, tf.acosh]:
x_val = make_xval([3, 4])
def func(x):
op_ = op(x)
return tf.identity(op_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@skip_caffe2_backend()
@check_opset_min_version(7, "multinomial")
def test_multinomial(self):
x_val = np.array([[10., 10.]], dtype=np.float32)
def func(x):
op = multinomial(tf.math.log(x), 5, output_dtype=tf.int64)
return tf.identity(op, name=_TFOUTPUT)
# since returned indexes are random we can only check type and shape
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, check_value=False,
check_shape=True, check_dtype=True)
@skip_caffe2_backend()
@check_opset_min_version(7, "multinomial")
def test_multinomial1(self):
shape = [2, 10]
x_val = np.ones(np.prod(shape)).astype("float32").reshape(shape)
def func(x):
op = multinomial(x, 2, output_dtype=tf.int64)
return tf.identity(op, name=_TFOUTPUT)
# since returned indexes are random we can only check type and shape
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, check_value=False,
check_shape=True, check_dtype=True)
def test_maxpool(self):
for p in get_conv_getdata():
_, padding, x_shape, ksize, strides = p
x_val = make_xval(x_shape)
def func(x):
mp = tf.nn.max_pool(x, ksize, strides, padding=padding)
return tf.identity(mp, name=_TFOUTPUT)
self.logger.debug(str(p))
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_tf_min_version("1.15", "required for max_pool args")
def test_maxpool_int(self):
x_shape = [8, 16, 16, 3]
x_val = make_xval(x_shape).astype("int32")
def func(x):
mp = tf.nn.max_pool(x, ksize=[2], strides=[1, 2, 2, 1], padding="SAME")
return tf.identity(mp, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@skip_tf_cpu("only tf_gpu can run maxpool with NCHW format")
def test_maxpool_gpu(self):
# make sure converter behaves well when data format is NCHW
# and when data format is NCHW, only gpu version of tensorflow can run it.
ksize = [1, 1, 2, 2]
strides = [1, 1, 2, 2]
x_val = make_xval([1, 3, 50, 80])
for padding in ["SAME", "VALID"]:
def func(x):
mp = tf.nn.max_pool(x, ksize, strides, padding=padding, data_format="NCHW")
return tf.identity(mp, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_onnxruntime_incompatibility("AveragePool")
def test_avgpool(self):
for p in get_conv_getdata(kind=0):
_, padding, x_shape, ksize, strides = p
x_val = make_xval(x_shape)
def func(x):
mp = tf.nn.avg_pool(x, ksize, strides, padding=padding)
return tf.identity(mp, name=_TFOUTPUT)
self.logger.debug(str(p))
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-06)
@check_onnxruntime_incompatibility("AveragePool")
@skip_tf_cpu("only tf_gpu can run avgpool with NCHW format")
def test_avgpool_gpu(self):
ksize = [1, 1, 2, 2]
strides = [1, 1, 2, 2]
x_val = make_xval([1, 3, 50, 80])
for padding in ["SAME", "VALID"]:
def func(x):
mp = tf.nn.avg_pool(x, ksize, strides, padding=padding, data_format="NCHW")
return tf.identity(mp, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def _conv_test(self, x_val, w, strides=None, padding="VALID", dilations=None, rtol=1e-07):
if strides is None:
strides = _STRIDE1x1
if dilations is None:
dilations = _DILATIONS1x1
def func(x):
kernel = tf.constant(w, dtype=tf.float32, name='k')
conv = tf.nn.conv2d(x, kernel, strides=strides, padding=padding, dilations=dilations)
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=rtol)
def test_conv2d_1(self):
x_val = make_xval((1, 1, 5, 5)).transpose(NCHW_TO_NHWC)
w = np.array([[2., 1., 1.],
[1., 3., 1.],
[1., 1., 4.]], dtype=np.float32).reshape(_KERNEL3x3)
self._conv_test(x_val, w)
def test_conv2d_2(self):
x_val = np.array([[4, 3, 1, 0],
[2, 1, 0, 1],
[1, 2, 4, 1],
[3, 1, 0, 2]], dtype=np.float32).reshape([1, 4, 4, 1])
w = np.array([[1, 0, 1],
[2, 1, 0],
[0, 0, 1]], dtype=np.float32).reshape(_KERNEL3x3)
self._conv_test(x_val, w)
def test_conv2d_3(self):
x_val = make_xval((1, 1, 5, 5)).transpose(NCHW_TO_NHWC)
w = np.array([[2., 1., 1.],
[1., 3., 1.],
[1., 1., 4.]], dtype=np.float32).reshape(_KERNEL3x3)
self._conv_test(x_val, w)
def test_conv2d_4(self):
x_val = make_xval((1, 1, 5, 5)).transpose(NCHW_TO_NHWC)
w = np.random.random_sample(_KERNEL3x3).astype(np.float32)
self._conv_test(x_val, w, padding="SAME", rtol=1e-05)
def test_conv2d_5(self):
x_val = make_xval((1, 1, 5, 5)).transpose(NCHW_TO_NHWC)
kernel_shape = [3, 3, 1, 2]
w = np.random.random_sample(kernel_shape).astype(np.float32)
self._conv_test(x_val, w, padding="SAME", rtol=1e-05)
def test_conv2d_6(self):
x_shape = [1, 35, 35, 288] # out: [1, 17, 17, 384]
kernel_shape = [3, 3, 288, 384]
strides = [1, 2, 2, 1]
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
self._conv_test(x_val, kernel_val, strides=strides, padding="VALID", rtol=1e-05)
def test_conv2d_dilation_same(self):
x_shape = [1, 35, 35, 288] # NHWC
kernel_shape = [3, 3, 288, 384] # [filter_height, filter_width, in_channels, out_channels]
strides = [1, 1, 1, 1] # NHWC
dilations = [1, 3, 1, 1] # NHWC
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
self._conv_test(x_val, kernel_val, strides=strides, padding="SAME", dilations=dilations, rtol=1e-05)
def test_conv2d_dilation_strides_same(self):
x_shape = [1, 35, 35, 288] # NHWC
kernel_shape = [3, 3, 288, 384] # [filter_height, filter_width, in_channels, out_channels]
strides = [1, 2, 4, 1] # NHWC
dilations = [1, 3, 1, 1] # NHWC
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
self._conv_test(x_val, kernel_val, strides=strides, padding="SAME", dilations=dilations, rtol=1e-05)
def test_conv3d_1(self):
strides = [1, 1, 1, 1, 1]
dilations = [1, 1, 1, 1, 1]
x_val = np.random.random_sample([2, 10, 9, 8, 5]).astype(np.float32)
w = np.random.random_sample([2, 3, 4, 5, 6]).astype(np.float32)
padding = "VALID"
def func(x):
kernel = tf.constant(w, dtype=tf.float32, name='k')
conv = tf.nn.conv3d(x, kernel, strides=strides, padding=padding, data_format="NDHWC", dilations=dilations)
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-05)
def test_conv3d_2(self):
strides = [1, 2, 3, 1, 1]
dilations = [1, 1, 1, 1, 1]
x_val = np.random.random_sample([2, 10, 9, 8, 5]).astype(np.float32)
w = np.random.random_sample([2, 3, 4, 5, 6]).astype(np.float32)
padding = "VALID"
def func(x):
kernel = tf.constant(w, dtype=tf.float32, name='k')
conv = tf.nn.conv3d(x, kernel, strides=strides, padding=padding, data_format="NDHWC", dilations=dilations)
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-05)
def test_conv3d_3(self):
strides = [1, 2, 3, 1, 1]
dilations = [1, 1, 1, 1, 1]
x_val = np.random.random_sample([2, 10, 9, 8, 5]).astype(np.float32)
w = np.random.random_sample([2, 3, 4, 5, 6]).astype(np.float32)
padding = "SAME"
def func(x):
kernel = tf.constant(w, dtype=tf.float32, name='k')
conv = tf.nn.conv3d(x, kernel, strides=strides, padding=padding, data_format="NDHWC", dilations=dilations)
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-05)
def test_avgpool3d(self):
strides = [1, 1, 1, 1, 1]
ksize = [1, 2, 2, 3, 1]
x_val = np.random.random_sample([2, 10, 9, 8, 5]).astype(np.float32)
padding = "VALID"
def func(x):
mp = tf.nn.avg_pool3d(x, ksize, strides, padding=padding, data_format="NDHWC")
return tf.identity(mp, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def test_maxpool3d(self):
strides = [1, 1, 1, 1, 1]
ksize = [1, 2, 2, 3, 1]
x_val = np.random.random_sample([2, 10, 9, 8, 5]).astype(np.float32)
padding = "VALID"
def func(x):
mp = tf.nn.max_pool3d(x, ksize, strides, padding=padding, data_format="NDHWC")
return tf.identity(mp, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_tf_min_version("1.14", "tf.nn.avg_pool2d doesn't exist before tf 1.14")
def test_avgpool2d(self):
strides = [1, 1, 1, 1]
ksize = [1, 2, 3, 1]
x_val = make_xval([2, 10, 12, 3])
padding = "VALID"
def func(x):
mp = tf.nn.avg_pool2d(x, ksize, strides, padding=padding, data_format="NHWC")
return tf.identity(mp, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_tf_min_version("1.7", "tf only support dilation is 1 for now")
def test_conv2d_7(self):
x_shape = [1, 35, 35, 288] # out: [1, 17, 17, 384]
kernel_shape = [3, 3, 288, 384]
strides = [1, 2, 2, 1]
dilations = [1, 3, 3, 1]
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
self._conv_test(x_val, kernel_val, strides=strides, padding="VALID",
dilations=dilations, rtol=1e-05)
def test_conv2d_8(self):
for input_shape in [[10, 10], [5, 5]]:
x_val = make_xval((1, 1, *input_shape)).transpose(NCHW_TO_NHWC)
w = np.random.random_sample([3, 3, 1, 2]).astype(np.float32)
strides = [1, 2, 2, 1]
def func(x):
kernel = tf.constant(w, dtype=tf.float32, name='k')
conv = tf.nn.conv2d(x, kernel, strides=strides, padding="SAME")
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-5)
def test_conv2d_with_pad_valid(self):
x_val = make_xval((1, 1, 5, 5)).transpose(NCHW_TO_NHWC)
w = np.random.random_sample([3, 3, 1, 2]).astype(np.float32)
strides = [1, 1, 1, 1]
def func(x):
kernel = tf.constant(w, dtype=tf.float32, name='k')
x_pad = tf.pad(x, paddings=[[0, 0], [2, 2], [2, 2], [0, 0]])
conv = tf.nn.conv2d(x_pad, kernel, strides=strides, padding="VALID")
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-5)
def test_conv2d_with_pad_same(self):
x_val = make_xval((1, 1, 5, 5)).transpose(NCHW_TO_NHWC)
w = np.random.random_sample([3, 3, 1, 2]).astype(np.float32)
strides = [1, 1, 1, 1]
def func(x):
kernel = tf.constant(w, dtype=tf.float32, name='k')
x_pad = tf.pad(x, paddings=[[0, 0], [2, 2], [2, 2], [0, 0]])
conv = tf.nn.conv2d(x_pad, kernel, strides=strides, padding="SAME")
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-5)
def test_conv2d_transpose(self):
x_shape = [2, 6, 4, 3]
output_shape = [2, 13, 9, 2]
kernel_shape = [3, 3, 2, 3]
strides = [1, 2, 2, 1]
x_val = make_xval(x_shape)
kernel_val = make_xval(kernel_shape)
def func(x):
f = tf.constant(kernel_val, name="kernel", dtype=tf.float32)
conv = tf.nn.conv2d_transpose(x, f, output_shape, strides=strides, padding="VALID")
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-05)
@check_onnxruntime_min_version("0.5.0", "conv transpose is added since onnxruntime-0.5.0")
def test_conv2d_transpose2(self):
# output_shape is dynamic
extra_opset = [utils.make_opsetid(constants.MICROSOFT_DOMAIN, 1)]
process_args = {"extra_opset": extra_opset}
x_shape = [2, 6, 4, 3]
output_shape = np.array([2, 13, 9, 2]).astype(np.int32)
kernel_shape = [3, 3, 2, 3]
strides = [1, 2, 2, 1]
x_val = make_xval(x_shape)
kernel_val = make_xval(kernel_shape)
def func(x, output_shape_placeholder):
f = tf.constant(kernel_val, name="kernel", dtype=tf.float32)
conv = tf.nn.conv2d_transpose(x, f, output_shape_placeholder, strides=strides, padding="VALID")
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: output_shape},
rtol=1e-05, process_args=process_args)
def test_depthwiseconv_0(self):
x_shape = [1, 3, 4, 3]
kernel_shape = [3, 3, 3, 3]
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
def func(x):
kernel = tf.constant(kernel_val, dtype=tf.float32, name='k')
conv = tf.nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=0.08)
def test_depthwiseconv_1(self):
x_shape = [1, 112, 112, 32]
kernel_shape = [3, 3, 32, 1]
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
def func(x):
kernel = tf.constant(kernel_val, dtype=tf.float32, name='k')
conv = tf.nn.depthwise_conv2d(x, kernel, strides=_STRIDE1x1, padding='VALID')
return tf.identity(conv, name=_TFOUTPUT)
# rtol is a bit high, 2 values have a bit high error. Maybe use different input data.
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=0.08)
def test_depthwiseconv_3(self):
x_shape = [1, 112, 112, 32]
kernel_shape = [3, 3, 32, 1]
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
def func(x):
kernel = tf.constant(kernel_val, dtype=tf.float32, name='k')
conv = tf.nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')
return tf.identity(conv, name=_TFOUTPUT)
# rtol is a bit high, 2 values have a bit high error. Maybe use different input data.
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=0.01)
def test_depthwiseconv_shared_kernel(self):
x_shape = [1, 3, 4, 3]
kernel_shape = [3, 3, 3, 3]
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
def func(x, y):
kernel = tf.constant(kernel_val, dtype=tf.float32, name='k')
conv1 = tf.nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')
conv2 = tf.nn.depthwise_conv2d(y, kernel, strides=[1, 1, 1, 1], padding='VALID')
conv = tf.add(conv1, conv2)
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: x_val}, rtol=0.08)
@check_tf_min_version("1.14", "tf depthwise_conv2d dilations")
@check_opset_min_version(11, "non-const pads")
def test_depthwiseconv_dilations(self):
x_shape = [1, 32, 32, 1]
kernel_shape = [5, 5, 1, 1]
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
def func(x):
kernel = tf.constant(kernel_val, dtype=tf.float32, name='k')
conv = tf.nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='SAME', dilations=[3, 4])
return tf.identity(conv, name=_TFOUTPUT)
# rtol is a bit high, 2 values have a bit high error. Maybe use different input data.
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=0.01)
@check_tf_max_version("1.15", "not supported in tf-2.0")
def test_dropout(self):
x_val = np.ones([1, 24, 24, 3], dtype=np.float32)
# Define a scope for reusing the variables
def func(x):
is_training = tf.constant(False, tf.bool)
x_ = tf.identity(x)
fc1 = tf.layers.dropout(x_, rate=.1, training=is_training)
return tf.identity(fc1, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val},
graph_validator=lambda g: (check_op_count(g, "RandomUniform", 0) and
check_op_count(g, "RandomUniformLike", 0)))
def test_nn_dropout(self):
x_val = np.ones([1, 24, 24, 3], dtype=np.float32)
# Define a scope for reusing the variables
def func(x, keep_prob):
x_ = tf.identity(x)
fc1 = dropout(x_, keep_prob)
return tf.identity(fc1, name=_TFOUTPUT)
# when constant_fold is enabled, PlaceholderWithDefault will be folded into either a const or a placeholder.
# here we set it False to test PlaceholderWithDefault bug: https://github.com/onnx/tensorflow-onnx/pull/446
# Dropout with ratio 1.0 will be optimized so that only one Identity is left
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: np.array(1., dtype=np.float32)},
graph_validator=lambda g: (check_op_count(g, "RandomUniform", 0) and
check_op_count(g, "RandomUniformLike", 0)))
@check_tf_min_version("1.13")
def test_nn_dropout_with_rate(self):
rate = tf.constant(0., name="rate")
x_val = np.ones([1, 24, 24, 3], dtype=np.float32)
# Define a scope for reusing the variables
def func(x):
x_ = tf.identity(x)
fc1 = tf.nn.dropout(x_, rate=rate)
return tf.identity(fc1, name="output")
feed_dict = {"input_1:0": x_val}
input_names_with_port = ["input_1:0"]
output_names_with_port = ["output:0"]
self.run_test_case(func, feed_dict, input_names_with_port, output_names_with_port, constant_fold=False,
graph_validator=lambda g: (check_op_count(g, "RandomUniform", 0) and
check_op_count(g, "RandomUniformLike", 0)))
def test_conv2d_with_input_transpose(self):
x_shape = [2, 32, 32, 3]
kernel_shape = [3, 3, 3, 3]
x_val = make_xval(x_shape)
x_val_for_onnx = x_val.transpose(NHWC_TO_NCHW)
def func(x):
kernel = tf.constant(make_xval(kernel_shape), dtype=tf.float32, name='k')
conv = tf.nn.conv2d(x, kernel, strides=[1, 1, 1, 1], padding="SAME")
return tf.identity(conv, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-05,
process_args={"inputs_as_nchw": [_INPUT]},
onnx_feed_dict={_INPUT: x_val_for_onnx})
def test_lrn_default(self):
x_shape = [1, 3, 4, 3]
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
def func(x):
op = tf.nn.local_response_normalization(x)
return tf.identity(op, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-05)
def test_lrn(self):
# can't set bias = 0
x_shape = [1, 2, 2, 8]
x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
def func(x):
op = tf.nn.local_response_normalization(x, depth_radius=4, bias=2, alpha=2, beta=1)
return tf.identity(op, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-05)
@check_onnxruntime_incompatibility("Abs")
def test_abs(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func(x):
x_ = tf.abs(x)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_onnxruntime_incompatibility("Add")
def test_const(self):
x_val = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32).reshape((2, 2))
def func(x):
y = tf.constant(x_val, name="y")
return tf.add(x, y, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_onnxruntime_incompatibility("Add")
def test_add(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func(x):
x_ = tf.add(x, x)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def test_placeholder(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func(x):
return tf.identity(x, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def test_placeholder_with_default_use_default(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func():
x = tf.constant(x_val, name="x")
y = tf_placeholder_with_default(x, x_val.shape, name=_TFINPUT)
return tf.identity(y, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {}, as_session=True, premade_placeholders=True)
def test_placeholder_with_default_use_feed(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func():
x = tf.constant(x_val, name="x")
y = tf_placeholder_with_default(x, x_val.shape, name=_TFINPUT)
return tf.identity(y, name=_TFOUTPUT)
x_feed_val = np.array([11.0, 22.0, -33.0, -44.0], dtype=np.float32).reshape((2, 2))
self._run_test_case(func, [_OUTPUT], {_INPUT: x_feed_val}, as_session=True, premade_placeholders=True)
def test_placeholder_with_default_computed_use_default(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
y_val = np.array([2.0, -4.0, 6.0, -8.0], dtype=np.float32).reshape((2, 2))
def func():
x = tf_placeholder(tf.float32, x_val.shape, name=_TFINPUT)
y = tf_placeholder(tf.float32, y_val.shape, name=_TFINPUT1)
total = tf.add(x, y)
z = tf_placeholder_with_default(total, x_val.shape, name=_TFINPUT2)
total2 = tf.add(total, z)
return tf.identity(total2, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: y_val}, as_session=True,
premade_placeholders=True, process_args={'use_default': [_TFINPUT2]})
def test_placeholder_with_default_computed_ignore_default(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
y_val = np.array([2.0, -4.0, 6.0, -8.0], dtype=np.float32).reshape((2, 2))
z_val = np.array([3.0, 6.0, 9.0, 10.0], dtype=np.float32).reshape((2, 2))
def func():
x = tf_placeholder(tf.float32, x_val.shape, name=_TFINPUT)
y = tf_placeholder(tf.float32, y_val.shape, name=_TFINPUT1)
total = tf.add(x, y)
z = tf_placeholder_with_default(total, x_val.shape, name=_TFINPUT2)
total2 = tf.add(total, z)
return tf.identity(total2, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: y_val, _INPUT2: z_val}, as_session=True,
premade_placeholders=True, process_args={'ignore_default': [_TFINPUT2]})
@check_onnxruntime_incompatibility("Add")
def test_add_bcast(self):
x1_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
x2_val = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], dtype=np.float32).reshape((2, 2, 2))
def func(x1, x2):
x_ = tf.add(x1, x2)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x1_val, _INPUT1: x2_val})
@check_onnxruntime_incompatibility("Add")
def test_add_bcast1(self):
# example taken from onnx doc
x1_val = np.random.randn(3, 4, 5).astype(np.float32)
x2_val = np.random.randn(5).astype(np.float32)
def func(x1, x2):
x_ = tf.add(x1, x2)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x1_val, _INPUT1: x2_val})
def test_matmul0(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func(x):
x_ = tf.matmul(x, x)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@skip_tflite("Issue with matmul with 2 copies of same input")
def test_matmul1(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0, 5.0, 6.0], dtype=np.float32).reshape((2, 3))
def func(x):
x_ = tf.matmul(x, x, transpose_a=True)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def test_matmul2(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
y_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func(x, y):
x_ = tf.matmul(x, y, transpose_b=True)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: y_val})
@unittest.skipIf(get_test_config().is_mac and get_test_config().is_onnxruntime_backend
and get_test_config().backend_version == "0.2.1", "onnxruntime 0.2.1 has bug on mac")
def test_matmul3(self):
x_shape = [1, 12, 256, 64]
x_val = np.arange(np.prod(x_shape)).astype("float32").reshape((x_shape))
def func(x, y):
x_ = tf.matmul(x, y, transpose_b=True)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: x_val}, rtol=1e-5)
@check_onnxruntime_incompatibility("Sub")
def test_sub(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func(x):
x_ = tf.subtract(x, x)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_onnxruntime_incompatibility("Mul")
def test_multiply(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func(x):
x_ = tf.multiply(x, x)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_onnxruntime_incompatibility("Div")
def test_div(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func(x):
x_ = tf.realdiv(x, x)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_onnxruntime_incompatibility("Exp")
def test_exp(self):
x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
def func(x):
x_ = tf.exp(x)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-05)
@check_onnxruntime_incompatibility("Log")
def test_log(self):
x_val = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32).reshape((2, 2))
def func(x):
x_ = tf.math.log(x)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_onnxruntime_incompatibility("Log")
def test_log_double(self):
x_val = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float64).reshape((2, 2))
def func(x):
x_ = tf.math.log(x)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
def test_gather(self):
x_val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
idx = np.array([1, 0, 2], dtype=np.int32)
idx_flattened = np.array([i * x_val.shape[1] + idx for i in range(0, x_val.shape[0])])
def func(x):
x_ = tf.gather(tf.reshape(x, [-1]), tf.constant(idx_flattened))
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_tf_min_version("1.14")
@check_opset_min_version(12, "GatherND with batch_dims")
def test_gather_batch_dims_no_trans(self):
x_val = np.arange(2 * 2 * 3 * 5 * 4, dtype=np.float32).reshape((2, 2, 3, 5, 4))
idx_val = np.array([[[1, 0, 2, 0], [1, 1, 1, 0]], [[0, 0, 0, 0], [2, 1, 1, 0]]], dtype=np.int32)
def func(x, idx):
x_ = tf.gather(x, idx, batch_dims=2, axis=2)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: idx_val})
@check_tf_min_version("1.14")
@check_opset_min_version(12, "GatherND with batch_dims")
def test_gather_batch_dims(self):
x_val = np.arange(2 * 2 * 3 * 5 * 4, dtype=np.float32).reshape((2, 2, 3, 5, 4))
idx_val = np.array([[[1, 0, 2, 0], [1, 1, 1, 0]], [[0, 0, 0, 0], [2, 1, 1, 0]]], dtype=np.int32)
def func(x, idx):
x_ = tf.gather(x, idx, batch_dims=2, axis=3)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: idx_val})
@check_opset_min_version(10, "Slice")
def test_roll_axis_scalar(self):
x_val = np.arange(4 * 3 * 5 * 2, dtype=np.float32).reshape((4, 3, 5, 2))
shift_val = np.array(4, dtype=np.int64)
axes_val = np.array(2, dtype=np.int32)
def func(x, shift):
x_ = tf.roll(x, shift, axes_val)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: shift_val})
@check_opset_min_version(10, "Slice")
def test_roll_axis_vector(self):
x_val = np.arange(4 * 3 * 5 * 2, dtype=np.float32).reshape((4, 3, 5, 2))
shift_val = np.array([2, 3, 4], dtype=np.int32)
axes_val = np.array([1, 2, 1], dtype=np.int32)
def func(x, shift):
x_ = tf.roll(x, shift, axes_val)
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: shift_val})
@check_opset_min_version(10, "Slice")
def test_roll_neg_axis(self):
def func(input_ids):
shifted_input_ids = tf.cast(input_ids, tf.int32)
shifted_input_ids = tf.roll(shifted_input_ids, 1, axis=-1)
return tf.identity(shifted_input_ids, name=_TFOUTPUT)
x_val = np.array([[0, 1, 2, 3, 4, 5, 6, 7], [1, 2, 3, 4, 5, 6, 7, 8]], dtype=np.int64)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_tf_min_version("2.2")
def test_large_model_format(self):
x_val = np.array([2.0], dtype=np.float32)
y_const = np.arange(2000, dtype=np.float32)
def func(x):
x_ = tf.multiply(x, tf.constant(y_const))
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, large_model=True)
@check_target('rs6', 'GatherNd')
def test_gathernd(self):
x_val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
indices = np.array([[[0, 1], [1, 1]], [[1, 2], [0, 2]]], dtype=np.int32)
def func(x):
x_ = tf.gather_nd(x, tf.constant(indices))
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
x_val = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.float32)
indices = np.array([[[0], [2]], [[4], [7]], [[6], [1]]], dtype=np.int32)
def func(x):
x_ = tf.gather_nd(x, tf.constant(indices))
return tf.identity(x_, name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
@check_target('rs6', 'GatherNd')
def test_gathernd_less_index(self):
x_val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
indices = np.array([[[0], [1]], [[2], [0]]], dtype=np.int32)