def test_ceil_11():
"""
api: paddle.ceil
op version: 11
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'ceil', [11])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 3]).astype('float32')))
obj.run()
def test_nn_functional_LogSigmoid_dtype():
"""
api: paddle.nn.functional.log_softmax
op version: 12
"""
op = Net(dtype='float64')
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_functional_LogSigmoid', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_argmax_dtype():
"""
api: paddle.argmax
op version: 11
"""
op = Net(dtype="int32")
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'argmax', [11])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_tile_12():
"""
api: paddle.tile
op version: 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'tile', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_any_axis():
"""
api: paddle.any
op version: 12
"""
op = Net(axis=1)
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'any', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [4, 3, 10]).astype('bool')))
obj.run()
def test_log1p_7():
"""
api: paddle.log1p
op version: 7
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'log1p', [7])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_mask_select_12():
"""
api: paddle.mask_select
op version: 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'mask_select', [12])
obj.set_input_data("input_data",
paddle.to_tensor([10, 4, 5, 6]).astype('float32'),
paddle.to_tensor([1, 0, 1, 0]).astype('bool'))
obj.run()
def test_softshrink_threshold():
"""
api: paddle.softshrink
op version: 12
"""
op = Net(threshold=1)
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'softshrink', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_Conv2DTranspose_9_output_padding_1():
"""
api: paddle.Conv2DTranspose
op version: 9
"""
op = Net(output_padding=1, stride=[3, 2], padding=[1, 2])
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_Conv2DTranspose', [9])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 10, 10]).astype('float32')))
obj.run()
def test_concat_10():
"""
api: paddle.concat
op version: 9
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'concat', [10])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')),
paddle.to_tensor(randtool("float", 0, 1, [3, 10]).astype('float32')))
obj.run()
def test_Conv1D_11_padding_0():
"""
api: paddle.nn.Conv1D
op version: 11
"""
op = Net(padding=[[0, 0], [0, 0], [1, 2]])
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_Conv1D', [11])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 10]).astype('float32')))
obj.run()
def test_logical_xor_12():
"""
api: paddle.logical_xor
op version: 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'logical_xor', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('bool')),
paddle.to_tensor(randtool("float", 0, 1, [3, 10]).astype('bool')))
obj.run()
def test_meshgrid_3():
"""
api: paddle.meshgrid
op version: 11, 12
"""
op = Net_3()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'meshgrid', [11, 12])
obj.set_input_data("input_data",
paddle.to_tensor([1, 2, 3]).astype('float32'),
paddle.to_tensor([5, 6]).astype('float32'),
paddle.to_tensor([1, 2, 3, 4]).astype('float32'))
obj.run()
def test_initializer_Uniform_base():
"""
api: paddle.initializer.Uniform
op version: 9
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_initializer_Uniform', [9, 10, 11, 12])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 2]).astype('float32')))
obj.run()
def test_BatchNorm2D_11():
"""
api: paddle.nn.BatchNorm2D
op version: 11
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_BatchNorm2D', [11])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 10, 10]).astype('float32')))
obj.run()
def test_Conv2D_dilation_2_9():
"""
api: paddle.nn.Conv2D
op version: 9
"""
op = Net(in_channels=16, out_channels=16, dilation=3)
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'Conv2D', [9])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 16, 10, 10]).astype('float32')))
obj.run()
def test_MaxPool2D_base_Padding_0():
"""
api: paddle.MaxPool2D
op version: 9, 10, 11, 12
"""
op = Net(kernel_size=5, padding=[[0, 0], [0, 0], [1, 2], [3, 4]])
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_MaxPool2D', [9, 10, 11, 12])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 10, 10]).astype('float32')))
obj.run()
def test_Conv1DTranspose_12_Padding_tuple1():
"""
api: paddle.nn.Conv1DTranspose
op version: 12
"""
op = Net(padding=(1, 2))
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_Conv1DTranspose', [9, 10, 11, 12, 13])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 10]).astype('float32')))
obj.run()
def test_logsumexp_keepdim():
"""
api: paddle.logsumexp
op version: 12
"""
op = Net(keepdim=True)
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'logsumexp', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 3, 10]).astype('float32')))
obj.run()
def test_Conv3D_10():
"""
api: paddle.Conv3D
op version: 10
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'Conv2D_Dropout', [10])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 5, 10, 10]).astype('float32')))
obj.run()
def test_nonzero_base():
"""
api: paddle.nonzero
op version: 9, 10, 11, 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nonzero', [9, 10, 11, 12])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 3, 3]).astype('float32')))
obj.run()
def test_nn_functional_interpolate_nearest_date_format():
"""
api: paddle.nn.functional.interpolate
op version: 11
"""
op = Net(size=[4, 12], data_format='NHWC')
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_functional_interpolate', [11])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [2, 2, 2, 10]).astype('float32')))
obj.run()
def test_atan_9():
"""
api: paddle.atan
op version: 9
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'atan', [9])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 3, 3]).astype('float32')))
obj.run()
def test_nn_functional_interpolate_bicubic_align_corners():
"""
api: paddle.nn.functional.interpolate
op version: 11
"""
op = Net(mode='bicubic', scale_factor=1.5, align_corners=True)
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_functional_interpolate', [11])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [1, 2, 2, 5]).astype('float32')))
obj.run()
def test_Conv1D_11_padding_replicate():
"""
api: paddle.nn.Conv1D
op version: 11
"""
op = Net(padding=2, padding_mode='replicate')
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_Conv1D', [9, 10, 11, 12])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 10]).astype('float32')))
obj.run()
def test_nn_functional_interpolate_nearest_scale_factor_tuple():
"""
api: paddle.nn.functional.interpolate
op version: 11
"""
op = Net(scale_factor=(1, 2))
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_functional_interpolate', [11])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [2, 3, 6, 10]).astype('float32')))
obj.run()
def test_Conv2DTranspose_9_10_11_12():
"""
api: paddle.Conv2DTranspose
op version: 9, 10, 11, 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_Conv2DTranspose', [9, 10, 11, 12])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 10, 10]).astype('float32')))
obj.run()
def test_GroupNorm_12():
"""
api: paddle.nn.GroupNorm
op version: 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_GroupNorm', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [5, 10, 8, 8]).astype('float32')))
obj.run()
def test_Hardswish_10():
"""
api: paddle.nn.Hardswish
op version: 10
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_Hardswish', [10])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 10, 10]).astype('float32')))
obj.run()
def test_Conv2D_padding_1_9():
"""
api: paddle.nn.Conv3D
op version: 9
"""
op = Net(padding=[1, 2, 3, 4])
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'Conv2D', [9])
obj.set_input_data(
"input_data",
paddle.to_tensor(
randtool("float", -1, 1, [3, 1, 10, 10]).astype('float32')))
obj.run()
