def test_Conv2DTranspose_9_10_11_12_group():
"""
api: paddle.Conv2DTranspose
op version: 9
"""
op = Net(in_channels=16,
out_channels=16,
groups=16,
padding=[[0, 0], [0, 0], [1, 2], [2, 3]])
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, [16, 16, 10, 10]).astype('float32')))
obj.run()
# if __name__ == '__main__':
# test_Conv2DTranspose_9_10_11_12_group()
# test_Conv2DTranspose_9_output_padding_3()
# test_Conv2DTranspose_9_output_padding_2()
# test_Conv2DTranspose_9_output_padding_1()
# test_Conv2DTranspose_9_output_padding()
# test_Conv2DTranspose_9_10_11_12()
def test_topk_base():
"""
api: paddle.topk
op version: 9, 10, 11, 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-10, rtol=1e-11
obj = APIOnnx(op, 'topk', [11, 12])
obj.set_input_data("input_data",
paddle.to_tensor([[1, 4, 5, 7], [2, 6, 2, 5]]))
obj.run()
def test_broadcast_to_base():
"""
api: paddle.broadcast_to
op version: 9, 10, 11, 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-10, rtol=1e-11
obj = APIOnnx(op, 'broadcast_to', [9, 10, 11, 12])
obj.set_input_data("input_data", paddle.to_tensor([1, 2, 3],
dtype='int32'))
obj.run()
def test_floor_divide_base():
"""
api: paddle.floor_divide
op version: 10
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-10, rtol=1e-11
obj = APIOnnx(op, 'floor_divide', [9, 10, 11, 12])
obj.set_input_data("input_data", paddle.to_tensor([2, 3, 8, 7]),
paddle.to_tensor([1, 5, 3, 3]))
obj.run()
def test_GRU_base():
"""
api: paddle.nn.GRU
op version: 9, 10, 11, 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-10, rtol=1e-11
obj = APIOnnx(op, 'nn_GRU', [9, 10, 11, 12])
obj.set_input_data("input_data", paddle.randn((4, 23, 16)),
paddle.randn((2, 4, 32)))
obj.run()
def test_mod_12():
"""
api: paddle.mod
op version: 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'mod', [12])
obj.set_input_data("input_data", paddle.to_tensor([2, 3, 8, 7]),
paddle.to_tensor([1, 5, 3, 3]))
obj.run()
def test_floor_mod_broadcasting():
"""
api: paddle.floor_mod
op version: 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'floor_mod', [12])
obj.set_input_data("input_data", paddle.to_tensor([2, 3, 8, 7]),
paddle.to_tensor([3]))
obj.run()
def test_clip_11():
"""
api: paddle.clip
op version: 11
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'clip', [11])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_Embedding_base():
"""
api: paddle.Embedding
op version: 9, 10, 11, 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_Embedding', [9, 10, 11, 12])
obj.set_input_data(
"input_data",
paddle.to_tensor(np.arange(3, 6).reshape((3, 1)).astype(np.int64)))
obj.run()
def test_expand_9():
"""
api: paddle.expand
op version: 9
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'expand', [9])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_nn_functional_thresholded_relu_threshold():
"""
api: paddle.nn.thresholded_relu
op version: 12
"""
op = Net(threshold=2)
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_functional_thresholded_relu', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_logical_not_12():
"""
api: paddle.logical_not
op version: 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'logical_not', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('bool')))
obj.run()
def test_unsqueeze_9_multil_negative_axis():
"""
api: paddle.unsqueeze
op version: 9
"""
op = Net(axis=[1, 2, 3, -1])
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'unsqueeze', [9, 10, 11, 12, 13])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [5, 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_meshgrid_unlikeSize():
"""
api: paddle.meshgrid
op version: 11, 12
"""
op = Net()
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'))
obj.run()
def test_register_buffer():
"""
api: register_buffer
op version: 9
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'register_buffer', [9])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [1]).astype('float32')))
obj.run()
def test_unique_return_all():
"""
api: paddle.unique
op version: 12
"""
op = Net_mult_all()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'unique', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_nn_functional_LogSigmoid_axis():
"""
api: paddle.nn.functional.log_softmax
op version: 12
"""
op = Net(axis=1)
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_any_10():
"""
api: paddle.any
op version: 10
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'any', [10])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('bool')))
obj.run()
def test_hardtanh_12():
"""
api: paddle.hardtanh
op version: 12
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'hardtanh', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
obj.run()
def test_acos_7():
"""
api: paddle.acos
op version: 7
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'acos', [7])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 3, 3]).astype('float32')))
obj.run()
def test_hardshrink_10():
"""
api: paddle.nn.hardshrink
op version: 10
"""
op = Net()
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'nn_hardshrink', [10])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [3, 10]).astype('float32')))
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_all_keepdim():
"""
api: paddle.all
op version: 12
"""
op = Net(keepdim=True)
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'all', [12])
obj.set_input_data(
"input_data",
paddle.to_tensor(randtool("float", -1, 1, [4, 3, 10]).astype('bool')))
obj.run()
def test_gather_axis():
"""
api: paddle.gather
op version: 12
"""
op = Net(axis=1)
op.eval()
# net, name, ver_list, delta=1e-6, rtol=1e-5
obj = APIOnnx(op, 'gather', [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_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_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_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_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()
