def test_pixel_shuffle(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_pixel_shuffle_impl]
arg_dict["device"] = ["cpu", "cuda"]
arg_dict["shape"] = [(2, 144, 5, 5), (11, 144, 1, 1)]
arg_dict["h_upscale_factor"] = [2, 3, 4]
arg_dict["w_upscale_factor"] = [2, 3, 4]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
arg_dict["shape"] = [(8, 25, 18, 18), (1, 25, 2, 2)]
arg_dict["h_upscale_factor"] = [5]
arg_dict["w_upscale_factor"] = [5]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_tensordot_tensor_dim(test_case):
def _test_tensor_dim(test_case, device):
np_dim = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.int)
flow_dim = flow.tensor(np_dim).to(device)
torch_dim = torch.tensor(np_dim).to(device)
np_random_array = np.random.randn(2, 3, 4, 5)
flow_tensor = flow.tensor(np_random_array).to(device)
torch_tensor = torch.tensor(np_random_array).to(device)
flow_result = flow.tensordot(flow_tensor,
flow_tensor,
dims=flow_dim)
torch_result = torch.tensordot(torch_tensor,
torch_tensor,
dims=torch_dim)
test_case.assertTrue(
np.allclose(
flow_result.numpy(),
torch_result.cpu().numpy(),
rtol=0.0001,
atol=0.0001,
))
arg_dict = OrderedDict()
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
_test_tensor_dim(test_case, arg[0])
def test_ones_like(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_ones_like_float, _test_ones_like_int]
arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 4, 5, 6)]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_argwhere(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_argwhere]
arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 4, 5, 6), (2, 3, 0, 4)]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_reflection_pad2d(test_case):
arg_dict = OrderedDict()
arg_dict["shape"] = [(1, 2, 3, 4), (8, 3, 4, 4)]
arg_dict["padding"] = [2, (1, 1, 2, 2)]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
_test_reflection_pad2d(test_case, *arg)
def test_cos(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_cos, _test_cos_backward]
arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 3, 4, 5)]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_type_tensortype_cpu(test_case):
# test tensor.type(x: tensortype) rather than tensor.type_tensortype
arg_dict = OrderedDict()
arg_dict["shape"] = [(1, 2), (3, 4, 5), (2, 3, 4, 5)]
arg_dict["device"] = ["cpu", "cuda"]
arg_dict["src_dtype"] = [
flow.uint8,
flow.int8,
flow.int64,
flow.int32,
flow.float16,
flow.float32,
flow.float64,
]
tensortype_dict = {
flow.CharTensor: [flow.int8, flow.device("cpu")],
flow.ByteTensor: [flow.uint8, flow.device("cpu")],
flow.IntTensor: [flow.int32, flow.device("cpu")],
flow.LongTensor: [flow.int64, flow.device("cpu")],
flow.HalfTensor: [flow.float16, flow.device("cpu")],
flow.FloatTensor: [flow.float32, flow.device("cpu")],
flow.DoubleTensor: [flow.float64, flow.device("cpu")],
}
arg_dict["tgt_tensortype"] = list(tensortype_dict.keys())
for arg in GenArgList(arg_dict):
_test_type_tensortype(test_case, tensortype_dict, *arg)
def test_reduce(test_case):
arg_dict = OrderedDict()
arg_dict["dst"] = [0, 1, 2, 3]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
_test_reduce(test_case, *arg)
def test_triu(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_triu]
arg_dict["diagonal"] = [2, -1]
arg_dict["device"] = ["cuda", "cpu"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_autograd_interface(test_case):
arg_dict = OrderedDict()
arg_dict["case"] = [_test_autograd_backward, _test_autograd_grad]
arg_dict["shape"] = [(2, 3), (2, 3, 4, 5)]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_expm1(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_expm1_impl]
arg_dict["device"] = ["cpu", "cuda"]
arg_dict["shape"] = [(1, ), (2, 3), (2, 3, 4), (2, 3, 4, 5)]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_global_ZeroPad2d(test_case):
arg_dict = OrderedDict()
arg_dict["padding"] = [2, (1, 1, 2, 2)]
for arg in GenArgList(arg_dict):
for placement in all_placement():
for sbp in all_sbp(placement, max_dim=4):
_test_global_ZeroPad2d(test_case, placement, sbp, *arg)
def test_in_top_k(test_case):
arg_dict = OrderedDict()
arg_dict["shape"] = [(2, 3), (3, 4), (5, 6)]
arg_dict["k"] = [1, 2, 5]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
_test_in_top_k_impl(test_case, *arg)
def test_gpu_fixed_dropout(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [
fixed_gpu_seed_dropout_test,
]
for arg in GenArgList(arg_dict):
arg[0](test_case)
def test_tensor_str_1n2d(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [
_test_global_tensor_str_2d,
]
arg_dict["device"] = ["cuda", "cpu"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_ConstantPad2d(test_case):
arg_dict = OrderedDict()
arg_dict["shape"] = [(1, 2, 3, 4), (8, 3, 4, 4)]
arg_dict["padding"] = [2, (1, 1, 2, 2)]
arg_dict["value"] = [0.0]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
_test_ZeroPad2d(test_case, *arg)
def test_inplace_contiguous(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [
_tets_inplace_contiguous,
]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_gather(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_fused_bias_add_gelu]
arg_dict["channel"] = [2, 4, 6, 8]
arg_dict["axis"] = [1]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_list_indexing_using_scalar_tensor(test_case):
arg_dict = OrderedDict()
arg_dict["function_test"] = [
_test_list_indexing_using_scalar_tensor,
]
arg_dict["dtype"] = [flow.uint8, flow.int8, flow.int32, flow.int64]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_eager_boxing_1d_special_split_axis(test_case):
arg_dict = OrderedDict()
arg_dict["in_device"] = ["cpu", "cuda"]
arg_dict["out_device"] = ["cpu", "cuda"]
arg_dict["in_device_list"] = [[0, 1], [1, 2, 3], [0, 1, 2, 3]]
arg_dict["out_device_list"] = [[0, 1, 3], [0, 1, 2, 3]]
for arg in GenArgList(arg_dict):
_test_eager_boxing_1d_special_split_axis(test_case, *arg)
def test_eye_with_1n2d(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_eye_with_1n2d]
arg_dict["n"] = [4, 3, 2]
arg_dict["m"] = [4, 3, 2]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_clip_value(test_case):
arg_dict = OrderedDict()
arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 4, 5, 6)]
arg_dict["device"] = ["cpu", "cuda"]
arg_dict["clip_value"] = [0, 0.5, 1.0]
for arg in GenArgList(arg_dict):
_test_clip_grad_value_impl(test_case, *arg)
_test_graph_clip_grad_value_impl(test_case, *arg)
def test_clip_grad(test_case):
arg_dict = OrderedDict()
arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 4, 5, 6)]
arg_dict["device"] = ["cpu", "cuda"]
arg_dict["max_norm"] = [0, 0.5, 1.0]
arg_dict["norm_type"] = ["inf", "-inf", 0.0, 1.0, 2.0, 3.5]
for arg in GenArgList(arg_dict):
_test_clip_grad_norm_impl(test_case, *arg)
def test_nonzero(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_nonzero]
arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 4, 5, 6), (2, 3, 0, 4)]
arg_dict["as_tuple"] = [True, False]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_masked_select(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [
_test_masked_select, _test_masked_select_broadcast
]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_0d_randn(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_0d_rand]
arg_dict["device"] = ["cpu", "cuda"]
arg_dict["shape"] = [(2, 0, 4), (2, 0, 2)]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_batch_gather(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_batch_gather]
arg_dict["shape"] = [(3, 2, 2), (3, 2, 4, 2), (3, 3, 4, 2, 2), (4, 2)]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_util_ops(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_isnan, _test_isinf]
arg_dict["shape"] = [(2, 3, 4), (1, 2, 3)]
arg_dict["dtype"] = [flow.float, flow.int]
arg_dict["device"] = ["cpu", "cuda"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_fuse_bias_add_dropout(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [_test_fused_bias_add_dropout]
arg_dict["shape"] = [(16, 64, 72), (32, 16, 48)]
arg_dict["axis"] = [0, 1, 2, -1, -2, -3]
arg_dict["drop_prob"] = [0.0, 1.0]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])
def test_cast(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [
_test_different_dtype,
]
arg_dict["device"] = ["cpu", "cuda"]
arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 3, 4, 5), (2, 0, 4)]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])