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:])