From c6db808bbf443ad1eaa0a94b4ba7cdb7b0aaad07 Mon Sep 17 00:00:00 2001 From: yanzhenxiang2020 Date: Tue, 15 Sep 2020 14:31:56 +0800 Subject: [PATCH] fix aicpu ut --- mindspore/ops/_op_impl/aicpu/reverse_sequence.py | 1 + mindspore/ops/operations/nn_ops.py | 2 +- tests/st/ops/ascend/test_aicpu_ops/test_ctc_loss.py | 10 ++++------ .../ops/ascend/test_aicpu_ops/test_reverse_sequence.py | 4 ++-- 4 files changed, 8 insertions(+), 9 deletions(-) diff --git a/mindspore/ops/_op_impl/aicpu/reverse_sequence.py b/mindspore/ops/_op_impl/aicpu/reverse_sequence.py index 678a4a61f31..0882bb9a613 100644 --- a/mindspore/ops/_op_impl/aicpu/reverse_sequence.py +++ b/mindspore/ops/_op_impl/aicpu/reverse_sequence.py @@ -26,6 +26,7 @@ reverse_sequence_op_info = AiCPURegOp("ReverseSequence") \ .dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default) \ .dtype_format(DataType.I16_Default, DataType.I32_Default, DataType.I16_Default) \ .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \ + .dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \ .dtype_format(DataType.I64_Default, DataType.I32_Default, DataType.I64_Default) \ .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default) \ .dtype_format(DataType.U16_Default, DataType.I32_Default, DataType.U16_Default) \ diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index f6b7f429eb8..0fb89f576c9 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1892,7 +1892,7 @@ class RNNTLoss(PrimitiveWithInfer): - **acts** (Tensor) - Tensor of shape :math:`(B, T, U, V)`. Data type should be float16 or float32. - **labels** (Tensor[int32]) - Tensor of shape :math:`(B, U-1)`. - **input_lengths** (Tensor[int32]) - Tensor of shape :math:`(B,)`. - - **label_lebgths** (Tensor[int32]) - Tensor of shape :math:`(B,)`. + - **label_lengths** (Tensor[int32]) - Tensor of shape :math:`(B,)`. Outputs: - **costs** (Tensor[int32]) - Tensor of shape :math:`(B,)`. diff --git a/tests/st/ops/ascend/test_aicpu_ops/test_ctc_loss.py b/tests/st/ops/ascend/test_aicpu_ops/test_ctc_loss.py index 67949bf767c..45a45b730a8 100644 --- a/tests/st/ops/ascend/test_aicpu_ops/test_ctc_loss.py +++ b/tests/st/ops/ascend/test_aicpu_ops/test_ctc_loss.py @@ -17,7 +17,6 @@ import numpy as np import mindspore.context as context import mindspore.nn as nn from mindspore import Tensor -from mindspore.common.api import ms_function from mindspore.ops import operations as P context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") @@ -28,16 +27,15 @@ class Net(nn.Cell): super(Net, self).__init__() self.ctc_loss = P.CTCLoss() - @ms_function def construct(self, inputs, labels_indices, labels_values, sequence_length): return self.ctc_loss(inputs, labels_indices, labels_values, sequence_length) def test_net_float32(): - x = np.rand.randn(2, 2, 3).astype(np.float32) - labels_indices = np.array([[0, 0], [1, 0]]).astype(np.int64) - labels_values = np.array([2, 2]).astype(np.int32) + x = np.random.randn(2, 2, 3).astype(np.float32) + labels_indices = np.array([[0, 1], [1, 0]]).astype(np.int64) + labels_values = np.array([1, 2]).astype(np.int32) sequence_length = np.array([2, 2]).astype(np.int32) net = Net() output = net(Tensor(x), Tensor(labels_indices), Tensor(labels_values), Tensor(sequence_length)) - print(output.asnumpy()) + print(output) diff --git a/tests/st/ops/ascend/test_aicpu_ops/test_reverse_sequence.py b/tests/st/ops/ascend/test_aicpu_ops/test_reverse_sequence.py index 5927b62560f..f09edfeff73 100644 --- a/tests/st/ops/ascend/test_aicpu_ops/test_reverse_sequence.py +++ b/tests/st/ops/ascend/test_aicpu_ops/test_reverse_sequence.py @@ -40,7 +40,7 @@ def test_net_int8(): batch_dim = 1 net = Net(seq_dim, batch_dim) output = net(Tensor(x), Tensor(seq_lengths)) - expected = np.array([1, 5, 9], [4, 2, 6], [7, 8, 3]).astype(np.int8) + expected = np.array([[1, 5, 9], [4, 2, 6], [7, 8, 3]]).astype(np.int8) assert np.array_equal(output.asnumpy(), expected) @@ -51,5 +51,5 @@ def test_net_int32(): batch_dim = 0 net = Net(seq_dim, batch_dim) output = net(Tensor(x), Tensor(seq_lengths)) - expected = np.array([1, 2, 3], [5, 4, 6], [9, 8, 7]).astype(np.int32) + expected = np.array([[1, 2, 3], [5, 4, 6], [9, 8, 7]]).astype(np.int32) assert np.array_equal(output.asnumpy(), expected)