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fix aicpu ut

This commit is contained in:
yanzhenxiang2020 2020-09-15 14:31:56 +08:00
parent c543db0585
commit c6db808bbf
4 changed files with 8 additions and 9 deletions
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1
mindspore/ops/_op_impl/aicpu/reverse_sequence.py
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@ -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) \

2
mindspore/ops/operations/nn_ops.py
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@ -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,)`.

10
tests/st/ops/ascend/test_aicpu_ops/test_ctc_loss.py
View File

@ -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)

4
tests/st/ops/ascend/test_aicpu_ops/test_reverse_sequence.py
View File

@ -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)