add CTCGreedyDecoder ops

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -59,3 +59,4 @@ from .fused_sparse_ftrl import _fused_sparse_ftrl_aicpu
 from .fused_sparse_proximal_adagrad import _fused_sparse_proximal_adagrad_aicpu
 from .meshgrid import _meshgrid_aicpu
 from .trans_data import _trans_data_aicpu
+from .ctc_greedy_decoder import _ctc_greedy_decoder_aicpu

mindspore/ops/_op_impl/aicpu/ctc_greedy_decoder.py

@@ -0,0 +1,39 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""CTCGreedyDecoder op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+ctc_greedy_decoder_op_info = AiCPURegOp("CTCGreedyDecoder") \
+    .fusion_type("OPAQUE") \
+    .input(0, "inputs", "required") \
+    .input(1, "sequence_length", "required") \
+    .output(0, "decoded_indices", "required") \
+    .output(1, "decoded_values", "required") \
+    .output(2, "decoded_shape", "required") \
+    .output(3, "log_probability", "required") \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.I64_Default, DataType.I64_Default,
+                  DataType.I64_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.I32_Default, DataType.I64_Default, DataType.I64_Default,
+                  DataType.I64_Default, DataType.F64_Default) \
+    .dtype_format(DataType.F32_NCHW, DataType.I32_NCHW, DataType.I64_NCHW, DataType.I64_NCHW,
+                  DataType.I64_NCHW, DataType.F32_NCHW) \
+    .dtype_format(DataType.F64_NCHW, DataType.I32_NCHW, DataType.I64_NCHW, DataType.I64_NCHW,
+                  DataType.I64_NCHW, DataType.F64_NCHW) \
+    .get_op_info()
+
+@op_info_register(ctc_greedy_decoder_op_info)
+def _ctc_greedy_decoder_aicpu():
+    """CTCGreedyDecoder AiCPU register"""
+    return

tests/st/ops/ascend/test_aicpu_ops/test_ctc_greedy_decoder.py

@@ -0,0 +1,59 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.ctc = P.CTCGreedyDecoder()
+
+    def construct(self, inputs, sequence_length):
+        return self.ctc(inputs, sequence_length)
+
+
+def test_net_float32():
+    x = np.random.randn(2, 2, 3).astype(np.float32)
+    sequence_length = np.array([2, 2]).astype(np.int32)
+    net = Net()
+    output = net(Tensor(x), Tensor(sequence_length))
+    print(output)
+
+
+def test_net_assert():
+    x = np.array([[[0.44662005, 0.41900548, -0.8334965],
+                   [-0.28560895, -0.03626213, -0.04149306]],
+                  [[-0.70390207, 0.2977548, -0.4097819],
+                   [-0.6942656, -0.14625494, -0.90554816]]]).astype(np.float32)
+    sequence_length = np.array([2, 2]).astype(np.int32)
+    net = Net()
+    output = net(Tensor(x), Tensor(sequence_length))
+    print(output)
+
+    out_expect0 = np.array([0, 0, 0, 1, 1, 0]).reshape(3, 2)
+    out_expect1 = np.array([0, 1, 1])
+    out_expect2 = np.array([2, 2])
+    out_expect3 = np.array([-0.7443749, 0.18251707]).reshape(2, 1)
+    assert np.array_equal(output[0].asnumpy(), out_expect0)
+    assert np.array_equal(output[1].asnumpy(), out_expect1)
+    assert np.array_equal(output[2].asnumpy(), out_expect2)
+    assert np.array_equal(output[3].asnumpy(), out_expect3)