from ngraph.opset4 import cos
from ngraph.opset4 import cosh
from ngraph.opset4 import ctc_greedy_decoder
+from ngraph.opset4 import ctc_loss
from ngraph.opset4 import cum_sum
from ngraph.opset4 import cum_sum as cumsum
from ngraph.opset4 import deformable_convolution
from ngraph.opset1.ops import cos
from ngraph.opset1.ops import cosh
from ngraph.opset1.ops import ctc_greedy_decoder
+from ngraph.opset4.ops import ctc_loss
from ngraph.opset3.ops import cum_sum
from ngraph.opset3.ops import cum_sum as cumsum
from ngraph.opset1.ops import deformable_convolution
@nameable_op
+def ctc_loss(
+ logits: NodeInput,
+ logit_length: NodeInput,
+ labels: NodeInput,
+ label_length: NodeInput,
+ blank_index: Optional[NodeInput] = None,
+ preprocess_collapse_repeated: bool = False,
+ ctc_merge_repeated: bool = True,
+ unique: bool = False,
+ name: Optional[str] = None,
+) -> Node:
+ """Return a node which performs CTCLoss.
+
+ :param logits: 3-D tensor of logits.
+ :param logit_length: 1-D tensor of lengths for each object from a batch.
+ :param labels: 2-D tensor of labels for which likelihood is estimated using logits.
+ :param label_length: 1-D tensor of length for each label sequence.
+ :param blank_index: Scalar used to mark a blank index.
+ :param preprocess_collapse_repeated: Flag for preprocessing labels before loss calculation.
+ :param ctc_merge_repeated: Flag for merging repeated characters in a potential alignment.
+ :param unique: Flag to find unique elements in a target.
+ :return: The new node which performs CTCLoss
+ """
+ if blank_index is not None:
+ inputs = as_nodes(logits, logit_length, labels, label_length, blank_index)
+ else:
+ inputs = as_nodes(logits, logit_length, labels, label_length)
+
+ attributes = {
+ "preprocess_collapse_repeated": preprocess_collapse_repeated,
+ "ctc_merge_repeated": ctc_merge_repeated,
+ "unique": unique,
+ }
+
+ return _get_node_factory_opset4().create("CTCLoss", inputs, attributes)
+
+
+@nameable_op
def non_max_suppression(
boxes: NodeInput,
scores: NodeInput,
--- /dev/null
+# ******************************************************************************
+# Copyright 2017-2020 Intel Corporation
+#
+# 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 ngraph as ng
+from ngraph.impl import Type
+
+
+def test_ctc_loss_props():
+ ind_dtype = np.int32
+ float_dtype = np.float32
+ logits = ng.parameter([2, 100, 80], dtype=float_dtype, name="logits")
+ logit_length = ng.parameter([2], dtype=ind_dtype, name="logit_length")
+ labels = ng.parameter([2, 100], dtype=ind_dtype, name="labels")
+ label_length = ng.parameter([2], dtype=ind_dtype, name="label_length")
+ blank_index = ng.parameter([], dtype=ind_dtype, name="blank_index")
+ preprocess_collapse_repeated = False
+ ctc_merge_repeated = True
+ unique = False
+
+ node = ng.ctc_loss(logits, logit_length, labels, label_length, blank_index,
+ preprocess_collapse_repeated, ctc_merge_repeated, unique)
+ assert node.get_type_name() == "CTCLoss"
+ assert node.get_output_size() == 1
+ assert list(node.get_output_shape(0)) == [2]
+ assert node.get_output_element_type(0) == Type.f32