Add orc model utility file (#2340)

dygraph/ocr_recognition/utility.py

+"""Contains common utility functions."""
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import distutils.util
+import numpy as np
+import paddle.fluid as fluid
+import six
+
+
+def print_arguments(args):
+    """Print argparse's arguments.
+
+    Usage:
+
+    .. code-block:: python
+
+        parser = argparse.ArgumentParser()
+        parser.add_argument("name", default="Jonh", type=str, help="User name.")
+        args = parser.parse_args()
+        print_arguments(args)
+
+    :param args: Input argparse.Namespace for printing.
+    :type args: argparse.Namespace
+    """
+    print("-----------  Configuration Arguments -----------")
+    for arg, value in sorted(six.iteritems(vars(args))):
+        print("%s: %s" % (arg, value))
+    print("------------------------------------------------")
+
+
+def add_arguments(argname, type, default, help, argparser, **kwargs):
+    """Add argparse's argument.
+
+    Usage:
+
+    .. code-block:: python
+
+        parser = argparse.ArgumentParser()
+        add_argument("name", str, "Jonh", "User name.", parser)
+        args = parser.parse_args()
+    """
+    type = distutils.util.strtobool if type == bool else type
+    argparser.add_argument(
+        "--" + argname,
+        default=default,
+        type=type,
+        help=help + ' Default: %(default)s.',
+        **kwargs)
+
+
+def to_numpy(data):
+
+    flattened_data = np.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    return flattened_data
+
+
+def get_attention_feeder_data(data, need_label=True):
+    pixel_data = None
+    pixel_data = np.concatenate(
+        list(map(lambda x: x[0][np.newaxis, :], data)),
+        axis=0).astype("float32")
+
+    label_in = to_numpy(list(map(lambda x: x[1], data)))
+    label_out = to_numpy(list(map(lambda x: x[2], data)))
+
+    mask = list(map(lambda x: x[3], data))
+    mask = np.concatenate(mask, axis=0)
+
+    if need_label:
+        return {
+            "pixel": pixel_data,
+            "label_in": label_in,
+            "label_out": label_out,
+            'mask': mask,
+        }
+    else:
+        return {"pixel": pixel_data}
+
+
+def get_attention_feeder_for_infer(data, place):
+    batch_size = len(data)
+    init_ids_data = np.array([0 for _ in range(batch_size)], dtype='int64')
+    init_scores_data = np.array(
+        [1. for _ in range(batch_size)], dtype='float32')
+    init_ids_data = init_ids_data.reshape((batch_size, 1))
+    init_scores_data = init_scores_data.reshape((batch_size, 1))
+    init_recursive_seq_lens = [1] * batch_size
+    init_recursive_seq_lens = [init_recursive_seq_lens, init_recursive_seq_lens]
+    init_ids = fluid.create_lod_tensor(init_ids_data, init_recursive_seq_lens,
+                                       place)
+    init_scores = fluid.create_lod_tensor(init_scores_data,
+                                          init_recursive_seq_lens, place)
+
+    pixel_tensor = fluid.LoDTensor()
+    pixel_data = None
+    pixel_data = np.concatenate(
+        list(map(lambda x: x[0][np.newaxis, :], data)),
+        axis=0).astype("float32")
+    pixel_tensor.set(pixel_data, place)
+    return {
+        "pixel": pixel_tensor,
+        "init_ids": init_ids,
+        "init_scores": init_scores
+    }