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25e57c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | # Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Loads dataset for the Pixel Classification task."""
import dataclasses
from typing import Mapping, Optional, Tuple
import tensorflow as tf, tf_keras
from official.common import dataset_fn
from official.core import config_definitions as cfg
from official.core import input_reader
from official.nlp.data import data_loader
from official.nlp.data import data_loader_factory
LABEL_TYPES_MAP = {'int': tf.int64, 'float': tf.float32}
@dataclasses.dataclass
class PixelDataConfig(cfg.DataConfig):
"""Data config for text classification task."""
input_path: str = ''
global_batch_size: int = 32
is_training: bool = True
label_type: str = 'int'
num_channels: int = 3
input_size: Tuple[int, int] = (16, 4096)
patch_h: int = 16
patch_w: int = 16
# Whether to include the example id number.
include_example_id: bool = False
# Maps the key in TfExample to feature name.
# Either tfrecord, sstable, or recordio.
file_type: str = 'tfrecord'
@data_loader_factory.register_data_loader_cls(PixelDataConfig)
class PixelDataLoader(data_loader.DataLoader):
"""A class to load dataset for text classification task."""
def __init__(self, params):
self._params = params
self._include_example_id = params.include_example_id
def name_to_features_spec(self):
"""Defines features to decode. Subclass may override to append features."""
h, w = self._params.input_size
positions = h // self._params.patch_h * w // self._params.patch_w
name_to_features = {
'pixel_values': tf.io.FixedLenFeature(
[self._params.num_channels, h, w], tf.float32
),
'label': tf.io.FixedLenFeature([1], tf.int64),
'attention_mask': tf.io.FixedLenFeature([positions], tf.float32),
}
if self._include_example_id:
name_to_features['example_id'] = tf.io.FixedLenFeature([], tf.int64)
return name_to_features
def _decode(self, record: tf.Tensor):
"""Decodes a serialized tf.Example."""
example = tf.io.parse_single_example(record, self.name_to_features_spec())
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in example:
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example
def _parse(self, record: Mapping[str, tf.Tensor]):
"""Parses raw tensors into a dict of tensors to be consumed by the model."""
key_mapping = {
'pixel_values': 'pixel_values',
'label': 'label',
'attention_mask': 'attention_mask',
}
ret = {}
for record_key in record:
if record_key in key_mapping:
ret[key_mapping[record_key]] = record[record_key]
else:
ret[record_key] = record[record_key]
return ret
def load(self, input_context: Optional[tf.distribute.InputContext] = None):
"""Returns a tf.dataset.Dataset."""
reader = input_reader.InputReader(
dataset_fn=dataset_fn.pick_dataset_fn(self._params.file_type),
params=self._params,
decoder_fn=self._decode,
parser_fn=self._parse,
)
return reader.read(input_context)
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