mirror of
https://github.com/twitter/the-algorithm-ml.git
synced 2024-11-17 21:49:21 +01:00
82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
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import os
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import json
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from absl import app, flags, logging
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import tensorflow as tf
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from typing import Dict
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from tml.projects.home.recap.data import tfe_parsing
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from tml.core import config as tml_config_mod
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import tml.projects.home.recap.config as recap_config_mod
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flags.DEFINE_string("config_path", None, "Path to hyperparameters for model.")
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flags.DEFINE_integer("n_examples", 100, "Numer of examples to generate.")
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FLAGS = flags.FLAGS
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def _generate_random_example(
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tf_example_schema: Dict[str, tf.io.FixedLenFeature]
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) -> Dict[str, tf.Tensor]:
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example = {}
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for feature_name, feature_spec in tf_example_schema.items():
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dtype = feature_spec.dtype
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if (dtype == tf.int64) or (dtype == tf.int32):
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x = tf.experimental.numpy.random.randint(0, high=10, size=feature_spec.shape, dtype=dtype)
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elif (dtype == tf.float32) or (dtype == tf.float64):
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x = tf.random.uniform(shape=[feature_spec.shape], dtype=dtype)
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else:
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raise NotImplementedError(f"Unknown type {dtype}")
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example[feature_name] = x
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return example
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def _float_feature(value):
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return tf.train.Feature(float_list=tf.train.FloatList(value=value))
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def _int64_feature(value):
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return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
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def _serialize_example(x: Dict[str, tf.Tensor]) -> bytes:
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feature = {}
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serializers = {tf.float32: _float_feature, tf.int64: _int64_feature}
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for feature_name, tensor in x.items():
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feature[feature_name] = serializers[tensor.dtype](tensor)
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example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
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return example_proto.SerializeToString()
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def generate_data(data_path: str, config: recap_config_mod.RecapConfig):
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with tf.io.gfile.GFile(config.train_data.seg_dense_schema.schema_path, "r") as f:
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seg_dense_schema = json.load(f)["schema"]
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tf_example_schema = tfe_parsing.create_tf_example_schema(
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config.train_data,
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seg_dense_schema,
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)
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record_filename = os.path.join(data_path, "random.tfrecord.gz")
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with tf.io.TFRecordWriter(record_filename, "GZIP") as writer:
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random_example = _generate_random_example(tf_example_schema)
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serialized_example = _serialize_example(random_example)
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writer.write(serialized_example)
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def _generate_data_main(unused_argv):
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config = tml_config_mod.load_config_from_yaml(recap_config_mod.RecapConfig, FLAGS.config_path)
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# Find the path where to put the data
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data_path = os.path.dirname(config.train_data.inputs)
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logging.info("Putting random data in %s", data_path)
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generate_data(data_path, config)
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if __name__ == "__main__":
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app.run(_generate_data_main)
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