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86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
# Copyright 2020 DeepMind Technologies Limited.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Interface for loading sketchy data into tensorflow."""
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import tensorflow.compat.v2 as tf
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def load_frames(filenames, num_parallel_reads=1, num_map_threads=None):
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if not num_map_threads:
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num_map_threads = num_parallel_reads
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dataset = tf.data.TFRecordDataset(
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filenames, num_parallel_reads=num_parallel_reads)
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return dataset.map(_parse_example, num_parallel_calls=num_map_threads)
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_FEATURES = {
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# Actions
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'actions':
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tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
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# Observations
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'gripper/joints/velocity':
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tf.io.FixedLenFeature(shape=1, dtype=tf.float32),
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'gripper/joints/torque':
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tf.io.FixedLenFeature(shape=1, dtype=tf.float32),
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'gripper/grasp':
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tf.io.FixedLenFeature(shape=1, dtype=tf.int64),
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'gripper/joints/angle':
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tf.io.FixedLenFeature(shape=1, dtype=tf.float32),
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'sawyer/joints/velocity':
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tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
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'sawyer/pinch/pose':
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tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
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'sawyer/tcp/pose':
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tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
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'sawyer/tcp/effort':
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tf.io.FixedLenFeature(shape=6, dtype=tf.float32),
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'sawyer/joints/torque':
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tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
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'sawyer/tcp/velocity':
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tf.io.FixedLenFeature(shape=6, dtype=tf.float32),
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'sawyer/joints/angle':
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tf.io.FixedLenFeature(shape=7, dtype=tf.float32),
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'wrist/torque':
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tf.io.FixedLenFeature(shape=3, dtype=tf.float32),
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'wrist/force':
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tf.io.FixedLenFeature(shape=3, dtype=tf.float32),
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'pixels/basket_front_left':
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tf.io.FixedLenFeature(shape=1, dtype=tf.string),
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'pixels/basket_back_left':
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tf.io.FixedLenFeature(shape=1, dtype=tf.string),
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'pixels/basket_front_right':
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tf.io.FixedLenFeature(shape=1, dtype=tf.string),
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'pixels/royale_camera_driver_depth':
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tf.io.FixedLenFeature(shape=(171, 224, 1), dtype=tf.float32),
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'pixels/royale_camera_driver_gray':
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tf.io.FixedLenFeature(shape=1, dtype=tf.string),
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'pixels/usbcam0':
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tf.io.FixedLenFeature(shape=1, dtype=tf.string),
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'pixels/usbcam1':
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tf.io.FixedLenFeature(shape=1, dtype=tf.string),
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}
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def _parse_example(example):
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return _decode_images(tf.io.parse_single_example(example, _FEATURES))
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def _decode_images(record):
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for name, value in list(record.items()):
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if value.dtype == tf.string:
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record[name] = tf.io.decode_jpeg(value[0])
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return record
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