113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
# Copyright 2022 Google LLC
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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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# http://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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"""A FiLM Efficientnet contextual image tokenizer used in Robotics Transformer 1.
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"""
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from typing import Optional
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from robotics_transformer.film_efficientnet import pretrained_efficientnet_encoder
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from robotics_transformer.tokenizers import token_learner
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import tensorflow as tf
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class RT1ImageTokenizer(tf.keras.layers.Layer):
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"""Tokenizes based on vocab size."""
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def __init__(self,
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embedding_output_dim: int,
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use_token_learner: bool = False,
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num_tokens: int = 8,
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**kwargs):
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"""Instantiates a RT1ImageTokenizer.
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Args:
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embedding_output_dim: The output size of the tokens.
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use_token_learner: Whether to use token learner. See
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https://arxiv.org/abs/2106.11297
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num_tokens: Relevant only for token learner - the number of learned
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tokens.
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**kwargs: Keyword arguments to base class.
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"""
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super().__init__(**kwargs)
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self._embedding_output_dim = embedding_output_dim
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self._tokenizer = pretrained_efficientnet_encoder.EfficientNetEncoder(
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pooling=False, early_film=True)
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self._use_token_learner = use_token_learner
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if self._use_token_learner:
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self._num_tokens = num_tokens
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self._token_learner = token_learner.TokenLearnerModule(
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num_tokens=self._num_tokens)
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@property
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def tokens_per_context_image(self) -> int:
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if self._use_token_learner:
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num_tokens = self._num_tokens
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else:
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num_tokens = 81
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return num_tokens
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def __call__(self,
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image: tf.Tensor,
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context: Optional[tf.Tensor] = None,
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training: bool = False) -> tf.Tensor:
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"""Gets image tokens.
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Args:
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image: Images of shape (b, t, h, w, 3) to tokenize.
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context: An optional context vector (e.g., a natural language embedding).
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Expected to have shape (b, t, embedding_dim).
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training: Whether or not we are in training mode.
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Returns:
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tokens: has shape (batch, t, num_tokens_per_timestep, embedding_dim)
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"""
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image_shape = tf.shape(image)
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b = image_shape[0]
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t = image_shape[1]
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h = image_shape[2]
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w = image_shape[3]
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c = image_shape[4]
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# Fold the time axis into the batch axis.
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image = tf.reshape(image, [b * t, h, w, c])
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if context is not None:
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context_rank = tf.rank(context)
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assertion = tf.Assert(context_rank == 3, data=[context_rank])
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with tf.control_dependencies([assertion]):
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context = tf.reshape(context, [b * t, tf.shape(context)[-1]])
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tokens = self.get_image_embeddings(image, context, training)
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if self._use_token_learner:
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tokens = self._token_learner(tokens, training)
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# Unflatten the time axis, which was previously flattened into the batch.
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tokens = tf.reshape(tokens, [b, t, tf.shape(tokens)[1], -1])
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return tokens
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def get_image_embeddings(self,
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image: tf.Tensor,
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context: Optional[tf.Tensor],
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training: bool = False) -> tf.Tensor:
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"""Gets embeddings from image.
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Args:
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image: Expected to be float32 in range [0, 1] with shape (b, h, w, 3).
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context: Expected to be float32 with shape (b, embedding_dim)
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training: Whether or not we are in training mode.
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Returns:
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tokens of shape (b, num_tokens, emedding_dim)
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"""
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image_tokens = self._tokenizer(image, context=context, training=training)
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image_tokens = tf.reshape(image_tokens, [-1, 81, 512])
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return image_tokens
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