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75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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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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#
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from typing import List
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import tensorflow as tf
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from rl_coach.architectures.tensorflow_components.architecture import Conv2d
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from rl_coach.base_parameters import EmbedderScheme
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from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedder
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from rl_coach.core_types import InputImageEmbedding
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class ImageEmbedder(InputEmbedder):
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"""
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An input embedder that performs convolutions on the input and then flattens the result.
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The embedder is intended for image like inputs, where the channels are expected to be the last axis.
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The embedder also allows custom rescaling of the input prior to the neural network.
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"""
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schemes = {
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EmbedderScheme.Empty:
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[],
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EmbedderScheme.Shallow:
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[
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Conv2d([32, 3, 1])
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],
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# atari dqn
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EmbedderScheme.Medium:
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[
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Conv2d([32, 8, 4]),
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Conv2d([64, 4, 2]),
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Conv2d([64, 3, 1])
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],
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# carla
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EmbedderScheme.Deep: \
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[
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Conv2d([32, 5, 2]),
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Conv2d([32, 3, 1]),
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Conv2d([64, 3, 2]),
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Conv2d([64, 3, 1]),
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Conv2d([128, 3, 2]),
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Conv2d([128, 3, 1]),
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Conv2d([256, 3, 2]),
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Conv2d([256, 3, 1])
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]
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}
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def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
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scheme: EmbedderScheme=EmbedderScheme.Medium, batchnorm: bool=False, dropout: bool=False,
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name: str= "embedder", input_rescaling: float=255.0, input_offset: float=0.0, input_clipping=None):
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super().__init__(input_size, activation_function, scheme, batchnorm, dropout, name, input_rescaling,
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input_offset, input_clipping)
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self.return_type = InputImageEmbedding
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if len(input_size) != 3 and scheme != EmbedderScheme.Empty:
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raise ValueError("Image embedders expect the input size to have 3 dimensions. The given size is: {}"
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.format(input_size))
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