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67a90ee87e
* Allow arbitrary dimensional observation (non vector or image) * Added creating PlanarMapsObservationSpace to GymEnvironment when number of channels is not 1 or 3
54 lines
2.7 KiB
Python
54 lines
2.7 KiB
Python
from typing import Union
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from types import ModuleType
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import mxnet as mx
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from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
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from rl_coach.architectures.mxnet_components.embedders.embedder import InputEmbedder
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nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol]
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class TensorEmbedder(InputEmbedder):
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def __init__(self, params: InputEmbedderParameters):
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"""
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A tensor embedder is an input embedder that takes a tensor with arbitrary dimension and produces a vector
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embedding by passing it through a neural network. An example is video data or 3D image data (i.e. 4D tensors)
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or other type of data that is more than 1 dimension (i.e. not vector) but is not an image.
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NOTE: There are no pre-defined schemes for tensor embedder. User must define a custom scheme by passing
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a callable object as InputEmbedderParameters.scheme when defining the respective preset. This callable
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object must return a Gluon HybridBlock. The hybrid_forward() of this block must accept a single input,
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normalized observation, and return an embedding vector for each sample in the batch.
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Keep in mind that the scheme is a list of blocks, which are stacked by optional batchnorm,
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activation, and dropout in between as specified in InputEmbedderParameters.
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:param params: parameters object containing input_clipping, input_rescaling, batchnorm, activation_function
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and dropout properties.
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"""
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super(TensorEmbedder, self).__init__(params)
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self.input_rescaling = params.input_rescaling['tensor']
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self.input_offset = params.input_offset['tensor']
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@property
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def schemes(self) -> dict:
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"""
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Schemes are the pre-defined network architectures of various depths and complexities that can be used. Are used
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to create Block when InputEmbedder is initialised.
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Note: Tensor embedder doesn't define any pre-defined scheme. User must provide custom scheme in preset.
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:return: dictionary of schemes, with key of type EmbedderScheme enum and value being list of mxnet.gluon.Block.
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For tensor embedder, this is an empty dictionary.
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"""
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return {}
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def hybrid_forward(self, F: ModuleType, x: nd_sym_type, *args, **kwargs) -> nd_sym_type:
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"""
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Used for forward pass through embedder network.
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:param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized).
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:param x: image representing environment state, of shape (batch_size, in_channels, height, width).
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:return: embedding of environment state, of shape (batch_size, channels).
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"""
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return super(TensorEmbedder, self).hybrid_forward(F, x, *args, **kwargs)
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