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https://github.com/wassname/ray.git
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Richard Liaw
16e82b43d1
* Changes for preprocessors * removed comments * Changes + push for lint * linted * adding dependency for travis * linting won't pass * reordering * needed for testing * added comments * pip it * pip dependencies
73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import cv2
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import numpy as np
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class Preprocessor(object):
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"""Defines an abstract observation preprocessor function."""
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def __init__(self, options):
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self.options = options
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self._init()
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def _init(self):
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pass
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def transform_shape(self, obs_shape):
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"""Returns the preprocessed observation shape."""
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raise NotImplementedError
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def transform(self, observation):
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"""Returns the preprocessed observation."""
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raise NotImplementedError
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class AtariPixelPreprocessor(Preprocessor):
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def _init(self):
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self.grayscale = self.options.get("grayscale", False)
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self.zero_mean = self.options.get("zero_mean", True)
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self.dim = self.options.get("dim", 80)
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def transform_shape(self, obs_shape):
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if self.grayscale:
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return (self.dim, self.dim, 1)
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else:
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return (self.dim, self.dim, 3)
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# TODO(ekl) why does this need to return an extra size-1 dim (the [None])
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def transform(self, observation):
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"""Downsamples images from (210, 160, 3) by the configured factor."""
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scaled = observation[25:-25, :, :]
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if self.dim < 80:
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scaled = cv2.resize(scaled, (80, 80))
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scaled = cv2.resize(scaled, (self.dim, self.dim))
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if self.grayscale:
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scaled = scaled.mean(2)
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scaled = scaled.astype(np.float32)
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scaled = np.reshape(scaled, [self.dim, self.dim, 1])
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scaled = scaled[None]
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if self.zero_mean:
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scaled = (scaled - 128) / 128
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else:
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scaled *= 1.0 / 255.0
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return scaled
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# TODO(rliaw): Also should include the deepmind preprocessor
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class AtariRamPreprocessor(Preprocessor):
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def transform_shape(self, obs_shape):
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return (128,)
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def transform(self, observation):
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return (observation - 128) / 128
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class NoPreprocessor(Preprocessor):
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def transform_shape(self, obs_shape):
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return obs_shape
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def transform(self, observation):
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return observation
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