Source code for ray.rllib.models.preprocessors

from collections import OrderedDict
import cv2
import logging
import numpy as np
import gym

from ray.rllib.utils.annotations import override, PublicAPI

ATARI_OBS_SHAPE = (210, 160, 3)
ATARI_RAM_OBS_SHAPE = (128, )
VALIDATION_INTERVAL = 100

logger = logging.getLogger(__name__)


[docs]@PublicAPI class Preprocessor: """Defines an abstract observation preprocessor function. Attributes: shape (obj): Shape of the preprocessed output. """ @PublicAPI def __init__(self, obs_space, options=None): legacy_patch_shapes(obs_space) self._obs_space = obs_space if not options: from ray.rllib.models.catalog import MODEL_DEFAULTS self._options = MODEL_DEFAULTS.copy() else: self._options = options self.shape = self._init_shape(obs_space, self._options) self._size = int(np.product(self.shape)) self._i = 0 @PublicAPI def _init_shape(self, obs_space, options): """Returns the shape after preprocessing.""" raise NotImplementedError
[docs] @PublicAPI def transform(self, observation): """Returns the preprocessed observation.""" raise NotImplementedError
[docs] def write(self, observation, array, offset): """Alternative to transform for more efficient flattening.""" array[offset:offset + self._size] = self.transform(observation)
[docs] def check_shape(self, observation): """Checks the shape of the given observation.""" if self._i % VALIDATION_INTERVAL == 0: if type(observation) is list and isinstance( self._obs_space, gym.spaces.Box): observation = np.array(observation) try: if not self._obs_space.contains(observation): raise ValueError( "Observation outside expected value range", self._obs_space, observation) except AttributeError: raise ValueError( "Observation for a Box/MultiBinary/MultiDiscrete space " "should be an np.array, not a Python list.", observation) self._i += 1
@property @PublicAPI def size(self): return self._size @property @PublicAPI def observation_space(self): obs_space = gym.spaces.Box(-1., 1., self.shape, dtype=np.float32) # Stash the unwrapped space so that we can unwrap dict and tuple spaces # automatically in model.py if (isinstance(self, TupleFlatteningPreprocessor) or isinstance(self, DictFlatteningPreprocessor)): obs_space.original_space = self._obs_space return obs_space
class GenericPixelPreprocessor(Preprocessor): """Generic image preprocessor. Note: for Atari games, use config {"preprocessor_pref": "deepmind"} instead for deepmind-style Atari preprocessing. """ @override(Preprocessor) def _init_shape(self, obs_space, options): self._grayscale = options.get("grayscale") self._zero_mean = options.get("zero_mean") self._dim = options.get("dim") if self._grayscale: shape = (self._dim, self._dim, 1) else: shape = (self._dim, self._dim, 3) return shape @override(Preprocessor) def transform(self, observation): """Downsamples images from (210, 160, 3) by the configured factor.""" self.check_shape(observation) scaled = observation[25:-25, :, :] if self._dim < 84: scaled = cv2.resize(scaled, (84, 84)) # OpenAI: Resize by half, then down to 42x42 (essentially mipmapping). # If we resize directly we lose pixels that, when mapped to 42x42, # aren't close enough to the pixel boundary. scaled = cv2.resize(scaled, (self._dim, self._dim)) if self._grayscale: scaled = scaled.mean(2) scaled = scaled.astype(np.float32) # Rescale needed for maintaining 1 channel scaled = np.reshape(scaled, [self._dim, self._dim, 1]) if self._zero_mean: scaled = (scaled - 128) / 128 else: scaled *= 1.0 / 255.0 return scaled class AtariRamPreprocessor(Preprocessor): @override(Preprocessor) def _init_shape(self, obs_space, options): return (128, ) @override(Preprocessor) def transform(self, observation): self.check_shape(observation) return (observation - 128) / 128 class OneHotPreprocessor(Preprocessor): @override(Preprocessor) def _init_shape(self, obs_space, options): return (self._obs_space.n, ) @override(Preprocessor) def transform(self, observation): self.check_shape(observation) arr = np.zeros(self._obs_space.n, dtype=np.float32) arr[observation] = 1 return arr @override(Preprocessor) def write(self, observation, array, offset): array[offset + observation] = 1 class NoPreprocessor(Preprocessor): @override(Preprocessor) def _init_shape(self, obs_space, options): return self._obs_space.shape @override(Preprocessor) def transform(self, observation): self.check_shape(observation) return observation @override(Preprocessor) def write(self, observation, array, offset): array[offset:offset + self._size] = np.array( observation, copy=False).ravel() @property @override(Preprocessor) def observation_space(self): return self._obs_space class TupleFlatteningPreprocessor(Preprocessor): """Preprocesses each tuple element, then flattens it all into a vector. RLlib models will unpack the flattened output before _build_layers_v2(). """ @override(Preprocessor) def _init_shape(self, obs_space, options): assert isinstance(self._obs_space, gym.spaces.Tuple) size = 0 self.preprocessors = [] for i in range(len(self._obs_space.spaces)): space = self._obs_space.spaces[i] logger.debug("Creating sub-preprocessor for {}".format(space)) preprocessor = get_preprocessor(space)(space, self._options) self.preprocessors.append(preprocessor) size += preprocessor.size return (size, ) @override(Preprocessor) def transform(self, observation): self.check_shape(observation) array = np.zeros(self.shape) self.write(observation, array, 0) return array @override(Preprocessor) def write(self, observation, array, offset): assert len(observation) == len(self.preprocessors), observation for o, p in zip(observation, self.preprocessors): p.write(o, array, offset) offset += p.size class DictFlatteningPreprocessor(Preprocessor): """Preprocesses each dict value, then flattens it all into a vector. RLlib models will unpack the flattened output before _build_layers_v2(). """ @override(Preprocessor) def _init_shape(self, obs_space, options): assert isinstance(self._obs_space, gym.spaces.Dict) size = 0 self.preprocessors = [] for space in self._obs_space.spaces.values(): logger.debug("Creating sub-preprocessor for {}".format(space)) preprocessor = get_preprocessor(space)(space, self._options) self.preprocessors.append(preprocessor) size += preprocessor.size return (size, ) @override(Preprocessor) def transform(self, observation): self.check_shape(observation) array = np.zeros(self.shape) self.write(observation, array, 0) return array @override(Preprocessor) def write(self, observation, array, offset): if not isinstance(observation, OrderedDict): observation = OrderedDict(sorted(observation.items())) assert len(observation) == len(self.preprocessors), \ (len(observation), len(self.preprocessors)) for o, p in zip(observation.values(), self.preprocessors): p.write(o, array, offset) offset += p.size @PublicAPI def get_preprocessor(space): """Returns an appropriate preprocessor class for the given space.""" legacy_patch_shapes(space) obs_shape = space.shape if isinstance(space, gym.spaces.Discrete): preprocessor = OneHotPreprocessor elif obs_shape == ATARI_OBS_SHAPE: preprocessor = GenericPixelPreprocessor elif obs_shape == ATARI_RAM_OBS_SHAPE: preprocessor = AtariRamPreprocessor elif isinstance(space, gym.spaces.Tuple): preprocessor = TupleFlatteningPreprocessor elif isinstance(space, gym.spaces.Dict): preprocessor = DictFlatteningPreprocessor else: preprocessor = NoPreprocessor return preprocessor def legacy_patch_shapes(space): """Assigns shapes to spaces that don't have shapes. This is only needed for older gym versions that don't set shapes properly for Tuple and Discrete spaces. """ if not hasattr(space, "shape"): if isinstance(space, gym.spaces.Discrete): space.shape = () elif isinstance(space, gym.spaces.Tuple): shapes = [] for s in space.spaces: shape = legacy_patch_shapes(s) shapes.append(shape) space.shape = tuple(shapes) return space.shape