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import numpy as np
import tensorflow as tf
from tensorflow.keras import layers as tfkl
from tensorflow_probability import distributions as tfd
from tensorflow.keras.mixed_precision import experimental as prec
from . import tools
class RSSME(tools.Module):
def __init__(self, stoch=30, deter=200, hidden=200, num_models=7, act=tf.nn.elu):
super().__init__()
self._activation = act
self._stoch_size = stoch
self._deter_size = deter
self._hidden_size = hidden
self._cell = tfkl.GRUCell(self._deter_size)
self._k = num_models
def initial(self, batch_size):
dtype = prec.global_policy().compute_dtype
return dict(
mean=tf.zeros([batch_size, self._stoch_size], dtype),
std=tf.zeros([batch_size, self._stoch_size], dtype),
stoch=tf.zeros([batch_size, self._stoch_size], dtype),
deter=self._cell.get_initial_state(None, batch_size, dtype))
def observe(self, embed, action, state=None):
if state is None:
state = self.initial(tf.shape(action)[0])
embed = tf.transpose(embed, [1, 0, 2])
action = tf.transpose(action, [1, 0, 2])
post, prior = tools.static_scan(lambda prev, inputs: self.obs_step(prev[0], *inputs),
(action, embed), (state, state))
post = {k: tf.transpose(v, [1, 0, 2]) for k, v in post.items()}
prior = {k: tf.transpose(v, [1, 0, 2]) for k, v in prior.items()}
return post, prior
def imagine(self, action, state=None):
if state is None:
state = self.initial(tf.shape(action)[0])
assert isinstance(state, dict), state
action = tf.transpose(action, [1, 0, 2])
prior = tools.static_scan(self.img_step, action, state)
prior = {k: tf.transpose(v, [1, 0, 2]) for k, v in prior.items()}
return prior
def get_feat(self, state):
return tf.concat([state['stoch'], state['deter']], -1)
def get_feat_size(self, state):
return self._stoch_size + self._deter_size
def get_dist(self, state):
return tfd.MultivariateNormalDiag(state['mean'], state['std'])
def obs_step(self, prev_state, prev_action, embed):
prior = self.img_step(prev_state, prev_action)
x = tf.concat([prior['deter'], embed], -1)
x = self.get('obs1', tfkl.Dense, self._hidden_size, self._activation)(x)
x = self.get('obs2', tfkl.Dense, 2 * self._stoch_size, None)(x)
mean, std = tf.split(x, 2, -1)
std = tf.nn.softplus(std) + 0.1
stoch = self.get_dist({'mean': mean, 'std': std}).sample()
post = {'mean': mean, 'std': std, 'stoch': stoch, 'deter': prior['deter']}
return post, prior
def img_step(self, prev_state, prev_action, k=None):
if k is None:
k = np.random.choice(self._k)
x = tf.concat([prev_state['stoch'], prev_action], -1)
x = self.get('img1', tfkl.Dense, self._hidden_size, self._activation)(x)
x, deter = self._cell(x, [prev_state['deter']])
deter = deter[0] # Keras wraps the state in a list.
x = self.get('img2_{}'.format(k), tfkl.Dense, self._hidden_size, self._activation)(x)
x = self.get('img3_{}'.format(k), tfkl.Dense, 2 * self._stoch_size, None)(x)
mean, std = tf.split(x, 2, -1)
std = tf.nn.softplus(std) + 0.1
stoch = self.get_dist({'mean': mean, 'std': std}).sample()
prior = {'mean': mean, 'std': std, 'stoch': stoch, 'deter': deter}
return prior
class MultivariateNormalDiag(tools.Module):
def __init__(self, hidden_size, latent_size, scale=None):
super().__init__()
self.latent_size = latent_size
self.scale = scale
self.dense1 = tf.keras.layers.Dense(hidden_size, activation=tf.nn.leaky_relu)
self.dense2 = tf.keras.layers.Dense(hidden_size, activation=tf.nn.leaky_relu)
self.output_layer = tf.keras.layers.Dense(2 * latent_size if self.scale
is None else latent_size)
def __call__(self, *inputs):
if len(inputs) > 1:
inputs = tf.concat(inputs, axis=-1)
else:
inputs, = inputs
out = self.dense1(inputs)
out = self.dense2(out)
out = self.output_layer(out)
loc = out[..., :self.latent_size]
if self.scale is None:
assert out.shape[-1] == 2 * self.latent_size
scale_diag = tf.nn.softplus(out[..., self.latent_size:]) + 1e-5
else:
assert out.shape[-1].value == self.latent_size
scale_diag = tf.ones_like(loc) * self.scale
return loc, scale_diag
class ConstantMultivariateNormalDiag(tools.Module):
def __init__(self, latent_size, scale=None):
super().__init__()
self.latent_size = latent_size
self.scale = scale
def __call__(self, *inputs):
# first input should not have any dimensions after the batch_shape, step_type
batch_shape = tf.shape(inputs[0]) # input is only used to infer batch_shape
shape = tf.concat([batch_shape, [self.latent_size]], axis=0)
loc = tf.zeros(shape)
if self.scale is None:
scale_diag = tf.ones(shape)
else:
scale_diag = tf.ones(shape) * self.scale
return loc, scale_diag
class ConvEncoderLarge(tools.Module):
def __init__(self, depth=32, act=tf.nn.relu):
self._act = act
self._depth = depth
def __call__(self, obs):
kwargs = dict(strides=2, activation=self._act)
x = tf.reshape(obs['image'], (-1,) + tuple(obs['image'].shape[-3:]))
x = self.get('h1', tfkl.Conv2D, 1 * self._depth, 4, **kwargs)(x)
x = self.get('h2', tfkl.Conv2D, 2 * self._depth, 4, **kwargs)(x)
x = self.get('h3', tfkl.Conv2D, 4 * self._depth, 4, **kwargs)(x)
x = self.get('h4', tfkl.Conv2D, 8 * self._depth, 4, **kwargs)(x)
x = self.get('h5', tfkl.Conv2D, 8 * self._depth, 4, **kwargs)(x)
shape = tf.concat([tf.shape(obs['image'])[:-3], [32 * self._depth]], 0)
return tf.reshape(x, shape)
class ConvDecoderLarge(tools.Module):
def __init__(self, depth=32, act=tf.nn.relu, shape=(128, 128, 3)):
self._act = act
self._depth = depth
self._shape = shape
def __call__(self, features):
kwargs = dict(strides=2, activation=self._act)
x = self.get('h1', tfkl.Dense, 32 * self._depth, None)(features)
x = tf.reshape(x, [-1, 1, 1, 32 * self._depth])
x = self.get('h2', tfkl.Conv2DTranspose, 4 * self._depth, 5, **kwargs)(x)
x = self.get('h3', tfkl.Conv2DTranspose, 2 * self._depth, 5, **kwargs)(x)
x = self.get('h4', tfkl.Conv2DTranspose, 1 * self._depth, 5, **kwargs)(x)
x = self.get('h5', tfkl.Conv2DTranspose, 1 * self._depth, 6, **kwargs)(x)
x = self.get('h6', tfkl.Conv2DTranspose, self._shape[-1], 6, strides=2)(x)
mean = tf.reshape(x, tf.concat([tf.shape(features)[:-1], self._shape], 0))
return tfd.Independent(tfd.Normal(mean, 1), len(self._shape))
class ConvEncoder(tools.Module):
def __init__(self, depth=32, act=tf.nn.relu):
self._act = act
self._depth = depth
def __call__(self, obs):
kwargs = dict(strides=2, activation=self._act)
x = tf.reshape(obs['image'], (-1,) + tuple(obs['image'].shape[-3:]))
x = self.get('h1', tfkl.Conv2D, 1 * self._depth, 4, **kwargs)(x)
x = self.get('h2', tfkl.Conv2D, 2 * self._depth, 4, **kwargs)(x)
x = self.get('h3', tfkl.Conv2D, 4 * self._depth, 4, **kwargs)(x)
x = self.get('h4', tfkl.Conv2D, 8 * self._depth, 4, **kwargs)(x)
shape = tf.concat([tf.shape(obs['image'])[:-3], [32 * self._depth]], 0)
return tf.reshape(x, shape)
class ConvDecoder(tools.Module):
def __init__(self, depth=32, act=tf.nn.relu, shape=(64, 64, 3)):
self._act = act
self._depth = depth
self._shape = shape
def __call__(self, features):
kwargs = dict(strides=2, activation=self._act)
x = self.get('h1', tfkl.Dense, 32 * self._depth, None)(features)
x = tf.reshape(x, [-1, 1, 1, 32 * self._depth])
x = self.get('h2', tfkl.Conv2DTranspose, 4 * self._depth, 5, **kwargs)(x)
x = self.get('h3', tfkl.Conv2DTranspose, 2 * self._depth, 5, **kwargs)(x)
x = self.get('h4', tfkl.Conv2DTranspose, 1 * self._depth, 6, **kwargs)(x)
x = self.get('h5', tfkl.Conv2DTranspose, self._shape[-1], 6, strides=2)(x)
mean = tf.reshape(x, tf.concat([tf.shape(features)[:-1], self._shape], 0))
return tfd.Independent(tfd.Normal(mean, 1), len(self._shape))
class DenseDecoder(tools.Module):
def __init__(self, shape, layers, units, dist='normal', act=tf.nn.elu):
self._shape = shape
self._layers = layers
self._units = units
self._dist = dist
self._act = act
def __call__(self, features):
x = features
for index in range(self._layers):
x = self.get(f'h{index}', tfkl.Dense, self._units, self._act)(x)
x = self.get(f'hout', tfkl.Dense, np.prod(self._shape))(x)
x = tf.reshape(x, tf.concat([tf.shape(features)[:-1], self._shape], 0))
if self._dist == 'normal':
return tfd.Independent(tfd.Normal(x, 1), len(self._shape))
if self._dist == 'binary':
return tfd.Independent(tfd.Bernoulli(x), len(self._shape))
raise NotImplementedError(self._dist)
class DenseNetwork(tools.Module):
def __init__(self, shape, layers, units, act=tf.nn.elu):
self._shape = shape
self._layers = layers
self._units = units
self._act = act
def __call__(self, features):
x = features
for index in range(self._layers):
x = self.get(f'h{index}', tfkl.Dense, self._units, self._act)(x)
x = self.get(f'hout', tfkl.Dense, self._shape)(x)
return x
class ActorNetwork(tools.Module):
def __init__(self, shape, layers, units, act=tf.nn.elu, mean_scale=1.0):
self._shape = shape
self._layers = layers
self._units = units
self._act = act
self._mean_scale = mean_scale
def __call__(self, features):
x = features
for index in range(self._layers):
x = self.get(f'h{index}', tfkl.Dense, self._units, self._act)(x)
x = self.get(f'hout', tfkl.Dense, self._shape)(x)
x = self._mean_scale * tf.tanh(x)
return x
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