baselines/lompo/models.py

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