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network_imporvements branch merge

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This commit is contained in:
Shadi Endrawis
2018-10-02 13:41:46 +03:00
parent 72ea933384
commit 51726a5b80
110 changed files with 1639 additions and 1161 deletions
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rl_coach/presets/CARLA_CIL.py
+58 -38
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@@ -1,19 +1,19 @@
import numpy as np
import os
from logger import screen
import numpy as np
import tensorflow as tf
# make sure you have $CARLA_ROOT/PythonClient in your PYTHONPATH
from carla.driving_benchmark.experiment_suites import CoRL2017
from rl_coach.logger import screen
from rl_coach.agents.cil_agent import CILAgentParameters
from rl_coach.architectures.tensorflow_components.architecture import Conv2d, Dense
from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters
from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense, BatchnormActivationDropout
from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
from rl_coach.base_parameters import VisualizationParameters
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
from rl_coach.environments.carla_environment import CarlaEnvironmentParameters, CameraTypes
from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
from rl_coach.environments.carla_environment import CarlaEnvironmentParameters
from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
from rl_coach.filters.filter import InputFilter
from rl_coach.filters.observation.observation_crop_filter import ObservationCropFilter
@@ -27,7 +27,6 @@ from rl_coach.schedules import ConstantSchedule
from rl_coach.spaces import ImageObservationSpace
from rl_coach.utilities.carla_dataset_to_replay_buffer import create_dataset
####################
# Graph Scheduling #
####################
@@ -44,38 +43,64 @@ agent_params = CILAgentParameters()
# forward camera and measurements input
agent_params.network_wrappers['main'].input_embedders_parameters = {
'CameraRGB': InputEmbedderParameters(scheme=[Conv2d([32, 5, 2]),
Conv2d([32, 3, 1]),
Conv2d([64, 3, 2]),
Conv2d([64, 3, 1]),
Conv2d([128, 3, 2]),
Conv2d([128, 3, 1]),
Conv2d([256, 3, 1]),
Conv2d([256, 3, 1]),
Dense([512]),
Dense([512])],
dropout=True,
batchnorm=True),
'measurements': InputEmbedderParameters(scheme=[Dense([128]),
Dense([128])])
'CameraRGB': InputEmbedderParameters(
scheme=[
Conv2d(32, 5, 2),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
Conv2d(32, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
Conv2d(64, 3, 2),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
Conv2d(64, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
Conv2d(128, 3, 2),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
Conv2d(128, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
Conv2d(256, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
Conv2d(256, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
Dense(512),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.3),
Dense(512),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.3)
],
activation_function='none' # we define the activation function for each layer explicitly
),
'measurements': InputEmbedderParameters(
scheme=[
Dense(128),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5),
Dense(128),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5)
],
activation_function='none' # we define the activation function for each layer explicitly
)
}
# TODO: batch norm is currently applied to the fc layers which is not desired
# TODO: dropout should be configured differenetly per layer [1.0] * 8 + [0.7] * 2 + [0.5] * 2 + [0.5] * 1 + [0.5, 1.] * 5
# simple fc middleware
agent_params.network_wrappers['main'].middleware_parameters = FCMiddlewareParameters(scheme=[Dense([512])])
agent_params.network_wrappers['main'].middleware_parameters = \
FCMiddlewareParameters(
scheme=[
Dense(512),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5)
],
activation_function='none'
)
# output branches
agent_params.network_wrappers['main'].heads_parameters = [
RegressionHeadParameters(),
RegressionHeadParameters(),
RegressionHeadParameters(),
RegressionHeadParameters()
RegressionHeadParameters(
scheme=[
Dense(256),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5),
Dense(256),
BatchnormActivationDropout(activation_function=tf.tanh)
],
num_output_head_copies=4 # follow lane, left, right, straight
)
]
# agent_params.network_wrappers['main'].num_output_head_copies = 4 # follow lane, left, right, straight
agent_params.network_wrappers['main'].rescale_gradient_from_head_by_factor = [1, 1, 1, 1]
agent_params.network_wrappers['main'].loss_weights = [1, 1, 1, 1]
# TODO: there should be another head predicting the speed which is connected directly to the forward camera embedding
agent_params.network_wrappers['main'].batch_size = 120
@@ -125,7 +150,6 @@ if not os.path.exists(agent_params.memory.load_memory_from_file_path):
# Environment #
###############
env_params = CarlaEnvironmentParameters()
env_params.level = 'town1'
env_params.cameras = ['CameraRGB']
env_params.camera_height = 600
env_params.camera_width = 800
@@ -134,9 +158,5 @@ env_params.allow_braking = True
env_params.quality = CarlaEnvironmentParameters.Quality.EPIC
env_params.experiment_suite = CoRL2017('Town01')
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST)]
vis_params.dump_mp4 = True
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=vis_params)
schedule_params=schedule_params, vis_params=VisualizationParameters())