def load_agent_config(config_path):
"""
Load an agent configuration from file, with inheritance.
:param config_path: path to a json config file
:return: the configuration dict
"""
with open(config_path) as f:
agent_config = json.loads(f.read())
if "base_config" in agent_config:
base_config = load_agent_config(agent_config["base_config"])
del agent_config["base_config"]
agent_config = Configurable.rec_update(base_config, agent_config)
return agent_config
def __init__(self, config):
super().__init__()
Configurable.__init__(self, config)
self.activation = activation_factory(self.config["activation"])
self.conv1 = nn.Conv2d(self.config["in_channels"],
32,
kernel_size=3,
stride=1,
padding=1)
self.conv2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1)
self.maxpool1 = nn.MaxPool2d(3, stride=2)
self.bn1 = nn.BatchNorm2d(32)
# MLP Head
# Number of Linear input connections depends on output of conv2d layers
# and therefore the input image size, so compute it.
def conv2d_size_out(size, kernel_size=3, stride=1, padding=1):
return (size + 2 * padding - (kernel_size - 1) - 1) // stride + 1
def maxpool_size_out(size, kernel_size=3, stride=2, padding=0):
return (size + 2 * padding - (kernel_size - 1) - 1) // stride + 1
convw = conv2d_size_out(
conv2d_size_out(
maxpool_size_out(
conv2d_size_out(
conv2d_size_out(
conv2d_size_out(self.config["in_width"]))))))
convh = conv2d_size_out(
conv2d_size_out(
maxpool_size_out(
conv2d_size_out(
conv2d_size_out(
conv2d_size_out(self.config["in_height"]))))))
assert convh > 0 and convw > 0
self.config["head_mlp"]["in"] = convw * convh * 32
self.config["head_mlp"]["out"] = self.config["out"]
self.head = model_factory(self.config["head_mlp"])
def load_environment_config(env_config):
"""
Load an environment from a configuration file.
:param env_config: the configuration, or path to the environment configuration file
:return: the environment
"""
if not isinstance(env_config, dict):
with open(env_config) as f:
env_config = json.loads(f.read())
if "base_config" in env_config:
base_config = load_environment_config(env_config["base_config"])
# del env_config["base_config"]
env_config = Configurable.rec_update(base_config, env_config)
return env_config
def __init__(self, config):
super().__init__()
Configurable.__init__(self, config)
self.activation = activation_factory(self.config["activation"])
self.conv1 = nn.Conv3d(self.config["in_channels"],
32,
kernel_size=(1, 7, 7),
stride=(1, 1, 1),
padding=(0, 3, 3))
self.conv2 = nn.Conv3d(32,
32,
kernel_size=(3, 5, 5),
stride=(1, 1, 1),
padding=(1, 2, 2))
self.conv3 = nn.Conv3d(32, 64, kernel_size=(3, 5, 5), stride=(1, 1, 1))
self.conv4 = nn.Conv3d(64, 64, kernel_size=(1, 5, 5), stride=(1, 1, 1))
self.maxpool1 = nn.MaxPool3d(kernel_size=(3, 5, 5), stride=(1, 2, 2))
self.bn1 = nn.BatchNorm3d(64)
# MLP Head
# Number of Linear input connections depends on output of conv2d layers
# and therefore the input image size, so compute it.
def conv2d_size_out(size, kernel_size=5, stride=1, padding=0):
return (size + 2 * padding - (kernel_size - 1) - 1) // stride + 1
def maxpool_size_out(size, kernel_size=5, stride=2, padding=0):
return (size + 2 * padding - (kernel_size - 1) - 1) // stride + 1
convw = conv2d_size_out(
conv2d_size_out(
maxpool_size_out(
conv2d_size_out(conv2d_size_out(self.config["in_width"],
kernel_size=7,
stride=1,
padding=3),
kernel_size=5,
stride=1,
padding=2))))
convh = conv2d_size_out(
conv2d_size_out(
maxpool_size_out(
conv2d_size_out(conv2d_size_out(self.config["in_height"],
kernel_size=7,
stride=1,
padding=3),
kernel_size=5,
stride=1,
padding=2))))
convd = conv2d_size_out(conv2d_size_out(maxpool_size_out(
conv2d_size_out(conv2d_size_out(self.config["in_depth"],
kernel_size=1,
stride=1,
padding=0),
kernel_size=3,
stride=1,
padding=1),
kernel_size=3,
stride=1),
kernel_size=3,
stride=1),
kernel_size=1,
stride=1)
assert convh > 0 and convw > 0
self.config["head_mlp"]["in"] = convw * convh * convd * 64
self.config["head_mlp"]["out"] = self.config["out"]
self.fc1 = nn.Linear(self.config["head_mlp"]["in"], 256)
self.fc2 = nn.Linear(256, self.config["head_mlp"]["out"])