def __init__(
self,
observation_space,
hidden_size,
goal_sensor_uuid=None,
detach=False,
additional_sensors=[] # low dim sensors corresponding to registered name
):
super().__init__()
self.goal_sensor_uuid = goal_sensor_uuid
self.additional_sensors = additional_sensors
self._n_input_goal = 0
self._n_input_goal = 0
# if goal_sensor_uuid is not None and goal_sensor_uuid != "no_sensor":
# self.goal_sensor_uuid = goal_sensor_uuid
# self._initialize_goal_encoder(observation_space)
self._hidden_size = hidden_size
resnet_baseplanes = 32
backbone="resnet18"
visual_resnet = ResNetEncoder(
observation_space,
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=False,
obs_transform=ResizeCenterCropper(size=(256, 256)),
backbone_only=True,
)
self.detach = detach
self.visual_resnet = visual_resnet
self.visual_encoder = nn.Sequential(
Flatten(),
nn.Linear(
np.prod(visual_resnet.output_shape), hidden_size
),
nn.Sigmoid()
)
self.visual_decoder = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Upsample(scale_factor=2),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Upsample(scale_factor=2),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Upsample(scale_factor=2),
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Upsample(scale_factor=2),
nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Upsample(scale_factor=2),
nn.Conv2d(32, 3, kernel_size=3, stride=1, padding=1),
)
self.train()
def __init__(
self,
observation_space,
output_size=128,
checkpoint="NONE",
backbone="resnet50",
resnet_baseplanes=32,
normalize_visual_inputs=False,
trainable=False,
spatial_output: bool = False,
):
super().__init__()
self.visual_encoder = ResNetEncoder(
spaces.Dict({"depth": observation_space.spaces["depth"]}),
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=normalize_visual_inputs,
obs_transform=None,
)
for param in self.visual_encoder.parameters():
param.requires_grad_(trainable)
if checkpoint != "NONE":
ddppo_weights = torch.load(checkpoint)
weights_dict = {}
for k, v in ddppo_weights["state_dict"].items():
split_layer_name = k.split(".")[2:]
if split_layer_name[0] != "visual_encoder":
continue
layer_name = ".".join(split_layer_name[1:])
weights_dict[layer_name] = v
del ddppo_weights
self.visual_encoder.load_state_dict(weights_dict, strict=True)
self.spatial_output = spatial_output
if not self.spatial_output:
self.output_shape = (output_size, )
self.visual_fc = nn.Sequential(
Flatten(),
nn.Linear(np.prod(self.visual_encoder.output_shape),
output_size),
nn.ReLU(True),
)
else:
self.spatial_embeddings = nn.Embedding(
self.visual_encoder.output_shape[1] *
self.visual_encoder.output_shape[2],
64,
)
self.output_shape = list(self.visual_encoder.output_shape)
self.output_shape[0] += self.spatial_embeddings.embedding_dim
self.output_shape = tuple(self.output_shape)
def __init__(
self,
observation_space,
action_space,
hidden_size,
net=SingleBelief,
aux_tasks=[],
config=None,
**kwargs,
):
super().__init__(
observation_space,
action_space,
hidden_size,
net,
aux_tasks=aux_tasks,
config=config,
**kwargs
)
self.medium = config.midlevel_medium
self.visual_encoder = None
self.visual_resize = nn.Sequential(
Flatten(),
nn.Linear(2048, hidden_size),
nn.ReLU(True)
)
def _setup_net(self, rgb_input, feature_dim):
self.cnn = nn.Sequential(
nn.Conv2d(in_channels=rgb_input,
out_channels=32,
kernel_size=3,
stride=2),
nn.ReLU(True),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3,
stride=2),
nn.ReLU(True),
nn.Conv2d(in_channels=64,
out_channels=128,
kernel_size=3,
stride=2),
nn.ReLU(True),
nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=3,
stride=2),
nn.ReLU(True),
)
self.flatten = nn.Sequential(
Flatten(),
nn.Linear(15 * 15 * 256, feature_dim),
# nn.ReLU(True),
)
self._init_weight()
print(self.cnn)
_print_model_parameters(self.cnn)
def __init__(
self,
observation_space,
action_space,
goal_sensor_uuid,
hidden_size,
num_recurrent_layers,
rnn_type,
backbone,
resnet_baseplanes,
normalize_visual_inputs,
obs_transform=ResizeCenterCropper(size=(256, 256)),
):
super().__init__()
self.goal_sensor_uuid = goal_sensor_uuid
self.prev_action_embedding = nn.Embedding(action_space.n + 1, 32)
self._n_prev_action = 32
self._n_input_goal = (
observation_space.spaces[self.goal_sensor_uuid].shape[0] + 1)
self.tgt_embeding = nn.Linear(self._n_input_goal, 32)
self._n_input_goal = 32
self._hidden_size = hidden_size
rnn_input_size = self._n_input_goal + self._n_prev_action
self.visual_encoder = ResNetEncoder(
observation_space,
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=normalize_visual_inputs,
obs_transform=obs_transform,
)
if not self.visual_encoder.is_blind:
self.visual_fc = nn.Sequential(
Flatten(),
nn.Linear(np.prod(self.visual_encoder.output_shape),
hidden_size),
nn.ReLU(True),
)
self.state_encoder = RNNStateEncoder(
(0 if self.is_blind else self._hidden_size) + rnn_input_size,
self._hidden_size,
rnn_type=rnn_type,
num_layers=num_recurrent_layers,
)
self.train()
def _init_model(self, cnn_dims, output_size):
r"""cnn_dims: initial cnn dimensions.
"""
if self.is_blind:
self.cnn = nn.Sequential()
return
# kernel size for different CNN layers
self._cnn_layers_kernel_size = [(8, 8), (4, 4), (3, 3)]
# strides for different CNN layers
self._cnn_layers_stride = [(4, 4), (2, 2), (1, 1)]
for kernel_size, stride in zip(self._cnn_layers_kernel_size,
self._cnn_layers_stride):
cnn_dims = self._conv_output_dim(
dimension=cnn_dims,
padding=np.array([0, 0], dtype=np.float32),
dilation=np.array([1, 1], dtype=np.float32),
kernel_size=np.array(kernel_size, dtype=np.float32),
stride=np.array(stride, dtype=np.float32),
)
self.cnn = nn.Sequential(
nn.Conv2d(
in_channels=self._n_input_rgb + self._n_input_depth,
out_channels=32,
kernel_size=self._cnn_layers_kernel_size[0],
stride=self._cnn_layers_stride[0],
),
nn.ReLU(True),
nn.Conv2d(
in_channels=32,
out_channels=64,
kernel_size=self._cnn_layers_kernel_size[1],
stride=self._cnn_layers_stride[1],
),
nn.ReLU(True),
nn.Conv2d(
in_channels=64,
out_channels=32,
kernel_size=self._cnn_layers_kernel_size[2],
stride=self._cnn_layers_stride[2],
),
Contiguous(),
Flatten(),
nn.Linear(32 * cnn_dims[0] * cnn_dims[1], output_size),
nn.ReLU(True),
)
self.layer_init()
def forward(self, x):
# 21 1 240 240
x = self.maxpool(x) # 21 1 120 120
x = self.conv1(x) # 21 8 30 30
x = nn.ReLU()(x)
x = self.maxpool(x) # 21 8 15 15
x = self.conv2(x) # 21 16 8 8
x = nn.ReLU()(x)
x = self.maxpool(x) # 21 16 4 4
x = self.conv3(x) # 21 32 2 2
x = nn.ReLU()(x)
x = Flatten()(x.contiguous()) # 21 128
x = self.fc(x) # 21 256
return x
def __init__(
self,
observation_space,
hidden_size,
goal_sensor_uuid=None,
additional_sensors=[
] # low dim sensors corresponding to registered name
):
super().__init__()
self.goal_sensor_uuid = goal_sensor_uuid
self.additional_sensors = additional_sensors
self._n_input_goal = 0
self._n_input_goal = 0
if goal_sensor_uuid is not None and goal_sensor_uuid != "no_sensor":
self.goal_sensor_uuid = goal_sensor_uuid
self._initialize_goal_encoder(observation_space)
self._hidden_size = hidden_size
resnet_baseplanes = 32
backbone = "resnet18"
visual_resnet = ResNetEncoder(
observation_space,
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=False,
)
self.visual_encoder = nn.Sequential(
visual_resnet,
Flatten(),
nn.Linear(np.prod(visual_resnet.output_shape), hidden_size),
nn.ReLU(True),
)
final_embedding_size = (0 if self.is_blind else
self._hidden_size) + self._n_input_goal
for sensor in additional_sensors:
final_embedding_size += observation_space.spaces[sensor].shape[0]
self.state_encoder = RNNStateEncoder(final_embedding_size,
self._hidden_size)
self.train()
def __init__(
self,
observation_space,
action_space,
hidden_size,
net=SingleBelief,
aux_tasks=[], # bruh are we even forwarding these things...
config=None,
**kwargs, # Note, we forward kwargs to the net
):
assert issubclass(net, SingleBelief), "Belief policy must use belief net"
super().__init__(net(
observation_space=observation_space,
hidden_size=hidden_size,
config=config, # Forward
**kwargs,
), action_space.n)
self.aux_tasks = aux_tasks
resnet_baseplanes = 32
backbone="resnet18"
visual_resnet = resnet.ResNetEncoder(
observation_space,
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=config.use_mean_and_var
)
self.visual_encoder = nn.Sequential(
visual_resnet,
Flatten(),
nn.Linear(
np.prod(visual_resnet.output_shape), hidden_size
),
nn.ReLU(True),
)
def forward(self, x):
# print("x: ", x.shape) # 1 23 480 480
# x = self.maxpool(x) # 1 23 240 240
x = self.conv1(x)
x = nn.ReLU()(x)
# print("x: ", x.shape) # 1 32 240 240
x = self.maxpool(x)
x = self.conv2(x)
x = nn.ReLU()(x)
# print("x: ", x.shape) # 1 64 120 120
x = self.maxpool(x)
x = self.conv3(x)
x = nn.ReLU()(x)
# print("x: ", x.shape) # 1 128 60 60
x = self.maxpool(x)
x = self.conv4(x)
x = nn.ReLU()(x)
# print("x: ", x.shape) # 1 64 30 30
x = self.maxpool(x)
x = self.conv5(x)
x = nn.ReLU()(x)
# print("x: ", x.shape) # 1 32 15 15
# print("x: ", x.shape) # 1 32 7 7
x = Flatten()(x.contiguous())
# print("x: ", x.shape) # 1*7200
x = self.fc(x) # 1*512
x = nn.ReLU()(x)
return x
def __init__(
self,
observation_space,
action_space,
goal_sensor_uuid,
hidden_size,
num_recurrent_layers,
rnn_type,
backbone,
resnet_baseplanes,
normalize_visual_inputs,
use_info_bot,
use_odometry,
):
super().__init__()
self.goal_sensor_uuid = goal_sensor_uuid
self._hidden_size = hidden_size
self.prev_action_embedding = nn.Embedding(action_space.n + 1, hidden_size)
self._n_prev_action = self.prev_action_embedding.embedding_dim
self._n_input_goal = observation_space.spaces[self.goal_sensor_uuid].shape[0]
self._tgt_proj = nn.Linear(self._n_input_goal, hidden_size)
self._n_input_goal = 32
self.ib = True
self.use_info_bot = use_info_bot
self.use_odometry = use_odometry
if self.ib:
self.bottleneck = VIBCompleteLayer(self._hidden_size, self._n_input_goal, self.use_info_bot, self.use_odometry)
self.visual_encoder = ResNetEncoder(
observation_space,
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=normalize_visual_inputs,
)
if not self.visual_encoder.is_blind:
after_compression_flat_size = 2048
num_compression_channels = int(
round(
after_compression_flat_size
/ (
self.visual_encoder.output_shape[1]
* self.visual_encoder.output_shape[2]
)
)
)
self.compression = nn.Sequential(
resnet.BasicBlock(
self.visual_encoder.output_shape[0],
self.visual_encoder.output_shape[0],
1,
),
resnet.BasicBlock(
self.visual_encoder.output_shape[0],
num_compression_channels,
1,
downsample=nn.Conv2d(
self.visual_encoder.output_shape[0], num_compression_channels, 1
),
),
)
self.visual_fc = nn.Sequential(
Flatten(),
nn.Linear(
np.prod(self.visual_encoder.compression_shape),
self._hidden_size - self._hidden_size // 4,
bias=False,
),
nn.LayerNorm(self._hidden_size - self._hidden_size // 4),
nn.ReLU(True),
)
self.visual_flow_encoder = nn.Sequential(
Flatten(),
nn.Linear(
np.prod(self.visual_encoder.compression_shape),
self._hidden_size // 2,
bias=False,
),
nn.LayerNorm(self._hidden_size // 2),
nn.ReLU(True),
nn.Linear(self._hidden_size // 2, self._hidden_size // 4, bias=False),
nn.LayerNorm(self._hidden_size // 4),
nn.ReLU(True),
)
self.delta_egomotion_predictor = nn.Linear(self._hidden_size // 4, 3)
if rnn_type != "transformer":
self.state_encoder = RNNStateEncoder(
self._hidden_size,
self._hidden_size,
rnn_type=rnn_type,
num_layers=num_recurrent_layers,
)
else:
self.state_encoder = TransformerStateEncoder(
input_size=self._hidden_size, d_model=self._hidden_size
)
self.goal_mem_layer = nn.Sequential(
nn.Linear(
self._hidden_size + (self._n_input_goal if self.ib else 0),
self.output_size,
),
nn.ReLU(True),
)
self.pg_with_gps_pred = nn.Sequential(
nn.Linear(self._hidden_size, self._hidden_size // 2),
nn.ReLU(True),
nn.Linear(self._hidden_size // 2, 3),
)
self.train()
self.register_buffer("ego_error_threshold", torch.tensor([[0.01]]))
def __init__(self, observation_space, output_size, detector):
super().__init__()
self.detector = detector
if "rgb" in observation_space.spaces:
self._n_input_rgb = observation_space.spaces["rgb"].shape[2]
else:
self._n_input_rgb = 0
if "depth" in observation_space.spaces:
self._n_input_depth = observation_space.spaces["depth"].shape[2]
else:
self._n_input_depth = 0
self._no_classes = observation_space.spaces["goalclass"].shape[0]
self._detector_channels = 765 // (3 * 3)
# kernel size for different CNN layers
self._cnn_layers_kernel_size = [(8, 8), (4, 4), (3, 3)]
# strides for different CNN layers
self._cnn_layers_stride = [(4, 4), (2, 2), (1, 1)]
if self._n_input_rgb > 0:
cnn_dims = np.array(observation_space.spaces["rgb"].shape[:2],
dtype=np.float32)
elif self._n_input_depth > 0:
cnn_dims = np.array(observation_space.spaces["depth"].shape[:2],
dtype=np.float32)
if self.is_blind:
self.cnn_1 = nn.Sequential()
self.cnn_2 = nn.Sequential()
else:
for kernel_size, stride in zip(self._cnn_layers_kernel_size,
self._cnn_layers_stride):
cnn_dims = self._conv_output_dim(
dimension=cnn_dims,
padding=np.array([0, 0], dtype=np.float32),
dilation=np.array([1, 1], dtype=np.float32),
kernel_size=np.array(kernel_size, dtype=np.float32),
stride=np.array(stride, dtype=np.float32),
)
self.cnn_1 = nn.Sequential(
nn.Conv2d(
in_channels=self._n_input_rgb + self._n_input_depth,
out_channels=32,
kernel_size=self._cnn_layers_kernel_size[0],
stride=self._cnn_layers_stride[0],
), nn.ReLU(True),
nn.Conv2d(
in_channels=32,
out_channels=64,
kernel_size=self._cnn_layers_kernel_size[1],
stride=self._cnn_layers_stride[1],
), nn.ReLU(True))
self.detector_cnn = nn.Sequential(
nn.Conv2d(
in_channels=self._detector_channels + self._no_classes,
out_channels=64,
kernel_size=1,
stride=1,
),
nn.ReLU(True),
)
self.cnn_2 = nn.Sequential(
nn.Conv2d(
in_channels=64 + 64,
out_channels=128,
kernel_size=self._cnn_layers_kernel_size[2],
stride=self._cnn_layers_stride[2],
),
nn.ReLU(True),
nn.Conv2d(
in_channels=128,
out_channels=32,
kernel_size=1,
stride=1,
),
# nn.ReLU(True),
Flatten(),
nn.Linear(32 * (cnn_dims[0] + 2) * (cnn_dims[1] + 2),
output_size),
nn.ReLU(True),
)
self.layer_init()
def __init__(
self,
observation_space,
hidden_size,
goal_sensor_uuid=None,
detach=False,
imagenet=False,
additional_sensors=[
] # low dim sensors corresponding to registered name
):
self.detach = detach
self.imagenet = imagenet
super().__init__()
self.goal_sensor_uuid = goal_sensor_uuid
self.additional_sensors = additional_sensors
self._n_input_goal = 0
self._n_input_goal = 0
if goal_sensor_uuid is not None and goal_sensor_uuid != "no_sensor":
self.goal_sensor_uuid = goal_sensor_uuid
self._initialize_goal_encoder(observation_space)
self._hidden_size = hidden_size
resnet_baseplanes = 64
backbone = "resnet50"
# backbone="resnet18"
if imagenet:
visual_resnet = TorchVisionResNet50()
visual_resnet.eval()
else:
visual_resnet = ResNetEncoder(
observation_space,
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=False,
)
self.detach = detach
self.model_encoder = ResNetEncoder(
observation_space,
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=False,
dense=True,
)
self.target_encoder = ResNetEncoder(
observation_space,
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=False,
dense=True,
)
self.visual_resnet = visual_resnet
if imagenet:
self.visual_encoder = nn.Sequential(
Flatten(),
nn.Linear(2048, hidden_size),
nn.ReLU(True),
)
self.target_image_encoder = nn.Sequential(
Flatten(),
nn.Linear(2048, hidden_size),
nn.ReLU(True),
)
else:
self.visual_encoder = nn.Sequential(
Flatten(),
nn.Linear(np.prod(visual_resnet.output_shape), hidden_size),
nn.ReLU(True),
)
self.target_image_encoder = nn.Sequential(
Flatten(),
nn.Linear(np.prod(visual_resnet.output_shape), hidden_size),
nn.ReLU(True),
)
final_embedding_size = (0 if self.is_blind else
self._hidden_size) + self._n_input_goal
for sensor in additional_sensors:
final_embedding_size += observation_space.spaces[sensor].shape[0]
if self.goal_sensor_uuid == 'imagegoal':
final_embedding_size = 1024
self.state_encoder = nn.Sequential(
nn.Linear(final_embedding_size, hidden_size), nn.ReLU(True),
nn.Linear(hidden_size, hidden_size))
self.state_policy_encoder = RNNStateEncoder(final_embedding_size,
self._hidden_size)
self.train()
def _init_perception_model(self, observation_space):
if "rgb" in observation_space.spaces:
self._n_input_rgb = observation_space.spaces["rgb"].shape[2]
else:
self._n_input_rgb = 0
if "depth" in observation_space.spaces:
self._n_input_depth = observation_space.spaces["depth"].shape[2]
else:
self._n_input_depth = 0
# kernel size for different CNN layers
self._cnn_layers_kernel_size = [(8, 8), (4, 4), (3, 3)]
# strides for different CNN layers
self._cnn_layers_stride = [(4, 4), (2, 2), (1, 1)]
if self._n_input_rgb > 0:
cnn_dims = np.array(observation_space.spaces["rgb"].shape[:2],
dtype=np.float32)
elif self._n_input_depth > 0:
cnn_dims = np.array(observation_space.spaces["depth"].shape[:2],
dtype=np.float32)
if self.is_blind:
return nn.Sequential()
else:
for kernel_size, stride in zip(self._cnn_layers_kernel_size,
self._cnn_layers_stride):
cnn_dims = self._conv_output_dim(
dimension=cnn_dims,
padding=np.array([0, 0], dtype=np.float32),
dilation=np.array([1, 1], dtype=np.float32),
kernel_size=np.array(kernel_size, dtype=np.float32),
stride=np.array(stride, dtype=np.float32),
)
return nn.Sequential(
nn.Conv2d(
in_channels=self._n_input_rgb + self._n_input_depth,
out_channels=32,
kernel_size=self._cnn_layers_kernel_size[0],
stride=self._cnn_layers_stride[0],
),
nn.ReLU(),
nn.Conv2d(
in_channels=32,
out_channels=64,
kernel_size=self._cnn_layers_kernel_size[1],
stride=self._cnn_layers_stride[1],
),
nn.ReLU(),
nn.Conv2d(
in_channels=64,
out_channels=32,
kernel_size=self._cnn_layers_kernel_size[2],
stride=self._cnn_layers_stride[2],
),
Flatten(),
nn.Linear(32 * cnn_dims[0] * cnn_dims[1], self._hidden_size),
nn.ReLU(),
)
def __init__(self,
observation_space,
output_size,
drop_prob=0.5,
channel_scale=1):
super().__init__()
if "rgb" in observation_space.spaces:
self._n_input_rgb = observation_space.spaces["rgb"].shape[2]
else:
self._n_input_rgb = 0
if "depth" in observation_space.spaces:
self._n_input_depth = observation_space.spaces["depth"].shape[2]
else:
self._n_input_depth = 0
# kernel size for different CNN layers
self._cnn_layers_kernel_size = [(8, 8), (4, 4), (3, 3)]
# strides for different CNN layers
self._cnn_layers_stride = [(4, 4), (2, 2), (1, 1)]
self._drop_prob = drop_prob
print("i am here---" * 100)
if self._n_input_rgb > 0:
cnn_dims = np.array(observation_space.spaces["rgb"].shape[:2],
dtype=np.float32)
elif self._n_input_depth > 0:
cnn_dims = np.array(observation_space.spaces["depth"].shape[:2],
dtype=np.float32)
if self.is_blind:
self.cnn = nn.Sequential()
else:
for kernel_size, stride in zip(self._cnn_layers_kernel_size,
self._cnn_layers_stride):
cnn_dims = self._conv_output_dim(
dimension=cnn_dims,
padding=np.array([0, 0], dtype=np.float32),
dilation=np.array([1, 1], dtype=np.float32),
kernel_size=np.array(kernel_size, dtype=np.float32),
stride=np.array(stride, dtype=np.float32),
)
ds = channel_scale
self.cnn = nn.Sequential(
nn.Conv2d(
in_channels=self._n_input_rgb + self._n_input_depth,
out_channels=32 * ds,
kernel_size=self._cnn_layers_kernel_size[0],
stride=self._cnn_layers_stride[0],
),
nn.BatchNorm2d(32 * ds, affine=False),
nn.ReLU(True),
nn.Conv2d(
in_channels=32 * ds,
out_channels=64 * ds,
kernel_size=self._cnn_layers_kernel_size[1],
stride=self._cnn_layers_stride[1],
),
nn.BatchNorm2d(64 * ds, affine=False),
nn.ReLU(True),
nn.Conv2d(
in_channels=64 * ds,
out_channels=32 * ds,
kernel_size=self._cnn_layers_kernel_size[2],
stride=self._cnn_layers_stride[2],
),
nn.BatchNorm2d(32 * ds, affine=False),
nn.ELU(True),
Flatten(),
nn.Linear(32 * ds * cnn_dims[0] * cnn_dims[1], output_size),
nn.BatchNorm1d(output_size, affine=False),
nn.ReLU(True),
)
self.layer_init()
def __init__(
self,
observation_space,
action_space,
hidden_size,
num_recurrent_layers,
rnn_type,
backbone,
resnet_baseplanes,
normalize_visual_inputs,
obs_transform=ResizeCenterCropper(size=(256, 256)),
force_blind_policy=False,
):
super().__init__()
self.prev_action_embedding = nn.Embedding(action_space.n + 1, 32)
self._n_prev_action = 32
rnn_input_size = self._n_prev_action
if (IntegratedPointGoalGPSAndCompassSensor.cls_uuid
in observation_space.spaces):
n_input_goal = (observation_space.spaces[
IntegratedPointGoalGPSAndCompassSensor.cls_uuid].shape[0] + 1)
self.tgt_embeding = nn.Linear(n_input_goal, 32)
rnn_input_size += 32
if ObjectGoalSensor.cls_uuid in observation_space.spaces:
self._n_object_categories = (int(
observation_space.spaces[ObjectGoalSensor.cls_uuid].high[0]) +
1)
self.obj_categories_embedding = nn.Embedding(
self._n_object_categories, 32)
rnn_input_size += 32
if EpisodicGPSSensor.cls_uuid in observation_space.spaces:
input_gps_dim = observation_space.spaces[
EpisodicGPSSensor.cls_uuid].shape[0]
self.gps_embedding = nn.Linear(input_gps_dim, 32)
rnn_input_size += 32
if PointGoalSensor.cls_uuid in observation_space.spaces:
input_pointgoal_dim = observation_space.spaces[
PointGoalSensor.cls_uuid].shape[0]
self.pointgoal_embedding = nn.Linear(input_pointgoal_dim, 32)
rnn_input_size += 32
if HeadingSensor.cls_uuid in observation_space.spaces:
input_heading_dim = (
observation_space.spaces[HeadingSensor.cls_uuid].shape[0] + 1)
assert input_heading_dim == 2, "Expected heading with 2D rotation."
self.heading_embedding = nn.Linear(input_heading_dim, 32)
rnn_input_size += 32
if ProximitySensor.cls_uuid in observation_space.spaces:
input_proximity_dim = observation_space.spaces[
ProximitySensor.cls_uuid].shape[0]
self.proximity_embedding = nn.Linear(input_proximity_dim, 32)
rnn_input_size += 32
if EpisodicCompassSensor.cls_uuid in observation_space.spaces:
assert (observation_space.spaces[EpisodicCompassSensor.cls_uuid].
shape[0] == 1), "Expected compass with 2D rotation."
input_compass_dim = 2 # cos and sin of the angle
self.compass_embedding = nn.Linear(input_compass_dim, 32)
rnn_input_size += 32
if ImageGoalSensor.cls_uuid in observation_space.spaces:
goal_observation_space = spaces.Dict(
{"rgb": observation_space.spaces[ImageGoalSensor.cls_uuid]})
self.goal_visual_encoder = ResNetEncoder(
goal_observation_space,
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=normalize_visual_inputs,
obs_transform=obs_transform,
)
self.goal_visual_fc = nn.Sequential(
Flatten(),
nn.Linear(np.prod(self.goal_visual_encoder.output_shape),
hidden_size),
nn.ReLU(True),
)
rnn_input_size += hidden_size
self._hidden_size = hidden_size
self.visual_encoder = ResNetEncoder(
observation_space if not force_blind_policy else spaces.Dict({}),
baseplanes=resnet_baseplanes,
ngroups=resnet_baseplanes // 2,
make_backbone=getattr(resnet, backbone),
normalize_visual_inputs=normalize_visual_inputs,
obs_transform=obs_transform,
)
if not self.visual_encoder.is_blind:
self.visual_fc = nn.Sequential(
Flatten(),
nn.Linear(np.prod(self.visual_encoder.output_shape),
hidden_size),
nn.ReLU(True),
)
self.state_encoder = RNNStateEncoder(
(0 if self.is_blind else self._hidden_size) + rnn_input_size,
self._hidden_size,
rnn_type=rnn_type,
num_layers=num_recurrent_layers,
)
self.train()
def __init__(
self,
observation_space,
output_size,
obs_transform: nn.Module = ResizeCenterCropper(size=(256, 256)),
):
super().__init__()
self.obs_transform = obs_transform
if self.obs_transform is not None:
observation_space = obs_transform.transform_observation_space(
observation_space)
if "rgb" in observation_space.spaces:
self._n_input_rgb = observation_space.spaces["rgb"].shape[2]
else:
self._n_input_rgb = 0
if "depth" in observation_space.spaces:
self._n_input_depth = observation_space.spaces["depth"].shape[2]
else:
self._n_input_depth = 0
# kernel size for different CNN layers
self._cnn_layers_kernel_size = [(8, 8), (4, 4), (3, 3)]
# strides for different CNN layers
self._cnn_layers_stride = [(4, 4), (2, 2), (1, 1)]
if self._n_input_rgb > 0:
cnn_dims = np.array(observation_space.spaces["rgb"].shape[:2],
dtype=np.float32)
elif self._n_input_depth > 0:
cnn_dims = np.array(observation_space.spaces["depth"].shape[:2],
dtype=np.float32)
if self.is_blind:
self.cnn = nn.Sequential()
else:
for kernel_size, stride in zip(self._cnn_layers_kernel_size,
self._cnn_layers_stride):
cnn_dims = self._conv_output_dim(
dimension=cnn_dims,
padding=np.array([0, 0], dtype=np.float32),
dilation=np.array([1, 1], dtype=np.float32),
kernel_size=np.array(kernel_size, dtype=np.float32),
stride=np.array(stride, dtype=np.float32),
)
self.cnn = nn.Sequential(
nn.Conv2d(
in_channels=self._n_input_rgb + self._n_input_depth,
out_channels=32,
kernel_size=self._cnn_layers_kernel_size[0],
stride=self._cnn_layers_stride[0],
),
nn.ReLU(True),
nn.Conv2d(
in_channels=32,
out_channels=64,
kernel_size=self._cnn_layers_kernel_size[1],
stride=self._cnn_layers_stride[1],
),
nn.ReLU(True),
nn.Conv2d(
in_channels=64,
out_channels=32,
kernel_size=self._cnn_layers_kernel_size[2],
stride=self._cnn_layers_stride[2],
),
# nn.ReLU(True),
Flatten(),
nn.Linear(32 * cnn_dims[0] * cnn_dims[1], output_size),
nn.ReLU(True),
)
self.layer_init()