def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
target_encoding = self.get_target_coordinates_encoding(observations)
x: Union[torch.Tensor, List[torch.Tensor]]
x = [target_encoding]
# if observations["rgb"].shape[0] != 1:
# print("rgb", (observations["rgb"][...,0,0,:].unsqueeze(-2).unsqueeze(-2) == observations["rgb"][...,0,0,:]).float().mean())
# if "depth" in observations:
# print("depth", (observations["depth"][...,0,0,:].unsqueeze(-2).unsqueeze(-2) == observations["depth"][...,0,0,:]).float().mean())
if not self.is_blind:
perception_embed = self.visual_encoder(observations)
if self.sensor_fusion:
perception_embed = self.sensor_fuser(perception_embed)
x = [perception_embed] + x
x = torch.cat(x, dim=-1)
x, rnn_hidden_states = self.state_encoder(x, memory.tensor("rnn"),
masks)
ac_output = ActorCriticOutput(distributions=self.actor(x),
values=self.critic(x),
extras={})
return ac_output, memory.set_tensor("rnn", rnn_hidden_states)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
out = self.linear(cast(torch.Tensor, observations[self.input_uuid]))
main_logits = out[..., :self.num_actions]
aux_logits = out[..., self.num_actions:-1]
values = out[..., -1:]
# noinspection PyArgumentList
return (
ActorCriticOutput(
distributions=cast(
DistributionType, CategoricalDistr(
logits=main_logits)), # step x sampler x ...
values=cast(torch.FloatTensor,
values.view(values.shape[:2] +
(-1, ))), # step x sampler x flattened
extras={
"auxiliary_distributions":
CategoricalDistr(logits=aux_logits)
},
),
None,
)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
if not self.is_blind:
perception_embed = self.visual_encoder(observations)
else:
# TODO manage blindness for all agents simultaneously or separate?
raise NotImplementedError()
# TODO alternative where all agents consume all observations
x, rnn_hidden_states = self.state_encoder(perception_embed,
memory.tensor("rnn"), masks)
dists, vals = self.actor_critic(x)
return (
ActorCriticOutput(
distributions=dists,
values=vals,
extras={},
),
memory.set_tensor("rnn", rnn_hidden_states),
)
def forward(self, observations, memory, prev_actions, masks):
dists, values = self.head(observations[self.input_uuid])
# noinspection PyArgumentList
return (
ActorCriticOutput(distributions=dists, values=values, extras={},),
None,
)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
"""Processes input batched observations to produce new actor and critic
values. Processes input batched observations (along with prior hidden
states, previous actions, and masks denoting which recurrent hidden
states should be masked) and returns an `ActorCriticOutput` object
containing the model's policy (distribution over actions) and
evaluation of the current state (value).
# Parameters
observations : Batched input observations.
memory : `Memory` containing the hidden states from initial timepoints.
prev_actions : Tensor of previous actions taken.
masks : Masks applied to hidden states. See `RNNStateEncoder`.
# Returns
Tuple of the `ActorCriticOutput` and recurrent hidden state.
"""
arm2obj_dist = self.get_relative_distance_embedding(
observations["relative_agent_arm_to_obj"])
obj2goal_dist = self.get_relative_distance_embedding(
observations["relative_obj_to_goal"])
perception_embed = self.visual_encoder(observations)
pickup_bool = observations["pickedup_object"]
before_pickup = pickup_bool == 0 # not used because of our initialization
after_pickup = pickup_bool == 1
distances = arm2obj_dist
distances[after_pickup] = obj2goal_dist[after_pickup]
x = [distances, perception_embed]
x_cat = torch.cat(x, dim=-1)
x_out, rnn_hidden_states = self.state_encoder(x_cat,
memory.tensor("rnn"),
masks)
actor_out = self.actor(x_out)
critic_out = self.critic(x_out)
actor_critic_output = ActorCriticOutput(distributions=actor_out,
values=critic_out,
extras={})
updated_memory = memory.set_tensor("rnn", rnn_hidden_states)
return (
actor_critic_output,
updated_memory,
)
def forward(self, observations, memory, prev_actions, masks):
out = self.linear(observations[self.input_uuid])
# noinspection PyArgumentList
return (
ActorCriticOutput(
# ensure [steps, samplers, ...]
distributions=CategoricalDistr(logits=out[..., :-1]),
# ensure [steps, samplers, flattened]
values=cast(torch.FloatTensor,
out[..., -1:].view(*out.shape[:2], -1)),
extras={},
),
None,
)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
x = self.goal_visual_encoder(observations)
x, rnn_hidden_states = self.state_encoder(
x, memory.tensor(self.memory_key), masks)
return (
ActorCriticOutput(distributions=self.actor(x),
values=self.critic(x),
extras={}),
memory.set_tensor(self.memory_key, rnn_hidden_states),
)
def forward( # type:ignore
self,
observations: Dict[str, Union[torch.FloatTensor, Dict[str, Any]]],
memory: Memory,
prev_actions: Any,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
means = self.actor(observations[self.input_uuid])
values = self.critic(observations[self.input_uuid])
return (
ActorCriticOutput(
cast(DistributionType, GaussianDistr(loc=means, scale=self.action_std)),
values,
{},
),
None, # no Memory
)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
x = self.goal_visual_encoder(observations)
x, rnn_hidden_states = self.state_encoder(x, memory.tensor("rnn"),
masks)
return (
ActorCriticOutput(
distributions=self.actor(x),
values=self.critic(x),
extras={"auxiliary_distributions": self.auxiliary_actor(x)}
if self.include_auxiliary_head else {},
),
memory.set_tensor("rnn", rnn_hidden_states),
)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
"""Processes input batched observations to produce new actor and critic
values. Processes input batched observations (along with prior hidden
states, previous actions, and masks denoting which recurrent hidden
states should be masked) and returns an `ActorCriticOutput` object
containing the model's policy (distribution over actions) and
evaluation of the current state (value).
# Parameters
observations : Batched input observations.
memory : `Memory` containing the hidden states from initial timepoints.
prev_actions : Tensor of previous actions taken.
masks : Masks applied to hidden states. See `RNNStateEncoder`.
# Returns
Tuple of the `ActorCriticOutput` and recurrent hidden state.
"""
target_encoding = self.get_object_type_encoding(
cast(Dict[str, torch.FloatTensor], observations)
)
x = [target_encoding]
if not self.is_blind:
perception_embed = self.visual_encoder(observations)
x = [perception_embed] + x
x_cat = torch.cat(x, dim=-1) # type: ignore
x_out, rnn_hidden_states = self.state_encoder(
x_cat, memory.tensor("rnn"), masks
)
return (
ActorCriticOutput(
distributions=self.actor(x_out), values=self.critic(x_out), extras={}
),
memory.set_tensor("rnn", rnn_hidden_states),
)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
cur_img = observations[self.rgb_uuid]
unshuffled_img = observations[self.unshuffled_rgb_uuid]
concat_img = torch.cat((cur_img, unshuffled_img), dim=-1)
x = self.visual_encoder({self.concat_rgb_uuid: concat_img})
x, rnn_hidden_states = self.state_encoder(x, memory.tensor("rnn"),
masks)
ac_output = ActorCriticOutput(distributions=self.actor(x),
values=self.critic(x),
extras={})
return ac_output, memory.set_tensor("rnn", rnn_hidden_states)
def adapt_result(ac_output, hidden_states, num_steps, num_samplers,
num_agents, num_layers, observations): # type: ignore
distributions = CategoricalDistr(
logits=ac_output.distributions.logits.view(num_steps, num_samplers,
-1), )
values = ac_output.values.view(num_steps, num_samplers, num_agents)
extras = ac_output.extras # ignore shape
# TODO confirm the shape of the auxiliary distribution is the same as the actor's
if "auxiliary_distributions" in extras:
extras["auxiliary_distributions"] = CategoricalDistr(
logits=extras["auxiliary_distributions"].logits.view(
num_steps,
num_samplers,
-1 # assume single-agent
), )
hidden_states = hidden_states.view(num_layers,
num_samplers * num_agents, -1)
# Unflatten all observation batch dims
def recursively_adapt_observations(obs):
for entry in obs:
if isinstance(obs[entry], Dict):
recursively_adapt_observations(obs[entry])
else:
assert isinstance(obs[entry], torch.Tensor)
if entry in ["minigrid_ego_image", "minigrid_mission"]:
final_dims = obs[entry].shape[
1:] # assumes no agents dim in observations!
obs[entry] = obs[entry].view(num_steps,
num_samplers * num_agents,
*final_dims)
recursively_adapt_observations(observations)
return (
ActorCriticOutput(distributions=distributions,
values=values,
extras=extras),
hidden_states,
)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
out = self.linear(cast(torch.Tensor, observations[self.key]))
assert len(out.shape) in [
3,
4,
], "observations must be [step, sampler, data] or [step, sampler, agent, data]"
if len(out.shape) == 3:
# [step, sampler, data] -> [step, sampler, agent, data]
out = out.unsqueeze(-2)
main_logits = out[..., :self.num_actions]
aux_logits = out[..., self.num_actions:-1]
values = out[..., -1:]
# noinspection PyArgumentList
return (
ActorCriticOutput(
distributions=cast(
DistributionType, CategoricalDistr(
logits=main_logits)), # step x sampler x ...
values=cast(torch.FloatTensor,
values.view(values.shape[:2] +
(-1, ))), # step x sampler x flattened
extras={
"auxiliary_distributions":
CategoricalDistr(logits=aux_logits),
},
),
None,
)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
cur_img_resnet = observations[self.rgb_uuid]
unshuffled_img_resnet = observations[self.unshuffled_rgb_uuid]
concat_img = torch.cat(
(
cur_img_resnet,
unshuffled_img_resnet,
cur_img_resnet * unshuffled_img_resnet,
),
dim=-3,
)
batch_shape, features_shape = concat_img.shape[:-3], concat_img.shape[
-3:]
concat_img_reshaped = concat_img.view(-1, *features_shape)
attention_probs = torch.softmax(
self.visual_attention(concat_img_reshaped).view(
concat_img_reshaped.shape[0], -1),
dim=-1,
).view(concat_img_reshaped.shape[0], 1,
*concat_img_reshaped.shape[-2:])
x = ((self.visual_encoder(concat_img_reshaped) *
attention_probs).mean(-1).mean(-1))
x = x.view(*batch_shape, -1)
x, rnn_hidden_states = self.state_encoder(x, memory.tensor("rnn"),
masks)
ac_output = ActorCriticOutput(distributions=self.actor(x),
values=self.critic(x),
extras={})
return ac_output, memory.set_tensor("rnn", rnn_hidden_states)
def forward(
self,
observations: ObservationType,
recurrent_hidden_states: torch.FloatTensor,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
):
(
observations,
recurrent_hidden_states,
prev_actions,
masks,
num_steps,
num_samplers,
num_agents,
num_layers,
) = self.adapt_inputs(observations, recurrent_hidden_states,
prev_actions, masks)
if self.lang_model != "gru":
ac_output, hidden_states = self.forward_loop(
observations=observations,
recurrent_hidden_states=recurrent_hidden_states,
prev_actions=prev_actions,
masks=masks, # type: ignore
)
return self.adapt_result(
ac_output,
hidden_states[-1:],
num_steps,
num_samplers,
num_agents,
num_layers,
observations,
)
assert recurrent_hidden_states.shape[0] == 1
images = cast(torch.FloatTensor, observations["minigrid_ego_image"])
if self.use_cnn2:
images_shape = images.shape
# noinspection PyArgumentList
images = images + torch.LongTensor(
[0, 11, 22]).view( # type:ignore
1, 1, 1, 3).to(images.device)
images = self.semantic_embedding(images).view( # type:ignore
*images_shape[:3], 24)
images = images.permute(0, 3, 1, 2).float() # type:ignore
_, nsamplers, _ = recurrent_hidden_states.shape
rollouts_len = images.shape[0] // nsamplers
masks = cast(torch.FloatTensor,
masks.view(rollouts_len, nsamplers, *masks.shape[1:]))
instrs: Optional[torch.Tensor] = None
if "minigrid_mission" in observations and self.use_instr:
instrs = cast(torch.FloatTensor, observations["minigrid_mission"])
instrs = instrs.view(rollouts_len, nsamplers, instrs.shape[-1])
needs_instr_reset_mask = masks != 1.0
needs_instr_reset_mask[0] = 1
needs_instr_reset_mask = needs_instr_reset_mask.squeeze(-1)
blocking_inds: List[int] = np.where(
needs_instr_reset_mask.view(rollouts_len,
-1).any(-1).cpu().numpy())[0].tolist()
blocking_inds.append(rollouts_len)
instr_embeddings: Optional[torch.Tensor] = None
if self.use_instr:
instr_reset_multi_inds = list((int(a), int(b)) for a, b in zip(
*np.where(needs_instr_reset_mask.cpu().numpy())))
time_ind_to_which_need_instr_reset: List[List] = [
[] for _ in range(rollouts_len)
]
reset_multi_ind_to_index = {
mi: i
for i, mi in enumerate(instr_reset_multi_inds)
}
for a, b in instr_reset_multi_inds:
time_ind_to_which_need_instr_reset[a].append(b)
unique_instr_embeddings = self._get_instr_embedding(
instrs[needs_instr_reset_mask])
instr_embeddings_list = [unique_instr_embeddings[:nsamplers]]
current_instr_embeddings_list = list(instr_embeddings_list[-1])
for time_ind in range(1, rollouts_len):
if len(time_ind_to_which_need_instr_reset[time_ind]) == 0:
instr_embeddings_list.append(instr_embeddings_list[-1])
else:
for sampler_needing_reset_ind in time_ind_to_which_need_instr_reset[
time_ind]:
current_instr_embeddings_list[
sampler_needing_reset_ind] = unique_instr_embeddings[
reset_multi_ind_to_index[(
time_ind, sampler_needing_reset_ind)]]
instr_embeddings_list.append(
torch.stack(current_instr_embeddings_list, dim=0))
instr_embeddings = torch.stack(instr_embeddings_list, dim=0)
# The following code can be used to compute the instr_embeddings in another way
# and thus verify that the above logic is (more likely to be) correct
# needs_instr_reset_mask = (masks != 1.0)
# needs_instr_reset_mask[0] *= 0
# needs_instr_reset_inds = needs_instr_reset_mask.view(nrollouts, -1).any(-1).cpu().numpy()
#
# # Get inds where a new task has started
# blocking_inds: List[int] = np.where(needs_instr_reset_inds)[0].tolist()
# blocking_inds.append(needs_instr_reset_inds.shape[0])
# if nrollouts != 1:
# pdb.set_trace()
# if blocking_inds[0] != 0:
# blocking_inds.insert(0, 0)
# if self.use_instr:
# instr_embeddings_list = []
# for ind0, ind1 in zip(blocking_inds[:-1], blocking_inds[1:]):
# instr_embeddings_list.append(
# self._get_instr_embedding(instrs[ind0])
# .unsqueeze(0)
# .repeat(ind1 - ind0, 1, 1)
# )
# tmp_instr_embeddings = torch.cat(instr_embeddings_list, dim=0)
# assert (instr_embeddings - tmp_instr_embeddings).abs().max().item() < 1e-6
# Embed images
# images = images.view(nrollouts, nsamplers, *images.shape[1:])
image_embeddings = self.image_conv(images)
if self.arch.startswith("expert_filmcnn"):
instr_embeddings_flatter = instr_embeddings.view(
-1, *instr_embeddings.shape[2:])
for controller in self.controllers:
image_embeddings = controller(image_embeddings,
instr_embeddings_flatter)
image_embeddings = F.relu(self.film_pool(image_embeddings))
image_embeddings = image_embeddings.view(rollouts_len, nsamplers, -1)
if self.use_instr and self.lang_model == "attgru":
raise NotImplementedError("Currently attgru is not implemented.")
memory = None
if self.use_memory:
assert recurrent_hidden_states.shape[0] == 1
hidden = (
recurrent_hidden_states[:, :, :self.semi_memory_size],
recurrent_hidden_states[:, :, self.semi_memory_size:],
)
embeddings_list = []
for ind0, ind1 in zip(blocking_inds[:-1], blocking_inds[1:]):
hidden = (hidden[0] * masks[ind0], hidden[1] * masks[ind0])
rnn_out, hidden = self.memory_rnn(image_embeddings[ind0:ind1],
hidden)
embeddings_list.append(rnn_out)
# embedding = hidden[0]
embedding = torch.cat(embeddings_list, dim=0)
memory = torch.cat(hidden, dim=-1)
else:
embedding = image_embeddings
if self.use_instr and not "filmcnn" in self.arch:
embedding = torch.cat((embedding, instr_embeddings), dim=-1)
if hasattr(self, "aux_info") and self.aux_info:
extra_predictions = {
info: self.extra_heads[info](embedding)
for info in self.extra_heads
}
else:
extra_predictions = dict()
embedding = embedding.view(rollouts_len * nsamplers, -1)
ac_output = ActorCriticOutput(
distributions=CategoricalDistr(logits=self.actor(embedding), ),
values=self.critic(embedding),
extras=extra_predictions if not self.include_auxiliary_head else {
**extra_predictions,
"auxiliary_distributions":
CategoricalDistr(logits=self.aux(embedding)),
},
)
hidden_states = memory
return self.adapt_result(
ac_output,
hidden_states,
num_steps,
num_samplers,
num_agents,
num_layers,
observations,
)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
"""Processes input batched observations to produce new actor and critic
values. Processes input batched observations (along with prior hidden
states, previous actions, and masks denoting which recurrent hidden
states should be masked) and returns an `ActorCriticOutput` object
containing the model's policy (distribution over actions) and
evaluation of the current state (value).
# Parameters
observations : Batched input observations.
memory : `Memory` containing the hidden states from initial timepoints.
prev_actions : Tensor of previous actions taken.
masks : Masks applied to hidden states. See `RNNStateEncoder`.
# Returns
Tuple of the `ActorCriticOutput` and recurrent hidden state.
"""
# 1.1 use perception model (i.e. encoder) to get observation embeddings
obs_embeds = self.forward_encoder(observations)
# 1.2 use embedding model to get prev_action embeddings
prev_actions_embeds = self.prev_action_embedder(prev_actions)
joint_embeds = torch.cat((obs_embeds, prev_actions_embeds),
dim=-1) # (T, N, *)
# 2. use RNNs to get single/multiple beliefs
beliefs_dict = {}
for key, model in self.state_encoders.items():
beliefs_dict[key], rnn_hidden_states = model(
joint_embeds, memory.tensor(key), masks)
memory.set_tensor(key, rnn_hidden_states) # update memory here
# 3. fuse beliefs for multiple belief models
beliefs, task_weights = self.fuse_beliefs(beliefs_dict,
obs_embeds) # fused beliefs
# 4. prepare output
extras = ({
aux_uuid: {
"beliefs":
(beliefs_dict[aux_uuid] if self.multiple_beliefs else beliefs),
"obs_embeds":
obs_embeds,
"aux_model": (self.aux_models[aux_uuid]
if aux_uuid in self.aux_models else None),
}
for aux_uuid in self.auxiliary_uuids
} if self.auxiliary_uuids is not None else {})
if self.multiple_beliefs:
extras[MultiAuxTaskNegEntropyLoss.UUID] = task_weights
actor_critic_output = ActorCriticOutput(
distributions=self.actor(beliefs),
values=self.critic(beliefs),
extras=extras,
)
return actor_critic_output, memory
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
in_walkthrough_phase_mask = observations[
self.in_walkthrough_phase_uuid]
in_unshuffle_phase_mask = ~in_walkthrough_phase_mask
in_walkthrough_float = in_walkthrough_phase_mask.float()
in_unshuffle_float = in_unshuffle_phase_mask.float()
# Don't reset hidden state at start of the unshuffle task
masks_no_unshuffle_reset = (masks.bool()
| in_unshuffle_phase_mask).float()
masks_with_unshuffle_reset = masks.float()
del masks # Just to make sure we don't accidentally use `masks when we want `masks_no_unshuffle_reset`
# Visual features
cur_img_resnet = observations[self.rgb_uuid]
unshuffled_img_resnet = observations[self.unshuffled_rgb_uuid]
concat_img = torch.cat(
(
cur_img_resnet,
unshuffled_img_resnet,
cur_img_resnet * unshuffled_img_resnet,
),
dim=-3,
)
batch_shape, features_shape = concat_img.shape[:-3], concat_img.shape[
-3:]
concat_img_reshaped = concat_img.view(-1, *features_shape)
attention_probs = torch.softmax(
self.visual_attention(concat_img_reshaped).view(
concat_img_reshaped.shape[0], -1),
dim=-1,
).view(concat_img_reshaped.shape[0], 1,
*concat_img_reshaped.shape[-2:])
vis_features = ((self.visual_encoder(concat_img_reshaped) *
attention_probs).mean(-1).mean(-1))
vis_features = vis_features.view(*batch_shape, -1)
# Various embeddings
prev_action_embeddings = self.prev_action_embedder(
((~masks_with_unshuffle_reset.bool()).long() *
(prev_actions.unsqueeze(-1) + 1))).squeeze(-2)
is_walkthrough_phase_embedding = self.is_walkthrough_phase_embedder(
in_walkthrough_phase_mask.long()).squeeze(-2)
to_cat = [
vis_features,
prev_action_embeddings,
is_walkthrough_phase_embedding,
]
rnn_hidden_states = memory.tensor("rnn")
rnn_outs = []
obs_for_rnn = torch.cat(to_cat, dim=-1)
last_walkthrough_encoding = memory.tensor("walkthrough_encoding")
for step in range(masks_with_unshuffle_reset.shape[0]):
rnn_out, rnn_hidden_states = self.state_encoder(
torch.cat(
(
obs_for_rnn[step:step + 1],
last_walkthrough_encoding *
masks_no_unshuffle_reset[step:step + 1],
),
dim=-1,
),
rnn_hidden_states,
masks_with_unshuffle_reset[step:step + 1],
)
rnn_outs.append(rnn_out)
walkthrough_encoding, _ = self.walkthrough_encoder(
rnn_out,
last_walkthrough_encoding,
masks_no_unshuffle_reset[step:step + 1],
)
last_walkthrough_encoding = (
last_walkthrough_encoding * in_unshuffle_float[step:step + 1] +
walkthrough_encoding * in_walkthrough_float[step:step + 1])
memory = memory.set_tensor("walkthrough_encoding",
last_walkthrough_encoding)
rnn_out = torch.cat(rnn_outs, dim=0)
walkthrough_dist, walkthrough_vals = self.walkthrough_ac(rnn_out)
unshuffle_dist, unshuffle_vals = self.unshuffle_ac(rnn_out)
assert len(in_walkthrough_float.shape) == len(
walkthrough_dist.logits.shape)
if self.walkthrough_good_action_logits is not None:
walkthrough_logits = (
walkthrough_dist.logits +
self.walkthrough_good_action_logits.view(
*((1, ) * (len(walkthrough_dist.logits.shape) - 1)), -1))
else:
walkthrough_logits = walkthrough_dist.logits
actor = CategoricalDistr(
logits=in_walkthrough_float * walkthrough_logits +
in_unshuffle_float * unshuffle_dist.logits)
values = (in_walkthrough_float * walkthrough_vals +
in_unshuffle_float * unshuffle_vals)
ac_output = ActorCriticOutput(distributions=actor,
values=values,
extras={})
return ac_output, memory.set_tensor("rnn", rnn_hidden_states)
def forward( # type:ignore
self,
observations: ObservationType,
memory: Memory,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
) -> Tuple[ActorCriticOutput[DistributionType], Optional[Memory]]:
in_walkthrough_phase_mask = observations[
self.in_walkthrough_phase_uuid]
in_unshuffle_phase_mask = ~in_walkthrough_phase_mask
in_walkthrough_float = in_walkthrough_phase_mask.float()
in_unshuffle_float = in_unshuffle_phase_mask.float()
# Don't reset hidden state at start of the unshuffle task
masks_no_unshuffle_reset = (masks.bool()
| in_unshuffle_phase_mask).float()
cur_img = observations[self.rgb_uuid]
unshuffled_img = observations[self.unshuffled_rgb_uuid]
concat_img = torch.cat((cur_img, unshuffled_img), dim=-1)
# Various embeddings
vis_features = self.visual_encoder({self.concat_rgb_uuid: concat_img})
prev_action_embeddings = self.prev_action_embedder(
((~masks.bool()).long() *
(prev_actions.unsqueeze(-1) + 1))).squeeze(-2)
is_walkthrough_phase_embedding = self.is_walkthrough_phase_embedder(
in_walkthrough_phase_mask.long()).squeeze(-2)
to_cat = [
vis_features,
prev_action_embeddings,
is_walkthrough_phase_embedding,
]
rnn_hidden_states = memory.tensor("rnn")
rnn_outs = []
obs_for_rnn = torch.cat(to_cat, dim=-1)
last_walkthrough_encoding = memory.tensor("walkthrough_encoding")
for step in range(masks.shape[0]):
rnn_out, rnn_hidden_states = self.state_encoder(
torch.cat(
(obs_for_rnn[step:step + 1], last_walkthrough_encoding),
dim=-1),
rnn_hidden_states,
masks[step:step + 1],
)
rnn_outs.append(rnn_out)
walkthrough_encoding, _ = self.walkthrough_encoder(
rnn_out,
last_walkthrough_encoding,
masks_no_unshuffle_reset[step:step + 1],
)
last_walkthrough_encoding = (
last_walkthrough_encoding * in_unshuffle_float[step:step + 1] +
walkthrough_encoding * in_walkthrough_float[step:step + 1])
memory = memory.set_tensor("walkthrough_encoding",
last_walkthrough_encoding)
rnn_out = torch.cat(rnn_outs, dim=0)
walkthrough_dist, walkthrough_vals = self.walkthrough_ac(rnn_out)
unshuffle_dist, unshuffle_vals = self.unshuffle_ac(rnn_out)
assert len(in_walkthrough_float.shape) == len(
walkthrough_dist.logits.shape)
if self.walkthrough_good_action_logits is not None:
walkthrough_logits = (
walkthrough_dist.logits +
self.walkthrough_good_action_logits.view(
*((1, ) * (len(walkthrough_dist.logits.shape) - 1)), -1))
else:
walkthrough_logits = walkthrough_dist.logits
actor = CategoricalDistr(
logits=in_walkthrough_float * walkthrough_logits +
in_unshuffle_float * unshuffle_dist.logits)
values = (in_walkthrough_float * walkthrough_vals +
in_unshuffle_float * unshuffle_vals)
ac_output = ActorCriticOutput(distributions=actor,
values=values,
extras={})
return ac_output, memory.set_tensor("rnn", rnn_hidden_states)
def forward_loop(
self,
observations: ObservationType,
recurrent_hidden_states: torch.FloatTensor,
prev_actions: torch.Tensor,
masks: torch.FloatTensor,
):
results = []
images = cast(torch.FloatTensor,
observations["minigrid_ego_image"]).float()
instrs: Optional[torch.Tensor] = None
if "minigrid_mission" in observations:
instrs = cast(torch.Tensor, observations["minigrid_mission"])
_, nsamplers, _ = recurrent_hidden_states.shape
rollouts_len = images.shape[0] // nsamplers
obs = babyai.rl.DictList()
images = images.view(rollouts_len, nsamplers, *images.shape[1:])
masks = masks.view(rollouts_len, nsamplers,
*masks.shape[1:]) # type:ignore
# needs_reset = (masks != 1.0).view(nrollouts, -1).any(-1)
if instrs is not None:
instrs = instrs.view(rollouts_len, nsamplers, instrs.shape[-1])
needs_instr_reset_mask = masks != 1.0
needs_instr_reset_mask[0] = 1
needs_instr_reset_mask = needs_instr_reset_mask.squeeze(-1)
instr_embeddings: Optional[torch.Tensor] = None
if self.use_instr:
instr_reset_multi_inds = list((int(a), int(b)) for a, b in zip(
*np.where(needs_instr_reset_mask.cpu().numpy())))
time_ind_to_which_need_instr_reset: List[List] = [
[] for _ in range(rollouts_len)
]
reset_multi_ind_to_index = {
mi: i
for i, mi in enumerate(instr_reset_multi_inds)
}
for a, b in instr_reset_multi_inds:
time_ind_to_which_need_instr_reset[a].append(b)
unique_instr_embeddings = self._get_instr_embedding(
instrs[needs_instr_reset_mask])
instr_embeddings_list = [unique_instr_embeddings[:nsamplers]]
current_instr_embeddings_list = list(instr_embeddings_list[-1])
for time_ind in range(1, rollouts_len):
if len(time_ind_to_which_need_instr_reset[time_ind]) == 0:
instr_embeddings_list.append(instr_embeddings_list[-1])
else:
for sampler_needing_reset_ind in time_ind_to_which_need_instr_reset[
time_ind]:
current_instr_embeddings_list[
sampler_needing_reset_ind] = unique_instr_embeddings[
reset_multi_ind_to_index[(
time_ind, sampler_needing_reset_ind)]]
instr_embeddings_list.append(
torch.stack(current_instr_embeddings_list, dim=0))
instr_embeddings = torch.stack(instr_embeddings_list, dim=0)
assert recurrent_hidden_states.shape[0] == 1
memory = recurrent_hidden_states[0]
# instr_embedding: Optional[torch.Tensor] = None
for i in range(rollouts_len):
obs.image = images[i]
if "minigrid_mission" in observations:
obs.instr = instrs[i]
# reset = needs_reset[i].item()
# if self.baby_ai_model.use_instr and (reset or i == 0):
# instr_embedding = self.baby_ai_model._get_instr_embedding(obs.instr)
results.append(
self.forward_once(obs,
memory=memory * masks[i],
instr_embedding=instr_embeddings[i]))
memory = results[-1]["memory"]
embedding = torch.cat([r["embedding"] for r in results], dim=0)
extra_predictions_list = [r["extra_predictions"] for r in results]
extra_predictions = {
key: torch.cat([ep[key] for ep in extra_predictions_list], dim=0)
for key in extra_predictions_list[0]
}
return (
ActorCriticOutput(
distributions=CategoricalDistr(logits=self.actor(embedding), ),
values=self.critic(embedding),
extras=extra_predictions
if not self.include_auxiliary_head else {
**extra_predictions,
"auxiliary_distributions":
cast(Any, CategoricalDistr(logits=self.aux(embedding))),
},
),
torch.stack([r["memory"] for r in results], dim=0),
)