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]]:
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( # 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=CategoricalDistr(logits=main_logits),
values=typing.cast(torch.FloatTensor, values),
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]]:
target_encoding = self.get_target_coordinates_encoding(observations)
x: Union[torch.Tensor, List[torch.Tensor]]
x = [target_encoding]
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]]:
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={}
),
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.
rnn_hidden_states : 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 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,
num_agents, -1), )
values = ac_output.values.view(num_steps, num_samplers, num_agents, 1)
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, num_agents, -1), )
hidden_states = hidden_states.view(num_layers,
num_samplers * num_agents, -1)
# Unflatten all observation batch dims
def recursively_adapt_observations(obs, num_steps, num_samplers,
num_agents):
for entry in obs:
if isinstance(obs[entry], Dict):
recursively_adapt_observations(obs[entry], num_steps,
num_samplers, num_agents)
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, num_steps, num_samplers,
num_agents)
return (
ActorCriticOutput(distributions=distributions,
values=values,
extras=extras),
hidden_states,
)
def forward(self, observations, memory, prev_actions, masks):
out = self.linear(observations[self.input_uuid])
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)
# noinspection PyArgumentList
return (
ActorCriticOutput(
distributions=CategoricalDistr(logits=out[..., :-1]),
values=cast(torch.FloatTensor, out[..., -1:]),
extras={},
),
None,
)
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,
)
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
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 = typing.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 = []
instr_embeddings_list.append(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_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 = []
instr_embeddings_list.append(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":
CategoricalDistr(logits=self.aux(embedding)),
},
),
torch.stack([r["memory"] for r in results], dim=0),
)
