class ModelGSFPV(nn.Module):
def __init__(self,
run_name="",
aux_class_features=False,
aux_grounding_features=False,
aux_lang=False,
recurrence=False):
super(ModelGSFPV, self).__init__()
self.model_name = "gs_fpv" + "_mem" if recurrence else ""
self.run_name = run_name
self.writer = LoggingSummaryWriter(log_dir="runs/" + run_name)
self.params = get_current_parameters()["Model"]
self.aux_weights = get_current_parameters()["AuxWeights"]
self.prof = SimpleProfiler(torch_sync=PROFILE, print=PROFILE)
self.iter = nn.Parameter(torch.zeros(1), requires_grad=False)
# Auxiliary Objectives
self.use_aux_class_features = aux_class_features
self.use_aux_grounding_features = aux_grounding_features
self.use_aux_lang = aux_lang
self.use_recurrence = recurrence
self.img_to_features_w = FPVToFPVMap(self.params["img_w"],
self.params["img_h"],
self.params["resnet_channels"],
self.params["feature_channels"])
self.lang_filter_gnd = MapLangSemanticFilter(
self.params["emb_size"], self.params["feature_channels"],
self.params["relevance_channels"])
self.lang_filter_goal = MapLangSpatialFilter(
self.params["emb_size"], self.params["relevance_channels"],
self.params["goal_channels"])
self.map_downsample = DownsampleResidual(
self.params["map_to_act_channels"], 2)
self.recurrence = RecurrentEmbedding(
self.params["gs_fpv_feature_map_size"],
self.params["gs_fpv_recurrence_size"])
# Sentence Embedding
self.sentence_embedding = SentenceEmbeddingSimple(
self.params["word_emb_size"], self.params["emb_size"],
self.params["emb_layers"])
in_features_size = self.params[
"gs_fpv_feature_map_size"] + self.params["emb_size"]
if self.use_recurrence:
in_features_size += self.params["gs_fpv_recurrence_size"]
self.features_to_action = DenseMlpBlock2(in_features_size,
self.params["mlp_hidden"], 4)
# Auxiliary Objectives
# --------------------------------------------------------------------------------------------------------------
self.add_auxiliary(
ClassAuxiliary2D("aux_class", None,
self.params["feature_channels"],
self.params["num_landmarks"], "fpv_features",
"lm_pos_fpv", "lm_indices"))
self.add_auxiliary(
ClassAuxiliary2D("aux_ground", None,
self.params["relevance_channels"], 2,
"fpv_features_g", "lm_pos_fpv", "lm_mentioned"))
if self.params["templates"]:
self.add_auxiliary(
ClassAuxiliary("aux_lang_lm", self.params["emb_size"],
self.params["num_landmarks"], 1,
"sentence_embed", "lm_mentioned_tplt"))
self.add_auxiliary(
ClassAuxiliary("aux_lang_side", self.params["emb_size"],
self.params["num_sides"], 1, "sentence_embed",
"side_mentioned_tplt"))
else:
self.add_auxiliary(
ClassAuxiliary("aux_lang_lm_nl", self.params["emb_size"], 2,
self.params["num_landmarks"], "sentence_embed",
"lang_lm_mentioned"))
self.action_loss = ActionLoss()
self.env_id = None
self.prev_instruction = None
self.seq_step = 0
# TODO: Try to hide these in a superclass or something. They take up a lot of space:
def cuda(self, device=None):
ModuleWithAuxiliaries.cuda(self, device)
self.sentence_embedding.cuda(device)
self.img_to_features_w.cuda(device)
self.lang_filter_gnd.cuda(device)
self.lang_filter_goal.cuda(device)
self.action_loss.cuda(device)
self.recurrence.cuda(device)
return self
def get_iter(self):
return int(self.iter.data[0])
def inc_iter(self):
self.iter += 1
def init_weights(self):
self.img_to_features_w.init_weights()
self.lang_filter_gnd.init_weights()
self.lang_filter_goal.init_weights()
self.sentence_embedding.init_weights()
def reset(self):
# TODO: This is error prone. Create a class StatefulModule, iterate submodules and reset all stateful modules
super(ModelGSFPV, self).reset()
self.sentence_embedding.reset()
self.img_to_features_w.reset()
self.recurrence.reset()
self.prev_instruction = None
print("GS_FPV_MEM_RESET")
def setEnvContext(self, context):
print("Set env context to: " + str(context))
self.env_id = context["env_id"]
def start_segment_rollout(self, *args):
self.reset()
def get_action(self, state, instruction):
"""
Given a DroneState (from PomdpInterface) and instruction, produce a numpy 4D action (x, y, theta, pstop)
:param state: DroneState object with the raw image from the simulator
:param instruction: Tokenized instruction given the corpus
#TODO: Absorb corpus within model
:return:
"""
# TODO: Simplify this
self.eval()
images_np_pure = state.image
state_np = state.state
#print("Act: " + debug_untokenize_instruction(instruction))
images_np = standardize_image(images_np_pure)
image_fpv = Variable(none_padded_seq_to_tensor([images_np]))
state = Variable(none_padded_seq_to_tensor([state_np]))
self.prev_instruction = instruction
img_in_t = image_fpv
img_in_t.volatile = True
instr_len = [len(instruction)] if instruction is not None else None
instruction = torch.LongTensor(instruction).unsqueeze(0)
instruction = cuda_var(instruction, self.is_cuda, self.cuda_device)
state.volatile = True
if self.is_cuda:
img_in_t = img_in_t.cuda(self.cuda_device)
state = state.cuda(self.cuda_device)
self.seq_step += 1
action = self(img_in_t, state, instruction, instr_len)
output_action = action.squeeze().data.cpu().numpy()
print("action: ", output_action)
stop_prob = output_action[3]
output_stop = 1 if stop_prob > self.params["stop_threshold"] else 0
output_action[3] = output_stop
return output_action
def deterministic_action(self, action_mean, action_std, stop_prob):
batch_size = action_mean.size(0)
action = Variable(
empty_float_tensor((batch_size, 4), self.is_cuda,
self.cuda_device))
action[:, 0:3] = action_mean[:, 0:3]
action[:, 3] = stop_prob
return action
def sample_action(self, action_mean, action_std, stop_prob):
action = torch.normal(action_mean, action_std)
stop = torch.bernoulli(stop_prob)
return action, stop
# This is called before beginning an execution sequence
def start_sequence(self):
self.seq_step = 0
self.reset()
print("RESETTED!")
return
# TODO: Move this somewhere and standardize
def cam_poses_from_states(self, states):
cam_pos = states[:, 9:12]
cam_rot = states[:, 12:16]
pose = Pose(cam_pos, cam_rot)
return pose
def forward(self, images, states, instructions, instr_lengths):
"""
:param images: BxCxHxW batch of images (observations)
:param states: BxK batch of drone states
:param instructions: BxM LongTensor where M is the maximum length of any instruction
:param instr_lengths: list of len B of integers, indicating length of each instruction
:param has_obs: list of booleans of length B indicating whether the given element in the sequence has an observation
:param yield_semantic_maps: If true, will not compute actions (full model), but return the semantic maps that
were built along the way in response to the images. This is ugly, but allows code reuse
:return:
"""
cam_poses = self.cam_poses_from_states(states)
self.prof.tick("out")
#print("Trn: " + debug_untokenize_instruction(instructions[0].data[:instr_lengths[0]]))
# Calculate the instruction embedding
if instructions is not None:
# TODO: Take batch of instructions and their lengths, return batch of embeddings. Store the last one as internal state
sent_embeddings = self.sentence_embedding(instructions,
instr_lengths)
self.keep_inputs("sentence_embed", sent_embeddings)
else:
sent_embeddings = self.sentence_embedding.get()
self.prof.tick("embed")
seq_size = len(images)
# Extract and project features onto the egocentric frame for each image
fpv_features = self.img_to_features_w(images,
cam_poses,
sent_embeddings,
self,
show="")
self.keep_inputs("fpv_features", fpv_features)
self.prof.tick("img_to_map_frame")
self.lang_filter_gnd.precompute_conv_weights(sent_embeddings)
self.lang_filter_goal.precompute_conv_weights(sent_embeddings)
gnd_features = self.lang_filter_gnd(fpv_features)
goal_features = self.lang_filter_goal(gnd_features)
self.keep_inputs("fpv_features_g", gnd_features)
visual_features = torch.cat([gnd_features, goal_features], dim=1)
lstm_in_features = visual_features.view([seq_size, 1, -1])
catlist = [lstm_in_features.view([seq_size, -1]), sent_embeddings]
if self.use_recurrence:
memory_features = self.recurrence(lstm_in_features)
catlist.append(memory_features[:, 0, :])
action_features = torch.cat(catlist, dim=1)
# Output the final action given the processed map
action_pred = self.features_to_action(action_features)
action_pred[:, 3] = torch.sigmoid(action_pred[:, 3])
out_action = self.deterministic_action(action_pred[:, 0:3], None,
action_pred[:, 3])
self.prof.tick("map_to_action")
return out_action
def maybe_cuda(self, tensor):
if self.is_cuda:
return tensor.cuda()
else:
return tensor
def cuda_var(self, tensor):
return cuda_var(tensor, self.is_cuda, self.cuda_device)
# Forward pass for training (with batch optimizations
def sup_loss_on_batch(self, batch, eval):
self.prof.tick("out")
action_loss_total = Variable(
empty_float_tensor([1], self.is_cuda, self.cuda_device))
if batch is None:
print("Skipping None Batch")
return action_loss_total
images = self.maybe_cuda(batch["images"])
instructions = self.maybe_cuda(batch["instr"])
instr_lengths = batch["instr_len"]
states = self.maybe_cuda(batch["states"])
actions = self.maybe_cuda(batch["actions"])
# Auxiliary labels
lm_pos_fpv = batch["lm_pos_fpv"]
lm_indices = batch["lm_indices"]
lm_mentioned = batch["lm_mentioned"]
lang_lm_mentioned = batch["lang_lm_mentioned"]
templates = get_current_parameters()["Environment"]["Templates"]
if templates:
lm_mentioned_tplt = batch["lm_mentioned_tplt"]
side_mentioned_tplt = batch["side_mentioned_tplt"]
# stops = self.maybe_cuda(batch["stops"])
masks = self.maybe_cuda(batch["masks"])
metadata = batch["md"]
seq_len = images.size(1)
batch_size = images.size(0)
count = 0
correct_goal_count = 0
goal_count = 0
# Loop thru batch
for b in range(batch_size):
seg_idx = -1
self.reset()
self.prof.tick("out")
b_seq_len = len_until_nones(metadata[b])
# TODO: Generalize this
# Slice the data according to the sequence length
b_metadata = metadata[b][:b_seq_len]
b_images = images[b][:b_seq_len]
b_instructions = instructions[b][:b_seq_len]
b_instr_len = instr_lengths[b][:b_seq_len]
b_states = states[b][:b_seq_len]
b_actions = actions[b][:b_seq_len]
b_lm_pos_fpv = lm_pos_fpv[b][:b_seq_len]
b_lm_indices = lm_indices[b][:b_seq_len]
b_lm_mentioned = lm_mentioned[b][:b_seq_len]
b_lm_pos_fpv = [
self.cuda_var(
(s / RESNET_FACTOR).long()) if s is not None else None
for s in b_lm_pos_fpv
]
b_lm_indices = [
self.cuda_var(s) if s is not None else None
for s in b_lm_indices
]
b_lm_mentioned = [
self.cuda_var(s) if s is not None else None
for s in b_lm_mentioned
]
# TODO: Figure out how to keep these properly. Perhaps as a whole batch is best
# TODO: Introduce a key-value store (encapsulate instead of inherit)
self.keep_inputs("lm_pos_fpv", b_lm_pos_fpv)
self.keep_inputs("lm_indices", b_lm_indices)
self.keep_inputs("lm_mentioned", b_lm_mentioned)
# TODO: Abstract all of these if-elses in a modular way once we know which ones are necessary
if templates:
b_lm_mentioned_tplt = lm_mentioned_tplt[b][:b_seq_len]
b_side_mentioned_tplt = side_mentioned_tplt[b][:b_seq_len]
b_side_mentioned_tplt = self.cuda_var(b_side_mentioned_tplt)
b_lm_mentioned_tplt = self.cuda_var(b_lm_mentioned_tplt)
self.keep_inputs("lm_mentioned_tplt", b_lm_mentioned_tplt)
self.keep_inputs("side_mentioned_tplt", b_side_mentioned_tplt)
else:
b_lang_lm_mentioned = self.cuda_var(
lang_lm_mentioned[b][:b_seq_len])
self.keep_inputs("lang_lm_mentioned", b_lang_lm_mentioned)
# ----------------------------------------------------------------------------
self.prof.tick("inputs")
actions = self(b_images, b_states, b_instructions, b_instr_len)
action_losses, _ = self.action_loss(b_actions,
actions,
batchreduce=False)
self.prof.tick("call")
action_losses = self.action_loss.batch_reduce_loss(action_losses)
action_loss = self.action_loss.reduce_loss(action_losses)
action_loss_total = action_loss
count += b_seq_len
self.prof.tick("loss")
action_loss_avg = action_loss_total / (count + 1e-9)
self.prof.tick("out")
# Doing this in the end (outside of se
aux_losses = self.calculate_aux_loss(reduce_average=True)
aux_loss = self.combine_aux_losses(aux_losses, self.aux_weights)
prefix = self.model_name + ("/eval" if eval else "/train")
self.writer.add_dict(prefix, get_current_meters(), self.get_iter())
self.writer.add_dict(prefix, aux_losses, self.get_iter())
self.writer.add_scalar(prefix + "/action_loss",
action_loss_avg.data.cpu()[0], self.get_iter())
self.prof.tick("auxiliaries")
total_loss = action_loss_avg + aux_loss
self.inc_iter()
self.prof.tick("summaries")
self.prof.loop()
self.prof.print_stats(1)
return total_loss
def get_dataset(self,
data=None,
envs=None,
dataset_names=None,
dataset_prefix=None,
eval=False):
# TODO: Maybe use eval here
#if self.fpv:
data_sources = []
data_sources.append(aup.PROVIDER_LM_POS_DATA)
data_sources.append(aup.PROVIDER_LANDMARKS_MENTIONED)
templates = get_current_parameters()["Environment"]["Templates"]
if templates:
data_sources.append(aup.PROVIDER_LANG_TEMPLATE)
return SegmentDataset(data=data,
env_list=envs,
dataset_names=dataset_names,
dataset_prefix=dataset_prefix,
aux_provider_names=data_sources,
segment_level=True)
class ModelTrajectoryTopDown(ModuleWithAuxiliaries):
def __init__(self, run_name="", model_class=MODEL_RSS,
aux_class_features=False, aux_grounding_features=False,
aux_class_map=False, aux_grounding_map=False, aux_goal_map=False,
aux_lang=False, aux_traj=False, rot_noise=False, pos_noise=False):
super(ModelTrajectoryTopDown, self).__init__()
self.model_name = "sm_trajectory" + str(model_class)
self.model_class = model_class
print("Init model of type: ", str(model_class))
self.run_name = run_name
self.writer = LoggingSummaryWriter(log_dir="runs/" + run_name)
self.params = get_current_parameters()["Model"]
self.aux_weights = get_current_parameters()["AuxWeights"]
self.prof = SimpleProfiler(torch_sync=PROFILE, print=PROFILE)
self.iter = nn.Parameter(torch.zeros(1), requires_grad=False)
# Auxiliary Objectives
self.use_aux_class_features = aux_class_features
self.use_aux_grounding_features = aux_grounding_features
self.use_aux_class_on_map = aux_class_map
self.use_aux_grounding_on_map = aux_grounding_map
self.use_aux_goal_on_map = aux_goal_map
self.use_aux_lang = aux_lang
self.use_aux_traj_on_map = aux_traj
self.use_aux_reg_map = self.aux_weights["regularize_map"]
self.use_rot_noise = rot_noise
self.use_pos_noise = pos_noise
# Path-pred FPV model definition
# --------------------------------------------------------------------------------------------------------------
self.img_to_features_w = FPVToGlobalMap(
source_map_size=self.params["global_map_size"], world_size_px=self.params["world_size_px"], world_size=self.params["world_size_m"],
res_channels=self.params["resnet_channels"], map_channels=self.params["feature_channels"],
img_w=self.params["img_w"], img_h=self.params["img_h"], img_dbg=IMG_DBG)
self.map_accumulator_w = LeakyIntegratorGlobalMap(source_map_size=self.params["global_map_size"], world_in_map_size=self.params["world_size_px"])
# Pre-process the accumulated map to do language grounding if necessary - in the world reference frame
if self.use_aux_grounding_on_map and not self.use_aux_grounding_features:
self.map_processor_a_w = LangFilterMapProcessor(
source_map_size=self.params["global_map_size"],
world_size=self.params["world_size_px"],
embed_size=self.params["emb_size"],
in_channels=self.params["feature_channels"],
out_channels=self.params["relevance_channels"],
spatial=False, cat_out=True)
else:
self.map_processor_a_w = IdentityMapProcessor(source_map_size=self.params["global_map_size"], world_size=self.params["world_size_px"])
if self.use_aux_goal_on_map:
self.map_processor_b_r = LangFilterMapProcessor(source_map_size=self.params["local_map_size"],
world_size=self.params["world_size_px"],
embed_size=self.params["emb_size"],
in_channels=self.params["relevance_channels"],
out_channels=self.params["goal_channels"],
spatial=True, cat_out=True)
else:
self.map_processor_b_r = IdentityMapProcessor(source_map_size=self.params["local_map_size"],
world_size=self.params["world_size_px"])
pred_channels = self.params["goal_channels"] + self.params["relevance_channels"]
# Common
# --------------------------------------------------------------------------------------------------------------
# Sentence Embedding
self.sentence_embedding = SentenceEmbeddingSimple(
self.params["word_emb_size"], self.params["emb_size"], self.params["emb_layers"])
self.map_transform_w_to_r = MapTransformerBase(source_map_size=self.params["global_map_size"],
dest_map_size=self.params["local_map_size"],
world_size=self.params["world_size_px"])
self.map_transform_r_to_w = MapTransformerBase(source_map_size=self.params["local_map_size"],
dest_map_size=self.params["global_map_size"],
world_size=self.params["world_size_px"])
# Batch select is used to drop and forget semantic maps at those timestaps that we're not planning in
self.batch_select = MapBatchSelect()
# Since we only have path predictions for some timesteps (the ones not dropped above), we use this to fill
# in the missing pieces by reorienting the past trajectory prediction into the frame of the current timestep
self.map_batch_fill_missing = MapBatchFillMissing(self.params["local_map_size"], self.params["world_size_px"])
# Passing true to freeze will freeze these weights regardless of whether they've been explicitly reloaded or not
enable_weight_saving(self.sentence_embedding, "sentence_embedding", alwaysfreeze=False)
# Output an action given the global semantic map
if self.params["map_to_action"] == "downsample2":
self.map_to_action = EgoMapToActionTriplet(
map_channels=self.params["map_to_act_channels"],
map_size=self.params["local_map_size"],
other_features_size=self.params["emb_size"])
elif self.params["map_to_action"] == "cropped":
self.map_to_action = CroppedMapToActionTriplet(
map_channels=self.params["map_to_act_channels"],
map_size=self.params["local_map_size"],
other_features_size=self.params["emb_size"]
)
# Don't freeze the trajectory to action weights, because it will be pre-trained during path-prediction training
# and finetuned on all timesteps end-to-end
enable_weight_saving(self.map_to_action, "map_to_action", alwaysfreeze=False, neverfreeze=True)
# Auxiliary Objectives
# --------------------------------------------------------------------------------------------------------------
# We add all auxiliaries that are necessary. The first argument is the auxiliary name, followed by parameters,
# followed by variable number of names of inputs. ModuleWithAuxiliaries will automatically collect these inputs
# that have been saved with keep_auxiliary_input() during execution
if aux_class_features:
self.add_auxiliary(ClassAuxiliary2D("aux_class", None, self.params["feature_channels"], self.params["num_landmarks"], self.params["dropout"],
"fpv_features", "lm_pos_fpv", "lm_indices"))
if aux_grounding_features:
self.add_auxiliary(ClassAuxiliary2D("aux_ground", None, self.params["relevance_channels"], 2, self.params["dropout"],
"fpv_features_g", "lm_pos_fpv", "lm_mentioned"))
if aux_class_map:
self.add_auxiliary(ClassAuxiliary2D("aux_class_map", self.params["world_size_px"], self.params["feature_channels"], self.params["num_landmarks"], self.params["dropout"],
"map_s_w_select", "lm_pos_map_select", "lm_indices_select"))
if aux_grounding_map:
self.add_auxiliary(ClassAuxiliary2D("aux_grounding_map", self.params["world_size_px"], self.params["relevance_channels"], 2, self.params["dropout"],
"map_a_w_select", "lm_pos_map_select", "lm_mentioned_select"))
if aux_goal_map:
self.add_auxiliary(GoalAuxiliary2D("aux_goal_map", self.params["goal_channels"], self.params["world_size_px"],
"map_b_w", "goal_pos_map"))
# RSS model uses templated data for landmark and side prediction
if self.use_aux_lang and self.params["templates"]:
self.add_auxiliary(ClassAuxiliary("aux_lang_lm", self.params["emb_size"], self.params["num_landmarks"], 1,
"sentence_embed", "lm_mentioned_tplt"))
self.add_auxiliary(ClassAuxiliary("aux_lang_side", self.params["emb_size"], self.params["num_sides"], 1,
"sentence_embed", "side_mentioned_tplt"))
# CoRL model uses alignment-model groundings
elif self.use_aux_lang:
# one output for each landmark, 2 classes per output. This is for finetuning, so use the embedding that's gonna be fine tuned
self.add_auxiliary(ClassAuxiliary("aux_lang_lm_nl", self.params["emb_size"], 2, self.params["num_landmarks"],
"sentence_embed", "lang_lm_mentioned"))
if self.use_aux_traj_on_map:
self.add_auxiliary(PathAuxiliary2D("aux_path", "map_b_r_select", "traj_gt_r_select"))
if self.use_aux_reg_map:
self.add_auxiliary(FeatureRegularizationAuxiliary2D("aux_regularize_features", None, "l1",
"map_s_w_select", "lm_pos_map_select"))
self.goal_good_criterion = GoalPredictionGoodCriterion(ok_distance=3.2)
self.goal_acc_meter = MovingAverageMeter(10)
self.print_auxiliary_info()
self.action_loss = ActionLoss()
self.env_id = None
self.prev_instruction = None
self.seq_step = 0
# TODO: Try to hide these in a superclass or something. They take up a lot of space:
def cuda(self, device=None):
ModuleWithAuxiliaries.cuda(self, device)
self.sentence_embedding.cuda(device)
self.map_accumulator_w.cuda(device)
self.map_processor_a_w.cuda(device)
self.map_processor_b_r.cuda(device)
self.img_to_features_w.cuda(device)
self.map_to_action.cuda(device)
self.action_loss.cuda(device)
self.map_batch_fill_missing.cuda(device)
self.map_transform_w_to_r.cuda(device)
self.map_transform_r_to_w.cuda(device)
self.batch_select.cuda(device)
self.map_batch_fill_missing.cuda(device)
return self
def get_iter(self):
return int(self.iter.data[0])
def inc_iter(self):
self.iter += 1
def init_weights(self):
self.img_to_features_w.init_weights()
self.map_accumulator_w.init_weights()
self.sentence_embedding.init_weights()
self.map_to_action.init_weights()
self.map_processor_a_w.init_weights()
self.map_processor_b_r.init_weights()
def reset(self):
# TODO: This is error prone. Create a class StatefulModule, iterate submodules and reset all stateful modules
super(ModelTrajectoryTopDown, self).reset()
self.sentence_embedding.reset()
self.img_to_features_w.reset()
self.map_accumulator_w.reset()
self.map_processor_a_w.reset()
self.map_processor_b_r.reset()
self.map_transform_w_to_r.reset()
self.map_transform_r_to_w.reset()
self.map_batch_fill_missing.reset()
self.prev_instruction = None
def setEnvContext(self, context):
print("Set env context to: " + str(context))
self.env_id = context["env_id"]
def save_viz(self, images_in):
imsave(get_viz_dir() + "fpv_" + str(self.seq_step) + ".png", images_in)
features_cam = self.get_inputs_batch("fpv_features")[-1, 0, 0:3]
save_tensor_as_img(features_cam, "F_c", self.env_id)
feature_map_torch = self.get_inputs_batch("f_w")[-1, 0, 0:3]
save_tensor_as_img(feature_map_torch, "F_w", self.env_id)
coverage_map_torch = self.get_inputs_batch("m_w")[-1, 0, 0:3]
save_tensor_as_img(coverage_map_torch, "M_w", self.env_id)
semantic_map_torch = self.get_inputs_batch("map_s_w_select")[-1, 0, 0:3]
save_tensor_as_img(semantic_map_torch, "S_w", self.env_id)
relmap_torch = self.get_inputs_batch("map_a_w_select")[-1, 0, 0:3]
save_tensor_as_img(relmap_torch, "R_w", self.env_id)
relmap_r_torch = self.get_inputs_batch("map_a_r_select")[-1, 0, 0:3]
save_tensor_as_img(relmap_r_torch, "R_r", self.env_id)
goalmap_torch = self.get_inputs_batch("map_b_w_select")[-1, 0, 0:3]
save_tensor_as_img(goalmap_torch, "G_w", self.env_id)
goalmap_r_torch = self.get_inputs_batch("map_b_r_select")[-1, 0, 0:3]
save_tensor_as_img(goalmap_r_torch, "G_r", self.env_id)
action = self.get_inputs_batch("action")[-1].data.cpu().squeeze().numpy()
action_fname = self.get_viz_dir() + "action_" + str(self.seq_step) + ".png"
Presenter().save_action(action, action_fname, "")
def get_action(self, state, instruction):
"""
Given a DroneState (from PomdpInterface) and instruction, produce a numpy 4D action (x, y, theta, pstop)
:param state: DroneState object with the raw image from the simulator
:param instruction: Tokenized instruction given the corpus
#TODO: Absorb corpus within model
:return:
"""
# TODO: Simplify this
self.eval()
images_np_pure = state.image
state_np = state.state
#print("Act: " + debug_untokenize_instruction(instruction))
images_np = standardize_image(images_np_pure)
image_fpv = Variable(none_padded_seq_to_tensor([images_np]))
state = Variable(none_padded_seq_to_tensor([state_np]))
# Add the batch dimension
first_step = True
if instruction == self.prev_instruction:
first_step = False
self.prev_instruction = instruction
img_in_t = image_fpv
img_in_t.volatile = True
instr_len = [len(instruction)] if instruction is not None else None
instruction = torch.LongTensor(instruction).unsqueeze(0)
instruction = cuda_var(instruction, self.is_cuda, self.cuda_device)
state.volatile = True
if self.is_cuda:
if img_in_t is not None:
img_in_t = img_in_t.cuda(self.cuda_device)
state = state.cuda(self.cuda_device)
step_enc = None
plan_now = None
self.seq_step += 1
action = self(img_in_t, state, instruction, instr_len, plan=plan_now, pos_enc=step_enc)
# Save materials for paper and presentation
if False:
self.save_viz(images_np_pure)
output_action = action.squeeze().data.cpu().numpy()
stop_prob = output_action[3]
output_stop = 1 if stop_prob > 0.5 else 0
output_action[3] = output_stop
return output_action
def deterministic_action(self, action_mean, action_std, stop_prob):
batch_size = action_mean.size(0)
action = Variable(empty_float_tensor((batch_size, 4), self.is_cuda, self.cuda_device))
action[:, 0:3] = action_mean[:, 0:3]
action[:, 3] = stop_prob
return action
def sample_action(self, action_mean, action_std, stop_prob):
action = torch.normal(action_mean, action_std)
stop = torch.bernoulli(stop_prob)
return action, stop
# This is called before beginning an execution sequence
def start_sequence(self):
self.seq_step = 0
self.reset()
print("RESETTED!")
return
# TODO: Move this somewhere and standardize
def cam_poses_from_states(self, states):
cam_pos = states[:, 9:12]
cam_rot = states[:, 12:16]
pos_variance = 0
rot_variance = 0
if self.use_pos_noise:
pos_variance = self.params["noisy_pos_variance"]
if self.use_rot_noise:
rot_variance = self.params["noisy_rot_variance"]
pose = Pose(cam_pos, cam_rot)
if self.use_pos_noise or self.use_rot_noise:
pose = get_noisy_poses_torch(pose, pos_variance, rot_variance, cuda=self.is_cuda, cuda_device=self.cuda_device)
return pose
def forward(self, images, states, instructions, instr_lengths, has_obs=None, plan=None, save_maps_only=False, pos_enc=None, noisy_poses=None):
"""
:param images: BxCxHxW batch of images (observations)
:param states: BxK batch of drone states
:param instructions: BxM LongTensor where M is the maximum length of any instruction
:param instr_lengths: list of len B of integers, indicating length of each instruction
:param has_obs: list of booleans of length B indicating whether the given element in the sequence has an observation
:param yield_semantic_maps: If true, will not compute actions (full model), but return the semantic maps that
were built along the way in response to the images. This is ugly, but allows code reuse
:return:
"""
cam_poses = self.cam_poses_from_states(states)
g_poses = None#[None for pose in cam_poses]
self.prof.tick("out")
#print("Trn: " + debug_untokenize_instruction(instructions[0].data[:instr_lengths[0]]))
# Calculate the instruction embedding
if instructions is not None:
# TODO: Take batch of instructions and their lengths, return batch of embeddings. Store the last one as internal state
sent_embeddings = self.sentence_embedding(instructions, instr_lengths)
self.keep_inputs("sentence_embed", sent_embeddings)
else:
sent_embeddings = self.sentence_embedding.get()
self.prof.tick("embed")
# Extract and project features onto the egocentric frame for each image
features_w, coverages_w = self.img_to_features_w(images, cam_poses, sent_embeddings, self, show="")
self.prof.tick("img_to_map_frame")
self.keep_inputs("f_w", features_w)
self.keep_inputs("m_w", coverages_w)
# Accumulate the egocentric features in a global map
maps_w = self.map_accumulator_w(features_w, coverages_w, add_mask=has_obs, show="acc" if IMG_DBG else "")
map_poses_w = g_poses
# TODO: Maybe keep maps_w if necessary
#self.keep_inputs("map_sm_local", maps_m)
self.prof.tick("map_accumulate")
# Throw away those timesteps that don't correspond to planning timesteps
maps_w_select, map_poses_w_select, cam_poses_select, noisy_poses_select, _, sent_embeddings_select, pos_enc = \
self.batch_select(maps_w, map_poses_w, cam_poses, noisy_poses, None, sent_embeddings, pos_enc, plan)
# Only process the maps on planning timesteps
if len(maps_w_select) > 0:
self.keep_inputs("map_s_w_select", maps_w_select)
self.prof.tick("batch_select")
# Process the map via the two map_procesors
# Do grounding of objects in the map chosen to do so
maps_w_select, map_poses_w_select = self.map_processor_a_w(maps_w_select, sent_embeddings_select, map_poses_w_select, show="")
self.keep_inputs("map_a_w_select", maps_w_select)
self.prof.tick("map_proc_gnd")
self.map_transform_w_to_r.set_maps(maps_w_select, map_poses_w_select)
maps_m_select, map_poses_m_select = self.map_transform_w_to_r.get_maps(cam_poses_select)
self.keep_inputs("map_a_r_select", maps_w_select)
self.prof.tick("transform_w_to_r")
self.keep_inputs("map_a_r_perturbed_select", maps_m_select)
self.prof.tick("map_perturb")
# Include positional encoding for path prediction
if pos_enc is not None:
sent_embeddings_pp = torch.cat([sent_embeddings_select, pos_enc.unsqueeze(1)], dim=1)
else:
sent_embeddings_pp = sent_embeddings_select
# Process the map via the two map_procesors (e.g. predict the trajectory that we'll be taking)
maps_m_select, map_poses_m_select = self.map_processor_b_r(maps_m_select, sent_embeddings_pp, map_poses_m_select)
self.keep_inputs("map_b_r_select", maps_m_select)
if True:
self.map_transform_r_to_w.set_maps(maps_m_select, map_poses_m_select)
maps_b_w_select, _ = self.map_transform_r_to_w.get_maps(None)
self.keep_inputs("map_b_w_select", maps_b_w_select)
self.prof.tick("map_proc_b")
else:
maps_m_select = None
maps_m, map_poses_m = self.map_batch_fill_missing(maps_m_select, cam_poses, plan, show="")
self.keep_inputs("map_b_r", maps_m)
self.prof.tick("map_fill_missing")
# Keep global maps for auxiliary objectives if necessary
if self.input_required("map_b_w"):
maps_b, _ = self.map_processor_b_r.get_maps(g_poses)
self.keep_inputs("map_b_w", maps_b)
self.prof.tick("keep_global_maps")
if run_metadata.IS_ROLLOUT:
pass
#Presenter().show_image(maps_m.data[0, 0:3], "plan_map_now", torch=True, scale=4, waitkey=1)
#Presenter().show_image(maps_w.data[0, 0:3], "sm_map_now", torch=True, scale=4, waitkey=1)
self.prof.tick("viz")
# Output the final action given the processed map
action_pred = self.map_to_action(maps_m, sent_embeddings)
out_action = self.deterministic_action(action_pred[:, 0:3], None, action_pred[:, 3])
self.keep_inputs("action", out_action)
self.prof.tick("map_to_action")
return out_action
# TODO: The below two methods seem to do the same thing
def maybe_cuda(self, tensor):
if self.is_cuda:
return tensor.cuda()
else:
return tensor
def cuda_var(self, tensor):
return cuda_var(tensor, self.is_cuda, self.cuda_device)
# Forward pass for training (with batch optimizations
def sup_loss_on_batch(self, batch, eval):
self.prof.tick("out")
action_loss_total = Variable(empty_float_tensor([1], self.is_cuda, self.cuda_device))
if batch is None:
print("Skipping None Batch")
return action_loss_total
images = self.maybe_cuda(batch["images"])
instructions = self.maybe_cuda(batch["instr"])
instr_lengths = batch["instr_len"]
states = self.maybe_cuda(batch["states"])
actions = self.maybe_cuda(batch["actions"])
# Auxiliary labels
lm_pos_fpv = batch["lm_pos_fpv"]
lm_pos_map = batch["lm_pos_map"]
lm_indices = batch["lm_indices"]
goal_pos_map = batch["goal_loc"]
TEMPLATES = True
if TEMPLATES:
lm_mentioned_tplt = batch["lm_mentioned_tplt"]
side_mentioned_tplt = batch["side_mentioned_tplt"]
else:
lm_mentioned = batch["lm_mentioned"]
lang_lm_mentioned = batch["lang_lm_mentioned"]
# stops = self.maybe_cuda(batch["stops"])
masks = self.maybe_cuda(batch["masks"])
# This is the first-timestep metadata
metadata = batch["md"]
seq_len = images.size(1)
batch_size = images.size(0)
count = 0
correct_goal_count = 0
goal_count = 0
# Loop thru batch
for b in range(batch_size):
seg_idx = -1
self.reset()
self.prof.tick("out")
b_seq_len = len_until_nones(metadata[b])
# TODO: Generalize this
# Slice the data according to the sequence length
b_metadata = metadata[b][:b_seq_len]
b_images = images[b][:b_seq_len]
b_instructions = instructions[b][:b_seq_len]
b_instr_len = instr_lengths[b][:b_seq_len]
b_states = states[b][:b_seq_len]
b_actions = actions[b][:b_seq_len]
b_lm_pos_fpv = lm_pos_fpv[b][:b_seq_len]
b_lm_pos_map = lm_pos_map[b][:b_seq_len]
b_lm_indices = lm_indices[b][:b_seq_len]
b_goal_pos = goal_pos_map[b][:b_seq_len]
if not TEMPLATES:
b_lang_lm_mentioned = lang_lm_mentioned[b][:b_seq_len]
b_lm_mentioned = lm_mentioned[b][:b_seq_len]
b_lm_pos_map = [self.cuda_var(s.long()) if s is not None else None for s in b_lm_pos_map]
b_lm_pos_fpv = [self.cuda_var((s / RESNET_FACTOR).long()) if s is not None else None for s in b_lm_pos_fpv]
b_lm_indices = [self.cuda_var(s) if s is not None else None for s in b_lm_indices]
b_goal_pos = self.cuda_var(b_goal_pos)
if not TEMPLATES:
b_lang_lm_mentioned = self.cuda_var(b_lang_lm_mentioned)
b_lm_mentioned = [self.cuda_var(s) if s is not None else None for s in b_lm_mentioned]
# TODO: Figure out how to keep these properly. Perhaps as a whole batch is best
# TODO: Introduce a key-value store (encapsulate instead of inherit)
self.keep_inputs("lm_pos_fpv", b_lm_pos_fpv)
self.keep_inputs("lm_pos_map", b_lm_pos_map)
self.keep_inputs("lm_indices", b_lm_indices)
self.keep_inputs("goal_pos_map", b_goal_pos)
if not TEMPLATES:
self.keep_inputs("lang_lm_mentioned", b_lang_lm_mentioned)
self.keep_inputs("lm_mentioned", b_lm_mentioned)
# TODO: Abstract all of these if-elses in a modular way once we know which ones are necessary
if TEMPLATES:
b_lm_mentioned_tplt = lm_mentioned_tplt[b][:b_seq_len]
b_side_mentioned_tplt = side_mentioned_tplt[b][:b_seq_len]
b_side_mentioned_tplt = self.cuda_var(b_side_mentioned_tplt)
b_lm_mentioned_tplt = self.cuda_var(b_lm_mentioned_tplt)
self.keep_inputs("lm_mentioned_tplt", b_lm_mentioned_tplt)
self.keep_inputs("side_mentioned_tplt", b_side_mentioned_tplt)
b_lm_mentioned = b_lm_mentioned_tplt
b_obs_mask = [True for _ in range(b_seq_len)]
b_plan_mask = [True for _ in range(b_seq_len)]
b_plan_mask_t_cpu = torch.Tensor(b_plan_mask) == True
b_plan_mask_t = self.maybe_cuda(b_plan_mask_t_cpu)
b_pos_enc = None
# ----------------------------------------------------------------------------
# Optional Auxiliary Inputs
# ----------------------------------------------------------------------------
if self.input_required("lm_pos_map_select"):
b_lm_pos_map_select = [lm_pos for i,lm_pos in enumerate(b_lm_pos_map) if b_plan_mask[i]]
self.keep_inputs("lm_pos_map_select", b_lm_pos_map_select)
if self.input_required("lm_indices_select"):
b_lm_indices_select = [lm_idx for i,lm_idx in enumerate(b_lm_indices) if b_plan_mask[i]]
self.keep_inputs("lm_indices_select", b_lm_indices_select)
if self.input_required("lm_mentioned_select"):
b_lm_mentioned_select = [lm_m for i,lm_m in enumerate(b_lm_mentioned) if b_plan_mask[i]]
self.keep_inputs("lm_mentioned_select", b_lm_mentioned_select)
# ----------------------------------------------------------------------------
self.prof.tick("inputs")
actions = self(b_images, b_states, b_instructions, b_instr_len,
has_obs=b_obs_mask, plan=b_plan_mask, pos_enc=b_pos_enc)
action_losses, _ = self.action_loss(b_actions, actions, batchreduce=False)
self.prof.tick("call")
action_losses = self.action_loss.batch_reduce_loss(action_losses)
action_loss = self.action_loss.reduce_loss(action_losses)
action_loss_total = action_loss
count += b_seq_len
self.prof.tick("loss")
action_loss_avg = action_loss_total / (count + 1e-9)
self.prof.tick("out")
# Doing this in the end (outside of se
aux_losses = self.calculate_aux_loss(reduce_average=True)
aux_loss = self.combine_aux_losses(aux_losses, self.aux_weights)
prefix = self.model_name + ("/eval" if eval else "/train")
self.writer.add_dict(prefix, get_current_meters(), self.get_iter())
self.writer.add_dict(prefix, aux_losses, self.get_iter())
self.writer.add_scalar(prefix + "/action_loss", action_loss_avg.data.cpu()[0], self.get_iter())
# TODO: Log value here
self.writer.add_scalar(prefix + "/goal_accuracy", self.goal_acc_meter.get(), self.get_iter())
self.prof.tick("auxiliaries")
total_loss = action_loss_avg + aux_loss
self.inc_iter()
self.prof.tick("summaries")
self.prof.loop()
self.prof.print_stats(1)
return total_loss
def get_dataset(self, data=None, envs=None, dataset_name=None, eval=False):
# TODO: Maybe use eval here
#if self.fpv:
data_sources = []
# If we're running auxiliary objectives, we need to include the data sources for the auxiliary labels
#if self.use_aux_class_features or self.use_aux_class_on_map or self.use_aux_grounding_features or self.use_aux_grounding_on_map:
#if self.use_aux_goal_on_map:
data_sources.append(aup.PROVIDER_LM_POS_DATA)
data_sources.append(aup.PROVIDER_GOAL_POS)
#data_sources.append(aup.PROVIDER_LANDMARKS_MENTIONED)
data_sources.append(aup.PROVIDER_LANG_TEMPLATE)
#if self.use_rot_noise or self.use_pos_noise:
# data_sources.append(aup.PROVIDER_POSE_NOISE)
return SegmentDataset(data=data, env_list=envs, dataset_name=dataset_name, aux_provider_names=data_sources, segment_level=True)
class ModelTopDownPathGoalPredictor(CudaModule):
def __init__(self,
run_name,
ignore_lang=False,
class_loss=True,
ground_loss=True):
super(ModelTopDownPathGoalPredictor, self).__init__()
self.run_name = run_name
self.model_name = "top_down_path_pred_pretrain"
self.writer = SummaryWriter(log_dir="runs/" + run_name)
self.ignore_lang = ignore_lang
self.class_loss = class_loss
self.ground_loss = ground_loss
# The feature net extracts the 2D feature map from the input image.
# The label_pool down-sizes the ground-truth labels, which are input at the same size as the input image
# The output predicted labels are the size of the feature map
self.feature_net = ResNet13Light(32, down_pad=True)
self.label_pool = nn.MaxPool2d(8)
if self.ground_loss:
self.lang_filter = MapLangSemanticFilter(sentence_embedding_size,
32, 3)
self.aux_ground_linear = nn.Linear(3, 2)
enable_weight_saving(self.lang_filter, "ground_filter")
enable_weight_saving(self.aux_ground_linear, "ground_aux_linear")
if RESNET:
self.unet = ResNetConditional(sentence_embedding_size, 35, 2)
else:
unet_c_in = 35 if self.ground_loss else 32
unet_hc1 = 48 if self.ground_loss else 48
unet_hb1 = 24 if self.ground_loss else 24
self.unet = Unet5ContextualBneck(unet_c_in,
2,
sentence_embedding_size,
hc1=unet_hc1,
hb1=unet_hb1,
hc2=128,
split_embedding=splitemb)
if attention:
self.sentence_embedding = SentenceEmbeddingSelfAttention(
word_embedding_size,
lstm_size,
sentence_embedding_layers,
attention_heads=attention_heads)
else:
self.sentence_embedding = SentenceEmbeddingSimple(
word_embedding_size, sentence_embedding_size,
sentence_embedding_layers)
self.gather2d = Gather2D()
if self.class_loss:
self.aux_class_linear = nn.Linear(32, 64)
enable_weight_saving(self.aux_class_linear, "class_aux_linear")
print("Sentence Embedding #Params: ",
get_n_params(self.sentence_embedding))
print("U-Net #Params: ", get_n_params(self.unet))
print("Class auxiliary: ", self.class_loss)
print("Ground auxiliary: ", self.ground_loss)
# Enable saving of pre-trained weights
enable_weight_saving(self.feature_net, "feature_resnet_light")
enable_weight_saving(self.unet, "unet")
enable_weight_saving(self.sentence_embedding, "sentence_embedding")
if NLL:
#self.mask_loss = nn.BCELoss()
self.mask_loss = nn.NLLLoss2d()
elif BCE:
self.mask_loss = nn.BCEWithLogitsLoss()
elif CE:
self.spatialsoftmax = SpatialSoftmax2d()
self.mask_loss = CrossEntropy2d()
else:
self.mask_loss = nn.MSELoss()
self.aux_loss = nn.CrossEntropyLoss(reduce=True, size_average=True)
self.epoch_numbers = {"train": 0, "eval": 0}
self.iter = nn.Parameter(torch.zeros(1), requires_grad=False)
self.dropout = nn.Dropout(0.5)
self.dropout2d = nn.Dropout2d(0.5)
self.dropout3d = nn.Dropout3d(0.5)
self.viz_images = []
self.instructions = []
def get_iter(self):
return int(self.iter.data[0])
def inc_iter(self):
self.iter += 1
def init_weights(self):
self.sentence_embedding.init_weights()
self.unet.init_weights()
if self.ground_loss:
self.aux_ground_linear.weight.data.normal_(0.001)
self.aux_ground_linear.bias.data.fill_(0)
if self.class_loss:
self.aux_class_linear.weight.data.normal_(0.001)
self.aux_class_linear.bias.data.fill_(0)
def cuda(self, device=None):
CudaModule.cuda(self, device)
self.sentence_embedding.cuda(device)
self.unet.cuda(device)
if self.ground_loss:
self.lang_filter.cuda(device)
return self
def write_eoe_summaries(self, inference_type, epoch_num):
pass
def write_summaires(self, prefix, idx, total_loss, main_loss, emb_loss,
class_loss, gnd_loss):
full_prefix = self.model_name + "/" + prefix + "/"
if self.writer is None:
return
self.writer.add_scalar(full_prefix + "total_loss", total_loss.data[0],
idx)
self.writer.add_scalar(full_prefix + "main_loss", main_loss.data[0],
idx)
self.writer.add_scalar(full_prefix + "class_loss", class_loss.data[0],
idx)
if class_loss is not None:
self.writer.add_scalar(full_prefix + "emb_loss", emb_loss.data[0],
idx)
if gnd_loss is not None:
self.writer.add_scalar(full_prefix + "gnd_loss", gnd_loss.data[0],
idx)
def get_dataset(self,
data=None,
envs=None,
eval=False,
dataset_name=None,
seg_level=True):
return TopDownDataset(env_list=envs,
instr_negatives=False,
instr_negatives_similar_only=False,
seg_level=seg_level,
yaw_rand_range=0.0 if eval else YAW_RANGE,
img_w=512,
img_h=512,
map_w=256,
map_h=256,
incl_path=True,
incl_endpoint=True)
def get_viz(self):
presenter = Presenter()
out = {"viz_img": []}
for i, img in enumerate(self.viz_images):
instruction = self.instructions[i]
if len(instruction.view([-1])) < 2:
instruction = [0]
else:
instruction = list(instruction.data.cpu().numpy().squeeze())
instruction_str = debug_untokenize_instruction(instruction)
viz_img = presenter.overlay_text(img, instruction_str)
out["viz_img"].append(viz_img)
return out
def forward(self, images, instructions, instruction_masks):
emb = self.sentence_embedding(instructions,
torch.sum(instruction_masks, 1))
# If the embedding returns an internal auxiliary, loss, pass it along
emb_loss = cuda_var(torch.zeros([1]), self.is_cuda, self.cuda_device)
if type(emb) is tuple:
emb, emb_loss = emb
feature_map = self.feature_net(images)
feature_map = self.dropout2d(feature_map)
if self.ground_loss:
self.lang_filter.precompute_conv_weights(emb)
ground_map = self.lang_filter(feature_map)
feature_map = torch.cat([feature_map, ground_map], dim=1)
# TODO: Testing breaking of gradients between ResNet and UNet
if cut_gradients:
feature_map_fwd = Variable(feature_map.data)
else:
feature_map_fwd = feature_map
#if self.ground_loss:
# feature_map_fwd = feature_map_fwd[:, 0:3, :, :]
pred_mask = self.unet(feature_map_fwd, emb)
return pred_mask, feature_map, emb_loss
def sup_loss_on_batch(self, batch, eval=False, viz=False):
if eval:
self.eval()
else:
self.train()
images = cuda_var(batch["images"], self.is_cuda, self.cuda_device)
instructions = cuda_var(batch["instr"], self.is_cuda, self.cuda_device)
instruction_masks = cuda_var(batch["instr_mask"], self.is_cuda,
self.cuda_device)
label_masks = cuda_var(batch["traj_labels"], self.is_cuda,
self.cuda_device)
# Each of the above is a list of lists of tensors, where the outer list is over the batch and the inner list
# is over the segments. Loop through and accumulate loss for each batch sequentially, and for each segment.
# Reset model state (embedding etc) between batches, but not between segments.
# We don't process each batch in batch-mode, because it's complicated, with the varying number of segments and all.
batch_size = len(images)
total_class_loss = Variable(empty_float_tensor([1], self.is_cuda,
self.cuda_device),
requires_grad=True)
total_ground_loss = Variable(empty_float_tensor([1], self.is_cuda,
self.cuda_device),
requires_grad=True)
count = 0
label_masks = self.label_pool(label_masks)
mask_pred, features, emb_loss = self(images, instructions,
instruction_masks)
if BCE:
mask_pred_flat = mask_pred.view(-1, 1)
label_masks_flat = label_masks - torch.min(label_masks)
label_masks_flat = label_masks_flat / (
torch.max(label_masks_flat) + 1e-9)
label_masks_flat = label_masks_flat.view(-1, 1).clamp(0, 1)
main_loss = self.mask_loss(mask_pred_flat, label_masks_flat)
elif NLL:
mask_pred_1 = F.softmax(mask_pred, 1, _stacklevel=5)
mask_pred_2 = 1 - mask_pred_1
mask_pred_1 = mask_pred_1.unsqueeze(1)
mask_pred_2 = mask_pred_2.unsqueeze(1)
mask_pred = torch.cat((mask_pred_1, mask_pred_2), dim=1)
label_masks = label_masks.clamp(0, 1)
if self.is_cuda:
label_masks = label_masks.type(torch.cuda.LongTensor)
else:
label_masks = label_masks.type(torch.LongTensor)
main_loss = self.mask_loss(mask_pred, label_masks)
elif CE:
# Crossentropy2D internally applies logsoftmax to mask_pred,
# but labels are already assumed to be a valid probability distribution, so no softmax is applied
main_loss = self.mask_loss(mask_pred, label_masks)
# So for nice plotting, we must manually do it
mask_pred = self.spatialsoftmax(mask_pred)
else:
main_loss = self.mask_loss(mask_pred, label_masks)
# sum emb loss if batch size > 1
if type(emb_loss) == tuple:
emb_loss = sum(emb_loss)
# Extract the feature vectors corresponding to every landmark's location in the map
# Apply a linear layer to classify which of the 64 landmarks it is
# The landmark positions have to be divided by the same factor as the ResNet scaling factor
lcount = 0
for i in range(batch_size):
if self.class_loss and len(batch["lm_pos"][i]) > 0:
lcount += 1
landmark_pos = cuda_var(batch["lm_pos"][i], self.is_cuda,
self.cuda_device)
landmark_indices = cuda_var(batch["lm_indices"][i],
self.is_cuda, self.cuda_device)
landmark_coords = (landmark_pos / 8).long()
lm_features = self.gather2d(features[i:i + 1, 0:32],
landmark_coords)
lm_pred = self.aux_class_linear(lm_features)
class_loss = self.aux_loss(lm_pred, landmark_indices)
total_class_loss = total_class_loss + class_loss
if self.ground_loss and len(batch["lm_pos"][i]) > 0:
landmark_pos = cuda_var(batch["lm_pos"][i], self.is_cuda,
self.cuda_device)
landmark_mentioned = cuda_var(batch["lm_mentioned"][i],
self.is_cuda, self.cuda_device)
landmark_coords = (landmark_pos / 8).long()
g_features = self.gather2d(features[i:i + 1, 32:35],
landmark_coords)
lm_pred = self.aux_ground_linear(g_features)
ground_loss = self.aux_loss(lm_pred, landmark_mentioned)
total_ground_loss = total_ground_loss + ground_loss
total_class_loss = total_class_loss / (lcount + 1e-9)
total_ground_loss = total_ground_loss / (lcount + 1e-9)
count += 1
# Just visualization and debugging code
if self.get_iter() % 50 == 0:
presenter = Presenter()
pred_viz_np = presenter.overlaid_image(images[0].data,
mask_pred[0].data)
labl_viz_np = presenter.overlaid_image(images[0].data,
label_masks[0].data)
comp = np.concatenate((pred_viz_np, labl_viz_np), axis=1)
presenter.show_image(comp, "path_pred")
if hasattr(self.sentence_embedding, "save_att_map"):
self.sentence_embedding.save_att_map(self.get_iter(), i)
total_loss = main_loss + 0.1 * total_class_loss + 0.001 * emb_loss + 0.1 * total_ground_loss
total_loss = total_loss / (count + 1e-9)
self.write_summaires("eval" if eval else "train", self.get_iter(),
total_loss, main_loss, emb_loss, total_class_loss,
total_ground_loss)
self.inc_iter()
return total_loss
class ModelGSMNBiDomain(nn.Module):
def __init__(self, run_name="", model_instance_name=""):
super(ModelGSMNBiDomain, self).__init__()
self.model_name = "gsmn_bidomain"
self.run_name = run_name
self.name = model_instance_name
if not self.name:
self.name = ""
self.writer = LoggingSummaryWriter(
log_dir=f"runs/{run_name}/{self.name}")
self.params = get_current_parameters()["Model"]
self.aux_weights = get_current_parameters()["AuxWeights"]
self.use_aux = self.params["UseAuxiliaries"]
self.prof = SimpleProfiler(torch_sync=PROFILE, print=PROFILE)
self.iter = nn.Parameter(torch.zeros(1), requires_grad=False)
self.tensor_store = KeyTensorStore()
self.aux_losses = AuxiliaryLosses()
self.rviz = None
if self.params.get("rviz"):
self.rviz = RvizInterface(
base_name="/gsmn/",
map_topics=["semantic_map", "grounding_map", "goal_map"],
markerarray_topics=["instruction"])
# Path-pred FPV model definition
# --------------------------------------------------------------------------------------------------------------
self.img_to_features_w = FPVToGlobalMap(
source_map_size=self.params["global_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"],
res_channels=self.params["resnet_channels"],
map_channels=self.params["feature_channels"],
img_w=self.params["img_w"],
img_h=self.params["img_h"],
cam_h_fov=self.params["cam_h_fov"],
img_dbg=IMG_DBG)
self.map_accumulator_w = LeakyIntegratorGlobalMap(
source_map_size=self.params["global_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"])
# Pre-process the accumulated map to do language grounding if necessary - in the world reference frame
if self.use_aux[
"grounding_map"] and not self.use_aux["grounding_features"]:
self.map_processor_a_w = LangFilterMapProcessor(
embed_size=self.params["emb_size"],
in_channels=self.params["feature_channels"],
out_channels=self.params["relevance_channels"],
spatial=False,
cat_out=True)
else:
self.map_processor_a_w = IdentityMapProcessor(
source_map_size=self.params["global_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"])
if self.use_aux["goal_map"]:
self.map_processor_b_r = LangFilterMapProcessor(
embed_size=self.params["emb_size"],
in_channels=self.params["relevance_channels"],
out_channels=self.params["goal_channels"],
spatial=self.params["spatial_goal_filter"],
cat_out=self.params["cat_rel_and_goal"])
else:
self.map_processor_b_r = IdentityMapProcessor(
source_map_size=self.params["local_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"])
# Common
# --------------------------------------------------------------------------------------------------------------
# Sentence Embedding
self.sentence_embedding = SentenceEmbeddingSimple(
self.params["word_emb_size"],
self.params["emb_size"],
self.params["emb_layers"],
dropout=0.0)
self.map_transform_w_to_r = MapTransformerBase(
source_map_size=self.params["global_map_size"],
dest_map_size=self.params["local_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"])
self.map_transform_r_to_w = MapTransformerBase(
source_map_size=self.params["local_map_size"],
dest_map_size=self.params["global_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"])
# Output an action given the global semantic map
if self.params["map_to_action"] == "downsample2":
self.map_to_action = EgoMapToActionTriplet(
map_channels=self.params["map_to_act_channels"],
map_size=self.params["local_map_size"],
other_features_size=self.params["emb_size"])
elif self.params["map_to_action"] == "cropped":
self.map_to_action = CroppedMapToActionTriplet(
map_channels=self.params["map_to_act_channels"],
map_size=self.params["local_map_size"])
# Auxiliary Objectives
# --------------------------------------------------------------------------------------------------------------
# We add all auxiliaries that are necessary. The first argument is the auxiliary name, followed by parameters,
# followed by variable number of names of inputs. ModuleWithAuxiliaries will automatically collect these inputs
# that have been saved with keep_auxiliary_input() during execution
if self.use_aux["class_features"]:
self.aux_losses.add_auxiliary(
ClassAuxiliary2D("aux_class", self.params["feature_channels"],
self.params["num_landmarks"],
self.params["dropout"], "fpv_features",
"lm_pos_fpv_features", "lm_indices",
"tensor_store"))
if self.use_aux["grounding_features"]:
self.aux_losses.add_auxiliary(
ClassAuxiliary2D("aux_ground",
self.params["relevance_channels"], 2,
self.params["dropout"], "fpv_features_g",
"lm_pos_fpv_features", "lm_mentioned",
"tensor_store"))
if self.use_aux["class_map"]:
self.aux_losses.add_auxiliary(
ClassAuxiliary2D("aux_class_map",
self.params["feature_channels"],
self.params["num_landmarks"],
self.params["dropout"], "map_S_W",
"lm_pos_map", "lm_indices", "tensor_store"))
if self.use_aux["grounding_map"]:
self.aux_losses.add_auxiliary(
ClassAuxiliary2D("aux_grounding_map",
self.params["relevance_channels"], 2,
self.params["dropout"], "map_R_W",
"lm_pos_map", "lm_mentioned", "tensor_store"))
if self.use_aux["goal_map"]:
self.aux_losses.add_auxiliary(
GoalAuxiliary2D("aux_goal_map", self.params["goal_channels"],
self.params["global_map_size"], "map_G_W",
"goal_pos_map"))
# RSS model uses templated data for landmark and side prediction
if self.use_aux["language"] and self.params["templates"]:
self.aux_losses.add_auxiliary(
ClassAuxiliary("aux_lang_lm", self.params["emb_size"],
self.params["num_landmarks"], 1,
"sentence_embed", "lm_mentioned_tplt"))
self.aux_losses.add_auxiliary(
ClassAuxiliary("aux_lang_side", self.params["emb_size"],
self.params["num_sides"], 1, "sentence_embed",
"side_mentioned_tplt"))
# CoRL model uses alignment-model groundings
elif self.use_aux["language"]:
# one output for each landmark, 2 classes per output. This is for finetuning, so use the embedding that's gonna be fine tuned
self.aux_losses.add_auxiliary(
ClassAuxiliary("aux_lang_lm_nl", self.params["emb_size"], 2,
self.params["num_landmarks"], "sentence_embed",
"lang_lm_mentioned"))
if self.use_aux["l1_regularization"]:
self.aux_losses.add_auxiliary(
FeatureRegularizationAuxiliary2D("aux_regularize_features",
"l1", "map_S_W"))
self.aux_losses.add_auxiliary(
FeatureRegularizationAuxiliary2D("aux_regularize_features",
"l1", "map_R_W"))
self.goal_acc_meter = MovingAverageMeter(10)
self.aux_losses.print_auxiliary_info()
self.action_loss = ActionLoss()
self.env_id = None
self.prev_instruction = None
self.seq_step = 0
def cuda(self, device=None):
CudaModule.cuda(self, device)
self.aux_losses.cuda(device)
self.sentence_embedding.cuda(device)
self.map_accumulator_w.cuda(device)
self.map_processor_a_w.cuda(device)
self.map_processor_b_r.cuda(device)
self.img_to_features_w.cuda(device)
self.map_to_action.cuda(device)
self.action_loss.cuda(device)
self.map_transform_w_to_r.cuda(device)
self.map_transform_r_to_w.cuda(device)
return self
def steal_cross_domain_modules(self, other_self):
# TODO: Consider whether to share auxiliary losses, and if so, all of them?
self.aux_losses = other_self.aux_losses
self.action_loss = other_self.action_loss
# TODO: Make sure that none of these things are stateful, or that there are resets after every forward pass
self.sentence_embedding = other_self.sentence_embedding
self.map_accumulator_w = other_self.map_accumulator_w
self.map_processor_a_w = other_self.map_processor_a_w
self.map_processor_b_r = other_self.map_processor_b_r
self.map_to_action = other_self.map_to_action
# We'll have a separate one of these for each domain
#self.img_to_features_w = other_self.img_to_features_w
# TODO: Check that statefulness is not an issue in sharing modules
# These have no parameters so no point sharing
#self.map_transform_w_to_r = other_self.map_transform_w_to_r
#self.map_transform_r_to_w = other_self.map_transform_r_to_w
def both_domain_parameters(self, other_self):
# This function iterates and yields parameters from this module and the other module, but does not yield
# shared parameters twice.
# First yield all of the other module's parameters
for p in other_self.parameters():
yield p
# Then yield all the parameters from the this module that are not shared with the other one
for p in self.img_to_features_w.parameters():
yield p
return
def get_iter(self):
return int(self.iter.data[0])
def inc_iter(self):
self.iter += 1
def load_img_feature_weights(self):
if self.params.get("load_feature_net"):
filename = self.params.get("feature_net_filename")
weights = load_pytorch_model(None, filename)
prefix = self.params.get("feature_net_tensor_name")
if prefix:
weights = find_state_subdict(weights, prefix)
# TODO: This breaks OOP conventions
self.img_to_features_w.img_to_features.load_state_dict(weights)
print(
f"Loaded pretrained weights from file {filename} with prefix {prefix}"
)
def init_weights(self):
self.img_to_features_w.init_weights()
self.load_img_feature_weights()
self.map_accumulator_w.init_weights()
self.sentence_embedding.init_weights()
self.map_to_action.init_weights()
self.map_processor_a_w.init_weights()
self.map_processor_b_r.init_weights()
def reset(self):
self.tensor_store.reset()
self.sentence_embedding.reset()
self.img_to_features_w.reset()
self.map_accumulator_w.reset()
self.map_transform_w_to_r.reset()
self.map_transform_r_to_w.reset()
self.load_img_feature_weights()
self.prev_instruction = None
def set_env_context(self, context):
print("Set env context to: " + str(context))
self.env_id = context["env_id"]
def save_viz(self, images_in, instruction):
# Save incoming images
imsave(
os.path.join(get_viz_dir_for_rollout(),
"fpv_" + str(self.seq_step) + ".png"), images_in)
#self.tensor_store.keep_input("fpv_img", images_in)
# Save all of these tensors from the tensor store as images
save_tensors_as_images(self.tensor_store, [
"images_w", "fpv_img", "fpv_features", "map_F_W", "map_M_W",
"map_S_W", "map_R_W", "map_R_R", "map_G_R", "map_G_W"
], str(self.seq_step))
# Save action as image
action = self.tensor_store.get_inputs_batch(
"action")[-1].data.cpu().squeeze().numpy()
action_fname = get_viz_dir_for_rollout() + "action_" + str(
self.seq_step) + ".png"
Presenter().save_action(action, action_fname, "")
instruction_fname = get_viz_dir_for_rollout() + "instruction.txt"
with open(instruction_fname, "w") as fp:
fp.write(instruction)
def get_action(self, state, instruction):
"""
Given a DroneState (from PomdpInterface) and instruction, produce a numpy 4D action (x, y, theta, pstop)
:param state: DroneState object with the raw image from the simulator
:param instruction: Tokenized instruction given the corpus
#TODO: Absorb corpus within model
:return:
"""
# TODO: Simplify this
self.eval()
images_np_pure = state.image
state_np = state.state
#print("Act: " + debug_untokenize_instruction(instruction))
images_np = standardize_image(images_np_pure)
image_fpv = Variable(none_padded_seq_to_tensor([images_np]))
state = Variable(none_padded_seq_to_tensor([state_np]))
# Add the batch dimension
first_step = True
if instruction == self.prev_instruction:
first_step = False
self.prev_instruction = instruction
instruction_str = debug_untokenize_instruction(instruction)
# TODO: Move this to PomdpInterface (for now it's here because this is already visualizing the maps)
if first_step:
if self.rviz is not None:
self.rviz.publish_instruction_text(
"instruction", debug_untokenize_instruction(instruction))
img_in_t = image_fpv
img_in_t.volatile = True
instr_len = [len(instruction)] if instruction is not None else None
instruction = torch.LongTensor(instruction).unsqueeze(0)
instruction = cuda_var(instruction, self.is_cuda, self.cuda_device)
state.volatile = True
if self.is_cuda:
if img_in_t is not None:
img_in_t = img_in_t.cuda(self.cuda_device)
state = state.cuda(self.cuda_device)
step_enc = None
plan_now = None
self.seq_step += 1
action = self(img_in_t,
state,
instruction,
instr_len,
plan=plan_now,
pos_enc=step_enc)
passive_mode_debug_projections = True
if passive_mode_debug_projections:
self.show_landmark_locations(loop=False, states=state)
self.reset()
# Run auxiliary objectives for debugging purposes (e.g. to compute classification predictions)
if self.params.get("run_auxiliaries_at_test_time"):
_, _ = self.aux_losses.calculate_aux_loss(self.tensor_store,
reduce_average=True)
overlaid = self.get_overlaid_classification_results(
whole_batch=False)
# Save materials for analysis and presentation
if self.params["write_figures"]:
self.save_viz(images_np_pure, instruction_str)
output_action = action.squeeze().data.cpu().numpy()
stop_prob = output_action[3]
output_stop = 1 if stop_prob > self.params["stop_p"] else 0
output_action[3] = output_stop
return output_action
def get_overlaid_classification_results(self, map_not_features=False):
if map_not_features:
predictions_name = "aux_class_map_predictions"
else:
predictions_name = "aux_class_predictions"
predictions = self.tensor_store.get_latest_input(predictions_name)
if predictions is None:
return None
predictions = predictions[0].detach()
# Get the 3 channels corresponding to no landmark, banana and gorilla
predictions = predictions[[0, 3, 24], :, :]
images = self.tensor_store.get_latest_input("images")[0].detach()
overlaid = Presenter().overlaid_image(images,
predictions,
gray_bg=True)
return overlaid
def deterministic_action(self, action_mean, action_std, stop_prob):
batch_size = action_mean.size(0)
action = Variable(
empty_float_tensor((batch_size, 4), self.is_cuda,
self.cuda_device))
action[:, 0:3] = action_mean[:, 0:3]
action[:, 3] = stop_prob
return action
# This is called before beginning an execution sequence
def start_sequence(self):
self.seq_step = 0
self.reset()
print("RESETTED!")
return
# TODO: Move this somewhere and standardize
def cam_poses_from_states(self, states):
cam_pos = states[:, 9:12]
cam_rot = states[:, 12:16]
pos_variance = 0
rot_variance = 0
if self.params.get("use_pos_noise"):
pos_variance = self.params["noisy_pos_variance"]
if self.params.get("use_rot_noise"):
rot_variance = self.params["noisy_rot_variance"]
pose = Pose(cam_pos, cam_rot)
if self.params.get("use_pos_noise") or self.params.get(
"use_rot_noise"):
pose = get_noisy_poses_torch(pose,
pos_variance,
rot_variance,
cuda=self.is_cuda,
cuda_device=self.cuda_device)
return pose
def forward(self,
images,
states,
instructions,
instr_lengths,
has_obs=None,
plan=None,
save_maps_only=False,
pos_enc=None,
noisy_poses=None,
halfway=False):
"""
:param images: BxCxHxW batch of images (observations)
:param states: BxK batch of drone states
:param instructions: BxM LongTensor where M is the maximum length of any instruction
:param instr_lengths: list of len B of integers, indicating length of each instruction
:param has_obs: list of booleans of length B indicating whether the given element in the sequence has an observation
:param yield_semantic_maps: If true, will not compute actions (full model), but return the semantic maps that
were built along the way in response to the images. This is ugly, but allows code reuse
:return:
"""
cam_poses = self.cam_poses_from_states(states)
g_poses = None #[None for pose in cam_poses]
self.prof.tick("out")
#str_instr = debug_untokenize_instruction(instructions[0].data[:instr_lengths[0]])
#print("Trn: " + str_instr)
# Calculate the instruction embedding
if instructions is not None:
# TODO: Take batch of instructions and their lengths, return batch of embeddings. Store the last one as internal state
sent_embeddings = self.sentence_embedding(instructions,
instr_lengths)
self.tensor_store.keep_inputs("sentence_embed", sent_embeddings)
else:
sent_embeddings = self.sentence_embedding.get()
self.prof.tick("embed")
# Extract and project features onto the egocentric frame for each image
features_w, coverages_w = self.img_to_features_w(images,
cam_poses,
sent_embeddings,
self.tensor_store,
show="")
# If we're running the model halway, return now. This is to compute enough features for the wasserstein critic, but no more
if halfway:
return None
# Don't back-prop into resnet if we're freezing these features (TODO: instead set requires grad to false)
if self.params.get("freeze_feature_net"):
features_w = features_w.detach()
self.prof.tick("img_to_map_frame")
self.tensor_store.keep_inputs("images", images)
self.tensor_store.keep_inputs("map_F_w", features_w)
self.tensor_store.keep_inputs("map_M_w", coverages_w)
if run_metadata.IS_ROLLOUT:
Presenter().show_image(features_w.data[0, 0:3],
"F",
torch=True,
scale=8,
waitkey=1)
# Accumulate the egocentric features in a global map
maps_s_w = self.map_accumulator_w(features_w,
coverages_w,
add_mask=has_obs,
show="acc" if IMG_DBG else "")
map_poses_w = g_poses
self.tensor_store.keep_inputs("map_S_W", maps_s_w)
self.prof.tick("map_accumulate")
Presenter().show_image(maps_s_w.data[0],
f"{self.name}_S_map_W",
torch=True,
scale=4,
waitkey=1)
# Do grounding of objects in the map chosen to do so
maps_r_w, map_poses_r_w = self.map_processor_a_w(maps_s_w,
sent_embeddings,
map_poses_w,
show="")
self.tensor_store.keep_inputs("map_R_W", maps_r_w)
Presenter().show_image(maps_r_w.data[0],
f"{self.name}_R_map_W",
torch=True,
scale=4,
waitkey=1)
self.prof.tick("map_proc_gnd")
# Transform to drone's reference frame
self.map_transform_w_to_r.set_maps(maps_r_w, map_poses_r_w)
maps_r_r, map_poses_r_r = self.map_transform_w_to_r.get_maps(cam_poses)
self.tensor_store.keep_inputs("map_R_R", maps_r_r)
self.prof.tick("transform_w_to_r")
# Predict goal location
maps_g_r, map_poses_g_r = self.map_processor_b_r(
maps_r_r, sent_embeddings, map_poses_r_r)
self.tensor_store.keep_inputs("map_G_R", maps_g_r)
# Transform back to map frame
self.map_transform_r_to_w.set_maps(maps_g_r, map_poses_g_r)
maps_g_w, _ = self.map_transform_r_to_w.get_maps(None)
self.tensor_store.keep_inputs("map_G_W", maps_g_w)
self.prof.tick("map_proc_b")
# Show and publish to RVIZ
Presenter().show_image(maps_g_w.data[0],
f"{self.name}_G_map_W",
torch=True,
scale=8,
waitkey=1)
if self.rviz:
self.rviz.publish_map(
"goal_map", maps_g_w[0].data.cpu().numpy().transpose(1, 2, 0),
self.params["world_size_m"])
# Output the final action given the processed map
action_pred = self.map_to_action(maps_g_r, sent_embeddings)
out_action = self.deterministic_action(action_pred[:, 0:3], None,
action_pred[:, 3])
self.tensor_store.keep_inputs("action", out_action)
self.prof.tick("map_to_action")
return out_action
# TODO: The below two methods seem to do the same thing
def maybe_cuda(self, tensor):
if self.is_cuda:
return tensor.cuda()
else:
return tensor
def cuda_var(self, tensor):
return cuda_var(tensor, self.is_cuda, self.cuda_device)
def unbatch(self, batch):
# TODO: Carefully consider this line. This is necessary to reset state between batches (e.g. delete all tensors in the tensor store)
self.reset()
# Get rid of the batch dimension for everything
images = self.maybe_cuda(batch["images"])[0]
seq_len = images.shape[0]
instructions = self.maybe_cuda(batch["instr"])[0][:seq_len]
instr_lengths = batch["instr_len"][0]
states = self.maybe_cuda(batch["states"])[0]
actions = self.maybe_cuda(batch["actions"])[0]
# Auxiliary labels
lm_pos_fpv = batch["lm_pos_fpv"][0]
lm_pos_map = batch["lm_pos_map"][0]
lm_indices = batch["lm_indices"][0]
goal_pos_map = batch["goal_loc"][0]
# TODO: Get rid of this. We will have lm_mentioned booleans and lm_mentioned_idx integers and that's it.
TEMPLATES = True
if TEMPLATES:
lm_mentioned_tplt = batch["lm_mentioned_tplt"][0]
side_mentioned_tplt = batch["side_mentioned_tplt"][0]
side_mentioned_tplt = self.cuda_var(side_mentioned_tplt)
lm_mentioned_tplt = self.cuda_var(lm_mentioned_tplt)
lang_lm_mentioned = None
else:
lm_mentioned_tplt = None
side_mentioned_tplt = None
lang_lm_mentioned = batch["lang_lm_mentioned"][0]
lm_mentioned = batch["lm_mentioned"][0]
# This is the first-timestep metadata
metadata = batch["md"][0]
lm_pos_map = [
torch.from_numpy(
transformations.pos_m_to_px(
p.numpy(), self.params["global_map_size"],
self.params["world_size_m"], self.params["world_size_px"]))
if p is not None else None for p in lm_pos_map
]
goal_pos_map = torch.from_numpy(
transformations.pos_m_to_px(goal_pos_map.numpy(),
self.params["global_map_size"],
self.params["world_size_m"],
self.params["world_size_px"]))
lm_pos_map = [
self.cuda_var(s.long()) if s is not None else None
for s in lm_pos_map
]
lm_pos_fpv_features = [
self.cuda_var(
(s /
self.img_to_features_w.img_to_features.get_downscale_factor()
).long()) if s is not None else None for s in lm_pos_fpv
]
lm_pos_fpv_img = [
self.cuda_var(s.long()) if s is not None else None
for s in lm_pos_fpv
]
lm_indices = [
self.cuda_var(s) if s is not None else None for s in lm_indices
]
goal_pos_map = self.cuda_var(goal_pos_map)
if not TEMPLATES:
lang_lm_mentioned = self.cuda_var(lang_lm_mentioned)
lm_mentioned = [
self.cuda_var(s) if s is not None else None for s in lm_mentioned
]
obs_mask = [True for _ in range(seq_len)]
plan_mask = [True for _ in range(seq_len)]
pos_enc = None
# TODO: Figure out how to keep these properly. Perhaps as a whole batch is best
self.tensor_store.keep_inputs("lm_pos_fpv_img", lm_pos_fpv_img)
self.tensor_store.keep_inputs("lm_pos_fpv_features",
lm_pos_fpv_features)
self.tensor_store.keep_inputs("lm_pos_map", lm_pos_map)
self.tensor_store.keep_inputs("lm_indices", lm_indices)
self.tensor_store.keep_inputs("goal_pos_map", goal_pos_map)
if not TEMPLATES:
self.tensor_store.keep_inputs("lang_lm_mentioned",
lang_lm_mentioned)
else:
self.tensor_store.keep_inputs("lm_mentioned_tplt",
lm_mentioned_tplt)
self.tensor_store.keep_inputs("side_mentioned_tplt",
side_mentioned_tplt)
self.tensor_store.keep_inputs("lm_mentioned", lm_mentioned)
# ----------------------------------------------------------------------------
# Optional Auxiliary Inputs
# ----------------------------------------------------------------------------
#if self.aux_losses.input_required("lm_pos_map"):
self.tensor_store.keep_inputs("lm_pos_map", lm_pos_map)
#if self.aux_losses.input_required("lm_indices"):
self.tensor_store.keep_inputs("lm_indices", lm_indices)
#if self.aux_losses.input_required("lm_mentioned"):
self.tensor_store.keep_inputs("lm_mentioned", lm_mentioned)
return images, instructions, instr_lengths, states, actions, \
lm_pos_fpv_img, lm_pos_fpv_features, lm_pos_map, lm_indices, goal_pos_map, \
lm_mentioned, lm_mentioned_tplt, side_mentioned_tplt, lang_lm_mentioned, \
metadata, obs_mask, plan_mask, pos_enc
def show_landmark_locations(self, loop=True, states=None):
# Show landmark locations in first-person images
img_all = self.tensor_store.get("images")
img_w_all = self.tensor_store.get("images_w")
import rollout.run_metadata as md
if md.IS_ROLLOUT:
# TODO: Discard this and move this to PomdpInterface or something
# (it's got nothing to do with the model)
# load landmark positions from configs
from data_io.env import load_env_config
from learning.datasets.aux_data_providers import get_landmark_locations_airsim
from learning.models.semantic_map.pinhole_camera_inv import PinholeCameraProjection
projector = PinholeCameraProjection(
map_size_px=self.params["global_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"],
img_x=self.params["img_w"],
img_y=self.params["img_h"],
cam_fov=self.params["cam_h_fov"],
#TODO: Handle correctly
domain="sim",
use_depth=False)
conf_json = load_env_config(md.ENV_ID)
landmark_names, landmark_indices, landmark_pos = get_landmark_locations_airsim(
conf_json)
cam_poses = self.cam_poses_from_states(states)
cam_pos = cam_poses.position[0]
cam_rot = cam_poses.orientation[0]
lm_pos_map_all = []
lm_pos_img_all = []
for i, landmark_in_world in enumerate(landmark_pos):
lm_pos_img, landmark_in_cam, status = projector.world_point_to_image(
cam_pos, cam_rot, landmark_in_world)
lm_pos_map = torch.from_numpy(
transformations.pos_m_to_px(
landmark_in_world[np.newaxis, :],
self.params["global_map_size"],
self.params["world_size_m"],
self.params["world_size_px"]))
lm_pos_map_all += [lm_pos_map[0]]
if lm_pos_img is not None:
lm_pos_img_all += [lm_pos_img]
lm_pos_img_all = [lm_pos_img_all]
lm_pos_map_all = [lm_pos_map_all]
else:
lm_pos_img_all = self.tensor_store.get("lm_pos_fpv_img")
lm_pos_map_all = self.tensor_store.get("lm_pos_map")
print("Plotting landmark points")
for i in range(len(img_all)):
p = Presenter()
overlay_fpv = p.overlay_pts_on_image(img_all[i][0],
lm_pos_img_all[i])
overlay_map = p.overlay_pts_on_image(img_w_all[i][0],
lm_pos_map_all[i])
p.show_image(overlay_fpv, "landmarks_on_fpv_img", scale=8)
p.show_image(overlay_map, "landmarks_on_map", scale=20)
if not loop:
break
def calc_tensor_statistics(self, prefix, tensor):
stats = {}
stats[f"{prefix}_mean"] = torch.mean(tensor).item()
stats[f"{prefix}_l2"] = torch.norm(tensor).item()
stats[f"{prefix}_stddev"] = torch.std(tensor).item()
return stats
def get_activation_statistics(self, keys):
stats = {}
from utils.dict_tools import dict_merge
for key in keys:
t = self.tensor_store.get_inputs_batch(key)
t_stats = self.calc_tensor_statistics(key, t)
stats = dict_merge(stats, t_stats)
return stats
# Forward pass for training (with batch optimizations
def sup_loss_on_batch(self, batch, eval, halfway=False):
self.prof.tick("out")
action_loss_total = Variable(
empty_float_tensor([1], self.is_cuda, self.cuda_device))
if batch is None:
print("Skipping None Batch")
return action_loss_total
images, instructions, instr_lengths, states, action_labels, \
lm_pos_fpv_img, lm_pos_fpv_features, lm_pos_map, lm_indices, goal_pos_map, \
lm_mentioned, lm_mentioned_tplt, side_mentioned_tplt, lang_lm_mentioned, \
metadata, obs_mask, plan_mask, pos_enc = self.unbatch(batch)
# ----------------------------------------------------------------------------
self.prof.tick("inputs")
pred_actions = self(images,
states,
instructions,
instr_lengths,
has_obs=obs_mask,
plan=plan_mask,
pos_enc=pos_enc,
halfway=halfway)
# Debugging landmark locations
if False:
self.show_landmark_locations()
# Don't compute any losses - those will not be used. All we care about are the intermediate activations
if halfway:
return None, self.tensor_store
action_losses, _ = self.action_loss(action_labels,
pred_actions,
batchreduce=False)
self.prof.tick("call")
action_losses = self.action_loss.batch_reduce_loss(action_losses)
action_loss = self.action_loss.reduce_loss(action_losses)
action_loss_total = action_loss
self.prof.tick("loss")
aux_losses, aux_metrics = self.aux_losses.calculate_aux_loss(
self.tensor_store, reduce_average=True)
aux_loss = self.aux_losses.combine_losses(aux_losses, self.aux_weights)
#overlaid = self.get_overlaid_classification_results()
#Presenter().show_image(overlaid, "classification", scale=2)
prefix = f"{self.model_name}/{'eval' if eval else 'train'}"
act_prefix = f"{self.model_name}_activations/{'eval' if eval else 'train'}"
# Mean, stddev, norm of maps
act_stats = self.get_activation_statistics(
["map_S_W", "map_R_W", "map_G_W"])
self.writer.add_dict(act_prefix, act_stats, self.get_iter())
self.writer.add_dict(prefix, get_current_meters(), self.get_iter())
self.writer.add_dict(prefix, aux_losses, self.get_iter())
self.writer.add_dict(prefix, aux_metrics, self.get_iter())
self.writer.add_scalar(prefix + "/action_loss",
action_loss_total.data.cpu().item(),
self.get_iter())
# TODO: Log value here
self.writer.add_scalar(prefix + "/goal_accuracy",
self.goal_acc_meter.get(), self.get_iter())
self.prof.tick("auxiliaries")
total_loss = action_loss_total + aux_loss
self.inc_iter()
self.prof.tick("summaries")
self.prof.loop()
self.prof.print_stats(1)
return total_loss, self.tensor_store
def get_dataset(self,
data=None,
envs=None,
dataset_names=None,
dataset_prefix=None,
eval=False):
# TODO: Maybe use eval here
#if self.fpv:
data_sources = []
# If we're running auxiliary objectives, we need to include the data sources for the auxiliary labels
#if self.use_aux_class_features or self.use_aux_class_on_map or self.use_aux_grounding_features or self.use_aux_grounding_on_map:
#if self.use_aux_goal_on_map:
data_sources.append(aup.PROVIDER_LM_POS_DATA)
data_sources.append(aup.PROVIDER_GOAL_POS)
#data_sources.append(aup.PROVIDER_LANDMARKS_MENTIONED)
data_sources.append(aup.PROVIDER_LANG_TEMPLATE)
#if self.use_rot_noise or self.use_pos_noise:
# data_sources.append(aup.PROVIDER_POSE_NOISE)
return SegmentDataset(data=data,
env_list=envs,
dataset_names=dataset_names,
dataset_prefix=dataset_prefix,
aux_provider_names=data_sources,
segment_level=True)
class PVN_Stage1_Bidomain_Original(nn.Module):
def __init__(self, run_name="", domain="sim"):
super(PVN_Stage1_Bidomain_Original, self).__init__()
self.model_name = "pvn_stage1"
self.run_name = run_name
self.domain = domain
self.writer = LoggingSummaryWriter(
log_dir=f"{get_logging_dir()}/runs/{run_name}/{self.domain}")
#self.writer = DummySummaryWriter()
self.root_params = get_current_parameters()["ModelPVN"]
self.params = self.root_params["Stage1"]
self.use_aux = self.root_params["UseAux"]
self.aux_weights = self.root_params["AuxWeights"]
if self.params.get("weight_override"):
aux_weights_override_name = "AuxWeightsRealOverride" if self.domain == "real" else "AuxWeightsSimOverride"
aux_weights_override = self.root_params.get(
aux_weights_override_name)
if aux_weights_override:
print(
f"Overriding auxiliary weights for domain: {self.domain}")
self.aux_weights = dict_merge(self.aux_weights,
aux_weights_override)
self.prof = SimpleProfiler(torch_sync=PROFILE, print=PROFILE)
self.iter = nn.Parameter(torch.zeros(1), requires_grad=False)
self.tensor_store = KeyTensorStore()
self.losses = AuxiliaryLosses()
# Auxiliary Objectives
self.do_perturb_maps = self.params["perturb_maps"]
print("Perturbing maps: ", self.do_perturb_maps)
# Path-pred FPV model definition
# --------------------------------------------------------------------------------------------------------------
self.num_feature_channels = self.params[
"feature_channels"] # + params["relevance_channels"]
self.num_map_channels = self.params["pathpred_in_channels"]
self.img_to_features_w = FPVToGlobalMap(
source_map_size=self.params["global_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"],
res_channels=self.params["resnet_channels"],
map_channels=self.params["feature_channels"],
img_w=self.params["img_w"],
img_h=self.params["img_h"],
cam_h_fov=self.params["cam_h_fov"],
domain=domain,
img_dbg=IMG_DBG)
self.map_accumulator_w = LeakyIntegratorGlobalMap(
source_map_size=self.params["global_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"])
self.add_init_pos_to_coverage = AddDroneInitPosToCoverage(
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"],
map_size_px=self.params["local_map_size"])
# Pre-process the accumulated map to do language grounding if necessary - in the world reference frame
self.map_processor_grounding = LangFilterMapProcessor(
embed_size=self.params["emb_size"],
in_channels=self.params["feature_channels"],
out_channels=self.params["relevance_channels"],
spatial=False,
cat_out=False)
ratio_prior_channels = self.params["feature_channels"]
# Process the global accumulated map
self.path_predictor_lingunet = RatioPathPredictor(
self.params["lingunet"],
prior_channels_in=self.params["feature_channels"],
posterior_channels_in=self.params["pathpred_in_channels"],
dual_head=self.params["predict_confidence"],
compute_prior=self.params["compute_prior"],
use_prior=self.params["use_prior_only"],
oob=self.params["clip_observability"])
print("UNet Channels: " + str(self.num_map_channels))
print("Feature Channels: " + str(self.num_feature_channels))
# TODO:O Verify that config has the same randomization parameters (yaw, pos, etc)
self.second_transform = self.do_perturb_maps or self.params[
"predict_in_start_frame"]
# Sentence Embedding
self.sentence_embedding = SentenceEmbeddingSimple(
self.params["word_emb_size"], self.params["emb_size"],
self.params["emb_layers"], self.params["emb_dropout"])
self.map_transform_local_to_local = MapTransformer(
source_map_size=self.params["local_map_size"],
dest_map_size=self.params["local_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"])
self.map_transform_global_to_local = MapTransformer(
source_map_size=self.params["global_map_size"],
dest_map_size=self.params["local_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"])
self.map_transform_local_to_global = MapTransformer(
source_map_size=self.params["local_map_size"],
dest_map_size=self.params["global_map_size"],
world_size_px=self.params["world_size_px"],
world_size_m=self.params["world_size_m"])
self.map_transform_s_to_p = self.map_transform_local_to_local
self.map_transform_w_to_s = self.map_transform_global_to_local
self.map_transform_w_to_r = self.map_transform_global_to_local
self.map_transform_r_to_s = self.map_transform_local_to_local
self.map_transform_r_to_w = self.map_transform_local_to_global
self.map_transform_p_to_w = self.map_transform_local_to_global
self.map_transform_p_to_r = self.map_transform_local_to_local
# Batch select is used to drop and forget semantic maps at those timestaps that we're not planning in
self.batch_select = MapBatchSelect()
# Since we only have path predictions for some timesteps (the ones not dropped above), we use this to fill
# in the missing pieces by reorienting the past trajectory prediction into the frame of the current timestep
self.map_batch_fill_missing = MapBatchFillMissing(
self.params["local_map_size"], self.params["world_size_px"],
self.params["world_size_m"])
self.spatialsoftmax = SpatialSoftmax2d()
self.visitation_softmax = VisitationSoftmax()
#TODO:O Use CroppedMapToActionTriplet in Wrapper as Stage2
# Auxiliary Objectives
# --------------------------------------------------------------------------------------------------------------
# We add all auxiliaries that are necessary. The first argument is the auxiliary name, followed by parameters,
# followed by variable number of names of inputs. ModuleWithAuxiliaries will automatically collect these inputs
# that have been saved with keep_auxiliary_input() during execution
if self.use_aux["class_features"]:
self.losses.add_auxiliary(
ClassAuxiliary2D("class_features",
self.params["feature_channels"],
self.params["num_landmarks"], 0,
"fpv_features", "lm_pos_fpv", "lm_indices"))
if self.use_aux["grounding_features"]:
self.losses.add_auxiliary(
ClassAuxiliary2D("grounding_features",
self.params["relevance_channels"], 2, 0,
"fpv_features_g", "lm_pos_fpv",
"lm_mentioned"))
if self.use_aux["class_map"]:
self.losses.add_auxiliary(
ClassAuxiliary2D("class_map", self.params["feature_channels"],
self.params["num_landmarks"], 0, "S_W_select",
"lm_pos_map_select", "lm_indices_select"))
if self.use_aux["grounding_map"]:
self.losses.add_auxiliary(
ClassAuxiliary2D("grounding_map",
self.params["relevance_channels"], 2, 0,
"R_W_select", "lm_pos_map_select",
"lm_mentioned_select"))
# CoRL model uses alignment-model groundings
if self.use_aux["lang"]:
# one output for each landmark, 2 classes per output. This is for finetuning, so use the embedding that's gonna be fine tuned
self.losses.add_auxiliary(
ClassAuxiliary("lang", self.params["emb_size"], 2,
self.params["num_landmarks"], "sentence_embed",
"lang_lm_mentioned"))
if self.use_aux["regularize_map"]:
self.losses.add_auxiliary(
FeatureRegularizationAuxiliary2D("regularize_map", "l1",
"S_W_select"))
lossfunc = self.params["path_loss_function"]
if self.params["clip_observability"]:
self.losses.add_auxiliary(
PathAuxiliary2D("visitation_dist", lossfunc,
self.params["clip_observability"],
"log_v_dist_s_select",
"v_dist_s_ground_truth_select", "SM_S_select"))
else:
self.losses.add_auxiliary(
PathAuxiliary2D("visitation_dist", lossfunc,
self.params["clip_observability"],
"log_v_dist_s_select",
"v_dist_s_ground_truth_select", "SM_S_select"))
self.goal_good_criterion = GoalPredictionGoodCriterion(
ok_distance=self.params["world_size_px"] * 0.1)
self.goal_acc_meter = MovingAverageMeter(10)
self.visible_goal_acc_meter = MovingAverageMeter(10)
self.invisible_goal_acc_meter = MovingAverageMeter(10)
self.visible_goal_frac_meter = MovingAverageMeter(10)
self.losses.print_auxiliary_info()
self.total_goals = 0
self.correct_goals = 0
self.env_id = None
self.env_img = None
self.seg_idx = None
self.prev_instruction = None
self.seq_step = 0
self.should_save_path_overlays = False
def make_picklable(self):
self.writer = DummySummaryWriter()
def steal_cross_domain_modules(self, other_self):
self.iter = other_self.iter
self.losses = other_self.losses
self.sentence_embedding = other_self.sentence_embedding
self.map_accumulator_w = other_self.map_accumulator_w
self.map_processor_grounding = other_self.map_processor_grounding
self.path_predictor_lingunet = other_self.path_predictor_lingunet
#self.img_to_features_w = other_self.img_to_features_w
def both_domain_parameters(self, other_self):
# This function iterates and yields parameters from this module and the other module, but does not yield
# shared parameters twice.
# First yield all of the other module's parameters
for p in other_self.parameters():
yield p
# Then yield all the parameters from the this module that are not shared with the other one
for p in self.img_to_features_w.parameters():
yield p
return
def get_iter(self):
return int(self.iter.data[0])
def inc_iter(self):
self.iter += 1
def load_state_dict(self, state_dict, strict=True):
super(PVN_Stage1_Bidomain_Original,
self).load_state_dict(state_dict, strict)
def init_weights(self):
self.img_to_features_w.init_weights()
self.map_accumulator_w.init_weights()
self.sentence_embedding.init_weights()
self.map_processor_grounding.init_weights()
self.path_predictor_lingunet.init_weights()
def reset(self):
# TODO: This is error prone. Create a class StatefulModule, iterate submodules and reset all stateful modules
self.tensor_store.reset()
self.sentence_embedding.reset()
self.img_to_features_w.reset()
self.map_accumulator_w.reset()
self.map_batch_fill_missing.reset()
self.prev_instruction = None
def setEnvContext(self, context):
print("Set env context to: " + str(context))
self.env_id = context["env_id"]
self.env_img = env.load_env_img(self.env_id, 256, 256)
self.env_img = self.env_img[:, :, [2, 1, 0]]
def set_save_path_overlays(self, save_path_overlays):
self.should_save_path_overlays = save_path_overlays
#TODO:O Figure out what to do with save_ground_truth_overlays
def print_metrics(self):
print(f"Model {self.model_name}:{self.domain} metrics:")
print(
f" Goal accuracy: {float(self.correct_goals) / self.total_goals}"
)
def goal_visible(self, masks, goal_pos):
goal_mask = masks.detach()[0, 0, :, :]
goal_pos = goal_pos[0].long().detach()
visible = bool(
(goal_mask[goal_pos[0], goal_pos[1]] > 0.5).detach().cpu().item())
return visible
# This is called before beginning an execution sequence
def start_sequence(self):
self.seq_step = 0
self.reset()
return
def cam_poses_from_states(self, states):
cam_pos = states[:, 9:12]
cam_rot = states[:, 12:16]
pose = Pose(cam_pos, cam_rot)
return pose
def forward(self,
images,
states,
instructions,
instr_lengths,
plan=None,
noisy_start_poses=None,
start_poses=None,
firstseg=None,
select_only=True,
halfway=False,
grad_noise=False,
rl=False):
"""
:param images: BxCxHxW batch of images (observations)
:param states: BxK batch of drone states
:param instructions: BxM LongTensor where M is the maximum length of any instruction
:param instr_lengths: list of len B of integers, indicating length of each instruction
:param plan: list of B booleans indicating True for timesteps where we do planning and False otherwise
:param noisy_start_poses: list of noisy start poses (for data-augmentation). These define the path-prediction frame at training time
:param start_poses: list of drone start poses (these should be equal in practice)
:param firstseg: list of booleans indicating True if a new segment starts at that timestep
:param select_only: boolean indicating whether to only compute visitation distributions for planning timesteps (default True)
:param rl: boolean indicating if we're doing reinforcement learning. If yes, output more than the visitation distribution
:return:
"""
cam_poses = self.cam_poses_from_states(states)
g_poses = None # None pose is a placeholder for the canonical global pose.
self.prof.tick("out")
self.tensor_store.keep_inputs("fpv", images)
# Calculate the instruction embedding
if instructions is not None:
# TODO: Take batch of instructions and their lengths, return batch of embeddings. Store the last one as internal state
# TODO: There's an assumption here that there's only a single instruction in the batch and it doesn't change
# UNCOMMENT THE BELOW LINE TO REVERT BACK TO GENERAL CASE OF SEPARATE INSTRUCTION PER STEP
if self.params["ignore_instruction"]:
# If we're ignoring instructions, just feed in an instruction that consists of a single zero-token
sent_embeddings = self.sentence_embedding(
torch.zeros_like(instructions[0:1, 0:1]),
torch.ones_like(instr_lengths[0:1]))
else:
sent_embeddings = self.sentence_embedding(
instructions[0:1], instr_lengths[0:1])
self.tensor_store.keep_inputs("sentence_embed", sent_embeddings)
else:
sent_embeddings = self.sentence_embedding.get()
self.prof.tick("embed")
# Extract and project features onto the egocentric frame for each image
F_W, M_W = self.img_to_features_w(images,
cam_poses,
sent_embeddings,
self.tensor_store,
show="",
halfway=halfway)
# For training the critic, this is as far as we need to poceed with the computation.
# self.img_to_features_w has stored computed feature maps inside the tensor store, which will then be retrieved by the critic
if halfway == True: # Warning: halfway must be True not truthy
return None, None
self.tensor_store.keep_inputs("F_w", F_W)
self.tensor_store.keep_inputs("M_w", M_W)
self.prof.tick("img_to_map_frame")
# Accumulate the egocentric features in a global map
reset_mask = firstseg if self.params["clear_history"] else None
# Consider the space very near the drone and right under it as observed - draw ones on the observability mask
# If we treat that space as unobserved, then there's going to be a gap in the visitation distribution, which
# makes training with RL more difficult, as there is no reward feedback if the drone doesn't cross that gap.
if self.params.get("cover_init_pos", False):
StartMasks_R = self.add_init_pos_to_coverage.get_init_pos_masks(
M_W.shape[0], M_W.device)
StartMasks_W, _ = self.map_transform_r_to_w(
StartMasks_R, cam_poses, None)
M_W = self.add_init_pos_to_coverage(M_W, StartMasks_W)
S_W, SM_W = self.map_accumulator_w(F_W,
M_W,
reset_mask=reset_mask,
show="acc" if IMG_DBG else "")
S_W_poses = g_poses
self.prof.tick("map_accumulate")
# If we're training Stage 2 with imitation learning from ground truth visitation distributions, we want to
# compute observability masks with the same code that's used in Stage 1 to avoid mistakes.
if halfway == "observability":
map_uncoverage_w = 1 - SM_W
return map_uncoverage_w
# Throw away those timesteps that don't correspond to planning timesteps
S_W_select, SM_W_select, S_W_poses_select, cam_poses_select, noisy_start_poses_select, start_poses_select, sent_embeddings_select = \
self.batch_select(S_W, SM_W, S_W_poses, cam_poses, noisy_start_poses, start_poses, sent_embeddings, plan)
#maps_m_prior_select, maps_m_posterior_select = None, None
# Only process the maps on plannieng timesteps
if len(S_W_select) == 0:
return None
self.tensor_store.keep_inputs("S_W_select", S_W_select)
self.prof.tick("batch_select")
# Process the map via the two map_procesors
# Do grounding of objects in the map chosen to do so
if self.use_aux["grounding_map"]:
R_W_select, RS_W_poses_select = self.map_processor_grounding(
S_W_select, sent_embeddings_select, S_W_poses_select, show="")
self.tensor_store.keep_inputs("R_W_select", R_W_select)
self.prof.tick("map_proc_gnd")
# Concatenate grounding map and semantic map along channel dimension
RS_W_select = torch.cat([S_W_select, R_W_select], 1)
else:
RS_W_select = S_W_select
RS_W_poses_select = S_W_poses_select
s_poses_select = start_poses_select if self.params[
"predict_in_start_frame"] else cam_poses_select
RS_S_select, RS_S_poses_select = self.map_transform_w_to_s(
RS_W_select, RS_W_poses_select, s_poses_select)
SM_S_select, SM_S_poses_select = self.map_transform_w_to_s(
SM_W_select, S_W_poses_select, s_poses_select)
assert SM_S_poses_select == RS_S_poses_select, "Masks and maps should have the same pose in start frame"
self.tensor_store.keep_inputs("RS_S_select", RS_S_select)
self.tensor_store.keep_inputs("SM_S_select", SM_S_select)
self.prof.tick("transform_w_to_s")
# Data augmentation for trajectory prediction
map_poses_clean_select = None
# TODO: Figure out if we can just swap out start poses for noisy poses and get rid of separate noisy poses
if self.do_perturb_maps:
assert noisy_start_poses_select is not None, "Noisy poses must be provided if we're perturbing maps"
RS_P_select, RS_P_poses_select = self.map_transform_s_to_p(
RS_S_select, RS_S_poses_select, noisy_start_poses_select)
else:
RS_P_select, RS_P_poses_select = RS_S_select, RS_S_poses_select
self.tensor_store.keep_inputs("RS_perturbed_select", RS_P_select)
self.prof.tick("map_perturb")
sent_embeddings_pp = sent_embeddings_select
# Run lingunet on the map to predict visitation distribution scores (pre-softmax)
# ---------
log_v_dist_p_select, v_dist_p_poses_select = self.path_predictor_lingunet(
RS_P_select,
sent_embeddings_pp,
RS_P_poses_select,
tensor_store=self.tensor_store)
# ---------
self.prof.tick("pathpred")
# TODO: Shouldn't we be transforming probability distributions instead of scores? Otherwise OOB space will have weird values
# Transform distributions back to world reference frame and keep them (these are the model outputs)
both_inner_w, v_dist_w_poses_select = self.map_transform_p_to_w(
log_v_dist_p_select.inner_distribution, v_dist_p_poses_select,
None)
log_v_dist_w_select = Partial2DDistribution(
both_inner_w, log_v_dist_p_select.outer_prob_mass)
self.tensor_store.keep_inputs("log_v_dist_w_select",
log_v_dist_w_select)
# Transform distributions back to start reference frame and keep them (for auxiliary objective)
both_inner_s, v_dist_s_poses_select = self.map_transform_p_to_r(
log_v_dist_p_select.inner_distribution, v_dist_p_poses_select,
start_poses_select)
log_v_dist_s_select = Partial2DDistribution(
both_inner_s, log_v_dist_p_select.outer_prob_mass)
self.tensor_store.keep_inputs("log_v_dist_s_select",
log_v_dist_s_select)
# prime number will mean that it will alternate between sim and real
if self.get_iter() % 23 == 0:
lsfm = SpatialSoftmax2d()
for i in range(S_W_select.shape[0]):
Presenter().show_image(S_W_select.detach().cpu()[i, 0:3],
f"{self.domain}_s_w_select",
scale=4,
waitkey=1)
Presenter().show_image(lsfm(
log_v_dist_s_select.inner_distribution).detach().cpu()[i],
f"{self.domain}_v_dist_s_select",
scale=4,
waitkey=1)
Presenter().show_image(lsfm(
log_v_dist_p_select.inner_distribution).detach().cpu()[i],
f"{self.domain}_v_dist_p_select",
scale=4,
waitkey=1)
Presenter().show_image(RS_P_select.detach().cpu()[i, 0:3],
f"{self.domain}_rs_p_select",
scale=4,
waitkey=1)
break
self.prof.tick("transform_back")
# If we're predicting the trajectory only on some timesteps, then for each timestep k, use the map from
# timestep k if predicting on timestep k. otherwise use the map from timestep j - the last timestep
# that had a trajectory prediction, rotated in the frame of timestep k.
if select_only:
# If we're just pre-training the trajectory prediction, don't waste time on generating the missing maps
log_v_dist_w = log_v_dist_w_select
v_dist_w_poses = v_dist_w_poses_select
else:
raise NotImplementedError("select_only must be True")
return_list = [log_v_dist_w, v_dist_w_poses]
if rl:
internals_for_rl = {
"map_coverage_w": SM_W,
"map_uncoverage_w": 1 - SM_W
}
return_list.append(internals_for_rl)
return tuple(return_list)
def maybe_cuda(self, tensor):
return tensor.to(next(self.parameters()).device)
def cuda_var(self, tensor):
return tensor.to(next(self.parameters()).device)
def unbatch(self, batch, halfway=False):
# Inputs
images = self.maybe_cuda(batch["images"][0])
seq_len = len(images)
instructions = self.maybe_cuda(batch["instr"][0][:seq_len])
instr_lengths = batch["instr_len"][0][:seq_len]
states = self.maybe_cuda(batch["states"][0])
if not halfway:
plan_mask = batch["plan_mask"][
0] # True for every timestep that we do visitation prediction
firstseg_mask = batch["firstseg_mask"][
0] # True for every timestep that is a new instruction segment
# Labels (including for auxiliary losses)
lm_pos_fpv = batch["lm_pos_fpv"][
0] # All object 2D coordinates in the first-person image
lm_pos_map_m = batch["lm_pos_map"][
0] # All object 2D coordinates in the semantic map
lm_indices = batch["lm_indices"][0] # All object class indices
goal_pos_map_m = batch["goal_loc"][
0] # Goal location in the world in meters_and_metrics
lm_mentioned = batch["lm_mentioned"][
0] # 1/0 labels whether object was mentioned/not mentioned in template instruction
# TODO: We're taking the FIRST label here. SINGLE SEGMENT ASSUMPTION
lang_lm_mentioned = batch["lang_lm_mentioned"][0][
0] # integer labes as to which object was mentioned
start_poses = batch["start_poses"][0]
noisy_start_poses = get_noisy_poses_torch(
start_poses.numpy(),
self.params["pos_variance"],
self.params["rot_variance"],
cuda=False,
cuda_device=None)
# Ground truth visitation distributions (in start and global frames)
v_dist_w_ground_truth_select = self.maybe_cuda(
batch["traj_ground_truth"][0])
start_poses_select = self.batch_select.one(
start_poses, plan_mask, v_dist_w_ground_truth_select.device)
v_dist_s_ground_truth_select, poses_s = self.map_transform_w_to_s(
v_dist_w_ground_truth_select, None, start_poses_select)
#self.tensor_store.keep_inputs("v_dist_w_ground_truth_select", v_dist_w_ground_truth_select)
self.tensor_store.keep_inputs("v_dist_s_ground_truth_select",
v_dist_s_ground_truth_select)
#Presenter().show_image(v_dist_s_ground_truth_select.detach().cpu()[0,0], "v_dist_s_ground_truth_select", waitkey=1, scale=4)
#Presenter().show_image(v_dist_w_ground_truth_select.detach().cpu()[0,0], "v_dist_w_ground_truth_select", waitkey=1, scale=4)
lm_pos_map_px = [
torch.from_numpy(
transformations.pos_m_to_px(p.numpy(),
self.params["global_map_size"],
self.params["world_size_m"],
self.params["world_size_px"]))
if p is not None else None for p in lm_pos_map_m
]
goal_pos_map_px = torch.from_numpy(
transformations.pos_m_to_px(goal_pos_map_m.numpy(),
self.params["global_map_size"],
self.params["world_size_m"],
self.params["world_size_px"]))
resnet_factor = self.img_to_features_w.img_to_features.get_downscale_factor(
)
lm_pos_fpv = [
self.cuda_var(
(s / resnet_factor).long()) if s is not None else None
for s in lm_pos_fpv
]
lm_indices = [
self.cuda_var(s) if s is not None else None for s in lm_indices
]
lm_mentioned = [
self.cuda_var(s) if s is not None else None
for s in lm_mentioned
]
lang_lm_mentioned = self.cuda_var(lang_lm_mentioned)
lm_pos_map_px = [
self.cuda_var(s.long()) if s is not None else None
for s in lm_pos_map_px
]
goal_pos_map_px = self.cuda_var(goal_pos_map_px)
self.tensor_store.keep_inputs("lm_pos_fpv", lm_pos_fpv)
self.tensor_store.keep_inputs("lm_pos_map", lm_pos_map_px)
self.tensor_store.keep_inputs("lm_indices", lm_indices)
self.tensor_store.keep_inputs("lm_mentioned", lm_mentioned)
self.tensor_store.keep_inputs("lang_lm_mentioned",
lang_lm_mentioned)
self.tensor_store.keep_inputs("goal_pos_map", goal_pos_map_px)
lm_pos_map_select = [
lm_pos for i, lm_pos in enumerate(lm_pos_map_px)
if plan_mask[i]
]
lm_indices_select = [
lm_idx for i, lm_idx in enumerate(lm_indices) if plan_mask[i]
]
lm_mentioned_select = [
lm_m for i, lm_m in enumerate(lm_mentioned) if plan_mask[i]
]
goal_pos_map_select = [
pos for i, pos in enumerate(goal_pos_map_px) if plan_mask[i]
]
self.tensor_store.keep_inputs("lm_pos_map_select",
lm_pos_map_select)
self.tensor_store.keep_inputs("lm_indices_select",
lm_indices_select)
self.tensor_store.keep_inputs("lm_mentioned_select",
lm_mentioned_select)
self.tensor_store.keep_inputs("goal_pos_map_select",
goal_pos_map_select)
# We won't need this extra information
else:
noisy_poses, start_poses, noisy_start_poses = None, None, None
plan_mask, firstseg_mask = None, None
metadata = batch["md"][0][0]
env_id = metadata["env_id"]
self.tensor_store.set_flag("env_id", env_id)
return images, states, instructions, instr_lengths, plan_mask, firstseg_mask, start_poses, noisy_start_poses, metadata
# Forward pass for training
def sup_loss_on_batch(self,
batch,
eval,
halfway=False,
grad_noise=False,
disable_losses=[]):
self.prof.tick("out")
self.reset()
if batch is None:
print("Skipping None Batch")
zero = torch.zeros([1]).float().to(next(self.parameters()).device)
return zero, self.tensor_store
images, states, instructions, instr_len, plan_mask, firstseg_mask, \
start_poses, noisy_start_poses, metadata = self.unbatch(batch, halfway=halfway)
self.prof.tick("unbatch_inputs")
# ----------------------------------------------------------------------------
_ = self(images,
states,
instructions,
instr_len,
plan=plan_mask,
firstseg=firstseg_mask,
noisy_start_poses=start_poses if eval else noisy_start_poses,
start_poses=start_poses,
select_only=True,
halfway=halfway,
grad_noise=grad_noise)
# ----------------------------------------------------------------------------
if self.should_save_path_overlays:
self.save_path_overlays(metadata)
# If we run the model halfway, we only need to calculate features needed for the wasserstein loss
# If we want to include more features in wasserstein critic, have to run the forward pass a bit further
if halfway and not halfway == "v2":
return None, self.tensor_store
# The returned values are not used here - they're kept in the tensor store which is used as an input to a loss
self.prof.tick("call")
if not halfway:
# Calculate goal-prediction accuracy:
goal_pos = self.tensor_store.get_inputs_batch("goal_pos_map",
cat_not_stack=True)
success_goal = self.goal_good_criterion(
self.tensor_store.get_inputs_batch("log_v_dist_w_select",
cat_not_stack=True),
goal_pos)
acc = 1.0 if success_goal else 0.0
self.goal_acc_meter.put(acc)
goal_visible = self.goal_visible(
self.tensor_store.get_inputs_batch("M_w", cat_not_stack=True),
goal_pos)
if goal_visible:
self.visible_goal_acc_meter.put(acc)
else:
self.invisible_goal_acc_meter.put(acc)
self.visible_goal_frac_meter.put(1.0 if goal_visible else 0.0)
self.correct_goals += acc
self.total_goals += 1
self.prof.tick("goal_acc")
if halfway == "v2":
disable_losses = ["visitation_dist", "lang"]
losses, metrics = self.losses.calculate_aux_loss(
tensor_store=self.tensor_store,
reduce_average=True,
disable_losses=disable_losses)
loss = self.losses.combine_losses(losses, self.aux_weights)
self.prof.tick("calc_losses")
prefix = self.model_name + ("/eval" if eval else "/train")
iteration = self.get_iter()
self.writer.add_dict(prefix, get_current_meters(), iteration)
self.writer.add_dict(prefix, losses, iteration)
self.writer.add_dict(prefix, metrics, iteration)
if not halfway:
self.writer.add_scalar(prefix + "/goal_accuracy",
self.goal_acc_meter.get(), iteration)
self.writer.add_scalar(prefix + "/visible_goal_accuracy",
self.visible_goal_acc_meter.get(),
iteration)
self.writer.add_scalar(prefix + "/invisible_goal_accuracy",
self.invisible_goal_acc_meter.get(),
iteration)
self.writer.add_scalar(prefix + "/visible_goal_fraction",
self.visible_goal_frac_meter.get(),
iteration)
self.inc_iter()
self.prof.tick("summaries")
self.prof.loop()
self.prof.print_stats(1)
return loss, self.tensor_store
def get_dataset(self,
data=None,
envs=None,
domain=None,
dataset_names=None,
dataset_prefix=None,
eval=False,
halfway_only=False):
# TODO: Maybe use eval here
data_sources = []
# If we're running auxiliary objectives, we need to include the data sources for the auxiliary labels
#if self.use_aux_class_features or self.use_aux_class_on_map or self.use_aux_grounding_features or self.use_aux_grounding_on_map:
#if self.use_aux_goal_on_map:
if not halfway_only:
data_sources.append(aup.PROVIDER_LM_POS_DATA)
data_sources.append(aup.PROVIDER_GOAL_POS)
# Adding these in this order will compute poses with added noise and compute trajectory ground truth
# in the reference frame of these noisy poses
data_sources.append(aup.PROVIDER_START_POSES)
if self.do_perturb_maps:
print("PERTURBING MAPS!")
# TODO: The noisy poses from the provider are not actually used!! Those should replace states instead!
data_sources.append(aup.PROVIDER_NOISY_POSES)
# TODO: Think this through. Perhaps we actually want dynamic ground truth given a noisy start position
if self.params["predict_in_start_frame"]:
data_sources.append(
aup.PROVIDER_TRAJECTORY_GROUND_TRUTH_STATIC)
else:
data_sources.append(
aup.PROVIDER_TRAJECTORY_GROUND_TRUTH_DYNAMIC_NOISY)
else:
print("NOT Perturbing Maps!")
data_sources.append(aup.PROVIDER_NOISY_POSES)
if self.params["predict_in_start_frame"]:
data_sources.append(
aup.PROVIDER_TRAJECTORY_GROUND_TRUTH_STATIC)
else:
data_sources.append(
aup.PROVIDER_TRAJECTORY_GROUND_TRUTH_DYNAMIC)
data_sources.append(aup.PROVIDER_LANDMARKS_MENTIONED)
templates = get_current_parameters()["Environment"]["templates"]
if templates:
data_sources.append(aup.PROVIDER_LANG_TEMPLATE)
return SegmentDataset(data=data,
env_list=envs,
domain=domain,
dataset_names=dataset_names,
dataset_prefix=dataset_prefix,
aux_provider_names=data_sources,
segment_level=True)