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
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 __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
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