# to be loaded to the model. You can find available models in the model zoo.
_C.MODEL.WEIGHTS = ""
# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR).
# To train on images of different number of channels, just set different mean & std.
# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
# When using pre-trained models in Detectron1 or any MSRA models,
# std has been absorbed into its conv1 weights, so the std needs to be set 1.
# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
# -----------------------------------------------------------------------------
# INPUT
# -----------------------------------------------------------------------------
_C.INPUT = CN()
# Size of the smallest side of the image during training
_C.INPUT.MIN_SIZE_TRAIN = (800, )
# Sample size of smallest side by choice or random selection from range give by
# INPUT.MIN_SIZE_TRAIN
_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice"
# Maximum size of the side of the image during training
_C.INPUT.MAX_SIZE_TRAIN = 1333
# Size of the smallest side of the image during testing. Set to zero to disable resize in testing.
_C.INPUT.MIN_SIZE_TEST = 800
# Maximum size of the side of the image during testing
_C.INPUT.MAX_SIZE_TEST = 1333
# `True` if cropping is used for data augmentation during training
_C.INPUT.CROP = CN({"ENABLED": False})
# Cropping type:
def get_derender_config(dataset_name):
_C = CfgNode()
_C.DEBUG = False
_C.TYPE = "derender"
_C.ATTRIBUTES = CfgNode()
_C.RESUME = False
_C.RESUME_DIR = ""
_C.SEED = -1
_C.CUDNN_BENCHMARK = False
# -----------------------------------------------------------------------------
# Model
# -----------------------------------------------------------------------------
_C.MODEL = CfgNode()
# Number of derender visual feature channels
_C.MODEL.FEATURE_CHANNELS = 512
# number of hidden layers after backbone
_C.MODEL.NUM_MID_LAYERS = 2
# Number of intermediate layer channels
_C.MODEL.MID_CHANNELS = 256
_C.INPUT = CfgNode()
_C.DATASETS = CfgNode()
_C.DATASETS.USE_PREDICTED_BOXES = False
_C.DATASETS.BASE_NAME = dataset_name
# -----------------------------------------------------------------------------
# DataLoader
# -----------------------------------------------------------------------------
_C.DATALOADER = CfgNode()
# Number of data loading threads
if _C.DEBUG:
_C.DATALOADER.NUM_WORKERS = 0
else:
_C.DATALOADER.NUM_WORKERS = 6
if _C.DEBUG:
_C.DATALOADER.VAL_BATCH_SIZE = 80
else:
_C.DATALOADER.VAL_BATCH_SIZE = 160
# ---------------------------------------------------------------------------- #
# Solver
# ---------------------------------------------------------------------------- #
_C.SOLVER = CfgNode()
#maximum number of seconds for training
_C.SOLVER.MAX_TIME_SECS = 43200
# Print metrics every _ seconds
_C.SOLVER.PRINT_METRICS_TIME = 180 #TODO: set back to 180
# write to tensorboard summary every_ secconds
_C.SOLVER.TENSORBOARD_SECS = 1
# Checkpoint every _ seconds
_C.SOLVER.CHECKPOINT_SECS = 600
# Run validation every _ seconds
_C.SOLVER.VALIDATION_SECS = 600 #TODO: change back to 300
_C.SOLVER.VALIDATION_MAX_SECS = 60
_C.SOLVER.BASE_LR = 6.658777172739463e-5
_C.SOLVER.BIAS_LR_FACTOR = 2
_C.SOLVER.OPT_TYPE = "Adam" #options "Adam" "SGD"
_C.SOLVER.MOMENTUM = 0.9960477666835778 #found via Bayesian Optimization
_C.SOLVER.ADAM_BETA = 0.9999427846237621
_C.SOLVER.WEIGHT_DECAY = 0.0005
_C.SOLVER.WEIGHT_DECAY_BIAS = 0
#Factor of reduction at iteration == el for el in SOLVER.STEPS
_C.SOLVER.GAMMA = 0.3
_C.SOLVER.STEPS = (80000, 100000)
# _C.SOLVER.STEPS = (3000,5000)
_C.SOLVER.WARMUP_FACTOR = 1.0 / 3
_C.SOLVER.WARMUP_ITERS = 500
_C.SOLVER.WARMUP_METHOD = "linear"
######### INTPHYS ###############
if _C.DATASETS.BASE_NAME == "intphys":
_C.DATASETS.TRAIN = ("intphys_train", )
# _C.DATASETS.TRAIN = ("intphys_val",)
_C.DATASETS.TEST = ("intphys_val", )
_C.DATASETS.USE_DEPTH = True
_C.DATALOADER.OBJECTS_PER_BATCH = 160 # TODO: get back to 160
_C.ATTRIBUTES.NAME = "intphys"
_C.MODEL.ADD_CAMERA = True
# Input channels for the model (segmented depth map, depth map)
_C.MODEL.IN_CHANNELS = 2
# The size of pooling kernel in the last layer of the resnet34
_C.MODEL.POOLING_KERNEL_SIZE = (8, 8)
####### ADEPT #################
elif _C.DATASETS.BASE_NAME == "adept":
_C.DATASETS.TRAIN = ("adept_train", )
# _C.DATASETS.TRAIN = ("adept_val",)
_C.DATASETS.TEST = ("adept_val", )
_C.DATASETS.USE_DEPTH = False
_C.ATTRIBUTES.NAME = "adept"
_C.MODEL.ADD_CAMERA = False
if _C.DEBUG:
_C.DATALOADER.OBJECTS_PER_BATCH = 20 # TODO: get back to 40
else:
_C.DATALOADER.OBJECTS_PER_BATCH = 120 # TODO: get back to 40
# Input channels for the model (segmented depth map, depth map)
_C.MODEL.IN_CHANNELS = 12
# The size of pooling kernel in the last layer of the resnet34
_C.MODEL.POOLING_KERNEL_SIZE = (10, 15)
elif _C.DATASETS.BASE_NAME == "ai2thor-intphys":
_C.DATASETS.TRAIN = ("ai2thor-intphys_train", )
# _C.DATASETS.TRAIN = ("ai2thor-intphys_val",)
_C.DATASETS.TEST = ("ai2thor-intphys_val", )
_C.DATASETS.USE_DEPTH = True
_C.ATTRIBUTES.NAME = "ai2thor"
_C.MODEL.ADD_CAMERA = False
if _C.DEBUG:
_C.DATALOADER.OBJECTS_PER_BATCH = 20
else:
_C.DATALOADER.OBJECTS_PER_BATCH = 120
# Input channels for the model (segmented depth map, depth map)
_C.MODEL.IN_CHANNELS = 2
# The size of pooling kernel in the last layer of the resnet34
_C.MODEL.POOLING_KERNEL_SIZE = (8, 16)
else:
raise NotImplementedError
return _C
