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train_config.py
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train_config.py
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from gazenet.utils.registrar import *
@TrainingConfigRegistrar.register
class TrainerBase(object):
inferer_name = ""
model_name = ""
model_properties = {}
log_dir = ""
logger = "" # comet, tensorboard
project_name = ""
experiment_name = model_name
checkpoint_model_dir = ""
train_dataset_properties = {}
val_dataset_properties = {}
test_dataset_properties = {}
@classmethod
def config_info(cls):
return {"summary": "This is base class and cannot be used directly.",
"example": "None"}
@TrainingConfigRegistrar.register
class GASPExp001_1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPEncConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv", "video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9", "det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv", "video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9", "det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv", "video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9", "det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: 1x1 convolutional variant. Not included in the paper.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp001_ALSTM1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: ALSTM variant.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp001_Add1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPEncAddConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: Additive variant.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp001_SE1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPSEEncConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: SE variant.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp001_GMU1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPEncGMUConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: GMU variant.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp001_GMUALSTM1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: AGMU variant.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp001_ALSTMGMU1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: LAGMU variant.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp001_DAMALSTMGMU1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPDAMEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: DAM + LAGMU variant.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp001_DAMGMU1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPDAMEncGMUConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: DAM + GMU variant.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp001_DAMGMUALSTM1x1Conv(object):
inferer_name = "GASPInference"
model_name = "GASPDAMEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Static GASP: DAM + AGMU variant.",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_2(object):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 2, "sequence_norm": False}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Sequential GASP: Sequential ALSTM variant. Exp_Variant_[N]{Norm} ->"
"N: Context size; Norm: Enable temporal normalization;",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_4(GASPExp002_SeqALSTM1x1Conv_2):
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 4, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_6(GASPExp002_SeqALSTM1x1Conv_2):
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 6, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_8(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 8, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_10(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 10, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_12(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 12, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_14(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 14, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_16(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 16, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_2Norm(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 2, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_4Norm(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 4, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_6Norm(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 6, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_8Norm(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 8, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_10Norm(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 10, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_12Norm(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 12, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_14Norm(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 14, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTM1x1Conv_16Norm(GASPExp002_SeqALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 16, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_2(object):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 2, "sequence_norm": False}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Sequential GASP: LARGMU variant. Exp_Variant_[N]{Norm} ->"
"N: Context size; Norm: Enable temporal normalization;",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_4(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 4, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_6(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 6, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_8(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 8, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_10(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 10, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_12(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 12, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_14(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 14, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_16(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 16, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_2Norm(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 2, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_4Norm(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 4, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_6Norm(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 6, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_8Norm(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 8, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_10Norm(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 10, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_12Norm(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 12, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_14Norm(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 14, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqALSTMGMU1x1Conv_16Norm(GASPExp002_SeqALSTMGMU1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncALSTMGMUConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 16, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_2(object):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 2, "sequence_norm": False}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Sequential GASP: ARGMU variant. Exp_Variant_[N]{Norm} ->"
"N: Context size; Norm: Enable temporal normalization;",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_4(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 4, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_6(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 6, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_8(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 8, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_10(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 10, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_12(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 12, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_14(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 14, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_16(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 16, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_2Norm(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 2, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_4Norm(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 4, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_6Norm(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 6, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_8Norm(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 8, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_10Norm(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 10, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_12Norm(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 12, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_14Norm(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 14, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqGMUALSTM1x1Conv_16Norm(GASPExp002_SeqGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 16, "sequence_norm": True}
@TrainingConfigRegistrar.register
class GASPExp002_SeqDAMGMUALSTM1x1Conv_2(object):
inferer_name = "GASPInference"
model_name = "SequenceGASPDAMEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 2, "sequence_norm": False}
log_dir = "logs"
logger = "comet" # comet, tensorboard
project_name = "gasp_runs"
experiment_name = model_name
checkpoint_model_dir = os.path.join("gazenet", "models", "saliency_prediction",
"gasp", "checkpoints", "pretrained_" +
str.lower(experiment_name))
train_dataset_properties = {"csv_file": "datasets/processed/train_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
val_dataset_properties = {"csv_file": "datasets/processed/validation_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
test_dataset_properties = {"csv_file": "datasets/processed/test_ave.csv",
"video_dir": "datasets/processed/Grouped_frames",
"inp_img_names_list": ["captured", "det_transformed_dave", "det_transformed_esr9",
"det_transformed_vidgaze", "det_transformed_gaze360"],
"gt_img_names_list": ["transformed_salmap", "transformed_fixmap"]}
@classmethod
def config_info(cls):
return {"summary": "Sequential GASP: DAM + ARGMU variant. Exp_Variant_[N]{Norm} ->"
"N: Context size; Norm: Enable temporal normalization;",
"example": "python3 gazenet/bin/train.py --train_config " + cls.__name__ +
" --gpus \"0\" --check_val_every_n_epoch 100 --max_epochs 10000 "
"--checkpoint_save_every_n_epoch 100 --checkpoint_save_n_top 3"}
@TrainingConfigRegistrar.register
class GASPExp002_SeqDAMGMUALSTM1x1Conv_4(GASPExp002_SeqDAMGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPDAMEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 4, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqDAMGMUALSTM1x1Conv_6(GASPExp002_SeqDAMGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPDAMEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 6, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqDAMGMUALSTM1x1Conv_8(GASPExp002_SeqDAMGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPDAMEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 8, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqDAMGMUALSTM1x1Conv_10(GASPExp002_SeqDAMGMUALSTM1x1Conv_2):
inferer_name = "GASPInference"
model_name = "SequenceGASPDAMEncGMUALSTMConv"
model_properties = {"modalities": 5, "batch_size": 4, "sequence_len": 10, "sequence_norm": False}
@TrainingConfigRegistrar.register
class GASPExp002_SeqDAMGMUALSTM1x1Conv_12(GASPExp002_SeqDAMGMUALSTM1x1Conv_2):