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CL_constant.py
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CL_constant.py
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import os
version = 1.0
STAGES_NAME = {
"functional": dict(
stages_name = ['object', 'attribute', 'relation', 'logical', 'knowledge', 'scenetext'],
abbv2stage = dict(o='object', a='attribute', r='relation', l='logical', k='knowledge', s='scenetext')
),
"scene": dict(
stages_name = ["a#ShopAndDining", "b#Workplace", "c#HomeOrHotel", "d#Transportation", "e#SportAndLeisure", "f#Outdoors"],
abbv2stage = dict(a="a#ShopAndDining", b="b#Workplace", c="c#HomeOrHotel", d="d#Transportation", e="e#SportAndLeisure", f="f#Outdoors")
),
}
ABBR2TASK = dict(
functional = dict (o='object', a='attribute', r='relation', l='logical', k='knowledge', s='scenetext'),
scene = dict(a='a#ShopAndDining', b='b#Workplace', c='c#HomeOrHotel', d="d#Transportation",e='e#SportAndLeisure', f='f#Outdoors'),
)
DATA_DIR = dict( # modify path
functional = "/Users/stan/code/functional_continual_learning_dev/Gen_data/v0.6",
scene = "/Users/stan/code/functional_continual_learning_dev/Gen_data/m_scene",
)
N_TESTING_SAMPLES = dict(
functional = dict(o=3000, a=3000, r=3000, l=3000, k=3000, s=3000),
scene = dict(a=3000, b=3000, c=3000, d=3000, e=3000, f=3000, g=3000),
)
TASK_DICT = dict(
functional = {
"object":{
"train": os.path.join(DATA_DIR["functional"],"fcl_mmf_object_train.npy"),
"val": os.path.join(DATA_DIR["functional"],"fcl_mmf_object_val.npy"),
"test": os.path.join(DATA_DIR["functional"],"fcl_mmf_object_val.npy"),
},
"attribute":{
"train": os.path.join(DATA_DIR["functional"],"fcl_mmf_attribute_train.npy"),
"val": os.path.join(DATA_DIR["functional"],"fcl_mmf_attribute_val.npy"),
"test": os.path.join(DATA_DIR["functional"],"fcl_mmf_attribute_val.npy"),
},
"relation":{
"train": os.path.join(DATA_DIR["functional"],"fcl_mmf_relation_train.npy"),
"val": os.path.join(DATA_DIR["functional"],"fcl_mmf_relation_val.npy"),
"test": os.path.join(DATA_DIR["functional"],"fcl_mmf_relation_val.npy"),
},
"logical":{
"train": os.path.join(DATA_DIR["functional"],"fcl_mmf_logical_train.npy"),
"val": os.path.join(DATA_DIR["functional"],"fcl_mmf_logical_val.npy"),
"test": os.path.join(DATA_DIR["functional"],"fcl_mmf_logical_val.npy"),
},
"knowledge":{
"train": os.path.join(DATA_DIR["functional"],"fcl_mmf_knowledge_train.npy"),
"val": os.path.join(DATA_DIR["functional"],"fcl_mmf_knowledge_val.npy"),
"test": os.path.join(DATA_DIR["functional"],"fcl_mmf_knowledge_val.npy"),
},
"scenetext":{
"train": os.path.join(DATA_DIR["functional"],"fcl_mmf_scenetext_train.npy"),
"val": os.path.join(DATA_DIR["functional"],"fcl_mmf_scenetext_val.npy"),
"test": os.path.join(DATA_DIR["functional"],"fcl_mmf_scenetext_val.npy"),
}
},
scene = {
"a#ShopAndDining":{
"train": os.path.join(DATA_DIR["scene"], "fcl_mmf_a#ShopAndDining_train.npy"),
"val": os.path.join(DATA_DIR["scene"], "fcl_mmf_a#ShopAndDining_val.npy"),
"test": os.path.join(DATA_DIR["scene"], "fcl_mmf_a#ShopAndDining_val.npy")
},
"b#Workplace":{
"train": os.path.join(DATA_DIR["scene"], "fcl_mmf_b#Workplace_train.npy"),
"val": os.path.join(DATA_DIR["scene"], "fcl_mmf_b#Workplace_val.npy"),
"test": os.path.join(DATA_DIR["scene"], "fcl_mmf_b#Workplace_val.npy")
},
"c#HomeOrHotel":{
"train": os.path.join(DATA_DIR["scene"], "fcl_mmf_c#HomeOrHotel_train.npy"),
"val": os.path.join(DATA_DIR["scene"], "fcl_mmf_c#HomeOrHotel_val.npy"),
"test": os.path.join(DATA_DIR["scene"], "fcl_mmf_c#HomeOrHotel_val.npy"),
},
"d#Transportation":{
"train": os.path.join(DATA_DIR["scene"], "fcl_mmf_d#Transportation_train.npy"),
"val": os.path.join(DATA_DIR["scene"], "fcl_mmf_d#Transportation_val.npy"),
"test": os.path.join(DATA_DIR["scene"], "fcl_mmf_d#Transportation_val.npy"),
},
"e#SportAndLeisure":{
"train": os.path.join(DATA_DIR["scene"], "fcl_mmf_e#SportAndLeisure_train.npy"),
"val": os.path.join(DATA_DIR["scene"], "fcl_mmf_e#SportAndLeisure_val.npy"),
"test": os.path.join(DATA_DIR["scene"], "fcl_mmf_e#SportAndLeisure_val.npy"),
},
"f#Outdoors":{
"train": os.path.join(DATA_DIR["scene"], "fcl_mmf_f#Outdoors_train.npy"),
"val": os.path.join(DATA_DIR["scene"], "fcl_mmf_f#Outdoors_val.npy"),
"test": os.path.join(DATA_DIR["scene"], "fcl_mmf_f#Outdoors_val.npy"),
},
},
)
FCL_DATA_ATTR = dict(
functional = {
"object": {
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"attribute": {
"train":{"data_size":18673},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"relation": {
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"logical":{
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"knowledge":{
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"scenetext": {
"train":{"data_size":16868},
"val":{"data_size":2422},
"test":{"data_size":2422},
}
},
scene = {
"a#ShopAndDining":{
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"b#Workplace": {
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"c#HomeOrHotel":{
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"d#Transportation":{
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"e#SportAndLeisure":{
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
"f#Outdoors":{
"train":{"data_size":20000},
"val":{"data_size":3000},
"test":{"data_size":3000},
},
},
)
# files under files/
GENERATED_SG_PTH = dict(
functional = "/Users/stan/code/functional_continual_learning_dev/SG_processing/generated_sg_all_stages_v6.json", # modify path here
scene = "/Users/stan/code/functional_continual_learning_dev/SG_processing/stage_sg_scene_setting_50u-50c.json", # modify path here
)
def get_task(task_abbv_order, task_abbv, cl_setting='functional'):
info = ABBR2TASK[cl_setting]
task_name = info[task_abbv]
task_index = task_abbv_order.index(task_abbv)
return task_index, task_name
def get_prev_task(task_abbv_order, cur_task_abbv, cl_setting='functional'):
info = ABBR2TASK[cl_setting]
cur_task_index = task_abbv_order.index(cur_task_abbv)
if cur_task_index == 0:
return None, None
else:
prev_task_abbv = task_abbv_order[cur_task_index-1]
prev_task = info[prev_task_abbv]
prev_task_index = cur_task_index - 1
return prev_task_index, prev_task