add modules

config.py

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+from yacs.config import CfgNode as CN
+import os
+import os.path as osp
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+_C = CN()
+
+dataset_path_2021 = '/data/datasets/aicity2021/AIC21_Track5_NL_Retrieval'
+data_path_2021 = '/data/datasets/aicity2021/AIC21_Track5_NL_Retrieval/new_baseline/AIC21_Track5_NL_Retrieval'
+save_mo_dir_2021 = osp.join(data_path_2021, "data/motion_map")
+
+dataset_path = '/home/dat09/3.hackathon/AICityChallenger/AICITY2022_Track2_SSM/data/datasets/aicity2022/Track2'
+data_path = '/home/dat09/3.hackathon/AICityChallenger/AICITY2022_Track2_SSM/data/datasets/aicity2022/Track2'
+save_mo_dir = osp.join(data_path, "mine/data/motion_map_iou")
+
+# DATA process related configurations.
+_C.DATA = CN()
+_C.DATA.CITYFLOW_PATH = dataset_path
+# _C.DATA.TRAIN_JSON_PATH = "data/train.json"
+# _C.DATA.EVAL_JSON_PATH = "data/val.json"
+_C.DATA.TRAIN_JSON_PATH = "data2022/train.json"
+_C.DATA.EVAL_JSON_PATH = "data2022/val.json"
+_C.DATA.EVAL_JSON_PATH_2021 = "data2021/train-tracks.json"
+_C.DATA.SIZE = 288
+_C.DATA.CROP_AREA = 1. ## new_w = CROP_AREA * old_w
+# _C.DATA.TEST_TRACKS_JSON_PATH = "data/test-tracks.json"
+_C.DATA.TEST_TRACKS_JSON_PATH = "data2022/test-tracks.json"
+_C.DATA.TEST_OUTPUT = "logs/Test_Output"
+_C.DATA.USE_MOTION = True
+_C.DATA.MOTION_PATH = save_mo_dir
+_C.DATA.MOTION_PATH_2021 = save_mo_dir_2021
+# _C.DATA.MOTION_PATH = "data/motion_map"
+_C.DATA.USE_OSS = False  # set True if using OSS
+_C.DATA.OSS_PATH = 's3://chenhaobo-shared'
+_C.DATA.CROP_AUG = False
+_C.DATA.TEXT_AUG_SPLIT = ''
+
+
+
+# Model specific configurations.
+_C.MODEL = CN()
+
+_C.MODEL.NAME = "base" #base or dual-stream
+_C.MODEL.BERT_TYPE = "BERT"
+_C.MODEL.BERT_NAME = "bert-base-uncased"
+_C.MODEL.IMG_ENCODER = "se_resnext50_32x4d" # se_resnext50_32x4d, efficientnet-b2, efficientnet-b3
+# _C.MODEL.NUM_CLASS = 2498
+_C.MODEL.NUM_CLASS = 2155  # 2155
+_C.MODEL.OUTPUT_SIZE = 1024
+_C.MODEL.EMBED_DIM = 1024
+_C.MODEL.car_idloss = True
+_C.MODEL.mo_idloss = True
+_C.MODEL.share_idloss = True
+_C.MODEL.RESNET_CHECKPOINT = osp.join(os.environ['HOME'], '.cache/torch/hub/checkpoints/resnet50-19c8e357.pth')
+
+# dual Text
+_C.MODEL.SAME_TEXT = False
+_C.MODEL.MERGE_DIM = 2048
+
+_C.MODEL.HEAD = CN()
+_C.MODEL.HEAD.ADD_TRAIN = True
+_C.MODEL.HEAD.CAT_TRAIN = True
+_C.MODEL.HEAD.CLS_NONLINEAR = True
+_C.MODEL.HEAD.CLS_WEIGHT = 0.5
+_C.MODEL.HEAD.CE_EPSILON = 0.
+_C.MODEL.HEAD.CLS_LOSS_SCALE = 32
+_C.MODEL.HEAD.CLS_LOSS_MARGIN = 0.35
+_C.MODEL.HEAD.SHARED_CLS = 'Linear'
+_C.MODEL.HEAD.CAR_CLS = 'Linear'
+_C.MODEL.HEAD.MO_CLS = 'Linear'
+
+_C.MODEL.HEAD.CLIP_LOSS = True
+_C.MODEL.HEAD.CLIP_LOSS_MARGIN = 0.
+
+_C.MODEL.HEAD.NLP_VIEW_LOSS = 'Triplet'
+_C.MODEL.HEAD.NLP_VIEW_LOSS_MARGIN = 0.
+_C.MODEL.HEAD.NLP_VIEW_SOFT = True
+
+_C.MODEL.METRIC = CN()
+_C.MODEL.METRIC.METRIC_WEIGHT = 1.
+_C.MODEL.METRIC.LOSS = ''
+_C.MODEL.METRIC.LOSS_SCALE = 80
+_C.MODEL.METRIC.LOSS_MARGIN = 0.4
+
+_C.MODEL.MAIN_FEAT_IDX = -1
+
+# Training configurations
+_C.TRAIN = CN()
+_C.TRAIN.ONE_EPOCH_REPEAT = 1
+_C.TRAIN.EPOCH = 400
+_C.TRAIN.BATCH_SIZE = 64
+_C.TRAIN.NUM_WORKERS = 6
+_C.TRAIN.PRINT_FREQ = 20
+_C.TRAIN.LR = CN()
+_C.TRAIN.LR.BASE_LR = 0.01
+_C.TRAIN.LR.WARMUP_EPOCH = 40
+_C.TRAIN.LR.DELAY = 120
+_C.TRAIN.BENCHMARK = False
+# _C.TRAIN.BENCHMARK = True  # set False for final training version
+_C.TRAIN.FREEZE_EPOCH = 0
+_C.TRAIN.SEED = 42
+_C.TRAIN.DETERMINISTIC = False
+
+# Test configurations
+_C.TEST = CN()
+_C.TEST.RESTORE_FROM = None
+# _C.TEST.QUERY_JSON_PATH = "data/test-queries.json"
+_C.TEST.QUERY_JSON_PATH = "data2022/test-queries.json"
+_C.TEST.BATCH_SIZE = 128
+_C.TEST.NUM_WORKERS = 6
+_C.TEST.CONTINUE = ""
+_C.TEST.RERANK = False
+
+
+_C.EVAL = CN()
+_C.EVAL.EPOCH = 20
+_C.EVAL.EVAL_BY_TEST = True
+_C.EVAL.EVAL_BY_TEST_NUM = 1  # eval_by_test epo = num * EVAL.EPOCH
+_C.EVAL.ON2021 = False
+# _C.EVAL.ON2021 = True
+
+# -----------------------------------------------------------------------------
+# INPUT from AICITY2021 DUN
+# -----------------------------------------------------------------------------
+_C.INPUT = CN()
+# Size of the image during training
+_C.INPUT.SIZE_TRAIN = [288, 288]
+# Size of the image during test
+_C.INPUT.SIZE_TEST = [288, 288]
+_C.INPUT.COLORJIT_PROB = 1.0
+_C.INPUT.AUGMIX_PROB = 0.0
+# Random probability for image horizontal flip
+_C.INPUT.PROB = 0.5
+# Random probability for random erasing
+_C.INPUT.RE_PROB = 0.5
+_C.INPUT.RE_SH = 0.4
+# Values to be used for image normalization
+_C.INPUT.PIXEL_MEAN = [0.485, 0.456, 0.406]
+# Values to be used for image normalization
+_C.INPUT.PIXEL_STD = [0.229, 0.224, 0.225]
+# Value of padding size
+_C.INPUT.PADDING = 10
+# color space
+_C.INPUT.COLOR_SPACE = 'rgb'
+# random patch
+_C.INPUT.RANDOM_PATCH_PROB = 0.0
+# random affine
+_C.INPUT.RANDOM_AFFINE_PROB = 0.0
+_C.INPUT.VERTICAL_FLIP_PROB = 0.0
+# random blur
+_C.INPUT.RANDOM_BLUR_PROB = 0.0
+
+# cut-off long-tailed data
+_C.INPUT.CUTOFF_LONGTAILED = False
+_C.INPUT.LONGTAILED_THR = 2
+
+
+def get_default_config():
+    return _C.clone()

configs/circle_loss.yaml

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+DATA:
+  USE_MOTION: True
+  SIZE: 288
+  TRAIN_JSON_PATH: "data2022/train_nlpaug.json"
+  EVAL_JSON_PATH: "data2022/val_nlpaug.json"
+  CROP_AREA: 1.6666667 ## new_w = CROP_AREA* old_w
+
+
+
+MODEL:
+  NAME: "dual-stream-v2"
+  BERT_TYPE: "ROBERTA"
+  BERT_NAME: "roberta-large"
+  IMG_ENCODER: "efficientnet-b2"
+  car_idloss: False
+  mo_idloss: False
+  share_idloss: False
+  HEAD:
+    CLIP_LOSS_MARGIN: 0.
+    CLS_NONLINEAR: True
+    CLS_LOSS_SCALE: 32
+    CLS_LOSS_MARGIN: 0.35
+    CAR_CLS: 'Linear'
+    MO_CLS: 'Linear'
+    SHARED_CLS: 'Linear'
+  METRIC:
+    METRIC_WEIGHT: 1.
+    LOSS: 'CircleLoss'
+    LOSS_SCALE: 80
+    LOSS_MARGIN: 0.4
+TRAIN: 
+  BATCH_SIZE: 64
+
+TEST:
+  QUERY_JSON_PATH: "data2022/test-queries_nlpaug.json"
+  RESTORE_FROM: "checkpoints/circle_loss.pth"
+

configs/dual_baseline_aug1.yaml

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+DATA:
+  USE_MOTION: True
+  SIZE: 288
+  TRAIN_JSON_PATH: "data2022/train_nlpaug.json"
+  EVAL_JSON_PATH: "data2022/val_nlpaug.json"
+  CROP_AREA: 1.6666667 ## new_w = CROP_AREA* old_w
+
+
+
+MODEL:
+  NAME: "dual-text-cat"
+  BERT_TYPE: "ROBERTA"
+  BERT_NAME: "roberta-large"
+  IMG_ENCODER: "efficientnet-b2"
+  car_idloss: False
+  mo_idloss: False
+  share_idloss: False
+  SAME_TEXT: True
+  MERGE_DIM: 1024
+TRAIN: 
+  BATCH_SIZE: 64
+
+TEST:
+  QUERY_JSON_PATH: "data2022/test-queries_nlpaug.json"
+  RESTORE_FROM: "checkpoints/dual_baseline_aug1.pth"
+

configs/dual_baseline_aug3.yaml

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+DATA:
+  USE_MOTION: True
+  SIZE: 288
+  TRAIN_JSON_PATH: "data2022/train_nlpaug_3.json"
+  EVAL_JSON_PATH: "data2022/val_nlpaug_3.json"
+  CROP_AREA: 1.6666667 ## new_w = CROP_AREA* old_w
+
+
+
+MODEL:
+  NAME: "dual-text-cat"
+  BERT_TYPE: "ROBERTA"
+  BERT_NAME: "roberta-large"
+  IMG_ENCODER: "efficientnet-b2"
+  car_idloss: False
+  mo_idloss: False
+  share_idloss: False
+  SAME_TEXT: True
+  MERGE_DIM: 1024
+TRAIN: 
+  BATCH_SIZE: 64
+
+TEST:
+  QUERY_JSON_PATH: "data2022/test-queries_nlpaug_3.json"
+  RESTORE_FROM: "checkpoints/dual_baseline_aug3.pth"
+

configs/single_baseline_aug1.yaml

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+DATA:
+  USE_MOTION: True
+  SIZE: 288
+  TRAIN_JSON_PATH: "data2022/train-tracks_nlpaug.json"
+  EVAL_JSON_PATH: "data2022/val_nlpaug.json"
+  CROP_AREA: 1.6666667 ## new_w = CROP_AREA* old_w
+
+
+
+MODEL:
+  NAME: "dual-stream"
+  BERT_TYPE: "ROBERTA"
+  BERT_NAME: "roberta-large"
+  IMG_ENCODER: "efficientnet-b2"
+  car_idloss: False
+  mo_idloss: False
+  share_idloss: False
+TRAIN:
+  BATCH_SIZE: 64
+
+TEST:
+  QUERY_JSON_PATH: "data2022/test-queries_nlpaug.json"
+  RESTORE_FROM: ""
+