json_stats: {"cur_iter": "12", "eta": "4:42:19", "split": "test_iter", "time_diff": 0.93468} Killed

/home/pana-tat/my_envs/slowfast/lib/python3.10/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
  warnings.warn(
/home/pana-tat/my_envs/slowfast/lib/python3.10/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
  warnings.warn(
config files: ['/home/pana-tat/work/video_classification/SlowFast/configs/Kinetics/SLOWFAST_8x8_R50.yaml', 'DATA.PATH_TO_DATA_DIR', '/home/pana-tat/work/video_classification/SlowFast/test.csv', 'TEST.CHECKPOINT_FILE_PATH', '/home/pana-tat/work/video_classification/PySlowFast_Model_Zoo/SLOWFAST_8x8_R50.pkl', 'TRAIN.ENABLE', 'False']
[05/09 18:58:29][INFO] test_net.py:  190: Test with config:
[05/09 18:58:29][INFO] test_net.py:  191: AUG:
  AA_TYPE: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  ENABLE: False
  GEN_MASK_LOADER: False
  INTERPOLATION: bicubic
  MASK_FRAMES: False
  MASK_RATIO: 0.0
  MASK_TUBE: False
  MASK_WINDOW_SIZE: [8, 7, 7]
  MAX_MASK_PATCHES_PER_BLOCK: None
  NUM_SAMPLE: 1
  RE_COUNT: 1
  RE_MODE: pixel
  RE_PROB: 0.25
  RE_SPLIT: False
AVA:
  ANNOTATION_DIR: /mnt/vol/gfsai-flash3-east/ai-group/users/haoqifan/ava/frame_list/
  BGR: False
  DETECTION_SCORE_THRESH: 0.9
  EXCLUSION_FILE: ava_val_excluded_timestamps_v2.2.csv
  FRAME_DIR: /mnt/fair-flash3-east/ava_trainval_frames.img/
  FRAME_LIST_DIR: /mnt/vol/gfsai-flash3-east/ai-group/users/haoqifan/ava/frame_list/
  FULL_TEST_ON_VAL: False
  GROUNDTRUTH_FILE: ava_val_v2.2.csv
  IMG_PROC_BACKEND: cv2
  LABEL_MAP_FILE: ava_action_list_v2.2_for_activitynet_2019.pbtxt
  TEST_FORCE_FLIP: False
  TEST_LISTS: ['val.csv']
  TEST_PREDICT_BOX_LISTS: ['ava_val_predicted_boxes.csv']
  TRAIN_GT_BOX_LISTS: ['ava_train_v2.2.csv']
  TRAIN_LISTS: ['train.csv']
  TRAIN_PCA_JITTER_ONLY: True
  TRAIN_PREDICT_BOX_LISTS: []
  TRAIN_USE_COLOR_AUGMENTATION: False
BENCHMARK:
  LOG_PERIOD: 100
  NUM_EPOCHS: 5
  SHUFFLE: True
BN:
  GLOBAL_SYNC: False
  NORM_TYPE: batchnorm
  NUM_BATCHES_PRECISE: 200
  NUM_SPLITS: 1
  NUM_SYNC_DEVICES: 1
  USE_PRECISE_STATS: True
  WEIGHT_DECAY: 0.0
CONTRASTIVE:
  BN_MLP: False
  BN_SYNC_MLP: False
  DELTA_CLIPS_MAX: inf
  DELTA_CLIPS_MIN: -inf
  DIM: 128
  INTERP_MEMORY: False
  KNN_ON: True
  LENGTH: 239975
  LOCAL_SHUFFLE_BN: True
  MEM_TYPE: 1d
  MLP_DIM: 2048
  MOCO_MULTI_VIEW_QUEUE: False
  MOMENTUM: 0.5
  MOMENTUM_ANNEALING: False
  NUM_CLASSES_DOWNSTREAM: 400
  NUM_MLP_LAYERS: 1
  PREDICTOR_DEPTHS: []
  QUEUE_LEN: 65536
  SEQUENTIAL: False
  SIMCLR_DIST_ON: True
  SWAV_QEUE_LEN: 0
  T: 0.07
  TYPE: mem
DATA:
  COLOR_RND_GRAYSCALE: 0.0
  DECODING_BACKEND: pyav
  DECODING_SHORT_SIZE: 256
  DUMMY_LOAD: False
  ENSEMBLE_METHOD: sum
  IN22K_TRAINVAL: False
  IN22k_VAL_IN1K: 
  INPUT_CHANNEL_NUM: [3, 3]
  INV_UNIFORM_SAMPLE: False
  IN_VAL_CROP_RATIO: 0.875
  LOADER_CHUNK_OVERALL_SIZE: 0
  LOADER_CHUNK_SIZE: 0
  MEAN: [0.45, 0.45, 0.45]
  MULTI_LABEL: False
  NUM_FRAMES: 32
  PATH_LABEL_SEPARATOR:  
  PATH_PREFIX: 
  PATH_TO_DATA_DIR: 
  PATH_TO_PRELOAD_IMDB: 
  RANDOM_FLIP: True
  REVERSE_INPUT_CHANNEL: False
  SAMPLING_RATE: 2
  SKIP_ROWS: 0
  SSL_BLUR_SIGMA_MAX: [0.0, 2.0]
  SSL_BLUR_SIGMA_MIN: [0.0, 0.1]
  SSL_COLOR_BRI_CON_SAT: [0.4, 0.4, 0.4]
  SSL_COLOR_HUE: 0.1
  SSL_COLOR_JITTER: False
  SSL_MOCOV2_AUG: False
  STD: [0.225, 0.225, 0.225]
  TARGET_FPS: 30
  TEST_CROP_SIZE: 256
  TIME_DIFF_PROB: 0.0
  TRAIN_CROP_NUM_SPATIAL: 1
  TRAIN_CROP_NUM_TEMPORAL: 1
  TRAIN_CROP_SIZE: 224
  TRAIN_JITTER_ASPECT_RELATIVE: []
  TRAIN_JITTER_FPS: 0.0
  TRAIN_JITTER_MOTION_SHIFT: False
  TRAIN_JITTER_SCALES: [256, 320]
  TRAIN_JITTER_SCALES_RELATIVE: []
  TRAIN_PCA_EIGVAL: [0.225, 0.224, 0.229]
  TRAIN_PCA_EIGVEC: [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.814], [-0.5836, -0.6948, 0.4203]]
  USE_OFFSET_SAMPLING: False
DATA_LOADER:
  ENABLE_MULTI_THREAD_DECODE: False
  NUM_WORKERS: 8
  PIN_MEMORY: True
DEMO:
  BUFFER_SIZE: 0
  CLIP_VIS_SIZE: 10
  COMMON_CLASS_NAMES: ['watch (a person)', 'talk to (e.g., self, a person, a group)', 'listen to (a person)', 'touch (an object)', 'carry/hold (an object)', 'walk', 'sit', 'lie/sleep', 'bend/bow (at the waist)']
  COMMON_CLASS_THRES: 0.7
  DETECTRON2_CFG: COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml
  DETECTRON2_THRESH: 0.9
  DETECTRON2_WEIGHTS: detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl
  DISPLAY_HEIGHT: 0
  DISPLAY_WIDTH: 0
  ENABLE: False
  FPS: 30
  GT_BOXES: 
  INPUT_FORMAT: BGR
  INPUT_VIDEO: 
  LABEL_FILE_PATH: 
  NUM_CLIPS_SKIP: 0
  NUM_VIS_INSTANCES: 2
  OUTPUT_FILE: 
  OUTPUT_FPS: -1
  PREDS_BOXES: 
  SLOWMO: 1
  STARTING_SECOND: 900
  THREAD_ENABLE: False
  UNCOMMON_CLASS_THRES: 0.3
  VIS_MODE: thres
  WEBCAM: -1
DETECTION:
  ALIGNED: True
  ENABLE: False
  ROI_XFORM_RESOLUTION: 7
  SPATIAL_SCALE_FACTOR: 16
DIST_BACKEND: nccl
LOG_MODEL_INFO: True
LOG_PERIOD: 10
MASK:
  DECODER_DEPTH: 0
  DECODER_EMBED_DIM: 512
  DECODER_SEP_POS_EMBED: False
  DEC_KV_KERNEL: []
  DEC_KV_STRIDE: []
  ENABLE: False
  HEAD_TYPE: separate
  MAE_ON: False
  MAE_RND_MASK: False
  NORM_PRED_PIXEL: True
  PER_FRAME_MASKING: False
  PRED_HOG: False
  PRETRAIN_DEPTH: [15]
  SCALE_INIT_BY_DEPTH: False
  TIME_STRIDE_LOSS: True
MIXUP:
  ALPHA: 0.8
  CUTMIX_ALPHA: 1.0
  ENABLE: False
  LABEL_SMOOTH_VALUE: 0.1
  PROB: 1.0
  SWITCH_PROB: 0.5
MODEL:
  ACT_CHECKPOINT: False
  ARCH: slowfast
  DETACH_FINAL_FC: False
  DROPCONNECT_RATE: 0.0
  DROPOUT_RATE: 0.5
  FC_INIT_STD: 0.01
  FP16_ALLREDUCE: False
  FROZEN_BN: False
  HEAD_ACT: softmax
  LOSS_FUNC: cross_entropy
  MODEL_NAME: SlowFast
  MULTI_PATHWAY_ARCH: ['slowfast']
  NUM_CLASSES: 400
  SINGLE_PATHWAY_ARCH: ['2d', 'c2d', 'i3d', 'slow', 'x3d', 'mvit', 'maskmvit']
MULTIGRID:
  BN_BASE_SIZE: 8
  DEFAULT_B: 0
  DEFAULT_S: 0
  DEFAULT_T: 0
  EPOCH_FACTOR: 1.5
  EVAL_FREQ: 3
  LONG_CYCLE: False
  LONG_CYCLE_FACTORS: [(0.25, 0.7071067811865476), (0.5, 0.7071067811865476), (0.5, 1), (1, 1)]
  LONG_CYCLE_SAMPLING_RATE: 0
  SHORT_CYCLE: False
  SHORT_CYCLE_FACTORS: [0.5, 0.7071067811865476]
MVIT:
  CLS_EMBED_ON: True
  DEPTH: 16
  DIM_MUL: []
  DIM_MUL_IN_ATT: False
  DROPOUT_RATE: 0.0
  DROPPATH_RATE: 0.1
  EMBED_DIM: 96
  HEAD_INIT_SCALE: 1.0
  HEAD_MUL: []
  LAYER_SCALE_INIT_VALUE: 0.0
  MLP_RATIO: 4.0
  MODE: conv
  NORM: layernorm
  NORM_STEM: False
  NUM_HEADS: 1
  PATCH_2D: False
  PATCH_KERNEL: [3, 7, 7]
  PATCH_PADDING: [2, 4, 4]
  PATCH_STRIDE: [2, 4, 4]
  POOL_FIRST: False
  POOL_KVQ_KERNEL: None
  POOL_KV_STRIDE: []
  POOL_KV_STRIDE_ADAPTIVE: None
  POOL_Q_STRIDE: []
  QKV_BIAS: True
  REL_POS_SPATIAL: False
  REL_POS_TEMPORAL: False
  REL_POS_ZERO_INIT: False
  RESIDUAL_POOLING: False
  REV:
    BUFFER_LAYERS: []
    ENABLE: False
    PRE_Q_FUSION: avg
    RESPATH_FUSE: concat
    RES_PATH: conv
  SEPARATE_QKV: False
  SEP_POS_EMBED: False
  USE_ABS_POS: True
  USE_FIXED_SINCOS_POS: False
  USE_MEAN_POOLING: False
  ZERO_DECAY_POS_CLS: True
NONLOCAL:
  GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
  INSTANTIATION: dot_product
  LOCATION: [[[], []], [[], []], [[], []], [[], []]]
  POOL: [[[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]]]
NUM_GPUS: 1
NUM_SHARDS: 1
OUTPUT_DIR: .
RESNET:
  DEPTH: 50
  INPLACE_RELU: True
  NUM_BLOCK_TEMP_KERNEL: [[3, 3], [4, 4], [6, 6], [3, 3]]
  NUM_GROUPS: 1
  SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [1, 1]]
  SPATIAL_STRIDES: [[1, 1], [2, 2], [2, 2], [2, 2]]
  STRIDE_1X1: False
  TRANS_FUNC: bottleneck_transform
  WIDTH_PER_GROUP: 64
  ZERO_INIT_FINAL_BN: True
  ZERO_INIT_FINAL_CONV: False
RNG_SEED: 0
SHARD_ID: 0
SLOWFAST:
  ALPHA: 4
  BETA_INV: 8
  FUSION_CONV_CHANNEL_RATIO: 2
  FUSION_KERNEL_SZ: 7
SOLVER:
  BASE_LR: 0.1
  BASE_LR_SCALE_NUM_SHARDS: False
  BETAS: (0.9, 0.999)
  CLIP_GRAD_L2NORM: None
  CLIP_GRAD_VAL: None
  COSINE_AFTER_WARMUP: False
  COSINE_END_LR: 0.0
  DAMPENING: 0.0
  GAMMA: 0.1
  LARS_ON: False
  LAYER_DECAY: 1.0
  LRS: []
  LR_POLICY: cosine
  MAX_EPOCH: 196
  MOMENTUM: 0.9
  NESTEROV: True
  OPTIMIZING_METHOD: sgd
  STEPS: []
  STEP_SIZE: 1
  WARMUP_EPOCHS: 34.0
  WARMUP_FACTOR: 0.1
  WARMUP_START_LR: 0.01
  WEIGHT_DECAY: 0.0001
  ZERO_WD_1D_PARAM: False
TASK: 
TENSORBOARD:
  CATEGORIES_PATH: 
  CLASS_NAMES_PATH: 
  CONFUSION_MATRIX:
    ENABLE: False
    FIGSIZE: [8, 8]
    SUBSET_PATH: 
  ENABLE: False
  HISTOGRAM:
    ENABLE: False
    FIGSIZE: [8, 8]
    SUBSET_PATH: 
    TOPK: 10
  LOG_DIR: 
  MODEL_VIS:
    ACTIVATIONS: False
    COLORMAP: Pastel2
    ENABLE: False
    GRAD_CAM:
      COLORMAP: viridis
      ENABLE: True
      LAYER_LIST: []
      USE_TRUE_LABEL: False
    INPUT_VIDEO: False
    LAYER_LIST: []
    MODEL_WEIGHTS: False
    TOPK_PREDS: 1
  PREDICTIONS_PATH: 
  WRONG_PRED_VIS:
    ENABLE: False
    SUBSET_PATH: 
    TAG: Incorrectly classified videos.
TEST:
  BATCH_SIZE: 64
  CHECKPOINT_FILE_PATH: 
  CHECKPOINT_TYPE: pytorch
  DATASET: kinetics
  ENABLE: True
  NUM_ENSEMBLE_VIEWS: 10
  NUM_SPATIAL_CROPS: 3
  NUM_TEMPORAL_CLIPS: [10]
  SAVE_RESULTS_PATH: 
TRAIN:
  AUTO_RESUME: True
  BATCH_SIZE: 64
  CHECKPOINT_CLEAR_NAME_PATTERN: ()
  CHECKPOINT_EPOCH_RESET: False
  CHECKPOINT_FILE_PATH: 
  CHECKPOINT_INFLATE: False
  CHECKPOINT_IN_INIT: False
  CHECKPOINT_PERIOD: 1
  CHECKPOINT_TYPE: pytorch
  DATASET: kinetics
  ENABLE: False
  EVAL_PERIOD: 10
  KILL_LOSS_EXPLOSION_FACTOR: 0.0
  MIXED_PRECISION: False
VIS_MASK:
  ENABLE: False
X3D:
  BN_LIN5: False
  BOTTLENECK_FACTOR: 1.0
  CHANNELWISE_3x3x3: True
  DEPTH_FACTOR: 1.0
  DIM_C1: 12
  DIM_C5: 2048
  SCALE_RES2: False
  WIDTH_FACTOR: 1.0
[05/09 18:58:30][INFO] misc.py:  185: Model:
SlowFast(
  (s1): VideoModelStem(
    (pathway0_stem): ResNetBasicStem(
      (conv): Conv3d(3, 64, kernel_size=(1, 7, 7), stride=(1, 2, 2), padding=(0, 3, 3), bias=False)
      (bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (pool_layer): MaxPool3d(kernel_size=[1, 3, 3], stride=[1, 2, 2], padding=[0, 1, 1], dilation=1, ceil_mode=False)
    )
    (pathway1_stem): ResNetBasicStem(
      (conv): Conv3d(3, 8, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=(2, 3, 3), bias=False)
      (bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (pool_layer): MaxPool3d(kernel_size=[1, 3, 3], stride=[1, 2, 2], padding=[0, 1, 1], dilation=1, ceil_mode=False)
    )
  )
  (s1_fuse): FuseFastToSlow(
    (conv_f2s): Conv3d(8, 16, kernel_size=(7, 1, 1), stride=(4, 1, 1), padding=(3, 0, 0), bias=False)
    (bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
  )
  (s2): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(80, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
      (branch1_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(80, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res0): ResBlock(
      (branch1): Conv3d(8, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
      (branch1_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(8, 8, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(8, 8, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(8, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(32, 8, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(8, 8, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(8, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(32, 8, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(8, 8, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(8, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (s2_fuse): FuseFastToSlow(
    (conv_f2s): Conv3d(32, 64, kernel_size=(7, 1, 1), stride=(4, 1, 1), padding=(3, 0, 0), bias=False)
    (bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
  )
  (pathway0_pool): MaxPool3d(kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=1, ceil_mode=False)
  (pathway1_pool): MaxPool3d(kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=1, ceil_mode=False)
  (s3): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(320, 512, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(320, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res0): ResBlock(
      (branch1): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(32, 16, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(16, 16, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(16, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(64, 16, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(16, 16, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(16, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(64, 16, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(16, 16, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(16, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(64, 16, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(16, 16, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(16, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (s3_fuse): FuseFastToSlow(
    (conv_f2s): Conv3d(64, 128, kernel_size=(7, 1, 1), stride=(4, 1, 1), padding=(3, 0, 0), bias=False)
    (bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
  )
  (s4): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(640, 1024, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(640, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res4): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res5): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res0): ResBlock(
      (branch1): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(64, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res4): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res5): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (s4_fuse): FuseFastToSlow(
    (conv_f2s): Conv3d(128, 256, kernel_size=(7, 1, 1), stride=(4, 1, 1), padding=(3, 0, 0), bias=False)
    (bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
  )
  (s5): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(1280, 2048, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(1280, 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(512, 512, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(2048, 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(512, 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(2048, 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(512, 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res0): ResBlock(
      (branch1): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (head): ResNetBasicHead(
    (predictors): ModuleList()
    (pathway0_avgpool): AvgPool3d(kernel_size=[8, 7, 7], stride=1, padding=0)
    (pathway1_avgpool): AvgPool3d(kernel_size=[32, 7, 7], stride=1, padding=0)
    (dropout): Dropout(p=0.5, inplace=False)
    (projection): Linear(in_features=2304, out_features=400, bias=True)
    (act): Softmax(dim=4)
  )
)
[05/09 18:48:22][INFO] misc.py:  187: Params: 34,566,488
[05/09 18:48:22][INFO] misc.py:  188: Mem: 0.1297292709350586 MB
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::max_pool3d encountered 4 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::add encountered 32 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::avg_pool3d encountered 2 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::softmax encountered 1 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::mean encountered 1 time(s)
[05/09 18:48:24][INFO] misc.py:  190: Flops: 66.068430848 G
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::batch_norm encountered 110 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::max_pool3d encountered 4 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::add encountered 32 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::avg_pool3d encountered 2 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::softmax encountered 1 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::mean encountered 1 time(s)
[05/09 18:48:24][INFO] misc.py:  191: Activations: 178.390592 M
[05/09 18:48:24][INFO] misc.py:  196: nvidia-smi
Tue May  9 18:48:24 2023       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0 Off |                  Off |
| 32%   41C    P2   104W / 450W |   3462MiB / 24564MiB |     15%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1006      G   /usr/lib/xorg/Xorg                  9MiB |
|    0   N/A  N/A      1456      G   /usr/bin/gnome-shell                8MiB |
|    0   N/A  N/A   2086273      C   python                           3440MiB |
+-----------------------------------------------------------------------------+
[05/09 18:48:24][INFO] misc.py:  185: Model:
SlowFast(
  (s1): VideoModelStem(
    (pathway0_stem): ResNetBasicStem(
      (conv): Conv3d(3, 64, kernel_size=(1, 7, 7), stride=(1, 2, 2), padding=(0, 3, 3), bias=False)
      (bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (pool_layer): MaxPool3d(kernel_size=[1, 3, 3], stride=[1, 2, 2], padding=[0, 1, 1], dilation=1, ceil_mode=False)
    )
    (pathway1_stem): ResNetBasicStem(
      (conv): Conv3d(3, 8, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=(2, 3, 3), bias=False)
      (bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (pool_layer): MaxPool3d(kernel_size=[1, 3, 3], stride=[1, 2, 2], padding=[0, 1, 1], dilation=1, ceil_mode=False)
    )
  )
  (s1_fuse): FuseFastToSlow(
    (conv_f2s): Conv3d(8, 16, kernel_size=(7, 1, 1), stride=(4, 1, 1), padding=(3, 0, 0), bias=False)
    (bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
  )
  (s2): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(80, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
      (branch1_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(80, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res0): ResBlock(
      (branch1): Conv3d(8, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
      (branch1_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(8, 8, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(8, 8, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(8, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(32, 8, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(8, 8, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(8, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(32, 8, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(8, 8, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(8, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (s2_fuse): FuseFastToSlow(
    (conv_f2s): Conv3d(32, 64, kernel_size=(7, 1, 1), stride=(4, 1, 1), padding=(3, 0, 0), bias=False)
    (bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
  )
  (pathway0_pool): MaxPool3d(kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=1, ceil_mode=False)
  (pathway1_pool): MaxPool3d(kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=1, ceil_mode=False)
  (s3): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(320, 512, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(320, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res0): ResBlock(
      (branch1): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(32, 16, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(16, 16, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(16, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(64, 16, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(16, 16, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(16, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(64, 16, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(16, 16, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(16, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(64, 16, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(16, 16, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(16, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (s3_fuse): FuseFastToSlow(
    (conv_f2s): Conv3d(64, 128, kernel_size=(7, 1, 1), stride=(4, 1, 1), padding=(3, 0, 0), bias=False)
    (bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
  )
  (s4): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(640, 1024, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(640, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res4): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res5): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res0): ResBlock(
      (branch1): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(64, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res4): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res5): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 32, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (s4_fuse): FuseFastToSlow(
    (conv_f2s): Conv3d(128, 256, kernel_size=(7, 1, 1), stride=(4, 1, 1), padding=(3, 0, 0), bias=False)
    (bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
  )
  (s5): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(1280, 2048, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(1280, 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(512, 512, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(2048, 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(512, 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(2048, 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(512, 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res0): ResBlock(
      (branch1): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
      (branch1_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(128, 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway1_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (head): ResNetBasicHead(
    (predictors): ModuleList()
    (pathway0_avgpool): AvgPool3d(kernel_size=[8, 7, 7], stride=1, padding=0)
    (pathway1_avgpool): AvgPool3d(kernel_size=[32, 7, 7], stride=1, padding=0)
    (dropout): Dropout(p=0.5, inplace=False)
    (projection): Linear(in_features=2304, out_features=400, bias=True)
    (act): Softmax(dim=4)
  )
)
[05/09 18:48:24][INFO] misc.py:  187: Params: 34,566,488
[05/09 18:48:24][INFO] misc.py:  188: Mem: 1.8723735809326172 MB
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::max_pool3d encountered 4 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::add encountered 32 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::avg_pool3d encountered 2 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::softmax encountered 1 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::mean encountered 1 time(s)
[05/09 18:48:24][INFO] misc.py:  190: Flops: 66.068430848 G
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::batch_norm encountered 110 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::max_pool3d encountered 4 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::add encountered 32 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::avg_pool3d encountered 2 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::softmax encountered 1 time(s)
[05/09 18:48:24][WARNING] jit_analysis.py:  499: Unsupported operator aten::mean encountered 1 time(s)
[05/09 18:48:24][INFO] misc.py:  191: Activations: 178.390592 M
[05/09 18:48:24][INFO] misc.py:  196: nvidia-smi
Tue May  9 18:48:24 2023       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0 Off |                  Off |
| 32%   41C    P2   111W / 450W |   3462MiB / 24564MiB |     15%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1006      G   /usr/lib/xorg/Xorg                  9MiB |
|    0   N/A  N/A      1456      G   /usr/bin/gnome-shell                8MiB |
|    0   N/A  N/A   2086273      C   python                           3440MiB |
+-----------------------------------------------------------------------------+
[05/09 18:48:25][INFO] checkpoint.py:  701: Unknown way of loading checkpoint. Using with random initialization, only for debugging.
[05/09 18:48:25][INFO] kinetics.py:   93: Constructing Kinetics test...
[05/09 18:48:25][INFO] kinetics.py:  158: Constructing kinetics dataloader (size: 1160550 skip_rows 0) from test.csv 
[05/09 18:48:25][INFO] test_net.py:  219: Testing model for 18134 iterations
[05/09 18:49:27][INFO] logging.py:   99: json_stats: {"cur_iter": "1", "eta": "12 days, 20:47:21", "split": "test_iter", "time_diff": 61.30152}
[05/09 18:49:28][INFO] logging.py:   99: json_stats: {"cur_iter": "2", "eta": "4:42:28", "split": "test_iter", "time_diff": 0.93468}
[05/09 18:49:29][INFO] logging.py:   99: json_stats: {"cur_iter": "3", "eta": "4:47:47", "split": "test_iter", "time_diff": 0.95234}
[05/09 18:49:30][INFO] logging.py:   99: json_stats: {"cur_iter": "4", "eta": "4:46:10", "split": "test_iter", "time_diff": 0.94702}
[05/09 18:49:31][INFO] logging.py:   99: json_stats: {"cur_iter": "5", "eta": "4:42:48", "split": "test_iter", "time_diff": 0.93592}
[05/09 18:49:32][INFO] logging.py:   99: json_stats: {"cur_iter": "6", "eta": "4:54:36", "split": "test_iter", "time_diff": 0.97505}
[05/09 18:49:33][INFO] logging.py:   99: json_stats: {"cur_iter": "7", "eta": "5:00:13", "split": "test_iter", "time_diff": 0.99366}
[05/09 18:49:34][INFO] logging.py:   99: json_stats: {"cur_iter": "8", "eta": "5:13:20", "split": "test_iter", "time_diff": 1.03716}
[05/09 18:49:50][INFO] logging.py:   99: json_stats: {"cur_iter": "9", "eta": "3 days, 8:53:17", "split": "test_iter", "time_diff": 16.06519}
[05/09 18:50:07][INFO] logging.py:   99: json_stats: {"cur_iter": "10", "eta": "3 days, 14:48:24", "split": "test_iter", "time_diff": 17.24163}
[05/09 18:50:08][INFO] logging.py:   99: json_stats: {"cur_iter": "11", "eta": "4:41:55", "split": "test_iter", "time_diff": 0.93334}
[05/09 18:50:09][INFO] logging.py:   99: json_stats: {"cur_iter": "12", "eta": "4:42:19", "split": "test_iter", "time_diff": 0.93468}
Killed
This happened when I tried to test the slowfast model by this command: python tools/run_net.py --cfg /home/pana-tat/work/video_classification/SlowFast/configs/Kinetics/SLOWFAST_8x8_R50.yaml DATA.PATH_TO_DATA_DIR '/home/pana-tat/work/video_classification/SlowFast/test.csv' TEST.CHECKPOINT_FILE_PATH '/home/pana-tat/work/video_classification/PySlowFast_Model_Zoo/SLOWFAST_8x8_R50.pkl' TRAIN.ENABLE False I have no idea how this error happened..
facebookresearch / SlowFast

json_stats: {"cur_iter": "12", "eta": "4:42:19", "split": "test_iter", "time_diff": 0.93468} Killed #648
