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tonysy/slowfast.ava.32x2.res50.short_test_log.txt

Created April 1, 2020 06:23
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slowfast.ava.32x2.res50.short_test_log.txt
[04/01 04:55:52] pyaction INFO: Running with full config:
{'AVA': {'ANNOTATION_DIR': '/public/sist/home/hexm/Projects/pyaction/data/ava/annotations/',
'BGR': False,
'DETECTION_SCORE_THRESH': 0.8,
'EXCLUSION_FILE': 'ava_val_excluded_timestamps_v2.2.csv',
'FRAME_DIR': '/public/sist/home/hexm/Projects/pyaction/data/ava/frames',
'FRAME_LIST_DIR': '/public/sist/home/hexm/Projects/pyaction/data/ava/frame_lists',
'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': ['person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv'],
'TRAIN_GT_BOX_LISTS': ['ava_train_v2.2.csv'],
'TRAIN_LISTS': ['train.csv'],
'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]],
'TRAIN_PCA_JITTER_ONLY': True,
'TRAIN_PREDICT_BOX_LISTS': ['ava_train_v2.2.csv',
'person_box_67091280_iou90/ava_detection_train_boxes_and_labels_include_negative_v2.2.csv'],
'TRAIN_USE_COLOR_AUGMENTATION': False},
'BN': {'EPSILON': 1e-05,
'MOMENTUM': 0.1,
'NUM_BATCHES_PRECISE': 200,
'USE_PRECISE_STATS': False,
'WEIGHT_DECAY': 0.0},
'DATA': {'CROP_SIZE': 224,
'INPUT_CHANNEL_NUM': [3, 3],
'MEAN': [0.45, 0.45, 0.45],
'NUM_FRAMES': 32,
'PATH_PREFIX': '',
'PATH_TO_DATA_DIR': '',
'SAMPLING_RATE': 2,
'STD': [0.225, 0.225, 0.225],
'TEST_CROP_SIZE': 256,
'TRAIN_CROP_SIZE': 224,
'TRAIN_JITTER_SCALES': [256, 320]},
'DATA_LOADER': {'ENABLE_MULTI_THREAD_DECODE': False,
'NUM_WORKERS': 16,
'PIN_MEMORY': True},
'DETECTION': {'ALIGNED': True,
'ENABLE': True,
'ROI_XFORM_RESOLUTION': 7,
'SPATIAL_SCALE_FACTOR': 16},
'DIST_BACKEND': 'nccl',
'LOG_PERIOD': 10,
'MODEL': {'ARCH': 'slowfast',
'DROPOUT_RATE': 0.5,
'FC_INIT_STD': 0.01,
'LOSS_FUNC': 'bce',
'MULTI_PATHWAY_ARCH': ['slowfast'],
'NUM_CLASSES': 80,
'SINGLE_PATHWAY_ARCH': ['c2d', 'i3d', 'slowonly']},
'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': 4,
'NUM_SHARDS': 1,
'OUTPUT_DIR': '/public/sist/home/hexm/Models/pyaction/model_logs/ava/slowfast.ava.32x2.res50.short',
'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], [2, 2]],
'SPATIAL_STRIDES': [[1, 1], [2, 2], [2, 2], [1, 1]],
'STRIDE_1X1': False,
'TRANS_FUNC': 'bottleneck_transform',
'WIDTH_PER_GROUP': 64,
'ZERO_INIT_FINAL_BN': True},
'RNG_SEED': 0,
'SHARD_ID': 0,
'SLOWFAST': {'ALPHA': 4,
'BETA_INV': 8,
'FUSION_CONV_CHANNEL_RATIO': 2,
'FUSION_KERNEL_SZ': 7},
'SOLVER': {'BASE_LR': 0.05,
'DAMPENING': 0.0,
'GAMMA': 0.1,
'LRS': [1, 0.1, 0.01, 0.001],
'LR_POLICY': 'steps_with_relative_lrs',
'MAX_EPOCH': 20,
'MOMENTUM': 0.9,
'NESTEROV': True,
'OPTIMIZING_METHOD': 'sgd',
'STEPS': [0, 10, 15, 20],
'STEP_SIZE': 1,
'WARMUP_EPOCHS': 5,
'WARMUP_FACTOR': 0.1,
'WARMUP_START_LR': 0.000125,
'WEIGHT_DECAY': 1e-07},
'TEST': {'BATCH_SIZE': 4,
'CHECKPOINT_FILE_PATH': '',
'CHECKPOINT_TYPE': 'pytorch',
'DATASET': 'ava',
'ENABLE': True,
'NUM_ENSEMBLE_VIEWS': 10,
'NUM_SPATIAL_CROPS': 3},
'TRAIN': {'AUTO_RESUME': True,
'BATCH_SIZE': 32,
'CHECKPOINT_FILE_PATH': '/public/sist/home/hexm/Projects/pyaction/model_zoo/ava/pretrain/SLOWFAST_8x8_R50.pkl',
'CHECKPOINT_INFLATE': False,
'CHECKPOINT_PERIOD': 1,
'CHECKPOINT_TYPE': 'caffe2',
'DATASET': 'ava',
'ENABLE': True,
'EVAL_PERIOD': 5}}
[04/01 04:55:52] pyaction INFO: different config with base class:
{'AVA': {'ANNOTATION_DIR': '/public/sist/home/hexm/Projects/pyaction/data/ava/annotations/',
'BGR': False,
'DETECTION_SCORE_THRESH': 0.8,
'EXCLUSION_FILE': 'ava_val_excluded_timestamps_v2.2.csv',
'FRAME_DIR': '/public/sist/home/hexm/Projects/pyaction/data/ava/frames',
'FRAME_LIST_DIR': '/public/sist/home/hexm/Projects/pyaction/data/ava/frame_lists',
'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': ['person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv'],
'TRAIN_GT_BOX_LISTS': ['ava_train_v2.2.csv'],
'TRAIN_LISTS': ['train.csv'],
'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]],
'TRAIN_PCA_JITTER_ONLY': True,
'TRAIN_PREDICT_BOX_LISTS': ['ava_train_v2.2.csv',
'person_box_67091280_iou90/ava_detection_train_boxes_and_labels_include_negative_v2.2.csv'],
'TRAIN_USE_COLOR_AUGMENTATION': False},
'DATA': {'NUM_FRAMES': 32, 'SAMPLING_RATE': 2},
'DATA_LOADER': {'NUM_WORKERS': 16},
'DETECTION': {'ALIGNED': True,
'ENABLE': True,
'ROI_XFORM_RESOLUTION': 7,
'SPATIAL_SCALE_FACTOR': 16},
'MODEL': {'LOSS_FUNC': 'bce', 'NUM_CLASSES': 80},
'NONLOCAL': {'GROUP': [[1, 1], [1, 1], [1, 1], [1, 1]],
'LOCATION': [[[], []], [[], []], [[], []], [[], []]]},
'NUM_GPUS': 4,
'OUTPUT_DIR': '/public/sist/home/hexm/Models/pyaction/model_logs/ava/slowfast.ava.32x2.res50.short',
'RESNET': {'NUM_BLOCK_TEMP_KERNEL': [[3, 3], [4, 4], [6, 6], [3, 3]],
'SPATIAL_DILATIONS': [[1, 1], [1, 1], [1, 1], [2, 2]],
'SPATIAL_STRIDES': [[1, 1], [2, 2], [2, 2], [1, 1]],
'ZERO_INIT_FINAL_BN': True},
'RNG_SEED': 0,
'SLOWFAST': {'ALPHA': 4,
'BETA_INV': 8,
'FUSION_CONV_CHANNEL_RATIO': 2,
'FUSION_KERNEL_SZ': 7},
'SOLVER': {'BASE_LR': 0.05,
'LRS': [1, 0.1, 0.01, 0.001],
'LR_POLICY': 'steps_with_relative_lrs',
'MAX_EPOCH': 20,
'STEPS': [0, 10, 15, 20],
'WARMUP_EPOCHS': 5,
'WARMUP_START_LR': 0.000125,
'WEIGHT_DECAY': 1e-07},
'TEST': {'BATCH_SIZE': 4, 'DATASET': 'ava'},
'TRAIN': {'BATCH_SIZE': 32,
'CHECKPOINT_FILE_PATH': '/public/sist/home/hexm/Projects/pyaction/model_zoo/ava/pretrain/SLOWFAST_8x8_R50.pkl',
'CHECKPOINT_TYPE': 'caffe2',
'DATASET': 'ava',
'EVAL_PERIOD': 5}}
[04/01 04:56:14] pa.utils.misc INFO: Model:
DistributedDataParallel(
(module): SlowFastModel(
(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], padding=[0, 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], dilation=[1, 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], padding=[0, 0, 0], 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], padding=[0, 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], dilation=[1, 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], padding=[0, 0, 0], 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], padding=[0, 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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], dilation=[1, 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], padding=[0, 0, 0], 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, 1, 1], 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, 1, 1], padding=[0, 2, 2], dilation=[1, 2, 2], 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], padding=[0, 0, 0], 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, 2, 2], dilation=[1, 2, 2], 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], padding=[0, 0, 0], 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, 2, 2], dilation=[1, 2, 2], 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], padding=[0, 0, 0], 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, 1, 1], 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, 1, 1], padding=[0, 2, 2], dilation=[1, 2, 2], 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], padding=[0, 0, 0], 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, 2, 2], dilation=[1, 2, 2], 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], padding=[0, 0, 0], 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, 2, 2], dilation=[1, 2, 2], 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], padding=[0, 0, 0], bias=False)
(c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
)
(head): ResNetRoIHead(
(s0_tpool): AvgPool3d(kernel_size=[8, 1, 1], stride=1, padding=0)
(s0_roi): ROIAlign(output_size=[7, 7], spatial_scale=0.0625, sampling_ratio=0, aligned=True)
(s0_spool): MaxPool2d(kernel_size=[7, 7], stride=1, padding=0, dilation=1, ceil_mode=False)
(s1_tpool): AvgPool3d(kernel_size=[32, 1, 1], stride=1, padding=0)
(s1_roi): ROIAlign(output_size=[7, 7], spatial_scale=0.0625, sampling_ratio=0, aligned=True)
(s1_spool): MaxPool2d(kernel_size=[7, 7], stride=1, padding=0, dilation=1, ceil_mode=False)
(dropout): Dropout(p=0.5, inplace=False)
(projection): Linear(in_features=2304, out_features=80, bias=True)
(act): Sigmoid()
)
)
)
[04/01 04:56:14] pa.utils.misc INFO: Params: 33,828,888
[04/01 04:56:14] pa.utils.misc INFO: Mem: 260.05322265625 MB
[04/01 04:56:14] pa.utils.misc INFO: FLOPs: 96.88860672 GFLOPs
[04/01 04:56:14] pa.utils.misc INFO: nvidia-smi
[04/01 04:56:19] pa.datasets.ava_helper INFO: Finished loading image paths from: /public/sist/home/hexm/Projects/pyaction/data/ava/frame_lists/val.csv
[04/01 04:56:20] pa.datasets.ava_helper INFO: Finished loading annotations from: /public/sist/home/hexm/Projects/pyaction/data/ava/annotations/person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv
[04/01 04:56:20] pa.datasets.ava_helper INFO: Detection threshold: 0.8
[04/01 04:56:20] pa.datasets.ava_helper INFO: Number of unique boxes: 100335
[04/01 04:56:20] pa.datasets.ava_helper INFO: Number of annotations: 0
[04/01 04:56:21] pa.datasets.ava_helper INFO: 52829 keyframes used.
[04/01 04:56:21] pa.datasets.ava_dataset INFO: === AVA dataset summary ===
[04/01 04:56:21] pa.datasets.ava_dataset INFO: Split: test
[04/01 04:56:21] pa.datasets.ava_dataset INFO: Number of videos: 64
[04/01 04:56:21] pa.datasets.ava_dataset INFO: Number of frames: 1729931
[04/01 04:56:21] pa.datasets.ava_dataset INFO: Number of key frames: 52829
[04/01 04:56:21] pa.datasets.ava_dataset INFO: Number of boxes: 100335.
[04/01 04:56:21] pyaction INFO: Testing model for 13208 iterations
[04/01 04:56:26] pa.datasets.ava_helper INFO: Finished loading image paths from: /public/sist/home/hexm/Projects/pyaction/data/ava/frame_lists/val.csv
[04/01 04:58:31] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "10", "eta": "1:36:09", "mode": "test", "time_diff": 0.437152}
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[04/01 06:17:19] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13080", "eta": "0:00:45", "mode": "test", "time_diff": 0.446353}
[04/01 06:17:22] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13090", "eta": "0:00:42", "mode": "test", "time_diff": 0.461683}
[04/01 06:17:26] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13100", "eta": "0:00:38", "mode": "test", "time_diff": 0.424148}
[04/01 06:17:30] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13110", "eta": "0:00:35", "mode": "test", "time_diff": 0.449752}
[04/01 06:17:33] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13120", "eta": "0:00:31", "mode": "test", "time_diff": 0.466457}
[04/01 06:17:37] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13130", "eta": "0:00:28", "mode": "test", "time_diff": 0.517542}
[04/01 06:17:39] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13140", "eta": "0:00:24", "mode": "test", "time_diff": 0.183760}
[04/01 06:17:41] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13150", "eta": "0:00:20", "mode": "test", "time_diff": 0.172823}
[04/01 06:17:44] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13160", "eta": "0:00:17", "mode": "test", "time_diff": 0.463541}
[04/01 06:17:46] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13170", "eta": "0:00:13", "mode": "test", "time_diff": 0.132943}
[04/01 06:17:51] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13180", "eta": "0:00:10", "mode": "test", "time_diff": 0.169522}
[04/01 06:17:54] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13190", "eta": "0:00:06", "mode": "test", "time_diff": 0.502176}
[04/01 06:17:57] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "13200", "eta": "0:00:03", "mode": "test", "time_diff": 0.190015}
[04/01 06:18:34] pa.utils.ava_eval_helper INFO: Evaluating with 50250 unique GT frames.
[04/01 06:18:34] pa.utils.ava_eval_helper INFO: Evaluating with 52829 unique detection frames
[04/01 06:18:50] pa.utils.ava_eval_helper INFO: AVA results wrote to /public/sist/home/hexm/Models/pyaction/model_logs/ava/slowfast.ava.32x2.res50.short/detections_latest.csv
[04/01 06:18:50] pa.utils.ava_eval_helper INFO: took 16 seconds.
[04/01 06:18:52] pa.utils.ava_eval_helper INFO: AVA results wrote to /public/sist/home/hexm/Models/pyaction/model_logs/ava/slowfast.ava.32x2.res50.short/groundtruth_latest.csv
[04/01 06:18:52] pa.utils.ava_eval_helper INFO: took 1 seconds.
[04/01 06:28:23] pa.utils.ava_eval_helper INFO:
{ 'PascalBoxes_PerformanceByCategory/AP@0.5IOU/answer phone': 0.6964049872484268,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/bend/bow (at the waist)': 0.3749015045375166,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/carry/hold (an object)': 0.5390645943094475,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/climb (e.g., a mountain)': 0.14270536611482598,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/close (e.g., a door, a box)': 0.15098123222294435,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/crouch/kneel': 0.1934699672286862,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/cut': 0.022646735807298934,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/dance': 0.5167403435126854,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/dress/put on clothing': 0.1004060889684076,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/drink': 0.2559653557257834,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/drive (e.g., a car, a truck)': 0.520203725048223,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/eat': 0.34720070137627296,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/enter': 0.08464596358174956,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/fall down': 0.08309754916696266,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/fight/hit (a person)': 0.43778408167081584,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/get up': 0.21304299870018295,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/give/serve (an object) to (a person)': 0.07549886667441384,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/grab (a person)': 0.07806817273903284,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hand clap': 0.315471505815232,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hand shake': 0.11080409042494209,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hand wave': 0.01583746202962471,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hit (an object)': 0.03846270825201372,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hug (a person)': 0.1307205375199812,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/jump/leap': 0.0674934054359243,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/kiss (a person)': 0.2522263853286618,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/lie/sleep': 0.4421914555839266,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/lift (a person)': 0.02580444009662227,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/lift/pick up': 0.02347613782599728,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/listen (e.g., to music)': 0.014959070455099055,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/listen to (a person)': 0.6100288224583863,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/martial art': 0.43505594168714923,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/open (e.g., a window, a car door)': 0.17949115650864866,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/play musical instrument': 0.40580640896040715,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/point to (an object)': 0.0007493836991721087,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/pull (an object)': 0.014453919007395507,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/push (an object)': 0.024186794252416658,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/push (another person)': 0.07736415772669145,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/put down': 0.029071628686159832,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/read': 0.29320306884836494,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/ride (e.g., a bike, a car, a horse)': 0.4276254289951251,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/run/jog': 0.5249560039729206,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/sail boat': 0.13840795869372474,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/shoot': 0.10873291489485315,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/sing to (e.g., self, a person, a group)': 0.2035125070328401,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/sit': 0.7782482800319931,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/smoke': 0.18392426755559985,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/stand': 0.8204829772092883,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/swim': 0.3881181712451062,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/take (an object) from (a person)': 0.05858826667612818,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/take a photo': 0.0024408039070484186,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/talk to (e.g., self, a person, a group)': 0.7922568897174611,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/text on/look at a cellphone': 0.05859421208396301,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/throw': 0.013896102323223813,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/touch (an object)': 0.31927656150983597,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/turn (e.g., a screwdriver)': 0.009540545528183815,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/walk': 0.7629543810619182,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/watch (a person)': 0.6712840682148187,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/watch (e.g., TV)': 0.21748922088762976,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/work on a computer': 0.05844019725077707,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/write': 0.08434224084159621,
'PascalBoxes_Precision/mAP@0.5IOU': 0.2493799785811755}
[04/01 06:28:23] pa.utils.ava_eval_helper INFO: AVA eval done in 605.039645 seconds.
[04/01 06:28:24] pa.utils.logging INFO: json_stats: {"map": 0.249380, "mode": "test"}
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