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tonysy/c2d.ava.8x8.res50.short_test_log.txt

Created April 1, 2020 06:18
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c2d.ava.8x8.res50.short_test_log.txt
[03/30 05:33:21] pyaction INFO: Running with full config:
{'AVA': {'ANNOTATION_DIR': '/public/sist/home/hexm/Projects/pyaction/data/ava/annotations/',
'BGR': False,
'DETECTION_SCORE_THRESH': 0.9,
'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],
'MEAN': [0.45, 0.45, 0.45],
'NUM_FRAMES': 4,
'PATH_PREFIX': '',
'PATH_TO_DATA_DIR': '/public/sist/home/hexm/Projects/pyaction/data/kinetics',
'SAMPLING_RATE': 16,
'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': 8,
'PIN_MEMORY': True},
'DETECTION': {'ALIGNED': True,
'ENABLE': True,
'ROI_XFORM_RESOLUTION': 7,
'SPATIAL_SCALE_FACTOR': 16},
'DIST_BACKEND': 'nccl',
'LOG_PERIOD': 10,
'MODEL': {'ARCH': 'slowonly',
'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]],
'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/c2d.ava.8x8.res50.short',
'RESNET': {'DEPTH': 50,
'INPLACE_RELU': True,
'NUM_BLOCK_TEMP_KERNEL': [[3], [4], [6], [3]],
'NUM_GROUPS': 1,
'SPATIAL_DILATIONS': [[1], [1], [1], [2]],
'SPATIAL_STRIDES': [[1], [2], [2], [1]],
'STRIDE_1X1': False,
'TRANS_FUNC': 'bottleneck_transform',
'WIDTH_PER_GROUP': 64,
'ZERO_INIT_FINAL_BN': True},
'RNG_SEED': 0,
'SHARD_ID': 0,
'SOLVER': {'BASE_LR': 0.1,
'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': 64,
'CHECKPOINT_FILE_PATH': '/public/sist/home/hexm/Projects/pyaction/model_zoo/ava/pretrain/C2D_8x8_R50.pkl',
'CHECKPOINT_INFLATE': False,
'CHECKPOINT_PERIOD': 1,
'CHECKPOINT_TYPE': 'caffe2',
'DATASET': 'ava',
'ENABLE': True,
'EVAL_PERIOD': 5}}
[03/30 05:33:21] pyaction INFO: different config with base class:
{'AVA': {'ANNOTATION_DIR': '/public/sist/home/hexm/Projects/pyaction/data/ava/annotations/',
'BGR': False,
'DETECTION_SCORE_THRESH': 0.9,
'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': {'INPUT_CHANNEL_NUM': [3],
'NUM_FRAMES': 4,
'PATH_TO_DATA_DIR': '/public/sist/home/hexm/Projects/pyaction/data/kinetics',
'SAMPLING_RATE': 16},
'DATA_LOADER': {'NUM_WORKERS': 8},
'DETECTION': {'ALIGNED': True,
'ENABLE': True,
'ROI_XFORM_RESOLUTION': 7,
'SPATIAL_SCALE_FACTOR': 16},
'MODEL': {'ARCH': 'slowonly', 'LOSS_FUNC': 'bce', 'NUM_CLASSES': 80},
'NUM_GPUS': 4,
'OUTPUT_DIR': '/public/sist/home/hexm/Models/pyaction/model_logs/ava/c2d.ava.8x8.res50.short',
'RESNET': {'SPATIAL_DILATIONS': [[1], [1], [1], [2]],
'SPATIAL_STRIDES': [[1], [2], [2], [1]],
'ZERO_INIT_FINAL_BN': True},
'RNG_SEED': 0,
'SOLVER': {'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': {'CHECKPOINT_FILE_PATH': '/public/sist/home/hexm/Projects/pyaction/model_zoo/ava/pretrain/C2D_8x8_R50.pkl',
'CHECKPOINT_TYPE': 'caffe2',
'DATASET': 'ava',
'EVAL_PERIOD': 5}}
[03/30 05:33:31] pa.utils.misc INFO: Model:
DistributedDataParallel(
(module): ResNetModel(
(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)
)
)
(s2): ResStage(
(pathway0_res0): ResBlock(
(branch1): Conv3d(64, 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(64, 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)
)
)
(pathway0_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(256, 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(256, 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)
)
)
(s4): ResStage(
(pathway0_res0): ResBlock(
(branch1): Conv3d(512, 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(512, 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)
)
)
(s5): ResStage(
(pathway0_res0): ResBlock(
(branch1): Conv3d(1024, 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(1024, 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)
)
)
(head): ResNetRoIHead(
(s0_tpool): AvgPool3d(kernel_size=[4, 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)
(dropout): Dropout(p=0.5, inplace=False)
(projection): Linear(in_features=2048, out_features=80, bias=True)
(act): Sigmoid()
)
)
)
[03/30 05:33:31] pa.utils.misc INFO: Params: 31,798,416
[03/30 05:33:31] pa.utils.misc INFO: Mem: 244.31103515625 MB
[03/30 05:33:31] pa.utils.misc INFO: FLOPs: 41.553133568 GFLOPs
[03/30 05:33:31] pa.utils.misc INFO: nvidia-smi
[03/30 05:33:36] pa.datasets.ava_helper INFO: Finished loading image paths from: /public/sist/home/hexm/Projects/pyaction/data/ava/frame_lists/val.csv
[03/30 05:33:37] 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
[03/30 05:33:37] pa.datasets.ava_helper INFO: Detection threshold: 0.9
[03/30 05:33:37] pa.datasets.ava_helper INFO: Number of unique boxes: 92629
[03/30 05:33:37] pa.datasets.ava_helper INFO: Number of annotations: 0
[03/30 05:33:37] pa.datasets.ava_helper INFO: 51635 keyframes used.
[03/30 05:33:37] pa.datasets.ava_dataset INFO: === AVA dataset summary ===
[03/30 05:33:37] pa.datasets.ava_dataset INFO: Split: test
[03/30 05:33:37] pa.datasets.ava_dataset INFO: Number of videos: 64
[03/30 05:33:37] pa.datasets.ava_dataset INFO: Number of frames: 1729931
[03/30 05:33:37] pa.datasets.ava_dataset INFO: Number of key frames: 51635
[03/30 05:33:37] pa.datasets.ava_dataset INFO: Number of boxes: 92629.
[03/30 05:33:37] pyaction INFO: Testing model for 12909 iterations
[03/30 05:33:42] pa.datasets.ava_helper INFO: Finished loading image paths from: /public/sist/home/hexm/Projects/pyaction/data/ava/frame_lists/val.csv
[03/30 05:34:14] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "10", "eta": "0:11:15", "mode": "test", "time_diff": 0.052327}
[03/30 05:34:15] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "20", "eta": "0:10:51", "mode": "test", "time_diff": 0.048700}
[03/30 05:34:15] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "30", "eta": "0:11:04", "mode": "test", "time_diff": 0.053750}
[03/30 05:34:16] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "40", "eta": "0:11:50", "mode": "test", "time_diff": 0.066151}
[03/30 05:34:16] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "50", "eta": "0:11:42", "mode": "test", "time_diff": 0.052133}
[03/30 05:34:17] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "60", "eta": "0:11:31", "mode": "test", "time_diff": 0.049627}
[03/30 05:34:17] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "70", "eta": "0:11:21", "mode": "test", "time_diff": 0.048809}
[03/30 05:34:18] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "80", "eta": "0:11:18", "mode": "test", "time_diff": 0.051495}
[03/30 05:34:19] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "90", "eta": "0:11:16", "mode": "test", "time_diff": 0.051671}
[03/30 05:34:19] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "100", "eta": "0:11:08", "mode": "test", "time_diff": 0.047495}
[03/30 05:34:20] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "110", "eta": "0:11:05", "mode": "test", "time_diff": 0.050051}
[03/30 05:34:20] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "120", "eta": "0:11:21", "mode": "test", "time_diff": 0.066860}
[03/30 05:34:22] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "130", "eta": "0:11:31", "mode": "test", "time_diff": 0.064433}
[03/30 05:34:22] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "140", "eta": "0:11:26", "mode": "test", "time_diff": 0.048900}
[03/30 05:34:23] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "150", "eta": "0:11:27", "mode": "test", "time_diff": 0.055612}
[03/30 05:34:23] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "160", "eta": "0:11:29", "mode": "test", "time_diff": 0.056868}
[03/30 05:34:24] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "170", "eta": "0:11:21", "mode": "test", "time_diff": 0.044805}
[03/30 05:34:25] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "180", "eta": "0:11:22", "mode": "test", "time_diff": 0.055588}
[03/30 05:34:25] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "190", "eta": "0:11:21", "mode": "test", "time_diff": 0.053199}
[03/30 05:34:26] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "200", "eta": "0:11:27", "mode": "test", "time_diff": 0.062615}
[03/30 05:34:26] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "210", "eta": "0:11:24", "mode": "test", "time_diff": 0.050494}
[03/30 05:34:27] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "220", "eta": "0:11:23", "mode": "test", "time_diff": 0.053674}
[03/30 05:34:27] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "230", "eta": "0:11:21", "mode": "test", "time_diff": 0.050474}
[03/30 05:34:28] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "240", "eta": "0:11:19", "mode": "test", "time_diff": 0.050790}
[03/30 05:34:28] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "250", "eta": "0:11:19", "mode": "test", "time_diff": 0.054380}
[03/30 05:34:29] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "260", "eta": "0:11:15", "mode": "test", "time_diff": 0.047409}
[03/30 05:34:29] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "270", "eta": "0:11:13", "mode": "test", "time_diff": 0.049914}
[03/30 05:34:30] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "280", "eta": "0:11:10", "mode": "test", "time_diff": 0.048964}
[03/30 05:34:30] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "290", "eta": "0:11:09", "mode": "test", "time_diff": 0.050580}
[03/30 05:34:31] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "300", "eta": "0:11:13", "mode": "test", "time_diff": 0.064215}
[03/30 05:34:31] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "310", "eta": "0:11:10", "mode": "test", "time_diff": 0.047699}
[03/30 05:34:32] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "320", "eta": "0:11:10", "mode": "test", "time_diff": 0.054891}
[03/30 05:34:33] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "330", "eta": "0:11:13", "mode": "test", "time_diff": 0.063051}
[03/30 05:34:33] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "340", "eta": "0:11:11", "mode": "test", "time_diff": 0.048114}
[03/30 05:34:34] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "350", "eta": "0:11:09", "mode": "test", "time_diff": 0.050927}
[03/30 05:34:35] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "360", "eta": "0:11:08", "mode": "test", "time_diff": 0.050827}
[03/30 05:34:35] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "370", "eta": "0:11:05", "mode": "test", "time_diff": 0.047343}
[03/30 05:34:36] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "380", "eta": "0:11:05", "mode": "test", "time_diff": 0.052775}
[03/30 05:34:36] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "390", "eta": "0:11:03", "mode": "test", "time_diff": 0.049507}
[03/30 05:34:37] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "400", "eta": "0:11:02", "mode": "test", "time_diff": 0.050674}
[03/30 05:34:37] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "410", "eta": "0:11:00", "mode": "test", "time_diff": 0.049512}
[03/30 05:34:38] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "420", "eta": "0:10:59", "mode": "test", "time_diff": 0.049675}
[03/30 05:34:38] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "430", "eta": "0:10:59", "mode": "test", "time_diff": 0.056653}
[03/30 05:34:39] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "440", "eta": "0:10:57", "mode": "test", "time_diff": 0.045124}
[03/30 05:34:39] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "450", "eta": "0:10:55", "mode": "test", "time_diff": 0.049167}
[03/30 05:34:40] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "460", "eta": "0:11:23", "mode": "test", "time_diff": 0.157328}
[03/30 05:34:41] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "470", "eta": "0:11:22", "mode": "test", "time_diff": 0.053837}
[03/30 05:34:41] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "480", "eta": "0:11:21", "mode": "test", "time_diff": 0.054365}
[03/30 05:34:42] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "490", "eta": "0:11:20", "mode": "test", "time_diff": 0.052581}
[03/30 05:34:42] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "500", "eta": "0:11:17", "mode": "test", "time_diff": 0.044779}
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[03/30 05:34:44] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "530", "eta": "0:13:00", "mode": "test", "time_diff": 0.063613}
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[03/30 05:34:45] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "550", "eta": "0:12:57", "mode": "test", "time_diff": 0.060951}
[03/30 05:34:46] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "560", "eta": "0:12:55", "mode": "test", "time_diff": 0.057483}
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[03/30 05:35:02] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "850", "eta": "0:11:56", "mode": "test", "time_diff": 0.050128}
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[03/30 05:35:04] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "890", "eta": "0:11:51", "mode": "test", "time_diff": 0.065870}
[03/30 05:35:04] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "900", "eta": "0:11:49", "mode": "test", "time_diff": 0.051279}
[03/30 05:35:05] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "910", "eta": "0:11:49", "mode": "test", "time_diff": 0.062786}
[03/30 05:35:05] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "920", "eta": "0:11:47", "mode": "test", "time_diff": 0.044904}
[03/30 05:35:06] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "930", "eta": "0:11:45", "mode": "test", "time_diff": 0.048310}
[03/30 05:35:06] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "940", "eta": "0:11:45", "mode": "test", "time_diff": 0.067571}
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[03/30 05:46:24] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "12850", "eta": "0:00:03", "mode": "test", "time_diff": 0.065742}
[03/30 05:46:24] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "12860", "eta": "0:00:02", "mode": "test", "time_diff": 0.052735}
[03/30 05:46:26] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "12870", "eta": "0:00:02", "mode": "test", "time_diff": 0.049053}
[03/30 05:46:26] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "12880", "eta": "0:00:01", "mode": "test", "time_diff": 0.065581}
[03/30 05:46:27] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "12890", "eta": "0:00:01", "mode": "test", "time_diff": 0.048976}
[03/30 05:46:27] pa.utils.logging INFO: json_stats: {"_type": "test_iter", "cur_iter": "12900", "eta": "0:00:00", "mode": "test", "time_diff": 0.046967}
[03/30 05:46:51] pa.utils.ava_eval_helper INFO: Evaluating with 50250 unique GT frames.
[03/30 05:46:51] pa.utils.ava_eval_helper INFO: Evaluating with 51635 unique detection frames
[03/30 05:47:07] pa.utils.ava_eval_helper INFO: AVA results wrote to /public/sist/home/hexm/Models/pyaction/model_logs/ava/c2d.ava.8x8.res50.short/detections_latest.csv
[03/30 05:47:07] pa.utils.ava_eval_helper INFO: took 15 seconds.
[03/30 05:47:08] pa.utils.ava_eval_helper INFO: AVA results wrote to /public/sist/home/hexm/Models/pyaction/model_logs/ava/c2d.ava.8x8.res50.short/groundtruth_latest.csv
[03/30 05:47:08] pa.utils.ava_eval_helper INFO: took 0 seconds.
[03/30 05:54:56] pa.utils.ava_eval_helper INFO:
{ 'PascalBoxes_PerformanceByCategory/AP@0.5IOU/answer phone': 0.6552575001602255,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/bend/bow (at the waist)': 0.34226543021533284,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/carry/hold (an object)': 0.49103339463754814,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/climb (e.g., a mountain)': 0.05414091102579498,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/close (e.g., a door, a box)': 0.09481362015897202,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/crouch/kneel': 0.17706509039298185,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/cut': 0.019057563219677234,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/dance': 0.43569583616202906,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/dress/put on clothing': 0.09354796418640031,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/drink': 0.21678611825415178,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/drive (e.g., a car, a truck)': 0.46350406520236354,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/eat': 0.1939225586877762,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/enter': 0.05647605606136552,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/fall down': 0.05490718109805054,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/fight/hit (a person)': 0.35391172127309334,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/get up': 0.11493429051068373,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/give/serve (an object) to (a person)': 0.042489803941729895,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/grab (a person)': 0.05388241702039771,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hand clap': 0.10892488306224504,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hand shake': 0.06490196587838731,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hand wave': 0.013403900761027345,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hit (an object)': 0.0035165727530306686,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hug (a person)': 0.1009063911836533,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/jump/leap': 0.032948576252470455,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/kiss (a person)': 0.18745537272773538,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/lie/sleep': 0.4248447184925216,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/lift (a person)': 0.012697462912190004,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/lift/pick up': 0.014662028731465514,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/listen (e.g., to music)': 0.01036083047180639,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/listen to (a person)': 0.5299436196836786,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/martial art': 0.42810534208001316,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/open (e.g., a window, a car door)': 0.14196959338044488,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/play musical instrument': 0.35247876553471075,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/point to (an object)': 0.0013055570096828374,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/pull (an object)': 0.010229315909903827,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/push (an object)': 0.01299852037715676,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/push (another person)': 0.02221244933320502,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/put down': 0.01771530979844713,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/read': 0.20751739935302135,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/ride (e.g., a bike, a car, a horse)': 0.29796733838316725,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/run/jog': 0.2993747055151045,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/sail boat': 0.09420920297488408,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/shoot': 0.049192136662056164,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/sing to (e.g., self, a person, a group)': 0.1271228629021034,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/sit': 0.751278877593281,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/smoke': 0.10610695849472832,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/stand': 0.7816353072075717,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/swim': 0.5516005080290796,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/take (an object) from (a person)': 0.023686466039957627,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/take a photo': 0.0018773366889517156,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/talk to (e.g., self, a person, a group)': 0.6829805776464459,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/text on/look at a cellphone': 0.022934924416500715,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/throw': 0.011721761613341704,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/touch (an object)': 0.3468541550962492,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/turn (e.g., a screwdriver)': 0.006031061757924358,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/walk': 0.6441306765172967,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/watch (a person)': 0.659668458600545,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/watch (e.g., TV)': 0.25085913104857765,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/work on a computer': 0.02751681166779495,
'PascalBoxes_PerformanceByCategory/AP@0.5IOU/write': 0.06513636616381671,
'PascalBoxes_Precision/mAP@0.5IOU': 0.20691126204857918}
[03/30 05:54:56] pa.utils.ava_eval_helper INFO: AVA eval done in 495.703450 seconds.
[03/30 05:54:57] pa.utils.logging INFO: json_stats: {"map": 0.206911, "mode": "test"}
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