2019-05-07 20:25:27,742 Namespace(cfg='../experiments/train/SiamFC.yaml', gpus='0', workers=32)
2019-05-07 20:25:27,743 {'CHECKPOINT_DIR': 'snapshot',
'GPUS': '0',
'OUTPUT_DIR': 'logs',
'PRINT_FREQ': 10,
'SIAMFC': {'DATASET': {'BLUR': 0,
'COLOR': 1,
'FLIP': 0,
'GOT10K': {'ANNOTATION': '/home/tjcv/dataset/SiamDW_trainset/GOT10K/train.json',
'PATH': '/home/tjcv/dataset/SiamDW_trainset/GOT10K/crop255'},
'ROTATION': 0,
'SCALE': 0.05,
'SHIFT': 4,
'VID': {'ANNOTATION': '/home/tjcv/dataset/SiamDW_trainset/VID/train.json',
'PATH': '/home/tjcv/dataset/SiamDW_trainset/VID/crop255'}},
'TEST': {'DATA': 'OTB2015',
'END_EPOCH': 50,
'MODEL': 'SiamFCIncep22',
'START_EPOCH': 30},
'TRAIN': {'BATCH': 32,
'END_EPOCH': 50,
'LR': 0.001,
'LR_END': 1e-07,
'LR_POLICY': 'log',
'MODEL': 'SiamFCRes22',
'MOMENTUM': 0.9,
'PAIRS': 600000,
'PRETRAIN': '../pretrain/CIResNet22_PRETRAIN.model',
'RESUME': False,
'SEARCH_SIZE': 255,
'START_EPOCH': 0,
'STRIDE': 8,
'TEMPLATE_SIZE': 127,
'WEIGHT_DECAY': 0.0001,
'WHICH_USE': 'VID'},
'TUNE': {'DATA': 'OTB2015',
'METHOD': 'GENE',
'MODEL': 'SiamFCIncep22'}},
'WORKERS': 32}
2019-05-07 20:25:30,937 trainable params:
2019-05-07 20:25:30,937 features.features.conv1.weight
2019-05-07 20:25:30,937 features.features.bn1.weight
2019-05-07 20:25:30,937 features.features.bn1.bias
2019-05-07 20:25:30,938 features.features.layer1.0.conv1.weight
2019-05-07 20:25:30,938 features.features.layer1.0.bn1.weight
2019-05-07 20:25:30,938 features.features.layer1.0.bn1.bias
2019-05-07 20:25:30,938 features.features.layer1.0.conv2.weight
2019-05-07 20:25:30,938 features.features.layer1.0.bn2.weight
2019-05-07 20:25:30,938 features.features.layer1.0.bn2.bias
2019-05-07 20:25:30,938 features.features.layer1.0.conv3.weight
2019-05-07 20:25:30,938 features.features.layer1.0.bn3.weight
2019-05-07 20:25:30,938 features.features.layer1.0.bn3.bias
2019-05-07 20:25:30,938 features.features.layer1.0.downsample.0.weight
2019-05-07 20:25:30,938 features.features.layer1.0.downsample.1.weight
2019-05-07 20:25:30,938 features.features.layer1.0.downsample.1.bias
2019-05-07 20:25:30,938 features.features.layer1.1.conv1.weight
2019-05-07 20:25:30,938 features.features.layer1.1.bn1.weight
2019-05-07 20:25:30,938 features.features.layer1.1.bn1.bias
2019-05-07 20:25:30,938 features.features.layer1.1.conv2.weight
2019-05-07 20:25:30,938 features.features.layer1.1.bn2.weight
2019-05-07 20:25:30,938 features.features.layer1.1.bn2.bias
2019-05-07 20:25:30,938 features.features.layer1.1.conv3.weight
2019-05-07 20:25:30,938 features.features.layer1.1.bn3.weight
2019-05-07 20:25:30,938 features.features.layer1.1.bn3.bias
2019-05-07 20:25:30,938 features.features.layer1.2.conv1.weight
2019-05-07 20:25:30,939 features.features.layer1.2.bn1.weight
2019-05-07 20:25:30,939 features.features.layer1.2.bn1.bias
2019-05-07 20:25:30,939 features.features.layer1.2.conv2.weight
2019-05-07 20:25:30,939 features.features.layer1.2.bn2.weight
2019-05-07 20:25:30,939 features.features.layer1.2.bn2.bias
2019-05-07 20:25:30,939 features.features.layer1.2.conv3.weight
2019-05-07 20:25:30,939 features.features.layer1.2.bn3.weight
2019-05-07 20:25:30,939 features.features.layer1.2.bn3.bias
2019-05-07 20:25:30,939 features.features.layer2.0.conv1.weight
2019-05-07 20:25:30,939 features.features.layer2.0.bn1.weight
2019-05-07 20:25:30,939 features.features.layer2.0.bn1.bias
2019-05-07 20:25:30,939 features.features.layer2.0.conv2.weight
2019-05-07 20:25:30,939 features.features.layer2.0.bn2.weight
2019-05-07 20:25:30,939 features.features.layer2.0.bn2.bias
2019-05-07 20:25:30,939 features.features.layer2.0.conv3.weight
2019-05-07 20:25:30,939 features.features.layer2.0.bn3.weight
2019-05-07 20:25:30,939 features.features.layer2.0.bn3.bias
2019-05-07 20:25:30,939 features.features.layer2.0.downsample.0.weight
2019-05-07 20:25:30,939 features.features.layer2.0.downsample.1.weight
2019-05-07 20:25:30,939 features.features.layer2.0.downsample.1.bias
2019-05-07 20:25:30,939 features.features.layer2.2.conv1.weight
2019-05-07 20:25:30,939 features.features.layer2.2.bn1.weight
2019-05-07 20:25:30,940 features.features.layer2.2.bn1.bias
2019-05-07 20:25:30,940 features.features.layer2.2.conv2.weight
2019-05-07 20:25:30,940 features.features.layer2.2.bn2.weight
2019-05-07 20:25:30,940 features.features.layer2.2.bn2.bias
2019-05-07 20:25:30,940 features.features.layer2.2.conv3.weight
2019-05-07 20:25:30,940 features.features.layer2.2.bn3.weight
2019-05-07 20:25:30,940 features.features.layer2.2.bn3.bias
2019-05-07 20:25:30,940 features.features.layer2.3.conv1.weight
2019-05-07 20:25:30,940 features.features.layer2.3.bn1.weight
2019-05-07 20:25:30,940 features.features.layer2.3.bn1.bias
2019-05-07 20:25:30,940 features.features.layer2.3.conv2.weight
2019-05-07 20:25:30,940 features.features.layer2.3.bn2.weight
2019-05-07 20:25:30,940 features.features.layer2.3.bn2.bias
2019-05-07 20:25:30,940 features.features.layer2.3.conv3.weight
2019-05-07 20:25:30,940 features.features.layer2.3.bn3.weight
2019-05-07 20:25:30,940 features.features.layer2.3.bn3.bias
2019-05-07 20:25:30,940 features.features.layer2.4.conv1.weight
2019-05-07 20:25:30,940 features.features.layer2.4.bn1.weight
2019-05-07 20:25:30,940 features.features.layer2.4.bn1.bias
2019-05-07 20:25:30,940 features.features.layer2.4.conv2.weight
2019-05-07 20:25:30,940 features.features.layer2.4.bn2.weight
2019-05-07 20:25:30,940 features.features.layer2.4.bn2.bias
2019-05-07 20:25:30,940 features.features.layer2.4.conv3.weight
2019-05-07 20:25:30,940 features.features.layer2.4.bn3.weight
2019-05-07 20:25:30,941 features.features.layer2.4.bn3.bias
2019-05-07 20:25:30,941 GPU NUM: 1
2019-05-07 20:25:30,945 model prepare done
2019-05-07 20:25:40,947 Epoch: [1][10/18750] lr: 0.0010000 Batch Time: 0.642s Data Time:0.334s Loss:11.23705
2019-05-07 20:25:40,947 Progress: 10 / 937500 [0%], Speed: 0.642 s/iter, ETA 6:23:12 (D:H:M)
2019-05-07 20:25:40,947
PROGRESS: 0.00%
2019-05-07 20:25:43,821 Epoch: [1][20/18750] lr: 0.0010000 Batch Time: 0.465s Data Time:0.167s Loss:8.01549
2019-05-07 20:25:43,821 Progress: 20 / 937500 [0%], Speed: 0.465 s/iter, ETA 5:01:01 (D:H:M)
2019-05-07 20:25:43,821
PROGRESS: 0.00%
2019-05-07 20:25:46,703 Epoch: [1][30/18750] lr: 0.0010000 Batch Time: 0.406s Data Time:0.112s Loss:5.91713
2019-05-07 20:25:46,703 Progress: 30 / 937500 [0%], Speed: 0.406 s/iter, ETA 4:09:41 (D:H:M)
2019-05-07 20:25:46,703
PROGRESS: 0.00%
2019-05-07 20:25:49,628 Epoch: [1][40/18750] lr: 0.0010000 Batch Time: 0.378s Data Time:0.084s Loss:4.73139
2019-05-07 20:25:49,629 Progress: 40 / 937500 [0%], Speed: 0.378 s/iter, ETA 4:02:19 (D:H:M)
2019-05-07 20:25:49,629
PROGRESS: 0.00%
2019-05-07 20:25:52,498 Epoch: [1][50/18750] lr: 0.0010000 Batch Time: 0.359s Data Time:0.067s Loss:3.99356
2019-05-07 20:25:52,498 Progress: 50 / 937500 [0%], Speed: 0.359 s/iter, ETA 3:21:35 (D:H:M)
2019-05-07 20:25:52,498
PROGRESS: 0.01%
...