torch.nn.modules.module.ModuleAttributeError: 'SiamRPNRes22' object has no attribute 'module'

[danho47]

I want to train SiamRPN, but I get this error,

/home/honda/SiamDW/siamese_tracking/../lib/core/config.py:183: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
exp_config = edict(yaml.load(f))
=> creating logs/SiamRPNRes22
=> creating logs/SiamRPNRes22/SiamRPNRes22_2020-11-17-22-10
Namespace(cfg='experiments/train/SiamRPN.yaml', gpus='1', workers=None)
{'CHECKPOINT_DIR': 'snapshot',
'GPUS': '1',
'OUTPUT_DIR': 'logs',
'PRINT_FREQ': 10,
'SIAMFC': {'DATASET': {'BLUR': 0,
'COLOR': 1,
'FLIP': 0,
'GOT10K': {'ANNOTATION': '/home/zhbli/Dataset/data3/got10k/train.json',
'PATH': '/home/zhbli/Dataset/data3/got10k/crop511'},
'ROTATION': 0,
'SCALE': 0.05,
'SHIFT': 4,
'VID': {'ANNOTATION': '/home/zhbli/Dataset/data2/vid/train.json',
'PATH': '/home/zhbli/Dataset/data2/vid/crop511'}},
'TEST': {'DATA': 'VOT2015',
'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': 'SiamFCIncep22',
'MOMENTUM': 0.9,
'PAIRS': 200000,
'PRETRAIN': 'resnet23_inlayer.model',
'RESUME': False,
'SEARCH_SIZE': 255,
'START_EPOCH': 0,
'STRIDE': 8,
'TEMPLATE_SIZE': 127,
'WEIGHT_DECAY': 0.0001,
'WHICH_USE': 'GOT10K'},
'TUNE': {'DATA': 'VOT2015',
'METHOD': 'GENE',
'MODEL': 'SiamFCIncep22'}},
'SIAMRPN': {'DATASET': {'BLUR': 0.2,
'COCO': {'ANNOTATION': '/home/honda/data/data/coco/train2017.json',
'PATH': '/home/honda/data/coco/crop271',
'RANGE': 1,
'USE': 100000},
'COLOR': 1,
'DET': {'ANNOTATION': '/home/honda/data/det/train.json',
'PATH': '/home/honda/data/det/crop271',
'RANGE': 100,
'USE': 100000},
'FLIP': 0,
'GOT10K': {'ANNOTATION': '/home/honda/data/data/got10k/train.json',
'PATH': '/home/honda/data/got10k/crop271',
'RANGE': 100,
'USE': 200000},
'LASOT': {'ANNOTATION': '/home/honda/data/data/lasot/train2017.json',
'PATH': '/home/honda/data/lasot/crop271',
'RANGE': 100,
'USE': 200000},
'ROTATION': 0,
'SCALE': 0.05,
'SHIFT': 4,
'VID': {'ANNOTATION': '/home/honda/data/Copy of '
'SiamDW_DATA/VID/train.json',
'PATH': '/home/honda/data/Copy of '
'SiamDW_DATA/VID/crop255',
'RANGE': 100,
'USE': 200000},
'YTB': {'ANNOTATION': '/home/honda/data/y2b/train.json',
'PATH': '/home/honda/data/y2b/crop271',
'RANGE': 3,
'USE': 200000}},
'TEST': {'DATA': 'VOT2016',
'END_EPOCH': 50,
'ISTRUE': True,
'MODEL': 'SiamRPNRes22',
'START_EPOCH': 20,
'THREADS': 16},
'TRAIN': {'ANCHORS_ALL_KEEP': 64,
'ANCHORS_POS_KEEP': 16,
'ANCHORS_RATIOS': [0.33, 0.5, 1, 2, 3],
'ANCHORS_SCALES': [8],
'ANCHORS_THR_HIGH': 0.6,
'ANCHORS_THR_LOW': 0.3,
'BATCH': 32,
'CLS_TYPE': 'thicker',
'CLS_WEIGHT': 1,
'END_EPOCH': 50,
'ISTRUE': True,
'LR': 0.01,
'LR_END': 1e-05,
'LR_POLICY': 'log',
'MODEL': 'SiamRPNRes22',
'MOMENTUM': 0.9,
'PRETRAIN': 'CIResNet22_PRETRAIN.model',
'REG_WEIGHT': 1,
'RESUME': False,
'SEARCH_SIZE': 255,
'START_EPOCH': 0,
'STRIDE': 8,
'TEMPLATE_SIZE': 127,
'WEIGHT_DECAY': 0.0005,
'WHICH_USE': ['YTB', 'VID']},
'TUNE': {'DATA': 'VOT2016',
'ISTRUE': False,
'METHOD': 'TPE',
'MODEL': 'SiamRPNRes22'}},
'WORKERS': 32}
/home/honda/SiamDW/siamese_tracking/../lib/models/modules.py:188: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.
nn.init.kaiming_normal(m.weight, mode='fan_out')
/home/honda/SiamDW/siamese_tracking/../lib/models/modules.py:190: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_.
nn.init.constant(m.weight, 1)
/home/honda/SiamDW/siamese_tracking/../lib/models/modules.py:191: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_.
nn.init.constant(m.bias, 0)
SiamRPNRes22(
(criterion): BCEWithLogitsLoss()
(features): ResNet22(
(features): ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck_CI(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck_CI(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck_CI(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck_CI(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(2): Bottleneck_CI(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck_CI(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(4): Bottleneck_CI(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
)
)
(connect_model): RPN_Up(
(template_cls): Conv2d(512, 5120, kernel_size=(3, 3), stride=(1, 1))
(template_reg): Conv2d(512, 10240, kernel_size=(3, 3), stride=(1, 1))
(search_cls): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(search_reg): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(adjust): Conv2d(20, 20, kernel_size=(1, 1), stride=(1, 1))
)
)
load pretrained model from ./pretrain/CIResNet22_PRETRAIN.model
remove prefix 'module.'
missing keys:{'connect_model.adjust.weight', 'features.features.layer2.3.bn2.num_batches_tracked', 'features.features.layer2.4.bn3.num_batches_tracked', 'connect_model.adjust.bias', 'features.features.layer1.2.bn3.num_batches_tracked', 'connect_model.search_cls.weight', 'features.features.layer2.2.bn2.num_batches_tracked', 'connect_model.template_cls.bias', 'features.features.layer2.0.bn3.num_batches_tracked', 'connect_model.template_reg.bias', 'features.features.layer1.2.bn2.num_batches_tracked', 'connect_model.search_reg.weight', 'features.features.layer2.2.bn1.num_batches_tracked', 'features.features.layer2.4.bn1.num_batches_tracked', 'features.features.layer1.0.bn2.num_batches_tracked', 'features.features.layer2.4.bn2.num_batches_tracked', 'features.features.layer1.2.bn1.num_batches_tracked', 'features.features.layer2.3.bn3.num_batches_tracked', 'features.features.layer1.0.bn3.num_batches_tracked', 'features.features.layer2.3.bn1.num_batches_tracked', 'features.features.layer1.0.downsample.1.num_batches_tracked', 'features.features.layer1.1.bn2.num_batches_tracked', 'features.features.layer1.0.bn1.num_batches_tracked', 'connect_model.template_cls.weight', 'features.features.layer2.0.bn2.num_batches_tracked', 'features.features.layer2.0.bn1.num_batches_tracked', 'features.features.layer1.1.bn3.num_batches_tracked', 'features.features.layer2.2.bn3.num_batches_tracked', 'features.features.layer2.0.downsample.1.num_batches_tracked', 'features.features.layer1.1.bn1.num_batches_tracked', 'connect_model.search_cls.bias', 'features.features.bn1.num_batches_tracked', 'connect_model.search_reg.bias', 'connect_model.template_reg.weight'}
unused checkpoint keys:set()
trainable params:
features.features.layer2.4.conv1.weight
features.features.layer2.4.bn1.weight
features.features.layer2.4.bn1.bias
features.features.layer2.4.conv2.weight
features.features.layer2.4.bn2.weight
features.features.layer2.4.bn2.bias
features.features.layer2.4.conv3.weight
features.features.layer2.4.bn3.weight
features.features.layer2.4.bn3.bias
connect_model.template_cls.weight
connect_model.template_cls.bias
connect_model.template_reg.weight
connect_model.template_reg.bias
connect_model.search_cls.weight
connect_model.search_cls.bias
connect_model.search_reg.weight
connect_model.search_reg.bias
connect_model.adjust.weight
connect_model.adjust.bias
GPU NUM: 1
model prepare done
train datas: ['YTB', 'VID']
YTB loaded.
VID loaded.
dataset length 400000
{'GPUS': '1', 'WORKERS': 32, 'PRINT_FREQ': 10, 'OUTPUT_DIR': 'logs', 'CHECKPOINT_DIR': 'snapshot', 'SIAMFC': {'TRAIN': {'MODEL': 'SiamFCIncep22', 'RESUME': False, 'START_EPOCH': 0, 'END_EPOCH': 50, 'TEMPLATE_SIZE': 127, 'SEARCH_SIZE': 255, 'STRIDE': 8, 'BATCH': 32, 'PAIRS': 200000, 'PRETRAIN': 'resnet23_inlayer.model', 'LR_POLICY': 'log', 'LR': 0.001, 'LR_END': 1e-07, 'MOMENTUM': 0.9, 'WEIGHT_DECAY': 0.0001, 'WHICH_USE': 'GOT10K'}, 'TEST': {'MODEL': 'SiamFCIncep22', 'DATA': 'VOT2015', 'START_EPOCH': 30, 'END_EPOCH': 50}, 'TUNE': {'MODEL': 'SiamFCIncep22', 'DATA': 'VOT2015', 'METHOD': 'GENE'}, 'DATASET': {'VID': {'PATH': '/home/zhbli/Dataset/data2/vid/crop511', 'ANNOTATION': '/home/zhbli/Dataset/data2/vid/train.json'}, 'GOT10K': {'PATH': '/home/zhbli/Dataset/data3/got10k/crop511', 'ANNOTATION': '/home/zhbli/Dataset/data3/got10k/train.json'}, 'SHIFT': 4, 'SCALE': 0.05, 'COLOR': 1, 'FLIP': 0, 'BLUR': 0, 'ROTATION': 0}}, 'SIAMRPN': {'DATASET': {'VID': {'PATH': '/home/honda/data/Copy of SiamDW_DATA/VID/crop255', 'ANNOTATION': '/home/honda/data/Copy of SiamDW_DATA/VID/train.json', 'RANGE': 100, 'USE': 200000}, 'YTB': {'PATH': '/home/honda/data/y2b/crop271', 'ANNOTATION': '/home/honda/data/y2b/train.json', 'RANGE': 3, 'USE': 200000}, 'COCO': {'PATH': '/home/honda/data/coco/crop271', 'ANNOTATION': '/home/honda/data/data/coco/train2017.json', 'RANGE': 1, 'USE': 100000}, 'DET': {'PATH': '/home/honda/data/det/crop271', 'ANNOTATION': '/home/honda/data/det/train.json', 'RANGE': 100, 'USE': 100000}, 'GOT10K': {'PATH': '/home/honda/data/got10k/crop271', 'ANNOTATION': '/home/honda/data/data/got10k/train.json', 'RANGE': 100, 'USE': 200000}, 'LASOT': {'PATH': '/home/honda/data/lasot/crop271', 'ANNOTATION': '/home/honda/data/data/lasot/train2017.json', 'RANGE': 100, 'USE': 200000}, 'SHIFT': 4, 'SCALE': 0.05, 'COLOR': 1, 'FLIP': 0, 'BLUR': 0.2, 'ROTATION': 0}, 'TRAIN': {'MODEL': 'SiamRPNRes22', 'RESUME': False, 'START_EPOCH': 0, 'END_EPOCH': 50, 'TEMPLATE_SIZE': 127, 'SEARCH_SIZE': 255, 'STRIDE': 8, 'BATCH': 32, 'PRETRAIN': 'CIResNet22_PRETRAIN.model', 'LR_POLICY': 'log', 'LR': 0.01, 'LR_END': 1e-05, 'MOMENTUM': 0.9, 'WEIGHT_DECAY': 0.0005, 'CLS_WEIGHT': 1, 'REG_WEIGHT': 1, 'WHICH_USE': ['YTB', 'VID'], 'ANCHORS_RATIOS': [0.33, 0.5, 1, 2, 3], 'ANCHORS_SCALES': [8], 'ANCHORS_THR_HIGH': 0.6, 'ANCHORS_THR_LOW': 0.3, 'ANCHORS_POS_KEEP': 16, 'ANCHORS_ALL_KEEP': 64, 'ISTRUE': True, 'CLS_TYPE': 'thicker'}, 'TEST': {'MODEL': 'SiamRPNRes22', 'DATA': 'VOT2016', 'START_EPOCH': 20, 'END_EPOCH': 50, 'ISTRUE': True, 'THREADS': 16}, 'TUNE': {'MODEL': 'SiamRPNRes22', 'DATA': 'VOT2016', 'METHOD': 'TPE', 'ISTRUE': False}}}
Traceback (most recent call last):
File "siamese_tracking/train_siamrpn.py", line 200, in
main()
File "siamese_tracking/train_siamrpn.py", line 190, in main
logger, cls_type = config.SIAMRPN.TRAIN.CLS_TYPE)
File "/home/honda/SiamDW/siamese_tracking/../lib/core/function.py", line 73, in siamrpn_train
model, optimizer = unfix_more(model, optimizer, epoch, cfg, cur_lr, logger)
File "/home/honda/SiamDW/siamese_tracking/../lib/core/function.py", line 142, in unfix_more
if model.module.features.unfix(epoch / cfg.SIAMFC.TRAIN.END_EPOCH):
File "/home/honda/anaconda3/envs/siamdw/lib/python3.7/site-packages/torch/nn/modules/module.py", line 779, in getattr
type(self).name, name))
torch.nn.modules.module.ModuleAttributeError: 'SiamRPNRes22' object has no attribute 'module'

Please, some advice