在miniImageNet数据集上运行RENet等报错 #63
Comments
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Please ensure that you have pull the latest commit ee25973, we have fix this problem in this version. |
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在我的版本中已经修改为了 |
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感谢您的回答！ batch:([tensor([[[[ 0.4896, 0.9282, 0.9423, ..., -1.3355, -1.6043, -1.3638], trainer.py:375 |
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你好，我按照下面的步骤尝试训练renet，但没有报错，或许可以供你参考： git clone https://github.com/RL-VIG/LibFewShot.git LibFewShot_ee25973
cd LibFewShot_ee25973
# 修改renet.config 和 run_trainer.py |
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十分感谢，未找到renet.config，请问是config/classifiers/RENet.yaml吗？ |
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是config/renet.yaml，刚刚写错了，抱歉。 |
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非常感谢，经过多次尝试，使用Conv64F可以正常使用，但是使用resnet18无法跑通。感谢帮助 |
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换Backbone的话需要对应修改分类头参数，分类头参数可以推导或者参考原文，可以附上更详细的报错信息供我们提供解决方案。 |
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(torchLFS) [inspur@localhost LibFewShot-main]$ python run_trainer.py
[11/14/22 10:04:36] INFO {'data_root': '/home/inspur/MAX_SPACE/Jiyu/LibFewShot-main/dataset_floder/miniImgFSL', 'image_size': 84, 'use_memory': False, 'augment': trainer.py:372
False, 'augment_times': 1, 'augment_times_query': 1, 'workers': 2, 'dataloader_num': 1, 'device_ids': 0, 'n_gpu': 1, 'seed': 2147483647,
'deterministic': True, 'port': 53758, 'log_name': None, 'log_level': 'info', 'log_interval': 100, 'log_paramerter': False, 'result_root':
'./results', 'save_interval': 10, 'save_part': ['emb_func'], 'tag': None, 'epoch': 5, 'test_epoch': 5, 'parallel_part': ['emb_func'],
'pretrain_path': None, 'resume': False, 'way_num': 5, 'shot_num': 1, 'query_num': 15, 'test_way': 5, 'test_shot': 1, 'test_query': 15,
'episode_size': 1, 'train_episode': 100, 'test_episode': 100, 'batch_size': 128, 'val_per_epoch': 1, 'optimizer': {'name': 'Adam',
'kwargs': {'lr': 0.01}, 'other': None}, 'lr_scheduler': {'name': 'StepLR', 'kwargs': {'gamma': 1.0, 'step_size': 20}}, 'warmup': 0,
'includes': ['headers/data.yaml', 'headers/device.yaml', 'headers/misc.yaml', 'headers/model.yaml', 'headers/optimizer.yaml',
'classifiers/RENet.yaml', 'backbones/resnet18.yaml'], 'classifier': {'name': 'RENet', 'kwargs': {'feat_dim': 512, 'lambda_epi': 0.25,
'temperature': 0.2, 'temperature_attn': 5.0, 'num_classes': 2}}, 'backbone': {'name': 'resnet18', 'kwargs': {'is_feature': False,
'avg_pool': True, 'is_flatten': True}}, 'tb_scale': 1.0, 'rank': 0}
[11/14/22 10:04:37] INFO RENet( trainer.py:372
(emb_func): ResNet(
(conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=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)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=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)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=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)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=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)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
)
(fc): Linear(in_features=512, out_features=2, bias=True)
(scr_layer): SCRLayer(
(model): Sequential(
(0): SelfCorrelationComputation(
(unfold): Unfold(kernel_size=(5, 5), dilation=1, padding=2, stride=1)
(relu): ReLU()
)
(1): SCR(
(conv1x1_in): Sequential(
(0): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(conv1): Sequential(
(0): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), bias=False)
(1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(conv2): Sequential(
(0): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), bias=False)
(1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(conv1x1_out): Sequential(
(0): Conv2d(64, 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)
)
)
)
)
(cca_layer): CCALayer(
(cca_module): CCA(
(conv): Sequential(
(0): SepConv4d(
(proj): Sequential(
(0): Conv2d(1, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Sequential(
(0): Conv3d(1, 1, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(1): BatchNorm3d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Sequential(
(0): Conv3d(1, 1, kernel_size=(3, 3, 1), stride=(1, 1, 1), padding=(1, 1, 0), bias=False)
(1): BatchNorm3d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(1): ReLU(inplace=True)
(2): SepConv4d(
(proj): Sequential(
(0): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv1): Sequential(
(0): Conv3d(16, 16, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(1): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv2): Sequential(
(0): Conv3d(16, 16, kernel_size=(3, 3, 1), stride=(1, 1, 1), padding=(1, 1, 0), bias=False)
(1): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
)
)
(cca_1x1): Sequential(
(0): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(loss_func): CrossEntropyLoss()
)
INFO Trainable params in the model: 11348186 trainer.py:372
[11/14/22 10:04:40] INFO load 38400 train image with 64 label. trainer.py:372
INFO load 9600 val image with 16 label. trainer.py:372
INFO load 12000 test image with 20 label. trainer.py:372
INFO Adam ( trainer.py:372
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
eps: 1e-08
initial_lr: 0.01
lr: 0.01
weight_decay: 0
)
INFO ============ Train on the train set ============ trainer.py:372
INFO learning rate: [0.01] trainer.py:372
Traceback (most recent call last):
File "run_trainer.py", line 24, in
main(0, config)
File "run_trainer.py", line 14, in main
trainer.train_loop(rank)
File "/home/inspur/MAX_SPACE/Jiyu/LibFewShot-main/core/trainer.py", line 83, in train_loop
train_acc = self._train(epoch_idx)
File "/home/inspur/MAX_SPACE/Jiyu/LibFewShot-main/core/trainer.py", line 170, in _train
[elem for each_batch in batch for elem in each_batch]
File "/home/inspur/anaconda3/envs/torchLFS/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/inspur/MAX_SPACE/Jiyu/LibFewShot-main/core/model/abstract_model.py", line 30, in forward
return self.set_forward_loss(x)
File "/home/inspur/MAX_SPACE/Jiyu/LibFewShot-main/core/model/finetuning/renet.py", line 415, in set_forward_loss
) = batch # RENet uses both episode and general dataloaders
ValueError: not enough values to unpack (expected 4, got 2)
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