RecursionError: maximum recursion depth exceeded

[sepehratwork]

This massage is logged to the terminal:

[2021-10-14 04:53:57,225 main.py:215 INFO train-abinet] ModelConfig(
(0): dataset_case_sensitive = False
(1): dataset_charset_path = data/charset_36.txt
(2): dataset_data_aug = True
(3): dataset_eval_case_sensitive = False
(4): dataset_image_height = 32
(5): dataset_image_width = 128
(6): dataset_max_length = 25
(7): dataset_multiscales = False
(8): dataset_num_workers = 14
(9): dataset_one_hot_y = True
(10): dataset_pin_memory = True
(11): dataset_smooth_factor = 0.1
(12): dataset_smooth_label = False
(13): dataset_test_batch_size = 384
(14): dataset_test_roots = ['data/training/MJ/MJ_train/', 'data/training/MJ/MJ_test/', 'data/training/MJ/MJ_valid/', 'data/training/ST']
(15): dataset_train_batch_size = 384
(16): dataset_train_roots = ['data/training/MJ/MJ_train/', 'data/training/MJ/MJ_test/', 'data/training/MJ/MJ_valid/', 'data/training/ST']
(17): dataset_use_sm = False
(18): global_name = train-abinet
(19): global_phase = train
(20): global_seed = None
(21): global_stage = train-super
(22): global_workdir = workdir/train-abinet
(23): model_alignment_loss_weight = 1.0
(24): model_checkpoint = None
(25): model_ensemble =
(26): model_iter_size = 3
(27): model_language_checkpoint = workdir/pretrain-language-model/pretrain-language-model.pth
(28): model_language_detach = True
(29): model_language_loss_weight = 1.0
(30): model_language_num_layers = 4
(31): model_language_use_self_attn = False
(32): model_name = modules.model_abinet_iter.ABINetIterModel
(33): model_strict = True
(34): model_use_vision = False
(35): model_vision_attention = position
(36): model_vision_backbone = transformer
(37): model_vision_backbone_ln = 3
(38): model_vision_checkpoint = workdir/pretrain-vision-model/best-pretrain-vision-model.pth
(39): model_vision_loss_weight = 1.0
(40): optimizer_args_betas = (0.9, 0.999)
(41): optimizer_bn_wd = False
(42): optimizer_clip_grad = 20
(43): optimizer_lr = 0.0001
(44): optimizer_scheduler_gamma = 0.1
(45): optimizer_scheduler_periods = [6, 4]
(46): optimizer_true_wd = False
(47): optimizer_type = Adam
(48): optimizer_wd = 0.0
(49): training_epochs = 10
(50): training_eval_iters = 3000
(51): training_save_iters = 3000
(52): training_show_iters = 50
(53): training_start_iters = 0
(54): training_stats_iters = 100000
)
[2021-10-14 04:53:57,226 main.py:222 INFO train-abinet] Construct dataset.
[2021-10-14 04:53:57,228 main.py:92 INFO train-abinet] 67199 training items found.
[2021-10-14 04:53:57,228 main.py:94 INFO train-abinet] 67199 valid items found.
[2021-10-14 04:53:57,228 main.py:226 INFO train-abinet] Construct model.
[2021-10-14 04:53:57,488 model_vision.py:37 INFO train-abinet] Read vision model from workdir/pretrain-vision-model/best-pretrain-vision-model.pth.
[2021-10-14 04:53:59,805 model_language.py:38 INFO train-abinet] Read language model from workdir/pretrain-language-model/pretrain-language-model.pth.
[2021-10-14 04:53:59,843 main.py:104 INFO train-abinet] ABINetIterModel(
(vision): BaseVision(
(backbone): ResTranformer(
(resnet): ResNet(
(conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(32, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): BasicBlock(
(conv1): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(32, 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)
(relu): ReLU(inplace)
(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)
(downsample): Sequential(
(0): Conv2d(32, 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)
)
)
(1): BasicBlock(
(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)
(relu): ReLU(inplace)
(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)
)
(2): BasicBlock(
(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)
(relu): ReLU(inplace)
(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)
)
(3): BasicBlock(
(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)
(relu): ReLU(inplace)
(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)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 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)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), 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=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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)
)
(2): BasicBlock(
(conv1): Conv2d(128, 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)
(relu): ReLU(inplace)
(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)
)
(3): BasicBlock(
(conv1): Conv2d(128, 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)
(relu): ReLU(inplace)
(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)
)
(4): BasicBlock(
(conv1): Conv2d(128, 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)
(relu): ReLU(inplace)
(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)
)
(5): BasicBlock(
(conv1): Conv2d(128, 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)
(relu): ReLU(inplace)
(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)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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=(1, 1), 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=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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)
)
(2): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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)
)
(3): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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)
)
(4): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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)
)
(5): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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)
)
)
(layer5): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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=(1, 1), 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=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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)
)
(2): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(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)
)
)
)
(pos_encoder): PositionalEncoding(
(dropout): Dropout(p=0.1)
)
(transformer): TransformerEncoder(
(layers): ModuleList(
(0): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.1)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1)
(dropout2): Dropout(p=0.1)
)
(1): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.1)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1)
(dropout2): Dropout(p=0.1)
)
(2): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.1)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1)
(dropout2): Dropout(p=0.1)
)
)
)
)
(attention): PositionAttention(
(k_encoder): Sequential(
(0): Sequential(
(0): Conv2d(512, 64, kernel_size=(3, 3), stride=(1, 2), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
(1): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
(2): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
(3): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
)
(k_decoder): Sequential(
(0): Sequential(
(0): Upsample(scale_factor=2.0, mode=nearest)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace)
)
(1): Sequential(
(0): Upsample(scale_factor=2.0, mode=nearest)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace)
)
(2): Sequential(
(0): Upsample(scale_factor=2.0, mode=nearest)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace)
)
(3): Sequential(
(0): Upsample(size=(8, 32), mode=nearest)
(1): Conv2d(64, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace)
)
)
(pos_encoder): PositionalEncoding(
(dropout): Dropout(p=0)
)
(project): Linear(in_features=512, out_features=512, bias=True)
)
(cls): Linear(in_features=512, out_features=37, bias=True)
)
(language): BCNLanguage(
(proj): Linear(in_features=37, out_features=512, bias=False)
(token_encoder): PositionalEncoding(
(dropout): Dropout(p=0.1)
)
(pos_encoder): PositionalEncoding(
(dropout): Dropout(p=0)
)
(model): TransformerDecoder(
(layers): ModuleList(
(0): TransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): Linear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.1)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm2): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(dropout2): Dropout(p=0.1)
(dropout3): Dropout(p=0.1)
)
(1): TransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): Linear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.1)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm2): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(dropout2): Dropout(p=0.1)
(dropout3): Dropout(p=0.1)
)
(2): TransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): Linear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.1)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm2): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(dropout2): Dropout(p=0.1)
(dropout3): Dropout(p=0.1)
)
(3): TransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): Linear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.1)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm2): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)
(dropout2): Dropout(p=0.1)
(dropout3): Dropout(p=0.1)
)
)
)
(cls): Linear(in_features=512, out_features=37, bias=True)
)
(alignment): BaseAlignment(
(w_att): Linear(in_features=1024, out_features=512, bias=True)
(cls): Linear(in_features=512, out_features=37, bias=True)
)
)
[2021-10-14 04:53:59,848 main.py:229 INFO train-abinet] Construct learner.
[2021-10-14 04:53:59,962 main.py:233 INFO train-abinet] Start training.
Traceback (most recent call last):
File "/ABINet/dataset.py", line 103, in get
return self._next_image(idx)
File "/ABINet/dataset.py", line 61, in _next_image
next_index = random.randint(0, len(self) - 1)
RecursionError: maximum recursion depth exceeded
[2021-10-14 04:53:59,994 dataset.py:119 INFO train-abinet] Corrupted image is found: MJ_train, 34607, , 0
Fatal Python error: Cannot recover from stack overflow.

Thread 0x00007f7923687700 (most recent call first):
File "/home/user/miniconda/envs/py36/lib/python3.6/selectors.py", line 376 in select
File "/home/user/miniconda/envs/py36/lib/python3.6/multiprocessing/connection.py", line 911 in wait
File "/home/user/miniconda/envs/py36/lib/python3.6/multiprocessing/connection.py", line 414 in _poll
File "/home/user/miniconda/envs/py36/lib/python3.6/multiprocessing/connection.py", line 257 in poll
File "/home/user/miniconda/envs/py36/lib/python3.6/multiprocessing/queues.py", line 104 in get
File "/home/user/miniconda/envs/py36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py", line 202 in run
File "/home/user/miniconda/envs/py36/lib/python3.6/threading.py", line 916 in _bootstrap_inner
File "/home/user/miniconda/envs/py36/lib/python3.6/threading.py", line 884 in _bootstrap

Thread 0x00007f797e1ef700 (most recent call first):
File "/home/user/miniconda/envs/py36/lib/python3.6/site-packages/fastai/callbacks/tensorboard.py", line 235 in _queue_processor
File "/home/user/miniconda/envs/py36/lib/python3.6/threading.py", line 864 in run
File "/home/user/miniconda/envs/py36/lib/python3.6/threading.py", line 916 in _bootstrap_inner
File "/home/user/miniconda/envs/py36/lib/python3.6/threading.py", line 884 in _bootstrap

Current thread 0x00007f7a07cd3700 (most recent call first):
File "/ABINet/dataset.py", line 61 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 120 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
File "/ABINet/dataset.py", line 62 in _next_image
File "/ABINet/dataset.py", line 103 in get
...
Aborted (core dumped)

Do you know what should I do with my dataset?
Thanks

[pmgautam]

I am also facing this issue. Has anyone solved this?

[yywwwwww]

hello,do you solve this problem?i have the same problem but i can not solve it.

[InsaneOnion]

I am also facing this issue. Has anyone solved this?