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FangShancheng / ABINet

Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
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RecursionError: maximum recursion depth exceeded #46

Open sepehratwork opened 2 years ago

sepehratwork commented 2 years ago

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 commented 1 year ago

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

yywwwwww commented 11 months ago

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

InsaneOnion commented 3 months ago

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