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slurm-3817.out
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slurm-3817.out
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INFO:faiss.loader:Loading faiss with AVX2 support.
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INFO:faiss.loader:Loading faiss with AVX2 support.
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INFO:root:called-params configs/in1k_vith14_ep300_FGDCC.yaml
INFO:root:loaded params...
{ 'data': { 'batch_size': 96,
'color_jitter_strength': 0.0,
'crop_scale': [0.3, 1.0],
'crop_size': 224,
'cutmix': 0.0,
'drop_path': 0.25,
'image_folder': '/home/rtcalumby/adam/luciano/plantnet_300K/',
'mixup': 0.0,
'nb_classes': 1081,
'num_workers': 16,
'pin_mem': True,
'reprob': 0.25,
'resume_epoch': 0,
'root_path': '/home/rtcalumby/adam/luciano/',
'use_color_distortion': False,
'use_gaussian_blur': False,
'use_horizontal_flip': False},
'logging': { 'folder': '/home/rtcalumby/adam/luciano/LifeCLEFPlant2022/logs/PlantNet300k_exp41',
'write_tag': 'jepa'},
'mask': { 'allow_overlap': False,
'aspect_ratio': [0.75, 1.5],
'enc_mask_scale': [0.85, 1.0],
'min_keep': 10,
'num_enc_masks': 1,
'num_pred_masks': 4,
'patch_size': 14,
'pred_mask_scale': [0.15, 0.2]},
'meta': { 'copy_data': False,
'load_checkpoint': True,
'model_name': 'vit_huge',
'pred_depth': 12,
'pred_emb_dim': 384,
'read_checkpoint': None,
'use_bfloat16': True},
'optimization': { 'ema': [0.9, 0.999],
'epochs': 75,
'final_lr': 1e-05,
'final_weight_decay': 0.4,
'ipe_scale': 1.0,
'label_smoothing': 0.1,
'lr': 2.5e-05,
'start_lr': 8.5e-05,
'warmup': 5,
'weight_decay': 0.05}}
INFO:root:Running... (rank: 0/1)
INFO:root:Initialized (rank/world-size) 0/1
INFO:root:MaskedAutoEncoder(
(encoder): Sequential(
(0): Linear(in_features=1280, out_features=1152, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=1152, out_features=1024, bias=True)
(3): GELU(approximate='none')
(4): Linear(in_features=1024, out_features=768, bias=True)
)
(decoder): Sequential(
(0): Linear(in_features=768, out_features=1024, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=1024, out_features=1152, bias=True)
(3): GELU(approximate='none')
(4): Linear(in_features=1152, out_features=1280, bias=True)
)
)
INFO:root:making imagenet data transforms
INFO:root:making imagenet data transforms
INFO:root:Finetuning dataset created
Training dataset, length: 245952
INFO:root:Finetuning dataset created
Val dataset, length: 31200
INFO:root:Using AdamW
['encoder', 'predictor', 'opt', 'scaler', 'target_encoder', 'epoch', 'loss', 'batch_size', 'world_size', 'lr']
INFO:root:loaded pretrained encoder from epoch 66 with msg: <All keys matched successfully>
INFO:root:PairedCrossAttentionClassifier(
(act): GELU(approximate='none')
(subclass_proj): Sequential(
(0): Linear(in_features=1280, out_features=1280, bias=True)
(1): GELU(approximate='none')
)
(parent_cross_attention): MultiHeadCrossAttention(
(query): Linear(in_features=1280, out_features=1280, bias=True)
(key): Linear(in_features=1280, out_features=1280, bias=True)
(value): Linear(in_features=1280, out_features=1280, bias=True)
)
(subclass_cross_attention): MultiHeadCrossAttention(
(query): Linear(in_features=1280, out_features=1280, bias=True)
(key): Linear(in_features=1280, out_features=1280, bias=True)
(value): Linear(in_features=1280, out_features=1280, bias=True)
)
(parent_feature_selection): Linear(in_features=2560, out_features=1280, bias=True)
(subclass_feature_selection): Linear(in_features=2560, out_features=1280, bias=True)
(parent_classifier): Linear(in_features=1280, out_features=1081, bias=True)
(subclass_classifier): Linear(in_features=1280, out_features=4324, bias=True)
(head_drop): Dropout(p=0.25, inplace=False)
)
INFO:root:Using AdamW
INFO:root:VisionTransformer(
(patch_embed): PatchEmbed(
(proj): Conv2d(3, 1280, kernel_size=(14, 14), stride=(14, 14))
)
(blocks): ModuleList(
(0-31): 32 x Block(
(norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=1280, out_features=3840, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=1280, out_features=1280, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): Identity()
(norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True)
(mlp): MLP(
(fc1): Linear(in_features=1280, out_features=5120, bias=True)
(act): GELU(approximate='none')
(fc2): Linear(in_features=5120, out_features=1280, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
)
(norm): LayerNorm((1280,), eps=1e-06, elementwise_affine=True)
)
INFO:root:Autoencoder Training...
INFO:root: - - Epoch: 1 - -
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INFO:root:[1, 0/ 2562] - [Autoencoder Training] [Autoencoder Loss: 1.9787] [autoencoder lr: 1.50e-04][mem: 7.84e+03](3140.2 ms)
INFO:root:[1, 100/ 2562] - [Autoencoder Training] [Autoencoder Loss: 1.9811] [autoencoder lr: 1.51e-04][mem: 8.33e+03](342.3 ms)
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