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_target_: src.models.anomaly_clip_module.AnomalyCLIPModule
optimizer:
_target_: torch.optim.AdamW
_partial_: true
lr: 0.001
weight_decay: 0.2
scheduler:
_target_: torch.optim.lr_scheduler.ReduceLROnPlateau
_partial_: true
mode: min
factor: 0.1
patience: 5
net:
_target_: src.models.components.anomaly_clip.AnomalyCLIP
arch: ViT-L/14@336px
image_size: 336
class_names: ["object"]
# class_names: ${prompt.class_names}
temperature: 0.07 # softmax
prompt_length: 12 # length of learnable prompts
context_length: 77 # defaut 77 for openai clip
truncate: false
feature_map_idx: [5, 11, 17, 23] # [0, 12, 23] [6, 12, 18] [5, 11, 17, 23] index of resnetblock in ViT
share_weight: false # whether the adapter shares weights for different feature maps
# state_template: ${prompt.state_template}
state_template:
normal: ["{}"]
anomaly: ["damaged {}"]
tokenizer:
_target_: src.models.components.clip.simple_tokenizer.SimpleTokenizer
adapter:
# _target_: torch.nn.Linear
# in_features: 1024 # clip vit feature dim, defaut 1024 for openai clip
# out_features: 1024
# bias: false
_target_: src.models.components.adapter.Linear
in_features: 1024 # clip vit feature dim, defaut 1024 for openai clip
out_features: 1024
hidden_features: null # set null, same as nn.Linear
dropout_prob: 0.0
bias: false
fusion:
_target_: src.models.components.cross_modal.DotProductFusion
embedding_dim: null # clip fusion featrue dim, only effective for learnable
loss:
cross_entropy:
_target_: torch.nn.CrossEntropyLoss
focal:
_target_: src.models.components.loss.FocalLoss
dice:
_target_: src.models.components.loss.BinaryDiceLoss
k_shot: false
filter: true
enable_validation: false
compile: false
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