i#SrSSKrSSKrSSKJrJrJr SSKrSSK r SSK J r SSK J r SSKJrJr SrSSKJr SS KJr SS KJr SS KJr \R8"\5r"S S \(a \ R>O\ 5r!"SS\(a \ R>O\ 5r""SS5r#g!\a Sr\R&"S5 Nf=f)z3Trainer for contrastive learning with triplet loss.N)OptionalDictAny) DataLoader TensorDatasetTFz9PyTorch not available, PreferenceTrainer will be disabled)PreferenceEncoder)PreferenceDataset) MemoryStore)PatternEmbedderc^\rSrSrSrS S\4U4SjjjrS\RS\RS\RS\R4S jr S r U=r $) TripletLosszTriplet loss for contrastive learning. Loss = max(0, d(anchor, positive) - d(anchor, negative) + margin) where d is Euclidean distance. margincb>[(d [S5e[[U]5 Xlg)zDInitialize triplet loss. Args: margin: Margin for triplet loss z#PyTorch is required for TripletLossN)TORCH_AVAILABLE ImportErrorsuperr __init__rselfr __class__s C/home/ubuntu/codebase/yexijia/保研/iCoLoc/src/learning/trainer.pyrTripletLoss.__init__!s( CD D k4)+ anchorpositivenegativereturnc[RRRX5n[RRRX5n[R"XE- UR -SS9nUR 5$)zCompute triplet loss. Args: anchor: Anchor vectors (batch_size, dim) positive: Positive vectors (batch_size, dim) negative: Negative vectors (batch_size, dim) Returns: Loss value min)torchnn functionalpairwise_distanceclamprmean)rrrrd_posd_neglosss rforwardTripletLoss.forward-sa##55fG##55fG{{5=4;;6C@yy{rr333333? __name__ __module__ __qualname____firstlineno____doc__floatrr#Tensorr,__static_attributes__ __classcell__rs@rr r sQ u  ellell,,+0<<rr c^\rSrSrSrS S\4U4SjjjrS\RS\RS\RS\R4S jr S r U=r $) CosineTripletLossCzcCosine-based triplet loss. Loss = max(0, -cos(anchor, positive) + cos(anchor, negative) + margin) rcb>[(d [S5e[[U]5 Xlg)zKInitialize cosine triplet loss. Args: margin: Margin for triplet loss z)PyTorch is required for CosineTripletLossN)rrrr=rrrs rrCosineTripletLoss.__init__Is) IJ J /1 rrrrrc[RRRX5n[RRRX5n[R"U*U-UR -SS9nUR 5$)zCompute cosine triplet loss. Args: anchor: Anchor vectors (batch_size, dim) positive: Positive vectors (batch_size, dim) negative: Negative vectors (batch_size, dim) Returns: Loss value r r!)r#r$r%cosine_similarityr'rr()rrrrcos_poscos_negr+s rr,CosineTripletLoss.forwardUsf((%%77I((%%77I{{G8g- ;Eyy{rr.r/r1r;s@rr=r=CsQ u  ellell,,+0<<rr=c\rSrSrSrSS\S\S\\S\ S\ S \ S \ S \ S \ 4S jjr SS\ S\ S\\ S\\ \44SjjrS\ 4SjrS\ 4SjrSrg)PreferenceTrainerkz:Trainer for preference encoder using contrastive learning.N memory_storeembeddermodel input_dim hidden_dimr learning_rateuse_cosine_lossuser_idc ~[(d [S5eXlX lXlX`lXplUc[XES9UlOX0lU(a[US9Ul O[US9Ul [R"URR5US9Ul[ R#SUSU35 g)aInitialize preference trainer. Args: memory_store: Memory store instance embedder: Pattern embedder model: Optional pre-trained preference encoder input_dim: Input embedding dimension hidden_dim: Hidden layer dimension margin: Margin for triplet loss learning_rate: Learning rate use_cosine_loss: Whether to use cosine-based triplet loss user_id: User identifier z)PyTorch is required for PreferenceTrainerN)rLrMr.)lrz*PreferenceTrainer initialized with margin=z, lr=)rrrIrJrPrrNrrKr= criterionr optimAdam parameters optimizerloggerinfo) rrIrJrKrLrMrrNrOrPs rrPreferenceTrainer.__init__ns.IJ J(   * =*YVDJJ .f=DN(7DNDJJ$9$9$; N @m_]^repochs batch_size save_pathrc [URURUR5nUR 5n[ U5S:Xa[ RS5 /SS.$[R"UVs/sHofSPM sn5n[R"UVs/sHofSPM sn5n[R"UVs/sHofSPM sn5n [XxU 5n [XSS9n S/0n [ RS [ U5S US 35 URR5 [U5GHMn S nSnU HunnnURU5nURU5nURU5nUR!UUU5nUR"R%5 UR'5 UR"R)5 UUR+5- nUS- nM US:aX- OS nU SR-U5 S U S-SUSUS3n[ RU5 [/SU35 U S:dGMU SU S- U- nUS:aSO US:aSOSn[/SUS[1U5S35 GMP U(aUR3U5 XS'U $s snfs snfs snf)zTrain the preference encoder. Args: epochs: Number of training epochs batch_size: Batch size save_path: Path to save the trained model Returns: Training history dictionary rz"No triplets available for training)r+r[T)r\shuffler+zStarting training with z triplets, z epochsr zEpoch /z, Loss: z.4fz u↓u↑u→u Loss变化:  r[)r rIrJrP get_tripletslenrXwarningr# FloatTensorrrrYrKtrainrangerSrW zero_gradbackwardstepitemappendprintabs save_model)rr[r\r]datasettripletstanchors positives negativestensor_dataset dataloaderhistoryepoch epoch_loss n_batches batch_anchorsbatch_positivesbatch_negatives anchor_proj positive_proj negative_projr+avg_loss progress_msg loss_changetrends rrhPreferenceTrainer.trains$D$5$5t}}dllS'') x=A  NN? @!, ,##8$<8aqT8$<=%%X&>XtX&>? %%X&>XtX&>? 'w9EtT 2, -c(m_KxwWX 6]EJICM? "jj7 $ ? ; $ ? ; ~~k=-P((* ##%diik) Q DN"2;Qz-CH FO " "8 ,$E!G9AfXXhs^LL KK % B|n% &qy%foeAg6A !,q{QeTY(q[1A#0FGHC#H  OOI &"o%=&>&>s9J4"J9 J>pathc[R"[RRU5SS9 [R "UR R5URR5UR RUR RUR RS.U5 [RSU35 g)z@Save the trained model. Args: path: Path to save the model T)exist_ok)model_state_dictoptimizer_state_dictrLrM output_dimzModel saved to N)osmakedirsrdirnamer#saverK state_dictrWrLrMrrXrY)rrs rrqPreferenceTrainer.save_models BGGOOD)D9 $ 5 5 7$(NN$=$=$?--**//**//      odV,-rc[R"U5nURRUS5 URRUS5 [ R SU35 g)zCLoad a trained model. Args: path: Path to load the model from rrzModel loaded from N)r#loadrKload_state_dictrWrXrY)rr checkpoints r load_modelPreferenceTrainer.load_modelsV ZZ%  "":.@#AB &&z2H'IJ (/0r)rSrJrNrrIrKrWrP)Nir0gMbP?Tuser_001) N)r2r3r4r5r6r r rrintr7boolstrrrrrhrqrr9rrrGrGksD 7;"%#&!$(-)- */_*/_*/_!!23/_ /_ ! /_  /_!&/_#'/_/_b9;)-LCL#L!#L26sCx.L\.s. 1s 1rrG)$r6rnumpynptypingrrrloggingr#torch.nnr$ torch.optimrTtorch.utils.datarrrrrfembedding.encoderrlearning.datasetr memory.storer learning.embedderr getLoggerr2rXModuleobjectr r=rGrrrrs9 &&Q:O 0.$-   8 $&"))F&R%_ &%P[1[1{QO OOOPQsB%%CC