26 lines
3.4 KiB
Plaintext
26 lines
3.4 KiB
Plaintext
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PK ve_meta_nas/data.pklFB
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ZZZZZZZZZZ<5A>cml_collections.config_dict.config_dict
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ConfigDict
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q )<29>q}q(X _fieldsq}q(X search_spaceqNX resumeq<06>X folder_nameqX 0404qX taskq X tr_meta_predictorq
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X exp_nameq NX
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model_typeq X meta_predictorq
X scorenet_ckpt_pathqX* checkpoints/score_model/model_best.pth.tarqX is_metaq<10>X trainingqh )<29>q}q(h}q(X sdeqX vesdeqX
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continuousq<17>X reduce_meanq<18>X noisedq<19>X
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batch_sizeqM X eval_batch_sizeqKdX n_itersq M'X
snapshot_freqq M<>X log_freqq KdX eval_freqq KdX snapshot_freq_for_preemptionq M<>X snapshot_samplingq!<21>X likelihood_weightingq"<22>X t_spotq#G?<3F> X load_pretrainedq$<24>X pretrained_model_pathq%huX _lockedq&<26>X
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_type_safeq'<27>X
_convert_dictq(<28>ubX samplingq)h )<29>q*}q+(h}q,(X methodq-X pcq.X predictorq/X euler_maruyamaq0X correctorq1X langevinq2X rtolq3G><3E><><EFBFBD><EFBFBD><EFBFBD>h<EFBFBD>X atolq4G><3E><><EFBFBD><EFBFBD><EFBFBD>h<EFBFBD>X
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ode_methodq5X dopri5q6X ode_stepq7G?<3F>z<EFBFBD>G<EFBFBD>{X n_steps_eachq8KX
noise_removalq9<71>X probability_flowq:<3A>X snrq;G?<3F>z<EFBFBD>G<EFBFBD>{X vis_rowq<KX vis_colq=KX classifier_scaleq>G?<3F> X regressq?<3F>X labelsq@X maxqAX weight_ratioqB<71>X weight_schedulingqC<71>h#G?<3F> X
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t_spot_endqDG X number_chain_stepsqEK2X check_datanameqFX cifar10qGuh&<26>h'<27>h(<28>ubX evalqHh )<29>qI}qJ(h}qK(X
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begin_ckptqLKX end_ckptqMKhM X enable_samplingqN<71>X num_samplesqOM X mmd_distanceqPX RBFqQX max_subgraphqR<71>X
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save_graphqS<71>uh&<26>h'<27>h(<28>ubX dataqTh )<29>qU}qV(h}qW(X centeredqX<71>X dequantizationqY<71>X rootqZX# ./data/processed/NASBench201_val.ptq[X nameq\X NASBench201q]X split_ratioq^G?<3F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>X dataset_idxq_X randomq`X max_nodeqaKX n_vocabqbKX
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START_TYPEqcK X END_TYPEqdKX
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num_graphsqeM =X num_channelsqfKX except_inoutqg<71>X triu_adjqh<71>X connect_prevqi<71>X
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label_listqj]qkX meta-accqlaX
aug_mask_algoqmX floydqnX num_trainqoK<6F>uh&<26>h'<27>h(<28>ubX modelqph )<29>qq}qr(h}qs(h\X MetaNeuralPredictorqtX ema_ratequG?<3F><>.H<><48> X
normalizationqvX GroupNormqwX nonlinearityqxX swishqyX nfqzK<7A>X num_gnn_layersq{KX size_condq|<7C>X embedding_typeq}X
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positionalq~X rw_depthqKX graph_layerq<72>X
PosTransLayerq<72>X edge_thq<68>G<EFBFBD><47> X headsq<73>KX
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attn_clampq<70><71>X
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input_typeq<65>X DAq<41>X hsq<73>M X nzq<7A>K8X
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num_sampleq<65>KX
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num_scalesq<73>M<EFBFBD>X beta_minq<6E>G?<3F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>X beta_maxq<78>G@4 X sigma_minq<6E>G?<3F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>X sigma_maxq<78>G@4 X dropoutq<74>G?<3F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>X
graph_encoderq<72>h )<29>q<EFBFBD>}q<>(h}q<>(X initial_hiddenq<6E>KX
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gcn_hiddenq<6E>K<EFBFBD>X
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gcn_layersq<73>KX
linear_hiddenq<6E>K<EFBFBD>uh&<26>h'<27>h(<28>ubuh&<26>h'<27>h(<28>ubX optimq<6D>h )<29>q<EFBFBD>}q<>(h}q<>(X weight_decayq<79>K X optimizerq<72>X Adamq<6D>X lrq<72>G><3E><><EFBFBD><EFBFBD><EFBFBD>h<EFBFBD>X beta1q<31>G?<3F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>X epsq<73>G>Ey<45><79>0<EFBFBD>:X warmupq<70>M<EFBFBD>X grad_clipq<70>G?<3F> uh&<26>h'<27>h(<28>ubX seedq<64>K*X deviceq<65>ctorch
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device
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q<EFBFBD>X cudaq<61>K <00>q<EFBFBD>Rq<52>X logq<67>h )<29>q<EFBFBD>}q<>(h}q<>(X use_wandbq<62><71>X wandb_project_nameq<65>X GTAD_v1q<31>X log_valid_sample_propq<70><71>X num_graphs_to_visualizeq<65>Kuh&<26>h'<27>h(<28>ubuh&<26>h'<27>h(<28>ub.PK<08><>R~ PK # ve_meta_nas/versionFB ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ3
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