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compute_validators.py
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compute_validators.py
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import os, glob
import json
import copy
import argparse
from tkinter import W
from sklearn.metrics import (
adjusted_rand_score,
adjusted_mutual_info_score,
homogeneity_score, completeness_score, v_measure_score,
fowlkes_mallows_score, silhouette_score, calinski_harabasz_score, davies_bouldin_score,
accuracy_score, f1_score, log_loss
)
import torch
from pytorch_adapt.datasets import get_mnist, get_mnistmr, get_office31, get_officehome, get_visda2017c
from pytorch_adapt.validators import (
AccuracyValidator, EntropyValidator, EntropyCombinedValidator,
BNMValidator, BNMWithSourceValidator,
IMValidator, IMCombinedValidator,
ClassClusterValidator, PerClassValidator,
SNDValidator, CORALValidator, MMDValidator, DeepEmbeddedValidator,
RankMeValidator,
HenryValidator, HenryV3Validator, HenryUpdatedValidator, GoukValidator, GoukV3Validator, GoukUpdatedValidator, ImprovedGoukValidator
)
# Create the parser
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default="office31")
parser.add_argument('--source', type=str, default="dslr")
parser.add_argument('--target', type=str, default="amazon")
parser.add_argument('--algorithm', type=str, default="source-only")
parser.add_argument('--G-arch', type=str, default="resnet50")
parser.add_argument('--init-source-only', action="store_true", default=False)
parser.add_argument('--batch-size', type=int, default=64)
parser.add_argument('--num-workers', type=int, default=64)
parser.add_argument('--feature-layer', type=str, default="fl3")
parser.add_argument('--validators', nargs='+', type=str,
default=[
"src_train_acc_score",
"src_val_acc_score",
"target_train_acc_score",
"target_val_acc_score",
"target_test_acc_score",
"src_train_bnm_score",
"src_val_bnm_score",
"target_train_bnm_score",
"target_val_bnm_score",
"src_train_target_train_bnm_score",
"src_train_target_val_bnm_score",
"src_val_target_train_bnm_score",
"src_val_target_val_bnm_score",
"src_train_entropy_score",
"src_val_entropy_score",
"target_train_entropy_score",
"target_val_entropy_score",
"src_train_target_train_entropy_score",
"src_train_target_val_entropy_score",
"src_val_target_train_entropy_score",
"src_val_target_val_entropy_score",
"src_train_im_score",
"src_val_im_score",
"target_train_im_score",
"target_val_im_score",
"src_train_target_train_im_score",
"src_train_target_val_im_score",
"src_val_target_train_im_score",
"src_val_target_val_im_score",
"src_train_class_<score_fn_name>_score",
"src_val_class_<score_fn_name>_score",
"target_train_class_<score_fn_name>_score",
"target_val_class_<score_fn_name>_score",
"src_train_target_train_class_<score_fn_name>_score",
"src_train_target_val_class_<score_fn_name>_score",
"src_val_target_train_class_<score_fn_name>_score",
"src_val_target_val_class_<score_fn_name>_score",
"src_train_logits_class_<score_fn_name>_score",
"src_val_logits_class_<score_fn_name>_score",
"target_train_logits_class_<score_fn_name>_score",
"target_val_logits_class_<score_fn_name>_score",
"src_train_target_train_logits_class_<score_fn_name>_score",
"src_train_target_val_logits_class_<score_fn_name>_score",
"src_val_target_train_logits_class_<score_fn_name>_score",
"src_val_target_val_logits_class_<score_fn_name>_score",
"src_train_snd_score",
"src_val_snd_score",
"target_train_snd_score",
"target_val_snd_score",
"src_train_target_train_mmd_score",
"src_train_target_val_mmd_score",
"src_val_target_train_mmd_score",
"src_val_target_val_mmd_score",
"src_train_target_train_logits_mmd_score",
"src_train_target_val_logits_mmd_score",
"src_val_target_train_logits_mmd_score",
"src_val_target_val_logits_mmd_score",
"src_train_target_train_preds_mmd_score",
"src_train_target_val_preds_mmd_score",
"src_val_target_train_preds_mmd_score",
"src_val_target_val_preds_mmd_score",
"src_train_target_train_mmd_per_class_score",
"src_train_target_val_mmd_per_class_score",
"src_val_target_train_mmd_per_class_score",
"src_val_target_val_mmd_per_class_score",
"src_train_target_train_logits_mmd_per_class_score",
"src_train_target_val_logits_mmd_per_class_score",
"src_val_target_train_logits_mmd_per_class_score",
"src_val_target_val_logits_mmd_per_class_score",
"src_train_target_train_preds_mmd_per_class_score",
"src_train_target_val_preds_mmd_per_class_score",
"src_val_target_train_preds_mmd_per_class_score",
"src_val_target_val_preds_mmd_per_class_score",
"src_train_target_train_coral_score",
"src_train_target_val_coral_score",
"src_val_target_train_coral_score",
"src_val_target_val_coral_score",
"src_train_target_train_logits_coral_score",
"src_train_target_val_logits_coral_score",
"src_val_target_train_logits_coral_score",
"src_val_target_val_logits_coral_score",
"src_train_target_train_preds_coral_score",
"src_train_target_val_preds_coral_score",
"src_val_target_train_preds_coral_score",
"src_val_target_val_preds_coral_score",
"src_train_target_train_coral_per_class_score",
"src_train_target_val_coral_per_class_score",
"src_val_target_train_coral_per_class_score",
"src_val_target_val_coral_per_class_score",
"src_train_target_train_logits_coral_per_class_score",
"src_train_target_val_logits_coral_per_class_score",
"src_val_target_train_logits_coral_per_class_score",
"src_val_target_val_logits_coral_per_class_score",
"src_train_target_train_preds_coral_per_class_score",
"src_train_target_val_preds_coral_per_class_score",
"src_val_target_train_preds_coral_per_class_score",
"src_val_target_val_preds_coral_per_class_score",
"src_train_rank_me_score",
"src_val_rank_me_score",
"target_train_rank_me_score",
"target_val_rank_me_score",
"src_train_logits_rank_me_score",
"src_val_logits_rank_me_score",
"target_train_logits_rank_me_score",
"target_val_logits_rank_me_score",
"src_train_preds_rank_me_score",
"src_val_preds_rank_me_score",
"target_train_preds_rank_me_score",
"target_val_preds_rank_me_score",
"src_train_target_train_rank_me_score",
"src_train_target_val_rank_me_score",
"src_val_target_train_rank_me_score",
"src_val_target_val_rank_me_score",
"src_train_target_train_logits_rank_me_score",
"src_train_target_val_logits_rank_me_score",
"src_val_target_train_logits_rank_me_score",
"src_val_target_val_logits_rank_me_score",
"src_train_target_train_preds_rank_me_score",
"src_train_target_val_preds_rank_me_score",
"src_val_target_train_preds_rank_me_score",
"src_val_target_val_preds_rank_me_score",
"target_train_henry_score",
"target_val_henry_score",
"src_train_gouk_score",
"src_val_gouk_score",
"target_train_henry_v3_score",
"target_val_henry_v3_score",
"src_train_gouk_v3_score",
"src_val_gouk_v3_score",
"target_train_henry_updated_score",
"target_val_henry_updated_score",
"src_train_gouk_updated_score",
"src_val_gouk_updated_score",
"src_train_improved_gouk_score",
"src_val_improved_gouk_score",
#"dev_features_max_score",
#"dev_logits_max_score",
#"dev_preds_max_score",
#"dev_features_standardize_score",
#"dev_logits_standardize_score",
#"dev_preds_standardize_score",
])
parser.add_argument('--device', type=str, default="cuda")
parser.add_argument('--results-root', type=str, default="/home/CORP/l.ericsson/code/pytorch-adapt/examples/visda/results/")
parser.add_argument('--data-root', type=str, default="/home/CORP/l.ericsson/data/")
parser.add_argument('--debug', action='store_true', default=False)
DATASETS = {
"mnistm": (get_mnist, 10),
"visda2017c": (get_visda2017c, 12),
"office31": (get_office31, 31),
"officehome": (get_officehome, 65),
"mnistmr": (get_mnistmr, 4),
}
VALIDATORS = {
"src_train_acc_score": lambda kwargs: (AccuracyValidator(), {"src_val": "src_train"}),
"src_val_acc_score": lambda kwargs: (AccuracyValidator(), {"src_val": "src_val"}),
"target_train_acc_score": lambda kwargs: (AccuracyValidator(), {"src_val": "target_train"}),
"target_val_acc_score": lambda kwargs: (AccuracyValidator(), {"src_val": "target_val"}),
"target_test_acc_score": lambda kwargs: (AccuracyValidator(), {"src_val": "target_test"}),
"src_train_bnm_score": lambda kwargs: (BNMValidator(), {"target_train": "src_train"}),
"src_val_bnm_score": lambda kwargs: (BNMValidator(), {"target_train": "src_val"}),
"target_train_bnm_score": lambda kwargs: (BNMValidator(), {"target_train": "target_train"}),
"target_val_bnm_score": lambda kwargs: (BNMValidator(), {"target_train": "target_val"}),
"src_train_target_train_bnm_score": lambda kwargs: (BNMWithSourceValidator(), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_bnm_score": lambda kwargs: (BNMWithSourceValidator(), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_bnm_score": lambda kwargs: (BNMWithSourceValidator(), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_bnm_score": lambda kwargs: (BNMWithSourceValidator(), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_entropy_score": lambda kwargs: (EntropyValidator(), {"target_train": "src_train"}),
"src_val_entropy_score": lambda kwargs: (EntropyValidator(), {"target_train": "src_val"}),
"target_train_entropy_score": lambda kwargs: (EntropyValidator(), {"target_train": "target_train"}),
"target_val_entropy_score": lambda kwargs: (EntropyValidator(), {"target_train": "target_val"}),
"src_train_target_train_entropy_score": lambda kwargs: (EntropyCombinedValidator(), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_entropy_score": lambda kwargs: (EntropyCombinedValidator(), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_entropy_score": lambda kwargs: (EntropyCombinedValidator(), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_entropy_score": lambda kwargs: (EntropyCombinedValidator(), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_im_score": lambda kwargs: (IMValidator(), {"target_train": "src_train"}),
"src_val_im_score": lambda kwargs: (IMValidator(), {"target_train": "src_val"}),
"target_train_im_score": lambda kwargs: (IMValidator(), {"target_train": "target_train"}),
"target_val_im_score": lambda kwargs: (IMValidator(), {"target_train": "target_val"}),
"src_train_target_train_im_score": lambda kwargs: (IMCombinedValidator(), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_im_score": lambda kwargs: (IMCombinedValidator(), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_im_score": lambda kwargs: (IMCombinedValidator(), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_im_score": lambda kwargs: (IMCombinedValidator(), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(), {"target_train": "src_train"}),
"src_val_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(), {"target_train": "src_val"}),
"target_train_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(), {"target_train": "target_train"}),
"target_val_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(), {"target_train": "target_val"}),
"src_train_target_train_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(with_src=True), {"src_train": "src_train", "target_train": "target_train"}),
"src_val_target_train_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(with_src=True), {"src_train": "src_val", "target_train": "target_train"}),
"src_train_target_val_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(with_src=True), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_val_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(with_src=True), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_logits_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(layer="logits"), {"target_train": "src_train"}),
"src_val_logits_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(layer="logits"), {"target_train": "src_val"}),
"target_train_logits_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(layer="logits"), {"target_train": "target_train"}),
"target_val_logits_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(layer="logits"), {"target_train": "target_val"}),
"src_train_target_train_logits_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(layer="logits", with_src=True), {"src_train": "src_train", "target_train": "target_train"}),
"src_val_target_train_logits_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(layer="logits", with_src=True), {"src_train": "src_val", "target_train": "target_train"}),
"src_train_target_val_logits_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(layer="logits", with_src=True), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_val_logits_class_<score_fn_name>_score": lambda kwargs: (ClassClusterValidator(layer="logits", with_src=True), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_snd_score": lambda kwargs: (SNDValidator(), {"target_train": "src_train"}),
"src_val_snd_score": lambda kwargs: (SNDValidator(), {"target_train": "src_val"}),
"target_train_snd_score": lambda kwargs: (SNDValidator(), {"target_train": "target_train"}),
"target_val_snd_score": lambda kwargs: (SNDValidator(), {"target_train": "target_val"}),
"src_train_target_train_mmd_score": lambda kwargs: (MMDValidator(mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_mmd_score": lambda kwargs: (MMDValidator(mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_mmd_score": lambda kwargs: (MMDValidator(mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_mmd_score": lambda kwargs: (MMDValidator(mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_logits_mmd_score": lambda kwargs: (MMDValidator(layer="logits", mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_logits_mmd_score": lambda kwargs: (MMDValidator(layer="logits", mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_logits_mmd_score": lambda kwargs: (MMDValidator(layer="logits", mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_logits_mmd_score": lambda kwargs: (MMDValidator(layer="logits", mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_preds_mmd_score": lambda kwargs: (MMDValidator(layer="preds", mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_preds_mmd_score": lambda kwargs: (MMDValidator(layer="preds", mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_preds_mmd_score": lambda kwargs: (MMDValidator(layer="preds", mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_preds_mmd_score": lambda kwargs: (MMDValidator(layer="preds", mmd_kwargs=dict(mmd_type="quadratic")), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_logits_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(layer="logits", mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_logits_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(layer="logits", mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_logits_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(layer="logits", mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_logits_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(layer="logits", mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_preds_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(layer="preds", mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_preds_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(layer="preds", mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_preds_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(layer="preds", mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_preds_mmd_per_class_score": lambda kwargs: (PerClassValidator(MMDValidator(layer="preds", mmd_kwargs=dict(mmd_type="quadratic"))), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_coral_score": lambda kwargs: (CORALValidator(), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_coral_score": lambda kwargs: (CORALValidator(), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_coral_score": lambda kwargs: (CORALValidator(), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_coral_score": lambda kwargs: (CORALValidator(), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_logits_coral_score": lambda kwargs: (CORALValidator(layer="logits"), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_logits_coral_score": lambda kwargs: (CORALValidator(layer="logits"), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_logits_coral_score": lambda kwargs: (CORALValidator(layer="logits"), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_logits_coral_score": lambda kwargs: (CORALValidator(layer="logits"), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_preds_coral_score": lambda kwargs: (CORALValidator(layer="preds"), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_preds_coral_score": lambda kwargs: (CORALValidator(layer="preds"), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_preds_coral_score": lambda kwargs: (CORALValidator(layer="preds"), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_preds_coral_score": lambda kwargs: (CORALValidator(layer="preds"), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator()), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator()), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator()), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator()), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_logits_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator(layer="logits")), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_logits_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator(layer="logits")), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_logits_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator(layer="logits")), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_logits_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator(layer="logits")), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_preds_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator(layer="preds")), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_preds_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator(layer="preds")), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_preds_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator(layer="preds")), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_preds_coral_per_class_score": lambda kwargs: (PerClassValidator(CORALValidator(layer="preds")), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_rank_me_score": lambda kwargs: (RankMeValidator(with_target=False), {"src_train": "src_train"}),
"src_val_rank_me_score": lambda kwargs: (RankMeValidator(with_target=False), {"src_train": "src_val"}),
"target_train_rank_me_score": lambda kwargs: (RankMeValidator(with_target=False), {"src_train": "target_train"}),
"target_val_rank_me_score": lambda kwargs: (RankMeValidator(with_target=False), {"src_train": "target_val"}),
"src_train_logits_rank_me_score": lambda kwargs: (RankMeValidator(layer="logits", with_target=False), {"src_train": "src_train"}),
"src_val_logits_rank_me_score": lambda kwargs: (RankMeValidator(layer="logits", with_target=False), {"src_train": "src_val"}),
"target_train_logits_rank_me_score": lambda kwargs: (RankMeValidator(layer="logits", with_target=False), {"src_train": "target_train"}),
"target_val_logits_rank_me_score": lambda kwargs: (RankMeValidator(layer="logits", with_target=False), {"src_train": "target_val"}),
"src_train_preds_rank_me_score": lambda kwargs: (RankMeValidator(layer="preds", with_target=False), {"src_train": "src_train"}),
"src_val_preds_rank_me_score": lambda kwargs: (RankMeValidator(layer="preds", with_target=False), {"src_train": "src_val"}),
"target_train_preds_rank_me_score": lambda kwargs: (RankMeValidator(layer="preds", with_target=False), {"src_train": "target_train"}),
"target_val_preds_rank_me_score": lambda kwargs: (RankMeValidator(layer="preds", with_target=False), {"src_train": "target_val"}),
"src_train_target_train_rank_me_score": lambda kwargs: (RankMeValidator(with_target=True), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_rank_me_score": lambda kwargs: (RankMeValidator(with_target=True), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_rank_me_score": lambda kwargs: (RankMeValidator(with_target=True), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_rank_me_score": lambda kwargs: (RankMeValidator(with_target=True), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_logits_rank_me_score": lambda kwargs: (RankMeValidator(layer="logits", with_target=True), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_logits_rank_me_score": lambda kwargs: (RankMeValidator(layer="logits", with_target=True), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_logits_rank_me_score": lambda kwargs: (RankMeValidator(layer="logits", with_target=True), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_logits_rank_me_score": lambda kwargs: (RankMeValidator(layer="logits", with_target=True), {"src_train": "src_val", "target_train": "target_val"}),
"src_train_target_train_preds_rank_me_score": lambda kwargs: (RankMeValidator(layer="preds", with_target=True), {"src_train": "src_train", "target_train": "target_train"}),
"src_train_target_val_preds_rank_me_score": lambda kwargs: (RankMeValidator(layer="preds", with_target=True), {"src_train": "src_train", "target_train": "target_val"}),
"src_val_target_train_preds_rank_me_score": lambda kwargs: (RankMeValidator(layer="preds", with_target=True), {"src_train": "src_val", "target_train": "target_train"}),
"src_val_target_val_preds_rank_me_score": lambda kwargs: (RankMeValidator(layer="preds", with_target=True), {"src_train": "src_val", "target_train": "target_val"}),
"target_train_henry_score": lambda kwargs: (HenryValidator(num_classes=kwargs["num_classes"]), {"src_val": "src_val", "target_train": "target_train"}),
"target_val_henry_score": lambda kwargs: (HenryValidator(num_classes=kwargs["num_classes"]), {"src_val": "src_val", "target_train": "target_val"}),
"target_train_henry_v3_score": lambda kwargs: (HenryV3Validator(num_classes=kwargs["num_classes"]), {"src_val": "src_val", "target_train": "target_train"}),
"target_val_henry_v3_score": lambda kwargs: (HenryV3Validator(num_classes=kwargs["num_classes"]), {"src_val": "src_val", "target_train": "target_val"}),
"target_train_henry_updated_score": lambda kwargs: (HenryUpdatedValidator(num_classes=kwargs["num_classes"], src_val_at_init=kwargs["src_val_at_init"]), {"src_val": "src_val", "target_train": "target_train"}),
"target_val_henry_updated_score": lambda kwargs: (HenryUpdatedValidator(num_classes=kwargs["num_classes"], src_val_at_init=kwargs["src_val_at_init"]), {"src_val": "src_val", "target_train": "target_val"}),
"src_train_gouk_score": lambda kwargs: (GoukValidator(num_classes=kwargs["num_classes"]), {"src_val": "src_train"}),
"src_val_gouk_score": lambda kwargs: (GoukValidator(num_classes=kwargs["num_classes"]), {"src_val": "src_val"}),
"src_train_gouk_v3_score": lambda kwargs: (GoukV3Validator(num_classes=kwargs["num_classes"]), {"src_val": "src_train"}),
"src_val_gouk_v3_score": lambda kwargs: (GoukV3Validator(num_classes=kwargs["num_classes"]), {"src_val": "src_val"}),
"src_train_gouk_updated_score": lambda kwargs: (GoukUpdatedValidator(num_classes=kwargs["num_classes"], src_val_at_init=kwargs["src_val_at_init"]), {"src_val": "src_train"}),
"src_val_gouk_updated_score": lambda kwargs: (GoukUpdatedValidator(num_classes=kwargs["num_classes"], src_val_at_init=kwargs["src_val_at_init"]), {"src_val": "src_val"}),
"src_train_improved_gouk_score": lambda kwargs: (ImprovedGoukValidator(num_classes=kwargs["num_classes"]), {"src_val": "src_train"}),
"src_val_improved_gouk_score": lambda kwargs: (ImprovedGoukValidator(num_classes=kwargs["num_classes"]), {"src_val": "src_val"}),
"dev_features_max_score": lambda kwargs: (
DeepEmbeddedValidator(
temp_folder=kwargs["dev_temp"],
layer="features",
normalization="max"
),
{"src_train": "src_train", "src_val": "src_val", "target_train": "target_train"}
),
"dev_logits_max_score": lambda kwargs: (
DeepEmbeddedValidator(
temp_folder=".dev_temp",
layer="logits",
normalization="max"
),
{"src_train": "src_train", "src_val": "src_val", "target_train": "target_train"}
),
"dev_preds_max_score": lambda kwargs: (
DeepEmbeddedValidator(
temp_folder=".dev_temp",
layer="preds",
normalization="max"
),
{"src_train": "src_train", "src_val": "src_val", "target_train": "target_train"}
),
"dev_features_standardize_score": lambda kwargs: (
DeepEmbeddedValidator(
temp_folder=".dev_temp",
layer="features",
normalization="standardize"
),
{"src_train": "src_train", "src_val": "src_val", "target_train": "target_train"}
),
"dev_logits_standardize_score": lambda kwargs: (
DeepEmbeddedValidator(
temp_folder=".dev_temp",
layer="logits",
normalization="standardize"
),
{"src_train": "src_train", "src_val": "src_val", "target_train": "target_train"}
),
"dev_preds_standardize_score": lambda kwargs: (
DeepEmbeddedValidator(
temp_folder=".dev_temp",
layer="preds",
normalization="standardize"
),
{"src_train": "src_train", "src_val": "src_val", "target_train": "target_train"}
),
}
cluster_score_fn_names = [
"ari", "ami", "v_measure", "fmi",
"silhouette", "chi", "dbi"
]
class Validator():
def __init__(self, validators, main_validator, patience):
self.validators = validators
self.main_validator = main_validator
self.patience = patience
self.scores = []
def compute(self, data, epoch):
d = {"epoch": epoch + 1}
for name, (validator, input_keys) in self.validators.items():
try:
val = validator(**{key: data[value] for key, value in input_keys.items()})
except:
val = {n: 0 for n in cluster_score_fn_names} if "<score_fn_name>" in name else 0
if "<score_fn_name>" in name:
for c_name in cluster_score_fn_names:
d[name.replace("<score_fn_name>", c_name)] = val[c_name]
else:
d[name] = val
self.scores.append(d)
def compute_validators(self, data, validators):
d = {}
for name, (validator, input_keys) in validators.items():
try:
val = validator(**{key: data[value] for key, value in input_keys.items()})
except:
val = {n: 0 for n in cluster_score_fn_names} if "<score_fn_name>" in name else 0
if "<score_fn_name>" in name:
for c_name in cluster_score_fn_names:
d[name.replace("<score_fn_name>", c_name)] = val[c_name]
else:
d[name] = val
return d
def necessary_splits(self):
splits = list(set([val for v in self.validators for val in self.validators[v][1].values()]))
split_splits = [key.split("_") for key in splits]
splits_with_labels = []
for split in split_splits:
s = f"{split[1]}_with_labels" if split[0] == "target" else split[1]
splits_with_labels.append([split[0], s])
return splits, splits_with_labels
def early_stopping(self):
history = torch.tensor(self.get(self.main_validator, recent=False))
best_val_step = torch.argmax(history)
self.best_epoch = self.scores[best_val_step]["epoch"]
current_val_step = len(self.scores) - 1
return current_val_step - best_val_step > self.patience
def get(self, name, recent=True):
if recent:
return self.scores[-1][name]
else:
return [self.scores[i][name] for i in range(len(self.scores))]
def get_best(self, name, best_epoch):
history = [s for s in self.scores]
for h in history:
if h["epoch"] == best_epoch:
return h
return None
def print(self, scores=None, recent=True):
s = scores if scores is not None else self.scores
history = [s[-1]] if recent else s
for scores in history:
for k, v in scores.items():
print(f"\t{k}:\t{v:.2f}")
print("\n")
def save(self, results_root, dataset, source, target, algorithm, filename, indent=4):
with open(os.path.join(results_root, dataset, source, target, algorithm, filename), "w") as f:
json.dump(self.scores[-1], f, indent=indent)
if __name__ == "__main__":
args = parser.parse_args()
if args.algorithm == "source-only":
args.validators = [v for v in args.validators if "acc_score" in v]
src_val_at_init = torch.load(os.path.join(args.results_root, args.dataset, args.source, args.target, "source-only", "best_features.pt"))["src_val"]
num_classes = DATASETS[args.dataset][1]
chosen_validators = {key: VALIDATORS[key]({
"num_classes": num_classes,
"src_val_at_init": src_val_at_init,
"dev_temp": os.path.join(".dev_temp", args.results_root, args.dataset, args.source, args.target, args.algorithm)}) for key in args.validators}
validator = Validator(chosen_validators, "target_test_acc_score", 200)
score_files = glob.glob(os.path.join(args.results_root, args.dataset, args.source, args.target, args.algorithm, "features*.pt"))
print(f"Computing validators for {len(score_files)} checkpoints in {args.dataset}-{args.source[0].upper()+args.target[0].upper()} {args.algorithm} ")
for f in score_files:
for fl in ["fl3", "fl6"]:
run_name = "{" + f.split("{")[1].split("}")[0] + "}"
epoch = int(f.split("_")[-1].split(".")[0])
scores_path = os.path.join(args.results_root, args.dataset, args.source, args.target, args.algorithm, f"post_scores_{run_name}_{epoch}_{fl}.json")
scores_keys = []
if os.path.exists(scores_path):
scores = json.load(open(scores_path, "r"))
scores_keys = [key for key in scores if key not in ["epoch"]]
not_computed = [v for v in args.validators if v not in scores_keys]
# remove clustering validators if computed
computed_clustering_scores = [s for s in cluster_score_fn_names if f"src_val_target_val_logits_class_{s}_score" in scores_keys]
if len(computed_clustering_scores) == len(cluster_score_fn_names):
for v in not_computed:
not_computed = [v for v in not_computed if "<score_fn_name>" not in v]
##############################
# add validators to override #
##############################
if args.algorithm != "source-only":
not_computed.extend([
"src_train_rank_me_score",
"src_val_rank_me_score",
"target_train_rank_me_score",
"target_val_rank_me_score",
"src_train_logits_rank_me_score",
"src_val_logits_rank_me_score",
"target_train_logits_rank_me_score",
"target_val_logits_rank_me_score",
"src_train_preds_rank_me_score",
"src_val_preds_rank_me_score",
"target_train_preds_rank_me_score",
"target_val_preds_rank_me_score",
"src_train_target_train_rank_me_score",
"src_train_target_val_rank_me_score",
"src_val_target_train_rank_me_score",
"src_val_target_val_rank_me_score",
"src_train_target_train_logits_rank_me_score",
"src_train_target_val_logits_rank_me_score",
"src_val_target_train_logits_rank_me_score",
"src_val_target_val_logits_rank_me_score",
"src_train_target_train_preds_rank_me_score",
"src_train_target_val_preds_rank_me_score",
"src_val_target_train_preds_rank_me_score",
"src_val_target_val_preds_rank_me_score",
])
if len(not_computed) == 0:
print(f"{args.dataset}-{args.source[0].upper()+args.target[0].upper()} {args.algorithm} {run_name}_{epoch}_{fl} already computed all validators, skipping.")
continue
else:
not_computed = args.validators
if not os.path.exists(scores_path):
print(f"Validating all for {args.dataset}-{args.source[0].upper()+args.target[0].upper()} {args.algorithm} {run_name}_{epoch}_{fl}.")
eval_data = torch.load(f)
for key in eval_data:
eval_data[key]["features"] = eval_data[key][fl]
# run MNIST faster
if args.dataset == "mnistm":
torch.manual_seed(0)
for key in eval_data:
idxs = torch.randperm(len(eval_data[key]["labels"]))[:1000]
for layer in eval_data[key]:
eval_data[key][layer] = eval_data[key][layer][idxs]
eval_data = {key: {key2: val2.to("cuda:0") for key2, val2 in val.items()} for key, val in eval_data.items()}
validator.compute(eval_data, epoch - 1)
validator.print()
validator.save(args.results_root, args.dataset, args.source, args.target, args.algorithm, f"post_scores_{run_name}_{epoch}_{fl}.json")
else:
print(f"Validating {not_computed} for {args.dataset}-{args.source[0].upper()+args.target[0].upper()} {args.algorithm} {run_name}_{epoch}_{fl}.")
eval_data = torch.load(f)
for key in eval_data:
eval_data[key]["features"] = eval_data[key][fl]
# run MNIST faster
if args.dataset == "mnistm":
torch.manual_seed(0)
for key in eval_data:
idxs = torch.randperm(len(eval_data[key]["labels"]))[:1000]
for layer in eval_data[key]:
eval_data[key][layer] = eval_data[key][layer][idxs]
eval_data = {key: {key2: val2.to("cuda:0") for key2, val2 in val.items()} for key, val in eval_data.items()}
uncomputed_validators = {key: VALIDATORS[key]({
"num_classes": num_classes,
"src_val_at_init": src_val_at_init,
"dev_temp": os.path.join(".dev_temp", args.results_root, args.dataset, args.source, args.target, args.algorithm)}) for key in not_computed}
validator_scores = validator.compute_validators(eval_data, uncomputed_validators)
print(validator_scores)
validator.scores.append(json.load(
open(os.path.join(
args.results_root, args.dataset, args.source, args.target, args.algorithm,
f"post_scores_{run_name}_{epoch}_{fl}.json"
), "r")
)
)
validator.scores[-1].update(validator_scores)
validator.print()
validator.save(args.results_root, args.dataset, args.source, args.target, args.algorithm, f"post_scores_{run_name}_{epoch}_{fl}.json")