release code

2025-08-16 20:46:31 +08:00
commit 3dc26db3b9
277 changed files with 60106 additions and 0 deletions
--- a/Dassl.ProGrad.pytorch/dassl/config/defaults.py
+++ b/Dassl.ProGrad.pytorch/dassl/config/defaults.py
@@ -0,0 +1,275 @@
+from yacs.config import CfgNode as CN
+
+###########################
+# Config definition
+###########################
+
+_C = CN()
+
+_C.VERSION = 1
+
+# Directory to save the output files (like log.txt and model weights)
+_C.OUTPUT_DIR = "./output"
+# Path to a directory where the files were saved previously
+_C.RESUME = ""
+# Set seed to negative value to randomize everything
+# Set seed to positive value to use a fixed seed
+_C.SEED = -1
+_C.USE_CUDA = True
+# Print detailed information
+# E.g. trainer, dataset, and backbone
+_C.VERBOSE = True
+
+###########################
+# Input
+###########################
+_C.INPUT = CN()
+_C.INPUT.SIZE = (224, 224)
+# Mode of interpolation in resize functions
+_C.INPUT.INTERPOLATION = "bilinear"
+# For available choices please refer to transforms.py
+_C.INPUT.TRANSFORMS = ()
+# If True, tfm_train and tfm_test will be None
+_C.INPUT.NO_TRANSFORM = False
+# Default mean and std come from ImageNet
+_C.INPUT.PIXEL_MEAN = [0.485, 0.456, 0.406]
+_C.INPUT.PIXEL_STD = [0.229, 0.224, 0.225]
+# Padding for random crop
+_C.INPUT.CROP_PADDING = 4
+# Cutout
+_C.INPUT.CUTOUT_N = 1
+_C.INPUT.CUTOUT_LEN = 16
+# Gaussian noise
+_C.INPUT.GN_MEAN = 0.0
+_C.INPUT.GN_STD = 0.15
+# RandomAugment
+_C.INPUT.RANDAUGMENT_N = 2
+_C.INPUT.RANDAUGMENT_M = 10
+# ColorJitter (brightness, contrast, saturation, hue)
+_C.INPUT.COLORJITTER_B = 0.4
+_C.INPUT.COLORJITTER_C = 0.4
+_C.INPUT.COLORJITTER_S = 0.4
+_C.INPUT.COLORJITTER_H = 0.1
+# Random gray scale's probability
+_C.INPUT.RGS_P = 0.2
+# Gaussian blur
+_C.INPUT.GB_P = 0.5  # propability of applying this operation
+_C.INPUT.GB_K = 21  # kernel size (should be an odd number)
+
+###########################
+# Dataset
+###########################
+_C.DATASET = CN()
+# Directory where datasets are stored
+_C.DATASET.ROOT = ""
+_C.DATASET.NAME = ""
+# List of names of source domains
+_C.DATASET.SOURCE_DOMAINS = ()
+# List of names of target domains
+_C.DATASET.TARGET_DOMAINS = ()
+# Number of labeled instances in total
+# Useful for the semi-supervised learning
+_C.DATASET.NUM_LABELED = -1
+# Number of images per class
+_C.DATASET.NUM_SHOTS = -1
+# Percentage of validation data (only used for SSL datasets)
+# Set to 0 if do not want to use val data
+# Using val data for hyperparameter tuning was done in Oliver et al. 2018
+_C.DATASET.VAL_PERCENT = 0.1
+# Fold index for STL-10 dataset (normal range is 0 - 9)
+# Negative number means None
+_C.DATASET.STL10_FOLD = -1
+# CIFAR-10/100-C's corruption type and intensity level
+_C.DATASET.CIFAR_C_TYPE = ""
+_C.DATASET.CIFAR_C_LEVEL = 1
+# Use all data in the unlabeled data set (e.g. FixMatch)
+_C.DATASET.ALL_AS_UNLABELED = False
+
+###########################
+# Dataloader
+###########################
+_C.DATALOADER = CN()
+_C.DATALOADER.NUM_WORKERS = 4
+# Apply transformations to an image K times (during training)
+_C.DATALOADER.K_TRANSFORMS = 1
+# img0 denotes image tensor without augmentation
+# Useful for consistency learning
+_C.DATALOADER.RETURN_IMG0 = False
+# Setting for the train_x data-loader
+_C.DATALOADER.TRAIN_X = CN()
+_C.DATALOADER.TRAIN_X.SAMPLER = "RandomSampler"
+_C.DATALOADER.TRAIN_X.BATCH_SIZE = 32
+# Parameter for RandomDomainSampler
+# 0 or -1 means sampling from all domains
+_C.DATALOADER.TRAIN_X.N_DOMAIN = 0
+# Parameter of RandomClassSampler
+# Number of instances per class
+_C.DATALOADER.TRAIN_X.N_INS = 16
+
+# Setting for the train_u data-loader
+_C.DATALOADER.TRAIN_U = CN()
+# Set to false if you want to have unique
+# data loader params for train_u
+_C.DATALOADER.TRAIN_U.SAME_AS_X = True
+_C.DATALOADER.TRAIN_U.SAMPLER = "RandomSampler"
+_C.DATALOADER.TRAIN_U.BATCH_SIZE = 32
+_C.DATALOADER.TRAIN_U.N_DOMAIN = 0
+_C.DATALOADER.TRAIN_U.N_INS = 16
+
+# Setting for the test data-loader
+_C.DATALOADER.TEST = CN()
+_C.DATALOADER.TEST.SAMPLER = "SequentialSampler"
+_C.DATALOADER.TEST.BATCH_SIZE = 32
+
+###########################
+# Model
+###########################
+_C.MODEL = CN()
+# Path to model weights (for initialization)
+_C.MODEL.INIT_WEIGHTS = ""
+_C.MODEL.BACKBONE = CN()
+_C.MODEL.BACKBONE.NAME = ""
+_C.MODEL.BACKBONE.PRETRAINED = True
+# Definition of embedding layers
+_C.MODEL.HEAD = CN()
+# If none, do not construct embedding layers, the
+# backbone's output will be passed to the classifier
+_C.MODEL.HEAD.NAME = ""
+# Structure of hidden layers (a list), e.g. [512, 512]
+# If undefined, no embedding layer will be constructed
+_C.MODEL.HEAD.HIDDEN_LAYERS = ()
+_C.MODEL.HEAD.ACTIVATION = "relu"
+_C.MODEL.HEAD.BN = True
+_C.MODEL.HEAD.DROPOUT = 0.0
+
+###########################
+# Optimization
+###########################
+_C.OPTIM = CN()
+_C.OPTIM.NAME = "adam"
+_C.OPTIM.LR = 0.0003
+_C.OPTIM.WEIGHT_DECAY = 5e-4
+_C.OPTIM.MOMENTUM = 0.9
+_C.OPTIM.SGD_DAMPNING = 0
+_C.OPTIM.SGD_NESTEROV = False
+_C.OPTIM.RMSPROP_ALPHA = 0.99
+_C.OPTIM.ADAM_BETA1 = 0.9
+_C.OPTIM.ADAM_BETA2 = 0.999
+# STAGED_LR allows different layers to have
+# different lr, e.g. pre-trained base layers
+# can be assigned a smaller lr than the new
+# classification layer
+_C.OPTIM.STAGED_LR = False
+_C.OPTIM.NEW_LAYERS = ()
+_C.OPTIM.BASE_LR_MULT = 0.1
+# Learning rate scheduler
+_C.OPTIM.LR_SCHEDULER = "single_step"
+# -1 or 0 means the stepsize is equal to max_epoch
+_C.OPTIM.STEPSIZE = (-1, )
+_C.OPTIM.GAMMA = 0.1
+_C.OPTIM.MAX_EPOCH = 10
+# Set WARMUP_EPOCH larger than 0 to activate warmup training
+_C.OPTIM.WARMUP_EPOCH = -1
+# Either linear or constant
+_C.OPTIM.WARMUP_TYPE = "linear"
+# Constant learning rate when type=constant
+_C.OPTIM.WARMUP_CONS_LR = 1e-5
+# Minimum learning rate when type=linear
+_C.OPTIM.WARMUP_MIN_LR = 1e-5
+# Recount epoch for the next scheduler (last_epoch=-1)
+# Otherwise last_epoch=warmup_epoch
+_C.OPTIM.WARMUP_RECOUNT = True
+
+###########################
+# Train
+###########################
+_C.TRAIN = CN()
+# How often (epoch) to save model during training
+# Set to 0 or negative value to only save the last one
+_C.TRAIN.CHECKPOINT_FREQ = 0
+# How often (batch) to print training information
+_C.TRAIN.PRINT_FREQ = 10
+# Use 'train_x', 'train_u' or 'smaller_one' to count
+# the number of iterations in an epoch (for DA and SSL)
+_C.TRAIN.COUNT_ITER = "train_x"
+
+###########################
+# Test
+###########################
+_C.TEST = CN()
+_C.TEST.EVALUATOR = "Classification"
+_C.TEST.PER_CLASS_RESULT = False
+# Compute confusion matrix, which will be saved
+# to $OUTPUT_DIR/cmat.pt
+_C.TEST.COMPUTE_CMAT = False
+# If NO_TEST=True, no testing will be conducted
+_C.TEST.NO_TEST = False
+# Use test or val set for FINAL evaluation
+_C.TEST.SPLIT = "test"
+# Which model to test after training
+# Either last_step or best_val
+_C.TEST.FINAL_MODEL = "last_step"
+
+###########################
+# Trainer specifics
+###########################
+_C.TRAINER = CN()
+_C.TRAINER.NAME = ""
+
+# MCD
+_C.TRAINER.MCD = CN()
+_C.TRAINER.MCD.N_STEP_F = 4  # number of steps to train F
+# MME
+_C.TRAINER.MME = CN()
+_C.TRAINER.MME.LMDA = 0.1  # weight for the entropy loss
+# SelfEnsembling
+_C.TRAINER.SE = CN()
+_C.TRAINER.SE.EMA_ALPHA = 0.999
+_C.TRAINER.SE.CONF_THRE = 0.95
+_C.TRAINER.SE.RAMPUP = 300
+
+# M3SDA
+_C.TRAINER.M3SDA = CN()
+_C.TRAINER.M3SDA.LMDA = 0.5  # weight for the moment distance loss
+_C.TRAINER.M3SDA.N_STEP_F = 4  # follow MCD
+# DAEL
+_C.TRAINER.DAEL = CN()
+_C.TRAINER.DAEL.WEIGHT_U = 0.5  # weight on the unlabeled loss
+_C.TRAINER.DAEL.CONF_THRE = 0.95  # confidence threshold
+_C.TRAINER.DAEL.STRONG_TRANSFORMS = ()
+
+# CrossGrad
+_C.TRAINER.CG = CN()
+_C.TRAINER.CG.EPS_F = 1.0  # scaling parameter for D's gradients
+_C.TRAINER.CG.EPS_D = 1.0  # scaling parameter for F's gradients
+_C.TRAINER.CG.ALPHA_F = 0.5  # balancing weight for the label net's loss
+_C.TRAINER.CG.ALPHA_D = 0.5  # balancing weight for the domain net's loss
+# DDAIG
+_C.TRAINER.DDAIG = CN()
+_C.TRAINER.DDAIG.G_ARCH = ""  # generator's architecture
+_C.TRAINER.DDAIG.LMDA = 0.3  # perturbation weight
+_C.TRAINER.DDAIG.CLAMP = False  # clamp perturbation values
+_C.TRAINER.DDAIG.CLAMP_MIN = -1.0
+_C.TRAINER.DDAIG.CLAMP_MAX = 1.0
+_C.TRAINER.DDAIG.WARMUP = 0
+_C.TRAINER.DDAIG.ALPHA = 0.5  # balancing weight for the losses
+
+# EntMin
+_C.TRAINER.ENTMIN = CN()
+_C.TRAINER.ENTMIN.LMDA = 1e-3  # weight on the entropy loss
+# Mean Teacher
+_C.TRAINER.MEANTEA = CN()
+_C.TRAINER.MEANTEA.WEIGHT_U = 1.0  # weight on the unlabeled loss
+_C.TRAINER.MEANTEA.EMA_ALPHA = 0.999
+_C.TRAINER.MEANTEA.RAMPUP = 5  # epochs used to ramp up the loss_u weight
+# MixMatch
+_C.TRAINER.MIXMATCH = CN()
+_C.TRAINER.MIXMATCH.WEIGHT_U = 100.0  # weight on the unlabeled loss
+_C.TRAINER.MIXMATCH.TEMP = 2.0  # temperature for sharpening the probability
+_C.TRAINER.MIXMATCH.MIXUP_BETA = 0.75
+_C.TRAINER.MIXMATCH.RAMPUP = 20000  # steps used to ramp up the loss_u weight
+# FixMatch
+_C.TRAINER.FIXMATCH = CN()
+_C.TRAINER.FIXMATCH.WEIGHT_U = 1.0  # weight on the unlabeled loss
+_C.TRAINER.FIXMATCH.CONF_THRE = 0.95  # confidence threshold
+_C.TRAINER.FIXMATCH.STRONG_TRANSFORMS = ()