zsic_unicl

config.py

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+# --------------------------------------------------------
+# Unified Contrastive Learning (UniCL)
+# Copyright (c) 2022 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Jianwei Yang ([email protected])
+# Based on Swin Transformer written by Zhe Liu
+# --------------------------------------------------------
+
+import os
+import yaml
+from yacs.config import CfgNode as CN
+
+_C = CN()
+_C.VERBOSE = False
+
+# Base config files
+_C.BASE = ['']
+
+# -----------------------------------------------------------------------------
+# Data settings
+# -----------------------------------------------------------------------------
+_C.DATA = CN()
+# Batch size for a single GPU, could be overwritten by command line argument
+_C.DATA.BATCH_SIZE = 128
+# Path to dataset, could be overwritten by command line argument
+_C.DATA.DATA_PATH = ''
+# Dataset name
+_C.DATA.DATASET = 'imagenet'
+# Input image size
+_C.DATA.IMG_SIZE = 224
+# Interpolation to resize image (random, bilinear, bicubic)
+_C.DATA.INTERPOLATION = 'bicubic'
+# Use zipped dataset instead of folder dataset
+# could be overwritten by command line argument
+_C.DATA.ZIP_MODE = False
+# Cache Data in Memory, could be overwritten by command line argument
+_C.DATA.CACHE_MODE = 'part'
+# Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.
+_C.DATA.PIN_MEMORY = True
+# Number of data loading threads
+_C.DATA.NUM_WORKERS = 8
+
+# -----------------------------------------------------------------------------
+# Model settings
+# -----------------------------------------------------------------------------
+_C.MODEL = CN()
+# Model name
+_C.MODEL.NAME = ''
+# Checkpoint to resume, could be overwritten by command line argument
+_C.MODEL.RESUME = ''
+# Number of classes, overwritten in data preparation
+_C.MODEL.NUM_CLASSES = 0
+# Label Smoothing
+_C.MODEL.LABEL_SMOOTHING = 0.1
+# Whether load pretrained model
+_C.MODEL.PRETRAINED = ''
+# Projection dimension
+_C.MODEL.DIM_PROJECTION = 512
+# Mode specific
+_C.MODEL.SPEC = CN(new_allowed=True)
+# -----------------------------------------------------------------------------
+# Build Image Encoder
+# -----------------------------------------------------------------------------
+_C.MODEL.IMAGE_ENCODER = CN()
+# Image encoder type
+_C.MODEL.IMAGE_ENCODER.TYPE = 'swin'
+# Input image size
+_C.MODEL.IMAGE_ENCODER.IMG_SIZE = 224
+# Dropout rate
+_C.MODEL.IMAGE_ENCODER.DROP_RATE = 0.0
+# Drop path rate
+_C.MODEL.IMAGE_ENCODER.DROP_PATH_RATE = 0.1
+
+# Swin Transformer parameters
+_C.MODEL.IMAGE_ENCODER.SWIN = CN()
+_C.MODEL.IMAGE_ENCODER.SWIN.PATCH_SIZE = 4
+_C.MODEL.IMAGE_ENCODER.SWIN.IN_CHANS = 3
+_C.MODEL.IMAGE_ENCODER.SWIN.EMBED_DIM = 96
+_C.MODEL.IMAGE_ENCODER.SWIN.DEPTHS = [2, 2, 6, 2]
+_C.MODEL.IMAGE_ENCODER.SWIN.NUM_HEADS = [3, 6, 12, 24]
+_C.MODEL.IMAGE_ENCODER.SWIN.WINDOW_SIZE = 7
+_C.MODEL.IMAGE_ENCODER.SWIN.MLP_RATIO = 4.
+_C.MODEL.IMAGE_ENCODER.SWIN.QKV_BIAS = True
+_C.MODEL.IMAGE_ENCODER.SWIN.QK_SCALE = None
+_C.MODEL.IMAGE_ENCODER.SWIN.APE = False
+_C.MODEL.IMAGE_ENCODER.SWIN.PATCH_NORM = True
+
+# FocalNet parameters
+_C.MODEL.IMAGE_ENCODER.FOCAL = CN()
+_C.MODEL.IMAGE_ENCODER.FOCAL.PATCH_SIZE = 4
+_C.MODEL.IMAGE_ENCODER.FOCAL.IN_CHANS = 3
+_C.MODEL.IMAGE_ENCODER.FOCAL.EMBED_DIM = 96
+_C.MODEL.IMAGE_ENCODER.FOCAL.DEPTHS = [2, 2, 6, 2]
+_C.MODEL.IMAGE_ENCODER.FOCAL.MLP_RATIO = 4.
+_C.MODEL.IMAGE_ENCODER.FOCAL.PATCH_NORM = True
+_C.MODEL.IMAGE_ENCODER.FOCAL.FOCAL_LEVELS = [2, 2, 2, 2]
+_C.MODEL.IMAGE_ENCODER.FOCAL.FOCAL_WINDOWS = [3, 3, 3, 3]
+_C.MODEL.IMAGE_ENCODER.FOCAL.FOCAL_FACTORS = [2, 2, 2, 2]
+_C.MODEL.IMAGE_ENCODER.FOCAL.USE_CONV_EMBED = False
+_C.MODEL.IMAGE_ENCODER.FOCAL.USE_LAYERSCALE = False
+_C.MODEL.IMAGE_ENCODER.FOCAL.USE_POSTLN = False
+
+# -----------------------------------------------------------------------------
+# Build Text Encoder
+# -----------------------------------------------------------------------------
+_C.MODEL.TEXT_ENCODER = CN()
+
+_C.MODEL.TEXT_ENCODER.NAME = 'transformer'
+_C.MODEL.TEXT_ENCODER.LOAD_PRETRAINED = False
+_C.MODEL.TEXT_ENCODER.PRETRAINED = ''
+_C.MODEL.TEXT_ENCODER.TOKENIZER = 'clip'
+_C.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77
+_C.MODEL.TEXT_ENCODER.WIDTH = 1024
+_C.MODEL.TEXT_ENCODER.HEADS = 16
+_C.MODEL.TEXT_ENCODER.LAYERS = 12
+_C.MODEL.TEXT_ENCODER.AUTOGRESSIVE = True
+
+# -----------------------------------------------------------------------------
+# Training settings
+# -----------------------------------------------------------------------------
+_C.TRAIN = CN()
+_C.TRAIN.START_EPOCH = 0
+_C.TRAIN.EPOCHS = 32
+_C.TRAIN.WARMUP_EPOCHS = 5
+_C.TRAIN.WEIGHT_DECAY = 0.1
+_C.TRAIN.BASE_LR = 5e-4
+_C.TRAIN.WARMUP_LR = 5e-7
+_C.TRAIN.MIN_LR = 5e-6
+# Clip gradient norm
+_C.TRAIN.CLIP_GRAD = 5.0
+# Auto resume from latest checkpoint
+_C.TRAIN.AUTO_RESUME = True
+# Gradient accumulation steps
+# could be overwritten by command line argument
+_C.TRAIN.ACCUMULATION_STEPS = 0
+# Whether to use gradient checkpointing to save memory
+# could be overwritten by command line argument
+_C.TRAIN.USE_CHECKPOINT = False
+
+# LR scheduler
+_C.TRAIN.LR_SCHEDULER = CN()
+_C.TRAIN.LR_SCHEDULER.NAME = 'cosine'
+# Epoch interval to decay LR, used in StepLRScheduler
+_C.TRAIN.LR_SCHEDULER.DECAY_EPOCHS = 30
+# LR decay rate, used in StepLRScheduler
+_C.TRAIN.LR_SCHEDULER.DECAY_RATE = 0.1
+
+# Optimizer
+_C.TRAIN.OPTIMIZER = CN()
+_C.TRAIN.OPTIMIZER.NAME = 'adamw'
+# Optimizer Epsilon
+_C.TRAIN.OPTIMIZER.EPS = 1e-8
+# Optimizer Betas
+_C.TRAIN.OPTIMIZER.BETAS = (0.9, 0.999)
+# SGD momentum
+_C.TRAIN.OPTIMIZER.MOMENTUM = 0.9
+
+# -----------------------------------------------------------------------------
+# Augmentation settings
+# -----------------------------------------------------------------------------
+_C.AUG = CN()
+# Color jitter factor
+_C.AUG.COLOR_JITTER = 0.4
+# Use AutoAugment policy. "v0" or "original"
+_C.AUG.AUTO_AUGMENT = 'rand-m9-mstd0.5-inc1'
+# Random erase prob
+_C.AUG.REPROB = 0.25
+# Random erase mode
+_C.AUG.REMODE = 'pixel'
+# Random erase count
+_C.AUG.RECOUNT = 1
+# Mixup alpha, mixup enabled if > 0
+_C.AUG.MIXUP = 0.8
+# Cutmix alpha, cutmix enabled if > 0
+_C.AUG.CUTMIX = 1.0
+# Cutmix min/max ratio, overrides alpha and enables cutmix if set
+_C.AUG.CUTMIX_MINMAX = None
+# Probability of performing mixup or cutmix when either/both is enabled
+_C.AUG.MIXUP_PROB = 1.0
+# Probability of switching to cutmix when both mixup and cutmix enabled
+_C.AUG.MIXUP_SWITCH_PROB = 0.5
+# How to apply mixup/cutmix params. Per "batch", "pair", or "elem"
+_C.AUG.MIXUP_MODE = 'batch'
+
+# -----------------------------------------------------------------------------
+# Testing settings
+# -----------------------------------------------------------------------------
+_C.TEST = CN()
+# Whether to use center crop when testing
+_C.TEST.CROP = True
+
+# -----------------------------------------------------------------------------
+# Misc
+# -----------------------------------------------------------------------------
+# Mixed precision opt level, if O0, no amp is used ('O0', 'O1', 'O2')
+# overwritten by command line argument
+_C.AMP_OPT_LEVEL = ''
+# Path to output folder, overwritten by command line argument
+_C.OUTPUT = ''
+# Tag of experiment, overwritten by command line argument
+_C.TAG = 'default'
+# Frequency to save checkpoint
+_C.SAVE_FREQ = 1
+# Frequency to logging info
+_C.PRINT_FREQ = 100
+# Fixed random seed
+_C.SEED = 0
+# Perform evaluation only, overwritten by command line argument
+_C.EVAL_MODE = False
+# Test throughput only, overwritten by command line argument
+_C.THROUGHPUT_MODE = False
+# Debug only so that skip dataloader initialization, overwritten by command line argument
+_C.DEBUG_MODE = False
+# local rank for DistributedDataParallel, given by command line argument
+_C.LOCAL_RANK = 0
+
+
+def _update_config_from_file(config, cfg_file):
+    config.defrost()
+    with open(cfg_file, 'r') as f:
+        yaml_cfg = yaml.load(f, Loader=yaml.FullLoader)
+
+    for cfg in yaml_cfg.setdefault('BASE', ['']):
+        if cfg:
+            _update_config_from_file(
+                config, os.path.join(os.path.dirname(cfg_file), cfg)
+            )
+    print('=> merge config from {}'.format(cfg_file))
+    config.merge_from_file(cfg_file)
+    config.freeze()
+
+
+def update_config(config, args):
+    _update_config_from_file(config, args.cfg)
+    config.freeze()
+
+
+def get_config(args):
+    """Get a yacs CfgNode object with default values."""
+    # Return a clone so that the defaults will not be altered
+    # This is for the "local variable" use pattern
+    config = _C.clone()
+    update_config(config, args)
+
+    return config