add code for classification

config.py

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+import os
+import yaml
+from yacs.config import CfgNode as CN
+
+_C = CN()
+
+# 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 type
+_C.MODEL.TYPE = 'cross-scale'
+# Model name
+_C.MODEL.NAME = 'tiny_patch4_group7_224'
+# Checkpoint to resume, could be overwritten by command line argument
+_C.MODEL.RESUME = ''
+_C.MODEL.FROM_PRETRAIN = ''
+# Number of classes, overwritten in data preparation
+_C.MODEL.NUM_CLASSES = 1000
+# Dropout rate
+_C.MODEL.DROP_RATE = 0.0
+# Drop path rate
+_C.MODEL.DROP_PATH_RATE = 0.1
+# Label Smoothing
+_C.MODEL.LABEL_SMOOTHING = 0.1
+
+# CrossFormer parameters
+_C.MODEL.CROS = CN()
+_C.MODEL.CROS.PATCH_SIZE = [4, 8, 16, 32]
+_C.MODEL.CROS.MERGE_SIZE = [[2, 4], [2,4], [2, 4]]
+_C.MODEL.CROS.IN_CHANS = 3
+_C.MODEL.CROS.EMBED_DIM = 48
+_C.MODEL.CROS.DEPTHS = [2, 2, 6, 2]
+_C.MODEL.CROS.NUM_HEADS = [3, 6, 12, 24]
+_C.MODEL.CROS.GROUP_SIZE = [7, 7, 7, 7]
+_C.MODEL.CROS.MLP_RATIO = 4.
+_C.MODEL.CROS.QKV_BIAS = True
+_C.MODEL.CROS.QK_SCALE = None
+_C.MODEL.CROS.APE = False
+_C.MODEL.CROS.PATCH_NORM = True
+
+# -----------------------------------------------------------------------------
+# Training settings
+# -----------------------------------------------------------------------------
+_C.TRAIN = CN()
+_C.TRAIN.START_EPOCH = 0
+_C.TRAIN.EPOCHS = 300
+_C.TRAIN.WARMUP_EPOCHS = 20
+_C.TRAIN.WEIGHT_DECAY = 0.05
+_C.TRAIN.BASE_LR = 5e-4
+_C.TRAIN.WARMUP_LR = 5e-7
+_C.TRAIN.MIN_LR = 5e-7
+# 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 = 1000
+# Frequency to logging info
+_C.PRINT_FREQ = 10
+# 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
+# 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.defrost()
+    if args.opts:
+        config.merge_from_list(args.opts)
+
+    # merge from specific arguments
+    if args.batch_size:
+        config.DATA.BATCH_SIZE = args.batch_size
+    if args.data_path:
+        config.DATA.DATA_PATH = args.data_path
+    if args.zip:
+        config.DATA.ZIP_MODE = True
+    if args.cache_mode:
+        config.DATA.CACHE_MODE = args.cache_mode
+    if args.resume:
+        config.MODEL.RESUME = args.resume
+    if args.accumulation_steps:
+        config.TRAIN.ACCUMULATION_STEPS = args.accumulation_steps
+    if args.use_checkpoint:
+        config.TRAIN.USE_CHECKPOINT = True
+    if args.amp_opt_level:
+        config.AMP_OPT_LEVEL = args.amp_opt_level
+    if args.output:
+        config.OUTPUT = args.output
+    if args.tag:
+        config.TAG = args.tag
+    if args.eval:
+        config.EVAL_MODE = True
+    if args.num_workers >= 0:
+        config.DATA.NUM_WORKERS = args.num_workers
+    if args.throughput:
+        config.THROUGHPUT_MODE = True
+
+    # if args.patch_size:
+    #     config.MODEL.CROS.PATCH_SIZE = args.patch_size
+
+    config.MODEL.CROS.MLP_RATIO = args.mlp_ratio
+    # config.MODEL.MERGE_SIZE_AFTER = [args.merge_size_after1, args.merge_size_after2, args.merge_size_after3, []]
+    config.DATA.DATASET = args.data_set
+    config.TRAIN.WARMUP_EPOCHS = args.warmup_epochs
+    # set local rank for distributed training
+    config.LOCAL_RANK = args.local_rank
+    # output folder
+    config.OUTPUT = os.path.join(config.OUTPUT, config.MODEL.NAME, config.TAG)
+
+    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

configs/base_patch4_group7_224.yaml

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+MODEL:
+  TYPE: cross-scale
+  NAME: cros_base_patch4_group7_224
+  DROP_PATH_RATE: 0.3
+  CROS:
+    EMBED_DIM: 96
+    DEPTHS: [ 2, 2, 18, 2 ]
+    NUM_HEADS: [ 3, 6, 12, 24 ]
+    GROUP_SIZE: [ 7, 7, 7, 7 ]
+    PATCH_SIZE: [4, 8, 16, 32]
+    MERGE_SIZE: [[2, 4], [2,4], [2, 4]]

configs/large_patch4_group7_224.yaml

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+MODEL:
+  TYPE: cross-scale
+  NAME: cros_base_patch4_group7_224
+  DROP_PATH_RATE: 0.5
+  CROS:
+    EMBED_DIM: 128
+    DEPTHS: [ 2, 2, 18, 2 ]
+    NUM_HEADS: [ 4, 8, 16, 32 ]
+    GROUP_SIZE: [ 7, 7, 7, 7 ]
+    PATCH_SIZE: [4, 8, 16, 32]
+    MERGE_SIZE: [[2, 4], [2,4], [2, 4]]

configs/small_patch4_group7_224.yaml

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+MODEL:
+  TYPE: cross-scale
+  NAME: cros_tiny_patch4_group7_224
+  DROP_PATH_RATE: 0.2
+  CROS:
+    EMBED_DIM: 96
+    DEPTHS: [ 2, 2, 6, 2 ]
+    NUM_HEADS: [ 3, 6, 12, 24 ]
+    GROUP_SIZE: [ 7, 7, 7, 7 ]
+    PATCH_SIZE: [4, 8, 16, 32]
+    MERGE_SIZE: [[2, 4], [2,4], [2, 4]]

configs/tiny_patch4_group7_224.yaml

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+MODEL:
+  TYPE: cross-scale
+  NAME: cros_tiny_patch4_group7_224
+  DROP_PATH_RATE: 0.1
+  CROS:
+    EMBED_DIM: 64
+    DEPTHS: [ 1, 1, 8, 6 ]
+    NUM_HEADS: [ 2, 4, 8, 16 ]
+    GROUP_SIZE: [ 7, 7, 7, 7 ]
+    PATCH_SIZE: [4, 8, 16, 32]
+    MERGE_SIZE: [[2, 4], [2,4], [2, 4]]

data/__init__.py

@@ -0,0 +1 @@
+from .build import build_loader