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resnet18_ssql_simsiam_cifar.yaml
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resnet18_ssql_simsiam_cifar.yaml
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SEED: 100
MODEL:
ARCH: cifar_resnet18
INPUTSHAPE: [32, 32]
TRAIN:
EPOCHS: 400
USE_DDP: True
SYNC_BN: False
DATASET: cifar10
BATCH_SIZE: 128 # per-gpu, we use 4 gpu
PRINT_FREQ: 10
OPTIMIZER:
NAME: sgd
MOMENTUM: 0.9
WEIGHT_DECAY: 0.0005 # 1e-5
LR_SCHEDULER:
WARMUP_EPOCHS: 0
WARMUP_LR: 0.0002 # 1e-4
BASE_LR: 0.05 # 1e-2
MIN_LR: 0.
TYPE: cosine
LOSS:
CRITERION:
NAME: CosineSimilarity
METER:
NAME: average
ACC:
TOPK: [1, 5]
RUNNER:
NAME: ssl
AUG:
TRAIN:
HORIZONTAL_FLIP:
PROB: 0.5
RANDOMRESIZEDCROP:
ENABLE: True
SCALE: (0.08, 1.0)
INTERPOLATION: bilinear
NORMLIZATION:
MEAN: [0.4914, 0.4822, 0.4465]
STD: [0.2023, 0.1994, 0.2010]
EVALUATION:
RESIZE:
ENABLE: True
SIZE: [32, 32]
CENTERCROP:
ENABLE: True
NORMLIZATION:
MEAN: [0.4914, 0.4822, 0.4465]
STD: [0.2023, 0.1994, 0.2010]
QUANT:
TYPE: ptq
W:
BIT: 0
BIT_RANGE: [2, 9]
SYMMETRY: True
QUANTIZER: uniform
GRANULARITY : channelwise
OBSERVER_METHOD:
NAME: MINMAX
A:
BIT: 0
BIT_RANGE: [4, 9]
SYMMETRY: False
QUANTIZER: uniform
GRANULARITY : layerwise
OBSERVER_METHOD:
NAME: MINMAX
BIT_CONFIG: [{
"conv1": {"w": 8, "a": 8},
"layer1.0.conv2": {"a": 0},
"layer1.1.conv2": {"a": 0},
"layer2.0.conv2": {"a": 0},
"layer2.0.downsample": {"a": 0},
"layer2.1.conv2": {"a": 0},
"layer3.0.downsample": {"a": 0},
"layer3.0.conv2": {"a": 0},
"layer3.1.conv2": {"a": 0},
"layer4.0.downsample": {"a": 0},
"layer4.0.conv2": {"a": 0},
"layer4.1.conv2": {"a": 0},
"fc.0": {"w": 8, "a": 0},
"fc.3": {"w": 8, "a": 0},
"fc.6": {"w": 8, "a": 0},
}]
SSL:
TYPE: SSQL_SimSiam
SETTING:
DIM: 2048
HIDDEN_DIM: 2048
MOMENTUM: 0.99