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Refactor se in anynet to support regnety (#42)
Summary: Pull Request resolved: #42 Test Plan: Imported from GitHub, without a `Test Plan:` line. tested locally: ``` ~/fbsource/fbcode/buck-out/gen/vision/fair/pycls/tools/test_net.par --cfg configs/imagenet/regnet/RegNetX-200MF_dds_1gpu.yaml NUM_GPUS 1 TRAIN.BATCH_SIZE 64 TEST.BATCH_SIZE 32 TEST.WEIGHTS https://dl.fbaipublicfiles.com/pycls/checkpoints/RegNet-X-E100-F0200-V04.pyth ``` Reviewed By: vedanuj Differential Revision: D20983994 Pulled By: rajprateek fbshipit-source-id: 61536f340c1e5afca3b0eb3c73f44d3a99839b90
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‎configs/imagenet/effnet/EN-B0_nds_1gpu.yaml

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STEM_W: 32
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DEPTHS: [1, 2, 2, 3, 3, 4, 1]
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WIDTHS: [16, 24, 40, 80, 112, 192, 320]
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SE_ENABLED: True
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EXP_RATIOS: [1, 6, 6, 6, 6, 6, 6]
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SE_RATIO: 0.25
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STRIDES: [1, 2, 2, 2, 1, 2, 1]

‎configs/imagenet/effnet/EN-B0_step_1gpu.yaml

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STEM_W: 32
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DEPTHS: [1, 2, 2, 3, 3, 4, 1]
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WIDTHS: [16, 24, 40, 80, 112, 192, 320]
8-
SE_ENABLED: True
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EXP_RATIOS: [1, 6, 6, 6, 6, 6, 6]
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SE_RATIO: 0.25
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STRIDES: [1, 2, 2, 2, 1, 2, 1]

‎configs/imagenet/regnet/RegNetY-1.6GF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 27
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W0: 48
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WA: 20.71
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WM: 2.65
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GROUP_W: 24
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

‎configs/imagenet/regnet/RegNetY-12GF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 19
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W0: 168
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WA: 73.36
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WM: 2.37
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GROUP_W: 112
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

‎configs/imagenet/regnet/RegNetY-16GF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 18
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W0: 200
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WA: 106.23
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WM: 2.48
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GROUP_W: 112
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

‎configs/imagenet/regnet/RegNetY-200MF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 13
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W0: 24
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WA: 36.44
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WM: 2.49
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GROUP_W: 8
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

‎configs/imagenet/regnet/RegNetY-3.2GF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 21
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W0: 80
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WA: 42.63
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WM: 2.66
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GROUP_W: 24
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

‎configs/imagenet/regnet/RegNetY-32GF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 20
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W0: 232
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WA: 115.89
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WM: 2.53
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GROUP_W: 232
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

‎configs/imagenet/regnet/RegNetY-4.0GF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 22
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W0: 96
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WA: 31.41
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WM: 2.24
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GROUP_W: 64
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

‎configs/imagenet/regnet/RegNetY-400MF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 16
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W0: 48
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WA: 27.89
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WM: 2.09
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GROUP_W: 8
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

‎configs/imagenet/regnet/RegNetY-6.4GF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 25
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W0: 112
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WA: 33.22
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WM: 2.27
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GROUP_W: 72
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

‎configs/imagenet/regnet/RegNetY-600MF_dds_1gpu.yaml

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MODEL:
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TYPE: regnet
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NUM_CLASSES: 1000
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REGNET:
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SE_ON: True
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DEPTH: 15
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W0: 48
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WA: 32.54
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WM: 2.32
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GROUP_W: 16
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BOT_MUL: 1.0
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OPTIM:
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BASE_LR: 0.1
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LR_POLICY: cos
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MAX_EPOCH: 100
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MOMENTUM: 0.9
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NESTEROV: True
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WEIGHT_DECAY: 5e-5
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WARMUP_EPOCHS: 5
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WARMUP_FACTOR: 0.1
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TRAIN:
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DATASET: imagenet
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SPLIT: train
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BATCH_SIZE: 128
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IM_SIZE: 224
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EVAL_PERIOD: 1
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TEST:
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DATASET: imagenet
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SPLIT: val
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BATCH_SIZE: 100
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IM_SIZE: 256
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NUM_GPUS: 1
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DATA_LOADER:
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NUM_WORKERS: 4
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CUDNN:
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BENCHMARK: True
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OUT_DIR: .

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