mirror of https://github.com/JDAI-CV/fast-reid.git
liaoxingyu
parent
3041a52aef
commit
18a33f7962
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@ -65,7 +65,7 @@ SOLVER:
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TEST:
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EVAL_PERIOD: 2000
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IMS_PER_BATCH: 512
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IMS_PER_BATCH: 256
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CUDNN_BENCHMARK: True
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@ -0,0 +1,88 @@
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MODEL:
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META_ARCHITECTURE: 'MGN'
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OPEN_LAYERS: ["b1_pool", "b1_head", "b2_pool", "b2_head", "b21_pool", "b21_head", "b22_pool", "b22_head",
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"b3_pool", "b3_head", "b31_pool", "b31_head", "b32_pool", "b32_head", "b33_pool", "b33_head"]
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BACKBONE:
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NAME: "build_resnet_backbone"
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NORM: "BN"
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DEPTH: 50
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LAST_STRIDE: 1
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WITH_IBN: False
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WITH_NL: False
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PRETRAIN: True
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HEADS:
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NAME: "BNneckHead"
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NORM: "BN"
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NECK_FEAT: "after"
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CLS_LAYER: "circle"
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POOL_LAYER: "gempool"
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IN_FEAT: 256
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SCALE: 64
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MARGIN: 0.35
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LOSSES:
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NAME: ("CrossEntropyLoss", "TripletLoss",)
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CE:
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EPSILON: 0.1
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SCALE: 0.125
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TRI:
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MARGIN: 0.0
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HARD_MINING: True
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NORM_FEAT: False
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USE_COSINE_DIST: False
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SCALE: 0.20
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DATASETS:
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NAMES: ("DukeMTMC",)
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TESTS: ("DukeMTMC",)
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INPUT:
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SIZE_TRAIN: [384, 128]
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SIZE_TEST: [384, 128]
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DO_AUTOAUG: True
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REA:
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ENABLED: True
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PROB: 0.5
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MEAN: [123.675, 116.28, 103.53]
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DO_PAD: True
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DATALOADER:
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PK_SAMPLER: True
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NUM_INSTANCE: 16
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NUM_WORKERS: 16
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SOLVER:
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OPT: "Adam"
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MAX_ITER: 18000
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BASE_LR: 0.00035
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BIAS_LR_FACTOR: 2.
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WEIGHT_DECAY: 0.0005
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WEIGHT_DECAY_BIAS: 0.0
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IMS_PER_BATCH: 64
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SCHED: "DelayedCosineAnnealingLR"
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DELAY_ITERS: 9000
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ETA_MIN_LR: 0.00000077
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WARMUP_FACTOR: 0.01
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WARMUP_ITERS: 2000
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FREEZE_ITERS: 2000
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LOG_PERIOD: 200
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CHECKPOINT_PERIOD: 6000
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TEST:
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EVAL_PERIOD: 2000
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IMS_PER_BATCH: 256
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PRECISE_BN:
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ENABLED: False
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DATASET: 'DukeMTMC'
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CUDNN_BENCHMARK: True
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@ -69,7 +69,7 @@ SOLVER:
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TEST:
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EVAL_PERIOD: 2000
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IMS_PER_BATCH: 512
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IMS_PER_BATCH: 256
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CUDNN_BENCHMARK: True
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@ -0,0 +1,17 @@
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_BASE_: "../Base-AGW.yml"
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MODEL:
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BACKBONE:
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NAME: "build_resnet_backbone"
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DEPTH: 101
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WITH_IBN: True
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PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet101_ibn_a.pth.tar"
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HEADS:
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NUM_CLASSES: 702
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DATASETS:
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NAMES: ("DukeMTMC",)
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TESTS: ("DukeMTMC",)
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OUTPUT_DIR: "logs/dukemtmc/agw_R101-ibn"
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@ -5,9 +5,12 @@ MODEL:
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NAME: "build_resnest_backbone"
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HEADS:
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NECK_FEAT: "after"
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NUM_CLASSES: 702
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LOSSES:
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TRI:
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MARGIN: 0.3
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DATASETS:
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NAMES: ("DukeMTMC",)
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TESTS: ("DukeMTMC",)
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@ -4,7 +4,7 @@ MODEL:
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BACKBONE:
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DEPTH: 101
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WITH_IBN: True
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PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet101_ibn_a.pth.tar"
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PRETRAIN_PATH: "/export/home/lxy/.cache/torch/checkpoints/resnet101_ibn_a.pth.tar"
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HEADS:
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NUM_CLASSES: 702
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@ -0,0 +1,14 @@
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_BASE_: "../Base-MGN.yml"
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MODEL:
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BACKBONE:
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PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar"
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HEADS:
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NUM_CLASSES: 702
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DATASETS:
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NAMES: ("DukeMTMC",)
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TESTS: ("DukeMTMC",)
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OUTPUT_DIR: "logs/dukemtmc/mgn_R50-ibn"
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@ -3,7 +3,6 @@ _BASE_: "../Base-Strongerbaseline.yml"
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MODEL:
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BACKBONE:
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DEPTH: 101
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WITH_NL: False
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WITH_IBN: True
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PRETRAIN_PATH: "/export/home/lxy/.cache/torch/checkpoints/resnet101_ibn_a.pth.tar"
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@ -0,0 +1,26 @@
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_BASE_: "../Base-AGW.yml"
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MODEL:
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BACKBONE:
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DEPTH: 101
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WITH_IBN: True
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PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet101_ibn_a.pth.tar"
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HEADS:
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NUM_CLASSES: 1041
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DATASETS:
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NAMES: ("MSMT17",)
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TESTS: ("MSMT17",)
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SOLVER:
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MAX_ITER: 42000
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STEPS: [19000, 33000]
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WARMUP_ITERS: 4700
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CHECKPOINT_PERIOD: 5000
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TEST:
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EVAL_PERIOD: 5000
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OUTPUT_DIR: "logs/msmt17/agw_R101-ibn"
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@ -7,6 +7,10 @@ MODEL:
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HEADS:
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NUM_CLASSES: 1041
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LOSSES:
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TRI:
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MARGIN: 0.3
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DATASETS:
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NAMES: ("MSMT17",)
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TESTS: ("MSMT17",)
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@ -0,0 +1,26 @@
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_BASE_: "../Base-MGN.yml"
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MODEL:
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BACKBONE:
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WITH_IBN: True
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PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar"
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HEADS:
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NUM_CLASSES: 1041
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DATASETS:
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NAMES: ("MSMT17",)
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TESTS: ("MSMT17",)
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SOLVER:
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MAX_ITER: 28000
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DELAY_ITERS: 14000
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WARMUP_ITERS: 4700
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FREEZE_ITERS: 4700
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TEST:
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EVAL_PERIOD: 5000
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PRECISE_BN:
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DATASET: 'MSMT17'
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OUTPUT_DIR: "logs/msmt17/mgn_R50-ibn"
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@ -0,0 +1,22 @@
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_BASE_: "../Base-MGN.yml"
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MODEL:
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HEADS:
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NUM_CLASSES: 1041
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DATASETS:
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NAMES: ("MSMT17",)
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TESTS: ("MSMT17",)
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SOLVER:
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MAX_ITER: 28000
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DELAY_ITERS: 14000
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WARMUP_ITERS: 4700
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FREEZE_ITERS: 4700
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TEST:
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EVAL_PERIOD: 5000
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PRECISE_BN:
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DATASET: 'MSMT17'
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OUTPUT_DIR: "logs/msmt17/mgn_R50"
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@ -3,7 +3,6 @@ _BASE_: "../Base-Strongerbaseline.yml"
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MODEL:
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BACKBONE:
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DEPTH: 101
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WITH_NL: False
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WITH_IBN: True
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PRETRAIN_PATH: "/export/home/lxy/.cache/torch/checkpoints/resnet101_ibn_a.pth.tar"
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_BASE_: "../Base-AGW.yml"
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MODEL:
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BACKBONE:
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NAME: "build_resnet_backbone"
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DEPTH: 101
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WITH_IBN: True
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PRETRAIN_PATH: "/export/home/lxy/.cache/torch/checkpoints/resnet101_ibn_a.pth.tar"
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HEADS:
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NUM_CLASSES: 751
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DATASETS:
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NAMES: ("Market1501",)
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TESTS: ("Market1501",)
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OUTPUT_DIR: "logs/market1501/agw_R101-ibn"
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@ -5,9 +5,13 @@ MODEL:
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NAME: "build_resnest_backbone"
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HEADS:
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NECK_FEAT: "after"
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NECK_FEAT: "before"
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NUM_CLASSES: 751
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LOSSES:
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TRI:
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MARGIN: 0.3
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DATASETS:
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NAMES: ("Market1501",)
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TESTS: ("Market1501",)
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@ -0,0 +1,14 @@
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_BASE_: "../Base-MGN.yml"
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MODEL:
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BACKBONE:
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PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar"
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HEADS:
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NUM_CLASSES: 751
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DATASETS:
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NAMES: ("Market1501",)
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TESTS: ("Market1501",)
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OUTPUT_DIR: "logs/market/mgn_R50-ibn"
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@ -3,9 +3,8 @@ _BASE_: "../Base-Strongerbaseline.yml"
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MODEL:
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BACKBONE:
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DEPTH: 101
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WITH_NL: False
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WITH_IBN: True
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PRETRAIN_PATH: "/export/home/lxy/.cache/torch/checkpoints/resnet101_ibn_a.pth.tar"
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PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet101_ibn_a.pth.tar"
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HEADS:
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NUM_CLASSES: 751
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@ -0,0 +1,229 @@
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# encoding: utf-8
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"""
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import copy
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import torch
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from torch import nn
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from fastreid.layers import GeneralizedMeanPoolingP, get_norm
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from fastreid.modeling.backbones import build_backbone
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from fastreid.modeling.backbones.resnet import Bottleneck
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from fastreid.modeling.heads import build_reid_heads
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from fastreid.modeling.losses import reid_losses, CrossEntropyLoss
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from fastreid.utils.weight_init import weights_init_kaiming
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from .build import META_ARCH_REGISTRY
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@META_ARCH_REGISTRY.register()
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class MGN(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self._cfg = cfg
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# backbone
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bn_norm = cfg.MODEL.BACKBONE.NORM
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num_splits = cfg.MODEL.BACKBONE.NORM_SPLIT
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with_se = cfg.MODEL.BACKBONE.WITH_SE
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backbone = build_backbone(cfg)
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self.backbone = nn.Sequential(
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backbone.conv1,
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backbone.bn1,
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backbone.relu,
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backbone.maxpool,
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backbone.layer1,
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backbone.layer2,
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backbone.layer3[0]
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)
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res_conv4 = nn.Sequential(*backbone.layer3[1:])
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res_g_conv5 = backbone.layer4
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res_p_conv5 = nn.Sequential(
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Bottleneck(1024, 512, bn_norm, num_splits, False, with_se, downsample=nn.Sequential(
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nn.Conv2d(1024, 2048, 1, bias=False), get_norm(bn_norm, 2048, num_splits))),
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Bottleneck(2048, 512, bn_norm, num_splits, False, with_se),
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Bottleneck(2048, 512, bn_norm, num_splits, False, with_se))
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res_p_conv5.load_state_dict(backbone.layer4.state_dict())
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if cfg.MODEL.HEADS.POOL_LAYER == 'avgpool':
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pool_layer = nn.AdaptiveAvgPool2d(1)
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elif cfg.MODEL.HEADS.POOL_LAYER == 'maxpool':
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pool_layer = nn.AdaptiveMaxPool2d(1)
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elif cfg.MODEL.HEADS.POOL_LAYER == 'gempool':
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pool_layer = GeneralizedMeanPoolingP()
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else:
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pool_layer = nn.Identity()
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# head
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in_feat = cfg.MODEL.HEADS.IN_FEAT
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num_classes = cfg.MODEL.HEADS.NUM_CLASSES
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# branch1
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self.b1 = nn.Sequential(
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copy.deepcopy(res_conv4), copy.deepcopy(res_g_conv5)
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)
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self.b1_pool = self._build_pool_reduce(pool_layer, reduce_dim=in_feat)
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self.b1_head = build_reid_heads(cfg, in_feat, num_classes, nn.Identity())
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# branch2
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self.b2 = nn.Sequential(
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copy.deepcopy(res_conv4), copy.deepcopy(res_p_conv5)
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)
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self.b2_pool = self._build_pool_reduce(pool_layer, reduce_dim=in_feat)
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self.b2_head = build_reid_heads(cfg, in_feat, num_classes, nn.Identity())
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self.b21_pool = self._build_pool_reduce(pool_layer, reduce_dim=in_feat)
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self.b21_head = build_reid_heads(cfg, in_feat, num_classes, nn.Identity())
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self.b22_pool = self._build_pool_reduce(pool_layer, reduce_dim=in_feat)
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self.b22_head = build_reid_heads(cfg, in_feat, num_classes, nn.Identity())
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# branch3
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self.b3 = nn.Sequential(
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copy.deepcopy(res_conv4), copy.deepcopy(res_p_conv5)
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)
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self.b3_pool = self._build_pool_reduce(pool_layer, reduce_dim=in_feat)
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self.b3_head = build_reid_heads(cfg, in_feat, num_classes, nn.Identity())
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self.b31_pool = self._build_pool_reduce(pool_layer, reduce_dim=in_feat)
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self.b31_head = build_reid_heads(cfg, in_feat, num_classes, nn.Identity())
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self.b32_pool = self._build_pool_reduce(pool_layer, reduce_dim=in_feat)
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self.b32_head = build_reid_heads(cfg, in_feat, num_classes, nn.Identity())
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self.b33_pool = self._build_pool_reduce(pool_layer, reduce_dim=in_feat)
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self.b33_head = build_reid_heads(cfg, in_feat, num_classes, nn.Identity())
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@staticmethod
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def _build_pool_reduce(pool_layer, input_dim=2048, reduce_dim=256):
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pool_reduce = nn.Sequential(
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pool_layer,
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nn.Conv2d(input_dim, reduce_dim, 1, bias=False),
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nn.BatchNorm2d(reduce_dim),
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nn.ReLU(True),
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)
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pool_reduce.apply(weights_init_kaiming)
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return pool_reduce
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def forward(self, inputs):
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images = inputs["images"]
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if not self.training:
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pred_feat = self.inference(images)
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try:
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return pred_feat, inputs["targets"], inputs["camid"]
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except KeyError:
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return pred_feat
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targets = inputs["targets"]
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# Training
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features = self.backbone(images) # (bs, 2048, 16, 8)
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# branch1
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b1_feat = self.b1(features)
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b1_pool_feat = self.b1_pool(b1_feat)
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b1_logits, b1_pool_feat, _ = self.b1_head(b1_pool_feat, targets)
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# branch2
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b2_feat = self.b2(features)
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# global
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b2_pool_feat = self.b2_pool(b2_feat)
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b2_logits, b2_pool_feat, _ = self.b2_head(b2_pool_feat, targets)
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b21_feat, b22_feat = torch.chunk(b2_feat, 2, dim=2)
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# part1
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b21_pool_feat = self.b21_pool(b21_feat)
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b21_logits, b21_pool_feat, _ = self.b21_head(b21_pool_feat, targets)
|
||||
# part2
|
||||
b22_pool_feat = self.b22_pool(b22_feat)
|
||||
b22_logits, b22_pool_feat, _ = self.b22_head(b22_pool_feat, targets)
|
||||
|
||||
# branch3
|
||||
b3_feat = self.b3(features)
|
||||
# global
|
||||
b3_pool_feat = self.b3_pool(b3_feat)
|
||||
b3_logits, b3_pool_feat, _ = self.b3_head(b3_pool_feat, targets)
|
||||
|
||||
b31_feat, b32_feat, b33_feat = torch.chunk(b3_feat, 3, dim=2)
|
||||
# part1
|
||||
b31_pool_feat = self.b31_pool(b31_feat)
|
||||
b31_logits, b31_pool_feat, _ = self.b31_head(b31_pool_feat, targets)
|
||||
# part2
|
||||
b32_pool_feat = self.b32_pool(b32_feat)
|
||||
b32_logits, b32_pool_feat, _ = self.b32_head(b32_pool_feat, targets)
|
||||
# part3
|
||||
b33_pool_feat = self.b33_pool(b33_feat)
|
||||
b33_logits, b33_pool_feat, _ = self.b33_head(b33_pool_feat, targets)
|
||||
|
||||
return (b1_logits, b2_logits, b3_logits, b21_logits, b22_logits, b31_logits, b32_logits, b33_logits), \
|
||||
(b1_pool_feat, b2_pool_feat, b3_pool_feat,
|
||||
torch.cat((b21_pool_feat, b22_pool_feat), dim=1),
|
||||
torch.cat((b31_pool_feat, b32_pool_feat, b33_pool_feat), dim=1)), \
|
||||
targets
|
||||
|
||||
def inference(self, images):
|
||||
assert not self.training
|
||||
features = self.backbone(images) # (bs, 2048, 16, 8)
|
||||
|
||||
# branch1
|
||||
b1_feat = self.b1(features)
|
||||
b1_pool_feat = self.b1_pool(b1_feat)
|
||||
b1_pool_feat = self.b1_head(b1_pool_feat)
|
||||
|
||||
# branch2
|
||||
b2_feat = self.b2(features)
|
||||
# global
|
||||
b2_pool_feat = self.b2_pool(b2_feat)
|
||||
b2_pool_feat = self.b2_head(b2_pool_feat)
|
||||
|
||||
b21_feat, b22_feat = torch.chunk(b2_feat, 2, dim=2)
|
||||
# part1
|
||||
b21_pool_feat = self.b21_pool(b21_feat)
|
||||
b21_pool_feat = self.b21_head(b21_pool_feat)
|
||||
# part2
|
||||
b22_pool_feat = self.b22_pool(b22_feat)
|
||||
b22_pool_feat = self.b22_head(b22_pool_feat)
|
||||
|
||||
# branch3
|
||||
b3_feat = self.b3(features)
|
||||
# global
|
||||
b3_pool_feat = self.b3_pool(b3_feat)
|
||||
b3_pool_feat = self.b3_head(b3_pool_feat)
|
||||
|
||||
b31_feat, b32_feat, b33_feat = torch.chunk(b3_feat, 3, dim=2)
|
||||
# part1
|
||||
b31_pool_feat = self.b31_pool(b31_feat)
|
||||
b31_pool_feat = self.b31_head(b31_pool_feat)
|
||||
# part2
|
||||
b32_pool_feat = self.b32_pool(b32_feat)
|
||||
b32_pool_feat = self.b32_head(b32_pool_feat)
|
||||
# part3
|
||||
b33_pool_feat = self.b33_pool(b33_feat)
|
||||
b33_pool_feat = self.b33_head(b33_pool_feat)
|
||||
|
||||
pred_feat = torch.cat([b1_pool_feat, b2_pool_feat, b3_pool_feat, b21_pool_feat,
|
||||
b22_pool_feat, b31_pool_feat, b32_pool_feat, b33_pool_feat], dim=1)
|
||||
return pred_feat
|
||||
|
||||
def losses(self, outputs):
|
||||
logits, feats, targets = outputs
|
||||
loss_dict = {}
|
||||
loss_dict.update(reid_losses(self._cfg, logits[0], feats[0], targets, 'b1_'))
|
||||
loss_dict.update(reid_losses(self._cfg, logits[1], feats[1], targets, 'b2_'))
|
||||
loss_dict.update(reid_losses(self._cfg, logits[2], feats[2], targets, 'b3_'))
|
||||
loss_dict.update(reid_losses(self._cfg, logits[3], feats[3], targets, 'b21_'))
|
||||
loss_dict.update(reid_losses(self._cfg, logits[5], feats[4], targets, 'b31_'))
|
||||
|
||||
part_ce_loss = [
|
||||
(CrossEntropyLoss(self._cfg)(logits[4], None, targets), 'b22_'),
|
||||
(CrossEntropyLoss(self._cfg)(logits[6], None, targets), 'b32_'),
|
||||
(CrossEntropyLoss(self._cfg)(logits[7], None, targets), 'b33_')
|
||||
]
|
||||
named_ce_loss = {}
|
||||
for item in part_ce_loss:
|
||||
named_ce_loss[item[1] + [*item[0]][0]] = [*item[0].values()][0]
|
||||
loss_dict.update(named_ce_loss)
|
||||
return loss_dict
|
||||
Loading…
Reference in New Issue