add deephash configs and dch algorithm
lubin
parent
a25d37ea2b
commit
2507be1a51
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@ -17,13 +17,14 @@ from .cosmargin import CosMargin
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from .circlemargin import CircleMargin
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from .fc import FC
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from .vehicle_neck import VehicleNeck
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from paddle.nn import Tanh
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__all__ = ['build_gear']
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def build_gear(config):
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support_dict = [
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'ArcMargin', 'CosMargin', 'CircleMargin', 'FC', 'VehicleNeck'
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'ArcMargin', 'CosMargin', 'CircleMargin', 'FC', 'VehicleNeck', 'Tanh'
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]
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module_name = config.pop('name')
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assert module_name in support_dict, Exception(
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@ -0,0 +1,104 @@
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# global configs
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Global:
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checkpoints: null
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pretrained_model: null
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output_dir: ./output
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device: gpu
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save_interval: 15
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eval_during_train: True
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eval_interval: 15
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epochs: 150
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print_batch_step: 10
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use_visualdl: False
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# used for static mode and model export
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image_shape: [3, 224, 224]
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save_inference_dir: ./inference
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eval_mode: retrieval
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use_dali: False
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to_static: False
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#feature postprocess
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feature_normalize: False
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feature_binarize: "sign"
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# model architecture
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Arch:
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name: RecModel
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infer_output_key: features
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infer_add_softmax: False
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Backbone:
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name: AlexNet
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pretrained: True
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class_num: 48
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# loss function config for traing/eval process
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Loss:
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Train:
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- DCHLoss:
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weight: 1.0
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gamma: 20.0
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_lambda: 0.1
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n_class: 10
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Eval:
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- DCHLoss:
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weight: 1.0
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gamma: 20.0
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_lambda: 0.1
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n_class: 10
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Optimizer:
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name: SGD
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lr:
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name: Piecewise
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learning_rate: 0.005
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decay_epochs: [200]
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values: [0.005, 0.0005]
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regularizer:
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name: 'L2'
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coeff: 0.00001
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# data loader for train and eval
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DataLoader:
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Train:
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dataset:
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name: CustomizedCifar10
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mode: 'train'
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sampler:
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batch_size: 128
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drop_last: False
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shuffle: True
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loader:
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num_workers: 4
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use_shared_memory: True
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Eval:
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Query:
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dataset:
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name: CustomizedCifar10
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mode: 'test'
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sampler:
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batch_size: 128
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drop_last: False
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shuffle: False
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loader:
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num_workers: 4
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use_shared_memory: True
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Gallery:
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dataset:
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name: CustomizedCifar10
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mode: 'train'
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sampler:
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batch_size: 128
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drop_last: False
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shuffle: False
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loader:
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num_workers: 4
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use_shared_memory: True
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Metric:
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Eval:
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- mAP: {}
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- Recallk:
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topk: [1, 5]
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@ -0,0 +1,105 @@
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# global configs
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Global:
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checkpoints: null
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pretrained_model: null
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output_dir: ./output
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device: gpu
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save_interval: 15
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eval_during_train: True
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eval_interval: 15
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epochs: 150
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print_batch_step: 10
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use_visualdl: False
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# used for static mode and model export
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image_shape: [3, 224, 224]
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save_inference_dir: ./inference
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eval_mode: retrieval
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use_dali: False
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to_static: False
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#feature postprocess
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feature_normalize: False
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feature_binarize: "sign"
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# model architecture
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Arch:
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name: RecModel
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infer_output_key: features
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infer_add_softmax: False
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Backbone:
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name: AlexNet
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pretrained: True
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class_num: 48
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Neck:
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name: Tanh
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Head:
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name: FC
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class_num: 10
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embedding_size: 48
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# loss function config for traing/eval process
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Loss:
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Train:
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- DSHSDLoss:
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weight: 1.0
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alpha: 0.05
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Eval:
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- DSHSDLoss:
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weight: 1.0
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alpha: 0.05
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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name: Piecewise
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learning_rate: 0.00001
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decay_epochs: [200]
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values: [0.00001, 0.000001]
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# data loader for train and eval
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DataLoader:
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Train:
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dataset:
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name: CustomizedCifar10
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mode: 'train'
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sampler:
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batch_size: 128
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drop_last: False
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shuffle: True
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loader:
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num_workers: 4
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use_shared_memory: True
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Eval:
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Query:
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dataset:
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name: CustomizedCifar10
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mode: 'test'
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sampler:
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batch_size: 128
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drop_last: False
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shuffle: False
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loader:
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num_workers: 4
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use_shared_memory: True
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Gallery:
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dataset:
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name: CustomizedCifar10
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mode: 'train'
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sampler:
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batch_size: 128
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drop_last: False
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shuffle: False
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loader:
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num_workers: 4
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use_shared_memory: True
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Metric:
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Eval:
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- mAP: {}
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- Recallk:
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topk: [1, 5]
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@ -0,0 +1,101 @@
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# global configs
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Global:
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checkpoints: null
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pretrained_model: null
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output_dir: ./output
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device: gpu
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save_interval: 15
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eval_during_train: True
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eval_interval: 15
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epochs: 150
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print_batch_step: 10
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use_visualdl: False
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# used for static mode and model export
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image_shape: [3, 224, 224]
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save_inference_dir: ./inference
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eval_mode: retrieval
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use_dali: False
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to_static: False
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#feature postprocess
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feature_normalize: False
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feature_binarize: "sign"
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# model architecture
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Arch:
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name: RecModel
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infer_output_key: features
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infer_add_softmax: False
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Backbone:
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name: AlexNet
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pretrained: True
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class_num: 48
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# loss function config for traing/eval process
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Loss:
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Train:
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- LCDSHLoss:
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weight: 1.0
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_lambda: 3
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n_class: 10
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Eval:
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- LCDSHLoss:
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weight: 1.0
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_lambda: 3
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n_class: 10
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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name: Piecewise
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learning_rate: 0.00001
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decay_epochs: [200]
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values: [0.00001, 0.000001]
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# data loader for train and eval
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DataLoader:
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Train:
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dataset:
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name: CustomizedCifar10
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mode: 'train'
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sampler:
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batch_size: 128
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drop_last: False
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shuffle: True
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loader:
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num_workers: 4
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use_shared_memory: True
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Eval:
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Query:
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dataset:
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name: CustomizedCifar10
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mode: 'test'
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sampler:
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batch_size: 128
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drop_last: False
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shuffle: False
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loader:
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num_workers: 4
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use_shared_memory: True
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Gallery:
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dataset:
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name: CustomizedCifar10
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mode: 'train'
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sampler:
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batch_size: 128
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drop_last: False
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shuffle: False
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loader:
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num_workers: 4
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use_shared_memory: True
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Metric:
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Eval:
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- mAP: {}
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- Recallk:
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topk: [1, 5]
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@ -28,6 +28,7 @@ from ppcls.data.dataloader.vehicle_dataset import CompCars, VeriWild
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from ppcls.data.dataloader.logo_dataset import LogoDataset
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from ppcls.data.dataloader.icartoon_dataset import ICartoonDataset
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from ppcls.data.dataloader.mix_dataset import MixDataset
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from ppcls.data.dataloader.customized_cifar10 import CustomizedCifar10
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# sampler
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from ppcls.data.dataloader.DistributedRandomIdentitySampler import DistributedRandomIdentitySampler
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@ -4,6 +4,7 @@ from ppcls.data.dataloader.common_dataset import create_operators
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from ppcls.data.dataloader.vehicle_dataset import CompCars, VeriWild
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from ppcls.data.dataloader.logo_dataset import LogoDataset
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from ppcls.data.dataloader.icartoon_dataset import ICartoonDataset
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from ppcls.data.dataloader.customized_cifar10 import CustomizedCifar10
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from ppcls.data.dataloader.mix_dataset import MixDataset
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from ppcls.data.dataloader.mix_sampler import MixSampler
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from ppcls.data.dataloader.pk_sampler import PKSampler
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@ -0,0 +1,65 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from paddle.vision.datasets import Cifar10
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from paddle.vision import transforms
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from paddle.dataset.common import _check_exists_and_download
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import numpy as np
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import os
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from PIL import Image
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class CustomizedCifar10(Cifar10):
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def __init__(self,
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data_file=None,
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mode='train',
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download=True,
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backend=None):
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assert mode.lower() in ['train', 'test', 'train', 'test'], \
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"mode should be 'train10', 'test10', 'train100' or 'test100', but got {}".format(mode)
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self.mode = mode.lower()
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if backend is None:
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backend = paddle.vision.get_image_backend()
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if backend not in ['pil', 'cv2']:
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raise ValueError(
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"Expected backend are one of ['pil', 'cv2'], but got {}"
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.format(backend))
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self.backend = backend
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self._init_url_md5_flag()
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self.data_file = data_file
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if self.data_file is None:
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assert download, "data_file is not set and downloading automatically is disabled"
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self.data_file = _check_exists_and_download(
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data_file, self.data_url, self.data_md5, 'cifar', download)
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self.transform = transforms.Compose([
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transforms.Resize(224), transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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self._load_data()
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self.dtype = paddle.get_default_dtype()
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def __getitem__(self, index):
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img, target = self.data[index]
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img = np.reshape(img, [3, 32, 32])
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img = img.transpose([1, 2, 0]).astype("uint8")
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img = Image.fromarray(img)
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img = self.transform(img)
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return (img, target)
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@ -24,7 +24,9 @@ from .distillationloss import DistillationDistanceLoss
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from .distillationloss import DistillationRKDLoss
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from .multilabelloss import MultiLabelLoss
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from .deephashloss import DSHSDLoss, LCDSHLoss
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from .deephashloss import DSHSDLoss
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from .deephashloss import LCDSHLoss
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from .deephashloss import DCHLoss
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class CombinedLoss(nn.Layer):
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@ -15,6 +15,7 @@
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import paddle
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import paddle.nn as nn
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class DSHSDLoss(nn.Layer):
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"""
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# DSHSD(IEEE ACCESS 2019)
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@ -23,6 +24,7 @@ class DSHSDLoss(nn.Layer):
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# [DSHSD] epoch:250, bit:48, dataset:nuswide_21, MAP:0.809, Best MAP: 0.815
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# [DSHSD] epoch:135, bit:48, dataset:imagenet, MAP:0.647, Best MAP: 0.647
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"""
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def __init__(self, alpha, multi_label=False):
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super(DSHSDLoss, self).__init__()
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self.alpha = alpha
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@ -37,9 +39,9 @@ class DSHSDLoss(nn.Layer):
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axis=2)
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# label to ont-hot
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label = paddle.flatten(label)
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n_class = logits.shape[1]
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label = paddle.nn.functional.one_hot(label, n_class).astype("float32")
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label = paddle.nn.functional.one_hot(
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label, n_class).astype("float32").squeeze()
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s = (paddle.matmul(
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label, label, transpose_y=True) == 0).astype("float32")
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@ -65,6 +67,7 @@ class LCDSHLoss(nn.Layer):
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# [LCDSH] epoch:145, bit:48, dataset:cifar10-1, MAP:0.798, Best MAP: 0.798
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# [LCDSH] epoch:183, bit:48, dataset:nuswide_21, MAP:0.833, Best MAP: 0.834
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"""
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def __init__(self, n_class, _lambda):
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super(LCDSHLoss, self).__init__()
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self._lambda = _lambda
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@ -73,11 +76,11 @@ class LCDSHLoss(nn.Layer):
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def forward(self, input, label):
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feature = input["features"]
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# label to ont-hot
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label = paddle.flatten(label)
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label = paddle.nn.functional.one_hot(label, self.n_class).astype("float32")
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s = 2 * (paddle.matmul(label, label, transpose_y=True) > 0).astype("float32") - 1
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label = paddle.nn.functional.one_hot(
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label, self.n_class).astype("float32").squeeze()
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s = 2 * (paddle.matmul(
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label, label, transpose_y=True) > 0).astype("float32") - 1
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inner_product = paddle.matmul(feature, feature, transpose_y=True) * 0.5
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inner_product = inner_product.clip(min=-50, max=50)
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@ -90,3 +93,58 @@ class LCDSHLoss(nn.Layer):
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return {"lcdshloss": L1 + self._lambda * L2}
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class DCHLoss(paddle.nn.Layer):
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"""
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# paper [Deep Cauchy Hashing for Hamming Space Retrieval]
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URL:(http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-cauchy-hashing-cvpr18.pdf)
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# [DCH] epoch:150, bit:48, dataset:cifar10-1, MAP:0.768, Best MAP: 0.810
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# [DCH] epoch:150, bit:48, dataset:coco, MAP:0.665, Best MAP: 0.670
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# [DCH] epoch:150, bit:48, dataset:imagenet, MAP:0.586, Best MAP: 0.586
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# [DCH] epoch:150, bit:48, dataset:nuswide_21, MAP:0.778, Best MAP: 0.794
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"""
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def __init__(self, gamma, _lambda, n_class):
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super(DCHLoss, self).__init__()
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self.gamma = gamma
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self._lambda = _lambda
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self.n_class = n_class
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def d(self, hi, hj):
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assert hi.shape[1] == hj.shape[
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1], "feature len of hi and hj is different, please check whether the featurs are right"
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K = hi.shape[1]
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inner_product = paddle.matmul(hi, hj, transpose_y=True)
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len_i = hi.pow(2).sum(axis=1, keepdim=True).pow(0.5)
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len_j = hj.pow(2).sum(axis=1, keepdim=True).pow(0.5)
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norm = paddle.matmul(len_i, len_j, transpose_y=True)
|
||||
cos = inner_product / norm.clip(min=0.0001)
|
||||
return (1 - cos.clip(max=0.99)) * K / 2
|
||||
|
||||
def forward(self, input, label):
|
||||
u = input["features"]
|
||||
y = paddle.nn.functional.one_hot(
|
||||
label, self.n_class).astype("float32").squeeze()
|
||||
|
||||
s = paddle.matmul(y, y, transpose_y=True).astype("float32")
|
||||
if (1 - s).sum() != 0 and s.sum() != 0:
|
||||
positive_w = s * s.numel() / s.sum()
|
||||
negative_w = (1 - s) * s.numel() / (1 - s).sum()
|
||||
w = positive_w + negative_w
|
||||
else:
|
||||
w = 1
|
||||
|
||||
d_hi_hj = self.d(u, u)
|
||||
|
||||
cauchy_loss = w * (s * paddle.log(d_hi_hj / self.gamma) +
|
||||
paddle.log(1 + self.gamma / d_hi_hj))
|
||||
|
||||
all_one = paddle.ones_like(u, dtype="float32")
|
||||
quantization_loss = paddle.log(1 + self.d(u.abs(), all_one) /
|
||||
self.gamma)
|
||||
|
||||
loss = cauchy_loss.mean() + self._lambda * quantization_loss.mean()
|
||||
|
||||
return {"dchloss": loss}
|
||||
|
|
|
|||
|
|
@ -22,6 +22,53 @@ import paddle
|
|||
from ppcls.utils import logger
|
||||
|
||||
|
||||
class SGD(object):
|
||||
"""
|
||||
Args:
|
||||
learning_rate (float|Tensor|LearningRateDecay, optional): The learning rate used to update ``Parameter``.
|
||||
It can be a float value, a ``Tensor`` with a float type or a LearningRateDecay. The default value is 0.001.
|
||||
parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \
|
||||
This parameter is required in dygraph mode. \
|
||||
The default value is None in static mode, at this time all parameters will be updated.
|
||||
weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
|
||||
It canbe a float value as coeff of L2 regularization or \
|
||||
:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
|
||||
If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
|
||||
the regularization setting here in optimizer will be ignored for this parameter. \
|
||||
Otherwise, the regularization setting here in optimizer will take effect. \
|
||||
Default None, meaning there is no regularization.
|
||||
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
|
||||
some derived class of ``GradientClipBase`` . There are three cliping strategies
|
||||
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
|
||||
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
|
||||
name (str, optional): The default value is None. Normally there is no need for user
|
||||
to set this property.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
learning_rate=0.001,
|
||||
parameters=None,
|
||||
weight_decay=None,
|
||||
grad_clip=None,
|
||||
name=None):
|
||||
self.learning_rate = learning_rate
|
||||
self.parameters = parameters
|
||||
self.weight_decay = weight_decay
|
||||
self.grad_clip = grad_clip
|
||||
self.name = name
|
||||
|
||||
def __call__(self, model_list):
|
||||
# model_list is None in static graph
|
||||
parameters = sum([m.parameters() for m in model_list],
|
||||
[]) if model_list else None
|
||||
opt = optim.SGD(learning_rate=self.learning_rate,
|
||||
parameters=parameters,
|
||||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
name=self.name)
|
||||
return opt
|
||||
|
||||
|
||||
class Momentum(object):
|
||||
"""
|
||||
Simple Momentum optimizer with velocity state.
|
||||
|
|
|
|||
Loading…
Reference in New Issue