├── mplug_owl2 ├── serve │ ├── __init__.py │ ├── examples │ │ ├── extreme_ironing.jpg │ │ └── Rebecca_(1939_poster)_Small.jpeg │ └── register_workers.py ├── evaluate │ ├── __init__.py │ └── mmbench_converter.py ├── local_serve │ ├── __init__.py │ └── examples │ │ ├── extreme_ironing.jpg │ │ └── Rebecca_(1939_poster)_Small.jpeg ├── __init__.py ├── .DS_Store ├── __pycache__ │ ├── __init__.cpython-310.pyc │ ├── mm_utils.cpython-310.pyc │ ├── constants.cpython-310.pyc │ └── conversation.cpython-310.pyc ├── model │ ├── __pycache__ │ │ ├── builder.cpython-310.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── multiway.cpython-310.pyc │ │ ├── modeling_llama2.cpython-310.pyc │ │ ├── modeling_qwen.cpython-310.pyc │ │ ├── visual_encoder.cpython-310.pyc │ │ ├── configuration_qwen.cpython-310.pyc │ │ ├── modeling_mplug_owl2.cpython-310.pyc │ │ ├── configuration_mplug_owl2.cpython-310.pyc │ │ └── modeling_attn_mask_utils.cpython-310.pyc │ ├── __init__.py │ ├── utils.py │ ├── multiway.py │ └── configuration_qwen.py ├── constants.py └── train │ └── train_mem.py ├── timm ├── version.py ├── data │ ├── parsers │ │ ├── __init__.py │ │ ├── constants.py │ │ ├── __pycache__ │ │ │ ├── parser.cpython-310.pyc │ │ │ ├── parser.cpython-311.pyc │ │ │ ├── parser.cpython-39.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── __init__.cpython-311.pyc │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── class_map.cpython-310.pyc │ │ │ ├── class_map.cpython-311.pyc │ │ │ ├── class_map.cpython-39.pyc │ │ │ ├── constants.cpython-310.pyc │ │ │ ├── constants.cpython-311.pyc │ │ │ ├── constants.cpython-39.pyc │ │ │ ├── parser_factory.cpython-310.pyc │ │ │ ├── parser_factory.cpython-311.pyc │ │ │ ├── parser_factory.cpython-39.pyc │ │ │ ├── parser_image_tar.cpython-310.pyc │ │ │ ├── parser_image_tar.cpython-311.pyc │ │ │ ├── parser_image_tar.cpython-39.pyc │ │ │ ├── parser_image_folder.cpython-310.pyc │ │ │ ├── parser_image_folder.cpython-311.pyc │ │ │ ├── parser_image_folder.cpython-39.pyc │ │ │ ├── parser_image_in_tar.cpython-310.pyc │ │ │ ├── parser_image_in_tar.cpython-311.pyc │ │ │ └── parser_image_in_tar.cpython-39.pyc │ │ ├── parser.py │ │ ├── class_map.py │ │ └── parser_factory.py │ ├── __pycache__ │ │ ├── mixup.cpython-39.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── config.cpython-310.pyc │ │ ├── config.cpython-311.pyc │ │ ├── config.cpython-39.pyc │ │ ├── dataset.cpython-310.pyc │ │ ├── dataset.cpython-311.pyc │ │ ├── dataset.cpython-39.pyc │ │ ├── loader.cpython-310.pyc │ │ ├── loader.cpython-311.pyc │ │ ├── loader.cpython-39.pyc │ │ ├── mixup.cpython-310.pyc │ │ ├── mixup.cpython-311.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── __init__.cpython-311.pyc │ │ ├── constants.cpython-310.pyc │ │ ├── constants.cpython-311.pyc │ │ ├── constants.cpython-39.pyc │ │ ├── transforms.cpython-39.pyc │ │ ├── auto_augment.cpython-310.pyc │ │ ├── auto_augment.cpython-311.pyc │ │ ├── auto_augment.cpython-39.pyc │ │ ├── real_labels.cpython-310.pyc │ │ ├── real_labels.cpython-311.pyc │ │ ├── real_labels.cpython-39.pyc │ │ ├── transforms.cpython-310.pyc │ │ ├── transforms.cpython-311.pyc │ │ ├── dataset_factory.cpython-39.pyc │ │ ├── random_erasing.cpython-310.pyc │ │ ├── random_erasing.cpython-311.pyc │ │ ├── random_erasing.cpython-39.pyc │ │ ├── dataset_factory.cpython-310.pyc │ │ ├── dataset_factory.cpython-311.pyc │ │ ├── transforms_factory.cpython-39.pyc │ │ ├── distributed_sampler.cpython-310.pyc │ │ ├── distributed_sampler.cpython-311.pyc │ │ ├── distributed_sampler.cpython-39.pyc │ │ ├── transforms_factory.cpython-310.pyc │ │ └── transforms_factory.cpython-311.pyc │ ├── constants.py │ ├── __init__.py │ └── real_labels.py ├── .DS_Store ├── __pycache__ │ ├── __init__.cpython-39.pyc │ ├── version.cpython-310.pyc │ ├── version.cpython-311.pyc │ ├── version.cpython-39.pyc │ ├── __init__.cpython-310.pyc │ └── __init__.cpython-311.pyc ├── models │ ├── __pycache__ │ │ ├── dla.cpython-39.pyc │ │ ├── dpn.cpython-39.pyc │ │ ├── hub.cpython-39.pyc │ │ ├── pit.cpython-39.pyc │ │ ├── tnt.cpython-39.pyc │ │ ├── vgg.cpython-39.pyc │ │ ├── beit.cpython-310.pyc │ │ ├── beit.cpython-311.pyc │ │ ├── beit.cpython-39.pyc │ │ ├── cait.cpython-310.pyc │ │ ├── cait.cpython-311.pyc │ │ ├── cait.cpython-39.pyc │ │ ├── coat.cpython-310.pyc │ │ ├── coat.cpython-311.pyc │ │ ├── coat.cpython-39.pyc │ │ ├── convit.cpython-39.pyc │ │ ├── cspnet.cpython-39.pyc │ │ ├── dla.cpython-310.pyc │ │ ├── dla.cpython-311.pyc │ │ ├── dpn.cpython-310.pyc │ │ ├── dpn.cpython-311.pyc │ │ ├── hrnet.cpython-310.pyc │ │ ├── hrnet.cpython-311.pyc │ │ ├── hrnet.cpython-39.pyc │ │ ├── hub.cpython-310.pyc │ │ ├── hub.cpython-311.pyc │ │ ├── levit.cpython-310.pyc │ │ ├── levit.cpython-311.pyc │ │ ├── levit.cpython-39.pyc │ │ ├── nasnet.cpython-39.pyc │ │ ├── nest.cpython-310.pyc │ │ ├── nest.cpython-311.pyc │ │ ├── nest.cpython-39.pyc │ │ ├── nfnet.cpython-310.pyc │ │ ├── nfnet.cpython-311.pyc │ │ ├── nfnet.cpython-39.pyc │ │ ├── pit.cpython-310.pyc │ │ ├── pit.cpython-311.pyc │ │ ├── regnet.cpython-39.pyc │ │ ├── resnet.cpython-39.pyc │ │ ├── rexnet.cpython-39.pyc │ │ ├── senet.cpython-310.pyc │ │ ├── senet.cpython-311.pyc │ │ ├── senet.cpython-39.pyc │ │ ├── sknet.cpython-310.pyc │ │ ├── sknet.cpython-311.pyc │ │ ├── sknet.cpython-39.pyc │ │ ├── tnt.cpython-310.pyc │ │ ├── tnt.cpython-311.pyc │ │ ├── twins.cpython-310.pyc │ │ ├── twins.cpython-311.pyc │ │ ├── twins.cpython-39.pyc │ │ ├── vgg.cpython-310.pyc │ │ ├── vgg.cpython-311.pyc │ │ ├── vovnet.cpython-39.pyc │ │ ├── xcit.cpython-310.pyc │ │ ├── xcit.cpython-311.pyc │ │ ├── xcit.cpython-39.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── byoanet.cpython-310.pyc │ │ ├── byoanet.cpython-311.pyc │ │ ├── byoanet.cpython-39.pyc │ │ ├── byobnet.cpython-310.pyc │ │ ├── byobnet.cpython-311.pyc │ │ ├── byobnet.cpython-39.pyc │ │ ├── convit.cpython-310.pyc │ │ ├── convit.cpython-311.pyc │ │ ├── convnext.cpython-39.pyc │ │ ├── crossvit.cpython-39.pyc │ │ ├── cspnet.cpython-310.pyc │ │ ├── cspnet.cpython-311.pyc │ │ ├── densenet.cpython-39.pyc │ │ ├── factory.cpython-310.pyc │ │ ├── factory.cpython-311.pyc │ │ ├── factory.cpython-39.pyc │ │ ├── features.cpython-39.pyc │ │ ├── ghostnet.cpython-39.pyc │ │ ├── helpers.cpython-310.pyc │ │ ├── helpers.cpython-311.pyc │ │ ├── helpers.cpython-39.pyc │ │ ├── nasnet.cpython-310.pyc │ │ ├── nasnet.cpython-311.pyc │ │ ├── pnasnet.cpython-310.pyc │ │ ├── pnasnet.cpython-311.pyc │ │ ├── pnasnet.cpython-39.pyc │ │ ├── registry.cpython-39.pyc │ │ ├── regnet.cpython-310.pyc │ │ ├── regnet.cpython-311.pyc │ │ ├── res2net.cpython-310.pyc │ │ ├── res2net.cpython-311.pyc │ │ ├── res2net.cpython-39.pyc │ │ ├── resnest.cpython-310.pyc │ │ ├── resnest.cpython-311.pyc │ │ ├── resnest.cpython-39.pyc │ │ ├── resnet.cpython-310.pyc │ │ ├── resnet.cpython-311.pyc │ │ ├── resnetv2.cpython-39.pyc │ │ ├── rexnet.cpython-310.pyc │ │ ├── rexnet.cpython-311.pyc │ │ ├── selecsls.cpython-39.pyc │ │ ├── tresnet.cpython-310.pyc │ │ ├── tresnet.cpython-311.pyc │ │ ├── tresnet.cpython-39.pyc │ │ ├── vovnet.cpython-310.pyc │ │ ├── vovnet.cpython-311.pyc │ │ ├── xception.cpython-39.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── __init__.cpython-311.pyc │ │ ├── convmixer.cpython-310.pyc │ │ ├── convmixer.cpython-311.pyc │ │ ├── convmixer.cpython-39.pyc │ │ ├── convnext.cpython-310.pyc │ │ ├── convnext.cpython-311.pyc │ │ ├── crossvit.cpython-310.pyc │ │ ├── crossvit.cpython-311.pyc │ │ ├── densenet.cpython-310.pyc │ │ ├── densenet.cpython-311.pyc │ │ ├── features.cpython-310.pyc │ │ ├── features.cpython-311.pyc │ │ ├── fx_features.cpython-39.pyc │ │ ├── ghostnet.cpython-310.pyc │ │ ├── ghostnet.cpython-311.pyc │ │ ├── hardcorenas.cpython-39.pyc │ │ ├── mlp_mixer.cpython-310.pyc │ │ ├── mlp_mixer.cpython-311.pyc │ │ ├── mlp_mixer.cpython-39.pyc │ │ ├── mobilenetv3.cpython-39.pyc │ │ ├── registry.cpython-310.pyc │ │ ├── registry.cpython-311.pyc │ │ ├── resnetv2.cpython-310.pyc │ │ ├── resnetv2.cpython-311.pyc │ │ ├── selecsls.cpython-310.pyc │ │ ├── selecsls.cpython-311.pyc │ │ ├── visformer.cpython-310.pyc │ │ ├── visformer.cpython-311.pyc │ │ ├── visformer.cpython-39.pyc │ │ ├── xception.cpython-310.pyc │ │ ├── xception.cpython-311.pyc │ │ ├── efficientnet.cpython-310.pyc │ │ ├── efficientnet.cpython-311.pyc │ │ ├── efficientnet.cpython-39.pyc │ │ ├── fx_features.cpython-310.pyc │ │ ├── fx_features.cpython-311.pyc │ │ ├── gluon_resnet.cpython-310.pyc │ │ ├── gluon_resnet.cpython-311.pyc │ │ ├── gluon_resnet.cpython-39.pyc │ │ ├── hardcorenas.cpython-310.pyc │ │ ├── hardcorenas.cpython-311.pyc │ │ ├── inception_v3.cpython-310.pyc │ │ ├── inception_v3.cpython-311.pyc │ │ ├── inception_v3.cpython-39.pyc │ │ ├── inception_v4.cpython-310.pyc │ │ ├── inception_v4.cpython-311.pyc │ │ ├── inception_v4.cpython-39.pyc │ │ ├── mobilenetv3.cpython-310.pyc │ │ ├── mobilenetv3.cpython-311.pyc │ │ ├── gluon_xception.cpython-310.pyc │ │ ├── gluon_xception.cpython-311.pyc │ │ ├── gluon_xception.cpython-39.pyc │ │ ├── swin_transformer.cpython-39.pyc │ │ ├── xception_aligned.cpython-39.pyc │ │ ├── swin_transformer.cpython-310.pyc │ │ ├── swin_transformer.cpython-311.pyc │ │ ├── vision_transformer.cpython-39.pyc │ │ ├── xception_aligned.cpython-310.pyc │ │ ├── xception_aligned.cpython-311.pyc │ │ ├── efficientnet_blocks.cpython-310.pyc │ │ ├── efficientnet_blocks.cpython-311.pyc │ │ ├── efficientnet_blocks.cpython-39.pyc │ │ ├── efficientnet_builder.cpython-310.pyc │ │ ├── efficientnet_builder.cpython-311.pyc │ │ ├── efficientnet_builder.cpython-39.pyc │ │ ├── inception_resnet_v2.cpython-310.pyc │ │ ├── inception_resnet_v2.cpython-311.pyc │ │ ├── inception_resnet_v2.cpython-39.pyc │ │ ├── vision_transformer.cpython-310.pyc │ │ ├── vision_transformer.cpython-311.pyc │ │ ├── vision_transformer_hybrid.cpython-310.pyc │ │ ├── vision_transformer_hybrid.cpython-311.pyc │ │ └── vision_transformer_hybrid.cpython-39.pyc │ ├── layers │ │ ├── __pycache__ │ │ │ ├── cbam.cpython-39.pyc │ │ │ ├── drop.cpython-39.pyc │ │ │ ├── eca.cpython-310.pyc │ │ │ ├── eca.cpython-311.pyc │ │ │ ├── eca.cpython-39.pyc │ │ │ ├── mlp.cpython-310.pyc │ │ │ ├── mlp.cpython-311.pyc │ │ │ ├── mlp.cpython-39.pyc │ │ │ ├── norm.cpython-39.pyc │ │ │ ├── cbam.cpython-310.pyc │ │ │ ├── cbam.cpython-311.pyc │ │ │ ├── config.cpython-39.pyc │ │ │ ├── drop.cpython-310.pyc │ │ │ ├── drop.cpython-311.pyc │ │ │ ├── linear.cpython-39.pyc │ │ │ ├── norm.cpython-310.pyc │ │ │ ├── norm.cpython-311.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── __init__.cpython-311.pyc │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── blur_pool.cpython-39.pyc │ │ │ ├── config.cpython-310.pyc │ │ │ ├── config.cpython-311.pyc │ │ │ ├── evo_norm.cpython-310.pyc │ │ │ ├── evo_norm.cpython-311.pyc │ │ │ ├── evo_norm.cpython-39.pyc │ │ │ ├── halo_attn.cpython-39.pyc │ │ │ ├── helpers.cpython-310.pyc │ │ │ ├── helpers.cpython-311.pyc │ │ │ ├── helpers.cpython-39.pyc │ │ │ ├── linear.cpython-310.pyc │ │ │ ├── linear.cpython-311.pyc │ │ │ ├── norm_act.cpython-310.pyc │ │ │ ├── norm_act.cpython-311.pyc │ │ │ ├── norm_act.cpython-39.pyc │ │ │ ├── padding.cpython-310.pyc │ │ │ ├── padding.cpython-311.pyc │ │ │ ├── padding.cpython-39.pyc │ │ │ ├── std_conv.cpython-310.pyc │ │ │ ├── std_conv.cpython-311.pyc │ │ │ ├── std_conv.cpython-39.pyc │ │ │ ├── activations.cpython-39.pyc │ │ │ ├── blur_pool.cpython-310.pyc │ │ │ ├── blur_pool.cpython-311.pyc │ │ │ ├── classifier.cpython-310.pyc │ │ │ ├── classifier.cpython-311.pyc │ │ │ ├── classifier.cpython-39.pyc │ │ │ ├── cond_conv2d.cpython-39.pyc │ │ │ ├── conv2d_same.cpython-39.pyc │ │ │ ├── conv_bn_act.cpython-39.pyc │ │ │ ├── create_act.cpython-310.pyc │ │ │ ├── create_act.cpython-311.pyc │ │ │ ├── create_act.cpython-39.pyc │ │ │ ├── create_attn.cpython-39.pyc │ │ │ ├── halo_attn.cpython-310.pyc │ │ │ ├── halo_attn.cpython-311.pyc │ │ │ ├── inplace_abn.cpython-39.pyc │ │ │ ├── patch_embed.cpython-39.pyc │ │ │ ├── pool2d_same.cpython-39.pyc │ │ │ ├── split_attn.cpython-310.pyc │ │ │ ├── split_attn.cpython-311.pyc │ │ │ ├── split_attn.cpython-39.pyc │ │ │ ├── trace_utils.cpython-39.pyc │ │ │ ├── weight_init.cpython-39.pyc │ │ │ ├── activations.cpython-310.pyc │ │ │ ├── activations.cpython-311.pyc │ │ │ ├── activations_me.cpython-39.pyc │ │ │ ├── cond_conv2d.cpython-310.pyc │ │ │ ├── cond_conv2d.cpython-311.pyc │ │ │ ├── conv2d_same.cpython-310.pyc │ │ │ ├── conv2d_same.cpython-311.pyc │ │ │ ├── conv_bn_act.cpython-310.pyc │ │ │ ├── conv_bn_act.cpython-311.pyc │ │ │ ├── create_attn.cpython-310.pyc │ │ │ ├── create_attn.cpython-311.pyc │ │ │ ├── create_conv2d.cpython-310.pyc │ │ │ ├── create_conv2d.cpython-311.pyc │ │ │ ├── create_conv2d.cpython-39.pyc │ │ │ ├── gather_excite.cpython-310.pyc │ │ │ ├── gather_excite.cpython-311.pyc │ │ │ ├── gather_excite.cpython-39.pyc │ │ │ ├── global_context.cpython-39.pyc │ │ │ ├── inplace_abn.cpython-310.pyc │ │ │ ├── inplace_abn.cpython-311.pyc │ │ │ ├── lambda_layer.cpython-310.pyc │ │ │ ├── lambda_layer.cpython-311.pyc │ │ │ ├── lambda_layer.cpython-39.pyc │ │ │ ├── mixed_conv2d.cpython-310.pyc │ │ │ ├── mixed_conv2d.cpython-311.pyc │ │ │ ├── mixed_conv2d.cpython-39.pyc │ │ │ ├── non_local_attn.cpython-39.pyc │ │ │ ├── patch_embed.cpython-310.pyc │ │ │ ├── patch_embed.cpython-311.pyc │ │ │ ├── pool2d_same.cpython-310.pyc │ │ │ ├── pool2d_same.cpython-311.pyc │ │ │ ├── separable_conv.cpython-39.pyc │ │ │ ├── space_to_depth.cpython-39.pyc │ │ │ ├── squeeze_excite.cpython-39.pyc │ │ │ ├── test_time_pool.cpython-39.pyc │ │ │ ├── trace_utils.cpython-310.pyc │ │ │ ├── trace_utils.cpython-311.pyc │ │ │ ├── weight_init.cpython-310.pyc │ │ │ ├── weight_init.cpython-311.pyc │ │ │ ├── activations_jit.cpython-310.pyc │ │ │ ├── activations_jit.cpython-311.pyc │ │ │ ├── activations_jit.cpython-39.pyc │ │ │ ├── activations_me.cpython-310.pyc │ │ │ ├── activations_me.cpython-311.pyc │ │ │ ├── bottleneck_attn.cpython-310.pyc │ │ │ ├── bottleneck_attn.cpython-311.pyc │ │ │ ├── bottleneck_attn.cpython-39.pyc │ │ │ ├── create_norm_act.cpython-310.pyc │ │ │ ├── create_norm_act.cpython-311.pyc │ │ │ ├── create_norm_act.cpython-39.pyc │ │ │ ├── global_context.cpython-310.pyc │ │ │ ├── global_context.cpython-311.pyc │ │ │ ├── non_local_attn.cpython-310.pyc │ │ │ ├── non_local_attn.cpython-311.pyc │ │ │ ├── selective_kernel.cpython-39.pyc │ │ │ ├── separable_conv.cpython-310.pyc │ │ │ ├── separable_conv.cpython-311.pyc │ │ │ ├── space_to_depth.cpython-310.pyc │ │ │ ├── space_to_depth.cpython-311.pyc │ │ │ ├── split_batchnorm.cpython-310.pyc │ │ │ ├── split_batchnorm.cpython-311.pyc │ │ │ ├── split_batchnorm.cpython-39.pyc │ │ │ ├── squeeze_excite.cpython-310.pyc │ │ │ ├── squeeze_excite.cpython-311.pyc │ │ │ ├── test_time_pool.cpython-310.pyc │ │ │ ├── test_time_pool.cpython-311.pyc │ │ │ ├── selective_kernel.cpython-310.pyc │ │ │ ├── selective_kernel.cpython-311.pyc │ │ │ ├── adaptive_avgmax_pool.cpython-310.pyc │ │ │ ├── adaptive_avgmax_pool.cpython-311.pyc │ │ │ └── adaptive_avgmax_pool.cpython-39.pyc │ │ ├── trace_utils.py │ │ ├── linear.py │ │ ├── helpers.py │ │ ├── norm.py │ │ ├── conv_bn_act.py │ │ ├── create_conv2d.py │ │ ├── patch_embed.py │ │ ├── conv2d_same.py │ │ ├── blur_pool.py │ │ ├── median_pool.py │ │ ├── space_to_depth.py │ │ ├── mixed_conv2d.py │ │ ├── test_time_pool.py │ │ ├── __init__.py │ │ ├── padding.py │ │ ├── classifier.py │ │ └── global_context.py │ └── __init__.py ├── utils │ ├── __pycache__ │ │ ├── agc.cpython-310.pyc │ │ ├── agc.cpython-311.pyc │ │ ├── agc.cpython-39.pyc │ │ ├── cuda.cpython-39.pyc │ │ ├── jit.cpython-310.pyc │ │ ├── jit.cpython-311.pyc │ │ ├── jit.cpython-39.pyc │ │ ├── log.cpython-310.pyc │ │ ├── log.cpython-311.pyc │ │ ├── log.cpython-39.pyc │ │ ├── misc.cpython-39.pyc │ │ ├── cuda.cpython-310.pyc │ │ ├── cuda.cpython-311.pyc │ │ ├── metrics.cpython-39.pyc │ │ ├── misc.cpython-310.pyc │ │ ├── misc.cpython-311.pyc │ │ ├── model.cpython-310.pyc │ │ ├── model.cpython-311.pyc │ │ ├── model.cpython-39.pyc │ │ ├── random.cpython-310.pyc │ │ ├── random.cpython-311.pyc │ │ ├── random.cpython-39.pyc │ │ ├── summary.cpython-39.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── __init__.cpython-311.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── clip_grad.cpython-39.pyc │ │ ├── metrics.cpython-310.pyc │ │ ├── metrics.cpython-311.pyc │ │ ├── model_ema.cpython-39.pyc │ │ ├── summary.cpython-310.pyc │ │ ├── summary.cpython-311.pyc │ │ ├── clip_grad.cpython-310.pyc │ │ ├── clip_grad.cpython-311.pyc │ │ ├── distributed.cpython-310.pyc │ │ ├── distributed.cpython-311.pyc │ │ ├── distributed.cpython-39.pyc │ │ ├── model_ema.cpython-310.pyc │ │ ├── model_ema.cpython-311.pyc │ │ ├── checkpoint_saver.cpython-310.pyc │ │ ├── checkpoint_saver.cpython-311.pyc │ │ └── checkpoint_saver.cpython-39.pyc │ ├── random.py │ ├── __init__.py │ ├── misc.py │ ├── clip_grad.py │ ├── distributed.py │ ├── metrics.py │ ├── log.py │ ├── summary.py │ ├── agc.py │ ├── cuda.py │ └── jit.py ├── loss │ ├── __init__.py │ ├── cross_entropy.py │ ├── jsd.py │ └── binary_cross_entropy.py ├── __init__.py ├── scheduler │ ├── __init__.py │ ├── step_lr.py │ └── multistep_lr.py └── optim │ ├── __init__.py │ ├── sgdp.py │ └── lookahead.py ├── .DS_Store ├── src ├── sota.png ├── framework.pdf ├── framework.png ├── crossdataset.png └── motivation.pdf ├── configs ├── .DS_Store ├── base.yaml ├── AGIQA_3k │ └── MA_AGIQA.yaml └── AIGCQA_20k │ └── MA_AGIQA.yaml ├── dataset ├── .DS_Store ├── AIGCQA_20K │ ├── info_train.xlsx │ └── __pycache__ │ │ └── AIGCQA_30k.cpython-39.pyc └── AGIQA_3k │ └── __pycache__ │ └── agiqa.cpython-39.pyc ├── __pycache__ ├── test.cpython-39.pyc ├── config.cpython-310.pyc ├── config.cpython-311.pyc ├── config.cpython-39.pyc ├── performance.cpython-310.pyc └── performance.cpython-39.pyc ├── models └── __pycache__ │ ├── MoE.cpython-39.pyc │ ├── maniqa.cpython-39.pyc │ ├── swin.cpython-310.pyc │ ├── swin.cpython-311.pyc │ ├── swin.cpython-39.pyc │ ├── MA_AGIQA.cpython-39.pyc │ ├── maniqa.cpython-311.pyc │ ├── MA_AGIQA.cpython-310.pyc │ ├── transformer.cpython-39.pyc │ └── transformer.cpython-311.pyc ├── utils └── __pycache__ │ ├── process.cpython-39.pyc │ ├── process.cpython-310.pyc │ └── process.cpython-311.pyc ├── data └── __pycache__ │ ├── AIGC_general.cpython-310.pyc │ └── AIGC_general.cpython-39.pyc ├── config.py └── performance.py /mplug_owl2/serve/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /mplug_owl2/evaluate/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /mplug_owl2/local_serve/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /timm/version.py: 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random_seed(seed=42, rank=0): 7 | torch.manual_seed(seed + rank) 8 | np.random.seed(seed + rank) 9 | random.seed(seed + rank) 10 | -------------------------------------------------------------------------------- /mplug_owl2/constants.py: -------------------------------------------------------------------------------- 1 | CONTROLLER_HEART_BEAT_EXPIRATION = 30 2 | WORKER_HEART_BEAT_INTERVAL = 15 3 | 4 | LOGDIR = "./demo_logs" 5 | 6 | # Model Constants 7 | IGNORE_INDEX = -100 8 | IMAGE_TOKEN_INDEX = -200 9 | DEFAULT_IMAGE_TOKEN = "<|image|>" 10 | -------------------------------------------------------------------------------- /timm/loss/__init__.py: -------------------------------------------------------------------------------- 1 | from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel 2 | from .binary_cross_entropy import BinaryCrossEntropy 3 | from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy 4 | from .jsd import JsdCrossEntropy 5 | -------------------------------------------------------------------------------- /timm/__init__.py: -------------------------------------------------------------------------------- 1 | from .version import __version__ 2 | from .models import create_model, list_models, is_model, list_modules, model_entrypoint, \ 3 | is_scriptable, is_exportable, set_scriptable, set_exportable, has_model_default_key, is_model_default_key, \ 4 | get_model_default_value, is_model_pretrained 5 | -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | """ configuration json """ 4 | class Config(dict): 5 | __getattr__ = dict.__getitem__ 6 | __setattr__ = dict.__setitem__ 7 | 8 | @classmethod 9 | def load(cls, file): 10 | with open(file, 'r') as f: 11 | config = json.loads(f.read()) 12 | return Config(config) -------------------------------------------------------------------------------- /timm/data/constants.py: -------------------------------------------------------------------------------- 1 | DEFAULT_CROP_PCT = 0.875 2 | IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) 3 | IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) 4 | IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5) 5 | IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5) 6 | IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255) 7 | IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3) 8 | -------------------------------------------------------------------------------- /timm/scheduler/__init__.py: -------------------------------------------------------------------------------- 1 | from .cosine_lr import CosineLRScheduler 2 | from .multistep_lr import MultiStepLRScheduler 3 | from .plateau_lr import PlateauLRScheduler 4 | from .poly_lr import PolyLRScheduler 5 | from .step_lr import StepLRScheduler 6 | from .tanh_lr import TanhLRScheduler 7 | 8 | from .scheduler_factory import create_scheduler 9 | -------------------------------------------------------------------------------- /timm/models/layers/trace_utils.py: -------------------------------------------------------------------------------- 1 | try: 2 | from torch import _assert 3 | except ImportError: 4 | def _assert(condition: bool, message: str): 5 | assert condition, message 6 | 7 | 8 | def _float_to_int(x: float) -> int: 9 | """ 10 | Symbolic tracing helper to substitute for inbuilt `int`. 11 | Hint: Inbuilt `int` can't accept an argument of type `Proxy` 12 | """ 13 | return int(x) 14 | -------------------------------------------------------------------------------- /timm/optim/__init__.py: -------------------------------------------------------------------------------- 1 | from .adabelief import AdaBelief 2 | from .adafactor import Adafactor 3 | from .adahessian import Adahessian 4 | from .adamp import AdamP 5 | from .adamw import AdamW 6 | from .lamb import Lamb 7 | from .lars import Lars 8 | from .lookahead import Lookahead 9 | from .madgrad import MADGRAD 10 | from .nadam import Nadam 11 | from .nvnovograd import NvNovoGrad 12 | from .radam import RAdam 13 | from .rmsprop_tf import RMSpropTF 14 | from .sgdp import SGDP 15 | from .optim_factory import create_optimizer, create_optimizer_v2, optimizer_kwargs 16 | -------------------------------------------------------------------------------- /mplug_owl2/train/train_mem.py: -------------------------------------------------------------------------------- 1 | # Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright: 2 | # Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright: 3 | # Make it more memory efficient by monkey patching the LLaMA model with FlashAttn. 4 | 5 | # Need to call this before importing transformers. 6 | from mplug_owl2.train.llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn 7 | 8 | replace_llama_attn_with_flash_attn() 9 | 10 | from mplug_owl2.train.train import train 11 | 12 | if __name__ == "__main__": 13 | train() -------------------------------------------------------------------------------- /timm/data/parsers/parser.py: -------------------------------------------------------------------------------- 1 | from abc import abstractmethod 2 | 3 | 4 | class Parser: 5 | def __init__(self): 6 | pass 7 | 8 | @abstractmethod 9 | def _filename(self, index, basename=False, absolute=False): 10 | pass 11 | 12 | def filename(self, index, basename=False, absolute=False): 13 | return self._filename(index, basename=basename, absolute=absolute) 14 | 15 | def filenames(self, basename=False, absolute=False): 16 | return [self._filename(index, basename=basename, absolute=absolute) for index in range(len(self))] 17 | 18 | -------------------------------------------------------------------------------- /timm/data/__init__.py: -------------------------------------------------------------------------------- 1 | from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\ 2 | rand_augment_transform, auto_augment_transform 3 | from .config import resolve_data_config 4 | from .constants import * 5 | from .dataset import ImageDataset, IterableImageDataset, AugMixDataset 6 | from .dataset_factory import create_dataset 7 | from .loader import create_loader 8 | from .mixup import Mixup, FastCollateMixup 9 | from .parsers import create_parser 10 | from .real_labels import RealLabelsImagenet 11 | from .transforms import * 12 | from .transforms_factory import create_transform -------------------------------------------------------------------------------- /timm/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .agc import adaptive_clip_grad 2 | from .checkpoint_saver import CheckpointSaver 3 | from .clip_grad import dispatch_clip_grad 4 | from .cuda import ApexScaler, NativeScaler 5 | from .distributed import distribute_bn, reduce_tensor 6 | from .jit import set_jit_legacy, set_jit_fuser 7 | from .log import setup_default_logging, FormatterNoInfo 8 | from .metrics import AverageMeter, accuracy 9 | from .misc import natural_key, add_bool_arg 10 | from .model import unwrap_model, get_state_dict, freeze, unfreeze 11 | from .model_ema import ModelEma, ModelEmaV2 12 | from .random import random_seed 13 | from .summary import update_summary, get_outdir 14 | -------------------------------------------------------------------------------- /configs/base.yaml: -------------------------------------------------------------------------------- 1 | # optimization 2 | cpu_num : 1 3 | batch_size: 8 4 | learning_rate: 0.00001 5 | weight_decay: 0.00001 6 | n_epoch: 30 7 | val_freq: 1 8 | T_max: 50 9 | eta_min: 0 10 | num_avg_val: 1 # if training koniq10k, num_avg_val is set to 1 11 | num_workers: 8 12 | 13 | # data 14 | split_seed: 20 15 | train_keep_ratio: 1.0 16 | val_keep_ratio: 1.0 17 | crop_size: 224 18 | prob_aug: 0.7 19 | 20 | # model 21 | patch_size: 8 22 | img_size: 224 23 | embed_dim: 768 24 | dim_mlp: 768 25 | num_heads: [4, 4] 26 | window_size: 4 27 | depths: [2, 2] 28 | num_outputs: 1 29 | num_tab: 2 30 | scale: 0.8 31 | 32 | 33 | # load & save checkpoint 34 | log_path: "./output/log/" 35 | log_file: ".log" -------------------------------------------------------------------------------- /timm/utils/misc.py: -------------------------------------------------------------------------------- 1 | """ Misc utils 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import re 6 | 7 | 8 | def natural_key(string_): 9 | """See http://www.codinghorror.com/blog/archives/001018.html""" 10 | return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] 11 | 12 | 13 | def add_bool_arg(parser, name, default=False, help=''): 14 | dest_name = name.replace('-', '_') 15 | group = parser.add_mutually_exclusive_group(required=False) 16 | group.add_argument('--' + name, dest=dest_name, action='store_true', help=help) 17 | group.add_argument('--no-' + name, dest=dest_name, action='store_false', help=help) 18 | parser.set_defaults(**{dest_name: default}) 19 | -------------------------------------------------------------------------------- /configs/AGIQA_3k/MA_AGIQA.yaml: -------------------------------------------------------------------------------- 1 | # train/test setting 2 | n_epoch: 30 3 | 4 | # dataset path 5 | dataset_name: AGIQA_3k 6 | train_test_num: 1 7 | set_seed: 20 8 | 9 | # data path 10 | data_path: "/mnt/petrelfs/yangshuo/data/AGIQA-3k-Database/AGIQA-3K" 11 | label_path: "dataset/AGIQA_3k" 12 | csv_path : "dataset/AGIQA_3k/data.csv" # total 13 | json_name: "AGIQA_3k.json" 14 | 15 | # load & save checkpoint 16 | log_path: "./output/log/AGIQA_3k/" 17 | log_file: ".log" 18 | 19 | # getFeature 20 | model_path: "MAGAer13/mplug-owl2-llama2-7b" 21 | model_base: null 22 | device: "cuda" 23 | conv-mode: null 24 | temperature: 0.2 25 | max-new-tokens: 512 26 | 27 | tensor_root: '/mnt/hwfile/gveval/yangshuo/output/mplug_tensor/AGIQA_3k' 28 | question_type: 2 -------------------------------------------------------------------------------- /configs/AIGCQA_20k/MA_AGIQA.yaml: -------------------------------------------------------------------------------- 1 | # train/test setting 2 | n_epoch: 30 3 | 4 | # dataset path 5 | dataset_name: AIGCQA_20k 6 | train_test_num: 1 7 | set_seed: 20 8 | 9 | # data path 10 | data_path: "/mnt/petrelfs/yangshuo/data/AIGCQA_20k" 11 | label_path: "dataset/AIGCQA_20k" 12 | csv_path : "dataset/AIGCQA_20K/info_train.csv" # total 13 | json_name: "AIGCQA_20k.json" 14 | 15 | # load & save checkpoint 16 | log_path: "./output/log/AIGCQA_20k/" 17 | log_file: ".log" 18 | 19 | # getFeature 20 | model_path: "MAGAer13/mplug-owl2-llama2-7b" 21 | model_base: null 22 | device: "cuda" 23 | conv-mode: null 24 | temperature: 0.2 25 | max-new-tokens: 512 26 | 27 | tensor_root: '/mnt/hwfile/gveval/yangshuo/output/mplug_tensor/AIGCQA_20k' 28 | question_type: 2 -------------------------------------------------------------------------------- /timm/models/layers/linear.py: -------------------------------------------------------------------------------- 1 | """ Linear layer (alternate definition) 2 | """ 3 | import torch 4 | import torch.nn.functional as F 5 | from torch import nn as nn 6 | 7 | 8 | class Linear(nn.Linear): 9 | r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b` 10 | 11 | Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting 12 | weight & bias to input.dtype to work around an issue w/ torch.addmm in this use case. 13 | """ 14 | def forward(self, input: torch.Tensor) -> torch.Tensor: 15 | if torch.jit.is_scripting(): 16 | bias = self.bias.to(dtype=input.dtype) if self.bias is not None else None 17 | return F.linear(input, self.weight.to(dtype=input.dtype), bias=bias) 18 | else: 19 | return F.linear(input, self.weight, self.bias) 20 | -------------------------------------------------------------------------------- /timm/data/parsers/class_map.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | 4 | def load_class_map(map_or_filename, root=''): 5 | if isinstance(map_or_filename, dict): 6 | assert dict, 'class_map dict must be non-empty' 7 | return map_or_filename 8 | class_map_path = map_or_filename 9 | if not os.path.exists(class_map_path): 10 | class_map_path = os.path.join(root, class_map_path) 11 | assert os.path.exists(class_map_path), 'Cannot locate specified class map file (%s)' % map_or_filename 12 | class_map_ext = os.path.splitext(map_or_filename)[-1].lower() 13 | if class_map_ext == '.txt': 14 | with open(class_map_path) as f: 15 | class_to_idx = {v.strip(): k for k, v in enumerate(f)} 16 | else: 17 | assert False, f'Unsupported class map file extension ({class_map_ext}).' 18 | return class_to_idx 19 | 20 | -------------------------------------------------------------------------------- /mplug_owl2/serve/register_workers.py: -------------------------------------------------------------------------------- 1 | """ 2 | Manually register workers. 3 | 4 | Usage: 5 | python3 -m fastchat.serve.register_worker --controller http://localhost:21001 --worker-name http://localhost:21002 6 | """ 7 | 8 | import argparse 9 | 10 | import requests 11 | 12 | if __name__ == "__main__": 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument("--controller-address", type=str) 15 | parser.add_argument("--worker-name", type=str) 16 | parser.add_argument("--check-heart-beat", action="store_true") 17 | args = parser.parse_args() 18 | 19 | url = args.controller_address + "/register_worker" 20 | data = { 21 | "worker_name": args.worker_name, 22 | "check_heart_beat": args.check_heart_beat, 23 | "worker_status": None, 24 | } 25 | r = requests.post(url, json=data) 26 | assert r.status_code == 200 -------------------------------------------------------------------------------- /timm/models/layers/helpers.py: -------------------------------------------------------------------------------- 1 | """ Layer/Module Helpers 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | from itertools import repeat 6 | import collections.abc 7 | 8 | 9 | # From PyTorch internals 10 | def _ntuple(n): 11 | def parse(x): 12 | if isinstance(x, collections.abc.Iterable): 13 | return x 14 | return tuple(repeat(x, n)) 15 | return parse 16 | 17 | 18 | to_1tuple = _ntuple(1) 19 | to_2tuple = _ntuple(2) 20 | to_3tuple = _ntuple(3) 21 | to_4tuple = _ntuple(4) 22 | to_ntuple = _ntuple 23 | 24 | 25 | def make_divisible(v, divisor=8, min_value=None, round_limit=.9): 26 | min_value = min_value or divisor 27 | new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) 28 | # Make sure that round down does not go down by more than 10%. 29 | if new_v < round_limit * v: 30 | new_v += divisor 31 | return new_v 32 | -------------------------------------------------------------------------------- /timm/utils/clip_grad.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from timm.utils.agc import adaptive_clip_grad 4 | 5 | 6 | def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0): 7 | """ Dispatch to gradient clipping method 8 | 9 | Args: 10 | parameters (Iterable): model parameters to clip 11 | value (float): clipping value/factor/norm, mode dependant 12 | mode (str): clipping mode, one of 'norm', 'value', 'agc' 13 | norm_type (float): p-norm, default 2.0 14 | """ 15 | if mode == 'norm': 16 | torch.nn.utils.clip_grad_norm_(parameters, value, norm_type=norm_type) 17 | elif mode == 'value': 18 | torch.nn.utils.clip_grad_value_(parameters, value) 19 | elif mode == 'agc': 20 | adaptive_clip_grad(parameters, value, norm_type=norm_type) 21 | else: 22 | assert False, f"Unknown clip mode ({mode})." 23 | 24 | -------------------------------------------------------------------------------- /timm/models/layers/norm.py: -------------------------------------------------------------------------------- 1 | """ Normalization layers and wrappers 2 | """ 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | 7 | 8 | class GroupNorm(nn.GroupNorm): 9 | def __init__(self, num_channels, num_groups=32, eps=1e-5, affine=True): 10 | # NOTE num_channels is swapped to first arg for consistency in swapping norm layers with BN 11 | super().__init__(num_groups, num_channels, eps=eps, affine=affine) 12 | 13 | def forward(self, x): 14 | return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps) 15 | 16 | 17 | class LayerNorm2d(nn.LayerNorm): 18 | """ LayerNorm for channels of '2D' spatial BCHW tensors """ 19 | def __init__(self, num_channels): 20 | super().__init__(num_channels) 21 | 22 | def forward(self, x: torch.Tensor) -> torch.Tensor: 23 | return F.layer_norm( 24 | x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2) 25 | -------------------------------------------------------------------------------- /timm/utils/distributed.py: -------------------------------------------------------------------------------- 1 | """ Distributed training/validation utils 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import torch 6 | from torch import distributed as dist 7 | 8 | from .model import unwrap_model 9 | 10 | 11 | def reduce_tensor(tensor, n): 12 | rt = tensor.clone() 13 | dist.all_reduce(rt, op=dist.ReduceOp.SUM) 14 | rt /= n 15 | return rt 16 | 17 | 18 | def distribute_bn(model, world_size, reduce=False): 19 | # ensure every node has the same running bn stats 20 | for bn_name, bn_buf in unwrap_model(model).named_buffers(recurse=True): 21 | if ('running_mean' in bn_name) or ('running_var' in bn_name): 22 | if reduce: 23 | # average bn stats across whole group 24 | torch.distributed.all_reduce(bn_buf, op=dist.ReduceOp.SUM) 25 | bn_buf /= float(world_size) 26 | else: 27 | # broadcast bn stats from rank 0 to whole group 28 | torch.distributed.broadcast(bn_buf, 0) 29 | -------------------------------------------------------------------------------- /mplug_owl2/model/utils.py: -------------------------------------------------------------------------------- 1 | from transformers import AutoConfig 2 | 3 | 4 | def auto_upgrade(config): 5 | cfg = AutoConfig.from_pretrained(config) 6 | if 'mplug_owl2' in config and 'mplug_owl2' not in cfg.model_type: 7 | assert cfg.model_type == 'mplug_owl2' 8 | print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.") 9 | print("You must upgrade the checkpoint to the new code base (this can be done automatically).") 10 | confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]") 11 | if confirm.lower() in ["y", "yes"]: 12 | print("Upgrading checkpoint...") 13 | assert len(cfg.architectures) == 1 14 | setattr(cfg.__class__, "model_type", "mplug_owl2") 15 | cfg.architectures[0] = 'LlavaLlamaForCausalLM' 16 | cfg.save_pretrained(config) 17 | print("Checkpoint upgraded.") 18 | else: 19 | print("Checkpoint upgrade aborted.") 20 | exit(1) -------------------------------------------------------------------------------- /timm/utils/metrics.py: -------------------------------------------------------------------------------- 1 | """ Eval metrics and related 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | 6 | 7 | class AverageMeter: 8 | """Computes and stores the average and current value""" 9 | def __init__(self): 10 | self.reset() 11 | 12 | def reset(self): 13 | self.val = 0 14 | self.avg = 0 15 | self.sum = 0 16 | self.count = 0 17 | 18 | def update(self, val, n=1): 19 | self.val = val 20 | self.sum += val * n 21 | self.count += n 22 | self.avg = self.sum / self.count 23 | 24 | 25 | def accuracy(output, target, topk=(1,)): 26 | """Computes the accuracy over the k top predictions for the specified values of k""" 27 | maxk = min(max(topk), output.size()[1]) 28 | batch_size = target.size(0) 29 | _, pred = output.topk(maxk, 1, True, True) 30 | pred = pred.t() 31 | correct = pred.eq(target.reshape(1, -1).expand_as(pred)) 32 | return [correct[:min(k, maxk)].reshape(-1).float().sum(0) * 100. / batch_size for k in topk] 33 | -------------------------------------------------------------------------------- /performance.py: -------------------------------------------------------------------------------- 1 | from scipy import stats 2 | import numpy as np 3 | from scipy.optimize import curve_fit 4 | 5 | 6 | def logistic_func(X, bayta1, bayta2, bayta3, bayta4): 7 | logisticPart = 1 + np.exp(np.negative(np.divide(X - bayta3, np.abs(bayta4)))) 8 | yhat = bayta2 + np.divide(bayta1 - bayta2, logisticPart) 9 | return yhat 10 | 11 | def fit_function(y_label, y_output): 12 | beta = [np.max(y_label), np.min(y_label), np.mean(y_output), 0.5] 13 | popt, _ = curve_fit(logistic_func, y_output, \ 14 | y_label, p0=beta, maxfev=100000000) 15 | y_output_logistic = logistic_func(y_output, *popt) 16 | 17 | return y_output_logistic 18 | 19 | 20 | def performance_fit(y_label, y_output): 21 | y_output_logistic = fit_function(y_label, y_output) 22 | PLCC = stats.pearsonr(y_output_logistic, y_label)[0] 23 | SRCC = stats.spearmanr(y_output, y_label)[0] 24 | KRCC = stats.stats.kendalltau(y_output, y_label)[0] 25 | RMSE = np.sqrt(((y_output_logistic-y_label) ** 2).mean()) 26 | 27 | return SRCC, PLCC, KRCC, RMSE -------------------------------------------------------------------------------- /timm/utils/log.py: -------------------------------------------------------------------------------- 1 | """ Logging helpers 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import logging 6 | import logging.handlers 7 | 8 | 9 | class FormatterNoInfo(logging.Formatter): 10 | def __init__(self, fmt='%(levelname)s: %(message)s'): 11 | logging.Formatter.__init__(self, fmt) 12 | 13 | def format(self, record): 14 | if record.levelno == logging.INFO: 15 | return str(record.getMessage()) 16 | return logging.Formatter.format(self, record) 17 | 18 | 19 | def setup_default_logging(default_level=logging.INFO, log_path=''): 20 | console_handler = logging.StreamHandler() 21 | console_handler.setFormatter(FormatterNoInfo()) 22 | logging.root.addHandler(console_handler) 23 | logging.root.setLevel(default_level) 24 | if log_path: 25 | file_handler = logging.handlers.RotatingFileHandler(log_path, maxBytes=(1024 ** 2 * 2), backupCount=3) 26 | file_formatter = logging.Formatter("%(asctime)s - %(name)20s: [%(levelname)8s] - %(message)s") 27 | file_handler.setFormatter(file_formatter) 28 | logging.root.addHandler(file_handler) 29 | -------------------------------------------------------------------------------- /timm/data/parsers/parser_factory.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from .parser_image_folder import ParserImageFolder 4 | from .parser_image_tar import ParserImageTar 5 | from .parser_image_in_tar import ParserImageInTar 6 | 7 | 8 | def create_parser(name, root, split='train', **kwargs): 9 | name = name.lower() 10 | name = name.split('/', 2) 11 | prefix = '' 12 | if len(name) > 1: 13 | prefix = name[0] 14 | name = name[-1] 15 | 16 | # FIXME improve the selection right now just tfds prefix or fallback path, will need options to 17 | # explicitly select other options shortly 18 | if prefix == 'tfds': 19 | from .parser_tfds import ParserTfds # defer tensorflow import 20 | parser = ParserTfds(root, name, split=split, **kwargs) 21 | else: 22 | assert os.path.exists(root) 23 | # default fallback path (backwards compat), use image tar if root is a .tar file, otherwise image folder 24 | # FIXME support split here, in parser? 25 | if os.path.isfile(root) and os.path.splitext(root)[1] == '.tar': 26 | parser = ParserImageInTar(root, **kwargs) 27 | else: 28 | parser = ParserImageFolder(root, **kwargs) 29 | return parser 30 | -------------------------------------------------------------------------------- /timm/loss/cross_entropy.py: -------------------------------------------------------------------------------- 1 | """ Cross Entropy w/ smoothing or soft targets 2 | 3 | Hacked together by / Copyright 2021 Ross Wightman 4 | """ 5 | 6 | import torch 7 | import torch.nn as nn 8 | import torch.nn.functional as F 9 | 10 | 11 | class LabelSmoothingCrossEntropy(nn.Module): 12 | """ NLL loss with label smoothing. 13 | """ 14 | def __init__(self, smoothing=0.1): 15 | super(LabelSmoothingCrossEntropy, self).__init__() 16 | assert smoothing < 1.0 17 | self.smoothing = smoothing 18 | self.confidence = 1. - smoothing 19 | 20 | def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: 21 | logprobs = F.log_softmax(x, dim=-1) 22 | nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) 23 | nll_loss = nll_loss.squeeze(1) 24 | smooth_loss = -logprobs.mean(dim=-1) 25 | loss = self.confidence * nll_loss + self.smoothing * smooth_loss 26 | return loss.mean() 27 | 28 | 29 | class SoftTargetCrossEntropy(nn.Module): 30 | 31 | def __init__(self): 32 | super(SoftTargetCrossEntropy, self).__init__() 33 | 34 | def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: 35 | loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1) 36 | return loss.mean() 37 | -------------------------------------------------------------------------------- /timm/utils/summary.py: -------------------------------------------------------------------------------- 1 | """ Summary utilities 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import csv 6 | import os 7 | from collections import OrderedDict 8 | try: 9 | import wandb 10 | except ImportError: 11 | pass 12 | 13 | def get_outdir(path, *paths, inc=False): 14 | outdir = os.path.join(path, *paths) 15 | if not os.path.exists(outdir): 16 | os.makedirs(outdir) 17 | elif inc: 18 | count = 1 19 | outdir_inc = outdir + '-' + str(count) 20 | while os.path.exists(outdir_inc): 21 | count = count + 1 22 | outdir_inc = outdir + '-' + str(count) 23 | assert count < 100 24 | outdir = outdir_inc 25 | os.makedirs(outdir) 26 | return outdir 27 | 28 | 29 | def update_summary(epoch, train_metrics, eval_metrics, filename, write_header=False, log_wandb=False): 30 | rowd = OrderedDict(epoch=epoch) 31 | rowd.update([('train_' + k, v) for k, v in train_metrics.items()]) 32 | rowd.update([('eval_' + k, v) for k, v in eval_metrics.items()]) 33 | if log_wandb: 34 | wandb.log(rowd) 35 | with open(filename, mode='a') as cf: 36 | dw = csv.DictWriter(cf, fieldnames=rowd.keys()) 37 | if write_header: # first iteration (epoch == 1 can't be used) 38 | dw.writeheader() 39 | dw.writerow(rowd) 40 | -------------------------------------------------------------------------------- /mplug_owl2/model/multiway.py: -------------------------------------------------------------------------------- 1 | 2 | import torch 3 | import torch.utils.checkpoint 4 | from torch import nn 5 | 6 | 7 | class MultiwayNetwork(nn.Module): 8 | 9 | def __init__(self, module_provider, num_multiway=2, out_features=None): 10 | super(MultiwayNetwork, self).__init__() 11 | 12 | self.multiway = torch.nn.ModuleList([module_provider() for _ in range(num_multiway)]) 13 | self.out_features=out_features 14 | def forward(self, hidden_states, multiway_indices): 15 | 16 | if len(self.multiway) == 1: 17 | return self.multiway[0](hidden_states) 18 | if self.out_features: 19 | output_hidden_states = torch.empty( 20 | hidden_states.size(0), hidden_states.size(1), self.out_features, 21 | dtype=hidden_states.dtype 22 | ).to(hidden_states.device) 23 | else: 24 | output_hidden_states = torch.empty_like(hidden_states) 25 | for idx, subway in enumerate(self.multiway): 26 | local_indices = multiway_indices.eq(idx).nonzero(as_tuple=True) 27 | hidden = hidden_states[local_indices].unsqueeze(1).contiguous() 28 | if hidden.numel(): 29 | output = subway(hidden) 30 | if isinstance(output, tuple): 31 | output = output[0] 32 | output = output.squeeze(1) 33 | output_hidden_states[local_indices] = output 34 | 35 | return output_hidden_states.contiguous() -------------------------------------------------------------------------------- /mplug_owl2/evaluate/mmbench_converter.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import io 3 | import base64 4 | import json 5 | from PIL import Image 6 | 7 | ''' 8 | This scripts convert mmbench_dev tsv file to jsonl 9 | ''' 10 | 11 | datas = pd.read_csv("data/mmbench/mmbench_dev_20230712/mmbench_dev_20230712.tsv", sep='\t') 12 | 13 | global_choices = ['A', 'B', 'C', 'D'] 14 | 15 | def decode_base64_to_image(base64_string): 16 | image_data = base64.b64decode(base64_string) 17 | image = Image.open(io.BytesIO(image_data)) 18 | return image 19 | 20 | 21 | with open('./data/mmbench/mmbench_dev_20230712/mmbench_dev_20230712.jsonl', 'w') as f: 22 | for idx in range(len(datas)): 23 | data = datas.iloc[idx] 24 | 25 | index = int(data['index']) 26 | question = data['question'] 27 | hint = data['hint'] if not pd.isna(data['hint']) else 'N/A' 28 | 29 | choices = [] 30 | for opt in global_choices: 31 | if pd.isna(data[opt]): 32 | continue 33 | choices.append(data[opt]) 34 | 35 | answer = global_choices.index(data['answer']) 36 | 37 | image = decode_base64_to_image(data['image']) 38 | image.save("data/mmbench/mmbench_dev_20230712/images/%d.jpg" % index) 39 | 40 | f.write(json.dumps({ 41 | "index": index, 42 | "image": "data/mmbench/mmbench_dev_20230712/images/%d.jpg" % index, 43 | "hint": hint, 44 | "question": question, 45 | "choices": choices, 46 | "answer": answer, 47 | }) + "\n") -------------------------------------------------------------------------------- /timm/models/layers/conv_bn_act.py: -------------------------------------------------------------------------------- 1 | """ Conv2d + BN + Act 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | from torch import nn as nn 6 | 7 | from .create_conv2d import create_conv2d 8 | from .create_norm_act import convert_norm_act 9 | 10 | 11 | class ConvBnAct(nn.Module): 12 | def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilation=1, groups=1, 13 | bias=False, apply_act=True, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU, aa_layer=None, 14 | drop_block=None): 15 | super(ConvBnAct, self).__init__() 16 | use_aa = aa_layer is not None 17 | 18 | self.conv = create_conv2d( 19 | in_channels, out_channels, kernel_size, stride=1 if use_aa else stride, 20 | padding=padding, dilation=dilation, groups=groups, bias=bias) 21 | 22 | # NOTE for backwards compatibility with models that use separate norm and act layer definitions 23 | norm_act_layer = convert_norm_act(norm_layer, act_layer) 24 | self.bn = norm_act_layer(out_channels, apply_act=apply_act, drop_block=drop_block) 25 | self.aa = aa_layer(channels=out_channels) if stride == 2 and use_aa else None 26 | 27 | @property 28 | def in_channels(self): 29 | return self.conv.in_channels 30 | 31 | @property 32 | def out_channels(self): 33 | return self.conv.out_channels 34 | 35 | def forward(self, x): 36 | x = self.conv(x) 37 | x = self.bn(x) 38 | if self.aa is not None: 39 | x = self.aa(x) 40 | return x 41 | -------------------------------------------------------------------------------- /timm/models/layers/create_conv2d.py: -------------------------------------------------------------------------------- 1 | """ Create Conv2d Factory Method 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | 6 | from .mixed_conv2d import MixedConv2d 7 | from .cond_conv2d import CondConv2d 8 | from .conv2d_same import create_conv2d_pad 9 | 10 | 11 | def create_conv2d(in_channels, out_channels, kernel_size, **kwargs): 12 | """ Select a 2d convolution implementation based on arguments 13 | Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d. 14 | 15 | Used extensively by EfficientNet, MobileNetv3 and related networks. 16 | """ 17 | if isinstance(kernel_size, list): 18 | assert 'num_experts' not in kwargs # MixNet + CondConv combo not supported currently 19 | assert 'groups' not in kwargs # MixedConv groups are defined by kernel list 20 | # We're going to use only lists for defining the MixedConv2d kernel groups, 21 | # ints, tuples, other iterables will continue to pass to normal conv and specify h, w. 22 | m = MixedConv2d(in_channels, out_channels, kernel_size, **kwargs) 23 | else: 24 | depthwise = kwargs.pop('depthwise', False) 25 | # for DW out_channels must be multiple of in_channels as must have out_channels % groups == 0 26 | groups = in_channels if depthwise else kwargs.pop('groups', 1) 27 | if 'num_experts' in kwargs and kwargs['num_experts'] > 0: 28 | m = CondConv2d(in_channels, out_channels, kernel_size, groups=groups, **kwargs) 29 | else: 30 | m = create_conv2d_pad(in_channels, out_channels, kernel_size, groups=groups, **kwargs) 31 | return m 32 | -------------------------------------------------------------------------------- /timm/models/layers/patch_embed.py: -------------------------------------------------------------------------------- 1 | """ Image to Patch Embedding using Conv2d 2 | 3 | A convolution based approach to patchifying a 2D image w/ embedding projection. 4 | 5 | Based on the impl in https://github.com/google-research/vision_transformer 6 | 7 | Hacked together by / Copyright 2020 Ross Wightman 8 | """ 9 | from torch import nn as nn 10 | 11 | from .helpers import to_2tuple 12 | from .trace_utils import _assert 13 | 14 | 15 | class PatchEmbed(nn.Module): 16 | """ 2D Image to Patch Embedding 17 | """ 18 | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True): 19 | super().__init__() 20 | img_size = to_2tuple(img_size) 21 | patch_size = to_2tuple(patch_size) 22 | self.img_size = img_size 23 | self.patch_size = patch_size 24 | self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) 25 | self.num_patches = self.grid_size[0] * self.grid_size[1] 26 | self.flatten = flatten 27 | 28 | self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) 29 | self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() 30 | 31 | def forward(self, x): 32 | B, C, H, W = x.shape 33 | _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).") 34 | _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).") 35 | x = self.proj(x) 36 | if self.flatten: 37 | x = x.flatten(2).transpose(1, 2) # BCHW -> BNC 38 | x = self.norm(x) 39 | return x 40 | -------------------------------------------------------------------------------- /timm/models/layers/conv2d_same.py: -------------------------------------------------------------------------------- 1 | """ Conv2d w/ Same Padding 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import torch 6 | import torch.nn as nn 7 | import torch.nn.functional as F 8 | from typing import Tuple, Optional 9 | 10 | from .padding import pad_same, get_padding_value 11 | 12 | 13 | def conv2d_same( 14 | x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1), 15 | padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1), groups: int = 1): 16 | x = pad_same(x, weight.shape[-2:], stride, dilation) 17 | return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups) 18 | 19 | 20 | class Conv2dSame(nn.Conv2d): 21 | """ Tensorflow like 'SAME' convolution wrapper for 2D convolutions 22 | """ 23 | 24 | def __init__(self, in_channels, out_channels, kernel_size, stride=1, 25 | padding=0, dilation=1, groups=1, bias=True): 26 | super(Conv2dSame, self).__init__( 27 | in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias) 28 | 29 | def forward(self, x): 30 | return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) 31 | 32 | 33 | def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs): 34 | padding = kwargs.pop('padding', '') 35 | kwargs.setdefault('bias', False) 36 | padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs) 37 | if is_dynamic: 38 | return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs) 39 | else: 40 | return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs) 41 | 42 | 43 | -------------------------------------------------------------------------------- /timm/loss/jsd.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | from .cross_entropy import LabelSmoothingCrossEntropy 6 | 7 | 8 | class JsdCrossEntropy(nn.Module): 9 | """ Jensen-Shannon Divergence + Cross-Entropy Loss 10 | 11 | Based on impl here: https://github.com/google-research/augmix/blob/master/imagenet.py 12 | From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - 13 | https://arxiv.org/abs/1912.02781 14 | 15 | Hacked together by / Copyright 2020 Ross Wightman 16 | """ 17 | def __init__(self, num_splits=3, alpha=12, smoothing=0.1): 18 | super().__init__() 19 | self.num_splits = num_splits 20 | self.alpha = alpha 21 | if smoothing is not None and smoothing > 0: 22 | self.cross_entropy_loss = LabelSmoothingCrossEntropy(smoothing) 23 | else: 24 | self.cross_entropy_loss = torch.nn.CrossEntropyLoss() 25 | 26 | def __call__(self, output, target): 27 | split_size = output.shape[0] // self.num_splits 28 | assert split_size * self.num_splits == output.shape[0] 29 | logits_split = torch.split(output, split_size) 30 | 31 | # Cross-entropy is only computed on clean images 32 | loss = self.cross_entropy_loss(logits_split[0], target[:split_size]) 33 | probs = [F.softmax(logits, dim=1) for logits in logits_split] 34 | 35 | # Clamp mixture distribution to avoid exploding KL divergence 36 | logp_mixture = torch.clamp(torch.stack(probs).mean(axis=0), 1e-7, 1).log() 37 | loss += self.alpha * sum([F.kl_div( 38 | logp_mixture, p_split, reduction='batchmean') for p_split in probs]) / len(probs) 39 | return loss 40 | -------------------------------------------------------------------------------- /timm/data/real_labels.py: -------------------------------------------------------------------------------- 1 | """ Real labels evaluator for ImageNet 2 | Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159 3 | Based on Numpy example at https://github.com/google-research/reassessed-imagenet 4 | 5 | Hacked together by / Copyright 2020 Ross Wightman 6 | """ 7 | import os 8 | import json 9 | import numpy as np 10 | 11 | 12 | class RealLabelsImagenet: 13 | 14 | def __init__(self, filenames, real_json='real.json', topk=(1, 5)): 15 | with open(real_json) as real_labels: 16 | real_labels = json.load(real_labels) 17 | real_labels = {f'ILSVRC2012_val_{i + 1:08d}.JPEG': labels for i, labels in enumerate(real_labels)} 18 | self.real_labels = real_labels 19 | self.filenames = filenames 20 | assert len(self.filenames) == len(self.real_labels) 21 | self.topk = topk 22 | self.is_correct = {k: [] for k in topk} 23 | self.sample_idx = 0 24 | 25 | def add_result(self, output): 26 | maxk = max(self.topk) 27 | _, pred_batch = output.topk(maxk, 1, True, True) 28 | pred_batch = pred_batch.cpu().numpy() 29 | for pred in pred_batch: 30 | filename = self.filenames[self.sample_idx] 31 | filename = os.path.basename(filename) 32 | if self.real_labels[filename]: 33 | for k in self.topk: 34 | self.is_correct[k].append( 35 | any([p in self.real_labels[filename] for p in pred[:k]])) 36 | self.sample_idx += 1 37 | 38 | def get_accuracy(self, k=None): 39 | if k is None: 40 | return {k: float(np.mean(self.is_correct[k])) * 100 for k in self.topk} 41 | else: 42 | return float(np.mean(self.is_correct[k])) * 100 43 | -------------------------------------------------------------------------------- /timm/models/layers/blur_pool.py: -------------------------------------------------------------------------------- 1 | """ 2 | BlurPool layer inspired by 3 | - Kornia's Max_BlurPool2d 4 | - Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar` 5 | 6 | Hacked together by Chris Ha and Ross Wightman 7 | """ 8 | 9 | import torch 10 | import torch.nn as nn 11 | import torch.nn.functional as F 12 | import numpy as np 13 | from .padding import get_padding 14 | 15 | 16 | class BlurPool2d(nn.Module): 17 | r"""Creates a module that computes blurs and downsample a given feature map. 18 | See :cite:`zhang2019shiftinvar` for more details. 19 | Corresponds to the Downsample class, which does blurring and subsampling 20 | 21 | Args: 22 | channels = Number of input channels 23 | filt_size (int): binomial filter size for blurring. currently supports 3 (default) and 5. 24 | stride (int): downsampling filter stride 25 | 26 | Returns: 27 | torch.Tensor: the transformed tensor. 28 | """ 29 | def __init__(self, channels, filt_size=3, stride=2) -> None: 30 | super(BlurPool2d, self).__init__() 31 | assert filt_size > 1 32 | self.channels = channels 33 | self.filt_size = filt_size 34 | self.stride = stride 35 | self.padding = [get_padding(filt_size, stride, dilation=1)] * 4 36 | coeffs = torch.tensor((np.poly1d((0.5, 0.5)) ** (self.filt_size - 1)).coeffs.astype(np.float32)) 37 | blur_filter = (coeffs[:, None] * coeffs[None, :])[None, None, :, :].repeat(self.channels, 1, 1, 1) 38 | self.register_buffer('filt', blur_filter, persistent=False) 39 | 40 | def forward(self, x: torch.Tensor) -> torch.Tensor: 41 | x = F.pad(x, self.padding, 'reflect') 42 | return F.conv2d(x, self.filt, stride=self.stride, groups=x.shape[1]) 43 | -------------------------------------------------------------------------------- /timm/utils/agc.py: -------------------------------------------------------------------------------- 1 | """ Adaptive Gradient Clipping 2 | 3 | An impl of AGC, as per (https://arxiv.org/abs/2102.06171): 4 | 5 | @article{brock2021high, 6 | author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan}, 7 | title={High-Performance Large-Scale Image Recognition Without Normalization}, 8 | journal={arXiv preprint arXiv:}, 9 | year={2021} 10 | } 11 | 12 | Code references: 13 | * Official JAX impl (paper authors): https://github.com/deepmind/deepmind-research/tree/master/nfnets 14 | * Phil Wang's PyTorch gist: https://gist.github.com/lucidrains/0d6560077edac419ab5d3aa29e674d5c 15 | 16 | Hacked together by / Copyright 2021 Ross Wightman 17 | """ 18 | import torch 19 | 20 | 21 | def unitwise_norm(x, norm_type=2.0): 22 | if x.ndim <= 1: 23 | return x.norm(norm_type) 24 | else: 25 | # works for nn.ConvNd and nn,Linear where output dim is first in the kernel/weight tensor 26 | # might need special cases for other weights (possibly MHA) where this may not be true 27 | return x.norm(norm_type, dim=tuple(range(1, x.ndim)), keepdim=True) 28 | 29 | 30 | def adaptive_clip_grad(parameters, clip_factor=0.01, eps=1e-3, norm_type=2.0): 31 | if isinstance(parameters, torch.Tensor): 32 | parameters = [parameters] 33 | for p in parameters: 34 | if p.grad is None: 35 | continue 36 | p_data = p.detach() 37 | g_data = p.grad.detach() 38 | max_norm = unitwise_norm(p_data, norm_type=norm_type).clamp_(min=eps).mul_(clip_factor) 39 | grad_norm = unitwise_norm(g_data, norm_type=norm_type) 40 | clipped_grad = g_data * (max_norm / grad_norm.clamp(min=1e-6)) 41 | new_grads = torch.where(grad_norm < max_norm, g_data, clipped_grad) 42 | p.grad.detach().copy_(new_grads) 43 | -------------------------------------------------------------------------------- /timm/utils/cuda.py: -------------------------------------------------------------------------------- 1 | """ CUDA / AMP utils 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import torch 6 | 7 | try: 8 | from apex import amp 9 | has_apex = True 10 | except ImportError: 11 | amp = None 12 | has_apex = False 13 | 14 | from .clip_grad import dispatch_clip_grad 15 | 16 | 17 | class ApexScaler: 18 | state_dict_key = "amp" 19 | 20 | def __call__(self, loss, optimizer, clip_grad=None, clip_mode='norm', parameters=None, create_graph=False): 21 | with amp.scale_loss(loss, optimizer) as scaled_loss: 22 | scaled_loss.backward(create_graph=create_graph) 23 | if clip_grad is not None: 24 | dispatch_clip_grad(amp.master_params(optimizer), clip_grad, mode=clip_mode) 25 | optimizer.step() 26 | 27 | def state_dict(self): 28 | if 'state_dict' in amp.__dict__: 29 | return amp.state_dict() 30 | 31 | def load_state_dict(self, state_dict): 32 | if 'load_state_dict' in amp.__dict__: 33 | amp.load_state_dict(state_dict) 34 | 35 | 36 | class NativeScaler: 37 | state_dict_key = "amp_scaler" 38 | 39 | def __init__(self): 40 | self._scaler = torch.cuda.amp.GradScaler() 41 | 42 | def __call__(self, loss, optimizer, clip_grad=None, clip_mode='norm', parameters=None, create_graph=False): 43 | self._scaler.scale(loss).backward(create_graph=create_graph) 44 | if clip_grad is not None: 45 | assert parameters is not None 46 | self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place 47 | dispatch_clip_grad(parameters, clip_grad, mode=clip_mode) 48 | self._scaler.step(optimizer) 49 | self._scaler.update() 50 | 51 | def state_dict(self): 52 | return self._scaler.state_dict() 53 | 54 | def load_state_dict(self, state_dict): 55 | self._scaler.load_state_dict(state_dict) 56 | -------------------------------------------------------------------------------- /timm/models/layers/median_pool.py: -------------------------------------------------------------------------------- 1 | """ Median Pool 2 | Hacked together by / Copyright 2020 Ross Wightman 3 | """ 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | from .helpers import to_2tuple, to_4tuple 7 | 8 | 9 | class MedianPool2d(nn.Module): 10 | """ Median pool (usable as median filter when stride=1) module. 11 | 12 | Args: 13 | kernel_size: size of pooling kernel, int or 2-tuple 14 | stride: pool stride, int or 2-tuple 15 | padding: pool padding, int or 4-tuple (l, r, t, b) as in pytorch F.pad 16 | same: override padding and enforce same padding, boolean 17 | """ 18 | def __init__(self, kernel_size=3, stride=1, padding=0, same=False): 19 | super(MedianPool2d, self).__init__() 20 | self.k = to_2tuple(kernel_size) 21 | self.stride = to_2tuple(stride) 22 | self.padding = to_4tuple(padding) # convert to l, r, t, b 23 | self.same = same 24 | 25 | def _padding(self, x): 26 | if self.same: 27 | ih, iw = x.size()[2:] 28 | if ih % self.stride[0] == 0: 29 | ph = max(self.k[0] - self.stride[0], 0) 30 | else: 31 | ph = max(self.k[0] - (ih % self.stride[0]), 0) 32 | if iw % self.stride[1] == 0: 33 | pw = max(self.k[1] - self.stride[1], 0) 34 | else: 35 | pw = max(self.k[1] - (iw % self.stride[1]), 0) 36 | pl = pw // 2 37 | pr = pw - pl 38 | pt = ph // 2 39 | pb = ph - pt 40 | padding = (pl, pr, pt, pb) 41 | else: 42 | padding = self.padding 43 | return padding 44 | 45 | def forward(self, x): 46 | x = F.pad(x, self._padding(x), mode='reflect') 47 | x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1], self.stride[1]) 48 | x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] 49 | return x 50 | -------------------------------------------------------------------------------- /timm/models/layers/space_to_depth.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | 5 | class SpaceToDepth(nn.Module): 6 | def __init__(self, block_size=4): 7 | super().__init__() 8 | assert block_size == 4 9 | self.bs = block_size 10 | 11 | def forward(self, x): 12 | N, C, H, W = x.size() 13 | x = x.view(N, C, H // self.bs, self.bs, W // self.bs, self.bs) # (N, C, H//bs, bs, W//bs, bs) 14 | x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs) 15 | x = x.view(N, C * (self.bs ** 2), H // self.bs, W // self.bs) # (N, C*bs^2, H//bs, W//bs) 16 | return x 17 | 18 | 19 | @torch.jit.script 20 | class SpaceToDepthJit(object): 21 | def __call__(self, x: torch.Tensor): 22 | # assuming hard-coded that block_size==4 for acceleration 23 | N, C, H, W = x.size() 24 | x = x.view(N, C, H // 4, 4, W // 4, 4) # (N, C, H//bs, bs, W//bs, bs) 25 | x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs) 26 | x = x.view(N, C * 16, H // 4, W // 4) # (N, C*bs^2, H//bs, W//bs) 27 | return x 28 | 29 | 30 | class SpaceToDepthModule(nn.Module): 31 | def __init__(self, no_jit=False): 32 | super().__init__() 33 | if not no_jit: 34 | self.op = SpaceToDepthJit() 35 | else: 36 | self.op = SpaceToDepth() 37 | 38 | def forward(self, x): 39 | return self.op(x) 40 | 41 | 42 | class DepthToSpace(nn.Module): 43 | 44 | def __init__(self, block_size): 45 | super().__init__() 46 | self.bs = block_size 47 | 48 | def forward(self, x): 49 | N, C, H, W = x.size() 50 | x = x.view(N, self.bs, self.bs, C // (self.bs ** 2), H, W) # (N, bs, bs, C//bs^2, H, W) 51 | x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # (N, C//bs^2, H, bs, W, bs) 52 | x = x.view(N, C // (self.bs ** 2), H * self.bs, W * self.bs) # (N, C//bs^2, H * bs, W * bs) 53 | return x 54 | -------------------------------------------------------------------------------- /timm/models/__init__.py: -------------------------------------------------------------------------------- 1 | from .beit import * 2 | from .byoanet import * 3 | from .byobnet import * 4 | from .cait import * 5 | from .coat import * 6 | from .convit import * 7 | from .convmixer import * 8 | from .convnext import * 9 | from .crossvit import * 10 | from .cspnet import * 11 | from .densenet import * 12 | from .dla import * 13 | from .dpn import * 14 | from .efficientnet import * 15 | from .ghostnet import * 16 | from .gluon_resnet import * 17 | from .gluon_xception import * 18 | from .hardcorenas import * 19 | from .hrnet import * 20 | from .inception_resnet_v2 import * 21 | from .inception_v3 import * 22 | from .inception_v4 import * 23 | from .levit import * 24 | from .mlp_mixer import * 25 | from .mobilenetv3 import * 26 | from .nasnet import * 27 | from .nest import * 28 | from .nfnet import * 29 | from .pit import * 30 | from .pnasnet import * 31 | from .regnet import * 32 | from .res2net import * 33 | from .resnest import * 34 | from .resnet import * 35 | from .resnetv2 import * 36 | from .rexnet import * 37 | from .selecsls import * 38 | from .senet import * 39 | from .sknet import * 40 | from .swin_transformer import * 41 | from .tnt import * 42 | from .tresnet import * 43 | from .twins import * 44 | from .vgg import * 45 | from .visformer import * 46 | from .vision_transformer import * 47 | from .vision_transformer_hybrid import * 48 | from .vovnet import * 49 | from .xception import * 50 | from .xception_aligned import * 51 | from .xcit import * 52 | 53 | from .factory import create_model, split_model_name, safe_model_name 54 | from .helpers import load_checkpoint, resume_checkpoint, model_parameters 55 | from .layers import TestTimePoolHead, apply_test_time_pool 56 | from .layers import convert_splitbn_model 57 | from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable, is_no_jit, set_no_jit 58 | from .registry import register_model, model_entrypoint, list_models, is_model, list_modules, is_model_in_modules,\ 59 | has_model_default_key, is_model_default_key, get_model_default_value, is_model_pretrained 60 | -------------------------------------------------------------------------------- /timm/models/layers/mixed_conv2d.py: -------------------------------------------------------------------------------- 1 | """ PyTorch Mixed Convolution 2 | 3 | Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595) 4 | 5 | Hacked together by / Copyright 2020 Ross Wightman 6 | """ 7 | 8 | import torch 9 | from torch import nn as nn 10 | 11 | from .conv2d_same import create_conv2d_pad 12 | 13 | 14 | def _split_channels(num_chan, num_groups): 15 | split = [num_chan // num_groups for _ in range(num_groups)] 16 | split[0] += num_chan - sum(split) 17 | return split 18 | 19 | 20 | class MixedConv2d(nn.ModuleDict): 21 | """ Mixed Grouped Convolution 22 | 23 | Based on MDConv and GroupedConv in MixNet impl: 24 | https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py 25 | """ 26 | def __init__(self, in_channels, out_channels, kernel_size=3, 27 | stride=1, padding='', dilation=1, depthwise=False, **kwargs): 28 | super(MixedConv2d, self).__init__() 29 | 30 | kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size] 31 | num_groups = len(kernel_size) 32 | in_splits = _split_channels(in_channels, num_groups) 33 | out_splits = _split_channels(out_channels, num_groups) 34 | self.in_channels = sum(in_splits) 35 | self.out_channels = sum(out_splits) 36 | for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)): 37 | conv_groups = in_ch if depthwise else 1 38 | # use add_module to keep key space clean 39 | self.add_module( 40 | str(idx), 41 | create_conv2d_pad( 42 | in_ch, out_ch, k, stride=stride, 43 | padding=padding, dilation=dilation, groups=conv_groups, **kwargs) 44 | ) 45 | self.splits = in_splits 46 | 47 | def forward(self, x): 48 | x_split = torch.split(x, self.splits, 1) 49 | x_out = [c(x_split[i]) for i, c in enumerate(self.values())] 50 | x = torch.cat(x_out, 1) 51 | return x 52 | -------------------------------------------------------------------------------- /timm/scheduler/step_lr.py: -------------------------------------------------------------------------------- 1 | """ Step Scheduler 2 | 3 | Basic step LR schedule with warmup, noise. 4 | 5 | Hacked together by / Copyright 2020 Ross Wightman 6 | """ 7 | import math 8 | import torch 9 | 10 | from .scheduler import Scheduler 11 | 12 | 13 | class StepLRScheduler(Scheduler): 14 | """ 15 | """ 16 | 17 | def __init__(self, 18 | optimizer: torch.optim.Optimizer, 19 | decay_t: float, 20 | decay_rate: float = 1., 21 | warmup_t=0, 22 | warmup_lr_init=0, 23 | t_in_epochs=True, 24 | noise_range_t=None, 25 | noise_pct=0.67, 26 | noise_std=1.0, 27 | noise_seed=42, 28 | initialize=True, 29 | ) -> None: 30 | super().__init__( 31 | optimizer, param_group_field="lr", 32 | noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed, 33 | initialize=initialize) 34 | 35 | self.decay_t = decay_t 36 | self.decay_rate = decay_rate 37 | self.warmup_t = warmup_t 38 | self.warmup_lr_init = warmup_lr_init 39 | self.t_in_epochs = t_in_epochs 40 | if self.warmup_t: 41 | self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values] 42 | super().update_groups(self.warmup_lr_init) 43 | else: 44 | self.warmup_steps = [1 for _ in self.base_values] 45 | 46 | def _get_lr(self, t): 47 | if t < self.warmup_t: 48 | lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps] 49 | else: 50 | lrs = [v * (self.decay_rate ** (t // self.decay_t)) for v in self.base_values] 51 | return lrs 52 | 53 | def get_epoch_values(self, epoch: int): 54 | if self.t_in_epochs: 55 | return self._get_lr(epoch) 56 | else: 57 | return None 58 | 59 | def get_update_values(self, num_updates: int): 60 | if not self.t_in_epochs: 61 | return self._get_lr(num_updates) 62 | else: 63 | return None 64 | -------------------------------------------------------------------------------- /timm/utils/jit.py: -------------------------------------------------------------------------------- 1 | """ JIT scripting/tracing utils 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import os 6 | 7 | import torch 8 | 9 | 10 | def set_jit_legacy(): 11 | """ Set JIT executor to legacy w/ support for op fusion 12 | This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes 13 | in the JIT exectutor. These API are not supported so could change. 14 | """ 15 | # 16 | assert hasattr(torch._C, '_jit_set_profiling_executor'), "Old JIT behavior doesn't exist!" 17 | torch._C._jit_set_profiling_executor(False) 18 | torch._C._jit_set_profiling_mode(False) 19 | torch._C._jit_override_can_fuse_on_gpu(True) 20 | #torch._C._jit_set_texpr_fuser_enabled(True) 21 | 22 | 23 | def set_jit_fuser(fuser): 24 | if fuser == "te": 25 | # default fuser should be == 'te' 26 | torch._C._jit_set_profiling_executor(True) 27 | torch._C._jit_set_profiling_mode(True) 28 | torch._C._jit_override_can_fuse_on_cpu(False) 29 | torch._C._jit_override_can_fuse_on_gpu(True) 30 | torch._C._jit_set_texpr_fuser_enabled(True) 31 | elif fuser == "old" or fuser == "legacy": 32 | torch._C._jit_set_profiling_executor(False) 33 | torch._C._jit_set_profiling_mode(False) 34 | torch._C._jit_override_can_fuse_on_gpu(True) 35 | torch._C._jit_set_texpr_fuser_enabled(False) 36 | elif fuser == "nvfuser" or fuser == "nvf": 37 | os.environ['PYTORCH_CUDA_FUSER_DISABLE_FALLBACK'] = '1' 38 | os.environ['PYTORCH_CUDA_FUSER_DISABLE_FMA'] = '1' 39 | os.environ['PYTORCH_CUDA_FUSER_JIT_OPT_LEVEL'] = '0' 40 | torch._C._jit_set_texpr_fuser_enabled(False) 41 | torch._C._jit_set_profiling_executor(True) 42 | torch._C._jit_set_profiling_mode(True) 43 | torch._C._jit_can_fuse_on_cpu() 44 | torch._C._jit_can_fuse_on_gpu() 45 | torch._C._jit_override_can_fuse_on_cpu(False) 46 | torch._C._jit_override_can_fuse_on_gpu(False) 47 | torch._C._jit_set_nvfuser_guard_mode(True) 48 | torch._C._jit_set_nvfuser_enabled(True) 49 | else: 50 | assert False, f"Invalid jit fuser ({fuser})" 51 | -------------------------------------------------------------------------------- /timm/models/layers/test_time_pool.py: -------------------------------------------------------------------------------- 1 | """ Test Time Pooling (Average-Max Pool) 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | 6 | import logging 7 | from torch import nn 8 | import torch.nn.functional as F 9 | 10 | from .adaptive_avgmax_pool import adaptive_avgmax_pool2d 11 | 12 | 13 | _logger = logging.getLogger(__name__) 14 | 15 | 16 | class TestTimePoolHead(nn.Module): 17 | def __init__(self, base, original_pool=7): 18 | super(TestTimePoolHead, self).__init__() 19 | self.base = base 20 | self.original_pool = original_pool 21 | base_fc = self.base.get_classifier() 22 | if isinstance(base_fc, nn.Conv2d): 23 | self.fc = base_fc 24 | else: 25 | self.fc = nn.Conv2d( 26 | self.base.num_features, self.base.num_classes, kernel_size=1, bias=True) 27 | self.fc.weight.data.copy_(base_fc.weight.data.view(self.fc.weight.size())) 28 | self.fc.bias.data.copy_(base_fc.bias.data.view(self.fc.bias.size())) 29 | self.base.reset_classifier(0) # delete original fc layer 30 | 31 | def forward(self, x): 32 | x = self.base.forward_features(x) 33 | x = F.avg_pool2d(x, kernel_size=self.original_pool, stride=1) 34 | x = self.fc(x) 35 | x = adaptive_avgmax_pool2d(x, 1) 36 | return x.view(x.size(0), -1) 37 | 38 | 39 | def apply_test_time_pool(model, config, use_test_size=True): 40 | test_time_pool = False 41 | if not hasattr(model, 'default_cfg') or not model.default_cfg: 42 | return model, False 43 | if use_test_size and 'test_input_size' in model.default_cfg: 44 | df_input_size = model.default_cfg['test_input_size'] 45 | else: 46 | df_input_size = model.default_cfg['input_size'] 47 | if config['input_size'][-1] > df_input_size[-1] and config['input_size'][-2] > df_input_size[-2]: 48 | _logger.info('Target input size %s > pretrained default %s, using test time pooling' % 49 | (str(config['input_size'][-2:]), str(df_input_size[-2:]))) 50 | model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size']) 51 | test_time_pool = True 52 | return model, test_time_pool 53 | -------------------------------------------------------------------------------- /timm/loss/binary_cross_entropy.py: -------------------------------------------------------------------------------- 1 | """ Binary Cross Entropy w/ a few extras 2 | 3 | Hacked together by / Copyright 2021 Ross Wightman 4 | """ 5 | from typing import Optional 6 | 7 | import torch 8 | import torch.nn as nn 9 | import torch.nn.functional as F 10 | 11 | 12 | class BinaryCrossEntropy(nn.Module): 13 | """ BCE with optional one-hot from dense targets, label smoothing, thresholding 14 | NOTE for experiments comparing CE to BCE /w label smoothing, may remove 15 | """ 16 | def __init__( 17 | self, smoothing=0.1, target_threshold: Optional[float] = None, weight: Optional[torch.Tensor] = None, 18 | reduction: str = 'mean', pos_weight: Optional[torch.Tensor] = None): 19 | super(BinaryCrossEntropy, self).__init__() 20 | assert 0. <= smoothing < 1.0 21 | self.smoothing = smoothing 22 | self.target_threshold = target_threshold 23 | self.reduction = reduction 24 | self.register_buffer('weight', weight) 25 | self.register_buffer('pos_weight', pos_weight) 26 | 27 | def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: 28 | assert x.shape[0] == target.shape[0] 29 | if target.shape != x.shape: 30 | # NOTE currently assume smoothing or other label softening is applied upstream if targets are already sparse 31 | num_classes = x.shape[-1] 32 | # FIXME should off/on be different for smoothing w/ BCE? Other impl out there differ 33 | off_value = self.smoothing / num_classes 34 | on_value = 1. - self.smoothing + off_value 35 | target = target.long().view(-1, 1) 36 | target = torch.full( 37 | (target.size()[0], num_classes), 38 | off_value, 39 | device=x.device, dtype=x.dtype).scatter_(1, target, on_value) 40 | if self.target_threshold is not None: 41 | # Make target 0, or 1 if threshold set 42 | target = target.gt(self.target_threshold).to(dtype=target.dtype) 43 | return F.binary_cross_entropy_with_logits( 44 | x, target, 45 | self.weight, 46 | pos_weight=self.pos_weight, 47 | reduction=self.reduction) 48 | -------------------------------------------------------------------------------- /timm/models/layers/__init__.py: -------------------------------------------------------------------------------- 1 | from .activations import * 2 | from .adaptive_avgmax_pool import \ 3 | adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d 4 | from .blur_pool import BlurPool2d 5 | from .classifier import ClassifierHead, create_classifier 6 | from .cond_conv2d import CondConv2d, get_condconv_initializer 7 | from .config import is_exportable, is_scriptable, is_no_jit, set_exportable, set_scriptable, set_no_jit,\ 8 | set_layer_config 9 | from .conv2d_same import Conv2dSame, conv2d_same 10 | from .conv_bn_act import ConvBnAct 11 | from .create_act import create_act_layer, get_act_layer, get_act_fn 12 | from .create_attn import get_attn, create_attn 13 | from .create_conv2d import create_conv2d 14 | from .create_norm_act import get_norm_act_layer, create_norm_act, convert_norm_act 15 | from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path 16 | from .eca import EcaModule, CecaModule, EfficientChannelAttn, CircularEfficientChannelAttn 17 | from .evo_norm import EvoNormBatch2d, EvoNormSample2d 18 | from .gather_excite import GatherExcite 19 | from .global_context import GlobalContext 20 | from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible 21 | from .inplace_abn import InplaceAbn 22 | from .linear import Linear 23 | from .mixed_conv2d import MixedConv2d 24 | from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp 25 | from .non_local_attn import NonLocalAttn, BatNonLocalAttn 26 | from .norm import GroupNorm, LayerNorm2d 27 | from .norm_act import BatchNormAct2d, GroupNormAct 28 | from .padding import get_padding, get_same_padding, pad_same 29 | from .patch_embed import PatchEmbed 30 | from .pool2d_same import AvgPool2dSame, create_pool2d 31 | from .squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite 32 | from .selective_kernel import SelectiveKernel 33 | from .separable_conv import SeparableConv2d, SeparableConvBnAct 34 | from .space_to_depth import SpaceToDepthModule 35 | from .split_attn import SplitAttn 36 | from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model 37 | from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame 38 | from .test_time_pool import TestTimePoolHead, apply_test_time_pool 39 | from .trace_utils import _assert, _float_to_int 40 | from .weight_init import trunc_normal_, variance_scaling_, lecun_normal_ 41 | -------------------------------------------------------------------------------- /timm/scheduler/multistep_lr.py: -------------------------------------------------------------------------------- 1 | """ MultiStep LR Scheduler 2 | 3 | Basic multi step LR schedule with warmup, noise. 4 | """ 5 | import torch 6 | import bisect 7 | from timm.scheduler.scheduler import Scheduler 8 | from typing import List 9 | 10 | class MultiStepLRScheduler(Scheduler): 11 | """ 12 | """ 13 | 14 | def __init__(self, 15 | optimizer: torch.optim.Optimizer, 16 | decay_t: List[int], 17 | decay_rate: float = 1., 18 | warmup_t=0, 19 | warmup_lr_init=0, 20 | t_in_epochs=True, 21 | noise_range_t=None, 22 | noise_pct=0.67, 23 | noise_std=1.0, 24 | noise_seed=42, 25 | initialize=True, 26 | ) -> None: 27 | super().__init__( 28 | optimizer, param_group_field="lr", 29 | noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed, 30 | initialize=initialize) 31 | 32 | self.decay_t = decay_t 33 | self.decay_rate = decay_rate 34 | self.warmup_t = warmup_t 35 | self.warmup_lr_init = warmup_lr_init 36 | self.t_in_epochs = t_in_epochs 37 | if self.warmup_t: 38 | self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values] 39 | super().update_groups(self.warmup_lr_init) 40 | else: 41 | self.warmup_steps = [1 for _ in self.base_values] 42 | 43 | def get_curr_decay_steps(self, t): 44 | # find where in the array t goes, 45 | # assumes self.decay_t is sorted 46 | return bisect.bisect_right(self.decay_t, t+1) 47 | 48 | def _get_lr(self, t): 49 | if t < self.warmup_t: 50 | lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps] 51 | else: 52 | lrs = [v * (self.decay_rate ** self.get_curr_decay_steps(t)) for v in self.base_values] 53 | return lrs 54 | 55 | def get_epoch_values(self, epoch: int): 56 | if self.t_in_epochs: 57 | return self._get_lr(epoch) 58 | else: 59 | return None 60 | 61 | def get_update_values(self, num_updates: int): 62 | if not self.t_in_epochs: 63 | return self._get_lr(num_updates) 64 | else: 65 | return None 66 | -------------------------------------------------------------------------------- /timm/models/layers/padding.py: -------------------------------------------------------------------------------- 1 | """ Padding Helpers 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import math 6 | from typing import List, Tuple 7 | 8 | import torch.nn.functional as F 9 | 10 | 11 | # Calculate symmetric padding for a convolution 12 | def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int: 13 | padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2 14 | return padding 15 | 16 | 17 | # Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution 18 | def get_same_padding(x: int, k: int, s: int, d: int): 19 | return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0) 20 | 21 | 22 | # Can SAME padding for given args be done statically? 23 | def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, **_): 24 | return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0 25 | 26 | 27 | # Dynamically pad input x with 'SAME' padding for conv with specified args 28 | def pad_same(x, k: List[int], s: List[int], d: List[int] = (1, 1), value: float = 0): 29 | ih, iw = x.size()[-2:] 30 | pad_h, pad_w = get_same_padding(ih, k[0], s[0], d[0]), get_same_padding(iw, k[1], s[1], d[1]) 31 | if pad_h > 0 or pad_w > 0: 32 | x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2], value=value) 33 | return x 34 | 35 | 36 | def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bool]: 37 | dynamic = False 38 | if isinstance(padding, str): 39 | # for any string padding, the padding will be calculated for you, one of three ways 40 | padding = padding.lower() 41 | if padding == 'same': 42 | # TF compatible 'SAME' padding, has a performance and GPU memory allocation impact 43 | if is_static_pad(kernel_size, **kwargs): 44 | # static case, no extra overhead 45 | padding = get_padding(kernel_size, **kwargs) 46 | else: 47 | # dynamic 'SAME' padding, has runtime/GPU memory overhead 48 | padding = 0 49 | dynamic = True 50 | elif padding == 'valid': 51 | # 'VALID' padding, same as padding=0 52 | padding = 0 53 | else: 54 | # Default to PyTorch style 'same'-ish symmetric padding 55 | padding = get_padding(kernel_size, **kwargs) 56 | return padding, dynamic 57 | -------------------------------------------------------------------------------- /timm/models/layers/classifier.py: -------------------------------------------------------------------------------- 1 | """ Classifier head and layer factory 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | from torch import nn as nn 6 | from torch.nn import functional as F 7 | 8 | from .adaptive_avgmax_pool import SelectAdaptivePool2d 9 | 10 | 11 | def _create_pool(num_features, num_classes, pool_type='avg', use_conv=False): 12 | flatten_in_pool = not use_conv # flatten when we use a Linear layer after pooling 13 | if not pool_type: 14 | assert num_classes == 0 or use_conv,\ 15 | 'Pooling can only be disabled if classifier is also removed or conv classifier is used' 16 | flatten_in_pool = False # disable flattening if pooling is pass-through (no pooling) 17 | global_pool = SelectAdaptivePool2d(pool_type=pool_type, flatten=flatten_in_pool) 18 | num_pooled_features = num_features * global_pool.feat_mult() 19 | return global_pool, num_pooled_features 20 | 21 | 22 | def _create_fc(num_features, num_classes, use_conv=False): 23 | if num_classes <= 0: 24 | fc = nn.Identity() # pass-through (no classifier) 25 | elif use_conv: 26 | fc = nn.Conv2d(num_features, num_classes, 1, bias=True) 27 | else: 28 | fc = nn.Linear(num_features, num_classes, bias=True) 29 | return fc 30 | 31 | 32 | def create_classifier(num_features, num_classes, pool_type='avg', use_conv=False): 33 | global_pool, num_pooled_features = _create_pool(num_features, num_classes, pool_type, use_conv=use_conv) 34 | fc = _create_fc(num_pooled_features, num_classes, use_conv=use_conv) 35 | return global_pool, fc 36 | 37 | 38 | class ClassifierHead(nn.Module): 39 | """Classifier head w/ configurable global pooling and dropout.""" 40 | 41 | def __init__(self, in_chs, num_classes, pool_type='avg', drop_rate=0., use_conv=False): 42 | super(ClassifierHead, self).__init__() 43 | self.drop_rate = drop_rate 44 | self.global_pool, num_pooled_features = _create_pool(in_chs, num_classes, pool_type, use_conv=use_conv) 45 | self.fc = _create_fc(num_pooled_features, num_classes, use_conv=use_conv) 46 | self.flatten = nn.Flatten(1) if use_conv and pool_type else nn.Identity() 47 | 48 | def forward(self, x): 49 | x = self.global_pool(x) 50 | if self.drop_rate: 51 | x = F.dropout(x, p=float(self.drop_rate), training=self.training) 52 | x = self.fc(x) 53 | x = self.flatten(x) 54 | return x 55 | -------------------------------------------------------------------------------- /timm/optim/sgdp.py: -------------------------------------------------------------------------------- 1 | """ 2 | SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py 3 | 4 | Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217 5 | Code: https://github.com/clovaai/AdamP 6 | 7 | Copyright (c) 2020-present NAVER Corp. 8 | MIT license 9 | """ 10 | 11 | import torch 12 | import torch.nn.functional as F 13 | from torch.optim.optimizer import Optimizer, required 14 | import math 15 | 16 | from .adamp import projection 17 | 18 | 19 | class SGDP(Optimizer): 20 | def __init__(self, params, lr=required, momentum=0, dampening=0, 21 | weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1): 22 | defaults = dict( 23 | lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, 24 | nesterov=nesterov, eps=eps, delta=delta, wd_ratio=wd_ratio) 25 | super(SGDP, self).__init__(params, defaults) 26 | 27 | @torch.no_grad() 28 | def step(self, closure=None): 29 | loss = None 30 | if closure is not None: 31 | with torch.enable_grad(): 32 | loss = closure() 33 | 34 | for group in self.param_groups: 35 | weight_decay = group['weight_decay'] 36 | momentum = group['momentum'] 37 | dampening = group['dampening'] 38 | nesterov = group['nesterov'] 39 | 40 | for p in group['params']: 41 | if p.grad is None: 42 | continue 43 | grad = p.grad 44 | state = self.state[p] 45 | 46 | # State initialization 47 | if len(state) == 0: 48 | state['momentum'] = torch.zeros_like(p) 49 | 50 | # SGD 51 | buf = state['momentum'] 52 | buf.mul_(momentum).add_(grad, alpha=1. - dampening) 53 | if nesterov: 54 | d_p = grad + momentum * buf 55 | else: 56 | d_p = buf 57 | 58 | # Projection 59 | wd_ratio = 1. 60 | if len(p.shape) > 1: 61 | d_p, wd_ratio = projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps']) 62 | 63 | # Weight decay 64 | if weight_decay != 0: 65 | p.mul_(1. - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum)) 66 | 67 | # Step 68 | p.add_(d_p, alpha=-group['lr']) 69 | 70 | return loss 71 | -------------------------------------------------------------------------------- /timm/optim/lookahead.py: -------------------------------------------------------------------------------- 1 | """ Lookahead Optimizer Wrapper. 2 | Implementation modified from: https://github.com/alphadl/lookahead.pytorch 3 | Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610 4 | 5 | Hacked together by / Copyright 2020 Ross Wightman 6 | """ 7 | import torch 8 | from torch.optim.optimizer import Optimizer 9 | from collections import defaultdict 10 | 11 | 12 | class Lookahead(Optimizer): 13 | def __init__(self, base_optimizer, alpha=0.5, k=6): 14 | # NOTE super().__init__() not called on purpose 15 | if not 0.0 <= alpha <= 1.0: 16 | raise ValueError(f'Invalid slow update rate: {alpha}') 17 | if not 1 <= k: 18 | raise ValueError(f'Invalid lookahead steps: {k}') 19 | defaults = dict(lookahead_alpha=alpha, lookahead_k=k, lookahead_step=0) 20 | self._base_optimizer = base_optimizer 21 | self.param_groups = base_optimizer.param_groups 22 | self.defaults = base_optimizer.defaults 23 | self.defaults.update(defaults) 24 | self.state = defaultdict(dict) 25 | # manually add our defaults to the param groups 26 | for name, default in defaults.items(): 27 | for group in self._base_optimizer.param_groups: 28 | group.setdefault(name, default) 29 | 30 | @torch.no_grad() 31 | def update_slow(self, group): 32 | for fast_p in group["params"]: 33 | if fast_p.grad is None: 34 | continue 35 | param_state = self._base_optimizer.state[fast_p] 36 | if 'lookahead_slow_buff' not in param_state: 37 | param_state['lookahead_slow_buff'] = torch.empty_like(fast_p) 38 | param_state['lookahead_slow_buff'].copy_(fast_p) 39 | slow = param_state['lookahead_slow_buff'] 40 | slow.add_(fast_p - slow, alpha=group['lookahead_alpha']) 41 | fast_p.copy_(slow) 42 | 43 | def sync_lookahead(self): 44 | for group in self._base_optimizer.param_groups: 45 | self.update_slow(group) 46 | 47 | @torch.no_grad() 48 | def step(self, closure=None): 49 | loss = self._base_optimizer.step(closure) 50 | for group in self._base_optimizer.param_groups: 51 | group['lookahead_step'] += 1 52 | if group['lookahead_step'] % group['lookahead_k'] == 0: 53 | self.update_slow(group) 54 | return loss 55 | 56 | def state_dict(self): 57 | return self._base_optimizer.state_dict() 58 | 59 | def load_state_dict(self, state_dict): 60 | self._base_optimizer.load_state_dict(state_dict) 61 | self.param_groups = self._base_optimizer.param_groups 62 | -------------------------------------------------------------------------------- /mplug_owl2/model/configuration_qwen.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Alibaba Cloud. 2 | # 3 | # This source code is licensed under the license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from transformers import PretrainedConfig 7 | 8 | 9 | class QWenConfig(PretrainedConfig): 10 | model_type = "qwen" 11 | keys_to_ignore_at_inference = ["past_key_values"] 12 | 13 | def __init__( 14 | self, 15 | multiway=False, 16 | vocab_size=151936, 17 | hidden_size=4096, 18 | num_hidden_layers=32, 19 | num_attention_heads=32, 20 | emb_dropout_prob=0.0, 21 | attn_dropout_prob=0.0, 22 | layer_norm_epsilon=1e-6, 23 | initializer_range=0.02, 24 | max_position_embeddings=8192, 25 | scale_attn_weights=True, 26 | use_cache=True, 27 | bf16=False, 28 | fp16=False, 29 | fp32=False, 30 | kv_channels=128, 31 | rotary_pct=1.0, 32 | rotary_emb_base=10000, 33 | use_dynamic_ntk=True, 34 | use_logn_attn=True, 35 | use_flash_attn="auto", 36 | intermediate_size=22016, 37 | no_bias=True, 38 | tie_word_embeddings=False, 39 | use_cache_quantization=False, 40 | use_cache_kernel=False, 41 | softmax_in_fp32=False, 42 | **kwargs, 43 | ): 44 | self.multiway = multiway 45 | self.vocab_size = vocab_size 46 | self.hidden_size = hidden_size 47 | self.intermediate_size = intermediate_size 48 | self.num_hidden_layers = num_hidden_layers 49 | self.num_attention_heads = num_attention_heads 50 | self.emb_dropout_prob = emb_dropout_prob 51 | self.attn_dropout_prob = attn_dropout_prob 52 | self.layer_norm_epsilon = layer_norm_epsilon 53 | self.initializer_range = initializer_range 54 | self.scale_attn_weights = scale_attn_weights 55 | self.use_cache = use_cache 56 | self.max_position_embeddings = max_position_embeddings 57 | self.bf16 = bf16 58 | self.fp16 = fp16 59 | self.fp32 = fp32 60 | self.kv_channels = kv_channels 61 | self.rotary_pct = rotary_pct 62 | self.rotary_emb_base = rotary_emb_base 63 | self.use_dynamic_ntk = use_dynamic_ntk 64 | self.use_logn_attn = use_logn_attn 65 | self.use_flash_attn = use_flash_attn 66 | self.no_bias = no_bias 67 | self.use_cache_quantization = use_cache_quantization 68 | self.use_cache_kernel = use_cache_kernel 69 | self.softmax_in_fp32 = softmax_in_fp32 70 | super().__init__( 71 | tie_word_embeddings=tie_word_embeddings, 72 | **kwargs 73 | ) -------------------------------------------------------------------------------- /timm/models/layers/global_context.py: -------------------------------------------------------------------------------- 1 | """ Global Context Attention Block 2 | 3 | Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond` 4 | - https://arxiv.org/abs/1904.11492 5 | 6 | Official code consulted as reference: https://github.com/xvjiarui/GCNet 7 | 8 | Hacked together by / Copyright 2021 Ross Wightman 9 | """ 10 | from torch import nn as nn 11 | import torch.nn.functional as F 12 | 13 | from .create_act import create_act_layer, get_act_layer 14 | from .helpers import make_divisible 15 | from .mlp import ConvMlp 16 | from .norm import LayerNorm2d 17 | 18 | 19 | class GlobalContext(nn.Module): 20 | 21 | def __init__(self, channels, use_attn=True, fuse_add=False, fuse_scale=True, init_last_zero=False, 22 | rd_ratio=1./8, rd_channels=None, rd_divisor=1, act_layer=nn.ReLU, gate_layer='sigmoid'): 23 | super(GlobalContext, self).__init__() 24 | act_layer = get_act_layer(act_layer) 25 | 26 | self.conv_attn = nn.Conv2d(channels, 1, kernel_size=1, bias=True) if use_attn else None 27 | 28 | if rd_channels is None: 29 | rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.) 30 | if fuse_add: 31 | self.mlp_add = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d) 32 | else: 33 | self.mlp_add = None 34 | if fuse_scale: 35 | self.mlp_scale = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d) 36 | else: 37 | self.mlp_scale = None 38 | 39 | self.gate = create_act_layer(gate_layer) 40 | self.init_last_zero = init_last_zero 41 | self.reset_parameters() 42 | 43 | def reset_parameters(self): 44 | if self.conv_attn is not None: 45 | nn.init.kaiming_normal_(self.conv_attn.weight, mode='fan_in', nonlinearity='relu') 46 | if self.mlp_add is not None: 47 | nn.init.zeros_(self.mlp_add.fc2.weight) 48 | 49 | def forward(self, x): 50 | B, C, H, W = x.shape 51 | 52 | if self.conv_attn is not None: 53 | attn = self.conv_attn(x).reshape(B, 1, H * W) # (B, 1, H * W) 54 | attn = F.softmax(attn, dim=-1).unsqueeze(3) # (B, 1, H * W, 1) 55 | context = x.reshape(B, C, H * W).unsqueeze(1) @ attn 56 | context = context.view(B, C, 1, 1) 57 | else: 58 | context = x.mean(dim=(2, 3), keepdim=True) 59 | 60 | if self.mlp_scale is not None: 61 | mlp_x = self.mlp_scale(context) 62 | x = x * self.gate(mlp_x) 63 | if self.mlp_add is not None: 64 | mlp_x = self.mlp_add(context) 65 | x = x + mlp_x 66 | 67 | return x 68 | --------------------------------------------------------------------------------