def test_resnet(): config_file = 'configs/benchmarks/resnet/r50_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (1, 3, 224, 224), (1, 100)) config_file = 'configs/benchmarks/resnet/rxt50_32x4d_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (1, 3, 224, 224), (1, 100)) config_file = 'configs/benchmarks/resnet/r50_torchvision_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = build_torchvision_resnet(cfg) print(model) test_data(model, (1, 3, 224, 224), (1, 100)) config_file = 'configs/benchmarks/resnet/rxt50_32x4d_torchvision_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = build_torchvision_resnet(cfg) print(model) test_data(model, (1, 3, 224, 224), (1, 100))
def test_resnetd(): config_file = 'configs/benchmarks/resnet/rd50_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (1, 3, 224, 224), (1, 100)) config_file = 'configs/benchmarks/resnet/rxtd50_32x4d_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (1, 3, 224, 224), (1, 100)) config_file = 'configs/benchmarks/resnet/rxtd50_32x4d_fast_avg_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (1, 3, 224, 224), (1, 100)) config_file = 'configs/benchmarks/resnet/rxtd50_32x4d_avg_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (1, 3, 224, 224), (1, 100))
def test_resnest(): # resnetd model = ResNet(arch="resnest50_2s2x40d", num_classes=1000) print(model) test_data(model, (3, 3, 224, 224), (3, 1000)) # resnetd model = ResNet(arch="resnest50_2s2x40d_fast", num_classes=1000) print(model) test_data(model, (3, 3, 224, 224), (3, 1000))
def test_resnet(): # for torchvision model = TorchvisionResNet(arch='resnet50', num_classes=1000) print(model) test_data(model, (3, 3, 224, 224), (3, 1000)) # for custom model = ResNet(arch="resnet50", num_classes=1000) print(model) test_data(model, (3, 3, 224, 224), (3, 1000)) # resnetxt_32x4d model = ResNet(arch="resnext50_32x4d", num_classes=1000) print(model) test_data(model, (3, 3, 224, 224), (3, 1000))
def test_sknet(): config_file = 'configs/benchmarks/resnet/sknet50_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (3, 3, 224, 224), (3, 100))
def test_resnet_gn(): cfg.MODEL.NORM.TYPE = 'GroupNorm' norm_layer = get_norm(cfg) print(norm_layer) # for custom model = ResNet(arch="resnet50", num_classes=1000, norm_layer=norm_layer) print(model) test_data(model, (1, 3, 224, 224), (1, 1000)) # resnetxt_32x4d model = ResNet(arch="resnext50_32x4d", num_classes=1000, norm_layer=norm_layer) print(model) test_data(model, (3, 3, 224, 224), (3, 1000))
def test_attention_resnetd(with_attentions=(1, 1, 1, 1), reduction=16, attention_type='SqueezeAndExcitationBlock2D'): # for custom model = ResNet(arch="resnetd50", with_attentions=with_attentions, reduction=reduction, attention_type=attention_type, num_classes=1000) print(model) test_data(model, (3, 3, 224, 224), (3, 1000)) # resnetxt_32x4d model = ResNet(arch="resnext50_32x4d", with_attentions=with_attentions, reduction=reduction, attention_type=attention_type, num_classes=1000) print(model) test_data(model, (3, 3, 224, 224), (3, 1000))
def test_resnest(): config_file = 'configs/benchmarks/resnet/rstd50_2s2x40d_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (3, 3, 224, 224), (3, 100)) config_file = 'configs/benchmarks/resnet/rstd50_2s2x40d_fast_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (3, 3, 224, 224), (3, 100)) config_file = 'configs/benchmarks/resnet/rstd50_2s2x40d_fast_official_cifar100_224_e100_rmsprop.yaml' cfg.merge_from_file(config_file) model = ResNet(cfg) print(model) test_data(model, (3, 3, 224, 224), (3, 100))
def test_sknet(): # resnetd model = ResNet(arch="sknet50", num_classes=1000) print(model) test_data(model, (3, 3, 224, 224), (3, 1000))