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))