def model_factory(arch_name, arch_kwargs, dataset, with_info=False):
model = None
if dataset.startswith('cifar'):
if arch_name.startswith('resnet'):
model = getattr(resnet_cifar, arch_name)(**arch_kwargs)
elif arch_name.startswith('densenet'):
model = CifarDenseNet(**arch_kwargs)
elif arch_name.startswith('preresnet'):
model = PreResNet(**arch_kwargs)
else:
try:
model = getattr(init_model, arch_name)
except AttributeError:
raise AttributeError('未找到模型: <%s>, 请检查是否已将该模型注册到 <xmodel._init_model> .' % arch_name)
model = model(**arch_kwargs)
elif dataset.startswith('imagenet'):
# 导入ImageNet模型，只需在 xmodel._init_model 中添加自己的模型即可，无需在此处添加循环分支
try:
model = getattr(init_model, arch_name)
except AttributeError:
raise AttributeError('未找到模型: <%s>, 请检查是否已将该模型注册到 <xmodel._init_model> .' % arch_name)
model = model(**arch_kwargs)
if model is None:
raise NotImplementedError('Unkown <arch_name:%s> for <dataset:%s>, '
'check mdoel_factory.py' % (arch_name, dataset))
if with_info:
params = xtils.calculate_params_scale(model, format='million')
insize = 224 if dataset == 'imagenet' else 32
gflops = xtils.calculate_FLOPs_scale(model, input_size=insize, multiply_adds=True, use_gpu=False)
depth = xtils.calculate_layers_num(model, layers=('conv2d', 'deconv2d', 'fc'))
if with_info == 'return':
return model, params, gflops, depth
return model
'last_branch': 1, 'last_down': True, 'last_expand': 0,
'poolmode': 'avg', 'active': 'relu', 'summer': 'split', 'nclass': 10,
'afisok': False, 'afkeys': ('af1', 'af2'), 'convon': True,
'convlayers': 1, 'convdepth': 4}
wv2s = {'stages': 2, 'rock': 'Q', 'branch': 3, 'indepth': 24, 'growth': 12, 'multiway': 4,
'layers': (3, 3, 0), 'blocks': ('D', 'D', '-'), 'bottle': (3, 3, 3), 'classify': (0, 0, 0),
'trans': ('A', 'A', '-'), 'reduction': (0.5, 0, 0),
'last_branch': 1, 'last_down': True, 'last_expand': 0,
'poolmode': 'avg', 'active': 'relu', 'summer': 'split', 'nclass': 10,
'afisok': False, 'afkeys': ('af1', 'af2'), 'convon': True,
'convlayers': 1, 'convdepth': 4}
wv3s = {'stages': 3, 'rock': 'Q', 'branch': 3, 'indepth': 24, 'growth': 12, 'multiway': 4,
'layers': (10, 10, 10), 'blocks': ('D', 'D', 'S'), 'bottle': (0, 0, 0), 'classify': (0, 0, 0),
'trans': ('A', 'A', 'A'), 'reduction': (0.5, 0.5, 0),
'last_branch': 1, 'last_down': False, 'last_expand': 10,
'poolmode': 'avg', 'active': 'relu', 'summer': 'split', 'nclass': 10,
'afisok': False, 'afkeys': ('af1', 'af2', 'af3'), 'convon': True,
'convlayers': 1, 'convdepth': 4}
model = WaveNet(**wv3s)
print('\n', model, '\n')
out = model(torch.randn(4, 3, 32, 32))
# utils.tensorboard_add_model(model, x)
xtils.calculate_params_scale(model, format='million')
xtils.calculate_FLOPs_scale(model, input_size=32, use_gpu=False, multiply_adds=False)
xtils.calculate_layers_num(model, layers=('conv2d', 'deconv2d', 'fc'))
xtils.calculate_time_cost(model, insize=32, toc=3, use_gpu=False)
return model
def preresnet110(num_classes=10):
"""Constructs a ResNet-110 model for CIFAR-10 (by default)
Args:
num_classes (uint): number of classes
"""
model = CifarPreResNet(ResNetBasicblock, 110, num_classes)
return model
def PreResNet(block=ResNetBasicblock, depth=20, num_classes=10):
return CifarPreResNet(block, depth, num_classes)
if __name__ == '__main__':
import xtils
# model = CifarPreResNet(ResNetBasicblock, 20, 10)
model = preresnet20(10)
# model = resnet50(False)
# model = densenet121(False)
print(model)
xtils.calculate_layers_num(model, layers=('conv2d', 'classifier'))
xtils.calculate_FLOPs_scale(model,
input_size=32,
multiply_adds=True,
use_gpu=False)
xtils.calculate_params_scale(model, format='million')
model = MultiScaleNet(**ms6)
print(model)
model.set_train_which(part=['bone+mhead', 'auxhead', 'summary'][2],
name_which='head-8-2-2@6')
# model.set_eval_which(part=['bone+mhead', 'bone+mhead+auxhead', 'bone+mhead+auxhead+summary'][1])
# paramsx = [p for p in model.parameters() if p.requires_grad]
#
# model.set_train_which(part=['bone+mhead', 'auxhead', 'summary'][0])
# paramsy = [p for p in model.parameters() if p.requires_grad]
#
# share = list(set(paramsx).intersection(set(paramsy)))
insize = [32, 224][model.dataset == 'imagenet']
x = torch.randn(1, 3, insize, insize)
z = model(x)
print('z-->', len(z), '\n')
xtils.calculate_layers_num(model)
xtils.calculate_FLOPs_scale(model,
input_size=insize,
use_gpu=False,
multiply_adds=False)
xtils.calculate_params_scale(model, format='million')
xtils.calculate_time_cost(model,
insize=insize,
toc=1,
pritout=True,
use_gpu=False)
'NUM_BRANCHES': 4,
'BLOCK': 'BASIC',
'NUM_BLOCKS': [4, 4, 4, 4],
'NUM_CHANNELS': [48, 96, 192, 384],
'FUSE_METHOD': 'SUM'
}
}
}
}
# 测试结果
# hrw18 [email protected] [email protected]
if __name__ == '__main__':
import xtils
hrarch = [hrw18, hrw30, hrw32, hrw40, hrw44, hrw48][0]
# model = get_cls_hrnet(hrarch)
# model = HighResolutionNet(**{'cfg': hrarch})
model = HRNets(**hrarch)
print(model)
xtils.calculate_layers_num(model, layers=('conv2d', 'linear'))
xtils.calculate_FLOPs_scale(model, input_size=224, multiply_adds=False)
xtils.calculate_params_scale(model, format='million')
xtils.calculate_time_cost(model,
insize=224,
use_gpu=False,
toc=1,
pritout=True)
elif netis == 'mobilev3':
model = MobileV3(**mbvdl)
elif netis == 'fishnet':
# fish99: 16.63M 4.31G 100L 693M 0.01121s
# fish150: 24.96M 6.45G 151L 804M 0.01696S
model = [fishnet99(), fishnet150(), fishnet201()][0]
elif netis == 'hrnet':
model = HRNets(**hrw18)
elif netis == 'effnet':
model = EFFNets(**effb3)
else:
raise NotImplementedError
xtils.calculate_params_scale(model, format='million')
xtils.calculate_FLOPs_scale(model, input_size=224, multiply_adds=True)
xtils.calculate_layers_num(model, layers=['conv2d', 'deconv2d', 'linear'])
model = model.to(device)
model.eval()
bn, c, h, w = 1, 3, 224, 224
x = torch.Tensor(bn, c, h, w)
x = x.to(device)
x.requires_grad = False
N = 2000
tic = time.time()
for i in range(N):
out = model(x)
toc = time.time() - tic
def model_factory(arch_name, arch_kwargs, dataset, with_info=False):
model = None
if arch_name.startswith('resnet'):
if dataset == 'cifar':
model = getattr(resnet_cifar, arch_name)(**arch_kwargs)
elif dataset == 'imagenet':
# model = getattr(tvmodels, arch_name)(**arch_kwargs) # pretrained model_url
model = getattr(tvm_resnet,
arch_name)(**arch_kwargs) # pretrained model_path
elif arch_name.startswith('densenet'):
if dataset == 'cifar':
model = CifarDenseNet(**arch_kwargs)
elif dataset == 'imagenet':
# model = getattr(tvmodels, arch_name)(**arch_kwargs) # pretrained model_url
model = getattr(tvm_densenet,
arch_name)(**arch_kwargs) # pretrained model_path
elif arch_name.startswith('vgg'):
if dataset == 'imagenet':
# model = getattr(tvmodels, arch_name)(**arch_kwargs) # pretrained model_url
model = getattr(tvm_vggs, arch_name)(**arch_kwargs)
elif arch_name.startswith('preresnet'):
if dataset == 'cifar10':
model = PreResNet(**arch_kwargs)
elif arch_name.startswith('fishnet'):
if dataset == 'imagenet':
model = getattr(fishnet,
arch_name)(**arch_kwargs) # pretrained model_path
elif arch_name.startswith('mobilev3'):
if dataset == 'imagenet':
model = MobileV3(**arch_kwargs) # pretrained model_path
# 导入自定义模型，只需在 xmodel._init_model 中添加自己的模型即可，无需在此处添加循环分支
else:
try:
model = getattr(init_model, arch_name)
except AttributeError:
raise AttributeError(
'未找到模型: <%s>, 请检查是否已将该模型注册到 <xmodel._init_model> .' %
arch_name)
model = model(**arch_kwargs)
if model is None:
raise NotImplementedError('Unkown <arch_name:%s> for <dataset:%s>, '
'check mdoel_factory.py' %
(arch_name, dataset))
if with_info:
# print(model)
parameters = xtils.calculate_params_scale(model, format='million')
if dataset == 'imagenet' and True:
gflops = xtils.calculate_FLOPs_scale(model,
input_size=224,
multiply_adds=True,
use_gpu=False)
model_depth = xtils.calculate_layers_num(model,
layers=('conv2d', 'deconv2d',
'fc'))
return model, parameters, model_depth
return model
