'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)
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)
# def test():
# net = MobileNetV3_Small()
# x = torch.randn(2, 3, 224, 224)
# y = net(x)
# print(y.size())
# test()
if __name__ == '__main__':
import xtils, os
model = [MobileNetV3_Large(), MobileNetV3_Small()][0]
print('\n', model, '\n')
ckpt = ['mobilev3_x_large.pth.tar', 'mobilev3_x_small.pth.tar'][0]
ckpt = os.path.join('/data/zhangjp/PreTrainedModels', ckpt)
model = xtils.load_ckpt_weights(model,
ckptf=ckpt,
device='cpu',
mgpus_to_sxpu='m2s')
# utils.tensorboard_add_model(model, x)
xtils.calculate_params_scale(model, format='million')
xtils.calculate_FLOPs_scale(model,
use_gpu=False,
input_size=224,
multiply_adds=True)
xtils.calculate_layers_num(model, layers=('conv2d', 'deconv2d', 'fc'))
xtils.calculate_time_cost(model, insize=224, use_gpu=False)
import xtils, time, random
seed = [2019, time.time()][0]
random.seed(seed)
torch.manual_seed(seed)
# # # ImageNet
model = XResNet(**xresnet_imgnet)
print(model)
xtils.calculate_layers_num(model,
layers=('conv2d', 'deconv2d', 'classifier'))
xtils.calculate_FLOPs_scale(model,
input_size=224,
multiply_adds=True,
use_gpu=False)
xtils.calculate_params_scale(model, format='million')
xtils.calculate_time_cost(model,
insize=224,
toc=1,
use_gpu=False,
pritout=True)
# CIFAR10 & CIFAR100
#
# model = XResNet(**xresnet_cifar100)
# print(model)
# utils.calculate_layers_num(model, layers=('conv2d', 'classifier'))
# utils.calculate_FLOPs_scale(model, input_size=32, multiply_adds=True, use_gpu=False)
# utils.calculate_params_scale(model, format='million')
# utils.calculate_time_cost(model, insize=32, toc=3, use_gpu=False, pritout=True)