如何计算深度学习模型参数量和推理速度
大家好，我是DASOU；
1.FLOPs和Params计算
1.1概念理解
计算公式：
对卷积层：(K_h * K_w * C_in * C_out) * (H_out * W_out)
对全连接层：C_in * C_out
模型参数量计算公式为：
对卷积层：（K_h * K_w * C_in）* C_out
对全连接层：C_in * C_out
注意：
1.params只与你定义的网络结构有关，和forward的任何操作无关。即定义好了网络结构，参数就已经决定了。FLOPs和不同的层运算结构有关。如果forward时在同一层（同一名字命名的层）多次运算，FLOPs不会增加
2.Model_size = 4*params 模型大小约为参数量的4倍
补充：
1.2计算方法
'''
code by zzg-2020-05-19
pip install thop
'''
import torch
from thop import profile
from models.yolo_nano import YOLONano
device = torch.device("cpu")
#input_shape of model,batch_size=1
net = YOLONano(num_classes=20, image_size=416) ##定义好的网络模型
input = torch.randn(1, 3, 416, 416)
flops, params = profile(net, inputs=(input, ))
print("FLOPs=", str(flops/1e9) +'{}'.format("G"))
print("params=", str(params/1e6)+'{}'.format("M")
'''
在PyTorch中，可以使用torchstat这个库来查看网络模型的一些信息，包括总的参数量params、MAdd、显卡内存占用量和FLOPs等
pip install torchstat
'''
from torchstat import stat
from torchvision.models import resnet50
model = resnet50()
stat(model, (3, 224, 224))
#pip install ptflops
from ptflops import get_model_complexity_info
from torchvision.models import resnet50
model = resnet50()
flops, params = get_model_complexity_info(model, (3, 224, 224), as_strings=True, print_per_layer_stat=True)
print('Flops: ' + flops)
print('Params: ' + params)
2.模型推理速度计算
2.1 模型推理速度正确计算
model = EfficientNet.from_pretrained(‘efficientnet-b0’)
device = torch.device(“cuda”)
model.to(device)
dummy_input = torch.randn(1, 3, 224, 224,dtype=torch.float).to(device)
starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
repetitions = 300
timings=np.zeros((repetitions,1))
#GPU-WARM-UP
for _ in range(10):
_ = model(dummy_input)
# MEASURE PERFORMANCE
with torch.no_grad():
for rep in range(repetitions):
starter.record()
_ = model(dummy_input)
ender.record()
# WAIT FOR GPU SYNC
torch.cuda.synchronize()
curr_time = starter.elapsed_time(ender)
timings[rep] = curr_time
mean_syn = np.sum(timings) / repetitions
std_syn = np.std(timings)
mean_fps = 1000. / mean_syn
print(' * Mean@1 {mean_syn:.3f}ms Std@5 {std_syn:.3f}ms FPS@1 {mean_fps:.2f}'.format(mean_syn=mean_syn, std_syn=std_syn, mean_fps=mean_fps))
print(mean_syn)
2.2 模型吞吐量计算
（批次数 X 批次大小）/（以秒为单位的总时间）
model = EfficientNet.from_pretrained(‘efficientnet-b0’)
device = torch.device(“cuda”)
model.to(device)
dummy_input = torch.randn(optimal_batch_size, 3,224,224, dtype=torch.float).to(device)
repetitions=100
total_time = 0
with torch.no_grad():
for rep in range(repetitions):
starter, ender = torch.cuda.Event(enable_timing=True),torch.cuda.Event(enable_timing=True)
starter.record()
_ = model(dummy_input)
ender.record()
torch.cuda.synchronize()
curr_time = starter.elapsed_time(ender)/1000
total_time += curr_time
Throughput = (repetitions*optimal_batch_size)/total_time
print(‘Final Throughput:’,Throughput)
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