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PyTorch (实验性）在 PyTorch 中使用 Eager 模式进行静态量化
2020-09-10 11:31:44 更新
原文： https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html
由编辑：赛斯·魏德曼
本教程介绍了如何进行训练后的静态量化，并说明了两种更先进的技术-每通道量化和量化感知训练-可以进一步提高模型的准确性。 请注意，目前仅支持 CPU 量化，因此在本教程中我们将不使用 GPU / CUDA。
在本教程结束时，您将看到 PyTorch 中的量化如何导致模型大小显着减小同时提高速度。 此外，您将在此处看到如何轻松应用中显示的一些高级量化技术，从而使量化后的模型获得的准确性降低得多。
警告：我们使用了许多其他 PyTorch 仓库中的样板代码，例如，定义MobileNetV2模型架构，定义数据加载器等。 我们当然鼓励您阅读它； 但是如果要使用量化功能，请随时跳至“ 4。 训练后静态量化”部分。
我们将从进行必要的导入开始：
import numpy as np
import torch
import torch.nn as nn
import torchvision
from torch.utils.data import DataLoader
from torchvision import datasets
import torchvision.transforms as transforms
import os
import time
import sys
import torch.quantization
## # Setup warnings
import warnings
warnings.filterwarnings(
action='ignore',
category=DeprecationWarning,
module=r'.*'
)
warnings.filterwarnings(
action='default',
module=r'torch.quantization'
)
## Specify random seed for repeatable results
torch.manual_seed(191009)1.模型架构
我们首先定义 MobileNetV2 模型体系结构，并进行了一些值得注意的修改以实现量化：
- 用
nn.quantized.FloatFunctional代替加法运算 - 在网络的开头和结尾处插入
QuantStub和DeQuantStub。 - 用 ReLU 替换 ReLU6
注意：此代码取自此处。
from torch.quantization import QuantStub, DeQuantStub
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
class ConvBNReLU(nn.Sequential):
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
padding = (kernel_size - 1) // 2
super(ConvBNReLU, self).__init__(
nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
nn.BatchNorm2d(out_planes, momentum=0.1),
# Replace with ReLU
nn.ReLU(inplace=False)
)
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup
layers = []
if expand_ratio != 1:
# pw
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
layers.extend([
# dw
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup, momentum=0.1),
])
self.conv = nn.Sequential(*layers)
# Replace torch.add with floatfunctional
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
if self.use_res_connect:
return self.skip_add.add(x, self.conv(x))
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(self, num_classes=1000, width_mult=1.0, inverted_residual_setting=None, round_nearest=8):
"""
MobileNet V2 main class
Args:
num_classes (int): Number of classes
width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
inverted_residual_setting: Network structure
round_nearest (int): Round the number of channels in each layer to be a multiple of this number
Set to 1 to turn off rounding
"""
super(MobileNetV2, self).__init__()
block = InvertedResidual
input_channel = 32
last_channel = 1280
if inverted_residual_setting is None:
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1],
]
# only check the first element, assuming user knows t,c,n,s are required
if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
raise ValueError("inverted_residual_setting should be non-empty "
"or a 4-element list, got {}".format(inverted_residual_setting))
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
features = [ConvBNReLU(3, input_channel, stride=2)]
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(block(input_channel, output_channel, stride, expand_ratio=t))
input_channel = output_channel
# building last several layers
features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
# make it nn.Sequential
self.features = nn.Sequential(*features)
self.quant = QuantStub()
self.dequant = DeQuantStub()
# building classifier
self.classifier = nn.Sequential(
nn.Dropout(0.2),
nn.Linear(self.last_channel, num_classes),
)
# weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
def forward(self, x):
x = self.quant(x)
x = self.features(x)
x = x.mean([2, 3])
x = self.classifier(x)
x = self.dequant(x)
return x
# Fuse Conv+BN and Conv+BN+Relu modules prior to quantization
# This operation does not change the numerics
def fuse_model(self):
for m in self.modules():
if type(m) == ConvBNReLU:
torch.quantization.fuse_modules(m, ['0', '1', '2'], inplace=True)
if type(m) == InvertedResidual:
for idx in range(len(m.conv)):
if type(m.conv[idx]) == nn.Conv2d:
torch.quantization.fuse_modules(m.conv, [str(idx), str(idx + 1)], inplace=True)2.助手功能
接下来，我们定义一些帮助程序功能以帮助模型评估。 这些主要来自这里。
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def evaluate(model, criterion, data_loader, neval_batches):
model.eval()
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
cnt = 0
with torch.no_grad():
for image, target in data_loader:
output = model(image)
loss = criterion(output, target)
cnt += 1
acc1, acc5 = accuracy(output, target, topk=(1, 5))
print('.', end = '')
top1.update(acc1[0], image.size(0))
top5.update(acc5[0], image.size(0))
if cnt >= neval_batches:
return top1, top5
return top1, top5
def load_model(model_file):
model = MobileNetV2()
state_dict = torch.load(model_file)
model.load_state_dict(state_dict)
model.to('cpu')
return model
def print_size_of_model(model):
torch.save(model.state_dict(), "temp.p")
print('Size (MB):', os.path.getsize("temp.p")/1e6)
os.remove('temp.p')3.定义数据集和数据加载器
作为最后的主要设置步骤，我们为训练和测试集定义了数据加载器。
ImageNet 数据
我们为本教程创建的特定数据集仅包含来自 ImageNet 数据的 1000 张图像，每个类别都有一张(该数据集的大小刚好超过 250 MB，可以相对轻松地下载）。 此自定义数据集的 URL 为：
https://s3.amazonaws.com/pytorch-tutorial-assets/imagenet_1k.zip要使用 Python 在本地下载此数据，可以使用：
import requests
url = 'https://s3.amazonaws.com/pytorch-tutorial-assets/imagenet_1k.zip`
filename = '~/Downloads/imagenet_1k_data.zip'
r = requests.get(url)
with open(filename, 'wb') as f:
f.write(r.content)为了运行本教程，我们下载了这些数据，并使用 Makefile 中的这些行将其移到正确的位置。
另一方面，要使用整个 ImageNet 数据集运行本教程中的代码，可以在后面的之后使用torchvision下载数据。 例如，要下载训练集并对其进行一些标准转换，可以使用：
import torchvision
import torchvision.transforms as transforms
imagenet_dataset = torchvision.datasets.ImageNet(
'~/.data/imagenet',
split='train',
download=True,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])下载完数据后，我们在下面显示了一些函数，这些函数定义了用于读取该数据的数据加载器。 这些功能主要来自此处。
def prepare_data_loaders(data_path):
traindir = os.path.join(data_path, 'train')
valdir = os.path.join(data_path, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
dataset = torchvision.datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
dataset_test = torchvision.datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
train_sampler = torch.utils.data.RandomSampler(dataset)
test_sampler = torch.utils.data.SequentialSampler(dataset_test)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=train_batch_size,
sampler=train_sampler)
data_loader_test = torch.utils.data.DataLoader(
dataset_test, batch_size=eval_batch_size,
sampler=test_sampler)
return data_loader, data_loader_test接下来，我们将加载经过预先训练的 MobileNetV2 模型。 我们在中提供从torchvision 中下载数据的 URL。
data_path = 'data/imagenet_1k'
saved_model_dir = 'data/'
float_model_file = 'mobilenet_pretrained_float.pth'
scripted_float_model_file = 'mobilenet_quantization_scripted.pth'
scripted_quantized_model_file = 'mobilenet_quantization_scripted_quantized.pth'
train_batch_size = 30
eval_batch_size = 30
data_loader, data_loader_test = prepare_data_loaders(data_path)
criterion = nn.CrossEntropyLoss()
float_model = load_model(saved_model_dir + float_model_file).to('cpu')接下来，我们将“融合模块”； 通过节省内存访问量，这可以使模型更快，同时还可以提高数值精度。 尽管这可以用于任何模型，但在量化模型中尤为常见。
print('\n Inverted Residual Block: Before fusion \n\n', float_model.features[1].conv)
float_model.eval()
## Fuses modules
float_model.fuse_model()
## Note fusion of Conv+BN+Relu and Conv+Relu
print('\n Inverted Residual Block: After fusion\n\n',float_model.features[1].conv)得出：
Inverted Residual Block: Before fusion
Sequential(
(0): ConvBNReLU(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
Inverted Residual Block: After fusion
Sequential(
(0): ConvBNReLU(
(0): ConvReLU2d(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32)
(1): ReLU()
)
(1): Identity()
(2): Identity()
)
(1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1))
(2): Identity()
)最后，要获得“基准”精度，让我们看看带有融合模块的未量化模型的精度
num_eval_batches = 10
print("Size of baseline model")
print_size_of_model(float_model)
top1, top5 = evaluate(float_model, criterion, data_loader_test, neval_batches=num_eval_batches)
print('Evaluation accuracy on %d images, %2.2f'%(num_eval_batches * eval_batch_size, top1.avg))
torch.jit.save(torch.jit.script(float_model), saved_model_dir + scripted_float_model_file)得出:
Size of baseline model
Size (MB): 13.981375
..........Evaluation accuracy on 300 images, 78.00我们看到 300 张图像的准确率达到 78％，这是 ImageNet 的坚实基础，尤其是考虑到我们的模型只有 14.0 MB 时。
这将是我们比较的基准。 接下来，让我们尝试不同的量化方法
4.训练后静态量化
训练后的静态量化不仅涉及像动态量化中那样将权重从 float 转换为 int，而且还执行额外的步骤，即首先通过网络馈送一批数据并计算不同激活的结果分布(具体而言，这是 通过在记录此数据的不同点插入<cite>观察者</cite>模块来完成）。 然后使用这些分布来确定在推理时如何具体量化不同的激活(一种简单的技术将简单地将整个激活范围划分为 256 个级别，但我们也支持更复杂的方法）。 重要的是，此附加步骤使我们能够在操作之间传递量化值，而不是在每次操作之间将这些值转换为浮点数，然后再转换为整数，从而显着提高了速度。
num_calibration_batches = 10
myModel = load_model(saved_model_dir + float_model_file).to('cpu')
myModel.eval()
## Fuse Conv, bn and relu
myModel.fuse_model()
## Specify quantization configuration
## Start with simple min/max range estimation and per-tensor quantization of weights
myModel.qconfig = torch.quantization.default_qconfig
print(myModel.qconfig)
torch.quantization.prepare(myModel, inplace=True)
## Calibrate first
print('Post Training Quantization Prepare: Inserting Observers')
print('\n Inverted Residual Block:After observer insertion \n\n', myModel.features[1].conv)
## Calibrate with the training set
evaluate(myModel, criterion, data_loader, neval_batches=num_calibration_batches)
print('Post Training Quantization: Calibration done')
## Convert to quantized model
torch.quantization.convert(myModel, inplace=True)
print('Post Training Quantization: Convert done')
print('\n Inverted Residual Block: After fusion and quantization, note fused modules: \n\n',myModel.features[1].conv)
print("Size of model after quantization")
print_size_of_model(myModel)
top1, top5 = evaluate(myModel, criterion, data_loader_test, neval_batches=num_eval_batches)
print('Evaluation accuracy on %d images, %2.2f'%(num_eval_batches * eval_batch_size, top1.avg))得出:
QConfig(activation=functools.partial(<class 'torch.quantization.observer.MinMaxObserver'>, reduce_range=True), weight=functools.partial(<class 'torch.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric))
Post Training Quantization Prepare: Inserting Observers
Inverted Residual Block:After observer insertion
Sequential(
(0): ConvBNReLU(
(0): ConvReLU2d(
(0): Conv2d(
32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32
(activation_post_process): MinMaxObserver(min_val=None, max_val=None)
)
(1): ReLU(
(activation_post_process): MinMaxObserver(min_val=None, max_val=None)
)
)
(1): Identity()
(2): Identity()
)
(1): Conv2d(
32, 16, kernel_size=(1, 1), stride=(1, 1)
(activation_post_process): MinMaxObserver(min_val=None, max_val=None)
)
(2): Identity()
)
..........Post Training Quantization: Calibration done
Post Training Quantization: Convert done
Inverted Residual Block: After fusion and quantization, note fused modules:
Sequential(
(0): ConvBNReLU(
(0): QuantizedConvReLU2d(32, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.15092508494853973, zero_point=0, padding=(1, 1), groups=32)
(1): Identity()
(2): Identity()
)
(1): QuantizedConv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), scale=0.1737997829914093, zero_point=72)
(2): Identity()
)
Size of model after quantization
Size (MB): 3.58906
..........Evaluation accuracy on 300 images, 63.33对于这个量化模型，我们发现在这 300 张相同的图像上，准确率仅低至〜62％。 但是，我们确实将模型的大小减小到了 3.6 MB 以下，几乎减少了 4 倍。
此外，我们可以简单地通过使用不同的量化配置来显着提高准确性。 我们使用推荐的配置对 x86 架构进行量化，重复相同的练习。 此配置执行以下操作：
- 量化每个通道的权重
- 使用直方图观察器，该直方图观察器收集激活的直方图，然后以最佳方式选择量化参数。
per_channel_quantized_model = load_model(saved_model_dir + float_model_file)
per_channel_quantized_model.eval()
per_channel_quantized_model.fuse_model()
per_channel_quantized_model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
print(per_channel_quantized_model.qconfig)
torch.quantization.prepare(per_channel_quantized_model, inplace=True)
evaluate(per_channel_quantized_model,criterion, data_loader, num_calibration_batches)
torch.quantization.convert(per_channel_quantized_model, inplace=True)
top1, top5 = evaluate(per_channel_quantized_model, criterion, data_loader_test, neval_batches=num_eval_batches)
print('Evaluation accuracy on %d images, %2.2f'%(num_eval_batches * eval_batch_size, top1.avg))
torch.jit.save(torch.jit.script(per_channel_quantized_model), saved_model_dir + scripted_quantized_model_file)得出:
QConfig(activation=functools.partial(<class 'torch.quantization.observer.HistogramObserver'>, reduce_range=True), weight=functools.partial(<class 'torch.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
....................Evaluation accuracy on 300 images, 77.33仅更改这种量化配置方法，就可以将准确性提高到 76％以上！ 尽管如此，这仍比上述 78％的基准差 1-2％。 因此，让我们尝试量化意识的训练。
5.量化意识训练
量化意识训练(QAT）是通常导致最高准确性的量化方法。 使用 QAT，在训练的正向和反向过程中，所有权重和激活都被“伪量化”：也就是说，浮点值会四舍五入以模拟 int8 值，但所有计算仍将使用浮点数进行。 因此，在训练过程中进行所有权重调整，同时“意识到”模型将最终被量化的事实。 因此，在量化之后，此方法通常比动态量化或训练后静态量化具有更高的准确性。
实际执行 QAT 的总体工作流程与之前非常相似：
- 我们可以使用与以前相同的模型：量化意识训练不需要额外的准备。
- 我们需要使用
qconfig来指定要在权重和激活之后插入哪种伪量化，而不是指定观察者
我们首先定义一个训练函数：
def train_one_epoch(model, criterion, optimizer, data_loader, device, ntrain_batches):
model.train()
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
avgloss = AverageMeter('Loss', '1.5f')
cnt = 0
for image, target in data_loader:
start_time = time.time()
print('.', end = '')
cnt += 1
image, target = image.to(device), target.to(device)
output = model(image)
loss = criterion(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
acc1, acc5 = accuracy(output, target, topk=(1, 5))
top1.update(acc1[0], image.size(0))
top5.update(acc5[0], image.size(0))
avgloss.update(loss, image.size(0))
if cnt >= ntrain_batches:
print('Loss', avgloss.avg)
print('Training: * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return
print('Full imagenet train set: * Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f}'
.format(top1=top1, top5=top5))
return我们像以前一样融合模块
qat_model = load_model(saved_model_dir + float_model_file)
qat_model.fuse_model()
optimizer = torch.optim.SGD(qat_model.parameters(), lr = 0.0001)
qat_model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')最后，prepare_qat执行“伪量化”，为量化感知训练准备模型
torch.quantization.prepare_qat(qat_model, inplace=True)
print('Inverted Residual Block: After preparation for QAT, note fake-quantization modules \n',qat_model.features[1].conv)得出:
Inverted Residual Block: After preparation for QAT, note fake-quantization modules
Sequential(
(0): ConvBNReLU(
(0): ConvBnReLU2d(
32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False
(activation_post_process): FakeQuantize(
fake_quant_enabled=True, observer_enabled=True, scale=None, zero_point=None
(activation_post_process): MovingAverageMinMaxObserver(min_val=None, max_val=None)
)
(weight_fake_quant): FakeQuantize(
fake_quant_enabled=True, observer_enabled=True, scale=None, zero_point=None
(activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=None, max_val=None)
)
)
(1): Identity()
(2): Identity()
)
(1): ConvBn2d(
32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
(activation_post_process): FakeQuantize(
fake_quant_enabled=True, observer_enabled=True, scale=None, zero_point=None
(activation_post_process): MovingAverageMinMaxObserver(min_val=None, max_val=None)
)
(weight_fake_quant): FakeQuantize(
fake_quant_enabled=True, observer_enabled=True, scale=None, zero_point=None
(activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=None, max_val=None)
)
)
(2): Identity()
)高精度训练量化模型需要在推断时对数字进行精确建模。 因此，对于量化感知训练，我们通过以下方式修改训练循环：
- 在训练快要结束时切换批处理规范以使用运行均值和方差，以更好地匹配推理数字。
- 我们还冻结了量化器参数(比例和零点），并对权重进行了微调。
num_train_batches = 20
## Train and check accuracy after each epoch
for nepoch in range(8):
train_one_epoch(qat_model, criterion, optimizer, data_loader, torch.device('cpu'), num_train_batches)
if nepoch > 3:
# Freeze quantizer parameters
qat_model.apply(torch.quantization.disable_observer)
if nepoch > 2:
# Freeze batch norm mean and variance estimates
qat_model.apply(torch.nn.intrinsic.qat.freeze_bn_stats)
# Check the accuracy after each epoch
quantized_model = torch.quantization.convert(qat_model.eval(), inplace=False)
quantized_model.eval()
top1, top5 = evaluate(quantized_model,criterion, data_loader_test, neval_batches=num_eval_batches)
print('Epoch %d :Evaluation accuracy on %d images, %2.2f'%(nepoch, num_eval_batches * eval_batch_size, top1.avg))得出:
....................Loss tensor(2.0660, grad_fn=<DivBackward0>)
Training: * Acc@1 53.000 Acc@5 77.167
..........Epoch 0 :Evaluation accuracy on 300 images, 78.67
....................Loss tensor(2.0398, grad_fn=<DivBackward0>)
Training: * Acc@1 56.000 Acc@5 77.667
..........Epoch 1 :Evaluation accuracy on 300 images, 74.67
....................Loss tensor(2.0917, grad_fn=<DivBackward0>)
Training: * Acc@1 52.833 Acc@5 77.333
..........Epoch 2 :Evaluation accuracy on 300 images, 75.33
....................Loss tensor(1.9406, grad_fn=<DivBackward0>)
Training: * Acc@1 55.000 Acc@5 79.333
..........Epoch 3 :Evaluation accuracy on 300 images, 77.67
....................Loss tensor(1.8255, grad_fn=<DivBackward0>)
Training: * Acc@1 59.833 Acc@5 82.000
..........Epoch 4 :Evaluation accuracy on 300 images, 77.00
....................Loss tensor(1.8275, grad_fn=<DivBackward0>)
Training: * Acc@1 58.167 Acc@5 80.167
..........Epoch 5 :Evaluation accuracy on 300 images, 76.67
....................Loss tensor(1.9429, grad_fn=<DivBackward0>)
Training: * Acc@1 56.333 Acc@5 79.833
..........Epoch 6 :Evaluation accuracy on 300 images, 76.33
....................Loss tensor(1.8643, grad_fn=<DivBackward0>)
Training: * Acc@1 57.333 Acc@5 81.000
..........Epoch 7 :Evaluation accuracy on 300 images, 75.67在这里，我们只对少数几个时期执行量化感知训练。 尽管如此，量化感知训练在整个 imagenet 数据集上的准确性仍超过 71％，接近 71.9％的浮点准确性。
有关量化意识训练的更多信息：
- QAT 是后期训练量化技术的超集，可以进行更多调试。 例如，我们可以分析模型的准确性是否受到权重或激活量化的限制。
- 由于我们使用伪量化来对实际量化算术的数值建模，因此我们还可以在浮点中模拟量化模型的准确性。
- 我们也可以轻松地模拟训练后量化。
量化加速
最后，让我们确认一下我们上面提到的内容：量化模型实际上执行推理的速度更快吗？ 让我们测试一下：
def run_benchmark(model_file, img_loader):
elapsed = 0
model = torch.jit.load(model_file)
model.eval()
num_batches = 5
# Run the scripted model on a few batches of images
for i, (images, target) in enumerate(img_loader):
if i < num_batches:
start = time.time()
output = model(images)
end = time.time()
elapsed = elapsed + (end-start)
else:
break
num_images = images.size()[0] * num_batches
print('Elapsed time: %3.0f ms' % (elapsed/num_images*1000))
return elapsed
run_benchmark(saved_model_dir + scripted_float_model_file, data_loader_test)
run_benchmark(saved_model_dir + scripted_quantized_model_file, data_loader_test)得出:
Elapsed time: 16 ms
Elapsed time: 10 ms在 MacBook Pro 上本地运行此程序，常规模型的运行时间为 61 毫秒，而量化模型的运行时间仅为 20 毫秒，这说明了量化模型与浮点模型相比，典型的 2-4 倍加速。
结论
在本教程中，我们展示了两种量化方法-训练后静态量化和量化感知训练-描述它们在“幕后”进行的操作以及如何在 PyTorch 中使用它们。
谢谢阅读！ 与往常一样，我们欢迎您提供任何反馈，因此，如果有任何问题，请在此处创建一个问题。
脚本的总运行时间：(9 分钟 43.065 秒）
Download Python source code: static_quantization_tutorial.py Download Jupyter notebook: static_quantization_tutorial.ipynb