來(lái)源：Python 技術(shù)「ID: pythonall」
在日常生活中總是有給圖像分類的場(chǎng)景，比如垃圾分類、不同場(chǎng)景的圖像分類等；今天的文章主要是基于圖像識(shí)別場(chǎng)景進(jìn)行模型構(gòu)建。圖像識(shí)別是通過(guò) Python深度學(xué)習(xí)來(lái)進(jìn)行模型訓(xùn)練，再使用模型對(duì)上傳的電子表單進(jìn)行自動(dòng)審核與比對(duì)后反饋相應(yīng)的結(jié)果。主要是利用 Python Torchvision 來(lái)構(gòu)造模型，Torchvision 服務(wù)于Pytorch 深度學(xué)習(xí)框架，主要是用來(lái)生成圖片、視頻數(shù)據(jù)集以及訓(xùn)練模型。
構(gòu)建模型為了直觀，需要使用 Jupyter notebook 進(jìn)行模型的構(gòu)建，Jupyter notebook 的安裝及使用詳見(jiàn)公眾號(hào)歷史文章 一文吃透 Jupyter Notebook,進(jìn)入 JupyterNotebook 頁(yè)面后即可進(jìn)行編輯。詳細(xì)頁(yè)面如下：
圖像識(shí)別需要用到深度學(xué)習(xí)相關(guān)模塊，所以需要導(dǎo)入相應(yīng)的包，具體導(dǎo)入的包如下：
%reload_ext autoreload
%autoreload 2
import torch
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision import transforms as tfs
from torchvision import models
from torch import nn
import matplotlib.pyplot as plt
%matplotlib inline
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
模型的訓(xùn)練主要方式是基于 GPU 或者 CPU 訓(xùn)練，在沒(méi)有 GPU 的條件下就在 CPU 下進(jìn)行訓(xùn)練，模型的訓(xùn)練需要花費(fèi)一定的時(shí)間，訓(xùn)練時(shí)長(zhǎng)根據(jù)訓(xùn)練集的數(shù)據(jù)和硬件性能而定，訓(xùn)練結(jié)果精確性根據(jù)數(shù)據(jù)的多少和準(zhǔn)確性而且，深度學(xué)習(xí)需要大量的素材才能判斷出精確的結(jié)果，所以需要申明使用 CPU 進(jìn)行訓(xùn)練：
# 是否使用GPU
use_gpu = False
將拿到的數(shù)據(jù)進(jìn)行訓(xùn)練集的數(shù)據(jù)預(yù)處理并設(shè)置訓(xùn)練分層數(shù)，再將拿到的圖片進(jìn)行水平翻轉(zhuǎn)后對(duì)圖片進(jìn)行剪裁， 剪裁后將圖片進(jìn)行隨機(jī)翻轉(zhuǎn)，增強(qiáng)隨機(jī)對(duì)比度以及圖片顏色變化
# 數(shù)據(jù)增強(qiáng)
train_transform = tfs.Compose([
# 訓(xùn)練集的數(shù)據(jù)預(yù)處理
tfs.Resize([224, 224]),
tfs.RandomHorizontalFlip(),
tfs.RandomCrop(128),
tfs.ToTensor(),
tfs.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])
])
test_transform = tfs.Compose([
tfs.Resize([224,224]),
# tfs.RandomCrop(128),
tfs.ToTensor(),
tfs.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])
])
# 每一個(gè)batch的數(shù)據(jù)集數(shù)目
batch_size = 10
模型訓(xùn)練需要準(zhǔn)備數(shù)據(jù)集和驗(yàn)證集，只有足夠的照片才能得到更精準(zhǔn)的答案。訓(xùn)練集和驗(yàn)證集部分代碼如下：
# 構(gòu)建訓(xùn)練集和驗(yàn)證集
#
train_set = ImageFolder('./dataset1/train', train_transform)
train_data = DataLoader(train_set, batch_size, shuffle=True, num_workers=0)
valid_set = ImageFolder('./dataset1/valid', test_transform)
valid_data = DataLoader(valid_set, 2*batch_size, shuffle=False, num_workers=0)
train_set.class_to_idx
len(valid_data)
# 數(shù)據(jù)集準(zhǔn)備
try:
if iter(train_data).next()[0].shape[0] == batch_size and \
iter(valid_data).next()[0].shape[0] == 2*batch_size:
print('Dataset is ready!')
else:
print('Not success, maybe the batch size is wrong')
except:
print('not success, image transform is wrong!')
# 構(gòu)建模型
def get_model():
model = models.resnet50(pretrained=True)
model.fc = nn.Linear(2048, 3)
return model
try:
model = get_model()
with torch.no_grad():
scorce = model(iter(train_data).next()[0])
print(scorce.shape[0], scorce.shape[1])
if scorce.shape[0] == batch_size and scorce.shape[1] == 3:
print('Model is ready!')
else:
print('Model is failed!')
except:
print('model is wrong')
if use_gpu:
model = model.cuda()
# 構(gòu)建loss函數(shù)和優(yōu)化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr = 1e-4)
# 訓(xùn)練的epoches數(shù)目
max_epoch = 20
數(shù)據(jù)集和訓(xùn)練集準(zhǔn)備好后進(jìn)行模型訓(xùn)練和訓(xùn)練結(jié)果可視化，部分代碼如下：
def train(model, train_data, valid_data, max_epoch, criterion, optimizer):
freq_print = int(len(train_data) / 3)
metric_log = dict()
metric_log['train_loss'] = list()
metric_log['train_acc'] = list()
if valid_data is not None:
metric_log['valid_loss'] = list()
metric_log['valid_acc'] = list()
for e in range(max_epoch):
model.train()
running_loss = 0
running_acc = 0
for i, data in enumerate(train_data, 1):
img, label = data
if use_gpu:
img = img.cuda()
label = label.cuda()
# forward前向傳播
out = model(img)
# 計(jì)算誤差
loss = criterion(out, label.long())
# 反向傳播，更新參數(shù)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 計(jì)算準(zhǔn)確率
_, pred = out.max(1)
num_correct = (pred == label.long()).sum().item()
acc = num_correct/img.shape[0]
running_loss += loss.item()
running_acc +=acc
if i % freq_print == 0:
print('[{}]/[{}], train loss: {:.3f}, train acc: {:.3f}' \
.format(i, len(train_data), running_loss / i, running_acc / i))
metric_log['train_loss'].append(running_loss / len(train_data))
metric_log['train_acc'].append(running_acc / len(train_data))
if valid_data is not None:
model.eval()
running_loss = 0
running_acc = 0
for data in valid_data:
img, label = data
if use_gpu:
img = img.cuda()
label = label.cuda()
# forward前向傳播
out = model(img)
# 計(jì)算誤差
loss = criterion(out, label.long())
# 計(jì)算準(zhǔn)確度
_, pred = out.max(1)
num_correct = (pred==label.long()).sum().item()
acc = num_correct/img.shape[0]
running_loss += loss.item()
running_acc += acc
metric_log['valid_loss'].append(running_loss/len(valid_data))
metric_log['valid_acc'].append(running_acc/len(valid_data))
print_str = 'epoch: {}, train loss: {:.3f}, train acc: {:.3f}, \
valid loss: {:.3f}, valid accuracy: {:.3f}'.format(
e+1, metric_log['train_loss'][-1], metric_log['train_acc'][-1],
metric_log['valid_loss'][-1], metric_log['valid_acc'][-1])
else:
print_str = 'epoch: {}, train loss: {:.3f}, train acc: {:.3f}'.format(
e+1,
metric_log['train_loss'][-1],
metric_log['train_acc'][-1])
print(print_str)
# 可視化
nrows = 1
ncols = 2
figsize= (10, 5)
_, figs = plt.subplots(nrows, ncols, figsize=figsize)
if valid_data is not None:
figs[0].plot(metric_log['train_loss'], label='train loss')
figs[0].plot(metric_log['valid_loss'], label='valid loss')
figs[0].axes.set_xlabel('loss')
figs[0].legend(loc='best')
figs[1].plot(metric_log['train_acc'], label='train acc')
figs[1].plot(metric_log['valid_acc'], label='valid acc')
figs[1].axes.set_xlabel('acc')
figs[1].legend(loc='best')
else:
figs[0].plot(metric_log['train_loss'], label='train loss')
figs[0].axes.set_xlabel('loss')
figs[0].legend(loc='best')
figs[1].plot(metric_log['train_acc'], label='train acc')
figs[1].axes.set_xlabel('acc')
figs[1].legend(loc='best')
# 用作調(diào)參
train(model, train_data, valid_data, max_epoch, criterion, optimizer)
# 保存模型
torch.save(model.state_dict(), './model/save_model2.pth')
今天的文章主要是講圖像識(shí)別模型如何構(gòu)建。希望對(duì)大家有所幫助。
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