分割對(duì)于圖像解釋任務(wù)至關(guān)重要，那就不要落后于流行趨勢(shì)，讓我們來(lái)實(shí)施它，我們很快就會(huì)成為專業(yè)人士！
什么是語(yǔ)義分割？
它描述了將圖像的每個(gè)像素與類別標(biāo)簽（例如花、人、道路、天空、海洋或汽車）相關(guān)聯(lián)的過(guò)程，即我們要輸入圖像，然后為該圖像中的每個(gè)像素輸出一個(gè)類別決策。例如下面這個(gè)輸入圖像，這是一只坐在床上的狗：
因此，在輸出中，我們希望為每個(gè)像素定義一組類別，即狗、床、后面的桌子和櫥柜。在語(yǔ)義分割之后，圖像看起來(lái)像這樣：
關(guān)于語(yǔ)義分割的一件有趣的事情是它不區(qū)分實(shí)例，即如果此圖像中有兩只狗，它們將僅被描述為一個(gè)標(biāo)簽，即 dog ，而不是 dog1 和 dog2。
語(yǔ)義分割一般用于：
自動(dòng)駕駛
工業(yè)檢驗(yàn)
衛(wèi)星圖像中值得注意的區(qū)域分類
醫(yī)學(xué)影像監(jiān)查
語(yǔ)義分割實(shí)現(xiàn)：
第一種方法是滑動(dòng)窗口，我們將輸入圖像分解成許多小的局部圖像，但是這種方法在計(jì)算上會(huì)很昂貴。所以，我們?cè)趯?shí)踐中并沒(méi)有真正使用這個(gè)方法。
另一種方法是完全卷積網(wǎng)絡(luò)，其中網(wǎng)絡(luò)有一整堆卷積層，沒(méi)有完全連接的層，從而保留了輸入的空間大小，這在計(jì)算上也是極其昂貴的。
第三個(gè)也是最好的一個(gè)方法，那就是對(duì)圖像進(jìn)行上采樣和下采樣。因此，我們不需要對(duì)圖像的完整空間分辨率進(jìn)行所有卷積，我們可能會(huì)在原始分辨率下遍歷少量卷積層，然后對(duì)該特征圖進(jìn)行下采樣，然后對(duì)其進(jìn)行上采樣。
在這里，我們只想在網(wǎng)絡(luò)的后半部分提高我們預(yù)測(cè)的空間分辨率，以便我們的輸出圖像現(xiàn)在可以與我們的輸入圖像具有相同的維度。它的計(jì)算效率要高得多，因?yàn)槲覀兛梢允咕W(wǎng)絡(luò)非常深，并以更便宜的空間分辨率運(yùn)行。
讓我們?cè)诖a中實(shí)現(xiàn)這一點(diǎn)：
導(dǎo)入處理所需的必要庫(kù) ，即
#SSCV IIITH 2K19
import random
import time
import numpy as np
import torch
print(torch.__version__)
import math
from PIL import Image, ImageOps
from torch.optim import SGD, Adam, lr_scheduler
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision.transforms import Resize
from torchvision.transforms import ToTensor, ToPILImage
from dataset import cityscapes
from dataset import idd_lite
import sys
print(sys.executable)
from transform import Relabel, ToLabel, Colorize
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
import importlib
from iouEval import iouEval, getColorEntry #importing iouEval class from the iouEval.py file
from shutil import copyfile
NUM_CHANNELS = 3 #RGB Images
NUM_CLASSES = 8 #IDD Lite has 8 labels or Level1 hierarchy of labels
USE_CUDA = torch.cuda.is_available()
IMAGE_HEIGHT = 160
DATA_ROOT = ‘/tmp/school/6-segmentation/user/1/6-segmentation/idd1_lite’
BATCH_SIZE = 2
NUM_WORKERS = 4
NUM_EPOCHS = 100
ENCODER_ONLY = True
device = torch.device(“cuda” )
#device = ‘cuda’
color_transform = Colorize(NUM_CLASSES)
image_transform = ToPILImage()
IOUTRAIN = False
IOUVAL = True
class MyCoTransform(object):def __init__(self, enc, augment=True, height=160):self.enc=encself.augment = augmentself.height = heightpassdef __call__(self, input, target):# Resizing data to required sizeinput = Resize((self.height,320), Image.BILINEAR)(input)target = Resize((self.height,320), Image.NEAREST)(target)
if(self.augment):# Random horizontal fliphflip = random.random()if (hflip < 0.5):input = input.transpose(Image.FLIP_LEFT_RIGHT)target = target.transpose(Image.FLIP_LEFT_RIGHT)#Random translation 0–2 pixels (fill rest with padding)transX = random.randint(0, 2) transY = random.randint(0, 2)
input = ImageOps.expand(input, border=(transX,transY,0,0), fill=0)target = ImageOps.expand(target, border=(transX,transY,0,0), fill=7) #pad label filling with 7input = input.crop((0, 0, input.size[0]-transX, input.size[1]-transY))target = target.crop((0, 0, target.size[0]-transX, target.size[1]-transY))
input = ToTensor()(input)target = ToLabel()(target)target = Relabel(255,7)(target)return input, target
best_acc = 0
co_transform = MyCoTransform(ENCODER_ONLY, augment=True, height=IMAGE_HEIGHT)
co_transform_val = MyCoTransform(ENCODER_ONLY, augment=False, height=IMAGE_HEIGHT)
#train data
dataset_train = idd_lite(DATA_ROOT, co_transform, ‘train’)
print(len(dataset_train))
#test data
dataset_val = idd_lite(DATA_ROOT, co_transform_val, ‘val’)
print(len(dataset_val))
loader_train = DataLoader(dataset_train, num_workers=NUM_WORKERS, batch_size=BATCH_SIZE, shuffle=True)
loader_val = DataLoader(dataset_val, num_workers=NUM_WORKERS, batch_size=BATCH_SIZE, shuffle=False)
答案是負(fù)對(duì)數(shù)，在較小值的時(shí)候效果不好，并且在較大值的時(shí)候效果也不好。因?yàn)槲覀儗p失函數(shù)加到所有正確的類別上，實(shí)際發(fā)生的情況是，每當(dāng)網(wǎng)絡(luò)為正確的類別，分配高置信度時(shí)，損失就低，但是當(dāng)網(wǎng)絡(luò)為正確的類別時(shí)分配低置信度，損失就高。
criterion = torch.nn.CrossEntropyLoss()
model_file = importlib.import_module(‘erfnet’)
model = model_file.Net(NUM_CLASSES).to(device)
optimizer = Adam(model.parameters(), 5e-4, (0.9, 0.999), eps=1e-08, weight_decay=1e-4)
start_epoch = 1
import os
steps_loss = 50
my_start_time = time.time()
for epoch in range(start_epoch, NUM_EPOCHS+1):print(“ — — — TRAINING — EPOCH”, epoch, “ — — -”)
epoch_loss = []time_train = []
doIouTrain = IOUTRAIN doIouVal = IOUVAL
if (doIouTrain):iouEvalTrain = iouEval(NUM_CLASSES)
model.train()for step, (images, labels) in enumerate(loader_train):
start_time = time.time()inputs = images.to(device)targets = labels.to(device)outputs = model(inputs, only_encode=ENCODER_ONLY)
# zero the parameter gradientsoptimizer.zero_grad()# forward + backward + optimizeloss = criterion(outputs, targets[:, 0])loss.backward()optimizer.step()
epoch_loss.append(loss.item())time_train.append(time.time() — start_time)
if (doIouTrain):#start_time_iou = time.time()iouEvalTrain.addBatch(outputs.max(1)[1].unsqueeze(1).data, targets.data)#print (“Time to add confusion matrix: “, time.time() — start_time_iou)
# print statisticsif steps_loss > 0 and step % steps_loss == 0:average = sum(epoch_loss) / len(epoch_loss)print(‘loss: {average:0.4} (epoch: {epoch}, step: {step})’, “// Avg time/img: %.4f s” % (sum(time_train) / len(time_train) / BATCH_SIZE))
average_epoch_loss_train = sum(epoch_loss) / len(epoch_loss)
iouTrain = 0if (doIouTrain):iouTrain, iou_classes = iouEvalTrain.getIoU()iouStr = getColorEntry(iouTrain)+’{:0.2f}’.format(iouTrain*100) + ‘\033[0m’print (“EPOCH IoU on TRAIN set: “, iouStr, “%”)
my_end_time = time.time()
print(my_end_time — my_start_time)
在訓(xùn)練了 100 個(gè) epoch 之后，我們會(huì)看到：
#Validate on val images after each epoch of training
print(“ — — — VALIDATING — EPOCH”, epoch, “ — — -”)
model.eval()
epoch_loss_val = []
time_val = []
if (doIouVal):iouEvalVal = iouEval(NUM_CLASSES)
for step, (images, labels) in enumerate(loader_val):start_time = time.time()
inputs = images.to(device) targets = labels.to(device)with torch.no_grad():outputs = model(inputs, only_encode=ENCODER_ONLY) #outputs = model(inputs)loss = criterion(outputs, targets[:, 0])epoch_loss_val.append(loss.item())time_val.append(time.time() — start_time)
#Add batch to calculate TP, FP and FN for iou estimationif (doIouVal):#start_time_iou = time.time()iouEvalVal.addBatch(outputs.max(1)[1].unsqueeze(1).data, targets.data)#print (“Time to add confusion matrix: “, time.time() — start_time_iou)if steps_loss > 0 and step % steps_loss == 0:average = sum(epoch_loss_val) / len(epoch_loss_val)print(‘VAL loss: {average:0.4} (epoch: {epoch}, step: {step})’, “// Avg time/img: %.4f s” % (sum(time_val) / len(time_val) / BATCH_SIZE))
average_epoch_loss_val = sum(epoch_loss_val) / len(epoch_loss_val)
iouVal = 0
if (doIouVal):
iouVal, iou_classes = iouEvalVal.getIoU()print(iou_classes)iouStr = getColorEntry(iouVal)+’{:0.2f}’.format(iouVal*100) + ‘\033[0m’print (“EPOCH IoU on VAL set: “, iouStr, “%”)
# Qualitative Analysis
dataiter = iter(loader_val)
images, labels = dataiter.next()
if USE_CUDA:images = images.to(device)
inputs = images.to(device)
with torch.no_grad():outputs = model(inputs, only_encode=ENCODER_ONLY)
label = outputs[0].max(0)[1].byte().cpu().data
label_color = Colorize()(label.unsqueeze(0))
label_save = ToPILImage()(label_color)
plt.figure()
plt.imshow(ToPILImage()(images[0].cpu()))
plt.figure()
plt.imshow(label_save)
輸出圖像
很快我們就可以準(zhǔn)備好我們的模型了！
因此，簡(jiǎn)而言之，現(xiàn)在我們將能夠輕松地將圖像的每個(gè)像素與類標(biāo)簽相關(guān)聯(lián)，并可以調(diào)整超參數(shù)以查看顯示的更改。本文展示了語(yǔ)義分割的基礎(chǔ)知識(shí)，要對(duì)實(shí)例進(jìn)行分類，我們需要進(jìn)行實(shí)例分割，這是語(yǔ)義分割的高級(jí)版本。
往期精彩回顧適合初學(xué)者入門人工智能的路線及資料下載中國(guó)大學(xué)慕課《機(jī)器學(xué)習(xí)》（黃海廣主講）機(jī)器學(xué)習(xí)及深度學(xué)習(xí)筆記等資料打印機(jī)器學(xué)習(xí)在線手冊(cè)深度學(xué)習(xí)筆記專輯《統(tǒng)計(jì)學(xué)習(xí)方法》的代碼復(fù)現(xiàn)專輯
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