0519-85602926 15861139266
数据增强
数据增强是一种通过使用现有图像的不同变体创建新的训练图像来更好地概括我们的模型的技术。我们当前的训练集中只有 800 张图像，因此数据增强对于确保我们的模型不会过拟合非常重要。
对于这个问题，我使用了翻转、旋转、中心裁剪和随机裁剪。
这里唯一需要记住的是确保包围盒也以与图像相同的方式进行转换。
# modified from fast.ai
def crop(im, r, c, target_r, target_c):
return im[r:r+target_r, c:c+target_c]
# random crop to the original size
def random_crop(x, r_pix=8):
""" Returns a random crop"""
r, c,*_ = x.shape
c_pix = round(r_pix*c/r)
rand_r = random.uniform(0, 1)
rand_c = random.uniform(0, 1)
start_r = np.floor(2*rand_r*r_pix).astype(int)
start_c = np.floor(2*rand_c*c_pix).astype(int)
return crop(x, start_r, start_c, r-2*r_pix, c-2*c_pix)
def center_crop(x, r_pix=8):
r, c,*_ = x.shape
c_pix = round(r_pix*c/r)
return crop(x, r_pix, c_pix, r-2*r_pix, c-2*c_pix)
def rotate_cv(im, deg, y=False, mode=cv2.BORDER_REFLECT, interpolation=cv2.INTER_AREA):
""" Rotates an image by deg degrees"""
r,c,*_ = im.shape
M = cv2.getRotationMatrix2D((c/2,r/2),deg,1)
if y:
return cv2.warpAffine(im, M,(c,r), borderMode=cv2.BORDER_CONSTANT)
return cv2.warpAffine(im,M,(c,r), borderMode=mode, flags=cv2.WARP_FILL_OUTLIERS+interpolation)
def random_cropXY(x, Y, r_pix=8):
""" Returns a random crop"""
r, c,*_ = x.shape
c_pix = round(r_pix*c/r)
rand_r = random.uniform(0, 1)
rand_c = random.uniform(0, 1)
start_r = np.floor(2*rand_r*r_pix).astype(int)
start_c = np.floor(2*rand_c*c_pix).astype(int)
xx = crop(x, start_r, start_c, r-2*r_pix, c-2*c_pix)
YY = crop(Y, start_r, start_c, r-2*r_pix, c-2*c_pix)
return xx, YY
def transformsXY(path, bb, transforms):
x = cv2.imread(str(path)).astype(np.float32)
x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB)/255
Y = create_mask(bb, x)
if transforms:
rdeg = (np.random.random()-.50)*20
x = rotate_cv(x, rdeg)
Y = rotate_cv(Y, rdeg, y=True)
if np.random.random() > 0.5:
x = np.fliplr(x).copy()
Y = np.fliplr(Y).copy()
x, Y = random_cropXY(x, Y)
else:
x, Y = center_crop(x), center_crop(Y)
return x, mask_to_bb(Y)
def create_corner_rect(bb, color='red'):
bb = np.array(bb, dtype=np.float32)
return plt.Rectangle((bb[1], bb[0]), bb[3]-bb[1], bb[2]-bb[0], color=color,
fill=False, lw=3)
def show_corner_bb(im, bb):
plt.imshow(im)
plt.gca().add_patch(create_corner_rect(bb))
PyTorch 数据集
现在我们已经有了数据增强，我们可以进行训练验证拆分并创建我们的 PyTorch 数据集。我们使用 ImageNet 统计数据对图像进行标准化，因为我们使用的是预训练的 ResNet 模型并在训练时在我们的数据集中应用数据增强。
X_train, X_val, y_train, y_val = train_test_split(X, Y, test_size=0.2, random_state=42)
def normalize(im):
"""Normalizes images with Imagenet stats."""
imagenet_stats = np.array([[0.485, 0.456, 0.406], [0.229, 0.224, 0.225]])
return (im - imagenet_stats[0])/imagenet_stats[1]
class RoadDataset(Dataset):
def __init__(self, paths, bb, y, transforms=False):
self.transforms = transforms
self.paths = paths.values
self.bb = bb.values
self.y = y.values
def __len__(self):
return len(self.paths)
def __getitem__(self, idx):
path = self.paths[idx]
y_class = self.y[idx]
x, y_bb = transformsXY(path, self.bb[idx], self.transforms)
x = normalize(x)
x = np.rollaxis(x, 2)
return x, y_class, y_bb
train_ds = RoadDataset(X_train['new_path'],X_train['new_bb'] ,y_train, transforms=True)
valid_ds = RoadDataset(X_val['new_path'],X_val['new_bb'],y_val)
batch_size = 64
train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True)
valid_dl = DataLoader(valid_ds, batch_size=batch_size)
PyTorch 模型
对于这个模型，我使用了一个非常简单的预先训练的 resNet-34模型。由于我们有两个任务要完成，这里有两个最后的层: 包围盒回归器和图像分类器。
class BB_model(nn.Module):
def __init__(self):
super(BB_model, self).__init__()
resnet = models.resnet34(pretrained=True)
layers = list(resnet.children())[:8]
self.features1 = nn.Sequential(*layers[:6])
self.features2 = nn.Sequential(*layers[6:])
self.classifier = nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, 4))
self.bb = nn.Sequential(nn.BatchNorm1d(512), nn.Linear(512, 4))
def forward(self, x):
x = self.features1(x)
x = self.features2(x)
x = F.relu(x)
x = nn.AdaptiveAvgPool2d((1,1))(x)
x = x.view(x.shape[0], -1)
return self.classifier(x), self.bb(x)
训练
对于损失，我们需要同时考虑分类损失和边界框回归损失，因此我们使用交叉熵和 L1 损失（真实值和预测坐标之间的所有绝对差之和）的组合。我已经将 L1 损失缩放了 1000 倍，因为分类和回归损失都在相似的范围内。除此之外，它是一个标准的 PyTorch 训练循环（使用 GPU）：
def update_optimizer(optimizer, lr):
for i, param_group in enumerate(optimizer.param_groups):
param_group["lr"] = lr
def train_epocs(model, optimizer, train_dl, val_dl, epochs=10,C=1000):
idx = 0
for i in range(epochs):
model.train()
total = 0
sum_loss = 0
for x, y_class, y_bb in train_dl:
batch = y_class.shape[0]
x = x.cuda().float()
y_class = y_class.cuda()
y_bb = y_bb.cuda().float()
out_class, out_bb = model(x)
loss_class = F.cross_entropy(out_class, y_class, reduction="sum")
loss_bb = F.l1_loss(out_bb, y_bb, reduction="none").sum(1)
loss_bb = loss_bb.sum()
loss = loss_class + loss_bb/C
optimizer.zero_grad()
loss.backward()
optimizer.step()
idx += 1
total += batch
sum_loss += loss.item()
train_loss = sum_loss/total
val_loss, val_acc = val_metrics(model, valid_dl, C)
print("train_loss %.3f val_loss %.3f val_acc %.3f" % (train_loss, val_loss, val_acc))
return sum_loss/total
def val_metrics(model, valid_dl, C=1000):
model.eval()
total = 0
sum_loss = 0
correct = 0
for x, y_class, y_bb in valid_dl:
batch = y_class.shape[0]
x = x.cuda().float()
y_class = y_class.cuda()
y_bb = y_bb.cuda().float()
out_class, out_bb = model(x)
loss_class = F.cross_entropy(out_class, y_class, reduction="sum")
loss_bb = F.l1_loss(out_bb, y_bb, reduction="none").sum(1)
loss_bb = loss_bb.sum()
loss = loss_class + loss_bb/C
_, pred = torch.max(out_class, 1)
correct += pred.eq(y_class).sum().item()
sum_loss += loss.item()
total += batch
return sum_loss/total, correct/total
model = BB_model().cuda()
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = torch.optim.Adam(parameters, lr=0.006)
train_epocs(model, optimizer, train_dl, valid_dl, epochs=15)
测试
现在我们已经完成了训练，我们可以选择一个随机图像并在上面测试我们的模型。尽管我们只有相当少量的训练图像，但是我们最终在测试图像上得到了一个相当不错的预测。
使用手机拍摄真实照片并测试模型将是一项有趣的练习。另一个有趣的实验是不执行任何数据增强并训练模型并比较两个模型。
# resizing test image
im = read_image('./road_signs/images_resized/road789.png')
im = cv2.resize(im, (int(1.49*300), 300))
cv2.imwrite('./road_signs/road_signs_test/road789.jpg', cv2.cvtColor(im, cv2.COLOR_RGB2BGR))
# test Dataset
test_ds = RoadDataset(pd.DataFrame([{'path':'./road_signs/road_signs_test/road789.jpg'}])['path'],pd.DataFrame([{'bb':np.array([0,0,0,0])}])['bb'],pd.DataFrame([{'y':[0]}])['y'])
x, y_class, y_bb = test_ds[0]
xx = torch.FloatTensor(x[None,])
xx.shape
# prediction
out_class, out_bb = model(xx.cuda())
out_class, out_bb
总结
现在我们已经介绍了目标检测的基本原理，并从头开始实现它，您可以将这些想法扩展到多对象情况，并尝试更复杂的模型，如 RCNN 和 YOLO！
