问题描述
我正在尝试训练 torchvision Faster R-CNN 模型,以便在我的自定义数据上进行对象检测。我在 torchvision 对象检测微调 tutorial 中使用了代码。但收到此错误:
Expected target Boxes to be a tensor of shape [N,4],got torch.Size([0])
这与我的自定义数据集中的负数据(空训练图像/无边界框)有关。我们如何更改下面的 Dataset class
以在包括负数据的数据集上启用训练 fast-rcnn?
class MyCustomDataset(Dataset):
def __init__(self,root,transforms):
self.root = root
self.transforms = transforms
# load all image files,sorting them to
# ensure that they are aligned
self.imgs = list(sorted(os.listdir(os.path.join(root,"PNGImages"))))
self.masks = list(sorted(os.listdir(os.path.join(root,"PedMasks"))))
def __len__(self):
return len(self.imgs)
def __getitem__(self,idx):
# load images ad masks
img_path = os.path.join(self.root,"PNGImages",self.imgs[idx])
mask_path = os.path.join(self.root,"PedMasks",self.masks[idx])
img = Image.open(img_path).convert("RGB")
# note that we haven't converted the mask to RGB,# because each color corresponds to a different instance
# with 0 being background
mask = Image.open(mask_path)
# convert the PIL Image into a numpy array
mask = np.array(mask)
# instances are encoded as different colors
obj_ids = np.unique(mask)
# first id is the background,so remove it
obj_ids = obj_ids[1:]
# split the color-encoded mask into a set of binary masks
masks = mask == obj_ids[:,None,None]
# get bounding Box coordinates for each mask
num_objs = len(obj_ids)
Boxes = []
for i in range(num_objs):
pos = np.where(masks[i])
xmin = np.min(pos[1])
xmax = np.max(pos[1])
ymin = np.min(pos[0])
ymax = np.max(pos[0])
Boxes.append([xmin,ymin,xmax,ymax])
# convert everything into a torch.Tensor
Boxes = torch.as_tensor(Boxes,dtype=torch.float32)
# there is only one class
labels = torch.ones((num_objs,),dtype=torch.int64)
image_id = torch.tensor([idx])
area = (Boxes[:,3] - Boxes[:,1]) * (Boxes[:,2] - Boxes[:,0])
# suppose all instances are not crowd
iscrowd = torch.zeros((num_objs,dtype=torch.int64)
target = {}
target["Boxes"] = Boxes
target["labels"] = labels
target["image_id"] = torch.tensor([idx])
target["area"] = area
target["iscrowd"] = iscrowd
return img,target
解决方法
我们需要对数据集类进行两项更改。
1- 空框输入为:
if num_objs == 0:
boxes = torch.zeros((0,4),dtype=torch.float32)
else:
boxes = torch.as_tensor(boxes,dtype=torch.float32)
2-为空边界框分配area=0
,更改用于计算面积的代码,并将其设为torch tensor
:
area = 0
for i in range(num_objs):
pos = np.where(masks[i])
xmin = np.min(pos[1])
xmax = np.max(pos[1])
ymin = np.min(pos[0])
ymax = np.max(pos[0])
area += (xmax-xmin)*(ymax-ymin)
area = torch.as_tensor(area,dtype=torch.float32)
我们将在现有的 for 循环中加入第 2 步。
因此,修改后的数据集类将如下所示:
class MyCustomDataset(Dataset):
def __init__(self,root,transforms):
self.root = root
self.transforms = transforms
# load all image files,sorting them to
# ensure that they are aligned
self.imgs = list(sorted(os.listdir(os.path.join(root,"PNGImages"))))
self.masks = list(sorted(os.listdir(os.path.join(root,"PedMasks"))))
def __len__(self):
return len(self.imgs)
def __getitem__(self,idx):
# load images ad masks
img_path = os.path.join(self.root,"PNGImages",self.imgs[idx])
mask_path = os.path.join(self.root,"PedMasks",self.masks[idx])
img = Image.open(img_path).convert("RGB")
# note that we haven't converted the mask to RGB,# because each color corresponds to a different instance
# with 0 being background
mask = Image.open(mask_path)
# convert the PIL Image into a numpy array
mask = np.array(mask)
# instances are encoded as different colors
obj_ids = np.unique(mask)
# first id is the background,so remove it
obj_ids = obj_ids[1:]
# split the color-encoded mask into a set of binary masks
masks = mask == obj_ids[:,None,None]
# get bounding box coordinates for each mask
num_objs = len(obj_ids)
boxes = []
area = 0
for i in range(num_objs):
pos = np.where(masks[i])
xmin = np.min(pos[1])
xmax = np.max(pos[1])
ymin = np.min(pos[0])
ymax = np.max(pos[0])
boxes.append([xmin,ymin,xmax,ymax])
area += (xmax-xmin)*(ymax-ymin)
area = torch.as_tensor(area,dtype=torch.float32)
# Handle empty bounding boxes
if num_objs == 0:
boxes = torch.zeros((0,dtype=torch.float32)
else:
boxes = torch.as_tensor(boxes,dtype=torch.float32)
# there is only one class
labels = torch.ones((num_objs,),dtype=torch.int64)
image_id = torch.tensor([idx])
#area = (boxes[:,3] - boxes[:,1]) * (boxes[:,2] - boxes[:,0])
# suppose all instances are not crowd
iscrowd = torch.zeros((num_objs,dtype=torch.int64)
target = {}
target["boxes"] = boxes
target["labels"] = labels
target["image_id"] = torch.tensor([idx])
target["area"] = area
target["iscrowd"] = iscrowd
return img,target