Here is my code:
firstly, I Creating a custom ItemBase subclass:
class ImageTuple(ItemBase):
def __init__(self, img1, img2):
self.img1,self.img2 = img1,img2
self.obj,self.data = (img1,img2),[-1+2*img1.data,-1+2*img2.data]
print(self.obj)
def __str__(self):
#return f'{self.obj}'
return f'{self.obj}'
def apply_tfms(self, tfms, **kwargs):
self.img1 = self.img1.apply_tfms(tfms, **kwargs)
self.img2 = self.img2.apply_tfms(tfms, **kwargs)
self.data = [-1+2*self.img1.data,-1+2*self.img2.data]
return self
def to_one(self): return Image(0.5+torch.cat(self.data,2)/2)
Secondly,I Creating a custom ItemList subclass:
class ImageTupleList(ImageList):
_label_cls=pfmList
def __init__(self, items, itemsB=None, **kwargs):
super().__init__(items, **kwargs)
self.itemsB = itemsB
self.copy_new.append('itemsB')
def get(self, i):
#ImageList的get方法，得到img1是Image
img1 = super().get(i)
fn = self.itemsB[i]
return ImageTuple(img1, open_image(fn))
def reconstruct(self, t:Tensor):
return ImageTuple(Image(t[0]/2+0.5),Image(t[1]/2+0.5))
@classmethod
def from_folders(cls, path, folderA, folderB, **kwargs):
#读文件夹folderB的名字
itemsB = ImageList.from_folder(path/folderB).items
res = super().from_folder(path/folderA, itemsB=itemsB, **kwargs)
res.path = path
return res
@classmethod
def from_custom(cls, path, folderA, itemsB, **kwargs):
res = super().from_folder(path/folderA, itemsB=itemsB, **kwargs).filter_by_func(lambda o:o.parts[-2]=='left')
res.path = path
return res
def show_xys(self, xs, ys, figsize:Tuple[int,int]=(12,6), **kwargs):
#"Show the `xs` and `ys` on a figure of `figsize`. `kwargs` are passed to the show method."
rows = int(math.sqrt(len(xs)))
fig, axs = plt.subplots(rows,rows,figsize=figsize)
for i, ax in enumerate(axs.flatten() if rows > 1 else [axs]):
xs[i].to_one().show(ax=ax, **kwargs)
plt.tight_layout()
def show_xyzs(self, xs, ys, zs, figsize:Tuple[int,int]=None, **kwargs):
#"""Show `xs` (inputs), `ys` (targets) and `zs` (predictions) on a figure of `figsize`.
#`kwargs` are passed to the show method."""
figsize = ifnone(figsize, (12,3*len(xs)))
fig,axs = plt.subplots(len(xs), 2, figsize=figsize)
fig.suptitle('Ground truth / Predictions', weight='bold', size=14)
for i,(x,z) in enumerate(zip(xs,zs)):
x.to_one().show(ax=axs[i,0], **kwargs)
z.to_one().show(ax=axs[i,1], **kwargs)
And my pfmList is:
class pfmList(ItemList):
# read an .pfm file into numpy array, used to load SceneFlow disparity files
#类内函数的第一个参数为self，否则会报错，因为调用self.pfm_imread()会自动传入self参数，即pfm_read(self,filename)导致参数不匹配
def pfm_imread(self,filename):
file = open(filename, 'rb')
color = None
width = None
height = None
scale = None
endian = None
header = file.readline().decode('utf-8').rstrip()
if header == 'PF':
color = True
elif header == 'Pf':
color = False
else:
raise Exception('Not a PFM file.')
dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode('utf-8'))
if dim_match:
width, height = map(int, dim_match.groups())
else:
raise Exception('Malformed PFM header.')
scale = float(file.readline().rstrip())
if scale < 0: # little-endian
endian = '<'
scale = -scale
else:
endian = '>' # big-endian
data = np.fromfile(file, endian + 'f')
shape = (height, width, 3) if color else (height, width)
data = np.reshape(data, shape)
data = np.flipud(data)
return data, scale
def get(self, i):
#得到(height*width)的视差
fn = super().get(i)
#/data/home/xubin/dataset/dataset/GANet_dataset/disparity/TRAIN/15mm_focallength/scene_backwards/slow/left/0058.png
#print(fn)
disparity, scale = self.pfm_imread(str(fn))
disparity = np.maximum(disparity, 0)
#扩展维度
disparity = np.expand_dims(disparity, 0)
#将numpy转为Image
img1 = torch.from_numpy(disparity)
return Image(img1)
def reconstruct(self, t:Tensor): return Image(t.float())
Through the above function，I Can get my GA_list as below:
Finally,I want to change my GA_list to databunch,but when I use transform, I get the error described earlier.I am confused because my img1 and img2 are indeed Image classes, they have the apply_tfms attribute.I haven’t found my problem yet. If you know my mistake, please tell me.
