tf.keras.utils.Sequence or tf.keras.utils.PyDataset is abnormal in tensorflow2.16 #67386
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comp:keras
Keras related issues
stat:awaiting response
Status - Awaiting response from author
TF 2.16
type:bug
Bug
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@zx-lhb |
sushreebarsa
added
the
stat:awaiting response
my dataset definitionclass Seg3DRandomDataset(tf.keras.utils.Sequence):
def __init__(self, images_dir, labels_dir, input_size, num_classes, batch_size,
hu_min_val, hu_max_val, mode:str,**kwargs):
super(Seg3DRandomDataset, self).__init__(**kwargs)
self.images_dir = images_dir
self.labels_dir = labels_dir
self.input_size = input_size
self.num_classes = num_classes
self.batch_size = batch_size
self.hu_min_val = hu_min_val
self.hu_max_val = hu_max_val
self.mode = mode
file_names = os.listdir(images_dir) # nii.gz
self.images_path = []
self.labels_path = []
for file_name in file_names:
image_path = os.path.join(images_dir, file_name)
label_path = os.path.join(labels_dir, file_name)
self.images_path.append(image_path)
self.labels_path.append(label_path)
def __len__(self):
return math.ceil(len(self.images_path) / self.batch_size)
def __getitem__(self, idx):
low = idx * self.batch_size
high = min(low + self.batch_size, len(self.images_path))
batch_x_path = self.images_path[low:high]
batch_y_path = self.labels_path[low:high]
batch_x = []
batch_y = []
for x_path, y_path in zip(batch_x_path, batch_y_path):
image = sitk.ReadImage(x_path)
label = sitk.ReadImage(y_path)
self.curimagePath = x_path
image = self.custom_window(image,self.hu_min_val,self.hu_max_val)
#
imageArray = sitk.GetArrayFromImage(image)
labelArray = sitk.GetArrayFromImage(label)
self.d_range, self.h_range, self.w_range = self.cal_board(labelArray)
imageArray = self.normalize_imageArray(imageArray[self.d_range[0]:self.d_range[1]+1, self.h_range[0]:self.h_range[1]+1, self.w_range[0]:self.w_range[1]+1])
labelArray = labelArray[self.d_range[0]:self.d_range[1]+1, self.h_range[0]:self.h_range[1]+1, self.w_range[0]:self.w_range[1]+1]
imageArray, labelArray = self.random_crop(imageArray, labelArray, self.input_size)
if self.mode == 'train':
if random.randint(0, 1) == 1:
imageArray = np.flip(imageArray, axis=1)
labelArray = np.flip(labelArray, axis=1)
if random.randint(0, 1) == 1:
imageArray = np.flip(imageArray, axis=2)
labelArray = np.flip(labelArray, axis=2)
if random.randint(0, 1) == 1:
rotate_angle = random.randint(0, 360)
imageArray = ndimage.rotate(imageArray, rotate_angle, axes=[1,2],reshape=False,mode='nearest',order=0)
labelArray = ndimage.rotate(labelArray, rotate_angle, axes=[1,2],reshape=False,mode='nearest',order=0)
# ->[1,d,h,w]
img_c = np.expand_dims(imageArray, axis=-1).astype(np.float32)
# labelArray onehot ->[d,h,w,num_classes]
lab_c = np.zeros((*labelArray.shape,self.num_classes),dtype=np.float32)
for i in range(self.num_classes):
lab_c[...,i] = (labelArray == i).astype(np.float32)
batch_x.append(img_c)
batch_y.append(lab_c)
return np.array(batch_x), np.array(batch_y)
def random_crop(self, imageArray, labelArray, crop_size):
D,H,W = imageArray.shape
d,h,w = crop_size
if D<d:
padding = np.zeros((d-D,H,W),dtype=np.float32)
imageArray = np.concatenate((imageArray, padding), axis=0)
labelArray = np.concatenate((labelArray, padding), axis=0)
if H<h:
D,H,W = imageArray.shape
padding = np.zeros((D,h-H,W),dtype=np.float32)
imageArray = np.concatenate((imageArray, padding), axis=1)
labelArray = np.concatenate((labelArray, padding), axis=1)
if W<w:
D,H,W = imageArray.shape
padding = np.zeros((D,H,w-W),dtype=np.float32)
imageArray = np.concatenate((imageArray, padding), axis=2)
labelArray = np.concatenate((labelArray, padding), axis=2)
D,H,W = imageArray.shape
d_start = random.randint(0, D-1)
d_end = d_start + d
if d_end > D:
d_end = D
d_start = D - d
h_start = random.randint(0, H-1)
h_end = h_start + h
if h_end > H:
h_end = H
h_start = H - h
w_start = random.randint(0, W-1)
w_end = w_start + w
if w_end > W:
w_end = W
w_start = W - w
img = imageArray[d_start:d_end, h_start:h_end, w_start:w_end]
lab = labelArray[d_start:d_end, h_start:h_end, w_start:w_end]
assert img.shape == (d,h,w) and lab.shape==(d,h,w), f"img.shape={img.shape}, lab.shape={lab.shape}, (d,h,w)={crop_size}"
return img, lab
def normalize_imageArray(self, image_array):
max_value = np.max(image_array)
min_value = np.min(image_array)
assert max_value==self.hu_max_val and min_value==self.hu_min_val,f"max_value={max_value}, hu_max_val={self.hu_max_val}; min_value={min_value}, hu_min_val={self.hu_min_val}, they are not equal!, image_path={self.curimagePath}"
img_01 = (image_array - min_value) / (max_value - min_value)
return np.clip(img_01, 0, 1)
def cal_board(self, label_array):
dots = np.argwhere(label_array != 0)
mins = np.min(dots, axis=0)
maxs = np.max(dots, axis=0)
d_range = [mins[0], maxs[0]]
h_range = [mins[1], maxs[1]]
w_range = [mins[2], maxs[2]]
return d_range, h_range, w_range
def custom_window(self,image,hu_min_val,hu_max_val):
ww_filter = sitk.IntensityWindowingImageFilter()
ww_filter.SetWindowMinimum(hu_min_val)
ww_filter.SetWindowMaximum(hu_max_val)
ww_filter.SetOutputMinimum(hu_min_val)
ww_filter.SetOutputMaximum(hu_max_val)
return ww_filter.Execute(image)
def on_epoch_end(self):
if self.mode == 'train':
seed = random.randint(1,100)
random.seed(seed)
random.shuffle(self.images_path)
random.seed(seed)
random.shuffle(self.labels_path)check datasetval_images_dir = "D:/data/lung/images/val"
val_labels_dir = "D:/data/lung/labels/val"
val_dataset = Seg3DRandomDataset(
images_dir=val_images_dir,labels_dir=val_labels_dir,
input_size=input_size,num_classes=num_classes,
batch_size=batch_size,
hu_min_val=-1000,hu_max_val=800,
mode="val"
)
for x,y in val_dataset:
print(x.shape,y.shape)
if(x.shape[0]==0):
input("zzzz")
input("zzz2")tf2.9 output
tf2.16 output
|
google-ml-butler
bot
removed
the
stat:awaiting response
sushreebarsa
added
the
stat:awaiting response
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Issue type
Bug
Have you reproduced the bug with TensorFlow Nightly?
No
Source
source
TensorFlow version
tf2.16
Custom code
Yes
OS platform and distribution
windows10
Mobile device
No response
Python version
3.12
Bazel version
No response
GCC/compiler version
No response
CUDA/cuDNN version
No response
GPU model and memory
No response
Current behavior?
when the dataset has run out, the dataset still generator something, but the shape is 0, so it cuase model.fit() failed when an epoch end. But I also test the same code in tf29, it wa OK.
Standalone code to reproduce the issue
Relevant log output
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