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Isuru0x01

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tensorflow-issues

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[ "@zx-lhb \r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!", "> @zx-lhb In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!\r\n\r\n# my dataset definition\r\n```python\r\nclass Seg3DRandomDataset(tf.keras.utils.Sequence):\r\n def __init__(self, images_dir, labels_dir, input_size, num_classes, batch_size,\r\n hu_min_val, hu_max_val, mode:str,**kwargs):\r\n super(Seg3DRandomDataset, self).__init__(**kwargs)\r\n\r\n self.images_dir = images_dir\r\n self.labels_dir = labels_dir\r\n self.input_size = input_size\r\n self.num_classes = num_classes\r\n self.batch_size = batch_size\r\n self.hu_min_val = hu_min_val\r\n self.hu_max_val = hu_max_val\r\n self.mode = mode\r\n \r\n file_names = os.listdir(images_dir) # nii.gz\r\n \r\n\r\n self.images_path = []\r\n self.labels_path = []\r\n for file_name in file_names:\r\n image_path = os.path.join(images_dir, file_name)\r\n label_path = os.path.join(labels_dir, file_name)\r\n self.images_path.append(image_path)\r\n self.labels_path.append(label_path)\r\n \r\n def __len__(self):\r\n return math.ceil(len(self.images_path) / self.batch_size)\r\n \r\n def __getitem__(self, idx):\r\n low = idx * self.batch_size\r\n high = min(low + self.batch_size, len(self.images_path))\r\n\r\n batch_x_path = self.images_path[low:high]\r\n batch_y_path = self.labels_path[low:high]\r\n \r\n batch_x = []\r\n batch_y = []\r\n for x_path, y_path in zip(batch_x_path, batch_y_path):\r\n image = sitk.ReadImage(x_path)\r\n label = sitk.ReadImage(y_path)\r\n self.curimagePath = x_path\r\n\r\n \r\n image = self.custom_window(image,self.hu_min_val,self.hu_max_val)\r\n # \r\n imageArray = sitk.GetArrayFromImage(image)\r\n labelArray = sitk.GetArrayFromImage(label)\r\n\r\n \r\n self.d_range, self.h_range, self.w_range = self.cal_board(labelArray)\r\n\r\n \r\n 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])\r\n 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]\r\n\r\n \r\n imageArray, labelArray = self.random_crop(imageArray, labelArray, self.input_size)\r\n\r\n if self.mode == 'train':\r\n if random.randint(0, 1) == 1: \r\n imageArray = np.flip(imageArray, axis=1)\r\n labelArray = np.flip(labelArray, axis=1)\r\n if random.randint(0, 1) == 1:\r\n imageArray = np.flip(imageArray, axis=2)\r\n labelArray = np.flip(labelArray, axis=2)\r\n if random.randint(0, 1) == 1:\r\n rotate_angle = random.randint(0, 360)\r\n imageArray = ndimage.rotate(imageArray, rotate_angle, axes=[1,2],reshape=False,mode='nearest',order=0)\r\n labelArray = ndimage.rotate(labelArray, rotate_angle, axes=[1,2],reshape=False,mode='nearest',order=0)\r\n \r\n\r\n \r\n # ->[1,d,h,w]\r\n img_c = np.expand_dims(imageArray, axis=-1).astype(np.float32)\r\n \r\n # labelArray onehot ->[d,h,w,num_classes]\r\n lab_c = np.zeros((*labelArray.shape,self.num_classes),dtype=np.float32)\r\n for i in range(self.num_classes):\r\n lab_c[...,i] = (labelArray == i).astype(np.float32)\r\n \r\n batch_x.append(img_c)\r\n batch_y.append(lab_c)\r\n\r\n \r\n return np.array(batch_x), np.array(batch_y)\r\n\r\n def random_crop(self, imageArray, labelArray, crop_size):\r\n D,H,W = imageArray.shape\r\n d,h,w = crop_size\r\n\r\n if D<d:\r\n padding = np.zeros((d-D,H,W),dtype=np.float32)\r\n imageArray = np.concatenate((imageArray, padding), axis=0)\r\n labelArray = np.concatenate((labelArray, padding), axis=0)\r\n if H<h:\r\n D,H,W = imageArray.shape\r\n padding = np.zeros((D,h-H,W),dtype=np.float32)\r\n imageArray = np.concatenate((imageArray, padding), axis=1)\r\n labelArray = np.concatenate((labelArray, padding), axis=1)\r\n if W<w:\r\n D,H,W = imageArray.shape\r\n padding = np.zeros((D,H,w-W),dtype=np.float32)\r\n imageArray = np.concatenate((imageArray, padding), axis=2)\r\n labelArray = np.concatenate((labelArray, padding), axis=2)\r\n \r\n D,H,W = imageArray.shape\r\n\r\n d_start = random.randint(0, D-1)\r\n d_end = d_start + d\r\n if d_end > D:\r\n d_end = D\r\n d_start = D - d\r\n\r\n h_start = random.randint(0, H-1)\r\n h_end = h_start + h\r\n if h_end > H:\r\n h_end = H\r\n h_start = H - h\r\n\r\n w_start = random.randint(0, W-1)\r\n w_end = w_start + w\r\n if w_end > W:\r\n w_end = W\r\n w_start = W - w\r\n \r\n img = imageArray[d_start:d_end, h_start:h_end, w_start:w_end]\r\n lab = labelArray[d_start:d_end, h_start:h_end, w_start:w_end]\r\n 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}\"\r\n \r\n return img, lab\r\n \r\n\r\n def normalize_imageArray(self, image_array): \r\n max_value = np.max(image_array)\r\n min_value = np.min(image_array)\r\n 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}\"\r\n\r\n img_01 = (image_array - min_value) / (max_value - min_value)\r\n return np.clip(img_01, 0, 1)\r\n \r\n\r\n def cal_board(self, label_array):\r\n dots = np.argwhere(label_array != 0)\r\n mins = np.min(dots, axis=0)\r\n maxs = np.max(dots, axis=0)\r\n\r\n d_range = [mins[0], maxs[0]]\r\n h_range = [mins[1], maxs[1]]\r\n w_range = [mins[2], maxs[2]]\r\n \r\n return d_range, h_range, w_range\r\n\r\n\r\n def custom_window(self,image,hu_min_val,hu_max_val):\r\n ww_filter = sitk.IntensityWindowingImageFilter()\r\n ww_filter.SetWindowMinimum(hu_min_val)\r\n ww_filter.SetWindowMaximum(hu_max_val)\r\n\r\n ww_filter.SetOutputMinimum(hu_min_val)\r\n ww_filter.SetOutputMaximum(hu_max_val)\r\n return ww_filter.Execute(image)\r\n\r\n def on_epoch_end(self):\r\n if self.mode == 'train':\r\n seed = random.randint(1,100)\r\n random.seed(seed)\r\n random.shuffle(self.images_path)\r\n random.seed(seed)\r\n random.shuffle(self.labels_path)\r\n```\r\n\r\n# check dataset\r\n```python\r\nval_images_dir = \"D:/data/lung/images/val\"\r\nval_labels_dir = \"D:/data/lung/labels/val\"\r\nval_dataset = Seg3DRandomDataset(\r\n images_dir=val_images_dir,labels_dir=val_labels_dir,\r\n input_size=input_size,num_classes=num_classes, \r\n batch_size=batch_size, \r\n hu_min_val=-1000,hu_max_val=800,\r\n mode=\"val\"\r\n)\r\nfor x,y in val_dataset:\r\n print(x.shape,y.shape)\r\n if(x.shape[0]==0):\r\n input(\"zzzz\")\r\n\r\ninput(\"zzz2\")\r\n```\r\n# tf2.9 output\r\n\r\n# tf2.16 output\r\n\r\n\r\n", "@zx-lhb Could you please share the gist with the error . I tried to replicate the issue reported here but facing a different error, please have a look at the [gist](https://colab.research.google.com/gist/sushreebarsa/a1d21f8e49e5e2e4b5b104041f1c9c59/67386.ipynb) and let us know. Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/67386\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/67386\">No</a>\n" ]

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[ "BTW please send XLA PRs via github.com/openxla/xla, it backs the same files, and soon will be the only way to submit such PRs.", "> BTW please send XLA PRs via github.com/openxla/xla, it backs the same files, and soon will be the only way to submit such PRs.\r\n\r\nXLA PR here: https://github.com/openxla/xla/pull/13106. @cheshire " ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/67370/checks?check_run_id=24843476150) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @brtal Can you please sign CLA? Thank you!", "Hi @brtal Can you please sign CLA? Thank you!\r\n\r\n" ]

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[ "Hi **@karelhorak-gen** ,\r\n- Sorry for the delay, I tried to run your code on colab using TF v2.15, 2.16.1, nightly and in 2.15 it is working fine, remaining versions getting the same issue. please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/63cf5b4bc484ac837ff24245b4c3356d/67334_2-15-2-16-1-nightly.ipynb) here for reference.\r\n- And Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)\r\nTo know more see;\r\n[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999).\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/67334\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/67334\">No</a>\n" ]