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[ "Hello, @ajayaraman!\r\nSorry for the late response!\r\nPlease ensure that the version of 'keras_cv' you're using is compatible with your Keras version. If you're using an older version of 'keras_cv', consider upgrading to a newer version. Also please check the Python you're using as it requires Python version 3.6 or higher. 'keras_cv' is not compatible with Python 2.7. 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/62182\">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/62182\">No</a>\n" ]

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[ "Hi @BmanClark \r\n\r\nCould you please provide the steps you have followed for TFLite conversion inorder to reproduce the issue?\r\n\r\nThanks.\r\n", "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.", "Sorry, I've been busy with my workaround... The issue still exists generally, but https://github.com/Picsart-AI-Research/MI-GAN/ has kindly added a new commit to replace the group deconvolutions in the network and avoid the issue. So to reproduce you need the previous commit of MI-GAN: 29807312b6d881b51246d83e8b6fa9b9e4f97978\r\n\r\nWith that network / github project I converted with [TinyNN](https://github.com/alibaba/TinyNeuralNetwork).\r\n\r\nSpecifically, once the TinyNN & MI-GAN projects were installed on python following their respective standard instructions, I used the following to convert the network and hit the issue.\r\n\r\n```python\r\nfrom tinynn.converter import TFLiteConverter\r\nimport torch\r\nimport sys\r\nimport argparse\r\nimport os\r\nimport numpy as np\r\nfrom lib.model_zoo.migan_inference import Generator as MIGAN\r\n\r\nmodel = MIGAN(resolution=512)\r\nmodel.load_state_dict(torch.load('./models/migan_512_places2.pt'))\r\ndummy_input = torch.randn(1, 4, res, res)\r\n\r\nwith torch.no_grad():\r\n model.cpu()\r\n model.eval()\r\n \r\n converter = TFLiteConverter(model, dummy_input, 'migan512.tflite')\r\n # converter.group_conv_rewrite = True # line to blow up network to thousands of nodes, but make conversion work by de-grouping\r\n converter.convert()\r\n``` \r\n\r\n", "Hi, this is the author of TinyNN, which is a tool to convert PyTorch models to TFLite. I've skimmed the conversation above. Actually, it is rather a feature request, not a bug. As group convolution is already supported in TFLite, it seems reasonable to get grouped transpose_convolution supported too. Internally, we have a fork of TFLite that maps the group deconvolution to a single op and then maps that to the underlying XNNPack implementation. Ideally, it could be supported here, as well.", "Hi @BmanClark @peterjc123 \r\n\r\nThanks for the information. \r\n\r\nI am just trying with a toy model to reproduce the issue and observed that TFLite is able to convert the transpose convolutions with groups without any error.\r\n\r\nPlease find this [gist](https://colab.research.google.com/gist/pjpratik/135971b72d0efec138e452f1b8fed66f/62181.ipynb). \r\n\r\nIf you could provide a similar toy model, that would help us investigate the bug or feature request better.\r\n\r\nThanks.\r\n", "@pjpratik Yes, it doesn't fail during conversion and evaluation, but if you inspect the generated TFLite model using Netron, you can see that it is translated to a full conv_transpose node rather than a grouped one. (The number of the input channels and the output channels of the weights are equal to 32) So there is no speedup and memory savings compared with full transposed convolutions during inference, making grouped deconvolutions meaningless.\r\n\r\n\r\n", "To make it clear, we need the following enhancements:\r\n1. Update the TFLite schema to enable grouped deconvolutions so that we can have grouped weights (which is smaller)\r\n2. Implement grouped deconvolution kernels to speedup inference", "Hi @peterjc123, would you be willing to make a contribution for this feature? That'll probably get it integrated the fastest.", "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.", "@pkgoogle Will take a look and work on this if I have time.", "@haozha111, can you please take a look? Thanks.", "This issue is stale because it has been open for 180 days with no activity. It will be closed if no further activity occurs. Thank you." ]

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[ "Hi @antoche ,\r\n\r\nThanks for reporting. The issue exists in TF2.13v and got fixed in Tf2.14V onwards. Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/1701a420793980bbda384b6a05815bcc/62180.ipynb).\r\n\r\nThank you!", "I'm getting the issue in 2.12.0 as well. When was it introduced? Is there a workaround?", "Hi @antoche ,\r\n\r\nAs per this [commit](https://github.com/tensorflow/tensorflow/commit/d765c6c264d1372a36e2da5b22621f0381f66930), `py_function` was made to use as decorator from Tf2.14 onwards.Not sure this is a feature implemented in Tf2.14 onwards.\r\n\r\nPlease use TF>=2.14 versions to avoid this error. ", "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/62180\">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/62180\">No</a>\n" ]

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[ "@learning-to-play Intel wants this to make it into 2.15 to address a memory-leak issue blocking several of their clients. The corresponding issue is #58676." ]

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[ "@DLumi Could you please check whether you are using the same TF version for training and inference?\r\nPlease provide a simple standalone code to replicate the issue reported here. \r\nThank you!", "@sushreebarsa I'm sorry, but did you even read what I wrote in the issue template? \r\nThere you can find as simple (and standalone) code as it can get. But you will have to switch environments to replicate that, given that the bug occurs on different TF versions.\r\n\r\nThe model in question was indeed saved (and later inferenced) on the same TF version. But I believe, it should also work if you save the model on another version, like 2.10. Did not check this one, as even nailing this down + filing the issue took quite a lot of time. So hope you can look into that.", "As per the result which is observed in the comparison, \r\n\r\n```\r\nMax absolute difference: 5.9604645e-08\r\nMax relative difference: 2.3390216e-07 \r\n```\r\n\r\nIt is in the range of` 1e-07`, which should be fine, considering the floating point precision errors. Thank you!", "@sachinprasadhs for the attached tensors the error is indeed _relatively_ low. \r\nBut depending on the inputs and the model, the error can get up to 1e-5, which is a bit too much to write it off as FP precision error.", "Hi @DLumi, could you try saving with .npz instead of pickle? Pickle saving is known to be different across python version and can lead to inconsistencies. Could you try using a CPU environment instead of a GPU environment? That might also reduce inconsistencies. We also suggest that you do not switch python versions for this test so we can isolate the potential issue to Tensorflow. 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/62175\">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/62175\">No</a>\n" ]

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[ "Hello @ujjwalnur ! As per the documentation [here](https://www.tensorflow.org/api_docs/python/tf/map_fn), some arguments are deprecated in this api **tf.map_fn** which will be removed in the future versions. Please use **fn_output_signature** instead. \r\nThank you!\r\n", "@sushreebarsa Your response does not help at all with respect to the actual problem. I am aware of the deprecated arguments and I have used `fn_output_signature` here in this function", "@ujjwalnur \r\nIn order to expedite the trouble-shooting process, please provide a complete code snippet to reproduce the issue reported here. Thank you!", "Here you go. \r\nPlease read through the code carefully to see the problem.\r\n\r\nIn the code I just simulate gradient backpropagation with respect to a fictional randomly simulated image classification dataset\r\n\r\nI have implemented three versions of the loss computation and there seems to be nothing out of place that could break gradient backpropagation.\r\n\r\nIn the past, #19897 and #37512 have mentioned issues with respect to gradient backpropgation with `tf.map_fn` and `tf.TensorArray`\r\n\r\n```\r\nimport tensorflow as tf\r\n\r\n\r\n# Let's create a model first\r\nclass MyModel(tf.keras.Model):\r\n def __init__(self):\r\n super(MyModel, self).__init__()\r\n self._l1 = tf.keras.layers.Conv2D(\r\n filters=64, kernel_size=3\r\n )\r\n self._l1 = tf.keras.Sequential(\r\n [\r\n tf.keras.layers.Conv2D(\r\n filters=64, kernel_size=3\r\n ),\r\n tf.keras.layers.GlobalAveragePooling2D(keepdims=False),\r\n tf.keras.layers.Dense(units=10)\r\n ]\r\n )\r\n self._l2 = tf.keras.Sequential(\r\n [\r\n tf.keras.layers.Conv2D(\r\n filters=64, kernel_size=3\r\n ),\r\n tf.keras.layers.GlobalAveragePooling2D(keepdims=False),\r\n tf.keras.layers.Dense(units=10)\r\n ]\r\n )\r\n self._l3 = tf.keras.Sequential(\r\n [\r\n tf.keras.layers.Conv2D(\r\n filters=64, kernel_size=3\r\n ),\r\n tf.keras.layers.GlobalAveragePooling2D(keepdims=False),\r\n tf.keras.layers.Dense(units=10)\r\n ]\r\n )\r\n\r\n def call(self, inputs, training=None, mask=None):\r\n y1 = self._l1(inputs)\r\n y2 = self._l2(inputs)\r\n y3 = self._l3(inputs)\r\n out = tf.stack([y1, y2, y3])\r\n # Output shape is (3, batch_size, 10)\r\n\r\n return out\r\n\r\n\r\nmodel = MyModel()\r\n\r\n\r\n# just a random image classification dataset\r\ndef get_dataset():\r\n db = tf.data.Dataset.range(100)\r\n db = db.map(\r\n lambda x: (tf.random.uniform(shape=(224, 224, 3), dtype=tf.float32),\r\n tf.one_hot(tf.random.uniform(shape=(), minval=0, maxval=9, dtype=tf.int64), depth=10)\r\n )\r\n )\r\n db = db.batch(5)\r\n return db\r\n\r\n\r\ndataset = get_dataset()\r\n\r\n\r\n# Computes the loss. But gradient backpropagation fails.\r\n# Here tf.map_fn is used to compute the loss of the output of l1, l2 and l3 ( See Model ) with respect to GT labeles.\r\ndef get_loss_v1(logits, labels):\r\n loss = tf.map_fn(\r\n fn=lambda x: tf.reduce_mean(\r\n tf.keras.losses.categorical_crossentropy(\r\n from_logits=True,\r\n y_pred=tf.gather(logits, x),\r\n y_true=labels\r\n )\r\n ),\r\n elems=tf.range(3),\r\n fn_output_signature=tf.float32\r\n )\r\n return loss\r\n\r\n\r\n# Computes the loss. But gradient backpropagation fails.\r\n# Here appending to a list is used to compute the loss of the output of l1, l2 and l3 ( See Model ) with respect to GT labeles.\r\n# This method should be avoided as it makes use of python list. TensorArray is recommended to be used here.\r\n\r\n# Check out https://github.com/tensorflow/tensorflow/issues/37512\r\ndef get_loss_v2(logits, labels):\r\n losses = list()\r\n for i in tf.range(3):\r\n y_pred = tf.gather(logits, i)\r\n losses.append(\r\n tf.reduce_mean(\r\n tf.keras.losses.categorical_crossentropy(\r\n from_logits=True,\r\n y_pred=y_pred,\r\n y_true=labels\r\n )\r\n )\r\n )\r\n return losses\r\n\r\n\r\n# Computes the loss. But gradient backpropagation fails.\r\n# Here tf.TensorArray with tf.while_loop is used to compute the loss of the output of l1, l2 and l3 ( See Model ) with respect to GT labeles.\r\ndef get_loss_v3(logits, labels):\r\n losses = tf.TensorArray(dtype=tf.float32,\r\n size=0, dynamic_size=True, clear_after_read=False, element_shape=()\r\n )\r\n index = tf.constant(0)\r\n\r\n def body(counter_var, log, lbl, l):\r\n l = l.write(l.size(), tf.reduce_mean(\r\n tf.keras.losses.categorical_crossentropy(\r\n from_logits=True,\r\n y_pred=tf.gather(log, counter_var),\r\n y_true=lbl\r\n )\r\n ))\r\n return counter_var + 1, log, lbl, l\r\n\r\n output = tf.while_loop(\r\n cond=lambda i, *_: tf.less(i, 3),\r\n loop_vars=(index, logits, labels, losses),\r\n body=body,\r\n parallel_iterations=1\r\n )\r\n loss_val = output[-1].stack()\r\n return loss_val\r\n\r\n\r\ndef train_step(images, labels):\r\n with tf.GradientTape(persistent=True) as tape:\r\n logits = model(images, training=True)\r\n loss_val = get_loss_v1(logits, labels)\r\n # loss_val = get_loss_v2(logits, labels)\r\n # loss_val = get_loss_v3(logits, labels)\r\n print(loss_val) # This prints the forward losses properly\r\n grads = list()\r\n for i in tf.range(3):\r\n tgt = tf.gather(loss_val, i)\r\n grads.append(\r\n tape.gradient(\r\n target=tgt,\r\n sources=model.trainable_variables\r\n )\r\n )\r\n print(grads) # This always prints None\r\n\r\n\r\nfor data in dataset:\r\n images, labels = data\r\n train_step(images, labels)\r\n```\r\n\r\n", "This issue has been pending for such long time \r\n\r\nhttps://github.com/tensorflow/tensorflow/issues/3972 (since 2016)\r\n\r\nway to go tf team!", "@ujjwalnur I tried to replicate the issue with the provided code above, could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/afd079e1e243168e512be789fca68149/62174.ipynb) and confirm the same?\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/62174\">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/62174\">No</a>\n", "@ujjwalnur Reopening the issue as the issue still persists. \r\n@SuryanarayanaY Could you please have a look at this ticket?\r\nThank you!" ]

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[ "Hi @nijat-ahmadli \r\n\r\nThis seems to be an issue with Android < 8 compatibility with latest TF Versions. Can you please provide the Android version you are using?\r\n\r\nPlease check this relevant issue #61951 \r\n\r\nThanks.", "Hi @pjpratik Thanks for your reply.\r\nI am using following versions for the project:\r\n```\r\n minSdkVersion 21\r\n targetSdkVersion 33\r\n compileSdkVersion 33\r\n```", "Hi @nijat-ahmadli, let's follow the progress on https://github.com/tensorflow/tensorflow/issues/61951 as this looks basically the same.", "Duplicate of #61951" ]

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[ "Hi @gab48 ,\r\n\r\nThough the argument `reshuffle_each_iteration=None` in the function, it defaults to `True` only which is documented in the argument description of this function. Please refer to argument description below as per document source [link](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle:~:text=(Optional.)%20A%20boolean%2C%20which%20if%20true%20indicates%20that%20the%20dataset%20should%20be%20pseudorandomly%20reshuffled%20each%20time%20it%20is%20iterated%20over.%20(Defaults%20to%20True.)).\r\n\r\n\r\n\r\nreshuffle_each_iteration | (Optional.) A boolean, which if true indicates that the dataset should be pseudorandomly reshuffled each time it is iterated over. (Defaults to True.)\r\n-- | --\r\n\r\n\r\n", "@gab48 It actually makes it more sense to set it as `None`.\r\n\r\n`None` makes it easier for someone to change the options using configuration files.", "@gab48 ,\r\n\r\nI agree to above comment. I think that's individual preference. But the default behaviour is properly documented and code also handled it as it is documented. I think there is no need to change anything here as there is no conflicts in documentation wrt code.\r\n\r\nThanks!", "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." ]

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[ "+1 \r\nI was having the same issue last week with CONV_2D op after quantization, any hints on the issue are appreciated. @sushreebarsa \r\nThank you,", "Hello, @Marouan-st! The provided drive link is not accessible to me. Could you please provide the access or share the colab gist to replicate the issue reported here. \r\nThank you! ", "> Hello, @Marouan-st! The provided drive link is not accessible to me. Could you please provide the access or share the colab gist to replicate the issue reported here. Thank you!\r\n\r\nOh I'm sorry... Access should be provided now.", "Hi, I have the same issue and can produce it with only a few lines of code.\r\n\r\n```\r\nimport tensorflow as tf\r\n\r\ninput_data = tf.random.normal([1, 1088, 1])\r\nfilters = tf.random.uniform([65, 1, 1])\r\n\r\[email protected]\r\ndef my_conv1d(input, filters):\r\n print(\"Tracing!\")\r\n output = tf.nn.conv1d(input, filters, 1, 'VALID')\r\n return output\r\n\r\ndef representative_dataset():\r\n for _ in range(5000):\r\n yield [tf.random.normal([1, 1088, 1]), tf.random.uniform([65, 1, 1])]\r\n\r\nconcrete_func = my_conv1d.get_concrete_function(input_data, filters)\r\n\r\nconverter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func],\r\n my_conv1d)\r\n\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter.representative_dataset = representative_dataset\r\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\r\n\r\ntflite_model = converter.convert()\r\n\r\ninterpreter = tf.lite.Interpreter(model_content=tflite_model)\r\ninterpreter.allocate_tensors()\r\n```\r\nThe call `interpreter.allocate_tensors()` produces:\r\n`RuntimeError: tensorflow/lite/kernels/conv.cc:374 affine_quantization->zero_point->data[i] != 0 (-128 != 0)Node number 4 (CONV_2D) failed to prepare.Failed to apply the default TensorFlow Lite delegate indexed at 0.`\r\n\r\nWhen I don't use\r\n```\r\nconverter.representative_dataset = representative_dataset\r\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\r\n```\r\nno error occours. Any ideas what to do?", "I was able to reproduce the issue. Please find this [gist](https://colab.research.google.com/gist/pjpratik/e601014213966dfdfdfdd31c5b805594/62171.ipynb).\r\n\r\nThe zero point calibration for the filters seems to be an issue here.\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.", "I was able to reproduce w/ the same gist above.\r\n\r\n@arfaian can you please take a look? Thanks." ]

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[ "Hello, @9527-csroad!\r\nTo use the Tesla T4 GPU in TensorFlow, please specify the `device:GPU device` type when creating a model or running a session with latest TF version as you are using an older version which is not actively supported. \r\nThank you!", "Thanks for your reply, @sushreebarsa . Which version should I install? You can see that no matter which version I installed, tensorflow always take so much GPU memory.\r\n```python\r\n(noya-mat) root@iZbp1b192y9va5podaabh6Z:/noya/matting# python3 optimize.py\r\n2023-11-06 15:31:45,263 - modelscope - INFO - PyTorch version 1.11.0+cu113 Found.\r\n2023-11-06 15:31:45,265 - modelscope - INFO - TensorFlow version 2.6.0 Found.\r\n2023-11-06 15:31:45,266 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer\r\n2023-11-06 15:31:45,303 - modelscope - INFO - Loading done! Current index file version is 1.9.4, with md5 6fd956e57d818f09de7f4c4f31828009 and a total number of 945 components indexed\r\n2023-11-06 15:31:46,762 - modelscope - WARNING - Model revision not specified, use revision: v1.1.0\r\n2023-11-06 15:31:47,362 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo\r\n2023-11-06 15:31:47,362 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo.\r\n2023-11-06 15:31:47,364 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo\r\n2023-11-06 15:31:48,284 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.\r\n2023-11-06 15:31:48,284 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'train': {'mosaic_mixup': {'mosaic_prob': 1.0, 'mosaic_scale': [0.1, 2.0], 'mosaic_size': [640, 640], 'mixup_prob': 0.15, 'mixup_scale': [0.5, 1.5], 'degrees': 10.0, 'translate': 0.2, 'shear': 2.0}, 'transform': {'image_mean': [0.0, 0.0, 0.0], 'image_std': [1.0, 1.0, 1.0], 'image_max_range': [640, 640], 'flip_prob': 0.5, 'autoaug_dict': {'box_prob': 0.3, 'num_subpolicies': 5, 'scale_splits': [2048, 10240, 51200], 'autoaug_params': [6, 9, 5, 3, 3, 4, 2, 4, 4, 4, 5, 2, 4, 1, 4, 2, 6, 4, 2, 2, 2, 6, 2, 2, 2, 0, 5, 1, 3, 0, 8, 5, 2, 8, 7, 5, 1, 3, 3, 3]}}}, 'evaluation': {'transform': {'image_mean': [0.0, 0.0, 0.0], 'image_std': [1.0, 1.0, 1.0], 'image_max_range': [640, 640], 'flip_prob': 0.0}}, 'model_dir': '/root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo'}. trying to build by task and model information.\r\n2023-11-06 15:31:49,116 - modelscope - WARNING - Model revision not specified, use revision: v1.0.0\r\n2023-11-06 15:31:50,585 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_unet_image-matting\r\n2023-11-06 15:31:50,585 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_unet_image-matting.\r\n2023-11-06 15:31:50,587 - modelscope - WARNING - No preprocessor field found in cfg.\r\n2023-11-06 15:31:50,587 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.\r\n2023-11-06 15:31:50,587 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/cv_unet_image-matting'}. trying to build by task and model information.\r\n2023-11-06 15:31:50,587 - modelscope - WARNING - Find task: portrait-matting, model type: None. Insufficient information to build preprocessor, skip building preprocessor\r\nWARNING:tensorflow:From /root/anaconda3/envs/noya-mat/lib/python3.8/site-packages/modelscope/utils/device.py:60: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nUse `tf.config.list_physical_devices('GPU')` instead.\r\n2023-11-06 15:31:50.589019: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-11-06 15:31:50.590884: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:31:50.598626: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:31:50.599297: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:31:55.306670: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:31:55.307388: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:31:55.307976: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:31:55.308559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /device:GPU:0 with 11456 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\n```\r\n\r\n```bash\r\npython3 optimize.py\r\n2023-11-06 15:40:24,458 - modelscope - INFO - PyTorch version 1.11.0+cu113 Found.\r\n2023-11-06 15:40:24,460 - modelscope - INFO - TensorFlow version 2.13.1 Found.\r\n2023-11-06 15:40:24,461 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer\r\n2023-11-06 15:40:24,498 - modelscope - INFO - Loading done! Current index file version is 1.9.4, with md5 6fd956e57d818f09de7f4c4f31828009 and a total number of 945 components indexed\r\n2023-11-06 15:40:25,950 - modelscope - WARNING - Model revision not specified, use revision: v1.1.0\r\n2023-11-06 15:40:26,489 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo\r\n2023-11-06 15:40:26,489 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo.\r\n2023-11-06 15:40:26,490 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo\r\n2023-11-06 15:40:27,396 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.\r\n2023-11-06 15:40:27,397 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'train': {'mosaic_mixup': {'mosaic_prob': 1.0, 'mosaic_scale': [0.1, 2.0], 'mosaic_size': [640, 640], 'mixup_prob': 0.15, 'mixup_scale': [0.5, 1.5], 'degrees': 10.0, 'translate': 0.2, 'shear': 2.0}, 'transform': {'image_mean': [0.0, 0.0, 0.0], 'image_std': [1.0, 1.0, 1.0], 'image_max_range': [640, 640], 'flip_prob': 0.5, 'autoaug_dict': {'box_prob': 0.3, 'num_subpolicies': 5, 'scale_splits': [2048, 10240, 51200], 'autoaug_params': [6, 9, 5, 3, 3, 4, 2, 4, 4, 4, 5, 2, 4, 1, 4, 2, 6, 4, 2, 2, 2, 6, 2, 2, 2, 0, 5, 1, 3, 0, 8, 5, 2, 8, 7, 5, 1, 3, 3, 3]}}}, 'evaluation': {'transform': {'image_mean': [0.0, 0.0, 0.0], 'image_std': [1.0, 1.0, 1.0], 'image_max_range': [640, 640], 'flip_prob': 0.0}}, 'model_dir': '/root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo'}. trying to build by task and model information.\r\n2023-11-06 15:40:28,238 - modelscope - WARNING - Model revision not specified, use revision: v1.0.0\r\n2023-11-06 15:40:28.780384: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-11-06 15:40:29.588528: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-11-06 15:40:30,385 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_unet_image-matting\r\n2023-11-06 15:40:30,385 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_unet_image-matting.\r\n2023-11-06 15:40:30,387 - modelscope - WARNING - No preprocessor field found in cfg.\r\n2023-11-06 15:40:30,387 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.\r\n2023-11-06 15:40:30,387 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/cv_unet_image-matting'}. trying to build by task and model information.\r\n2023-11-06 15:40:30,387 - modelscope - WARNING - Find task: portrait-matting, model type: None. Insufficient information to build preprocessor, skip building preprocessor\r\nWARNING:tensorflow:From /root/anaconda3/envs/noya-mat/lib/python3.8/site-packages/modelscope/utils/device.py:60: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nUse `tf.config.list_physical_devices('GPU')` instead.\r\n2023-11-06 15:40:30.390254: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-06 15:40:30.397435: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-06 15:40:30.398099: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-06 15:40:35.308779: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-06 15:40:35.309475: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-06 15:40:35.310045: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-06 15:40:35.310599: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /device:GPU:0 with 11426 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\n2023-11-06 15:40:36.160844: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:375] MLIR V1 optimization pass is not enabled\r\n2023-11-06 15:40:38.756156: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:425] Loaded runtime CuDNN library: 8.1.1 but source was compiled with: 8.6.0. CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library. If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration.\r\n2023-11-06 15:40:38.756592: W tensorflow/core/framework/op_kernel.cc:1828] OP_REQUIRES failed at conv_ops_impl.h:770 : UNIMPLEMENTED: DNN library is not found.\r\n2023-11-06 15:40:38.756662: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 17908648775275799695\r\nTraceback (most recent call last):\r\n File \"/root/anaconda3/envs/noya-mat/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1379, in _do_call\r\n return fn(*args)\r\n File \"/root/anaconda3/envs/noya-mat/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1362, in _run_fn\r\n return self._call_tf_sessionrun(options, feed_dict, fetch_list,\r\n File \"/root/anaconda3/envs/noya-mat/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1455, in _call_tf_sessionrun\r\n return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\r\ntensorflow.python.framework.errors_impl.UnimplementedError: 2 root error(s) found.\r\n (0) UNIMPLEMENTED: DNN library is not found.\r\n [[{{node coarse/Conv/Conv2D}}]]\r\n [[output_png/_3]]\r\n (1) UNIMPLEMENTED: DNN library is not found.\r\n [[{{node coarse/Conv/Conv2D}}]]\r\n0 successful operations.\r\n0 derived errors ignored.\r\n```\r\n\r\n```bash\r\n(noya-mat) root@iZbp1b192y9va5podaabh6Z:/noya/matting# python3 optimize.py\r\n2023-11-06 15:44:22,216 - modelscope - INFO - PyTorch version 1.11.0+cu113 Found.\r\n2023-11-06 15:44:22,219 - modelscope - INFO - TensorFlow version 2.11.0 Found.\r\n2023-11-06 15:44:22,219 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer\r\n2023-11-06 15:44:22,260 - modelscope - INFO - Loading done! Current index file version is 1.9.4, with md5 6fd956e57d818f09de7f4c4f31828009 and a total number of 945 components indexed\r\n2023-11-06 15:44:23,739 - modelscope - WARNING - Model revision not specified, use revision: v1.1.0\r\n2023-11-06 15:44:24,284 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo\r\n2023-11-06 15:44:24,284 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo.\r\n2023-11-06 15:44:24,285 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo\r\n2023-11-06 15:44:25,196 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.\r\n2023-11-06 15:44:25,196 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'train': {'mosaic_mixup': {'mosaic_prob': 1.0, 'mosaic_scale': [0.1, 2.0], 'mosaic_size': [640, 640], 'mixup_prob': 0.15, 'mixup_scale': [0.5, 1.5], 'degrees': 10.0, 'translate': 0.2, 'shear': 2.0}, 'transform': {'image_mean': [0.0, 0.0, 0.0], 'image_std': [1.0, 1.0, 1.0], 'image_max_range': [640, 640], 'flip_prob': 0.5, 'autoaug_dict': {'box_prob': 0.3, 'num_subpolicies': 5, 'scale_splits': [2048, 10240, 51200], 'autoaug_params': [6, 9, 5, 3, 3, 4, 2, 4, 4, 4, 5, 2, 4, 1, 4, 2, 6, 4, 2, 2, 2, 6, 2, 2, 2, 0, 5, 1, 3, 0, 8, 5, 2, 8, 7, 5, 1, 3, 3, 3]}}}, 'evaluation': {'transform': {'image_mean': [0.0, 0.0, 0.0], 'image_std': [1.0, 1.0, 1.0], 'image_max_range': [640, 640], 'flip_prob': 0.0}}, 'model_dir': '/root/.cache/modelscope/hub/damo/cv_tinynas_human-detection_damoyolo'}. trying to build by task and model information.\r\n2023-11-06 15:44:25,910 - modelscope - WARNING - Model revision not specified, use revision: v1.0.0\r\n2023-11-06 15:44:26.202622: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-11-06 15:44:27.097726: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /root/anaconda3/envs/noya-mat/lib/python3.8/site-packages/cv2/../../lib64:/usr/local/cuda-11.2/lib64:\r\n2023-11-06 15:44:27.097846: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /root/anaconda3/envs/noya-mat/lib/python3.8/site-packages/cv2/../../lib64:/usr/local/cuda-11.2/lib64:\r\n2023-11-06 15:44:27.097860: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2023-11-06 15:44:27,877 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/cv_unet_image-matting\r\n2023-11-06 15:44:27,877 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/cv_unet_image-matting.\r\n2023-11-06 15:44:27,881 - modelscope - WARNING - No preprocessor field found in cfg.\r\n2023-11-06 15:44:27,881 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.\r\n2023-11-06 15:44:27,881 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/cv_unet_image-matting'}. trying to build by task and model information.\r\n2023-11-06 15:44:27,881 - modelscope - WARNING - Find task: portrait-matting, model type: None. Insufficient information to build preprocessor, skip building preprocessor\r\nWARNING:tensorflow:From /root/anaconda3/envs/noya-mat/lib/python3.8/site-packages/modelscope/utils/device.py:60: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nUse `tf.config.list_physical_devices('GPU')` instead.\r\n2023-11-06 15:44:27.882293: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-11-06 15:44:27.883792: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:44:27.890937: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:44:27.891624: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:44:32.758004: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:44:32.758740: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:44:32.759339: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-11-06 15:44:32.759909: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /device:GPU:0 with 11432 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\n```\r\n\r\n", "Hi @9527-csroad ,\r\n\r\nCould you please confirm the exact problem? As per my understanding here you have a 15 GB GPU but while code execution you find that only 10.175GB is visible. Is that what you want report?\r\n\r\nI can also see Pytorch version also indeed installed here.It seems your environment has too many dependencies that is taking more memory resources ?\r\n", "Hello @SuryanarayanaY ,\r\nMy question is why it allocated so much GPU memory? You can find it take around 10GB while it not need so much memory（I'm not train a model but deploy a model). I deployed some models and they usually use 3GB - 6GB cuda memory, but now, the procedure take all of the rest of the cuda memory.", "> ```python\r\n> ```python\r\n> 2023-11-06 15:31:55.308559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /device:GPU:0 with 11456 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\n> ```\r\n> \r\n> \r\n> \r\n> \r\n> \r\n> \r\n> \r\n> \r\n> \r\n> \r\n> \r\n> ```\r\n\r\nHi @9527-csroad , If you are referring to this log then its just printing the GPU device and its available memory for process. It's not locking the memory though.", "If so, I'm sorry to waste your time. I have end this project long time, and I will pay attention to this next time. Thanks for your patience reply.\r\nBest regard.", "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/62170\">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/62170\">No</a>\n" ]

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[ "I am able to reproduce\r\n\r\n```sh\r\n./benchmark_model --graph=split_v.tflite\r\nINFO: STARTING!\r\nWARN: Duplicate flags: num_threads\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [split_v.tflite]\r\nINFO: Loaded model split_v.tflite\r\nINFO: Initialized TensorFlow Lite runtime.\r\nINFO: Applying 1 TensorFlow Lite delegate(s) lazily.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nSegmentation fault\r\n```\r\n\r\nHi @alankelly, can you please take a look?" ]

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[ "I was able to reproduce this issue in 2.14.\r\n\r\n<img width=\"746\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/118897289/acd98354-bf15-4b1f-85d0-873ec21a243b\">\r\n\r\n\r\n@pkgoogle Could you please look into this?\r\n\r\nThanks.", "I was able to reproduce off of nightly:\r\n\r\n```\r\n./benchmark_model --graph=reduce_prod.tflite\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [reduce_prod.tflite]\r\nINFO: Loaded model reduce_prod.tflite\r\nERROR: Invalid tensor index 10 in inputs. The subgraph has 3 tensors\r\n\r\nERROR: Invalid tensor index 24 in outputs. The subgraph has 3 tensors\r\n\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nSegmentation fault\r\n```\r\n\r\n@alankelly, can you please take a look?" ]

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[ "I was able to reproduce this issue in 2.14.\r\n\r\n<img width=\"746\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/118897289/f99130a9-f836-4dc5-85cd-c30f8e6b8088\">\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.", "I was able to reproduce on nightly:\r\n\r\n```\r\n./benchmark_model --graph=pad.tflite\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [pad.tflite]\r\nINFO: Loaded model pad.tflite\r\nERROR: Invalid tensor index 4098 in outputs. The subgraph has 3 tensors\r\n\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nSegmentation fault\r\n```\r\n\r\n@alankelly can you please take a look? Thanks." ]

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[ "@SiriusHsh Thank you for raising this issue!\r\nCould you please check the following things;\r\na. Using the latest TF version\r\nb. Try using different benchmark tools of other TFlite versions and\r\nc. Switching to GPU instead of CPU\r\nPlease let us know if it helps?\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/62166\">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/62166\">No</a>\n", "I am able to reproduce:\r\n\r\n```\r\n./benchmark_model --graph=onehot.tflite\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [onehot.tflite]\r\nINFO: Loaded model onehot.tflite\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nSegmentation fault\r\n```\r\n\r\nHi @alankelly, can you please take a look? Thanks." ]

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[ "I am able to reproduce:\r\n\r\n```sh\r\n./benchmark_model --graph=maximum.tflite\r\nINFO: STARTING!\r\nWARN: Duplicate flags: num_threads\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [maximum.tflite]\r\nINFO: Loaded model maximum.tflite\r\nINFO: Initialized TensorFlow Lite runtime.\r\nERROR: Invalid tensor index 65536 in inputs. The subgraph has 3 tensors\r\n\r\nINFO: Applying 1 TensorFlow Lite delegate(s) lazily.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nSegmentation fault\r\n```\r\n\r\nHi @alankelly, can you please take a look? Thanks." ]

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[ "I was able to reproduce this on 2.14.\r\n\r\n<img width=\"746\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/118897289/217679ae-139d-4bfe-9c08-19a98ef007a8\">\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.", "I was able to reproduce on nightly:\r\n\r\n```\r\n./benchmark_model --graph=reshape.tflite\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [reshape.tflite]\r\nINFO: Loaded model reshape.tflite\r\nERROR: Invalid tensor index 32 in inputs. The subgraph has 3 tensors\r\n\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nSegmentation fault\r\n```\r\n\r\n@alankelly, can you please take a look? Thanks." ]

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[ "I was able to reproduce this issue on r2.14.\r\n\r\n<img width=\"746\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/118897289/e1956661-acb0-463a-aa8c-36f6dc1f1d94\">\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.", "I was able to reproduce on nightly:\r\n\r\n```\r\n./benchmark_model --graph=exp.tflite\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [exp.tflite]\r\nINFO: Loaded model exp.tflite\r\nERROR: Invalid tensor index 1025 in inputs. The subgraph has 3 tensors\r\n\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nSegmentation fault\r\n```\r\n\r\n@alankelly, can you please take a look. Thanks." ]

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[ "I was able to reproduce this on 2.14.\r\n\r\n<img width=\"746\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/118897289/e7d720df-37f9-4a95-b47e-8b90b829ccd0\">\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.\r\n", "I was able to reproduce on nightly:\r\n\r\n```\r\n./benchmark_model --graph=dequantize.tflite\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [dequantize.tflite]\r\nINFO: Loaded model dequantize.tflite\r\nERROR: Invalid tensor index 256 in outputs. The subgraph has 3 tensors\r\n\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nSegmentation fault\r\n```\r\n\r\n@alankelly, can you please take a look? Thanks." ]

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[ "I am able to reproduce:\r\n\r\n```sh\r\n./benchmark_model --graph=densify.tflite\r\nINFO: STARTING!\r\nWARN: Duplicate flags: num_threads\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [densify.tflite]\r\nINFO: Loaded model densify.tflite\r\nINFO: Initialized TensorFlow Lite runtime.\r\nERROR: Invalid tensor index 8193 in inputs. The subgraph has 3 tensors\r\n\r\nINFO: Applying 1 TensorFlow Lite delegate(s) lazily.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nSegmentation fault\r\n```\r\n\r\nHi @alankelly, I'm going to consolidate these to you for efficiency purposes. Can you please take a look? Thanks." ]

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[ "I was able to reproduce as noted above, @alankelly, can you please take a look? Thanks." ]

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[ "I was able to reproduce in both 2.14 and nightly as well.\r\n\r\n<img width=\"995\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/118897289/3923ff7e-e267-46ba-91d2-c671cc682eac\">\r\n\r\n@pkgoogle Could you please look at this issue?\r\n\r\nThanks.\r\n", "I was able to reproduce on nightly as well:\r\n\r\n```\r\n./benchmark_model --graph=relu6.tflite\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [relu6.tflite]\r\nINFO: Loaded model relu6.tflite\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nERROR: failed to delegate RELU node #0\r\nERROR: Node number 2 (TfLiteXNNPackDelegate) failed to prepare.\r\nERROR: Restored original execution plan after delegate application failure.\r\nINFO: The input model file size (MB): 0.00062\r\nINFO: Initialized session in 1.422ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\nSegmentation fault\r\n```\r\n\r\n@alankelly, can you please take a look? Thanks." ]

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[ "Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/ " ]

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[ "@vladbelit could you PTAL ", "We have a similar change going out soon (nightly jobs), though there are still a couple of details being hashed out with regards to CI", "Hi @SamuelMarks Can you please rebase your branch and resolve conflicts? Thank you!", "This could probably be closed - the Linux x86/ARM 3.12 nightlies are up. They should soon be set to upload, once a few superficial test failures are resolved.\r\nMacOS should be added soon as well.", "Thanks for the PR @SamuelMarks but the internal effort landed and our continued development of the official directory would have needed this PR to change multiple times. " ]

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[ "Hi @rsuderman, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "@NatashaKnk would you review this please?\r\nthis is another patch that tries to head off issues when TOSA upstreams changes that require operands have equal ranks.", "Hi @NatashaKnk, Can you please review this PR ? Thank you!", "Hi @NatashaKnk, Can you please review this PR ? Thank you!" ]

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[ "@penpornk ", "Hi @penpornk, Can you please review this PR ? Thank you!", "Hi @penpornk, would you mind taking a look at this PR and let me know if any changes are needed? Thank you :) ", "> Thank you for the PR and sorry for the delay! Could you please paste some microbenchmarks/end-to-end model performance results before and after the changes?\r\n> \r\n> cc: @cantonios\r\n\r\nPlease find attached benchmarks on image classification models before and after this PR. It improves performance up to 8% with an average of a 4.5% improvement.\r\n\r\n\r\n", "Hi @penpornk are there any other changes required on my end to get this PR merged? Thanks.", "Hi @penpornk and @cantonios. Would it be possible to get this merged in soon so that it makes it for the next release? Thank you.", "@davsva01 Sorry for the delay! Our auto-import tool failed with TSL-only changes. I'll try to manually import the changes." ]

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[ "I really need your help to solve this problem!\r\n", "It is the final linkage step that raise an error stating can't find -lnativewindow", "Hi @wqy123456 ,\r\n\r\nCould you please try adding `--copt=-DMESA_EGL_NO_X11_HEADERS=1` to the command and let us know if it helps. For more details please refer similar issue #52155 .\r\n\r\nThanks!", "Hi,@SuryanarayanaY,I have solved this question by modifying build_defs.bzl.\r\n\r\nBelow are my modifications：\r\n\r\n#def nativewindow_linkopts():\r\n # copybara:uncomment_begin(google-only)\r\n # return min_supported_ndk_api(\"26\", [\"-lnativewindow\"])\r\n # copybara:uncomment_end\r\n # copybara:comment_begin(oss-only)\r\n # return [\"-lnativewindow\"]\r\n # copybara:comment_end\r\n\r\ndef gpu_delegate_linkopts():\r\n \"\"\"Additional link options needed when linking in the GPU Delegate.\"\"\"\r\n return select({\r\n \"//tensorflow:android\": [\r\n \"-lEGL\",\r\n # We don't need to link libGLESv3, because if it exists,\r\n # it is a symlink to libGLESv2.\r\n # See Compatibility Definition Document:\r\n # https://source.android.com/compatibility/10/android-10-cdd#7_1_4_1_opengl_es\r\n \"-lGLESv2\",\r\n ],\r\n \"//conditions:default\": [],\r\n }) \r\n #+ nativewindow_linkopts()", "Hi @wqy123456 ,\r\n\r\nI can see the file [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/delegates/gpu/build_defs.bzl) at master branch. It seems you have commented `nativewindow_linkopts` to make it workable for you right?\r\n\r\nI think this modification might be applicable for your own optimization and compiler options you have chosen. \r\n\r\nCould we mark it as resolved now? \r\n\r\nThank you!\r\n\r\n\r\n", "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/62151\">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/62151\">No</a>\n" ]

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[ "@hfwanguanghui Could you please make sure that you are using the correct version of CMake?\r\nAs you are using Linux so please confirm if you have used right command for CMake as below,\r\n```\r\ncmake -S tensorflow/lite -B build -DCMAKE_TOOLCHAIN_FILE=path/to/toolchain.cmake\r\n\r\n```\r\nThe toolchain file that you are using needs to be corrected. The toolchain file should specify the compiler and linker that you want to use for cross compiling TensorFlow Lite.\r\n\r\nCould you please upgrade to the latest stable TF v2.14 as you are using an older version?\r\n\r\nIf you are still having issues then please provide more information about your environment and the steps that you are using to build the package.\r\n\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/62150\">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/62150\">No</a>\n", "I have the same issue and I can't solve the problem \r\n[this is the link for my issue](https://github.com/tensorflow/tensorflow/issues/68616)" ]

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[ "Hi @rsuderman, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @rdzhabarov, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!" ]

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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/62148/checks?check_run_id=17813806352) 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 @yasiribmcon Can you please sign CLA. Thank you!", "Hi @gbaned Sorry for the delay, CLA has been signed now!" ]

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[ "@algat There could be the following reasons for this specific build failure such as;\r\na. The dependencies would be missing - the libraries such as Flatbuffers, and XNNPACK should be installed properly\r\nb. Incompatible dependencies - the version would be mismatched sometimes which will cause this failure\r\nc. CMake configuration- The right version of CMake along with configuration set up for this should be correct.\r\n\r\nBy seeing your error log we are assuming that the CMake build system is trying to include the header file and to fix this error, you need to set the `INTERFACE_INCLUDE_DIRECTORIES` property to an absolute path. \r\nHope it helps?\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.", "I confirm that compilation to an installable package on Ubuntu 23.10 is not possible.\r\n\r\n```bash\r\n$ cmake ../tensorflow/tensorflow/lite -DTFLITE_ENABLE_INSTALL=ON \\\r\n -DCMAKE_FIND_PACKAGE_PREFER_CONFIG=ON \\\r\n -DSYSTEM_FARMHASH=ON \\\r\n -DSYSTEM_PTHREADPOOL=ON \\\r\n -Dabsl_DIR=/usr/lib/x86_64-linux-gnu/cmake/absl \\\r\n -DEigen3_DIR=/usr/share/eigen3/cmake \\\r\n -DFlatBuffers_DIR=/usr/lib/x86_64-linux-gnu/cmake/flatbuffers \\\r\n -Dgemmlowp_DIR=/usr/lib/x86_64-linux-gnu/cmake/gemmlowp \\\r\n -DNEON_2_SSE_DIR=/usr/lib/cmake/NEON_2_SSE \\\r\n -Dcpuinfo_DIR=/usr/lib/x86_64-linux-gnu/cmake/cpuinfo \\\r\n -Druy_DIR=/usr/lib/x86_64-linux-gnu/cmake/ruy\r\n```\r\n\r\nproduces the following:\r\n\r\n```bash\r\n-- Setting build type to Release, for debug builds use'-DCMAKE_BUILD_TYPE=Debug'.\r\n-- The C compiler identification is GNU 13.2.0\r\n-- The CXX compiler identification is GNU 13.2.0\r\n-- Detecting C compiler ABI info\r\n-- Detecting C compiler ABI info - done\r\n-- Check for working C compiler: /usr/bin/cc - skipped\r\n-- Detecting C compile features\r\n-- Detecting C compile features - done\r\n-- Detecting CXX compiler ABI info\r\n-- Detecting CXX compiler ABI info - done\r\n-- Check for working CXX compiler: /usr/bin/c++ - skipped\r\n-- Detecting CXX compile features\r\n-- Detecting CXX compile features - done\r\n-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\r\n-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\r\n-- Found Threads: TRUE \r\n-- Found farmhash: /usr/lib/x86_64-linux-gnu/libfarmhash.so \r\nCMake Warning (dev) at /usr/share/cmake-3.27/Modules/FetchContent.cmake:1316 (message):\r\n The DOWNLOAD_EXTRACT_TIMESTAMP option was not given and policy CMP0135 is\r\n not set. The policy's OLD behavior will be used. When using a URL\r\n download, the timestamps of extracted files should preferably be that of\r\n the time of extraction, otherwise code that depends on the extracted\r\n contents might not be rebuilt if the URL changes. The OLD behavior\r\n preserves the timestamps from the archive instead, but this is usually not\r\n what you want. Update your project to the NEW behavior or specify the\r\n DOWNLOAD_EXTRACT_TIMESTAMP option with a value of true to avoid this\r\n robustness issue.\r\nCall Stack (most recent call first):\r\n tools/cmake/modules/OverridableFetchContent.cmake:398 (FetchContent_Declare)\r\n tools/cmake/modules/fft2d.cmake:22 (OverridableFetchContent_Declare)\r\n tools/cmake/modules/Findfft2d.cmake:18 (include)\r\n CMakeLists.txt:150 (find_package)\r\nThis warning is for project developers. Use -Wno-dev to suppress it.\r\n\r\n-- The ASM compiler identification is GNU\r\n-- Found assembler: /usr/bin/cc\r\n-- Downloading cpuinfo to /home/me/Documents/GitHub/tflite_build/cpuinfo-source (define CPUINFO_SOURCE_DIR to avoid it)\r\n-- Configuring done (0.0s)\r\n-- Generating done (0.0s)\r\n-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/cpuinfo-download\r\n[ 11%] Creating directories for 'cpuinfo'\r\n[ 22%] Performing download step (download, verify and extract) for 'cpuinfo'\r\n-- Downloading...\r\n dst='/home/me/Documents/GitHub/tflite_build/cpuinfo-download/cpuinfo-prefix/src/959002f82d7962a473d8bf301845f2af720e0aa4.zip'\r\n timeout='none'\r\n inactivity timeout='none'\r\n-- Using src='https://github.com/pytorch/cpuinfo/archive/959002f82d7962a473d8bf301845f2af720e0aa4.zip'\r\n-- [download 0% complete]\r\n-- [download 1% complete]\r\n-- [download 2% complete]\r\n-- [download 3% complete]\r\n-- [download 4% complete]\r\n-- [download 5% complete]\r\n-- [download 6% complete]\r\n-- [download 7% complete]\r\n-- [download 8% complete]\r\n-- [download 9% complete]\r\n-- [download 10% complete]\r\n-- [download 11% complete]\r\n-- [download 12% complete]\r\n-- [download 13% complete]\r\n-- [download 14% complete]\r\n-- [download 15% complete]\r\n-- [download 16% complete]\r\n-- [download 17% complete]\r\n-- [download 18% complete]\r\n-- [download 19% complete]\r\n-- [download 20% complete]\r\n-- [download 21% complete]\r\n-- [download 22% complete]\r\n-- [download 23% complete]\r\n-- [download 24% complete]\r\n-- [download 25% complete]\r\n-- [download 26% complete]\r\n-- [download 27% complete]\r\n-- [download 28% complete]\r\n-- [download 29% complete]\r\n-- [download 30% complete]\r\n-- [download 31% complete]\r\n-- [download 32% complete]\r\n-- [download 33% complete]\r\n-- [download 34% complete]\r\n-- [download 35% complete]\r\n-- [download 36% complete]\r\n-- [download 37% complete]\r\n-- [download 38% complete]\r\n-- [download 39% complete]\r\n-- [download 40% complete]\r\n-- [download 41% complete]\r\n-- [download 42% complete]\r\n-- [download 43% complete]\r\n-- [download 44% complete]\r\n-- [download 45% complete]\r\n-- [download 46% complete]\r\n-- [download 47% complete]\r\n-- [download 48% complete]\r\n-- [download 49% complete]\r\n-- [download 50% complete]\r\n-- [download 51% complete]\r\n-- [download 52% complete]\r\n-- [download 53% complete]\r\n-- [download 54% complete]\r\n-- [download 55% complete]\r\n-- [download 56% complete]\r\n-- [download 57% complete]\r\n-- [download 58% complete]\r\n-- [download 59% complete]\r\n-- [download 60% complete]\r\n-- [download 61% complete]\r\n-- [download 62% complete]\r\n-- [download 63% complete]\r\n-- [download 64% complete]\r\n-- [download 65% complete]\r\n-- [download 66% complete]\r\n-- [download 67% complete]\r\n-- [download 68% complete]\r\n-- [download 69% complete]\r\n-- [download 70% complete]\r\n-- [download 71% complete]\r\n-- [download 72% complete]\r\n-- [download 73% complete]\r\n-- [download 74% complete]\r\n-- [download 75% complete]\r\n-- [download 76% complete]\r\n-- [download 77% complete]\r\n-- [download 78% complete]\r\n-- [download 79% complete]\r\n-- [download 80% complete]\r\n-- [download 81% complete]\r\n-- [download 82% complete]\r\n-- [download 83% complete]\r\n-- [download 84% complete]\r\n-- [download 85% complete]\r\n-- [download 86% complete]\r\n-- [download 87% complete]\r\n-- [download 88% complete]\r\n-- [download 89% complete]\r\n-- [download 90% complete]\r\n-- [download 91% complete]\r\n-- [download 92% complete]\r\n-- [download 93% complete]\r\n-- [download 94% complete]\r\n-- [download 95% complete]\r\n-- [download 96% complete]\r\n-- [download 97% complete]\r\n-- [download 98% complete]\r\n-- [download 99% complete]\r\n-- [download 100% complete]\r\n-- verifying file...\r\n file='/home/me/Documents/GitHub/tflite_build/cpuinfo-download/cpuinfo-prefix/src/959002f82d7962a473d8bf301845f2af720e0aa4.zip'\r\n-- Downloading... done\r\n-- extracting...\r\n src='/home/me/Documents/GitHub/tflite_build/cpuinfo-download/cpuinfo-prefix/src/959002f82d7962a473d8bf301845f2af720e0aa4.zip'\r\n dst='/home/me/Documents/GitHub/tflite_build/cpuinfo-source'\r\n-- extracting... [tar xfz]\r\n-- extracting... [analysis]\r\n-- extracting... [rename]\r\n-- extracting... [clean up]\r\n-- extracting... done\r\n[ 33%] No update step for 'cpuinfo'\r\n[ 44%] No patch step for 'cpuinfo'\r\n[ 55%] No configure step for 'cpuinfo'\r\n[ 66%] No build step for 'cpuinfo'\r\n[ 77%] No install step for 'cpuinfo'\r\n[ 88%] No test step for 'cpuinfo'\r\n[100%] Completed 'cpuinfo'\r\n[100%] Built target cpuinfo\r\n-- Downloading FP16 to /home/me/Documents/GitHub/tflite_build/FP16-source (define FP16_SOURCE_DIR to avoid it)\r\n-- Configuring done (0.0s)\r\n-- Generating done (0.0s)\r\n-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/FP16-download\r\n[ 11%] Creating directories for 'fp16'\r\n[ 22%] Performing download step (download, verify and extract) for 'fp16'\r\n-- Downloading...\r\n dst='/home/me/Documents/GitHub/tflite_build/FP16-download/fp16-prefix/src/0a92994d729ff76a58f692d3028ca1b64b145d91.zip'\r\n timeout='none'\r\n inactivity timeout='none'\r\n-- Using src='https://github.com/Maratyszcza/FP16/archive/0a92994d729ff76a58f692d3028ca1b64b145d91.zip'\r\n-- [download 14% complete]\r\n-- [download 45% complete]\r\n-- [download 88% complete]\r\n-- [download 99% complete]\r\n-- [download 100% complete]\r\n-- verifying file...\r\n file='/home/me/Documents/GitHub/tflite_build/FP16-download/fp16-prefix/src/0a92994d729ff76a58f692d3028ca1b64b145d91.zip'\r\n-- Downloading... done\r\n-- extracting...\r\n src='/home/me/Documents/GitHub/tflite_build/FP16-download/fp16-prefix/src/0a92994d729ff76a58f692d3028ca1b64b145d91.zip'\r\n dst='/home/me/Documents/GitHub/tflite_build/FP16-source'\r\n-- extracting... [tar xfz]\r\n-- extracting... [analysis]\r\n-- extracting... [rename]\r\n-- extracting... [clean up]\r\n-- extracting... done\r\n[ 33%] No update step for 'fp16'\r\n[ 44%] No patch step for 'fp16'\r\n[ 55%] No configure step for 'fp16'\r\n[ 66%] No build step for 'fp16'\r\n[ 77%] No install step for 'fp16'\r\n[ 88%] No test step for 'fp16'\r\n[100%] Completed 'fp16'\r\n[100%] Built target fp16\r\n-- Downloading FXdiv to /home/me/Documents/GitHub/tflite_build/FXdiv-source (define FXDIV_SOURCE_DIR to avoid it)\r\n-- Configuring done (0.0s)\r\n-- Generating done (0.0s)\r\n-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/FXdiv-download\r\n[ 11%] Creating directories for 'fxdiv'\r\n[ 22%] Performing download step (download, verify and extract) for 'fxdiv'\r\n-- Downloading...\r\n dst='/home/me/Documents/GitHub/tflite_build/FXdiv-download/fxdiv-prefix/src/b408327ac2a15ec3e43352421954f5b1967701d1.zip'\r\n timeout='none'\r\n inactivity timeout='none'\r\n-- Using src='https://github.com/Maratyszcza/FXdiv/archive/b408327ac2a15ec3e43352421954f5b1967701d1.zip'\r\n-- [download 65% complete]\r\n-- [download 100% complete]\r\n-- verifying file...\r\n file='/home/me/Documents/GitHub/tflite_build/FXdiv-download/fxdiv-prefix/src/b408327ac2a15ec3e43352421954f5b1967701d1.zip'\r\n-- Downloading... done\r\n-- extracting...\r\n src='/home/me/Documents/GitHub/tflite_build/FXdiv-download/fxdiv-prefix/src/b408327ac2a15ec3e43352421954f5b1967701d1.zip'\r\n dst='/home/me/Documents/GitHub/tflite_build/FXdiv-source'\r\n-- extracting... [tar xfz]\r\n-- extracting... [analysis]\r\n-- extracting... [rename]\r\n-- extracting... [clean up]\r\n-- extracting... done\r\n[ 33%] No update step for 'fxdiv'\r\n[ 44%] No patch step for 'fxdiv'\r\n[ 55%] No configure step for 'fxdiv'\r\n[ 66%] No build step for 'fxdiv'\r\n[ 77%] No install step for 'fxdiv'\r\n[ 88%] No test step for 'fxdiv'\r\n[100%] Completed 'fxdiv'\r\n[100%] Built target fxdiv\r\n-- Downloading pthreadpool to /home/me/Documents/GitHub/tflite_build/pthreadpool-source (define PTHREADPOOL_SOURCE_DIR to avoid it)\r\n-- Configuring done (0.0s)\r\n-- Generating done (0.0s)\r\n-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/pthreadpool-download\r\n[ 11%] Creating directories for 'pthreadpool'\r\n[ 22%] Performing download step (download, verify and extract) for 'pthreadpool'\r\n-- Downloading...\r\n dst='/home/me/Documents/GitHub/tflite_build/pthreadpool-download/pthreadpool-prefix/src/4fe0e1e183925bf8cfa6aae24237e724a96479b8.zip'\r\n timeout='none'\r\n inactivity timeout='none'\r\n-- Using src='https://github.com/Maratyszcza/pthreadpool/archive/4fe0e1e183925bf8cfa6aae24237e724a96479b8.zip'\r\n-- [download 18% complete]\r\n-- [download 37% complete]\r\n-- [download 56% complete]\r\n-- [download 87% complete]\r\n-- [download 95% complete]\r\n-- [download 100% complete]\r\n-- verifying file...\r\n file='/home/me/Documents/GitHub/tflite_build/pthreadpool-download/pthreadpool-prefix/src/4fe0e1e183925bf8cfa6aae24237e724a96479b8.zip'\r\n-- Downloading... done\r\n-- extracting...\r\n src='/home/me/Documents/GitHub/tflite_build/pthreadpool-download/pthreadpool-prefix/src/4fe0e1e183925bf8cfa6aae24237e724a96479b8.zip'\r\n dst='/home/me/Documents/GitHub/tflite_build/pthreadpool-source'\r\n-- extracting... [tar xfz]\r\n-- extracting... [analysis]\r\n-- extracting... [rename]\r\n-- extracting... [clean up]\r\n-- extracting... done\r\n[ 33%] No update step for 'pthreadpool'\r\n[ 44%] No patch step for 'pthreadpool'\r\n[ 55%] No configure step for 'pthreadpool'\r\n[ 66%] No build step for 'pthreadpool'\r\n[ 77%] No install step for 'pthreadpool'\r\n[ 88%] No test step for 'pthreadpool'\r\n[100%] Completed 'pthreadpool'\r\n[100%] Built target pthreadpool\r\nCMake Deprecation Warning at /home/me/Documents/GitHub/tflite_build/FP16-source/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED):\r\n Compatibility with CMake < 3.5 will be removed from a future version of\r\n CMake.\r\n\r\n Update the VERSION argument <min> value or use a ...<max> suffix to tell\r\n CMake that the project does not need compatibility with older versions.\r\n\r\n\r\n-- Downloading PSimd to /home/me/Documents/GitHub/tflite_build/psimd-source (define PSIMD_SOURCE_DIR to avoid it)\r\nCMake Deprecation Warning at CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED):\r\n Compatibility with CMake < 3.5 will be removed from a future version of\r\n CMake.\r\n\r\n Update the VERSION argument <min> value or use a ...<max> suffix to tell\r\n CMake that the project does not need compatibility with older versions.\r\n\r\n\r\n-- Configuring done (0.0s)\r\n-- Generating done (0.0s)\r\n-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/psimd-download\r\n[ 11%] Creating directories for 'psimd'\r\n[ 22%] Performing download step (git clone) for 'psimd'\r\nKlone nach 'psimd-source' …\r\nIhr Branch ist auf demselben Stand wie 'origin/master'.\r\nBereits auf 'master'\r\n[ 33%] Performing update step for 'psimd'\r\n[ 44%] No patch step for 'psimd'\r\n[ 55%] No configure step for 'psimd'\r\n[ 66%] No build step for 'psimd'\r\n[ 77%] No install step for 'psimd'\r\n[ 88%] No test step for 'psimd'\r\n[100%] Completed 'psimd'\r\n[100%] Built target psimd\r\nCMake Deprecation Warning at /home/me/Documents/GitHub/tflite_build/psimd-source/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED):\r\n Compatibility with CMake < 3.5 will be removed from a future version of\r\n CMake.\r\n\r\n Update the VERSION argument <min> value or use a ...<max> suffix to tell\r\n CMake that the project does not need compatibility with older versions.\r\n\r\n\r\n-- Configuring done (35.6s)\r\nCMake Error in tools/cmake/modules/ml_dtypes/CMakeLists.txt:\r\n Target \"ml_dtypes\" INTERFACE_INCLUDE_DIRECTORIES property contains path:\r\n\r\n \"/home/me/Documents/GitHub/tflite_build/ml_dtypes\"\r\n\r\n which is prefixed in the build directory.\r\n\r\n\r\nCMake Error in tools/cmake/modules/ml_dtypes/CMakeLists.txt:\r\n Target \"ml_dtypes\" INTERFACE_INCLUDE_DIRECTORIES property contains path:\r\n\r\n \"/home/me/Documents/GitHub/tflite_build/ml_dtypes/ml_dtypes\"\r\n\r\n which is prefixed in the build directory.\r\n\r\n\r\nCMake Error: install(EXPORT \"tensorflow-liteTargets\" ...) includes target \"tensorflow-lite\" which requires target \"pthreadpool\" that is not in any export set.\r\nCMake Error: install(EXPORT \"tensorflow-liteTargets\" ...) includes target \"tensorflow-lite\" which requires target \"XNNPACK\" that is not in any export set.\r\n-- Generating done (0.4s)\r\nCMake Generate step failed. Build files cannot be regenerated correctly.\r\n```", "@algat \r\nThis issue is effectively a duplicate of #62381 .\r\nCan we close this issue here as it is already being tracked there?\r\n\r\n 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/62147\">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/62147\">No</a>\n" ]

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[ "@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.14 and [tf-nightly](https://colab.research.google.com/gist/tilakrayal/627c4a38027beada5fb0a4da7bc8e915/untitled1396.ipynb) But whereas in tensorflow v2.13, the colab was unable to get a crash and it was executed with the error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/f402321fdfdb57589971f9c77e499bb4/untitled1397.ipynb). " ]

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[ "Hi @RangaSamudrala ,\r\n\r\nThe `AdjustContrastV2` Op has no gradient registered. Hence you are getting the error. You can have a look into [source](https://www.tensorflow.org/api_docs/python/tf/raw_ops) here to find the list of Ops and have gradient support or not.", "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/62145\">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/62145\">No</a>\n" ]

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[ "@GwiHwan-Go I tried to replicate the issue reported in both TF [v2.14](https://colab.research.google.com/gist/sushreebarsa/4303b9fd7d7e08b6e3d08631d24c9fcd/62142.ipynb#scrollTo=n-zt9Xm8Z7oy) , tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/3260a844f3a8be89da01fce7e053ffbd/untitled890.ipynb#scrollTo=n-zt9Xm8Z7oy) and didn't face the error reported. Could you please have a look at this gists and confirm the same? The error log is attached below;\r\n```\r\n**InvalidArgumentError Traceback (most recent call last)\r\n[<ipython-input-2-b326ca390b51>](https://localhost:8080/#) in <cell line: 10>()\r\n 8 'padding': 'VALID', 'strides': [3],\r\n 9 'use_cudnn_on_gpu': True}\r\n---> 10 res = tf.raw_ops.Conv2DBackpropFilter(**args)\r\n 11 print(res)\r\n\r\n3 frames\r\n[/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py](https://localhost:8080/#) in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)\r\n 51 try:\r\n 52 ctx.ensure_initialized()\r\n---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\n 54 inputs, attrs, num_outputs)\r\n 55 except core._NotOkStatusException as e:\r\n\r\nInvalidArgumentError: {{function_node __wrapped__Conv2DBackpropFilter_device_/job:localhost/replica:0/task:0/device:CPU:0}} Sliding window strides field must specify 4 dimensions [Op:Conv2DBackpropFilter]**\r\n```\r\nThank you!", "I've upgraded TensorFlow from version v1.12.1-100714-gd8e55c05473 to 2.15.0-dev20231005, as indicated in the logs below. Despite the upgrade, I'm still encountering a \"core dumped\" error, also provided below.\r\n```\r\n023-10-18 15:47:55.233348: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-10-18 15:47:55.233380: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-10-18 15:47:55.234739: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-10-18 15:47:55.242401: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-10-18 15:47:56.170911: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-10-18 15:47:58.000540: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1924] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 727 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:02:00.0, compute capability: 7.5\r\n2023-10-18 15:47:58.001140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1924] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 6732 MB memory: -> device: 1, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:04:00.0, compute capability: 7.5\r\n2023-10-18 15:47:58.001643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1924] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 6732 MB memory: -> device: 2, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:83:00.0, compute capability: 7.5\r\n2023-10-18 15:47:58.002156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1924] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 6732 MB memory: -> device: 3, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:84:00.0, compute capability: 7.5\r\ntf version : 2.15.0-dev20231005\r\n2023-10-18 15:47:58.050863: F ./tensorflow/core/util/tensor_format.h:428] Check failed: index >= 0 && index < num_total_dims Invalid index from the dimension: 3, 0, C\r\nAborted (core dumped)\r\n```\r\nThis is my other environment information :\r\n\r\nCUDA used to build PyTorch: 11.8\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 22.04.3 LTS (x86_64)\r\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.22.1\r\nLibc version: glibc-2.35\r\n\r\nPython version: 3.9.17 (main, Jul 5 2023, 20:41:20) [GCC 11.2.0] (64-bit runtime)\r\nPython platform: Linux-5.15.0-86-generic-x86_64-with-glibc2.35\r\nIs CUDA available: True\r\nCUDA runtime version: 12.1.105\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration: \r\nGPU 0: NVIDIA GeForce RTX 2070\r\nGPU 1: NVIDIA GeForce RTX 2070\r\nGPU 2: NVIDIA GeForce RTX 2070\r\nGPU 3: NVIDIA GeForce RTX 2070\r\n\r\nNvidia driver version: 535.104.12\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nAddress sizes: 46 bits physical, 48 bits virtual\r\nByte Order: Little Endian\r\nCPU(s): 32\r\nOn-line CPU(s) list: 0-31\r\nVendor ID: GenuineIntel\r\nModel name: Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz\r\nCPU family: 6\r\nModel: 63\r\nThread(s) per core: 2\r\nCore(s) per socket: 8\r\nSocket(s): 2\r\nStepping: 2\r\nCPU max MHz: 3200.0000\r\nCPU min MHz: 1200.0000\r\nBogoMIPS: 4793.99\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm xsaveopt cqm_llc cqm_occup_llc dtherm ida arat pln pts md_clear flush_l1d\r\nVirtualization: VT-x\r\nL1d cache: 512 KiB (16 instances)\r\nL1i cache: 512 KiB (16 instances)\r\nL2 cache: 4 MiB (16 instances)\r\nL3 cache: 40 MiB (2 instances)\r\nNUMA node(s): 2\r\nNUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30\r\nNUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31\r\nVulnerability Gather data sampling: Not affected\r\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\r\nVulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable\r\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable\r\nVulnerability Meltdown: Mitigation; PTI\r\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\r\nVulnerability Retbleed: Not affected\r\nVulnerability Spec rstack overflow: Not affected\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Not affected\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.24.3\r\n", "@GwiHwan-Go I was able to replicate the issue on colab, please find the [gist](https://colab.research.google.com/gist/sushreebarsa/e894b385f0fa20e264f16c2923df4ab8/62142.ipynb) here.\r\n@sachinprasadhs Could you please have a look at this issue?\r\nThank you!", "What is the purpose of this bug report? If it's to report a security issue see the [security policy](https://github.com/tensorflow/tensorflow/security/policy) and report through the proper channel.", "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/62142\">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/62142\">No</a>\n" ]

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[ "@GwiHwan-Go I tried with the latest TF version 2.14 and the result is as shown in the below;\r\n```\r\ntf.Tensor(\r\n[ 1.6422114 0.17947054 -1.2081759 -1.3383625 0.62573224 1.6191798\r\n -0.49271154], shape=(7,), dtype=float32)\r\n[ ]\r\n\r\n```\r\nCould you please have a look at the [gist](https://colab.research.google.com/gist/sushreebarsa/59bba6ef2eb918b41d4b2e0d85961e33/62140.ipynb) and confirm the same?\r\nThank you!", "I've upgraded TensorFlow from version v1.12.1-100714-gd8e55c05473 to 2.15.0-dev20231005, as indicated in the logs below. Despite the upgrade, I'm still encountering a \"core dumped\" error, also provided below.\r\n```\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-10-20 13:47:43.827420: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2.15.0-dev20231005\r\n2023-10-20 13:47:45.912409: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1924] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3554 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:02:00.0, compute capability: 7.5\r\n2023-10-20 13:47:45.913276: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1924] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 6826 MB memory: -> device: 1, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:04:00.0, compute capability: 7.5\r\n2023-10-20 13:47:45.914124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1924] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 6826 MB memory: -> device: 2, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:83:00.0, compute capability: 7.5\r\n2023-10-20 13:47:45.914841: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1924] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 6826 MB memory: -> device: 3, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:84:00.0, compute capability: 7.5\r\n2023-10-20 13:47:46.023909: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (2 vs. 2)\r\nAborted (core dumped)\r\n```\r\nThis is my other environment information :\r\n\r\nCUDA used to build PyTorch: 11.8\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 22.04.3 LTS (x86_64)\r\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.22.1\r\nLibc version: glibc-2.35\r\n\r\nPython version: 3.9.17 (main, Jul 5 2023, 20:41:20) [GCC 11.2.0] (64-bit runtime)\r\nPython platform: Linux-5.15.0-86-generic-x86_64-with-glibc2.35\r\nIs CUDA available: True\r\nCUDA runtime version: 12.1.105\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration:\r\nGPU 0: NVIDIA GeForce RTX 2070\r\nGPU 1: NVIDIA GeForce RTX 2070\r\nGPU 2: NVIDIA GeForce RTX 2070\r\nGPU 3: NVIDIA GeForce RTX 2070\r\n\r\nNvidia driver version: 535.104.12\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nAddress sizes: 46 bits physical, 48 bits virtual\r\nByte Order: Little Endian\r\nCPU(s): 32\r\nOn-line CPU(s) list: 0-31\r\nVendor ID: GenuineIntel\r\nModel name: Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz\r\nCPU family: 6\r\nModel: 63\r\nThread(s) per core: 2\r\nCore(s) per socket: 8\r\nSocket(s): 2\r\nStepping: 2\r\nCPU max MHz: 3200.0000\r\nCPU min MHz: 1200.0000\r\nBogoMIPS: 4793.99\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm xsaveopt cqm_llc cqm_occup_llc dtherm ida arat pln pts md_clear flush_l1d\r\nVirtualization: VT-x\r\nL1d cache: 512 KiB (16 instances)\r\nL1i cache: 512 KiB (16 instances)\r\nL2 cache: 4 MiB (16 instances)\r\nL3 cache: 40 MiB (2 instances)\r\nNUMA node(s): 2\r\nNUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30\r\nNUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31\r\nVulnerability Gather data sampling: Not affected\r\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\r\nVulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable\r\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable\r\nVulnerability Meltdown: Mitigation; PTI\r\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\r\nVulnerability Retbleed: Not affected\r\nVulnerability Spec rstack overflow: Not affected\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Not affected\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.24.3", "@GwiHwan-Go I haven't faced this issue while using TF v2.14 as shown in my comment above. tf.raw_ops.BiasAddGrad was introduced in TensorFlow 2.14.0, so you need to use at least that version to use it. Sometimes due to invalid arguments will be causing such issues. Please make sure you have enough memory or disk space available to avoid such situations. 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/62140\">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/62140\">No</a>\n" ]

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[]

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[ "@rafaelubalmw,\r\nCould you please provide some more information and the usecase to analyse the issue in an effective way. 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." ]

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[ "Hi @MKrbm ,\r\n\r\nThanks for reaching us. `tensorflow-macos` is built and maintained by Apple. I hope you have followed the metal plugin instructions [here](https://developer.apple.com/metal/tensorflow-plugin/). As this works fine on Colab(Linux) and the issue persists on only Mac M1, I request you to raise an issue with apple developer forum [here](https://developer.apple.com/forums/tags/tensorflow-metal/).\r\n\r\n@kulinseth , Do you have any pointers here?", "@SuryanarayanaY Yes, I indeed followd the instructions. I will raisean issue there as you suggested. Thank you.\r\n", "Hi @MKrbm ,\r\n\r\nCOuld you checked the issue with latest Keras3 versions either 3.0.4 or keras-nightly and let us know the result?", "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/62137\">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/62137\">No</a>\n" ]

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[ "Broken commit was reverted by https://github.com/tensorflow/tensorflow/commit/341a0320140f94a57eb81bbba8f70615afca161e", "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/62136\">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/62136\">No</a>\n" ]

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[ "Hi @jansdhillon \r\n\r\nThe output shapes for the tflite model can be observed using `interpreter.get_output_details()`. The outputs might be ordered and need to be accessed accordingly. \r\n\r\nCould you please share a toy tflite model in order to debug the issue?\r\n\r\nThanks.", "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.", "@jansdhillon Were you able to solve this and could please post the solution to this issue? I am having the same problem at the moment", "> Hi @jansdhillon\r\n> \r\n> The output shapes for the tflite model can be observed using `interpreter.get_output_details()`. The outputs might be ordered and need to be accessed accordingly.\r\n> \r\n> Could you please share a toy tflite model in order to debug the issue?\r\n> \r\n> Thanks.\r\n\r\nI did run that, which you can see the output of in the last block of code. They all have a shape of [].", "> @jansdhillon Were you able to solve this and could please post the solution to this issue? I am having the same problem at the moment\r\n\r\nUnfortunately, no I was not. I was trying to use this for a project but wasn't able to get it working in time so I had to pivot.", "Alright thanks for responding I will try to reproduce! I have run model maker on two different datasets. With one the inference afer conversion works just fine. With the other (the current one) I got the same results as you. Maybe I can find out the reason", "I was not able to reproduce it again. I am also using WSL with Ubuntu 22.04. I tested the same model as you with model maker and this time loading worked. However, when exporting the model to Tflite I also got this\r\n\r\n `2023-11-13 00:52:04.885225: W tensorflow/core/common_runtime/graph_constructor.cc:803] Node 'resample_p7/PartitionedCall' has 1 outputs but the _output_shapes attribute specifies shapes for 3 outputs. Output shapes may be inaccurate.\r\n`\r\n\r\nBut it did not matter. Loading the model and inferring it worked fine afterwards. I was able to access to output tensors like this:\r\n\r\n```\r\n signature_fn = interpreter.get_signature_runner()\r\n output = signature_fn(images=image)\r\n\r\n count = int(np.squeeze(output['output_0']))\r\n scores = np.squeeze(output['output_1'])\r\n classes = np.squeeze(output['output_2'])\r\n boxes = np.squeeze(output['output_3'])\r\n\r\n```\r\n\r\n", "Hi @jansdhillon \r\n\r\nAs mentioned @alexw92 , use the signature function to get the output details. You can add the following code to the interpreter. This information can be found in the [documentation](https://www.tensorflow.org/lite/models/modify/model_maker/object_detection ) as well. \r\n\r\n\r\n\r\n```\r\ndef detect_objects(interpreter, image, threshold):\r\n \"\"\"Returns a list of detection results, each a dictionary of object info.\"\"\"\r\n\r\n signature_fn = interpreter.get_signature_runner()\r\n\r\n # Feed the input image to the model\r\n output = signature_fn(images=image)\r\n\r\n # Get all outputs from the model\r\n count = int(np.squeeze(output['output_0']))\r\n scores = np.squeeze(output['output_1'])\r\n classes = np.squeeze(output['output_2'])\r\n boxes = np.squeeze(output['output_3'])\r\n\r\n results = []\r\n for i in range(count):\r\n if scores[i] >= threshold:\r\n result = {\r\n 'bounding_box': boxes[i],\r\n 'class_id': classes[i],\r\n 'score': scores[i]\r\n }\r\n results.append(result)\r\n return results\r\n```\r\nPlease let us know if any problem persists.\r\n\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/62135\">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/62135\">No</a>\n", "@alexw92 Hi, I'm not sure if you've deleted your comment but I got a notification about it so I figured I'd respond.\r\n\r\nThat was the same issue I was having. I should have been more clear about what I was actually trying to do, which was use the tflite model directly in my Flutter app with tflite_flutter. However, as you mentioned, it seems like the only way to get the actual output is with Python, which obviously does not work with Flutter directly. \r\n\r\nOf course, you could use a Flask server but at that point what's the point of on-device inference, really. This is why I pivoted away from this approach.\r\n\r\nBut I do agree, it would be ideal if the TFLite Model Maker could create models that work outside with the Flutter package. I did have some better results using MediaPipe but ultimately scrapped Flutter entirely.", "@jansdhillon Hi, yes sorry I did delete it. I thought I could reopen the issue but it does not work :D\r\n\r\nYes I fully aggree! I had to do manual fixes which are not appropriate in flutter tflite package to make this work. We should not be forced to do it like that. And since both Tensorflow, Flutter and also now the Flutter TFLite Package are under the hood of Google there should also be some interest of Google to make these work together I guess. \r\n\r\nUntil this is getting resolved if you or anyone else want to come back to use Flutter TFLite you can refer to this [issue](https://github.com/tensorflow/flutter-tflite/issues/67) which describes what must be done to in Flutter TFLite to use model maker OD models." ]

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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/62134/checks?check_run_id=17761789154) 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 @Krysclick12 Can you please sign CLA. Thank you!", "Closing as spam" ]

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[ "@crroush,\r\nCould you please elaborate about your Feature and please specify the Use Cases for this feature. \r\n\r\nAlso TensorFlow builds are **configured** by the .`bazelrc` file in the repository's root directory. The ./configure or `./configure.py` scripts can be used to adjust common settings.\r\n\r\nhttps://www.tensorflow.org/install/source#configure_the_build\r\n\r\nThere is also a python version of this script, .**/configure.py**. If using a virtual environment, python `configure.py` prioritizes paths within the environment, whereas ./configure prioritizes paths outside the environment. In both cases you can change the default. Thank you!\r\n", "I was using the python script, but I cannot just pass the configuration into the python script, I have to interactively type them in, so I am unable to automate this process. I have to build it for 3-4 architectures, so I would rather this process not require an engineer typing in commands. \r\n\r\nI am not that familiar with bazel, so if I can drop in different .bazelrc with my custom configs and forgo the configure.py that will get me to the same goal. " ]

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[ "BTW this may not even fix it as the classifier for 12.2 is not even listed on https://pypi.org/classifiers/ yet, but at least it would be correct for when it does appear." ]

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[ "https://github.com/tensorflow/tensorflow/actions/runs/6516448173/job/17699971553", "Thank you. This is being rolled back for now. \r\nIn the meantime, a PR was created to add the classifier to PyPi: \r\nhttps://github.com/pypa/trove-classifiers/pull/155", "You could use 'Environment :: GPU :: NVIDIA CUDA :: 12' instead as that one already exists.", "Seems like a good idea until the 12.2 tag gets added", "PR updated", "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/62127\">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/62127\">No</a>\n" ]

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[ "Hi @elfringham Can you please resolve conflicts? Thank you!", "Issue was resolved by https://github.com/tensorflow/tensorflow/commit/5a39244519e46bfba66e377e5ee533bc2ab3969b" ]

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[ "Introduced by https://github.com/tensorflow/tensorflow/commit/e7aa1cadef44349d57019401e54d4fb390bf48f1", "Fixed by https://github.com/tensorflow/tensorflow/commit/5a39244519e46bfba66e377e5ee533bc2ab3969b", "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/62125\">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/62125\">No</a>\n" ]