Hi,

I am trying to convert a 'ssd_mobilenet_v2_320x320_coco17_tpu-8' TF2 model to .tflite.

i use export_tflite_ssd_graph.py for create tflite_graph.pb

But this either fails to create. I am working in Tensorflow 2.3.0 and Python 3.7.3.

Prerequisites

Please answer the following questions for yourself before submitting an issue.

• [ ] I am using the latest TensorFlow Model Garden release and TensorFlow 2.
• [ ] I am reporting the issue to the correct repository. (Model Garden official or research directory)
• [ ] I checked to make sure that this issue has not already been filed.

1. The entire URL of the file you are using

https://github.com/tensorflow/models/tree/master/research/object_detection

2. Describe the bug

Thanks for releasing the Object Detection API 2.0. I am trying to build the model on my own dataset. I downloaded the trained file from model zoo CenterNet HourGlass104 512x512. Then changed the configure file and test the code. A bug comes.

WARNING:tensorflow:Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.conv_block.norm.moving_variance
W0716 19:56:53.424076 140587994642240 util.py:144] Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.conv_block.norm.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.conv.kernel
W0716 19:56:53.424108 140587994642240 util.py:144] Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.conv.kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.axis
W0716 19:56:53.424140 140587994642240 util.py:144] Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.gamma
W0716 19:56:53.424172 140587994642240 util.py:144] Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.beta
W0716 19:56:53.424204 140587994642240 util.py:144] Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.moving_mean
W0716 19:56:53.424236 140587994642240 util.py:144] Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.moving_variance
W0716 19:56:53.424268 140587994642240 util.py:144] Unresolved object in checkpoint: (root).model._feature_extractor._network.hourglass_network.1.inner_block.0.inner_block.0.inner_block.0.inner_block.0.decoder_block.1.skip.norm.moving_variance
WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
W0716 19:56:53.424301 140587994642240 util.py:152] **A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.**

A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.

I do not know how to resolve this issue!

6. System information

• OS Platform and Distribution: Linux Ubuntu 18.04
• TensorFlow installed from (source or binary): installed as the official guide and no error occurs.
• TensorFlow version (use command below): tensorflow 2.2.0
• Python version: 3.6
• CUDA/cuDNN version: CUDA 10.2, CuDNN 7.6
• GPU model and memory: 2x 2080 Ti

Please let us know which model this issue is about (specify the top-level directory)

models/img2txt

Can someone release a pre-trained model for the img2txt model trained on COCO? Would be great for someone here who doesn't have the computational resource yet to do a full training run. Thanks!

Are the DELF authors able to give a little more detail about how they train their model? Any insight into things like

- cross-entropy loss and/or accuracy curves during fine-tuning training and/or attention training
- number of epochs of training; number of GPUs; wall clock time
- learning rates; how layers are frozen/unfrozen
- how/whether hyperparameters were tuned on a validation set

would be super helpful. Any specific pointers to other projects (maybe in this repo?) that used a roughly similar procedure would be helpful as well.

EDIT: Also, can you verify that both the fine-tuning and attention models were trained on this dataset, rather than the Google-Landmarks dataset introduced in your paper.

Thanks Ben

cc @andrefaraujo

ImportError Traceback (most recent call last) in () ----> 1 from utils import label_map_util 2 3 from utils import visualization_utils as vis_util

~/lab/dl/models/object_detection/utils/label_map_util.py in () 20 import tensorflow as tf 21 from google.protobuf import text_format ---> 22 from object_detection.protos import string_int_label_map_pb2 23 24

ImportError: cannot import name 'string_int_label_map_pb2'

System information

• What is the top-level directory of the model you are using: models/research/object_detection/
• Have I written custom code: No custom code for reproducing the bug. I have written custom code for diagnosing.
• OS Platform and Distribution: Linux Ubuntu 16.04
• TensorFlow installed from (source or binary): Anaconda conda-forge channel
• TensorFlow version: b'unknown' 1.4.1 (output from python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)")
• CUDA/cuDNN version: CUDA 8.0/cuDNN 6.0
• GPU model and memory: 1 TITAN X (Pascal) 12189MiB
• Exact command to reproduce: Run the provided object detection demo (ssd_mobilenet_v1_coco_2017_11_17 model) with a small modification in the last cell to record the inference speed:

    i = 0
    for _ in range(10):
      image_path = TEST_IMAGE_PATHS[1]
      i += 1
      image = Image.open(image_path)
      # the array based representation of the image will be used later in order to prepare the
      # result image with boxes and labels on it.
      image_np = load_image_into_numpy_array(image)
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      # Actual detection.
      options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
      run_metadata = tf.RunMetadata()
      start_time = time.time()
      (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      print('Iteration %d: %.3f sec'%(i, time.time()-start_time))

The results show that the inference speed is much shower than the reported inference speed, 30ms, in the model zoo page:

Iteration 1: 2.212 sec
Iteration 2: 0.069 sec
Iteration 3: 0.076 sec
Iteration 4: 0.068 sec
Iteration 5: 0.072 sec
Iteration 6: 0.072 sec
Iteration 7: 0.071 sec
Iteration 8: 0.079 sec
Iteration 9: 0.085 sec
Iteration 10: 0.071 sec

Describe the problem

Summary: By directly running the provided object detection demo, the observed inference speed of object detection models in the model zoo is much slower than the reported inference speed. With some hack, a higher inference speed than the reported speed can be achieved. After some diagnostics, it is highly likely that the slow inference speed is caused by:

• tf.where and other post-processing operations are running anomaly slow on GPU; or
• The frozen inference graph is lack of the ability to optimize the GPU/CPU assignment.

proof of the hypothesis: tf.where and other post-processing operations are running anomaly slow on GPU

By outputting trace file, we can diagnose the running time of each node in details. To output the trace file, modify the last cell of object detection demo as:

from tensorflow.python.client import timeline
with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    # Definite input and output Tensors for detection_graph
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    # Each box represents a part of the image where a particular object was detected.
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
    i = 0
    for _ in range(10):
      image_path = TEST_IMAGE_PATHS[1]
      i += 1
      image = Image.open(image_path)
      # the array based representation of the image will be used later in order to prepare the
      # result image with boxes and labels on it.
      image_np = load_image_into_numpy_array(image)
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      # Actual detection.
      options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
      run_metadata = tf.RunMetadata()
      start_time = time.time()   
      (boxes, scores, classes, num) = sess.run(\
      [detection_boxes, detection_scores, detection_classes, num_detections], \
      feed_dict={image_tensor: image_np_expanded}, \
      options=options, run_metadata=run_metadata)    
      print('Iteration %d: %.3f sec'%(i, time.time()-start_time))
      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
        image_np,
        np.squeeze(boxes),
        np.squeeze(classes).astype(np.int32),
        np.squeeze(scores),
        category_index,
        use_normalized_coordinates=True,
        line_thickness=8)
        
    plt.figure(figsize=IMAGE_SIZE)
    plt.imshow(image_np)
    
    fetched_timeline = timeline.Timeline(run_metadata.step_stats)
    chrome_trace = fetched_timeline.generate_chrome_trace_format()
    with open('Experiment_1.json' , 'w') as f:
      f.write(chrome_trace)

The output json file has been included in the .zip file in the source code section below. Visualizing the json file in chrome://tracing/ gives:

The CNN related operations end at ~13ms and the rest post-processing operations take about 133ms. We have noticed that adding the trace function will further slow down the inference speed. But it is shows clearly that the post-processing operations (post CNN) run very slowly on GPU.

As a comparison, one can run the object detection demo with GPU disabled, and profile the running trace using the same method. To disable GPU, add os.environ['CUDA_VISIBLE_DEVICES'] = '' in the first row of the last cell.

The output json file has been included in the .zip file in the source code section below. Visualizing this json file in chrome://tracing/ gives:

By running everything on CPU, the CNN operations end at roughly 63ms and the rest post-processing operations only takes about 15ms on CPU which is significantly faster than the time they take when running on GPU.

proof of the hypothesis: The frozen inference graph is lack of the ability to optimized the GPU/CPU assignment

We add some hack trying to see can we achieve a higher inference speed. The hack is manually assigning the CNN related nodes on GPU and the rest nodes on CPU. The idea is using GPU to accelerate only CNN operations and leave the post-processing operations on CPU.

The source code has been included in the .zip file in the source code section below.

With this hack, we are able to observe a higher inference speed than the reported speed.

Iteration 1: 1.021 sec
Iteration 2: 0.027 sec
Iteration 3: 0.026 sec
Iteration 4: 0.027 sec
Iteration 5: 0.026 sec
Iteration 6: 0.026 sec
Iteration 7: 0.026 sec
Iteration 8: 0.031 sec
Iteration 9: 0.031 sec
Iteration 10: 0.026 sec

To verify the hypothesis, here are some questions we need from the tensorflow team:

1. Are the numbers of inference speed reported on the detection model zoo page tested on the frozen inference graphs or original graphs?

2. Are the slow tf.where and other post-processing operations supposed to run on GPU or CPU? Is the slow running speed on GPU normal?

3. Is there a device assigning function to optimize the GPU/CPU use in the original tensorflow graphs? Is that function missing in the frozen inference graphs?

Source code / logs

tensorflowissue.zip

System information

• What is the top-level directory of the model you are using: /Appendix/tensorflow_models/research
• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Nope
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
• TensorFlow installed from (source or binary): binary
• TensorFlow version (use command below): 2.0.0-alpha0, and the models lib clone on Mar 11, 2019
• Bazel version (if compiling from source):
• CUDA/cuDNN version: 10.0
• GPU model and memory: 1050Ti
• Exact command to reproduce: PIPELINE_CONFIG_PATH='/home/jovyan/Codelab/model/pipeline.config' MODEL_DIR='/home/jovyan/Codelab/data' NUM_TRAIN_STEPS=50000 SAMPLE_1_OF_N_EVAL_EXAMPLES=1 python3 object_detection/model_main.py
  --pipeline_config_path=${PIPELINE_CONFIG_PATH}
  --model_dir=${MODEL_DIR}
  --num_train_steps=${NUM_TRAIN_STEPS}
  --sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES
  --alsologtostderr

tf_upgrade_v2 --intree . --outtree . --copyotherfiles False

Describe the problem

I try to use ObjectDetection API in TensorFlow 2.0.0-alpha0, but the program told me that AttributeError: module 'tensorflow' has no attribute 'contrib', clearly Google has delete the contrlib library. So next step I try to use the tf_upgrade_v2 utility to help me converting existing TensorFlow 1.x Python scripts to TensorFlow 2.0. But finally I failed. Here are the message from terminal.

Source code / logs

@wangtz

Hi,

I have installed the tensorflow-gpu 1.5 or 1.6.rc0 in accompany with Cuda-9.0 and CuDNN-7.0.5 When I start training using train.py, it detects the GPU, but it starts the training on the CPU and CPU load is 100%. The GPU memory gets filled and its core clocks increases but it does not show any consistent load on the cores.

name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.759
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2018-02-12 11:23:48.533753: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1308] Adding visible gpu devices: 0
2018-02-12 11:23:57.838951: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:989] Creating TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4742 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)

As mentioned above the error takes place in while executing the command in the windows cmd prompt.

D:/BB/bin/protoc object_detection/protos/*.proto --python_out=.

as for the reference in the installation process of object detection in this model.

in protobuf compilation.

With the latest changes for exporting a SavedModel d9d10fbbb938534af72f405983cabb85258ac5f3 I tried to run predictions on Google CloudML. When I uploaded my model here https://console.cloud.google.com/mlengine/models I got the following error Model validation failed: SavedModel must contain exactly one metagraph with tag: serve which I was able to fix here: #1810 After this fix I ran into another error: Model validation failed: Outer dimension for SignatureDef outputs must be unknown, outer dimension of 'detection_scores:0' is 1

Printing out the detection_signature (https://github.com/tensorflow/models/blob/master/object_detection/exporter.py#L281) it looks like this:

inputs {
  key: "inputs"
  value {
    name: "ToFloat:0"
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 1
      }
      dim {
        size: -1
      }
      dim {
        size: -1
      }
      dim {
        size: 3
      }
    }
  }
}
outputs {
  key: "detection_boxes"
  value {
    name: "detection_boxes:0"
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 1
      }
      dim {
        size: 300
      }
      dim {
        size: 4
      }
    }
  }
}
outputs {
  key: "detection_classes"
  value {
    name: "detection_classes:0"
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 1
      }
      dim {
        size: 300
      }
    }
  }
}
outputs {
  key: "detection_scores"
  value {
    name: "detection_scores:0"
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 1
      }
      dim {
        size: 300
      }
    }
  }
}
outputs {
  key: "num_detections"
  value {
    name: "num_detections:0"
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 1
      }
    }
  }
}
method_name: "tensorflow/serving/predict"

I am just getting started with tensorflow... it looks to me like the dimension in the shapes should be somehow removed?

Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 18.04
• Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: -
• TensorFlow installed from (source or binary): pip
• TensorFlow version (use command below): 2.3.0
• Python version: 3.6.9
• Bazel version (if compiling from source): -
• GCC/Compiler version (if compiling from source): -
• CUDA/cuDNN version: CUDA 11.4
• GPU model and memory: GeForce RTX 3090

I am trying to convert a re-trained TF2 object detection SSD MobilenetV2 model to a proprietary framework. I have successfully re-trained the network and it runs properly. However, I am having trouble with converting the saved_model.pb to the other framework. The conversion script from the SDK I am working with performs optimization on the saved_model.pb, using 'meta_optimizer.cc', which returns an empty graph after running through my re-trained model. I used 'exporter_main_v2.py' to export my re-trained checkpoint to the saved_model.pb which I am having trouble with.

The issue is not with my training or checkpoints, but with the exporting process from checkpoint to a saved_model.pb using 'exporter_main_v2.py'. I know this because I downloaded the SSD MobilenetV2 model from the TF2 Zoo to test with it. I have no issue converting the official saved_model.pb file found in the official repo, but when I try to convert the official checkpoints found in the repo to a saved_model.pb using 'exporter_main_v2.py', I face the same issue trying to convert the newly produced saved_model.pb file to the proprietary framework. This means that something wrong is happening when executing the 'exporter_main_v2.py' script.

Describe the expected behavior The exported saved_model.pb file should not be different than the official saved_model.pb file found in the official repo.

The following is what I get, showing 0 nodes and 0 edges

Standalone code to reproduce the issue The model I downloaded is: http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_320x320_coco17_tpu-8.tar.gz

The command I used to export the official checkpoint to a saved_model.pb is: python ~/models/research/object_detection/exporter_main_v2.py --input_type image_tensor --pipeline_config_path pipeline.config --trained_checkpoint_dir checkpoint/ --output_directory exported_model/

I tried to move quantize_movinet.py. However, when I try to quantize movinet for int8, such as python quantize_movinet.py --saved_model_dir=${SAVED_MODEL_DIR} --saved_model_with_states_dir=${SAVED_MODEL_WITH_STATES_DIR} --output_dataset_dir=${OUTPUT_DATASET_DIR} --output_tflite=${OUTPUT_TFLITE} --quantization_mode=int8 --save_dataset_to_tfrecords=True

I get following error UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8e in position 193: invalid start byte

What does it mean? I guess Kinetics600 dataset doesn't work well. So, I added here in line 86 input_path='C:\\Users\\~~\\kinetics600\\', However it doesn't work. Please tell me how to move "quantize_movinet.py." Thanks.

OS: Windows Python 3.9

Prerequisites

Please answer the following questions for yourself before submitting an issue.

I am reporting the issue to the correct repository. (Model Garden official or research directory). Yes I checked to make sure that this issue has not already been filed. Yes

• [ ] I am using the tensorflow == 2.3.0 without gpu support.

1. The entire URL of the file you are using

https://gitee.com/cleryer-1/image-denoise-using-wasserstein-gan

This is my modified version of code: https://drive.google.com/drive/folders/1xdRZ5IOnAx_VyhvBB67x19EoDe6CjHUZ?usp=sharing (i only changed image pathways in config.py )

2. Describe the bug

I want to denoise images using GAN. I'm trying to implement same code on my own test and training data which is as follows: (http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X2.zip) (http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X2.zip) It gave 2 different errors at different times, these are: 'NoneType' object is not iterable numpy.core._exceptions.MemoryError: Unable to allocate 4.69 GiB for an array with shape (102400, 64, 64, 3) and data type float32.

I also tried it on Google Colab Pro+ but it gives an same error. At the last line, "train()" line it creates an error. Should i change something on filepaths?

3. Steps to reproduce

I followed the link above and reshaped and added noise to my own data(256*256 size) with the help of "python image_operation.py -h" in conda. I also changed the amount of virtual memory but problem still occurs. I only changed path in the config.py file with original work.

4. Expected behavior

I expect train.py and test.py to give results.

5. Additional context

runfile('C:/Users/eren.alici/Desktop/pythons/train.py', wdir='C:/Users/eren.alici/Desktop/pythons') generating patches done Traceback (most recent call last):

File "C:\Users\eren.alici\Desktop\pythons\train.py", line 83, in train()

File "C:\Users\eren.alici\Desktop\pythons\train.py", line 24, in train truth, noise = get_patch(truth, noise)

File "C:\Users\eren.alici\Desktop\pythons\utils.py", line 198, in get_patch return np.array(out_raw), np.array(out_noise)

MemoryError: Unable to allocate 4.69 GiB for an array with shape (102400, 64, 64, 3) and data type float32

6. System information

• OS Platform and Distribution : Windows 10 64 bit Pro
• TensorFlow installed from: pip
• TensorFlow version (use command below): 2.3.0
• Python version: 3.8.10
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version: None
• GPU model and memory: None

edit: i tried to train just 20 image and it gives this error this time:

[C:\Users\asus\AppData\Local\Microsoft\WindowsApps\python3.8.exe C:\Users\asus\PycharmProjects\pythonProject1\train.py 2022-12-31 16:50:37.740732: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found 2022-12-31 16:50:37.743082: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. generating patches done 2022-12-31 16:51:48.724730: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found 2022-12-31 16:51:48.727183: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303) 2022-12-31 16:51:48.851474: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-OLN8V00 2022-12-31 16:51:48.851846: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-OLN8V00 2022-12-31 16:51:48.860257: 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 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-12-31 16:51:48.952904: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1e5e84fe770 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2022-12-31 16:51:48.953235: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version Traceback (most recent call last): File "C:\Users\asus\PycharmProjects\pythonProject1\train.py", line 84, in train() File "C:\Users\asus\PycharmProjects\pythonProject1\train.py", line 45, in train for times in epoch_bar(range(truth.shape[0] // BATCH_SIZE)): File "C:\Users\asus\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\progressbar\progressbar.py", line 150, in next value = next(self.__iterable) TypeError: 'NoneType' object is not an iterator](url)

edit: i tried to train just 20 image and it gives this error this time:

C:\Users\asus\AppData\Local\Microsoft\WindowsApps\python3.8.exe C:\Users\asus\PycharmProjects\pythonProject1\train.py 2022-12-31 16:50:37.740732: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found 2022-12-31 16:50:37.743082: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. generating patches done 2022-12-31 16:51:48.724730: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found 2022-12-31 16:51:48.727183: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303) 2022-12-31 16:51:48.851474: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-OLN8V00 2022-12-31 16:51:48.851846: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-OLN8V00 2022-12-31 16:51:48.860257: 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 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-12-31 16:51:48.952904: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1e5e84fe770 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2022-12-31 16:51:48.953235: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version Traceback (most recent call last): File "C:\Users\asus\PycharmProjects\pythonProject1\train.py", line 84, in train() File "C:\Users\asus\PycharmProjects\pythonProject1\train.py", line 45, in train for times in epoch_bar(range(truth.shape[0] // BATCH_SIZE)): File "C:\Users\asus\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\progressbar\progressbar.py", line 150, in next value = next(self.__iterable) TypeError: 'NoneType' object is not an iterator

Description

It tries to solve the #10874 issue.

• Make a dev container for the DELF project.

Type of change

• [X] New feature (non-breaking change which adds functionality)

The suggestion is for the DELF project. https://github.com/tensorflow/models/tree/master/research/delf

I had some trouble making the DELF model work. It needs some dependencies and some of them had collisions with other projects. I tried to use install_delf.sh script, but It also returns some errors. It takes some time and effort until I can use the project.

I think It is a good idea to build a dev container and dockerize the whole project. It's a lot easier for others to use it.

hi, i am training on my own dataset first i use the build_image_dataset.py to creat tfrecord then i run train.py but i always got this error: tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [310,310,4], [batch]: [310,310,3]

310 is the image size of my images.

can someone give some ideas? thanks very much!