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View Code? Open in Web Editor NEWYolov3 Object Detection implemented as APIs, using TensorFlow and Flask
License: Apache License 2.0
Yolov3 Object Detection implemented as APIs, using TensorFlow and Flask
License: Apache License 2.0
I entered this code in git bash and i saw these error
I didn't touch anything. Just i followed your step on youtube
https://www.youtube.com/watch?v=p44G9_xCM4I
$ python detect_video.py --video 'data/video/video.mp4' --output 'data/video/output.avi'
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\init_ops.py:1251: calling VarianceScaling.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0403 02:33:27.644036 21832 deprecation.py:506] From C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\init_ops.py:1251: calling VarianceScaling.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
2020-04-03 02:33:39.667521: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
I0403 02:33:47.461905 21832 detect_video.py:36] weights loaded
I0403 02:33:47.462905 21832 detect_video.py:39] classes loaded
Traceback (most recent call last):
File "detect_video.py", line 94, in
app.run(main)
File "C:\Users\USER.conda\envs\yolov3-cpu\lib\site-packages\absl\app.py", line 299, in run
_run_main(main, args)
File "C:\Users\USER.conda\envs\yolov3-cpu\lib\site-packages\absl\app.py", line 250, in _run_main
sys.exit(main(argv))
File "detect_video.py", line 76, in main
boxes, scores, classes, nums = yolo.predict(img_in)
File "C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py", line 1060, in predict
x, check_steps=True, steps_name='steps', steps=steps)
File "C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py", line 2509, in _standardize_user_data
training_utils.check_steps_argument(x, steps, steps_name)
File "C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training_utils.py", line 990, in check_steps_argument
input_type=input_type_str, steps_name=steps_name))
ValueError: When using data tensors as input to a model, you should specify the steps
argument.
(yolov3-cpu)
================================================================
USER@DESKTOP-GT9682B MINGW64 ~/Desktop/학교/프로젝트/yolo/AIGuys_yolo/Object-Detection-API (master)
$ python detect_video.py --video 0 --output 'data/video/output.avi'
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\init_ops.py:1251: calling VarianceScaling.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0403 02:40:38.728249 15728 deprecation.py:506] From C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\init_ops.py:1251: calling VarianceScaling.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
2020-04-03 02:40:47.488561: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
I0403 02:40:55.117871 15728 detect_video.py:36] weights loaded
I0403 02:40:55.119870 15728 detect_video.py:39] classes loaded
Traceback (most recent call last):
File "detect_video.py", line 94, in
app.run(main)
File "C:\Users\USER.conda\envs\yolov3-cpu\lib\site-packages\absl\app.py", line 299, in run
_run_main(main, args)
File "C:\Users\USER.conda\envs\yolov3-cpu\lib\site-packages\absl\app.py", line 250, in _run_main
sys.exit(main(argv))
File "detect_video.py", line 76, in main
boxes, scores, classes, nums = yolo.predict(img_in)
File "C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py", line 1060, in predict
x, check_steps=True, steps_name='steps', steps=steps)
File "C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py", line 2509, in _standardize_user_data
training_utils.check_steps_argument(x, steps, steps_name)
File "C:\Users\USER\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training_utils.py", line 990, in check_steps_argument
input_type=input_type_str, steps_name=steps_name))
ValueError: When using data tensors as input to a model, you should specify the steps
argument.
(yolov3-cpu)
what should i do??
Hello, I found a performance issue in the definition of transform_targets_for_outpu
t, yolov3_tf2/dataset.py, tf.range(tf.shape(y_true)[1])
will be calculated repeatedly during program execution, resulting in reduced efficiency. I think it should be created before the loop.
Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.
I was running this application in colab. When I ran the load_weights.py file, it showed FATAL Flags parsing error: Unknown command line flag 'f'
. But the code ran, so I moved forward and ran the detect_video.py with my weights file as /content/yolov3.weights
and classes file as /content/Object-Detection-API/data/labels/coco.names
. It showed DuplicateFlagError: The flag 'classes' is defined twice. First from /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py, Second from /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py. Description from the first occurrence: path to classes file
My colab notebook :- https://colab.research.google.com/drive/1bESeiPhNPbRGO7xWDK0X6uzlo3gu6p3C#scrollTo=mGWhScK0jAeQ
after running the convert_annotations.py file got txt files for each of the images indicating its labels and annotations. But shouldn't I list those files in the obj.data file? How did my model get the labels?
Dear Author,
I was running the Object Detection API code on Google Colab. But when I am running app.py code, it gives me this error at the production WSGI Server. **http://0.0.0.0:5000/ **
Error:
The webpage at http://0.0.0.0:5000/ might be temporarily down or it may have moved permanently to a new web address.
Kindly suggest me the steps to solve this issue.
from yolov3_tf2.models import YoloV3, YoloV3Tiny
File "E:\OD-2\Object-Detection-API\yolov3_tf2\models.py", line 5, in
from tensorflow.keras import Model
ModuleNotFoundError: No module named 'tensorflow.keras'
Hello!
I got very bad detection result for tiny YOLO model, it feels like model is highly underfitted.
I used detection.py
file and changed strings 14 - 16 to use tiny version like:
flags.DEFINE_string('weights', './weights/yolov3-tiny.tf',
'path to weights file')
flags.DEFINE_boolean('tiny', True, 'yolov3 or yolov3-tiny')
The weights of models were downloaded by links from READ.ME file and were converted to tensorflow format without any errors.
The code about muti-gpus should be:
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.set_visible_devices(physical_devices[0:1], 'GPU')
Hi,
when I'm running python load_weights.py --weights ./weights/yolov3-tiny.weights --output ./weights/yolov3-tiny.tf --tiny
on my Jetson Nano (Jetpack 4.3, tensorflow 2.1.0) an error occurs.
Error:
ValueError: cannot reshape array of size 324670 into shape (512,256,3,3)
Can somebody help me with that?
Best regards an thanks in advance.
Below you will find the complete output after running the python file:
(aiguyyolotest1) christopher@ccz:~/aiguyyolotest1/Object-Detection-API$ python load_weights.py --weights ./weights/yolov3-tiny.weights --output ./weights/yolov3-tiny.tf --tiny
2020-09-18 08:33:58.451661: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-09-18 08:34:03.677829: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer.so.6
2020-09-18 08:34:03.724180: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer_plugin.so.6
2020-09-18 08:34:18.304032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-09-18 08:34:18.352799: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:948] ARM64 does not support NUMA - returning NUMA node zero
2020-09-18 08:34:18.353007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:00:00.0 name: NVIDIA Tegra X1 computeCapability: 5.3
coreClock: 0.9216GHz coreCount: 1 deviceMemorySize: 3.87GiB deviceMemoryBandwidth: 23.84GiB/s
2020-09-18 08:34:18.353174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-09-18 08:34:18.353300: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-09-18 08:34:18.435096: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-09-18 08:34:18.543380: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-09-18 08:34:18.661994: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-09-18 08:34:18.731548: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-09-18 08:34:18.732000: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-09-18 08:34:18.733151: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:948] ARM64 does not support NUMA - returning NUMA node zero
2020-09-18 08:34:18.733627: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:948] ARM64 does not support NUMA - returning NUMA node zero
2020-09-18 08:34:18.733735: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-09-18 08:34:18.843884: W tensorflow/core/platform/profile_utils/cpu_utils.cc:98] Failed to find bogomips in /proc/cpuinfo; cannot determine CPU frequency
2020-09-18 08:34:18.844908: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3ce9c5e0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-09-18 08:34:18.844972: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-09-18 08:34:18.945611: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:948] ARM64 does not support NUMA - returning NUMA node zero
2020-09-18 08:34:18.945924: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3ce01a20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-09-18 08:34:18.945975: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA Tegra X1, Compute Capability 5.3
2020-09-18 08:34:18.946674: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:948] ARM64 does not support NUMA - returning NUMA node zero
2020-09-18 08:34:18.946802: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:00:00.0 name: NVIDIA Tegra X1 computeCapability: 5.3
coreClock: 0.9216GHz coreCount: 1 deviceMemorySize: 3.87GiB deviceMemoryBandwidth: 23.84GiB/s
2020-09-18 08:34:18.946871: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-09-18 08:34:18.946920: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-09-18 08:34:18.947109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-09-18 08:34:18.947211: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-09-18 08:34:18.947296: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-09-18 08:34:18.947378: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-09-18 08:34:18.947418: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-09-18 08:34:18.947742: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:948] ARM64 does not support NUMA - returning NUMA node zero
2020-09-18 08:34:18.948078: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:948] ARM64 does not support NUMA - returning NUMA node zero
2020-09-18 08:34:18.948156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-09-18 08:34:18.948256: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-09-18 08:34:31.465761: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-09-18 08:34:31.465973: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] 0
2020-09-18 08:34:31.466015: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N
2020-09-18 08:34:31.482722: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:948] ARM64 does not support NUMA - returning NUMA node zero
2020-09-18 08:34:31.483740: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:948] ARM64 does not support NUMA - returning NUMA node zero
2020-09-18 08:34:31.489128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 279 MB memory) -> physical GPU (device: 0, name: NVIDIA Tegra X1, pci bus id: 0000:00:00.0, compute capability: 5.3)
Model: "yolov3_tiny"
Layer (type) Output Shape Param # Connected to
input (InputLayer) [(None, None, None, 0
yolo_darknet (Model) ((None, None, None, 6298480 input[0][0]
yolo_conv_0 (Model) (None, None, None, 2 263168 yolo_darknet[1][1]
yolo_conv_1 (Model) (None, None, None, 3 33280 yolo_conv_0[1][0]
yolo_darknet[1][0]
yolo_output_0 (Model) (None, None, None, 3 1312511 yolo_conv_0[1][0]
yolo_output_1 (Model) (None, None, None, 3 951295 yolo_conv_1[1][0]
yolo_boxes_0 (Lambda) ((None, None, None, 0 yolo_output_0[1][0]
yolo_boxes_1 (Lambda) ((None, None, None, 0 yolo_output_1[1][0]
yolo_nms (Lambda) ((None, 100, 4), (No 0 yolo_boxes_0[0][0]
yolo_boxes_0[0][1]
yolo_boxes_0[0][2]
yolo_boxes_1[0][0]
yolo_boxes_1[0][1]
yolo_boxes_1[0][2]Total params: 8,858,734
Trainable params: 8,852,366
Non-trainable params: 6,368
I0918 08:34:40.257712 547578310672 load_weights.py:19] model created
I0918 08:34:40.266925 547578310672 utils.py:47] yolo_darknet/conv2d bn
I0918 08:34:40.282309 547578310672 utils.py:47] yolo_darknet/conv2d_1 bn
I0918 08:34:40.295813 547578310672 utils.py:47] yolo_darknet/conv2d_2 bn
I0918 08:34:40.312008 547578310672 utils.py:47] yolo_darknet/conv2d_3 bn
I0918 08:34:40.321672 547578310672 utils.py:47] yolo_darknet/conv2d_4 bn
I0918 08:34:40.345241 547578310672 utils.py:47] yolo_darknet/conv2d_5 bn
Traceback (most recent call last):
File "load_weights.py", line 34, in
app.run(main)
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 299, in run
_run_main(main, args)
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 250, in _run_main
sys.exit(main(argv))
File "load_weights.py", line 21, in main
load_darknet_weights(yolo, FLAGS.weights, FLAGS.tiny)
File "/home/christopher/aiguyyolotest1/Object-Detection-API/yolov3_tf2/utils.py", line 68, in load_darknet_weights
conv_shape).transpose([2, 3, 1, 0])
ValueError: cannot reshape array of size 324670 into shape (512,256,3,3)
Good morning,
I'm stuck today because I don't know how to add a python method on YoloV3 or YoloV4,
My goal is to trigger an alarm (to send an e-mail for example) when an object will be detected,
example:
When my Yolov3 program detects it will send an alert email or a message (sms)
I have a doubt, my model has currently 3 classes(Nike, Adidas. Visa), is it possible to add a new class to the model without retraining the whole model. Will transfer learning work?
I am trying to convert this yolov3 custom model: https://drive.google.com/drive/folders/17jysPykGMkNw66lDMd0kryybCvGOesKi?usp=sharing into tensorflow format
in the load_weights.py I changed line 10 to:
flags.DEFINE_integer('num_classes', 1, 'number of classes in the model')
and in the ./data/labels/coco.names file I changed it to only "mice" (it detects mice for video analysis purposes)
however it begins converting until it produces the error in the title of this issue after this line in the cmd:
please if someone could help that would be much appreciated
Hey guys, I'm trying to convert my custom weights files to TensorFlow format but keep running into the same error.
I've renamed my files to yolov3.weights and placed then in the same folder as the official pretrained weights. I've also changed the coco.names file accordingly.
I have also changed the number of classes to match in the app.py, detect_video.pi and detect.py
Please let me know what I'm doing wrong!
(yolov3-cpu) MacBook-Pro-Milan-2:Object-Detection-API Milan$ python load_weights.py Traceback (most recent call last): File "load_weights.py", line 4, in <module> from yolov3_tf2.models import YoloV3, YoloV3Tiny ImportError: No module named yolov3_tf2.models
Milan
after i run python load_weights,
this is the output:
2020-06-09 21:12:35.418124: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
but it times out after a couple of seconds and no .tf tile is present after
ive ran everything correctly and it worked for awhile. then, this happened....
Traceback (most recent call last):
File "C:\Users\finnx\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\training\py_checkpoint_reader.py", line 95, in NewCheckpointReader
return CheckpointReader(compat.as_bytes(filepattern))
RuntimeError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for ./weights/yolov3.tf
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "detect_video.py", line 94, in <module>
app.run(main)
File "C:\Users\finnx\anaconda3\envs\yolov3-gpu\lib\site-packages\absl\app.py", line 303, in run
_run_main(main, args)
File "C:\Users\finnx\anaconda3\envs\yolov3-gpu\lib\site-packages\absl\app.py", line 251, in _run_main
sys.exit(main(argv))
File "detect_video.py", line 35, in main
yolo.load_weights(FLAGS.weights)
File "C:\Users\finnx\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 234, in load_weights
return super(Model, self).load_weights(filepath, by_name, skip_mismatch)
File "C:\Users\finnx\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 1187, in load_weights
py_checkpoint_reader.NewCheckpointReader(filepath)
File "C:\Users\finnx\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\training\py_checkpoint_reader.py", line 99, in NewCheckpointReader
error_translator(e)
File "C:\Users\finnx\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\training\py_checkpoint_reader.py", line 35, in error_translator
raise errors_impl.NotFoundError(None, None, error_message)
tensorflow.python.framework.errors_impl.NotFoundError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for ./weights/yolov3.tf
PLEASE HELP!!!!
Hello @theAIGuysCode, great work !!!.
What changes would I have to implement to use YoloV4?
Thank you
if tiny:
yolo = YoloV3Tiny(classes=num_classes)
else:
yolo = YoloV3(classes=num_classes)
yolo.load_weights(weights_path).expect_partial()
print('weights loaded')
class_names = [c.strip() for c in open(classes_path).readlines()]
print('classes loaded')
This code throw error on server startup. "Truncated or oversized response headers received from daemon process on production server"
I have nvidia driver 384.130 version, cuda 9.0 ver, cudnn 7.6.4
I tried python app.py
, an error occurs “tensorflow.python.framework.errors_impl.InternalError.cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version.
so I downgraded tensorflow-gpu version from 2.1 to 1.14.
I run python app.py
agian. and then another error occurred “ModuleNotFoundERror : No module named ‘tensorflow.keras’”
I downloaded tensorflow 2.1 ver again..
How can I fix it? :(
Thanks.
Kindly make a repo for yolov4 deployment as well.
Same/similar approach is not working for it.
How shall I make an API for YOLOv4?
I was able to follow the entire video but getting the error messag below when loading this,
(yolov3-gpu) C:\Users\rob26\Desktop\Object-Detection-API>python load_weights.py
(As suggested, using Anaconda)...
(yolov3-gpu) C:\Users\rob26\Desktop\Object-Detection-API>python load_weights.py
2020-03-22 14:12:34.123439: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
2020-03-22 14:12:36.164552: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-03-22 14:12:36.190347: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1660 major: 7 minor: 5 memoryClockRate(GHz): 1.83
pciBusID: 0000:01:00.0
2020-03-22 14:12:36.196564: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2020-03-22 14:12:36.201758: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2020-03-22 14:12:36.210883: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-03-22 14:12:36.216014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1660 major: 7 minor: 5 memoryClockRate(GHz): 1.83
pciBusID: 0000:01:00.0
2020-03-22 14:12:36.221990: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2020-03-22 14:12:36.226487: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2020-03-22 14:12:36.784693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-22 14:12:36.788336: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2020-03-22 14:12:36.791547: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2020-03-22 14:12:36.796796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4630 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1660, pci bus id: 0000:01:00.0, compute capability: 7.5)
Model: "yolov3"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
yolo_darknet (Model) ((None, None, None, 40620640 input[0][0]
__________________________________________________________________________________________________
yolo_conv_0 (Model) (None, None, None, 5 11024384 yolo_darknet[1][2]
__________________________________________________________________________________________________
yolo_conv_1 (Model) (None, None, None, 2 2957312 yolo_conv_0[1][0]
yolo_darknet[1][1]
__________________________________________________________________________________________________
yolo_conv_2 (Model) (None, None, None, 1 741376 yolo_conv_1[1][0]
yolo_darknet[1][0]
__________________________________________________________________________________________________
yolo_output_0 (Model) (None, None, None, 3 4984063 yolo_conv_0[1][0]
__________________________________________________________________________________________________
yolo_output_1 (Model) (None, None, None, 3 1312511 yolo_conv_1[1][0]
__________________________________________________________________________________________________
yolo_output_2 (Model) (None, None, None, 3 361471 yolo_conv_2[1][0]
__________________________________________________________________________________________________
yolo_boxes_0 (Lambda) ((None, None, None, 0 yolo_output_0[1][0]
__________________________________________________________________________________________________
yolo_boxes_1 (Lambda) ((None, None, None, 0 yolo_output_1[1][0]
__________________________________________________________________________________________________
yolo_boxes_2 (Lambda) ((None, None, None, 0 yolo_output_2[1][0]
__________________________________________________________________________________________________
yolo_nms (Lambda) ((None, 100, 4), (No 0 yolo_boxes_0[0][0]
yolo_boxes_0[0][1]
yolo_boxes_0[0][2]
yolo_boxes_1[0][0]
yolo_boxes_1[0][1]
yolo_boxes_1[0][2]
yolo_boxes_2[0][0]
yolo_boxes_2[0][1]
yolo_boxes_2[0][2]
==================================================================================================
Total params: 62,001,757
Trainable params: 61,949,149
Non-trainable params: 52,608
__________________________________________________________________________________________________
I0322 14:12:41.026230 8916 load_weights.py:19] model created
I0322 14:12:41.028251 8916 utils.py:47] yolo_darknet/conv2d bn
I0322 14:12:41.031241 8916 utils.py:47] yolo_darknet/conv2d_1 bn
I0322 14:12:41.033211 8916 utils.py:47] yolo_darknet/conv2d_2 bn
I0322 14:12:41.036204 8916 utils.py:47] yolo_darknet/conv2d_3 bn
I0322 14:12:41.039179 8916 utils.py:47] yolo_darknet/conv2d_4 bn
I0322 14:12:41.042149 8916 utils.py:47] yolo_darknet/conv2d_5 bn
I0322 14:12:41.045166 8916 utils.py:47] yolo_darknet/conv2d_6 bn
I0322 14:12:41.047483 8916 utils.py:47] yolo_darknet/conv2d_7 bn
I0322 14:12:41.050596 8916 utils.py:47] yolo_darknet/conv2d_8 bn
I0322 14:12:41.052566 8916 utils.py:47] yolo_darknet/conv2d_9 bn
I0322 14:12:41.057577 8916 utils.py:47] yolo_darknet/conv2d_10 bn
I0322 14:12:41.060544 8916 utils.py:47] yolo_darknet/conv2d_11 bn
I0322 14:12:41.065555 8916 utils.py:47] yolo_darknet/conv2d_12 bn
I0322 14:12:41.067526 8916 utils.py:47] yolo_darknet/conv2d_13 bn
I0322 14:12:41.071515 8916 utils.py:47] yolo_darknet/conv2d_14 bn
I0322 14:12:41.074537 8916 utils.py:47] yolo_darknet/conv2d_15 bn
I0322 14:12:41.079493 8916 utils.py:47] yolo_darknet/conv2d_16 bn
I0322 14:12:41.082505 8916 utils.py:47] yolo_darknet/conv2d_17 bn
I0322 14:12:41.086475 8916 utils.py:47] yolo_darknet/conv2d_18 bn
I0322 14:12:41.089491 8916 utils.py:47] yolo_darknet/conv2d_19 bn
I0322 14:12:41.094455 8916 utils.py:47] yolo_darknet/conv2d_20 bn
I0322 14:12:41.097445 8916 utils.py:47] yolo_darknet/conv2d_21 bn
I0322 14:12:41.101435 8916 utils.py:47] yolo_darknet/conv2d_22 bn
I0322 14:12:41.104452 8916 utils.py:47] yolo_darknet/conv2d_23 bn
I0322 14:12:41.109441 8916 utils.py:47] yolo_darknet/conv2d_24 bn
I0322 14:12:41.112406 8916 utils.py:47] yolo_darknet/conv2d_25 bn
I0322 14:12:41.116420 8916 utils.py:47] yolo_darknet/conv2d_26 bn
I0322 14:12:41.129360 8916 utils.py:47] yolo_darknet/conv2d_27 bn
I0322 14:12:41.132377 8916 utils.py:47] yolo_darknet/conv2d_28 bn
I0322 14:12:41.144320 8916 utils.py:47] yolo_darknet/conv2d_29 bn
I0322 14:12:41.148334 8916 utils.py:47] yolo_darknet/conv2d_30 bn
I0322 14:12:41.160302 8916 utils.py:47] yolo_darknet/conv2d_31 bn
I0322 14:12:41.163483 8916 utils.py:47] yolo_darknet/conv2d_32 bn
I0322 14:12:41.175453 8916 utils.py:47] yolo_darknet/conv2d_33 bn
I0322 14:12:41.178956 8916 utils.py:47] yolo_darknet/conv2d_34 bn
I0322 14:12:41.189929 8916 utils.py:47] yolo_darknet/conv2d_35 bn
I0322 14:12:41.194916 8916 utils.py:47] yolo_darknet/conv2d_36 bn
I0322 14:12:41.205785 8916 utils.py:47] yolo_darknet/conv2d_37 bn
I0322 14:12:41.209749 8916 utils.py:47] yolo_darknet/conv2d_38 bn
I0322 14:12:41.220768 8916 utils.py:47] yolo_darknet/conv2d_39 bn
I0322 14:12:41.224793 8916 utils.py:47] yolo_darknet/conv2d_40 bn
I0322 14:12:41.236654 8916 utils.py:47] yolo_darknet/conv2d_41 bn
I0322 14:12:41.239645 8916 utils.py:47] yolo_darknet/conv2d_42 bn
I0322 14:12:41.251588 8916 utils.py:47] yolo_darknet/conv2d_43 bn
I0322 14:12:41.305469 8916 utils.py:47] yolo_darknet/conv2d_44 bn
I0322 14:12:41.312425 8916 utils.py:47] yolo_darknet/conv2d_45 bn
I0322 14:12:41.363314 8916 utils.py:47] yolo_darknet/conv2d_46 bn
I0322 14:12:41.370305 8916 utils.py:47] yolo_darknet/conv2d_47 bn
I0322 14:12:41.421196 8916 utils.py:47] yolo_darknet/conv2d_48 bn
I0322 14:12:41.428506 8916 utils.py:47] yolo_darknet/conv2d_49 bn
I0322 14:12:41.480397 8916 utils.py:47] yolo_darknet/conv2d_50 bn
I0322 14:12:41.488376 8916 utils.py:47] yolo_darknet/conv2d_51 bn
I0322 14:12:41.538240 8916 utils.py:47] yolo_conv_0/conv2d_52 bn
I0322 14:12:41.545222 8916 utils.py:47] yolo_conv_0/conv2d_53 bn
I0322 14:12:41.597058 8916 utils.py:47] yolo_conv_0/conv2d_54 bn
I0322 14:12:41.603073 8916 utils.py:47] yolo_conv_0/conv2d_55 bn
I0322 14:12:41.655930 8916 utils.py:47] yolo_conv_0/conv2d_56 bn
I0322 14:12:41.663909 8916 utils.py:47] yolo_output_0/conv2d_57 bn
I0322 14:12:41.713775 8916 utils.py:47] yolo_output_0/conv2d_58 bias
I0322 14:12:41.717764 8916 utils.py:47] yolo_conv_1/conv2d_59 bn
I0322 14:12:41.719759 8916 utils.py:47] yolo_conv_1/conv2d_60 bn
I0322 14:12:41.722752 8916 utils.py:47] yolo_conv_1/conv2d_61 bn
I0322 14:12:41.732725 8916 utils.py:47] yolo_conv_1/conv2d_62 bn
I0322 14:12:41.735716 8916 utils.py:47] yolo_conv_1/conv2d_63 bn
I0322 14:12:41.746687 8916 utils.py:47] yolo_conv_1/conv2d_64 bn
I0322 14:12:41.749615 8916 utils.py:47] yolo_output_1/conv2d_65 bn
I0322 14:12:41.760585 8916 utils.py:47] yolo_output_1/conv2d_66 bias
I0322 14:12:41.762580 8916 utils.py:47] yolo_conv_2/conv2d_67 bn
I0322 14:12:41.764575 8916 utils.py:47] yolo_conv_2/conv2d_68 bn
I0322 14:12:41.766757 8916 utils.py:47] yolo_conv_2/conv2d_69 bn
I0322 14:12:41.769778 8916 utils.py:47] yolo_conv_2/conv2d_70 bn
I0322 14:12:41.771775 8916 utils.py:47] yolo_conv_2/conv2d_71 bn
I0322 14:12:41.777057 8916 utils.py:47] yolo_conv_2/conv2d_72 bn
I0322 14:12:41.779037 8916 utils.py:47] yolo_output_2/conv2d_73 bn
I0322 14:12:41.782189 8916 utils.py:47] yolo_output_2/conv2d_74 bias
I0322 14:12:41.783190 8916 load_weights.py:22] weights loaded
2020-03-22 14:12:41.800300: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-03-22 14:12:43.102478: W tensorflow/stream_executor/cuda/redzone_allocator.cc:312] Internal: Invoking ptxas not supported on Windows
Relying on driver to perform ptx compilation. This message will be only logged once.
2020-03-22 14:12:43.215831: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cublas64_100.dll'; dlerror: cublas64_100.dll not found
2020-03-22 14:12:43.220370: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_INTERNAL_ERROR
2020-03-22 14:12:43.224648: W tensorflow/stream_executor/stream.cc:1919] attempting to perform BLAS operation using StreamExecutor without BLAS support
Traceback (most recent call last):
File "load_weights.py", line 34, in <module>
app.run(main)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\absl\app.py", line 299, in run
_run_main(main, args)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\absl\app.py", line 250, in _run_main
sys.exit(main(argv))
File "load_weights.py", line 25, in main
output = yolo(img)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 708, in call
convert_kwargs_to_constants=base_layer_utils.call_context().saving)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 860, in _run_internal_graph
output_tensors = layer(computed_tensors, **kwargs)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 708, in call
convert_kwargs_to_constants=base_layer_utils.call_context().saving)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 860, in _run_internal_graph
output_tensors = layer(computed_tensors, **kwargs)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py", line 197, in call
outputs = self._convolution_op(inputs, self.kernel)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 1134, in __call__
return self.conv_op(inp, filter)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 639, in __call__
return self.call(inp, filter)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 238, in __call__
name=self.name)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 2010, in conv2d
name=name)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1031, in conv2d
data_format=data_format, dilations=dilations, name=name, ctx=_ctx)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1130, in conv2d_eager_fallback
ctx=_ctx, name=name)
File "C:\Users\rob26\anaconda3\envs\yolov3-gpu\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InternalError: Blas SGEMM launch failed : m=25600, n=32, k=64 [Op:Conv2D]
my custom weights file is junglecamp0.6.weights, based on yolov3 (not yolov3-tiny)
31 classes
When I run the command " python load_weights.py --weights ./weights/junglecamp0.6.weights --output ./weights/junglecamp0.6.tf " it gives me the following error message:
ValueError: cannot reshape array of size 76070 into shape (256,128,3,3)
Hi. I'm trying object-detection-API.
I run all code. finally I run app.py
but error occurred.
failed to allocate 120.19M (126025728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2020-06-14 01:30:23.044898: F tensorflow/stream_executor/cuda/cuda_driver.cc:175] Check failed: err == cudaSuccess || err == cudaErrorInvalidValue Unexpected CUDA error: out of memory
It is result when i run nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.130 Driver Version: 384.130 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce 840M Off | 00000000:0A:00.0 Off | N/A |
| N/A 39C P5 N/A / N/A | 249MiB / 2002MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1065 G /usr/lib/xorg/Xorg 119MiB |
| 0 1822 G compiz 79MiB |
| 0 2280 G /opt/teamviewer/tv_bin/TeamViewer 9MiB |
| 0 4531 G ...AAAAAAAAAAAACAAAAAAAAAA= --shared-files 39MiB |
+-----------------------------------------------------------------------------+
I should fix it by Monday. plz help me plz
pleaseeeeee......
Thanks
Hi, I want to add a noise filtering step to increase the object detection accuracy. Therefore please tell me how to add a function cv2.medianBlur() to the code given?
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