Is yolov5 is contained in the module torchvision.model? I have been getting an attribute error when trying to call yolo in pytorch.
AttributeError: module 'torchvision.models' has no attribute 'yolov5'
Related
AttributeError: module 'mediapipe.python.solutions.holistic' has no attribute 'LEFT_HAND_LANDMARKS'
I am trying to use left hand landmarks in mediapipe holistic model but it gives error that it has no attribute of LEFT_HAND_LANDMARKS
I were expecting to get 21 landmarks of hand defined in mediapipe solution.
I am using someone else code (Code Link) which is implemented in Tensorflow1. I want to run this code into Tensorflow2 However I am getting this error:
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
AttributeError: module 'tensorflow' has no attribute 'contrib'
I upgraded this code by using this instruction:
!tf_upgrade_v2 \
--infile /research/dept8/gds/anafees/MyTest.py \
--outfile /research/dept8/gds/anafees/MyTest2.py
Most things are updated, however generated report showed:
168:21: ERROR: Using member tf.contrib.distribute.MirroredStrategy in deprecated module tf.contrib. tf.contrib.distribute.MirroredStrategy cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.
I search google; however, I could not find any suitable solution. I do not want to move back to Tensorflow1. Is there any alternate solution? Can anyone help?
Few libraries are deprecated in Tensorflow 2.x such tf.contrib, To make the code compatible with Tf 2 needs some changes in library.
Replace
tf.contrib.learn.datasets.load_dataset("mnist")
with
tf.keras.datasets.mnist.load_data()
And Replace
tf.contrib.distribute.MirroredStrategy
with
tf.distribute.MirroredStrategy(devices=None, cross_device_ops=None)
I'm trying to make encoder decoder like model where I'm getting the following error at model.fit
AttributeError: module 'tensorflow.python.framework.ops' has no attribute '_TensorLike'
model.fit(train_dataloader,
validation_data = test_dataloader,
steps_per_epoch=len(train_dataset)//8,
epochs=10)
I'm using keras 2.3.1 and segmentaion-model
How to resolve it?
This issue is fixed in latest keras version 2.6.0
Workaround for older Keras version
Change is_tensor in file keras/backend/tensorflow_backend.py, as of keras with tensorflow 2.3.0
from tensorflow.python.framework import tensor_util
def is_tensor(x):
return tensor_util.is_tensor(x)
#return isinstance(x, tf_ops._TensorLike) or tf_ops.is_dense_tensor_like(x)
I am getting this error when I am predicting image class using learn.predict(img)
the image file format is png
AttributeError: PngImageFile object has no attribute apply_tfms
Google PngImageFile => http://code.nabla.net/doc/PIL/api/PIL/PngImagePlugin/PIL.PngImagePlugin.PngImageFile.html
Google apply_tfms => https://docs.fast.ai/vision.image.html#Image.apply_tfms
The call is expecting a fastai Image but it seems you're passing in a PIL Image.
It's impossible to tell for sure without actual code, but that seems a likely reason.
When I use keras Tuner for tuning, tuner=RandomSearch(hypermodel=model,objective='mse',seed=42,max_trials=3,directory=r'E:\multivariate test\bayes',project_name=' helloworld') reported an AttributeError: module'tensorflow._api.v1.keras.metrics' has no attribute'Metric', error，how to solve this problem?
Maybe you are using TF v < 2.2. You can check this page [https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Metric]. There you can find this: tf.keras.metrics.Metric