I am trying to deploy a model based on Object Detection example to do some tests and I am getting this error:
"Expects arg[0] to be uint8 but float is provided"
In that case I am using this to load my data:
request.inputs['inputs'].CopyFrom(
tf.contrib.util.make_tensor_proto({FLAGS.input_image}))
where FLAGS.input_image is my image data in bytes.
I was thinking that maybe that I should convert my image bytes to something that this input understands, but I haven't found yet.
What could I do to fix this issue?
Thanks !!!!
To convert the image to bytes, use the following in client code (python)
with open(FLAGS.image, 'rb') as f:
data = f.read()
Also please find a sample client (for inception model in python) as follows https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/inception_client.py
Related
I am new in the tensorflow part and hope someone can help me.
I've seen this document https://www.tensorflow.org/api_docs/python/tf/audio/decode_wav and executed tf.audio.decode_wav( contents, desired_channels=-1, desired_samples=-1, name=None )
The only thing I've changed is the contents, changing to my path name.
But still get an error!
Any methods to convert it? And I want to output something like this. .tfrecord-00000-of-00008
Read the .wav file into a string of bytes and then decode it:
import tensorflow as tf
wav_contents = tf.io.read_file("file.wav")
audio, sample_rate = tf.audio.decode_wav(contents=wav_contents)
audio.shape
This example was borrowed from the TensorFlow tutorial on reading audio files.
I have a set of wav files that I want to generate a spectrogram of. But when I use the tf.audio.decode_wav function, I get the following error:
InvalidArgumentError: Bad audio format for WAV: Expected 1 (PCM), but
got7 [Op:DecodeWav]
How do I circumvent this error? Are there any other ways to generate a log mel spectrogram for wav files using tensorflow?
I am aware of librosa package, but I would prefer tensorflow.
The code is:
def decode_audio(audio_binary):
audio, _ = tf.audio.decode_wav(audio_binary)
return tf.squeeze(audio, axis=-1)
def get_waveform_and_label(file_path):
audio_binary = tf.io.read_file(file_path)
waveform = decode_audio(audio_binary)
return waveform
The error tells you that your files indicate that they have samples encoded as 8-bit mulaw.
As described in the TensorFlow documentation for tf.audio.decode_wav, only 16-bit PCM WAV is supported by this method.
You would need to re-encode your wave files prior to passing them to tensorflow. Something like ffmpeg could help here.
I can't seem to find any documentation on how to use this model.
I am trying to use it to print out the objects that appear in a video
any help would be greatly appreciated
I am just starting out so go easy on me
I am trying to use it to print out the objects that appear in a video
I interpret that your problem is to print out the name of the found objects.
I don't know how you implemented where you got Fast RCNN trained on OpenImages v4. Therefore, I will give you the way with the model from Tensorflow Hub. Google Colab. AI Hub
After some digging around and a LOT of trial and error I came up with this
#!/home/ahmed/anaconda3/envs/TensorFlow/bin/python3.8
import tensorflow as tf
import tensorflow_hub as hub
import time,imageio,sys,pickle
# sys.argv[1] is used for taking the video path from the terminal
video = sys.argv[1]
#passing the video file to ImageIO to be read later in form of frames
video = imageio.get_reader(video)
dictionary = {}
#download and extract the model( faster_rcnn/openimages_v4/inception_resnet_v2 or
# openimages_v4/ssd/mobilenet_v2) in the same folder
module_handle = "*Path to the model folder*"
detector = hub.load(module_handle).signatures['default']
#looping over every frame in the video
for index, frames in enumerate(video):
# converting the images ( video frames ) to tf.float32 which is the only acceptable input format
image = tf.image.convert_image_dtype(frames, tf.float32)[tf.newaxis]
# passing the converted image to the model
detector_output = detector(image)
class_names = detector_output["detection_class_entities"]
scores = detector_output["detection_scores"]
# in case there are multiple objects in the frame
for i in range(len(scores)):
if scores[i] > 0.3:
#converting form bytes to string
object = class_names[i].numpy().decode("ascii")
#adding the objects that appear in the frames in a dictionary and their frame numbers
if object not in dictionary:
dictionary[object] = [index]
else:
dictionary[object].append(index)
print(dictionary)
I've been trying to use a hyperspectral image dataset that was in .mat files. I found that using the scipy library with its loadmat function I can load the hyperspectral images and selecting some bands to see them as an RGB.
def RGBread(image):
images = loadmat(image).get('new_image')
return abs(images[:,:,(12,6,4)])
def SIread(image):
images = loadmat(image).get('new_image')
return abs(images[:,:,:])
After trying to implement the pix2pix architecture I found an unexpected error. When passing the list of the names of the dataset files by a function that is responsible for load the data(which are still .mat files), Tensor Flow does not have a direct method for this reading or coding, so I get these data with my RGBread and SIread method and then I turned them into tensors.
def load_image(filename, augment=True):
inimg = tf.cast( tf.convert_to_tensor(RGBread(ImagePATH+'/'+filename)
,dtype=tf.float32),tf.float32)[...,:3]
tgimg = tf.cast( tf.convert_to_tensor(SIread(ImagePATH+'/'+filename)
,dtype=tf.float32),tf.float32)[...,:12]
inimg, tgimg = resize(inimg, tgimg,IMG_HEIGH,IMG_WIDTH)
if augment:
inimg, tgimg = random_jitter(inimg, tgimg)
return inimg, tgimg
When loading an image with the load_image method, using the name and path of a single .mat file (a hyperspectral image) of my dataset as argument of my function the method worked perfectly.
plt.imshow(load_train_image(tr_urls[1])[0])
The problem started when I created my dataSet tensor, because my RGBread function does not receive a tensor as a parameter since loadmat('.mat') expects a string. Having the following error.
train_dataset = tf.data.Dataset.from_tensor_slices(tr_urls)
train_dataset = train_dataset.map(load_train_image,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
TypeError: expected str, bytes or os.PathLike object, not Tensor
After reading a lot about reading .mat files I found a user who recommended passing the data to TFrecord format. I've been trying to do it but I couldn't. Someone could help me?
Rasterio may be useful here.
https://rasterio.readthedocs.io/en/latest/
It can read hyperspectral .tif which can be passed to tf.data using a tf.keras data-generator. It may be a bit slow and perhaps should be done before training rather than at runtime.
An alternative is to ask whether you need the geotiff metadata. If not, you can preprocess and save as numpy arrays for tfrecords.
I am currently trying to get a trained TF seq2seq model working with Tensorflow.js. I need to get the json files for this. My input is a few sentences and the output is "embeddings". This model is working when I read in the checkpoint however I can't get it converted for tf.js. Part of the process for conversion is to get my latest checkpoint frozen as a protobuf (pb) file and then convert that to the json formats expected by tensorflow.js.
The above is my understanding and being that I haven't done this before, it may be wrong so please feel free to correct if I'm wrong in what I have deduced from reading.
When I try to convert to the tensorflow.js format I use the following command:
sudo tensorflowjs_converter --input_format=tf_frozen_model
--output_node_names='embeddings'
--saved_model_tags=serve
./saved_model/model.pb /web_model
This then displays the error listed in this post:
ValueError: Input 0 of node Variable/Assign was passed int32 from
Variable:0 incompatible with expected int32_ref.
One of the problems I'm running into is that I'm really not even sure how to troubleshoot this. So I was hoping that perhaps one of you maybe had some guidance or maybe you know what my issue may be.
I have upped the code I used to convert the checkpoint file to protobuf at the link below. I then added to the bottom of the notebook an import of that file that is then providing the same error I get when trying to convert to tensorflowjs format. (Just scroll to the bottom of the notebook)
https://github.com/xtr33me/textsumToTfjs/blob/master/convert_ckpt_to_pb.ipynb
Any help would be greatly appreciated!
Still unsure as to why I was getting the above error, however in the end I was able to resolve this issue by just switching over to using TF's SavedModel via tf.saved_model. A rough example of what worked for me can be found below should anyone in the future run into something similar. After saving out the below model, I was then able to perform the tensorflowjs_convert call on it and export the correct files.
if first_iter == True: #first time through
first_iter = False
#Lets try saving this badboy
cwd = os.getcwd()
path = os.path.join(cwd, 'simple')
shutil.rmtree(path, ignore_errors=True)
inputs_dict = {
"batch_decoder_input": tf.convert_to_tensor(batch_decoder_input)
}
outputs_dict = {
"batch_decoder_output": tf.convert_to_tensor(batch_decoder_output)
}
tf.saved_model.simple_save(
sess, path, inputs_dict, outputs_dict
)
print('Model Saved')
#End save model code