When i am trying sample code from Hugging face i get below error.
the code can be found from https://huggingface.co/facebook/tts_transformer-en-ljspeech
Code:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/fastspeech2-en-ljspeech",
arg_overrides={"vocoder": "hifigan", "fp16": False}
)
model = models[0]
TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
generator = task.build_generator(model, cfg)
text = "Hello, this is a test run."
sample = TTSHubInterface.get_model_input(task, text)
wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
ipd.Audio(wav, rate=rate)
Error:
TypeError Traceback (most recent call last)
Input In [1], in <module>
10 model = models[0]
11 TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
---> 12 generator = task.build_generator(model, cfg)
14 text = "Hello, this is a test run."
16 sample = TTSHubInterface.get_model_input(task, text)
File ~/office/virtual_environments/eye_for_bliend/Images/fairseq/fairseq/tasks/text_to_speech.py:151, in TextToSpeechTask.build_generator(self, models, cfg, vocoder, **unused)
149 if vocoder is None:
150 vocoder = self.build_default_vocoder()
--> 151 model = models[0]
152 if getattr(model, "NON_AUTOREGRESSIVE", False):
153 return NonAutoregressiveSpeechGenerator(model, vocoder, self.data_cfg)
TypeError: 'TTSTransformerModel' object is not subscriptable
What worked for me was to put the model in a list where you build the generator on line 12.
generator = task.build_generator([model], cfg)
Related
I have made a simple CNN to recognize three types of fish. I am trying to use CNN to classify the image that was not included in training or validation sets. The image is grunts-saltwater.jpg and is on Gdrive. Here is the code for predicting on existing CNN model:
grunts_url = "https://drive.google.com/file/d/1zuA6T_0a9mOUvNWHQ1OACPLaZMCtbIZd/view?usp=sharing"
grunts_path = tf.keras.utils.get_file('grunts-saltwater', origin=grunts_url)
img = keras.preprocessing.image.load_img(
grunts_path, target_size=(img_height, img_width))
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
However, I get the following error:
Downloading data from https://drive.google.com/file/d/1zuA6T_0a9mOUvNWHQ1OACPLaZMCtbIZd/view?usp=sharing
8192/Unknown - 0s 0us/step
---------------------------------------------------------------------------
UnidentifiedImageError Traceback (most recent call last)
<ipython-input-217-d031443047e1> in <module>()
3
4 img = keras.preprocessing.image.load_img(
----> 5 grunts_path, target_size=(img_height, img_width))
6
7 img_array = keras.preprocessing.image.img_to_array(img)
2 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/preprocessing/image.py in load_img(path, grayscale, color_mode, target_size, interpolation)
299 """
300 return image.load_img(path, grayscale=grayscale, color_mode=color_mode,
--> 301 target_size=target_size, interpolation=interpolation)
302
303
/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py in load_img(path, grayscale, color_mode, target_size, interpolation)
112 'The use of `load_img` requires PIL.')
113 with open(path, 'rb') as f:
--> 114 img = pil_image.open(io.BytesIO(f.read()))
115 if color_mode == 'grayscale':
116 # if image is not already an 8-bit, 16-bit or 32-bit grayscale image
/usr/local/lib/python3.6/dist-packages/PIL/Image.py in open(fp, mode)
2860 warnings.warn(message)
2861 raise UnidentifiedImageError(
-> 2862 "cannot identify image file %r" % (filename if filename else fp)
2863 )
2864
UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f9002c637d8>
Can you help with the issue, please? Thanks.
It is because the google drive link of the image is now a downloadable line rather it is a view link. If it is shared then you can convert it into a downloadable link such that http clients can download them.
To covert to downloadable line
Your shared image ID is 1zuA6T_0a9mOUvNWHQ1OACPLaZMCtbIZd so use the link "https://docs.google.com/uc?export=download&id=1zuA6T_0a9mOUvNWHQ1OACPLaZMCtbIZd"
Fixed code:
grunts_url = "https://docs.google.com/uc?export=download&id=1zuA6T_0a9mOUvNWHQ1OACPLaZMCtbIZd"
grunts_path = tf.keras.utils.get_file('grunts-saltwater', origin=grunts_url)
img = keras.preprocessing.image.load_img(grunts_path, target_size=(100, 100))
I managed to resolve the issue. I do not think I was referring to source URL correctly. Here is an example that worked.
gruntfish_url = "https://upload.wikimedia.org/wikipedia/commons/5/54/Blue_Stripe_Grunt._Haemulon_sciurus.jpg"
gruntfish_path = tf.keras.utils.get_file('Grunt.', origin=gruntfish_url)
I'm pretty new to tensorflow and I'm trying to run object_detection_tutorial. I'm getting TypeErrror and don't know how to fix it.
This is load_model function which misses 2 arguments:
tags: Set of string tags to identify the required MetaGraphDef. These should correspond to the tags used when saving the variables using the SavedModel save() API.
export_dir: Directory in which the SavedModel protocol buffer and variables to be loaded are located.
def load_model(model_name):
base_url = 'http://download.tensorflow.org/models/object_detection/'
model_file = model_name + '.tar.gz'
model_dir = tf.keras.utils.get_file(
fname=model_name,
origin=base_url + model_file,
untar=True)
model_dir = pathlib.Path(model_dir)/"saved_model"
model = tf.saved_model.load(str(model_dir))
model = model.signatures['serving_default']
return model
WARNING:tensorflow:From <ipython-input-9-f8a3c92a04a4>:11: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-12-e10c73a22cc9> in <module>
1 model_name = 'ssd_mobilenet_v1_coco_2017_11_17'
----> 2 detection_model = load_model(model_name)
<ipython-input-9-f8a3c92a04a4> in load_model(model_name)
9 model_dir = pathlib.Path(model_dir)/"saved_model"
10
---> 11 model = tf.saved_model.load(str(model_dir))
12 model = model.signatures['serving_default']
13
~/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
322 'in a future version' if date is None else ('after %s' % date),
323 instructions)
--> 324 return func(*args, **kwargs)
325 return tf_decorator.make_decorator(
326 func, new_func, 'deprecated',
TypeError: load() missing 2 required positional arguments: 'tags' and 'export_dir'
Can you help me fix this and run my first object detector :D?
I had the same problem and i'm trying to solve this for 1 week now. I guess the solution should be this;
model = tf.compat.v2.saved_model.load(str(model_dir), None)
More detail would be (from the official website) ;
Load a SavedModel from export_dir.
tf.saved_model.load(
export_dir,
tags=None
)
Aliases:
tf.compat.v1.saved_model.load_v2
tf.compat.v2.saved_model.load
I guessed it was a branch problem and using the tf_2_1_reference branch did the trick for me:
igian#iGians-MBP models % git checkout tf_2_1_reference
M research/object_detection/object_detection_tutorial.ipynb
Branch 'tf_2_1_reference' set up to track remote branch 'tf_2_1_reference' from 'origin'.
Switched to a new branch 'tf_2_1_reference'
igians#iGians-MBP models % jupyter notebook
Then executed each jupiter cell of the tutorial like a good newbie!
This is the branch i used: https://github.com/tensorflow/models/tree/tf_2_1_reference
If you would just like to make a perdiction then you can also use load the model as below:
from tensorflow.contrib import predictor
predict_fn = predictor.from_saved_model(model_dir)
"I'm trying to train the ner model using spacy. It works fine for CPU. But when I try executing it using GPU I'm getting the following error. Spacy version 2.1.4, CUDA version 10.1"
"I tried re-installing thinc but still I'm getting the error"
from __future__ import unicode_literals, print_function
import plac
import random
from pathlib import Path
import spacy
from spacy.util import minibatch, compounding
import json
spacy.require_gpu()
nlp = spacy.blank("en")
ner = nlp.create_pipe("ner")
ner = nlp.create_pipe("ner")
for _, annotations in TRAIN_DATA:
for ent in annotations.get("entities"):
ner.add_label(ent[2])
optimizer = nlp.begin_training()
"I'm getting the following error"
"CUDARuntimeError
Traceback (most recent call last)
in
----> 1 optimizer = nlp.begin_training()
G:\Anaconda3\lib\site-packages\spacy\language.py in begin_training(self, get_gold_tuples, sgd, component_cfg, **cfg)
547 if self.vocab.vectors.data.shape[1] >= 1:
548 self.vocab.vectors.data = Model.ops.asarray(self.vocab.vectors.data)
--> 549 link_vectors_to_models(self.vocab)
550 if self.vocab.vectors.data.shape[1]:
551 cfg["pretrained_vectors"] = self.vocab.vectors.name
G:\Anaconda3\lib\site-packages\spacy_ml.py in link_vectors_to_models(vocab)
297 else:
298 word.rank = 0
--> 299 data = ops.asarray(vectors.data)
300 # Set an entry here, so that vectors are accessed by StaticVectors
301 # (unideal, I know)
ops.pyx in thinc.neural.ops.CupyOps.asarray()
G:\Anaconda3\lib\site-packages\cupy\creation\from_data.py in array(obj, dtype, copy, order, subok, ndmin)
39
40 """
---> 41 return core.array(obj, dtype, copy, order, subok, ndmin)
42
43
cupy\core\core.pyx in cupy.core.core.array()
cupy\core\core.pyx in cupy.core.core.array()
cupy\core\core.pyx in cupy.core.core.ndarray.__init__()
cupy\cuda\memory.pyx in cupy.cuda.memory.alloc()
cupy\cuda\memory.pyx in cupy.cuda.memory.MemoryPool.malloc()
cupy\cuda\memory.pyx in cupy.cuda.memory.MemoryPool.malloc()
cupy\cuda\device.pyx in cupy.cuda.device.get_device_id()
cupy\cuda\runtime.pyx in cupy.cuda.runtime.getDevice()
cupy\cuda\runtime.pyx in cupy.cuda.runtime.check_status()
CUDARuntimeError: cudaErrorUnknown: unknown error"
The below is a part of my project code.
with tf.name_scope("test_accuracy"):
test_mean_abs_err, test_mean_abs_err_op = tf.metrics.mean_absolute_error(labels=label_pl, predictions=test_eval_predict)
test_accuracy, test_accuracy_op = tf.metrics.accuracy(labels=label_pl, predictions=test_eval_predict)
test_precision, test_precision_op = tf.metrics.precision(labels=label_pl, predictions=test_eval_predict)
test_recall, test_recall_op = tf.metrics.recall(labels=label_pl, predictions=test_eval_predict)
test_f1_measure = 2 * test_precision * test_recall / (test_precision + test_recall)
tf.summary.scalar('test_mean_abs_err', test_mean_abs_err)
tf.summary.scalar('test_accuracy', test_accuracy)
tf.summary.scalar('test_precision', test_precision)
tf.summary.scalar('test_recall', test_recall)
tf.summary.scalar('test_f1_measure', test_f1_measure)
# validation metric init op
validation_metrics_init_op = tf.variables_initializer(\
var_list=[test_mean_abs_err_op, test_accuracy_op, test_precision_op, test_recall_op], \
name='validation_metrics_init')
However, when I run it, errors occur like this:
Traceback (most recent call last):
File "./run_dnn.py", line 285, in <module>
train(wnd_conf)
File "./run_dnn.py", line 89, in train
name='validation_metrics_init')
File "/export/local/anaconda2/lib/python2.7/site-
packages/tensorflow/python/ops/variables.py", line 1176, in
variables_initializer
return control_flow_ops.group(*[v.initializer for v in var_list], name=name)
AttributeError: 'Tensor' object has no attribute 'initializer'
I realize that I cannot create a validation initializer like that. I want to re-calculate the corresponding metrics when I save a new checkpoint model and apply a new round of validation. So, I have to re-initialize the metrics to be zero.
But how to reset all these metrics to be zero? Many thanks to your help!
I sovled the problem in the following way after referring to the blog (Avoiding headaches with tf.metrics).
# validation metrics
validation_metrics_var_scope = "validation_metrics"
test_mean_abs_err, test_mean_abs_err_op = tf.metrics.mean_absolute_error(labels=label_pl, predictions=test_eval_predict, name=validation_metrics_var_scope)
test_accuracy, test_accuracy_op = tf.metrics.accuracy(labels=label_pl, predictions=test_eval_predict, name=validation_metrics_var_scope)
test_precision, test_precision_op = tf.metrics.precision(labels=label_pl, predictions=test_eval_predict, name=validation_metrics_var_scope)
test_recall, test_recall_op = tf.metrics.recall(labels=label_pl, predictions=test_eval_predict, name=validation_metrics_var_scope)
test_f1_measure = 2 * test_precision * test_recall / (test_precision + test_recall)
tf.summary.scalar('test_mean_abs_err', test_mean_abs_err)
tf.summary.scalar('test_accuracy', test_accuracy)
tf.summary.scalar('test_precision', test_precision)
tf.summary.scalar('test_recall', test_recall)
tf.summary.scalar('test_f1_measure', test_f1_measure)
# validation metric init op
validation_metrics_vars = tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=validation_metrics_var_scope)
validation_metrics_init_op = tf.variables_initializer(var_list=validation_metrics_vars, name='validation_metrics_init')
a minimal working example that can be run line by line in a python terminal:
import tensorflow as tf
s = tf.Session()
acc = tf.metrics.accuracy([0,1,0], [0.1, 0.9, 0.8])
ini = tf.variables_initializer(tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES))
s.run([ini])
s.run([acc])
Here is a snippet of my code taken directly from the tf.contrib.learn tutorial on tensorflow.org:
# Load Data Sets
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename = IRIS_TRAINING,
target_dtype = np.int,
features_dtype = np.float32)
Here is the error message:
AttributeError Traceback (most recent call last)
<ipython-input-14-7122d1244c55> in <module>()
11
12 # Load Data Sets
---> 13 training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
14 filename = IRIS_TRAINING,
15 target_dtype = np.int,
AttributeError: 'module' object has no attribute 'learn'
Clearly the module has the attribute learn since tensorflow has a section on learning tf.contrib.learn. What am I doing wrong? All guidance is appreciated.