I am writing a custom BERT model on my own corpus, I generated the vocab file using BertWordPieceTokenizer and then running below code
!python create_pretraining_data.py
--input_file=/content/drive/My Drive/internet_archive_scifi_v3.txt
--output_file=/content/sample_data/tf_examples.tfrecord
--vocab_file=/content/sample_data/sifi_13sep-vocab.txt
--do_lower_case=True
--max_seq_length=128
--max_predictions_per_seq=20
--masked_lm_prob=0.15
--random_seed=12345
--dupe_factor=5
Getting output as :
INFO:tensorflow:*** Reading from input files ***
INFO:tensorflow:*** Writing to output files ***
INFO:tensorflow: /content/sample_data/tf_examples.tfrecord
INFO:tensorflow:Wrote 0 total instances
Not sure why I am always getting 0 instances in tf_examples.tfrecord, what am I doing wrong?
I am using TF version 1.12
FYI..generated vocab file is 290 KB.
It can not read the input file, please use My\ Drive instead of My Drive:
--input_file=/content/drive/My\ Drive/internet_archive_scifi_v3.txt
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I have a tensorflow "graph-model" consisting of a model.json and several .bin files. In javascript I am able to read those files using
const weights = browser.runtime.getURL("web_model/model.json");
tf.loadGraphModel(weights)
However I would like to be able to use this model in python, in order to process the results better.
When I try to load the model in python with
new_model = keras.models.load_model('./web_model/model.json')
I get the following error:
File "h5py/h5f.pyx", line 106, in h5py.h5f.open
OSError: Unable to open file (file signature not found)
I don't understand, since the javascript code is able to run the model, I think python should be able to do the same as well. What am I doing wrong ?
i git yolov7(https://github.com/WongKinYiu) with yolov7.pt and try to run
detect.py(i just want to run the example). it seems to be normal. but the output image has no mask.Why?
here is my code and log:
(PyTorch) E:\yolov7>python detect.py --weights yolov7.pt --source inference\images\bus.jpg
Namespace(weights=['yolov7.pt'], source='inference\\images\\bus.jpg', img_size=640, conf_thres=0.25, iou_thres=0.45, device='', view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', exist_ok=False, no_trace=False)
YOLOR v0.1-103-g6ded32c torch 1.11.0 CUDA:0 (NVIDIA GeForce GTX 1650, 4095.6875MB)
Fusing layers...
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
Model Summary: 306 layers, 36905341 parameters, 6652669 gradients
Convert model to Traced-model...
traced_script_module saved!
model is traced!
E:\anaconda\envs\PyTorch\lib\site-packages\torch\functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\TensorShape.cpp:2228.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Done. (151.6ms) Inference, (9.3ms) NMS
The image with the result is saved in: runs\detect\exp4\bus.jpg
Done. (3.713s)
and here is my result:output image
You set the argument classes=None.
The classes variable refers to a list of classes, where you define the index of the entities saved inside the weights you are referencing for the inference.
From detect.py:
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
Since you told the model to check for zero classes, the model itself will not report anything.
I was also facing this issue. After downgrading cuda version to 10.2 my problem was solved. I used Cuda 10.2 with PyTorch 1.10.0 via pip installation. I hope it helps you too.
pip3 install torch==1.10.0+cu102 torchvision==0.11.1+cu102 torchaudio===0.10.0+cu102 -f https://download.pytorch.org/whl/cu102/torch_stable.html
Source of the answer: link
Since when you are working with GPU, It allows half precision by default, which you can change by editing your detect.py file.
Go to detect.py file and not exactly sure but on line 31, you will see this line of code:
half = device.type != 'cpu' # half precision only supported on CUDA
Replace that line with
half = False
and then save default.py file.
Now when you are using your detection command, make sure to use --device 0 that indicates your GPU must be utilize for detection.
python detect.py --weights yolov7.pt --device 0 --source inference\images\bus.jpg
I have an export.pkl model which has been trained on a cuda machine. I want to use it on a macbook:
from fastai.text import load_learner
from utils import get_corpus
learner = load_learner('./models')
corpus = get_corpus()
res = [ str(learner.predict(c)[0]) for c in corpus ]
I get the following error:
...
File "/Users/gautiergilabert/Envs/cc/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 146, in forward
"them on device: {}".format(self.src_device_obj, t.device))
RuntimeError: module must have its parameters and buffers on device cuda:0 (device_ids[0]) but found one of them on device: cpu
I have two questions:
I found the raise in my export.pkl:
for t in chain(self.module.parameters(), self.module.buffers()):
if t.device != self.src_device_obj:
raise RuntimeError("module must have its parameters and buffers "
"on device {} (device_ids[0]) but found one of "
"them on device: {}".format(self.src_device_obj, t.device))
It is said about the module in the docstring: module to be parallelized. I don't really understand what it is. My macbook ?
Apart my macbook, I would like to run the model on a cpu
Is there a way to make this export.pkl model works on a cpu ?
Is there a way to make another export.pkl on cuda and make it available on a cpu ?
Thanks
One way is to load the learner by specifying the model with empty dataset and loading the model weights afterwards. For resnet image classifier something like this should work:
from fastai.vision import *
# path where the model is saved under path/models/model-name
path = "model_path"
tfms = get_transforms()
data = ImageDataBunch.single_from_classes(".", classes=["class1", "class2"], ds_tfms=tfms)
learner = cnn_learner(data, models.resnet34, metrics=accuracy)
# loads model from model_path/models/model_name.pth
learner.load("model_name")
image = open_image("test.jpg")
pred_class, pred_idx, outputs = learner.predict(image)
I am preparing a custom model to run on android phone using instructions from https://www.tensorflow.org/mobile/prepare_models
First i retrained the model on custom images using below command:
$ python tensorflow/examples/image_retraining/retrain.py --image_dir tensorflow/examples/image_retraining/my_images/ --learning_rate=0.0005 --testing_percentage=15 --validation_percentage=15 --train_batch_size=32 --validation_batch_size=-1 --flip_left_right True --random_scale=30 --random_brightness=30 --eval_step_interval=100 --how_many_training_steps=100 --tfhub_module https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/1
and as next step, I tested the model using label_image.py which also works fine in predicting the input image. However, freeze_graph gives error
$ bazel-bin/tensorflow/python/tools/freeze_graph --input_graph=/tmp/output_graph.pb --output_graph=/tmp/frozen_graph.pb
However, I keep getting this error.
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position
57: invalid start byte
I noticed that your --input_graph=/tmp/output_graph.pb. Is your graph written as binary file (as_text=False), instead of pbtxt? If so, you will need to pass the --input_binary=true flag to freeze_graph.
if you write your graph as a binary file using:
tf.train.write_graph(sess.graph_def, 'tarinGraph', 'train2.pbtxt', as_text=False)
then you will need to pass the --input_binary=true flag to freeze_graph.
I am trying out ways to deploy tensorflow model on android/iOS devices. So I did:
1) use tf.saved_model.builder.SavedModelBuilder to get model in .pb file
2) use tf.saved_model.loader.load() to verify that I can restore the model
However, when I want to do further inspection of the model using import_pb_to_tensorboard.py following suggestions at
1) https://medium.com/#daj/how-to-inspect-a-pre-trained-tensorflow-model-5fd2ee79ced0
2) https://hackernoon.com/running-a-tensorflow-model-on-ios-and-android-ce89446c8143
I got this error:
File "/Users/rjtang/_hack/env.tensorflow_src/lib/python3.4/site-packages/google/protobuf/internal/python_message.py", line 1083, in MergeFromString
if self._InternalParse(serialized, 0, length) != length:
.....
File "/Users/rjtang/_hack/env.tensorflow_src/lib/python3.4/site-packages/google/protobuf/internal/decoder.py", line 612, in DecodeRepeatedField
if value.add()._InternalParse(buffer, pos, new_pos) != new_pos:
....
File "/Users/rjtang/_hack/env.tensorflow_src/lib/python3.4/site-packages/google/protobuf/internal/decoder.py", line 746, in DecodeMap
raise _DecodeError('Unexpected end-group tag.')
The code and the generated .pb files are here:
https://github.com/rjt10/hear_it/blob/master/urban_sound/saved_model.pb
https://github.com/rjt10/hear_it/blob/master/urban_sound/savedmodel_save.py
https://github.com/rjt10/hear_it/blob/master/urban_sound/savedmodel_load.py
The version of tensorflow that I use is built from source "HEAD detached at v1.4.1"
Well, I understand what's happening now. Tensorflow has at least 3 ways to save and load a model. The graph will be serialized as one of the following 3 protobuf objects:
GraphDef
MetaGraphDef
SavedModel
You just need to deserialize it properly, such as https://github.com/rjt10/hear_it/blob/master/urban_sound/model_check.py
For Android, TensorFlowInferenceInterface() expects a GraphDef, https://github.com/tensorflow/tensorflow/blob/e2be6d4c4fc9f1b7f6040b51b23190c14202e797/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java#L541
That explains why.