Created a simple dummy sequential model in tf.keras as shown below:
model = tf.keras.Sequential()
model.add(layers.Dense(10, input_shape=(100, 100)))
model.add(layers.Conv1D(3, 2))
model.add(layers.Flatten())
model.add(layers.Dense(10, activation='softmax', name='predict_10'))
Trained the model and saved it using tf.keras.models.saved_model.
To get the input input and output node names used saved_model_cli.
saved_model_cli show --dir "path/to/SavedModel" --all
Froze the saved model with freeze_graph.py utility.
python freeze_graph.py --input_saved_model_dir=<path/to/SavedModel> --output_graph=<path/freeze.pb> --input_binary=True --output_node_names=StatefulPartitionedCall
Model is frozen.
Now Here's the main issue:
To load the frozen graph I've used this guide Migrate tf1.x to tf2.x (wrap_frozen_graph)
Used
with tf.io.gfile.GFile("patf/to/freeze.pb", 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
load_frozen = wrap_frozen_graph(graph_def, inputs='dense_3_input:0', outputs='predict_10:0')
Output error
ValueError: Input 1 of node StatefulPartitionedCall was passed float from dense_3/kernel:0 incompatible with expected resource.
I'm getting same error when converting .pb to .dlc (Qualcomm).
Actually I want to run original model on Qualcomm's Hexagon DSP or GPU.
Related
I am trying to download the VGG19 model via TensorFlow
base_model = VGG19(input_shape = [256,256,3],
include_top = False,
weights = 'imagenet')
However the download always gets stuck before it finishes downloading. I've tried with different models too like InceptionV3 and the same happens there.
Fortunately, the prompt makes the link available where the model can be downloaded manually
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5
19546112/80134624 [======>.......................] - ETA: 11s
After downloading the model from the given link I try to import the model using
base_model = load_model('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5')
but I get this error
ValueError: No model found in config file.
How do I load in the downloaded .h5 model manually?
You're using load_model on weights, instead of a model. You need to have a defined model first, then load the weights.
weights = "path/to/weights"
model = VGG19 # the defined model
model.load_weights(weights) # the weights
Got the same problem when learning on tensorflow tutorial, too.
Transfer learning and fine-tuning: Create the base model from the pre-trained convnets
# Create the base model from the pre-trained model MobileNet V2
IMG_SIZE = (160, 160)
IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights=None)
# load model weights manually
weights = 'mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_160_no_top.h5'
base_model.load_weights(weights)
I tried download the model.h5, and load manually. It works.
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I'm trying to use a model from tensorflow hub on Kaggle.
Like so:
m = tf.keras.Sequential([
hub.KerasLayer("https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4", output_shape=[1280],
trainable=False), # Can be True, see below.
tf.keras.layers.Dense(num_classes, activation='softmax')
])
m.build([None, 224, 224, 3]) # Batch input shape.
It works well with GPU, but as soon as I switch to TPU with TF records I get the following error:
InvalidArgumentError: Unsuccessful TensorSliceReader constructor: Failed to get matching files on /tmp/tfhub_modules/87fb99f72aec02d017e12c0a3d86c5c182ec22ca/variables/variables: Unimplemented: File system scheme '[local]' not implemented (file: '/tmp/tfhub_modules/87fb99f72aec02d017e12c0a3d86c5c182ec22ca/variables/variables')
However the set up and tfrecords dataset are all correct as it works with a switching the pretrained model to a keras application of the same model (i.e. for example above using the mobilenet keras application).
I tried caching but I have been unsuccessful, is there something I have to beware when following this guide:
https://www.tensorflow.org/hub/caching
Thanks in advance!
The failure happens because the TPU is trying to load the TFHub model from /tmp/ which it doesn't have access to. You should be able to get this to work by:
with strategy.scope():
load_locally = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')
m = tf.keras.Sequential([
hub.KerasLayer(
"https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4",
output_shape=[1280],
load_options=load_locally,
trainable=False), # Can be True, see below.
tf.keras.layers.Dense(num_classes, activation='softmax')
])
Source: EfficientNetB7 on 100+ flowers.
I want to use a pre-trained model and add a segmentation head at the end of that, but the problem is that I just have the 'frozen_inference_graph.pb'. These are the files that I have from the model:
I have tried several ways:
1. loading the pre-trained model into a Keras model:
It seems to be impossible with the files that I have. It just gives me an AutoTrackable object instead of a model.
2. Accessing the Tensor Objects of the frozen model and make the model with tensors:
I found out how to access the tensors but couldn't make a Keras model with Tensor objects.
with self.graph.as_default():
graph = tf.import_graph_def(graph_def, name='')
graph = tf.compat.v1.import_graph_def(graph_def)
tf.compat.v1.Graph.as_default(graph)
self.sess = tf.Session(graph=self.graph)
self.tensors = [tensor for op in tf.compat.v1.get_default_graph().get_operations() for tensor in op.values()]
Here I can get the tensors but I can't use the tensors in the model:
model = tf.keras.models.Model(inputs=self.tensors[0], outputs=self.tensors[-1])
Is there any way to convert this frozen graph to a Keras model?
Or If there is another approach which I can train the model, I would be glad to know.
P.S. The pre-trained model is 'ssd_mobilenet_v3_small_coco_2020_01_14' which can be found Here.
You can use two methods:
The file 'frozen_inference_graph.pb' contains all necessary information about the weights and the model architecture. Use the following snippet to read the model and add a new layer:a
customModel = tf.keras.models.load_model('savedModel')
# savedModel is the folder with .pb data
pretrainedOutput = customModel.layers[-1].output
newOutput = tf.keras.layers.Dense(2)(pretrainedOutput) # change layer as needed
new_model = tf.keras.Model(inputs=customModel.inputs, outputs=[newOutput])
# create a new model with input of old model and new output tensors
where 'savedModel' is the name of the folder with 'frozen_inference_graph.pb' and other meta data. See details about using .pb files and finetuning the custom models in the TFguide.
Try using .meta file with model architecture and .ckpt to restore the weights in TF 1.x:
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph('my_test_model-1000.meta')
new_saver.restore(sess, tf.train.latest_checkpoint('./'))
Refer to the tutorial on how to load and customize restored models in TF 1.x.
I had lost my dataset by a careless mistake. I have only my tflite file left in my hand. Is there any solution to reverse back h5 file. I have been done decent research in this but no solutions found.
The conversion from a TensorFlow SaveModel or tf.keras H5 model to .tflite is an irreversible process. Specifically, the original model topology is optimized during the compilation by the TFLite converter, which leads to some loss of information. Also, the original tf.keras model's loss and optimizer configurations are discarded, because those aren't required for inference.
However, the .tflite file still contains some information that can help you restore the original trained model. Most importantly, the weight values are available, although they might be quantized, which could lead to some loss in precision.
The code example below shows you how to read weight values from a .tflite file after it's created from a simple trained tf.keras.Model.
import numpy as np
import tensorflow as tf
# First, create and train a dummy model for demonstration purposes.
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, input_shape=[5], activation="relu"),
tf.keras.layers.Dense(1, activation="sigmoid")])
model.compile(loss="binary_crossentropy", optimizer="sgd")
xs = np.ones([8, 5])
ys = np.zeros([8, 1])
model.fit(xs, ys, epochs=1)
# Convert it to a TFLite model file.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("converted.tflite", "wb").write(tflite_model)
# Use `tf.lite.Interpreter` to load the written .tflite back from the file system.
interpreter = tf.lite.Interpreter(model_path="converted.tflite")
all_tensor_details = interpreter.get_tensor_details()
interpreter.allocate_tensors()
for tensor_item in all_tensor_details:
print("Weight %s:" % tensor_item["name"])
print(interpreter.tensor(tensor_item["index"])())
These weight values loaded back from the .tflite file can be used with tf.keras.Model.set_weights() method, which will allow you to re-inject the weight values into a new instance of trainable Model that you have in Python. Obviously, this requires you to still have access to the code that defines the model's architecture.
When inspecting the Keras model yolov3-tiny.h5 using netron
I see that the input node is called input_1 and has type float32[?,?,?,3]. I would expect float32[?,416,416,3]
How can I force it to be float32[?,416,416,3]?
This is needed for downstream processing. The Keras model has to be converted to a frozen_model.pb in Tensorflow and then be further processed for deployement.
The deployement tools cannot handle an input with unknow w,h size.
Here is how I generated the Keras model. I downloaded the yolov3-tiny.cfg (https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg) and yolov3-tiny.weigths (https://pjreddie.com/media/files/yolov3-tiny.weights)
And then converted the model to a keras model using the following command :
python convert.py -p yolov3-tiny.cfg yolov3-tiny.weights model_data/yolov3-tiny.h5
(this code is obtained by cloning https://github.com/qqwweee/keras-yolo3)
Making a prediction using the saved Keras model works fine :
python yolo_video.py --image --model model_data/yolov3-tiny.h5
However when inspecting the Keras model yolov3-tiny.h5 using netron
I see that the input node is called input_1 and has type float32[?,?,?,3]
I would expect float32[?,416,416,3]
How can I force it to be float32[?,416,416,3]?
You could do that while converting the keras model file to tflite by passing input_shapes as a dictionary with input node name as the key and shape as the value.
Check out the TFLiteConverter class here
def _main_(args):
kmodel = args.kmodel
lmodel = args.lmodel
converter = tf.lite.TFLiteConverter.from_keras_model_file(kmodel, input_shapes={"input_1":[1,416,416,3]})
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
open("lmodel", "wb").write(tflite_model)
if __name__ == '__main__':
argparser = argparse.ArgumentParser(description='Quantize a trained yolo model')
argparser.add_argument('-k', '--kmodel', help='path to model weights file input')
argparser.add_argument('-l', '--lmodel', help='path to tensor flow lite output')
args = argparser.parse_args()
_main_(args)