I downloaded a ssd_mobilenet_v2_coco from tensorflow detection model zoo. And I used import_pb_to_tensorboard.py to show the structure on Tensorboard.
I find a node named 'image_tensor', this is the picture discribed in Tensorboard.
I want to use the function 'get_tensor_by_name()' to input a new image and get the ouputs. However, it failed.
I tried 'get_operation_by_name()' , it didn't work neither.
Here is the code:
import tensorflow as tf
def one_image(im_path, model_path):
sess= tf.Session()
with sess.as_default():
image_tensor = tf.image.decode_jpeg(tf.read_file(im_path), channels=3)
saver = tf.train.import_meta_graph(model_path + "/model.ckpt.meta")
saver.restore(sess, tf.train.latest_checkpoint(model_path))
graph = tf.get_default_graph()
# x = graph.get_tensor_by_name("import/image_tensor:0")
# out_put = graph.get_tensor_by_name("import/detection_classes:0")
x = graph.get_operation_by_name("import/image_tensor").outputs[0]
outputs = graph.get_operation_by_name("import/detection_classes").outputs[0]
out_put = sess.run(outputs, feed_dict={x: image_tensor.eval()})
print(out_put)
sess.close()
if __name__ == "__main__":
one_image("testimg-4-resize.jpg", "ssd_mobilenet_v2_coco_2018_03_29")
And here is the KeyError:
KeyError: "The name 'import/image_tensor' refers to an Operation not in the graph."
I am wondering how to get the tensor name from Tensorboard and whether there is another way to load model from 'only-ckpts'.
'only-ckpts' means files only include 'model.ckpt.data-00000-of-00001' , 'model.ckpt.index' and 'model.ckpt.meta'.
Any advice will be grateful. Thank you in advance.
The tool import_pb_to_tensorboard.py uses tf.import_graph_def to import the graph and uses default name argument, which is "import" as documented.
Your code imports the graph through tf.train.import_meta_graph and uses default import_scope argument, which will not prefix imported tensor or operation name. It is obvious then you have two options to correct this error:
Do the following in place of your import_meta_graph line:
saver = tf.train.import_meta_graph(model_path + "/model.ckpt.meta",
import_scope='import')
Remove import/ prefix when trying to get tensor or operation by name like this:
x = graph.get_tensor_by_name("image_tensor:0")
out_put = graph.get_tensor_by_name("detection_classes:0")
x = graph.get_operation_by_name("image_tensor").outputs[0]
outputs = graph.get_operation_by_name("detection_classes").outputs[0]
Related
I have a image where i have to use the pytorch-fasterrcnn-resnet50-fpn to extract the features of the image. Below is the code that I am trying
import torchvision
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained = True)
### strip the last layer
feature_extractor = torch.nn.Sequential(*list(model_ft.children())[:-1])
inputs = feature_extractor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
Here the type of image is PIL.JpegImagePlugin.JpegImageFile
The above code is not working for getting the features. Can anyone tell me how to solve this?
I am trying to generate text in a certain style using tensorflow, and even when I copy and paste the code from the tensorflow website it only predicts unknown characters even though there is a mask to prevent this. I'm thinking it has something to do with the version of python I'm running (3.9.15) since it doesnt even work with their code and dataset.
Has anyone else run into this issue?
class OneStep(tf.keras.Model):
def __init__(self, model, chars_from_ids, ids_from_chars, temperature=1.0):
super().__init__()
self.temperature = temperature
self.model = model
self.chars_from_ids = chars_from_ids
self.ids_from_chars = ids_from_chars
# Create a mask to prevent "[UNK]" from being generated.
skip_ids = self.ids_from_chars(['[UNK]'])[:, None]
sparse_mask = tf.SparseTensor(
# Put a -inf at each bad index.
values=[-float('inf')]*len(skip_ids),
indices=skip_ids,
# Match the shape to the vocabulary
dense_shape=[len(ids_from_chars.get_vocabulary())])
self.prediction_mask = tf.sparse.to_dense(sparse_mask)
#tf.function
def generate_one_step(self, inputs, states=None):
# Convert strings to token IDs.
input_chars = tf.strings.unicode_split(inputs, 'UTF-8')
input_ids = self.ids_from_chars(input_chars).to_tensor()
# Run the model.
# predicted_logits.shape is [batch, char, next_char_logits]
predicted_logits, states = self.model(inputs=input_ids, states=states,
return_state=True)
# Only use the last prediction.
predicted_logits = predicted_logits[:, -1, :]
predicted_logits = predicted_logits/self.temperature
# Apply the prediction mask: prevent "[UNK]" from being generated.
predicted_logits = predicted_logits + self.prediction_mask
# Sample the output logits to generate token IDs.
predicted_ids = tf.random.categorical(predicted_logits, num_samples=1)
predicted_ids = tf.squeeze(predicted_ids, axis=-1)
# Convert from token ids to characters
predicted_chars = self.chars_from_ids(predicted_ids)
# Return the characters and model state.
return predicted_chars, states
I lifted this straight from their tutorial and tried to run it in my enviroment and it just predicted '[UNK]'
I'm running a mac M1 with the latest version of tensorflow. So, that may also be an issue.
PLEASE HELP!!
Tutorial for reference: https://www.tensorflow.org/text/tutorials/text_generation
I am trying to convert a pre-trained torch model to ONNX, but recive the following error:
RuntimeError: step!=1 is currently not supported
I'm trying this on a pre-trained colorization model: https://github.com/richzhang/colorization
Here is the code I ran in Google Colab:
!git clone https://github.com/richzhang/colorization.git
cd colorization/
import colorizers
model = colorizer_siggraph17 = colorizers.siggraph17(pretrained=True).eval()
input_names = [ "input" ]
output_names = [ "output" ]
dummy_input = torch.randn(1, 1, 256, 256, device='cpu')
torch.onnx.export(model, dummy_input, "test_converted_model.onnx", verbose=True,
input_names=input_names, output_names=output_names)
I appreciate any help :)
UPDATE 1: #Proko suggestion solved the ONNX export issue. Now I have a new possibly related problem when I try to convert the ONNX to TensorRT. I get the following error:
[TensorRT] ERROR: Network must have at least one output
Here is the code I used:
import torch
import pycuda.driver as cuda
import pycuda.autoinit
import tensorrt as trt
import onnx
TRT_LOGGER = trt.Logger()
def build_engine(onnx_file_path):
# initialize TensorRT engine and parse ONNX model
builder = trt.Builder(TRT_LOGGER)
builder.max_workspace_size = 1 << 25
builder.max_batch_size = 1
if builder.platform_has_fast_fp16:
builder.fp16_mode = True
network = builder.create_network()
parser = trt.OnnxParser(network, TRT_LOGGER)
# parse ONNX
with open(onnx_file_path, 'rb') as model:
print('Beginning ONNX file parsing')
parser.parse(model.read())
print('Completed parsing of ONNX file')
# generate TensorRT engine optimized for the target platform
print('Building an engine...')
engine = builder.build_cuda_engine(network)
context = engine.create_execution_context()
print("Completed creating Engine")
return engine, context
ONNX_FILE_PATH = 'siggraph17.onnx' # Exported using the code above
engine,_ = build_engine(ONNX_FILE_PATH)
I tried to force the build_engine function to use the output of the network by:
network.mark_output(network.get_layer(network.num_layers-1).get_output(0))
but it did not work.
I appropriate any help!
Like I have mentioned in a comment, this is because slicing in torch.onnx supports only step = 1 but there are 2-step slicing in the model:
self.model2(conv1_2[:,:,::2,::2])
Your only option as for now is to rewrite slicing to be some other ops. You can do it by using range and reshape to obtain proper indices. Consider the following function "step-less-arange" (I hope it is generic enough for anyone with similar problem):
def sla(x, step):
diff = x % step
x += (diff > 0)*(step - diff) # add length to be able to reshape properly
return torch.arange(x).reshape((-1, step))[:, 0]
usage:
>> sla(11, 3)
tensor([0, 3, 6, 9])
Now you can replace every slice like this:
conv2_2 = self.model2(conv1_2[:,:,self.sla(conv1_2.shape[2], 2),:][:,:,:, self.sla(conv1_2.shape[3], 2)])
NOTE: you should optimize it. Indices are calculated for every call so it might be wise to pre-compute it.
I have tested it with my fork of the repo and I was able to save the model:
https://github.com/prokotg/colorization
What works for me was to add the opset_version=11 on torch.onnx.export
First I had tried use opset_version=10, but the API suggest 11 so it works.
So your function should be:
torch.onnx.export(model, dummy_input, "test_converted_model.onnx", verbose=True,opset_version=11,
input_names=input_names, output_names=output_names)
I can't work out how to invoke my .tflite model that does matmul on the coral accelerator using the python api.
The .tflite model is generated from some example code here. It works well using the tf.lite.Interpreter() class but I don't know how to transform it to work with the edgetpu class. I have tried edgetpu.basic.basic_engine.BasicEngine() by changing the models datatype from numpy.float32 to numpy.uint8, but that did not help. I am a complete beginner with TensorFlow and just want to use my tpu for matmul.
import numpy
import tensorflow as tf
import edgetpu
from edgetpu.basic.basic_engine import BasicEngine
def export_tflite_from_session(session, input_nodes, output_nodes, tflite_filename):
print("Converting to tflite...")
converter = tf.lite.TFLiteConverter.from_session(session, input_nodes, output_nodes)
tflite_model = converter.convert()
with open(tflite_filename, "wb") as f:
f.write(tflite_model)
print("Converted %s." % tflite_filename)
#This does matmul just fine but does not use the TPU
def test_tflite_model(tflite_filename, examples):
print("Loading TFLite interpreter for %s..." % tflite_filename)
interpreter = tf.lite.Interpreter(model_path=tflite_filename)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print("input details: %s" % input_details)
print("output details: %s" % output_details)
for i, input_tensor in enumerate(input_details):
interpreter.set_tensor(input_tensor['index'], examples[i])
interpreter.invoke()
model_output = []
for i, output_tensor in enumerate(output_details):
model_output.append(interpreter.get_tensor(output_tensor['index']))
return model_output
#this should use the TPU, but I don't know how to run the model or if it needs
#further processing. One matrix can be constant for my use case
def test_tpu(tflite_filename,examples):
print("Loading TFLite interpreter for %s..." % tflite_filename)
#TODO edgetpu.basic
interpreter = BasicEngine(tflite_filename)
interpreter.allocate_tensors()#does not work...
def main():
tflite_filename = "model.tflite"
shape_a = (2, 2)
shape_b = (2, 2)
a = tf.placeholder(dtype=tf.float32, shape=shape_a, name="A")
b = tf.placeholder(dtype=tf.float32, shape=shape_b, name="B")
c = tf.matmul(a, b, name="output")
numpy.random.seed(1234)
a_ = numpy.random.rand(*shape_a).astype(numpy.float32)
b_ = numpy.random.rand(*shape_b).astype(numpy.float32)
with tf.Session() as session:
session_output = session.run(c, feed_dict={a: a_, b: b_})
export_tflite_from_session(session, [a, b], [c], tflite_filename)
tflite_output = test_tflite_model(tflite_filename, [a_, b_])
tflite_output = tflite_output[0]
#test the TPU
tflite_output = test_tpu(tflite_filename, [a_, b_])
print("Input example:")
print(a_)
print(a_.shape)
print(b_)
print(b_.shape)
print("Session output:")
print(session_output)
print(session_output.shape)
print("TFLite output:")
print(tflite_output)
print(tflite_output.shape)
print(numpy.allclose(session_output, tflite_output))
if __name__ == '__main__':
main()
You're only converting your model once, and your model is not fully compiled for the Edge TPU. From the docs:
At the first point in the model graph where an unsupported operation occurs, the compiler partitions the graph into two parts. The first part of the graph that contains only supported operations is compiled into a custom operation that executes on the Edge TPU, and everything else executes on the CPU
There are several specific requirements that the model must meet:
quantization-aware training
constant tensor sizes and model parameters at compile time
tensors are 3-dimensional or smaller.
models only use operations supported by the Edge TPU.
There is an online compiler as well as a CLI version that is useful for translating .tflite models into Edge TPU compatible .tflite models.
Your code is also incomplete. You've passed your model to the class here:
interpreter = BasicEngine(tflite_filename)
but you're missing the step of actually running the inference on the tensor:
output = RunInference(interpreter)
I am currently trying to visualize the output of an intermediate layer in Keras 1.0 (which I could do with Keras 0.3) but it does not work anymore.
x = model.input
y = model.layers[3].output
f = theano.function([x], y)
But I get the following error:
MissingInputError: ("An input of the graph, used to compute DimShuffle{x,x,x,x}(keras_learning_phase), was not provided and not given a value.Use the Theano flag exception_verbosity='high',for more information on this error.", keras_learning_phase)
Prior to Keras 1.0, with my graph model, I could just do:
x = graph.inputs['input'].input
y = graph.nodes[layer].get_output(train=False)
f = theano.function([x], y, allow_input_downcast=True)
So I suspect it to come from the "train=False" parameter which I don't know how to set in the new version.
Thank you for your help
Try:
In the import statements first give
from keras import backend as K
from theano import function
then
f = K.function([model.layers[0].input, K.learning_phase()],
[model.layers[3].output])
# output in test mode = 0
layer_output = get_3rd_layer_output([X_test, 0])[0]
# output in train mode = 1
layer_output = get_3rd_layer_output([X_train, 1])[0]
This was just answered by François Chollet on github:
Your model apparently has a different behavior in training and test mode, and so needs to know what mode it should be using.
Use
iterate = K.function([input_img, K.learning_phase()], [loss, grads])
and pass 1 or 0 as value for the learning phase, based on whether you want the model in training mode or test mode.
https://github.com/fchollet/keras/issues/2417