I have an image tensor of the following dimensions
TensorShape([Dimension(1), Dimension(96), Dimension(96), Dimension(3)])
I want this tensor to be in following channel first dimension
TensorShape([Dimension(1), Dimension(2), Dimension(96), Dimension(96)])
I have tried
tf.transpose (image, perm = [0,3,1,2])
but it did not work It is returning in the same as previous.
Since, this is the requirement of Facenet algorithm , please suggest the way to do it.
You can try to convert the tensor into NumPy array and then use np.rollaxis and convert back to Tensorflow tensor.
Related
I have a class 'tensorflow.python.framework.ops.Tensor as output and need to convert this to a numpy array.
.numpy() doesn't work because it isn't a eagerTensor.
.eval doesn't work as well, because i'm using tensorflow >2.0
Is there any other way to fix this?
img_height=330
img_width=600
img_depth=23
save_model="saved_Models/wheatModel"
prediction_data_path=["data/stacked/MOD13Q1.A2017.2738.tif","data/stacked/MOD13Q1.A2017.889.tif","data/stacked/MOD13Q1.A2017.923.tif"]
prediction_data=dataConv.preparePredictionData(prediction_data_path)
prediction_reshaped=dataConv.reshapeFiles(prediction_data,img_width,img_height,img_depth)
x_ds =tf.stack(prediction_reshaped)
model = tf.keras.models.load_model(save_model)
model.predict(x_ds)
image=model.get_layer(name='prediction_image').output
n,output_width,output_height,output_depth,output_channels=image.shape
print(type(image))
image=tf.reshape(image,(output_width,output_height,output_depth))
print(type(image))
image.numpy()
So in the code above.
I load my trained model
predict the given images
get the output from the next to last layer
reshape this data
Now i want to convert this tensor to an numpyarray
This seems so basic, but for some reason, I can't find any clear documentation on it.
So lets say I know my ONNX model wants an input of shape [245, 245, 3]. The second argument in the constructor Ort::Value::CreateTensor wants a linear array of the data to fill the tensor. What is the order of the linear array?
For example, are the first three values in the linear array the BGR values for the 0-th pixel in the image, or are the first three values in the linear array the B-channel value of the first three pixels in the image? And as for ordering of pixels in the image: row-major?
The short answer is : ONNX only supports NCHW
As a reference, please check the section My converted TensorFlow model is slow - why? in onnxruntime.ai. This is the only "official" material that talking about the data format I found so far.
It's row-major. The format of inputs in ONNX is NCHW. C = number of channels. In this case C=3. The ordering of C (BGR or RGB) depends on the model. For e.g. the YOLO model takes an image 3(RGB) x 416px x 416px.
I am trying to predict() the output for a single data point d, using my trained Keras model loaded from a file. But I get a ValueError If predicting from data tensors, you should specify the 'step' argument. What does that mean?
I tried setting step=1, but then I get a different error ValueError: Cannot feed value of shape () for Tensor u'input_1:0', which has shape '(?, 600)'.
Here is my code:
d = np.concatenate((hidden[p[i]], hidden[x[i]])).resize((1,600))
hidden[p[i]] = autoencoder.predict(d,steps=)
The model is expecting (?,600) as input. I have concatenated two numpy arrays of shape (300,) each to get (600,), which is resized to (1,600). This (1,600) is my input to predict().
In my case, the input to predict was None (because I had a bug in another part of the code).
In official doc, steps refer to the total number of steps before stopping. So steps=1 means make predictions on one batch instead of making prediction on one record (single data point).
https://keras.io/models/sequential/
-> Define value of steps argument,
d = np.concatenate((hidden[p[i]],
hidden[x[i]])).resize((1,600))
hidden[p[i]] = autoencoder.predict(d,steps=1)
If you are using a test data generator, it is good practice to define the steps, as mentioned in the documentation.
If you are predicting a single instance, no need to define the steps. Just make sure the argument (i.e. instance 'd') is not None, otherwise that error will show up. Some reshaping may also be necessary.
in my case i got the same error, i just reshaped the data to predict with numpy function reshape() to the shape of the data originally used to train the model.
I have read an image as follows using opencv
image = cv2.imread('/data/TestImages/cat.jpg',cv2.IMREAD_UNCHANGED)
This read image cause the error message when it was called by segmentation, np_image, np_logits = sess.run([pred, image, logits])
The error message is as TypeError: Can not convert a ndarray into a Tensor or Operation.
Are there any mechanisms that can transform an image represented as ndarray to a Tensorflow tensor. Thanks.
You have to read up on the sess.run function. In the array you have as argument of your function you specify what you want to get OUT of your run command. In your case, you probably only want your pred and logits.
If you want to put something IN the network you have to specify a tf.placeholder in your graph, and feed your image like this:
np_pred,np_logits = sess.run([pred, logits],feed_dict={image_placeholder: image})
Hope this helps!
I have one image data tensor with shape of B*H*W*C and one position tensor with shape of B*H*W*2. The values in position tensor are pixel coordinates and I want to sample pixels in image data tensor according to these pixel coordinates. I have tried one way to do that like reshaping the tensor to one-dimension tensor, but I think it's really inconvenient. I wonder whether I could implement it by some more convenient approach like matrix mapping(e.g. remap in opencv).
I would first ask if you are sure the position matrix isn't redundant. If the position matrix entries simply correspond to the pixel locations in the image array, then for a given application however you access the position matrix could be used instead on the image data.
Perhaps as a starting point, running
sess = tf.Session()
np_img, np_pos = sess.run([tf_img, tf_pos], feed_dict={...})
will convert tensors to numpy arrays, which may make your operations easier.
Otherwise, a 1D-tensor isn't that bad and there are TF functions for reshaping easily.