I want to implement a convolution-deconvolution network for a image segmentation project. In the deconvolution part, I am planning to upsample the feature map by 2. e.g. The original feature map is of dimension 64*64*4 and I want to upsample it into 128*128*4. Does anyone know a tensor operation that does this? Thanks!
You could use tf.image.resize_images(). It takes batches of images or single images and supports the most common methods such as bilinear and nearest_neighbor.
Here's the link to the TensorFlow API reference: resizing
You can also take a look at how the upsampling operation is implemented in a higher-level API such as tflearn. You can find upsample_2d and upscore_layer in their Github repo: conv.py
Note: the output might be cast to tf.float32 in older TF versions
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I'm trying to implement a local response normalization layer in Tensorflow to be used in a Keras model:
Here is an image of the operation I am trying to implement:
Here is the Paper link, please refer to section 3.3 to see the description of this layer
I have a working NumPy implementation, however, this implementation uses for loops and inbuilt python min and max operators to compute the summation. However, these pythonic operations will cause errors when defining a custom keras layer, so I can't use this implementation.
The issue here lies in the fact that I need to iterate over all the elements in the feature map and generate a normalized value for each of them. Additionally, the upper and lower bound on the summation change depending on which value I am currently normalizing. I can't really think of a way to handle this without nested for loops, but this will not work in a Keras custom layer as it isn't a native TensorFlow function.
Could anyone point me towards tensorflow/keras backend functions that could help me in implementing this layer?
EDIT: I know that this layer is implemented as a keras layer, but I want to build intuition about custom layers, so I want to implement this layer using tensor ops.
As tf.data augmentations are executed only on CPUs. I need a way to run certain augmentations on the TPU for an audio project.
For example,
CPU: tf.recs read -> audio crop -> noise addition.
TPU: spectogram -> Mixup Augmentation.
Most augmentations can be done as a Keras Layer on top of the model, but MixUp requires both changes in input as well as label.
Is there a way to do it using tf keras APIs.
And if there is any way we can transfer part of tf.data to run on TPU that will also be helpful.
As you have rightly mentioned and as per the Tensorflow documentation also the preprocessing of tf.data is done on CPU only.
However, you can do some workaround to preprocess your tf.data using TPU/GPU by directly using transformation function in your model with something like below code.
input = tf.keras.layers.Input((512,512,3))
x = tf.keras.layers.Lambda(transform)(input)
You can follow this Kaggle post for detailed discussion on this topic.
See the Tensorflow guide that discusses preprocessing data before the model or inside the model. By including preprocessing inside the model, the GPU is leveraged instead of the CPU, it makes the model portable, and it helps reduce the training/serving skew. The guide also has multiple recipes to get you started too. It doesn't explicitly state this works for a TPU but it can be tried.
I notice in the code for the Tensorflow Object Detection API there are several references to Mask R-CNN however no mention of it in the documentation. Is it possible to train/run Mask R-CNN through this API, and if so how?
You may not like it, but the answer is (for the moment), is no. The API cannot be used to predict or recover masks
They only use a little part of the Mask R-CNN paper to predict boxes in a certain way, but predicting the instance masks is not yet implemented.
Now we can implement Mask with faster_rcnn_inception_v2 there is samples with 1.8.0 tensorflow version
I am trying to reimplement on tensorflow the Fast-Rcnn network that is already implemented in caffe, in order to use in Face/License Plates detection.
For that purpose, I converted the caffe weights into npy thanks using this script.
Here is how I present my model. To which I load the converted weights.
PS: I used the roi_pooling implementation by zplizzi.
Does anyone have any idea why I wouldn't get the same result testing same images with same Selective Search bboxes ? I was thinking it might be the flattening process that could differs from caffe to TF, maybe ?
*Edit:
Here is an example of results I get in caffe. While I get no car detection in TF.
Has anyone implement the FRCNN for TensorFlow version?
I found some related repos as following:
Implement roi pool layer
Implement fast RCNN based on py-faster-rcnn repo
but for 1: assume the roi pooling layer works (I haven't tried), and there are something need to be implemented as following:
ROI data layer e.g. roidb.
Linear Regression e.g. SmoothL1Loss
ROI pool layer post-processing for end-to-end training which should convert the ROI pooling layer's results to feed into CNN for classifier.
For 2: em...., it seems based on py-faster-rcnn which based on Caffe to prepared pre-processing (e.g. roidb) and feed data into Tensorflow to train the model, it seems weird, so I may not tried it.
So what I want to know is that, will Tensorflow support Faster RCNN in the future?. If not, do I have any mis-understand which mentioned above? or has any repo or someone support that?
Tensorflow has just released an official Object Detection API here, that can be used for instance with their various slim models.
This API contains implementation of various Pipelines for Object Detection, including popular Faster RCNN, with their pre-trained models as well.