dilation | Dilated Convolution for Semantic Image Segmentation | Machine Learning library

 by   fyu Python Version: Current License: MIT

kandi X-RAY | dilation Summary

kandi X-RAY | dilation Summary

dilation is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. dilation has no bugs, it has no vulnerabilities, it has a Permissive License and it has high support. However dilation build file is not available. You can download it from GitHub.

Properties of dilated convolution are discussed in our ICLR 2016 conference paper. This repository contains the network definitions and the trained models. You can use this code together with vanilla Caffe to segment images using the pre-trained models. If you want to train the models yourself, please check out the document for training.

            kandi-support Support

              dilation has a highly active ecosystem.
              It has 741 star(s) with 268 fork(s). There are 35 watchers for this library.
              It had no major release in the last 6 months.
              There are 30 open issues and 17 have been closed. On average issues are closed in 27 days. There are no pull requests.
              It has a positive sentiment in the developer community.
              The latest version of dilation is current.

            kandi-Quality Quality

              dilation has 0 bugs and 0 code smells.

            kandi-Security Security

              dilation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              dilation code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              dilation is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              dilation releases are not available. You will need to build from source code and install.
              dilation has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed dilation and discovered the below as its top functions. This is intended to give you an instant insight into dilation implemented functionality, and help decide if they suit your requirements.
            • Validate options
            • Predict from a dataset
            • Calculate the zoom probability of a map
            • Creates a solver
            • Build a joint graph
            • Build convolutional context
            • Builds the frontend VGG network
            • Make image data
            • Make a downsampled deconvolution
            • Make a softmaxWithLoss
            • Make input data
            • Make a softmax probability
            • Makes an accuracy layer
            • Create a caffenet context
            • Make bin labels data
            • Build a frontend VGG network
            • Create and return train and test networks
            • Make a network
            • Run train
            Get all kandi verified functions for this library.

            dilation Key Features

            No Key Features are available at this moment for dilation.

            dilation Examples and Code Snippets

            Construct a tensor product of input tensors .
            pythondot img1Lines of Code : 177dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def pool(
                input,  # pylint: disable=redefined-builtin
              """Performs an N-D pooling operation.
            2d convolutional convolutional convolution .
            pythondot img2Lines of Code : 147dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def atrous_conv2d(value, filters, rate, padding, name=None):
              """Atrous convolution (a.k.a. convolution with holes or dilated convolution).
              This function is a simpler wrapper around the more general
              `tf.nn.convolution`, and exists only for back  
            2D convolutional convolution .
            pythondot img3Lines of Code : 118dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def conv2d(  # pylint: disable=redefined-builtin,dangerous-default-value
                dilations=[1, 1, 1, 1],

            Community Discussions


            How can I change background color to red of an image using Python
            Asked 2022-Apr-10 at 11:58

            I have the following code that works great but it doesn't fill all background. I played with numbers but it either makes all image red or not changing the background.
            How can I change the background color of the image?

            Picture i want to change its background]:



            Answered 2022-Apr-09 at 21:47

            I thought we can simply use cv2.floodFill, and fill the white background with red color.
            The issue is that the image is not clean enough - there are JPEG artifacts, and rough edges.

            Using cv2.inRange may bring us closer, but assuming there are some white tulips (that we don't want to turn into red), we may have to use floodFill for filling only the background.

            I came up with the following stages:

            • Convert from RGB to HSV color space.
            • Apply threshold on the saturation channel - the white background is almost zero in HSV color space.
            • Apply opening morphological operation for removing artifacts.
            • Apply floodFill, on the threshold image - fill the background with the value 128.
              The background is going to be 128.
              Black pixels inside the area of the tulips is going to be 0.
              Most of the tulips area stays white.
            • Set all pixels where threshold equals 128 to red.

            Code sample:

            Source https://stackoverflow.com/questions/71806826


            Nonlinear mixed models: what am I doing wrong?
            Asked 2022-Feb-23 at 20:36

            I am working with a data set that is comprised of three columns: patient ID (ID), TIME, and cervical dilation (CD). I apologize in advance for being unable to share my data, as it is confidential, but I have included a sample table below. Each patient CD was recorded in time as they progressed through labor. Time is measured in hours and CD can be 1-10cm. The number of time points/CD scores vary from patient to patient. In this model t is set in reverse, where 10 cm (fully dilated) is set as t=0 for all patients. This is done so that all patients can be aligned at time of full dilation. My dataset has no NA's and all patients have 2 or more time points.

            ID TIME CD 1 0 10 1 3 8 1 6 5 2 0 10 2 1 9 2 4 7 2 9 4

            I know for this problem I need to use nonlinear mixed effects model. I know from literature that the function that defines this biological process is modeled best as a biexponential function of the form CD= Cexp(-At)+(10-C)exp(-Lt), where A is the active labor rate [cm/hour], L is the latent labor rate [cm/hour], C is the diameter of the cervix [cm] at the point where the patient transitions from latent to active labor, and t is time in hours.

            I have tried using both nlmer() and nlme() to fit this data, and I have used both the self-start biexponential function SSbiexp() as well as created my own function and its deriv(). Each parameter C, A, and L should have a random effect based on ID. Previous work has shown that C~4.98cm, A~0.41cm/hr, and L~0.07cm/hr. When using the SSbiexp(), there is a term for the second exponential component that is labeled here as C2, but should be the same as the (10-C) component of my self-made biexponential function.

            When using nlme() with SSbiexp() I receive the error: Singularity in backsolve at level 0, block 1



            Answered 2022-Feb-23 at 20:36

            Here's how far I've gotten:

            • the exponential rates are supposed to be specified as logs of the rates (to make sure that the rates themselves stay positive, i.e. that we have exponential decay curves rather than growth curves)
            • I simplified the model significantly, taking out the random effects in T1 and T2.

            Source https://stackoverflow.com/questions/71232029


            Shape must be rank 4 but is rank 2 for '{{node Conv2D}}
            Asked 2022-Jan-24 at 07:05

            I'm new to tensorflow and I'm trying to create a cnn and got this error ValueError: Shape must be rank 4 but is rank 2 for '{{node Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](concat, Variable_6/read)' with input shapes: [?,1568], [1568,784]. this error is related to weight or input, how can i solved this thank you . My code :



            Answered 2022-Jan-24 at 07:05

            I am not sure what you are trying to do, but you really need to read the docs regarding how conv2d operations work, because you are trying to feed a 2D tensor but actually need a 4D tensor. Anyway, here is a working example:

            Source https://stackoverflow.com/questions/70826308


            RuntimeError: Input type and weight type should be the same
            Asked 2022-Jan-24 at 07:04

            I’m trying to swap resNet blocks with resNext blocks in my current model. All worked and I even trained the model for 1000+ epochs with the resNet blocks but when I added the following class to the model, it returned this error. (ran without errors in my local CPU but got the error when running in colab)

            Added Class :



            Answered 2022-Jan-24 at 07:04

            Your problem in your new class GroupConv1D is that you store all your convolution modules in a regular python list self.conv_list instead of using nn Containers.
            All methods that affect nn.Modules (e.g., .to(device), .eval(), etc.) are applied recursively to all relevant members of the "root" nn.Module.
            However, how can pytorch tell which are the relevant members?
            For this you have containers: they group together sub-modules, registers and parameters such that pytorch can recursively apply all relevant nn.Module's methods to them.

            See, e.g., this answer.

            Source https://stackoverflow.com/questions/70829410


            Tesseract OCR gives really bad output even with typed text
            Asked 2021-Dec-20 at 05:05

            I've been trying to get tesseract OCR to extract some digits from a pre-cropped image and it's not working well at all even though the images are fairly clear. I've tried looking around for solutions but all the other questions I've seen on here involve a problem with cropping or skewed text.

            Here's an example of my code which tries to read the image and output to the command line.



            Answered 2021-Dec-20 at 03:04

            I've found a decent workaround. First off I've made the image larger. More area for tesseract to work with helped it a lot. Second, to get rid of non-digit outputs, I've used the following config on the image to string function:

            Source https://stackoverflow.com/questions/70410527


            How to check the input dimensions of a model in Flux.jl?
            Asked 2021-Nov-22 at 18:41

            I have a resnet model which I am working with. I originally trained the model using batches of images. Now that it is trained, I want to do inference on a single image (224x224 with 3 color channels). However, when I pass the image to my model via model(imgs[:, :, :, 2]) I get:



            Answered 2021-Nov-22 at 18:23

            Sorry, I am really not an expert, but is not the problem that imgs[:, :, :, 2] crates a 3-dimensional tensor? May-be imgs[:, :, :, 2:2] would work, as it makes a four dimensional tensor with the last dimension equal to one (since you have one image)

            Source https://stackoverflow.com/questions/70069956


            Error while using Vgg16 (transfter learning) - RuntimeError: Failed to run torchinfo
            Asked 2021-Nov-20 at 02:36

            I'm trying to use VGG16 with transfer learning, but getting errors:



            Answered 2021-Nov-20 at 02:36

            In case you're trying to change the final classifier, you should change the whole, not only one layer:

            Source https://stackoverflow.com/questions/70032269


            Understanding the PyTorch implementation of Conv2DTranspose
            Asked 2021-Oct-31 at 10:48

            I am trying to understand an example snippet that makes use of the PyTorch transposed convolution function, with documentation here, where in the docs the author writes:

            "The padding argument effectively adds dilation * (kernel_size - 1) - padding amount of zero padding to both sizes of the input."

            Consider the snippet below where a [1, 1, 4, 4] sample image of all ones is input to a ConvTranspose2D operation with arguments stride=2 and padding=1 with a weight matrix of shape (1, 1, 4, 4) that has entries from a range between 1 and 16 (in this case dilation=1 and added_padding = 1*(4-1)-1 = 2)



            Answered 2021-Oct-31 at 10:39

            The output spatial dimensions of nn.ConvTranspose2d are given by:

            Source https://stackoverflow.com/questions/69782823


            How to use grad convolution in google-jax?
            Asked 2021-Oct-15 at 04:33

            Thanks for reading my question!

            I was just learning about custom grad functions in Jax, and I found the approach JAX took with defining custom functions is quite elegant.

            One thing troubles me though.

            I created a wrapper to make lax convolution look like PyTorch conv2d.



            Answered 2021-Oct-15 at 04:33

            When I run your code with the most recent releases of jax and jaxlib (jax==0.2.22; jaxlib==0.1.72), I see the following error:

            Source https://stackoverflow.com/questions/69571976


            How to remove black dots of this image using OpenCV?
            Asked 2021-Oct-07 at 09:06

            Is it possible to remove those black dots without hampering the image text using OpenCV?

            So far, I have tried dilation, morphologyEx, erode etc.



            Answered 2021-Oct-07 at 09:06

            Maybe try to connect the letters to big blobs, and remove small blobs:

            Source https://stackoverflow.com/questions/69420248

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install dilation

            You can download it from GitHub.
            You can use dilation like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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