kandi X-RAY | caffe Summary
kandi X-RAY | caffe Summary
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors. Check out the project site for all the details like.
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Trending Discussions on caffe
I am currently trying to build OpenPose. First, I will try to describe the environment and then the error emerging from it. Caffe, being built from source, resides in its entirety in [/Users...]/openpose/3rdparty instead of the usual location (I redact some parts of the filepaths in this post for privacy). All of its include files can be found in [/Users...]/openpose/3rdparty/caffe/include/caffe. After entering this command:...
ANSWERAnswered 2021-Jun-15 at 18:43
You are using cmake. The makefiles generated by cmake don't conform to "standard" makefile conventions; in particular they don't use the
When you're using cmake, you're not expected to modify the compiler options by changing the invocation of make. Instead, you're expected to modify the compiler options by either editing the CMakeLists.txt file, or else by providing an overridden value to the
cmake command line that is used to generate your makefiles.
I am trying to get the second last value in each row of a data frame, meaning the first job a person has had. (Job1_latest is the most recent job and people had a different number of jobs in the past and I want to get the first one). I managed to get the last value per row with the code below:
first_job <- function(x) tail(x[!is.na(x)], 1)
first_job <- apply(data, 1, first_job)...
ANSWERAnswered 2021-May-11 at 13:56
You can get the value which is next to last non-NA value.
I need to find reference or description regarding workspace that is provided to
cudnnConvolutionBackwardFilter familiy of functions.
Can I reuse the workspace for next calls/layers assuming that different layers aren't executed in parallel on the GPU?
I'm looking into caffe's implementation of cudnn_conv_layer.cpp and instance of layer allocates its own and separate space for each of 3 functions. Which seems to be wasteful since logically I should be able to reuse the memory for multiple layers/functions.
However I can't find a reference that allows or disallows this explicitly and Caffe keeps separate workspace for each and every layer and I suspect that in total it may "waste" a lot of memory....
ANSWERAnswered 2021-Apr-23 at 16:35
Yes, you can reuse the workspace for calls from different layers. Workspace is just memory needed by the algorithm to work, not a sort of context that has to be initialized or keeps certain state, you can see it in the cuDNN user guide e.g. here or here (look e.g. for the documentation for
cudnnGetConvolutionForwardWorkspaceSize). Also that is why inside one layer the size of workspace is computed as the maximum of all possible workspaces that are needed by any of the algorithms applied (well, multiplied by
CUDNN_STREAMS_PER_GROUP and also by number of groups if more than one since groups can be executed in parallel).
That said in caffe it is quite possible for 2 instances of any layer to be computed in parallel and I don't think workspaces are that large compared to the actual data one have to store for one batch (though I'm not sure about this part since that depends on the NN architecture and algorithms used), but I have doubts you can win a lot by reusing the workspace in common cases.
In theory you could always allocate workspace right before corresponding library call and free it after, which would save even more memory, but it would probably degrade the performance to some extent.
Please bear with me. I'm new to CoreML and machine learning. I have a CoreML model that I was able to convert from a research paper implementation that used Caffe. It's a CSRNet, the objective being crowd-counting. After much wrangling, I'm able to load the MLmodel into Python using Coremltools, pre-process an image using Pillow and predict an output. The result is a MultiArray (from a density map), which I've then processed further to derive the actual numerical prediction.
How do I add a custom layer as an output to the model that takes the current output and performs the following functionality? I've read numerous articles, and am still at a loss. (Essentially, it sums the values all the values in the MultiArray) I'd like to be able to save the model/ layer and import it into Xcode so that the MLModel result is a single numerical value, and not a MultiArray.
This is the code I'm currently using to convert the output from the model into a number (in Python):...
ANSWERAnswered 2021-Apr-01 at 10:21
You can add a ReduceSumLayerParams to the end of the model. You'll need to do this in Python by hand. If you set its reduceAll parameter to true, it will compute the sum over the entire tensor.
However, in my opinion, it's just as easy to use the model as-is, and in your Swift code grab a pointer to the MLMultiArray's data and use
vDSP.sum(a) to compute the sum.
I am trying to use docker to help create caffe models using this tutorial, and I am getting an error that my path is not configured, however I followed the instructions to configure the file as shown in the error below:...
ANSWERAnswered 2021-Mar-29 at 19:28
I resolved the issue by deleting "/" from in front of "shared_folder":
Let's say I have a network model made in
I can use
CoreML Converters API to get a CoreML model file (
.mlmodel) from it.
Now, as I have a
.mlmodel file, and know
input shape and
output shape, how can a maximum RAM footprint be estimated?
I know that a model сan have a lot of layers, their size can be much bigger than input/output shape.
So the questions are:
- Can be a maximal
mlmodelmemory footprint be known with some formula/API, without compiling and running an app?
- Is a maximal footprint closer to a memory size of the biggest intermediate layer, or is it closer to a sum of the all layer's sizes?
Any advice is appreciated. As I am new to CoreML, you may give any feedback and I'll try to improve the question if needed....
ANSWERAnswered 2021-Mar-23 at 18:57
IMHO, whatever formula you come up with at the end of the day must be based on the number of trainable parameters of the network.
For classifying networks it can be found by iterating or the existing API can be used.
The whole difficulty arises to get the state of a certain reducer (because of the
This problem did not arise until I used it
ANSWERAnswered 2021-Mar-20 at 04:19
You just need to pass a function to the
useSelector that reads the state from the store, for example:
I am trying to compile OpenCV for Android with contrib modules, mainly I am interested in sfm. I did a lot of research and finaly I did the following in order to support sfm:
Compiled gflags Compiled Glog Compiled Ceres
After that I used this cmake command to build and generate (partial output is given below):...
ANSWERAnswered 2021-Jan-24 at 21:16
I just finished build opencv with android using this :
I am trying to detect human hand pose using OpenPose just like given in this video https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/.github/media/pose_face_hands.gif for hand part. I have downloaded the caffe model and prototxt file. Below is my code to implement the model....
ANSWERAnswered 2021-Jan-16 at 08:40
Try this code below
I am reviving this github issue because I believe it is valid and needs to be explained. tf.keras has a masking layer with docs that reads
For each timestep in the input tensor (dimension #1 in the tensor), if all values in the input tensor at that timestep are equal to mask_value, then the timestep will be masked (skipped) in all downstream layers (as long as they support masking).
If any downstream layer does not support masking yet receives such an input mask, an exception will be raised.
ANSWERAnswered 2021-Jan-16 at 00:52
I think this is already explained well in the Github issue you have linked. Underlying problem is that irrespective of whether an array is masked or not,
softmax() still operates on
0.0 values and returns a
non-zero value as mathematically expected (link).
The only way to get a zero output from a
softmax() is to pass a very small float value. If you set the masked values to the minimum possible machine limit for
Softmax() of this value will be zero.
To get machine limit on float64 you need
tf.float64.min which is equal to
-1.7976931348623157e+308. More info about machine limits on this post.
Here is an implementation for your reference on
tf.boolean_mask only, and the correct method of using
tf.where for creating the mask and passing it to
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