vmap | Surface velocity map generation using ASP
kandi X-RAY | vmap Summary
kandi X-RAY | vmap Summary
The ASP/VW correlator is fast, memory-efficient, multi-threaded, and generalized. This means it can be used for efficient feature tracking operations on two arbitrary input rasters acquired at different times.
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Top functions reviewed by kandi - BETA
- The main function of the script .
- Get stereo options .
- get argument parser
- Generate GDAL dataset
- Run a command .
- Plots the quiver plot
- Plots a streamline plot
- Make matplotlib plot
- Get the velocant vector from a file .
- Create a symbolic link .
vmap Key Features
vmap Examples and Code Snippets
Community Discussions
Trending Discussions on vmap
QUESTION
I am trying to implement a two layer neural network and get the gradient of the second layer per sample.
My code looks like this:
...ANSWER
Answered 2022-Feb-18 at 20:29The issue is exactly what the error message says: in order to vmap an operation over multiple arrays, the dimension of the mapped axes in each array must be equal. In your arrays, the dimensions are not equal: you passed in_axes=(0, None, 0, 0, 0)
for arguments W1, W2, b, x, y
, but W1.shape[0] = 10
, b.shape[0] = 1
, x.shape[0] = 11
, and y.shape[0] = 11
.
Because these are not equal, you get this error. To prevent this error, you should only vmap over array axes of the same length.
For example, if you want the gradients with respect to W2
computed per pair of W1, W2
inputs, it might look something like this (note the updated predict
function and updated in_axes
):
QUESTION
I have a function that I use for an interactive map, where I hover an element to show a specific region based on class that matches a country suffix. Im trying to figure out how to select multiple classes to show multiple regions.
Code:
...ANSWER
Answered 2022-Feb-18 at 14:25You can modify your select/deselect
function to accept array of classes, or instead loop the classes and invoke select/deselect
for each class name:
QUESTION
I am trying to use the tf.map_fn() , where my elems should be pointing to the channel dimension of my inputs(shape = 100,24,24,6), so my elems should be a list/tuple of tensors, pointing or accessing the values of the channel dimension(6) of the inputs .I am trying to do it by making a for loop in such a way :
@tf.function def call(self, inputs, training=True):
...ANSWER
Answered 2022-Feb-16 at 11:41First, instead of doing a for loop (try to avoid for efficiency), you can just reshape that way:
elems = tf.reshape(inputs,-1)
Second, what do you want to do exactly? What do you mean by "it doesn't work"? What is the error message? What is self.do_mapping
?
Best,
Keivan
QUESTION
I'm having some issues testing a basic model on Jax. For example, I'm trying to implement the value_and_grad()
function from Jax manually for a binary classification problem. Here is my model initializer:
ANSWER
Answered 2022-Feb-04 at 23:14Flax models must be marked as static for use in jit:
QUESTION
I use the Python psycopg2 module to copy the content of a csv file (a list of users) into a PostGreSQL database.
So I begin to parse the CSV with the Python pandas module. Then with a for loop, I try to insert my data in my SQL queries.
I have two problems :
a) When I execute the role query (query 2 - see below) to create new roles in the database, I get 'user' instead of user. How could I do to insert the roles with the right syntax ?
b) The queries 3 and 4 (see below) give the following error :
ANSWER
Answered 2022-Jan-31 at 21:12First of all, there is probably no need for pandas
here, since you only need to open the csv file and parse its contents. Using the built-in csv
module should be enough.
You can read the file like this:
QUESTION
I'm trying to understand JAX's auto-vectorization capabilities using vmap
and implemented a minimal working example based on JAX's documentation.
I don't understand how in_axes
is used correctly. In the example below I can set in_axes=(None, 0)
or in_axes=(None, 1)
leading to the same results. Why is that the case?
And why do I have to use in_axes=(None, 0)
and not something like in_axes=(0, )
?
ANSWER
Answered 2022-Jan-03 at 13:55in_axes=(None, 0)
means that the first argument (here params
) will not be mapped, while the second argument (here input_vec
) will be mapped along axis 0.
In the example below I can set
in_axes=(None, 0)
orin_axes=(None, 1)
leading to the same results. Why is that the case?
This is because input_vec
is a 2x2 matrix of ones, so whether you map along axis 0 or axis 1, the input vectors are length-2 vectors of ones. In more general cases, the two specifications are not equivalent, which you can see by either (1) making batch_size
differ from input_dims[0]
, or (2) filling your arrays with non-constant values.
why do I have to use
in_axes=(None, 0)
and not something likein_axes=(0, )
?
If you set in_axes=(0, )
for a function with two arguments, you get an error because the length of the in_axes
tuple must match the number of arguments passed to the function. That said, it is possible to pass a scalar in_axes=0
as a shorthand for in_axes=(0, 0)
, but for your function this would lead to a shape error because the leading dimension of the arrays in params
does not match the leading dimension of input_vec
.
QUESTION
In many nftables examples, I sometimes see statement like this:
...ANSWER
Answered 2021-Dec-16 at 06:41Concatenation
There's some more details about it in the wiki
QUESTION
Recently I was doing some research on CSP, and I found some weird declarations of CSP from dominant websites.
For Facebook, after logging in, the CSP is like:
...ANSWER
Answered 2021-Nov-25 at 09:54QUESTION
I tried my best to look for the solution online, but cannot seem to find one for my graph_tool
network. I have a pandas dataframe df
that has three columns: id_source
, id_target
, weight
. The id_source
and id_target
contain names in text form. I want to use them as vertex labels. However, when adding edges from a pandas dataframe, I must set hashed=True
. The code then looks like this
ANSWER
Answered 2021-Nov-20 at 17:06From the documentation for Graph.add_edge_list
:
Add a list of edges to the graph, given by edge_list, which can be an iterator of (source, target) pairs where both source and target are vertex indexes, or a numpy.ndarray of shape (E,2), where E is the number of edges, and each line specifies a (source, target) pair. If the list references vertices which do not exist in the graph, they will be created.
You have passed df.values
for edge_list
, which has three columns (ie a shape of (E, 3)), but two columns are expected (a shape of (E, 2)).
Try this:
QUESTION
I need solution to add the Kubernetes services inside the Traffic manager using terraform and in order to do that I need to have a public IP address for each cluster, but it seems that IP is created under different subscription after deployment.
Tried playing with azurerm_traffic_manager_endpoint about different Types like azureEndpoints and nestedEndpoints but it seems that the script is failing with the same error listed below.
Below is my script which I want to deploy and I will share the error:
Error:
...creating/updating nestedEndpoints Endpoint "vmap-tmep" (Traffic Manager Profile "vmap-tm" / Resource Group "RG-TEST-TEST"): trafficmanager.EndpointsClient#CreateOrUpdate: Failure responding to request: StatusCode=400 -- Original Error: autorest/azure: Service returned an error. Status=400 Code="BadRequest" Message="The 'resourceTargetId' property of endpoint 'vmap-tmep' is invalid or missing. The property must be specified only for the following endpoint types: AzureEndpoints, NestedEndpoints. You must have read access to the resource to which it refers."
ANSWER
Answered 2021-Nov-08 at 12:23As already discussed You need to change few things in your code in-order to use traffic manager for AKS.
You need to use
azureEndpoints
instead ofnestedEndpoints
as the Traffic Manager endpoint Type.As there are currently four services
(Cloud Service ,App Service, App Service Slots and Public IP's)
which support the Traffic manager . So , you have to use the Public IP which is being used by the AKS .You have to use the below block:
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