![]() ![]() Inside ssd.py at this line there is a call to contiguous() after permute(). about the next word predictionvector torch.cat((contextvectors, ht). I am trying to follow the pytorch code for the ssd implementation (Github link). These two lines were just to illustrate how we can still 'get back' to x from a slice of x, we can recover the apparent content by changing the tensor's metadata.# include const char* shape_of(torch::Tensor const & tensor), torch:: kByte) printf( "shape of tframe is %s, \n ", shape_of(tframe)) įrame = cv::Mat( 2, 2, CV_8UC3, data) std::cout ()) std::cout ()) std::cout << "cout show(tres. Seq) We want to iterate over the sequence so we permute it to (S. I assumed that there should be a GPU kernel function for permutation in PyTorch/aten/src/ATen/native/cuda/, but I didn’t find it in either TensorTransformation. and test datasets : device vice ( ' cuda ' if. Same with z: > torch.as_strided(z, size=x.shape, stride=x.stride())īoth will return a copy of x because torch.as_strided is allocating memory for the newly created tensor. image image. In fact, you can even go from y to x's representation using torch.as_strided: > torch.as_strided(y, size=x.shape, stride=x.stride()) Id prefer the following, which leaves the original image unmodified and simply adds a new axis as desired: image np.array (image) image omnumpy (image) image image np.newaxis, : unsqueeze works fine here too. Here again, you notice that x.data_ptr is the same as z.data_ptr. The main difference is that, instead of using the -operator similar to the Python API syntax, in the C++ API the indexing methods are: torch::Tensor::index ( link) torch::Tensor::indexput ( link) It’s also important to note that index types such as None / Ellipsis / Slice live in the torch::indexing namespace, and it’s recommended to. > z.shape, z.stride(), x_ptr = z.data_ptr() Permuting the first two axes then slicing on the first one: > z = x.permute(1, 0, 2) Path.loadarray Path.loadarray (p:pathlib.Path) Save numpy array to a compressed pytables file, using compression level lvlompression lib can be any of. > y.shape, y.stride(), x_ptr = y.data_ptr()Īs you can see, x and x shared the same storage. We have kept the data pointer for our 'base' tensor in x_ptr: Let us look at a simple example: > x = torch.ones(2,3,4).transpose(0,1) how you interact with that buffer (strides and shape) changes. Those two are essentially the same, the underlying data storage buffer is kept the same, only the metadata i.e. PyTorch torch.permute () rearranges the original tensor according to the desired ordering and returns a new multidimensional rotated tensor. Since permute doesn't affect the underlying memory layout, both operations are essentially equivalent. but in fact share the identical storage buffer with the original tensor. TLDR Slices seemingly contain less information. (x.shape, 3, ADDITIONAL_DIM, x.shape) + x.shape The irony is that tries to redirect to the function for more information, but (because it does not exist) it cannot link to it, as evident here. ![]() ![]() The torch::Tensor::permute function is perfect for this. In terms of memory locality it would make any difference to have a,b,c = torch.zeros( When I search for the permute function (torch.permute) I can only find the method (). The same Torch Script based approach is also used for all the other libtorch functionality. Will create three tensors with the same batch size x.shape. (3, x.shape, ADDITIONAL_DIM, x.shape) + x.shape When I create additional variables I am creating them with torch.zeros and then transposing, so that the largest stride goes to the axis 1, as well. Print(x.stride(), u.stride(), v.stride()) Results from arithmetic operations seems to keep the same memory layout x = torch.ones(2,3,4).transpose(0,1) I am able to permute the dimmension of the tensor: Im able to do this in pytorch But not in tensorflow A torch.rand(1, 2,5) A A.permute(0,2,1) A.shape torch.Size(1, 5, 2) Tensorflow (just a try,I dont know about this): A tf.random.normal(1, 2,5) A tf. My model calls a function that becomes simpler if I have another dimension in the first axis, so that I can use x instead of x. I know that usually the batch dimension is axis zero, and I imagine this has a reason: The underlying memory for each item in the batch is contiguous. stride () method that returns a list of integers specifying how many jumps to make along the 1-D array in order to access the next element along the N axes of the N-D array. ![]()
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