![]() Specified or is None, key defaults to an identity function and returns The key is a function computing a key value for each element. Make an iterator that returns consecutive keys and groups from the iterable. Has one more element than the input iterable.ĭef filterfalse ( predicate, iterable ): # filterfalse(lambda x: x%2, range(10)) -> 0 2 4 6 8 if predicate is None : predicate = bool for x in iterable : if not predicate ( x ): yield x itertools. However, if the keyword argument initial is provided, theĪccumulation leads off with the initial value so that the output Usually, the number of elements output matches the input iterable. The default operation of addition, elements may be any addable That can be accepted as arguments to func. Elements of the input iterable may be any type If func is supplied, it should be a function Results of other binary functions (specified via the optional Make an iterator that returns accumulated sums, or accumulated Streams of infinite length, so they should only be accessed by functions or ![]() The following module functions all construct and return iterators. R-length tuples, in sorted order, with repeated elementsĪA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD R-length tuples, in sorted order, no repeated elements R-length tuples, all possible orderings, no repeated elements Zip_longest('ABCD', 'xy', fillvalue='-') -> Ax By C- D-Ĭartesian product, equivalent to a nested for-loop It1, it2, … itn splits one iterator into n Seq, seq, starting when pred failsĮlements of seq where pred(elem) is falseįilterfalse(lambda x: x%2, range(10)) -> 0 2 4 6 8 Iterators terminating on the shortest input sequence:Ĭom_iterable() -> A B C D E FĬompress('ABCDEF', ) -> A C E F Sum(starmap(operator.mul, zip(vec1, vec2, strict=True))).Įlem, elem, elem, … endlessly or up to n times Operator can be mapped across two vectors to form an efficient dot-product: These tools and their built-in counterparts also work well with the high-speedįunctions in the operator module. The same effect can be achieved in Pythonīy combining map() and count() to form map(f, count()). Together, they form an “iteratorĪlgebra” making it possible to construct specialized tools succinctly andįor instance, SML provides a tabulation tool: tabulate(f) which produces a The module standardizes a core set of fast, memory efficient tools that are This module implements a number of iterator building blocks inspiredīy constructs from APL, Haskell, and SML. X2.Itertools - Functions creating iterators for efficient looping ¶ RuntimeError: view size is not compatible with input tensor''s size and stride (at least one dimension spans across two contiguous subspaces). # strides cannot cut it anymore - we get an error X1.stride() # -> (1, 4) efficient stride representation can handle this Take a look at this simple example: # original tensor However, if you only change the shape (i.e., using view or reshape) you incorrectly "mix" the values from the two channels: A.view(1,2,3,3) If you correctly transpose and then reshape, you get the correct split into even and odd channels: A.transpose(1,2).view(1,2,3,3) Here's an example, with B=1, N=3 and C=2, the first channel has even numbers 0.16, and the second channel has odd numbers 1.17: A = torch.arange(2*9).view(1,9,2) See this answer, and this one for more details. In contrast, transpose and permute change the underlying order of elements in the tensor. ![]() Reshape and view only affect the shape of a tensor, but d not change the underlying order of elements. Transposing/permuting and view/reshape are NOT the same! So in the end, yes you end up with two different results: C shares the same data as A, while B is a copy and has a different memory layout. building a view on top of a tensor doesn't change its memory layout, it's an abstraction level to better manipulate tensors. By layout I mean memory arrangement, I am not referring to its shape which is irrelevant: > B.flatten() In terms of memory layout A has the following memory arangement: > A.flatten() Here swapping axis=1 and axis=2 comes down to a batched transpose (in mathematical terms): > B = A.transpose(2, 1) If you take a smaller example, something we can grasp more easily: > A = torch.rand(1, 4, 3) What it comes down to is a contiguous memory data buffer. The view at the end is just a nice interface for you to access your data but it has no effect on the underlying data of your tensor. The difference between B and C is that you have used anspose which means you have swapped two axes, this means you have changed the layout of the memory. ![]()
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