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Diffstat (limited to 'notebooks/Untitled1.ipynb')
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diff --git a/notebooks/Untitled1.ipynb b/notebooks/Untitled1.ipynb index a2d6168..92b35c9 100644 --- a/notebooks/Untitled1.ipynb +++ b/notebooks/Untitled1.ipynb @@ -430,241 +430,6 @@ "plt.figure(figsize=(40, 20))\n", "plt.imshow(xxx, cmap='gray')" ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "c7c30e67-0cd7-4c23-adcc-56c86450bd37", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.device" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(t.device)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "a6f270cc-20a2-4aae-8006-cb956eeed44c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "mv =-torch.finfo(t.dtype).max" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "390d8a9d-2002-456f-93f9-b4e01b550024", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "a = torch.tensor([1., 1., 2., 2.])" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "55efcc9d-9f61-46fb-8417-0a3443332b93", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "b = torch.tensor([1., 1., 2., 2.]) != 2." - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "a0629f46-06b7-42dd-9fd7-d7d9da95faf6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([-3.4028e+38, -3.4028e+38, 2.0000e+00, 2.0000e+00])" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a.masked_fill_(b, mv)" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "516339e8-445a-4459-8fec-f028e3201bce", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([ True, True, False, False])" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c0e733d8-c17d-46f5-b484-9c74e46d7308", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from typing import Union\n", - "\n", - "import torch\n", - "\n", - "\n", - "def first_appearance(x: torch.Tensor, element: Union[int, float], dim: int = 1) -> torch.Tensor:\n", - " \"\"\"Return indices of first appearance of element in x, collapsing along dim.\n", - "\n", - " Based on https://discuss.pytorch.org/t/first-nonzero-index/24769/9\n", - "\n", - " Parameters\n", - " ----------\n", - " x\n", - " One or two-dimensional Tensor to search for element.\n", - " element\n", - " Item to search for inside x.\n", - " dim\n", - " Dimension of Tensor to collapse over.\n", - "\n", - " Returns\n", - " -------\n", - " torch.Tensor\n", - " Indices where element occurs in x. If element is not found,\n", - " return length of x along dim. One dimension smaller than x.\n", - "\n", - " Raises\n", - " ------\n", - " ValueError\n", - " if x is not a 1 or 2 dimensional Tensor\n", - "\n", - " Examples\n", - " --------\n", - " >>> first_appearance(torch.tensor([[1, 2, 3], [2, 3, 3], [1, 1, 1], [3, 1, 1]]), 3)\n", - " tensor([2, 1, 3, 0])\n", - " >>> first_appearance(torch.tensor([1, 2, 3]), 1, dim=0)\n", - " tensor(0)\n", - " \"\"\"\n", - " if x.dim() > 2 or x.dim() == 0:\n", - " raise ValueError(f\"only 1 or 2 dimensional Tensors allowed, got Tensor with dim {x.dim()}\")\n", - " matches = x == element\n", - " first_appearance_mask = (matches.cumsum(dim) == 1) & matches\n", - " does_match, match_index = first_appearance_mask.max(dim)\n", - " first_inds = torch.where(does_match, match_index, x.shape[dim])\n", - " return first_inds" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "26ff2314-b2df-408f-b83a-f5fc903145da", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([2, 3])" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "first_appearance(torch.tensor([[1, 1, 3], [1, 1, 1]]), 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e8c9dd16-4917-40bc-8504-084035882ced", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([ True, False])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "torch.any(torch.isin(torch.tensor([[1, 1, 3], [1, 1, 1]]), 3), 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "30eb1926-53d2-431a-b7c1-f95919887b84", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([2, 0])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "torch.tensor([[1, 1, 3], [1, 1, 1]]).argmax(dim=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a02c61b3-c84f-4778-90d0-fe6aafa12ccc", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { |