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-rw-r--r--notebooks/00-test-gpu.ipynb276
-rw-r--r--notebooks/03-look-at-iam-lines.ipynb139
-rw-r--r--notebooks/Untitled1.ipynb235
3 files changed, 276 insertions, 374 deletions
diff --git a/notebooks/00-test-gpu.ipynb b/notebooks/00-test-gpu.ipynb
new file mode 100644
index 0000000..7ea06ae
--- /dev/null
+++ b/notebooks/00-test-gpu.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "8468e45a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d1bc956b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.cuda.is_available()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "652cfb26",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.cuda.device_count()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0fc5e328",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.cuda.current_device()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "4ed93c82",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<torch.cuda.device at 0x7fbf7bf7b9a0>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.cuda.device(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c03841ce",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'NVIDIA GeForce RTX 2070'"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.cuda.get_device_name(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "2fba9680-0c73-4a62-b24d-ceea79874717",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'11.7'"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.version.cuda"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "7e62de10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t = torch.randn((2, 1, 3, 64))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "fc221b4e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([2, 1, 3, 64])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "ab80d75e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch.nn.functional as F"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "bfe1fc90",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([2, 1, 7, 128])"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "F.interpolate(t, scale_factor=[2.5, 2], mode=\"nearest\").shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "f69fa1fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f = torch.nn.Conv2d(1, 1, 3, 1, padding=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "cd6df204",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([2, 1, 64, 64])"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f(torch.randn(2, 1, 64, 64)).shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41693566",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/03-look-at-iam-lines.ipynb b/notebooks/03-look-at-iam-lines.ipynb
index 9e9b24c..049a8b6 100644
--- a/notebooks/03-look-at-iam-lines.ipynb
+++ b/notebooks/03-look-at-iam-lines.ipynb
@@ -134,39 +134,6 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(tensor([[[0., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 0., ..., 0., 0., 0.],\n",
- " ...,\n",
- " [0., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 0., ..., 0., 0., 0.]]]),\n",
- " tensor([ 1, 32, 27, 14, 33, 16, 21, 22, 27, 20, 40, 14, 33, 40, 33, 21, 18, 40,\n",
- " 29, 31, 28, 29, 40, 21, 28, 25, 17, 22, 27, 20, 40, 33, 21, 18, 40, 15,\n",
- " 14, 31, 20, 18, 45, 32, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]))"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "datamodule.data_val[16]"
- ]
- },
- {
- "cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
@@ -190,112 +157,6 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['<b>',\n",
- " '<s>',\n",
- " '<e>',\n",
- " '<p>',\n",
- " '0',\n",
- " '1',\n",
- " '2',\n",
- " '3',\n",
- " '4',\n",
- " '5',\n",
- " '6',\n",
- " '7',\n",
- " '8',\n",
- " '9',\n",
- " 'a',\n",
- " 'b',\n",
- " 'c',\n",
- " 'd',\n",
- " 'e',\n",
- " 'f',\n",
- " 'g',\n",
- " 'h',\n",
- " 'i',\n",
- " 'j',\n",
- " 'k',\n",
- " 'l',\n",
- " 'm',\n",
- " 'n',\n",
- " 'o',\n",
- " 'p',\n",
- " 'q',\n",
- " 'r',\n",
- " 's',\n",
- " 't',\n",
- " 'u',\n",
- " 'v',\n",
- " 'w',\n",
- " 'x',\n",
- " 'y',\n",
- " 'z',\n",
- " ' ',\n",
- " '!',\n",
- " '\"',\n",
- " '#',\n",
- " '&',\n",
- " \"'\",\n",
- " '(',\n",
- " ')',\n",
- " '*',\n",
- " '+',\n",
- " ',',\n",
- " '-',\n",
- " '.',\n",
- " '/',\n",
- " ':',\n",
- " ';',\n",
- " '?',\n",
- " '\\n']"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "datamodule.tokenizer.mapping"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([ 1, 34, 27, 22, 28, 27, 40, 36, 28, 34, 25, 17, 40, 15, 18, 40, 29, 31,\n",
- " 18, 29, 14, 31, 18, 17, 40, 33, 28, 40, 31, 18, 14, 16, 21, 2, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x =dataset[0][1]\n",
- "x["
- ]
- },
- {
- "cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
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": {
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-12-25 07:09:15 +0000