remove unused/repeated imports
This commit is contained in:
Aleksey Morozov
parent
6bc0bd8873
commit
90c1b12d63
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@ -36,73 +36,6 @@
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"Make sure you're running a GPU runtime; if not, select \"GPU\" as the hardware accelerator in Runtime > Change Runtime Type in the menu. The next cells will print the CUDA version of the runtime if it has a GPU, and install PyTorch 1.7.1."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "0BpdJkdBssk9",
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"outputId": "f26d1899-1e21-427a-f730-9451be9a2572"
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},
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"source": [
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"import subprocess\n",
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"\n",
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"CUDA_version = [s for s in subprocess.check_output([\"nvcc\", \"--version\"]).decode(\"UTF-8\").split(\", \") if s.startswith(\"release\")][0].split(\" \")[-1]\n",
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"print(\"CUDA version:\", CUDA_version)\n",
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"\n",
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"if CUDA_version == \"10.0\":\n",
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" torch_version_suffix = \"+cu100\"\n",
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"elif CUDA_version == \"10.1\":\n",
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" torch_version_suffix = \"+cu101\"\n",
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"elif CUDA_version == \"10.2\":\n",
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" torch_version_suffix = \"\"\n",
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"else:\n",
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" torch_version_suffix = \"+cu110\""
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"CUDA version: 10.1\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "RBVr18E5tse8",
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"outputId": "d4647553-46fa-4cc5-8a41-b152abc0b4d2"
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},
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"source": [
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"! pip install torch==1.7.1{torch_version_suffix} torchvision==0.8.2{torch_version_suffix} -f https://download.pytorch.org/whl/torch_stable.html ftfy regex"
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],
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
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"Requirement already satisfied: torch==1.7.1+cu101 in /usr/local/lib/python3.6/dist-packages (1.7.1+cu101)\n",
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"Requirement already satisfied: torchvision==0.8.2+cu101 in /usr/local/lib/python3.6/dist-packages (0.8.2+cu101)\n",
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"Requirement already satisfied: ftfy in /usr/local/lib/python3.6/dist-packages (5.8)\n",
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"Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (2019.12.20)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.7.1+cu101) (1.19.4)\n",
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"Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from torch==1.7.1+cu101) (0.8)\n",
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"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch==1.7.1+cu101) (3.7.4.3)\n",
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"Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.8.2+cu101) (7.0.0)\n",
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"Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from ftfy) (0.2.5)\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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@ -238,7 +171,7 @@
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"id": "d6cpiIFHp9N6"
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},
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"source": [
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"from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize\n",
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"from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor\n",
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"from PIL import Image\n",
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"\n",
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"preprocess = Compose([\n",
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@ -330,7 +263,7 @@
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" To avoid that, we want lookup tables between utf-8 bytes and unicode strings.\n",
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" And avoids mapping to whitespace/control characters the bpe code barfs on.\n",
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" \"\"\"\n",
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" bs = list(range(ord(\"!\"), ord(\"~\")+1))+list(range(ord(\"¡\"), ord(\"¬\")+1))+list(range(ord(\"®\"), ord(\"ÿ\")+1))\n",
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" bs = list(range(ord(\"!\"), ord(\"~\")+1)) + list(range(ord(\"¡\"), ord(\"¬\")+1)) + list(range(ord(\"®\"), ord(\"ÿ\")+1))\n",
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" cs = bs[:]\n",
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" n = 0\n",
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" for b in range(2**8):\n",
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@ -343,8 +276,9 @@
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"\n",
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"\n",
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"def get_pairs(word):\n",
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" \"\"\"Return set of symbol pairs in a word.\n",
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" Word is represented as tuple of symbols (symbols being variable-length strings).\n",
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" \"\"\"\n",
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" Return set of symbol pairs in a word. Word is represented as tuple of\n",
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" symbols (symbols being variable-length strings).\n",
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" \"\"\"\n",
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" pairs = set()\n",
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" prev_char = word[0]\n",
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@ -381,20 +315,21 @@
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" self.encoder = dict(zip(vocab, range(len(vocab))))\n",
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" self.decoder = {v: k for k, v in self.encoder.items()}\n",
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" self.bpe_ranks = dict(zip(merges, range(len(merges))))\n",
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" self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}\n",
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" self.cache = {'<|startoftext|>': '<|startoftext|>',\n",
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" '<|endoftext|>': '<|endoftext|>'}\n",
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" self.pat = re.compile(r\"\"\"<\\|startoftext\\|>|<\\|endoftext\\|>|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+\"\"\", re.IGNORECASE)\n",
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"\n",
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" def bpe(self, token):\n",
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" if token in self.cache:\n",
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" return self.cache[token]\n",
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" word = tuple(token[:-1]) + ( token[-1] + '</w>',)\n",
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" word = tuple(token[:-1]) + (token[-1] + '</w>',)\n",
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" pairs = get_pairs(word)\n",
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"\n",
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" if not pairs:\n",
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" return token+'</w>'\n",
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" return token + '</w>'\n",
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"\n",
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" while True:\n",
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" bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))\n",
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" bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))\n",
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" if bigram not in self.bpe_ranks:\n",
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" break\n",
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" first, second = bigram\n",
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@ -405,7 +340,7 @@
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" j = word.index(first, i)\n",
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" new_word.extend(word[i:j])\n",
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" i = j\n",
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" except:\n",
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" except ValueError:\n",
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" new_word.extend(word[i:])\n",
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" break\n",
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"\n",
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@ -429,14 +364,16 @@
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" bpe_tokens = []\n",
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" text = whitespace_clean(basic_clean(text)).lower()\n",
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" for token in re.findall(self.pat, text):\n",
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" token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))\n",
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" bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))\n",
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" token = ''.join(self.byte_encoder[b]\n",
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" for b in token.encode('utf-8'))\n",
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" bpe_tokens.extend(self.encoder[bpe_token]\n",
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" for bpe_token in self.bpe(token).split(' '))\n",
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" return bpe_tokens\n",
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"\n",
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" def decode(self, tokens):\n",
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" text = ''.join([self.decoder[token] for token in tokens])\n",
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" text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=\"replace\").replace('</w>', ' ')\n",
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" return text\n"
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" return text"
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],
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"execution_count": 9,
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"outputs": []
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@ -460,15 +397,8 @@
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"id": "tMc1AXzBlhzm"
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},
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"source": [
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"import os\n",
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"import skimage\n",
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"import IPython.display\n",
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"import matplotlib.pyplot as plt\n",
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"from PIL import Image\n",
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"import numpy as np\n",
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"\n",
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"from collections import OrderedDict\n",
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"import torch\n",
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"\n",
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"%matplotlib inline\n",
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"%config InlineBackend.figure_format = 'retina'\n",
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@ -481,7 +411,7 @@
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" \"rocket\": \"a rocket standing on a launchpad\",\n",
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" \"motorcycle_right\": \"a red motorcycle standing in a garage\",\n",
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" \"camera\": \"a person looking at a camera on a tripod\",\n",
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" \"horse\": \"a black-and-white silhouette of a horse\", \n",
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" \"horse\": \"a black-and-white silhouette of a horse\",\n",
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" \"coffee\": \"a cup of coffee on a saucer\"\n",
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"}"
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],
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@ -518,7 +448,7 @@
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" plt.xticks([])\n",
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" plt.yticks([])\n",
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"\n",
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"plt.tight_layout()\n"
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"plt.tight_layout()"
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],
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"execution_count": 11,
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"outputs": [
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@ -582,7 +512,8 @@
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"id": "w1l_muuhZ_Nk"
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},
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"source": [
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"text_input = torch.zeros(len(text_tokens), model.context_length, dtype=torch.long)\n",
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"text_input = torch.zeros(len(text_tokens), model.context_length,\n",
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" dtype=torch.long)\n",
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"sot_token = tokenizer.encoder['<|startoftext|>']\n",
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"eot_token = tokenizer.encoder['<|endoftext|>']\n",
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"\n",
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@ -651,10 +582,12 @@
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"plt.yticks(range(count), texts, fontsize=18)\n",
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"plt.xticks([])\n",
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"for i, image in enumerate(images):\n",
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" plt.imshow(image.permute(1, 2, 0), extent=(i - 0.5, i + 0.5, -1.6, -0.6), origin=\"lower\")\n",
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" plt.imshow(image.permute(1, 2, 0), extent=(i - 0.5, i + 0.5, -1.6, -0.6),\n",
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" origin=\"lower\")\n",
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"for x in range(similarity.shape[1]):\n",
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" for y in range(similarity.shape[0]):\n",
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" plt.text(x, y, f\"{similarity[y, x]:.2f}\", ha=\"center\", va=\"center\", size=12)\n",
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" plt.text(x, y, f\"{similarity[y, x]:.2f}\", ha=\"center\", va=\"center\",\n",
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" size=12)\n",
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"\n",
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"for side in [\"left\", \"top\", \"right\", \"bottom\"]:\n",
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" plt.gca().spines[side].set_visible(False)\n",
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@ -720,7 +653,8 @@
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"source": [
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"from torchvision.datasets import CIFAR100\n",
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"\n",
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"cifar100 = CIFAR100(os.path.expanduser(\"~/.cache\"), transform=preprocess, download=True)"
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"cifar100 = CIFAR100(os.path.expanduser(\"~/.cache\"), transform=preprocess,\n",
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" download=True)"
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],
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"execution_count": 18,
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"outputs": [
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@ -743,9 +677,12 @@
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"outputId": "6e0c62da-e7ee-44f0-8e20-6d5d0020437d"
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},
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"source": [
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"text_descriptions = [f\"This is a photo of a {label}\" for label in cifar100.classes]\n",
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"text_tokens = [[sot_token] + tokenizer.encode(desc) + [eot_token] for desc in text_descriptions]\n",
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"text_input = torch.zeros(len(text_tokens), model.context_length, dtype=torch.long)\n",
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"text_descriptions = [f\"This is a photo of a {label}\"\n",
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" for label in cifar100.classes]\n",
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"text_tokens = [[sot_token] + tokenizer.encode(desc) + [eot_token]\n",
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" for desc in text_descriptions]\n",
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"text_input = torch.zeros(len(text_tokens), model.context_length,\n",
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" dtype=torch.long)\n",
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"\n",
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"for i, tokens in enumerate(text_tokens):\n",
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" text_input[i, :len(tokens)] = torch.tensor(tokens)\n",
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@ -835,17 +772,6 @@
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}
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "0OENu-DQLzQY"
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},
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"source": [
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""
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],
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"execution_count": 21,
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"outputs": []
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}
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]
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}
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