{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://download.pytorch.org/whl/cu121\n", "Collecting torch==2.4.1+cu121\n", " Downloading https://download.pytorch.org/whl/cu121/torch-2.4.1%2Bcu121-cp311-cp311-win_amd64.whl (2444.9 MB)\n", " ---------------------------------------- 0.0/2.4 GB ? eta -:--:--\n", " ---------------------------------------- 0.0/2.4 GB ? eta -:--:--\n", " ---------------------------------------- 0.0/2.4 GB ? eta -:--:--\n", " ---------------------------------------- 0.0/2.4 GB ? eta -:--:--\n", " ---------------------------------------- 0.0/2.4 GB ? eta -:--:--\n", " ---------------------------------------- 0.0/2.4 GB 273.8 kB/s eta 2:28:50\n", " ---------------------------------------- 0.0/2.4 GB 273.8 kB/s eta 2:28:50\n", " ---------------------------------------- 0.0/2.4 GB 273.8 kB/s eta 2:28:50\n", " 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---------------- ----------------------- 1.0/2.4 GB 26.2 MB/s eta 0:00:55\n", " ---------------- ----------------------- 1.0/2.4 GB 26.2 MB/s eta 0:00:55\n", " ---------------- ----------------------- 1.0/2.4 GB 26.2 MB/s eta 0:00:55\n", " ---------------- ----------------------- 1.0/2.4 GB 18.7 MB/s eta 0:01:16\n", " ---------------- ----------------------- 1.0/2.4 GB 25.2 MB/s eta 0:00:57\n", " ---------------- ----------------------- 1.0/2.4 GB 25.2 MB/s eta 0:00:56\n", " ---------------- ----------------------- 1.0/2.4 GB 25.2 MB/s eta 0:00:56\n", " ---------------- ----------------------- 1.0/2.4 GB 18.7 MB/s eta 0:01:16\n", " ---------------- ----------------------- 1.0/2.4 GB 25.1 MB/s eta 0:00:56\n", " ---------------- ----------------------- 1.0/2.4 GB 25.1 MB/s eta 0:00:56\n", " ---------------- ----------------------- 1.0/2.4 GB 25.1 MB/s eta 0:00:56\n", " ---------------- ----------------------- 1.0/2.4 GB 18.7 MB/s eta 0:01:16\n", " ---------------- ----------------------- 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------------------- -------------------- 1.2/2.4 GB 15.6 MB/s eta 0:01:20\n", " ------------------- -------------------- 1.2/2.4 GB 15.6 MB/s eta 0:01:20\n", " ------------------- -------------------- 1.2/2.4 GB 17.7 MB/s eta 0:01:10\n", " ------------------- -------------------- 1.2/2.4 GB 17.3 MB/s eta 0:01:12\n", " ------------------- -------------------- 1.2/2.4 GB 15.2 MB/s eta 0:01:22\n", " ------------------- -------------------- 1.2/2.4 GB 15.2 MB/s eta 0:01:22\n", " ------------------- -------------------- 1.2/2.4 GB 18.2 MB/s eta 0:01:08\n", " ------------------- -------------------- 1.2/2.4 GB 18.2 MB/s eta 0:01:08\n", " ------------------- -------------------- 1.2/2.4 GB 15.6 MB/s eta 0:01:20\n", " ------------------- -------------------- 1.2/2.4 GB 15.6 MB/s eta 0:01:20\n", " ------------------- -------------------- 1.2/2.4 GB 17.7 MB/s eta 0:01:10\n", " ------------------- -------------------- 1.2/2.4 GB 17.7 MB/s eta 0:01:10\n", " ------------------- -------------------- 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------------------- -------------------- 1.2/2.4 GB 13.9 MB/s eta 0:01:28\n", " -------------------- ------------------- 1.2/2.4 GB 17.3 MB/s eta 0:01:11\n", " -------------------- ------------------- 1.2/2.4 GB 16.0 MB/s eta 0:01:17\n", " -------------------- ------------------- 1.2/2.4 GB 15.6 MB/s eta 0:01:19\n", " -------------------- ------------------- 1.2/2.4 GB 13.6 MB/s eta 0:01:30\n", " -------------------- ------------------- 1.2/2.4 GB 17.7 MB/s eta 0:01:09\n", " -------------------- ------------------- 1.2/2.4 GB 16.0 MB/s eta 0:01:17\n", " -------------------- ------------------- 1.2/2.4 GB 16.0 MB/s eta 0:01:17\n", " -------------------- ------------------- 1.2/2.4 GB 13.9 MB/s eta 0:01:28\n", " -------------------- ------------------- 1.2/2.4 GB 17.3 MB/s eta 0:01:11\n", " -------------------- ------------------- 1.2/2.4 GB 15.6 MB/s eta 0:01:18\n", " -------------------- ------------------- 1.2/2.4 GB 15.6 MB/s eta 0:01:18\n", " -------------------- ------------------- 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0:01:52\n", " --------------------- ------------------ 1.3/2.4 GB 9.6 MB/s eta 0:01:59\n", " --------------------- ------------------ 1.3/2.4 GB 9.2 MB/s eta 0:02:04\n", " --------------------- ------------------ 1.3/2.4 GB 10.4 MB/s eta 0:01:50\n", " --------------------- ------------------ 1.3/2.4 GB 10.1 MB/s eta 0:01:53\n", " --------------------- ------------------ 1.3/2.4 GB 9.8 MB/s eta 0:01:57\n", " --------------------- ------------------ 1.3/2.4 GB 9.2 MB/s eta 0:02:04\n", " --------------------- ------------------ 1.3/2.4 GB 10.4 MB/s eta 0:01:50\n", " --------------------- ------------------ 1.3/2.4 GB 10.1 MB/s eta 0:01:53\n", " --------------------- ------------------ 1.3/2.4 GB 9.8 MB/s eta 0:01:57\n", " --------------------- ------------------ 1.3/2.4 GB 9.2 MB/s eta 0:02:03\n", " --------------------- ------------------ 1.3/2.4 GB 10.6 MB/s eta 0:01:48\n", " --------------------- ------------------ 1.3/2.4 GB 10.2 MB/s eta 0:01:51\n", " --------------------- 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0:01:49\n", " ---------------------- ----------------- 1.4/2.4 GB 9.5 MB/s eta 0:01:54\n", " ---------------------- ----------------- 1.4/2.4 GB 10.9 MB/s eta 0:01:39\n", " ---------------------- ----------------- 1.4/2.4 GB 10.4 MB/s eta 0:01:44\n", " ---------------------- ----------------- 1.4/2.4 GB 9.9 MB/s eta 0:01:49\n", " ---------------------- ----------------- 1.4/2.4 GB 9.8 MB/s eta 0:01:51\n", " ---------------------- ----------------- 1.4/2.4 GB 10.7 MB/s eta 0:01:41\n", " ---------------------- ----------------- 1.4/2.4 GB 10.2 MB/s eta 0:01:46\n", " ---------------------- ----------------- 1.4/2.4 GB 10.2 MB/s eta 0:01:46\n", " ---------------------- ----------------- 1.4/2.4 GB 9.6 MB/s eta 0:01:52\n", " ---------------------- ----------------- 1.4/2.4 GB 10.6 MB/s eta 0:01:42\n", " ---------------------- ----------------- 1.4/2.4 GB 10.4 MB/s eta 0:01:44\n", " ---------------------- ----------------- 1.4/2.4 GB 10.1 MB/s eta 0:01:47\n", " ---------------------- 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------------------------- -------------- 1.5/2.4 GB 26.2 MB/s eta 0:00:35\n", " ------------------------- -------------- 1.5/2.4 GB 26.2 MB/s eta 0:00:35\n", " ------------------------- -------------- 1.5/2.4 GB 26.2 MB/s eta 0:00:35\n", " ------------------------- -------------- 1.5/2.4 GB 26.2 MB/s eta 0:00:35\n", " ------------------------- -------------- 1.6/2.4 GB 18.7 MB/s eta 0:00:48\n", " ------------------------- -------------- 1.6/2.4 GB 25.2 MB/s eta 0:00:36\n", " ------------------------- -------------- 1.6/2.4 GB 25.2 MB/s eta 0:00:36\n", " ------------------------- -------------- 1.6/2.4 GB 25.2 MB/s eta 0:00:36\n", " ------------------------- -------------- 1.6/2.4 GB 26.2 MB/s eta 0:00:34\n", " ------------------------- -------------- 1.6/2.4 GB 26.2 MB/s eta 0:00:34\n", " ------------------------- -------------- 1.6/2.4 GB 26.2 MB/s eta 0:00:34\n", " ------------------------- -------------- 1.6/2.4 GB 26.2 MB/s eta 0:00:34\n", " ------------------------- -------------- 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------------------------- -------------- 1.6/2.4 GB 23.4 MB/s eta 0:00:38\n", " ------------------------- -------------- 1.6/2.4 GB 23.4 MB/s eta 0:00:38\n", " ------------------------- -------------- 1.6/2.4 GB 18.2 MB/s eta 0:00:48\n", " ------------------------- -------------- 1.6/2.4 GB 17.7 MB/s eta 0:00:50\n", " ------------------------- -------------- 1.6/2.4 GB 26.2 MB/s eta 0:00:34\n", " ------------------------- -------------- 1.6/2.4 GB 26.2 MB/s eta 0:00:34\n", " ------------------------- -------------- 1.6/2.4 GB 19.9 MB/s eta 0:00:44\n", " ------------------------- -------------- 1.6/2.4 GB 24.2 MB/s eta 0:00:36\n", " ------------------------- -------------- 1.6/2.4 GB 26.2 MB/s eta 0:00:33\n", " ------------------------- -------------- 1.6/2.4 GB 26.2 MB/s eta 0:00:33\n", " ------------------------- -------------- 1.6/2.4 GB 19.2 MB/s eta 0:00:45\n", " ------------------------- -------------- 1.6/2.4 GB 27.3 MB/s eta 0:00:32\n", " ------------------------- -------------- 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-------------------------- ------------- 1.6/2.4 GB 25.1 MB/s eta 0:00:34\n", " -------------------------- ------------- 1.6/2.4 GB 18.7 MB/s eta 0:00:46\n", " -------------------------- ------------- 1.6/2.4 GB 24.2 MB/s eta 0:00:35\n", " -------------------------- ------------- 1.6/2.4 GB 24.2 MB/s eta 0:00:35\n", " -------------------------- ------------- 1.6/2.4 GB 24.2 MB/s eta 0:00:35\n", " -------------------------- ------------- 1.6/2.4 GB 18.7 MB/s eta 0:00:45\n", " -------------------------- ------------- 1.6/2.4 GB 25.1 MB/s eta 0:00:34\n", " -------------------------- ------------- 1.6/2.4 GB 25.1 MB/s eta 0:00:34\n", " -------------------------- ------------- 1.6/2.4 GB 25.1 MB/s eta 0:00:34\n", " -------------------------- ------------- 1.6/2.4 GB 19.3 MB/s eta 0:00:44\n", " -------------------------- ------------- 1.6/2.4 GB 25.2 MB/s eta 0:00:34\n", " -------------------------- ------------- 1.6/2.4 GB 25.2 MB/s eta 0:00:34\n", " -------------------------- ------------- 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--------------------------- ------------ 1.7/2.4 GB 26.2 MB/s eta 0:00:31\n", " --------------------------- ------------ 1.7/2.4 GB 26.2 MB/s eta 0:00:31\n", " --------------------------- ------------ 1.7/2.4 GB 26.2 MB/s eta 0:00:31\n", " --------------------------- ------------ 1.7/2.4 GB 25.2 MB/s eta 0:00:32\n", " --------------------------- ------------ 1.7/2.4 GB 25.2 MB/s eta 0:00:32\n", " --------------------------- ------------ 1.7/2.4 GB 25.2 MB/s eta 0:00:32\n", " --------------------------- ------------ 1.7/2.4 GB 25.2 MB/s eta 0:00:32\n", " --------------------------- ------------ 1.7/2.4 GB 25.2 MB/s eta 0:00:32\n", " --------------------------- ------------ 1.7/2.4 GB 25.2 MB/s eta 0:00:32\n", " --------------------------- ------------ 1.7/2.4 GB 25.2 MB/s eta 0:00:32\n", " --------------------------- ------------ 1.7/2.4 GB 25.2 MB/s eta 0:00:32\n", " --------------------------- ------------ 1.7/2.4 GB 25.1 MB/s eta 0:00:32\n", " --------------------------- ------------ 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-------------------------------- ------- 2.0/2.4 GB 14.2 MB/s eta 0:00:33\n", " -------------------------------- ------- 2.0/2.4 GB 17.7 MB/s eta 0:00:27\n", " -------------------------------- ------- 2.0/2.4 GB 16.4 MB/s eta 0:00:29\n", " -------------------------------- ------- 2.0/2.4 GB 16.4 MB/s eta 0:00:29\n", " -------------------------------- ------- 2.0/2.4 GB 14.2 MB/s eta 0:00:33\n", " -------------------------------- ------- 2.0/2.4 GB 17.7 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 16.4 MB/s eta 0:00:28\n", " -------------------------------- ------- 2.0/2.4 GB 16.4 MB/s eta 0:00:28\n", " -------------------------------- ------- 2.0/2.4 GB 14.5 MB/s eta 0:00:32\n", " -------------------------------- ------- 2.0/2.4 GB 17.7 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 16.4 MB/s eta 0:00:28\n", " -------------------------------- ------- 2.0/2.4 GB 16.4 MB/s eta 0:00:28\n", " -------------------------------- ------- 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-------------------------------- ------- 2.0/2.4 GB 17.7 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 17.2 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 14.9 MB/s eta 0:00:30\n", " -------------------------------- ------- 2.0/2.4 GB 17.7 MB/s eta 0:00:25\n", " -------------------------------- ------- 2.0/2.4 GB 16.8 MB/s eta 0:00:27\n", " -------------------------------- ------- 2.0/2.4 GB 16.8 MB/s eta 0:00:27\n", " -------------------------------- ------- 2.0/2.4 GB 14.6 MB/s eta 0:00:31\n", " -------------------------------- ------- 2.0/2.4 GB 17.2 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 16.8 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 16.4 MB/s eta 0:00:27\n", " -------------------------------- ------- 2.0/2.4 GB 14.6 MB/s eta 0:00:30\n", " -------------------------------- ------- 2.0/2.4 GB 18.2 MB/s eta 0:00:24\n", " -------------------------------- ------- 2.0/2.4 GB 16.8 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 16.8 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 14.2 MB/s eta 0:00:31\n", " -------------------------------- ------- 2.0/2.4 GB 18.2 MB/s eta 0:00:24\n", " -------------------------------- ------- 2.0/2.4 GB 16.8 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 16.8 MB/s eta 0:00:26\n", " -------------------------------- ------- 2.0/2.4 GB 14.2 MB/s eta 0:00:31\n", " --------------------------------- ------ 2.0/2.4 GB 18.7 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.0/2.4 GB 17.2 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 17.2 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 14.6 MB/s eta 0:00:30\n", " --------------------------------- ------ 2.0/2.4 GB 18.2 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:26\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:26\n", " --------------------------------- ------ 2.0/2.4 GB 14.6 MB/s eta 0:00:30\n", " --------------------------------- ------ 2.0/2.4 GB 17.3 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 16.4 MB/s eta 0:00:26\n", " --------------------------------- ------ 2.0/2.4 GB 16.4 MB/s eta 0:00:26\n", " --------------------------------- ------ 2.0/2.4 GB 14.6 MB/s eta 0:00:29\n", " --------------------------------- ------ 2.0/2.4 GB 17.3 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 14.2 MB/s eta 0:00:30\n", " --------------------------------- ------ 2.0/2.4 GB 17.7 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 17.2 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 14.9 MB/s eta 0:00:28\n", " --------------------------------- ------ 2.0/2.4 GB 14.2 MB/s eta 0:00:30\n", " --------------------------------- ------ 2.0/2.4 GB 17.7 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 14.9 MB/s eta 0:00:28\n", " --------------------------------- ------ 2.0/2.4 GB 14.2 MB/s eta 0:00:29\n", " --------------------------------- ------ 2.0/2.4 GB 17.2 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 14.9 MB/s eta 0:00:28\n", " --------------------------------- ------ 2.0/2.4 GB 16.4 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 14.9 MB/s eta 0:00:28\n", " --------------------------------- ------ 2.0/2.4 GB 16.4 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.0/2.4 GB 17.7 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 14.9 MB/s eta 0:00:28\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 17.3 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 17.3 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 14.9 MB/s eta 0:00:27\n", " --------------------------------- ------ 2.0/2.4 GB 17.7 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 16.8 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.0/2.4 GB 14.6 MB/s eta 0:00:28\n", " --------------------------------- ------ 2.1/2.4 GB 17.7 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 17.3 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 17.3 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 15.2 MB/s eta 0:00:26\n", " --------------------------------- ------ 2.1/2.4 GB 17.7 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 17.2 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 17.2 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 14.6 MB/s eta 0:00:27\n", " --------------------------------- ------ 2.1/2.4 GB 16.8 MB/s eta 0:00:24\n", " --------------------------------- ------ 2.1/2.4 GB 17.3 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 17.3 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 14.6 MB/s eta 0:00:27\n", " --------------------------------- ------ 2.1/2.4 GB 17.7 MB/s eta 0:00:22\n", " --------------------------------- ------ 2.1/2.4 GB 17.2 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 15.2 MB/s eta 0:00:26\n", " --------------------------------- ------ 2.1/2.4 GB 14.9 MB/s eta 0:00:26\n", " --------------------------------- ------ 2.1/2.4 GB 17.7 MB/s eta 0:00:22\n", " --------------------------------- ------ 2.1/2.4 GB 17.3 MB/s eta 0:00:23\n", " --------------------------------- ------ 2.1/2.4 GB 15.2 MB/s eta 0:00:26\n", " --------------------------------- ------ 2.1/2.4 GB 14.6 MB/s eta 0:00:27\n", " --------------------------------- ------ 2.1/2.4 GB 18.2 MB/s eta 0:00:21\n", " --------------------------------- ------ 2.1/2.4 GB 17.7 MB/s eta 0:00:22\n", " --------------------------------- ------ 2.1/2.4 GB 15.6 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.1/2.4 GB 15.2 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.1/2.4 GB 17.7 MB/s eta 0:00:22\n", " --------------------------------- ------ 2.1/2.4 GB 17.3 MB/s eta 0:00:22\n", " --------------------------------- ------ 2.1/2.4 GB 15.2 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.1/2.4 GB 14.2 MB/s eta 0:00:27\n", " --------------------------------- ------ 2.1/2.4 GB 17.2 MB/s eta 0:00:22\n", " --------------------------------- ------ 2.1/2.4 GB 17.2 MB/s eta 0:00:22\n", " --------------------------------- ------ 2.1/2.4 GB 15.2 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.1/2.4 GB 14.2 MB/s eta 0:00:27\n", " --------------------------------- ------ 2.1/2.4 GB 17.7 MB/s eta 0:00:21\n", " --------------------------------- ------ 2.1/2.4 GB 17.7 MB/s eta 0:00:21\n", " --------------------------------- ------ 2.1/2.4 GB 15.2 MB/s eta 0:00:25\n", " --------------------------------- ------ 2.1/2.4 GB 14.2 MB/s eta 0:00:26\n", " ---------------------------------- ----- 2.1/2.4 GB 17.2 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 17.2 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 14.9 MB/s eta 0:00:25\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:24\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 14.6 MB/s eta 0:00:25\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 14.9 MB/s eta 0:00:25\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 15.2 MB/s eta 0:00:24\n", " ---------------------------------- ----- 2.1/2.4 GB 16.4 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 17.2 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 14.9 MB/s eta 0:00:24\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 17.2 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 18.2 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 18.2 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 18.2 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 14.9 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 18.2 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 18.2 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 14.9 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 17.3 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 15.2 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 14.2 MB/s eta 0:00:24\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 14.9 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 13.9 MB/s eta 0:00:24\n", " ---------------------------------- ----- 2.1/2.4 GB 18.2 MB/s eta 0:00:18\n", " ---------------------------------- ----- 2.1/2.4 GB 18.2 MB/s eta 0:00:18\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 14.5 MB/s eta 0:00:23\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:18\n", " ---------------------------------- ----- 2.1/2.4 GB 16.4 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 16.8 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 15.6 MB/s eta 0:00:21\n", " ---------------------------------- ----- 2.1/2.4 GB 14.9 MB/s eta 0:00:22\n", " ---------------------------------- ----- 2.1/2.4 GB 17.7 MB/s eta 0:00:18\n", " ---------------------------------- ----- 2.1/2.4 GB 17.2 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 16.0 MB/s eta 0:00:20\n", " ---------------------------------- ----- 2.1/2.4 GB 17.2 MB/s eta 0:00:18\n", " ---------------------------------- ----- 2.1/2.4 GB 16.8 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 16.8 MB/s eta 0:00:19\n", " ---------------------------------- ----- 2.1/2.4 GB 18.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.1/2.4 GB 18.7 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.1/2.4 GB 17.7 MB/s eta 0:00:18\n", " ----------------------------------- ---- 2.1/2.4 GB 17.7 MB/s eta 0:00:18\n", " ----------------------------------- ---- 2.1/2.4 GB 17.7 MB/s eta 0:00:18\n", " ----------------------------------- ---- 2.1/2.4 GB 17.7 MB/s eta 0:00:18\n", " ----------------------------------- ---- 2.1/2.4 GB 18.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.1/2.4 GB 18.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.1/2.4 GB 18.7 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.1/2.4 GB 18.7 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.1/2.4 GB 18.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.1/2.4 GB 18.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.1/2.4 GB 18.7 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.1/2.4 GB 18.7 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.2/2.4 GB 17.7 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.2/2.4 GB 16.0 MB/s eta 0:00:19\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 16.4 MB/s eta 0:00:18\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 15.6 MB/s eta 0:00:19\n", " ----------------------------------- ---- 2.2/2.4 GB 18.7 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.7 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.7 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 15.6 MB/s eta 0:00:19\n", " ----------------------------------- ---- 2.2/2.4 GB 17.2 MB/s eta 0:00:17\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 17.2 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 19.3 MB/s eta 0:00:14\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 17.7 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 17.7 MB/s eta 0:00:16\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.7 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 19.3 MB/s eta 0:00:14\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 17.7 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.7 MB/s eta 0:00:14\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 18.2 MB/s eta 0:00:14\n", " ----------------------------------- ---- 2.2/2.4 GB 18.7 MB/s eta 0:00:14\n", " ----------------------------------- ---- 2.2/2.4 GB 18.7 MB/s eta 0:00:14\n", " ----------------------------------- ---- 2.2/2.4 GB 18.7 MB/s eta 0:00:14\n", " ----------------------------------- ---- 2.2/2.4 GB 17.7 MB/s eta 0:00:15\n", " ----------------------------------- ---- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ----------------------------------- ---- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ----------------------------------- ---- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ----------------------------------- ---- 2.2/2.4 GB 19.3 MB/s eta 0:00:13\n", " ----------------------------------- ---- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ----------------------------------- ---- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ----------------------------------- ---- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ------------------------------------ --- 2.2/2.4 GB 18.7 MB/s eta 0:00:13\n", " ------------------------------------ --- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ------------------------------------ --- 2.2/2.4 GB 20.5 MB/s eta 0:00:12\n", " ------------------------------------ --- 2.2/2.4 GB 20.5 MB/s eta 0:00:12\n", " ------------------------------------ --- 2.2/2.4 GB 19.3 MB/s eta 0:00:13\n", " ------------------------------------ --- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ------------------------------------ --- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ------------------------------------ --- 2.2/2.4 GB 23.4 MB/s eta 0:00:11\n", " ------------------------------------ --- 2.2/2.4 GB 17.7 MB/s eta 0:00:14\n", " ------------------------------------ --- 2.2/2.4 GB 26.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 2.2/2.4 GB 26.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 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------------------------------------ --- 2.2/2.4 GB 19.3 MB/s eta 0:00:12\n", " ------------------------------------ --- 2.2/2.4 GB 25.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 2.2/2.4 GB 25.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 2.2/2.4 GB 18.7 MB/s eta 0:00:12\n", " ------------------------------------ --- 2.2/2.4 GB 24.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 2.2/2.4 GB 26.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 2.2/2.4 GB 26.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 2.2/2.4 GB 19.2 MB/s eta 0:00:12\n", " ------------------------------------ --- 2.2/2.4 GB 26.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 2.2/2.4 GB 26.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 2.2/2.4 GB 26.2 MB/s eta 0:00:09\n", " ------------------------------------ --- 2.2/2.4 GB 18.7 MB/s eta 0:00:12\n", " ------------------------------------ --- 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------------------------------------ --- 2.2/2.4 GB 26.2 MB/s eta 0:00:08\n", " ------------------------------------ --- 2.2/2.4 GB 26.2 MB/s eta 0:00:08\n", " ------------------------------------ --- 2.2/2.4 GB 19.2 MB/s eta 0:00:11\n", " ------------------------------------ --- 2.3/2.4 GB 27.3 MB/s eta 0:00:08\n", " ------------------------------------ --- 2.3/2.4 GB 27.3 MB/s eta 0:00:08\n", " ------------------------------------ --- 2.3/2.4 GB 27.3 MB/s eta 0:00:08\n", " ------------------------------------ --- 2.3/2.4 GB 19.3 MB/s eta 0:00:10\n", " ------------------------------------ --- 2.3/2.4 GB 26.2 MB/s eta 0:00:08\n", " ------------------------------------ --- 2.3/2.4 GB 26.2 MB/s eta 0:00:08\n", " ------------------------------------ --- 2.3/2.4 GB 26.2 MB/s eta 0:00:08\n", " ------------------------------------ --- 2.3/2.4 GB 19.2 MB/s eta 0:00:10\n", " ------------------------------------ --- 2.3/2.4 GB 27.3 MB/s eta 0:00:07\n", " ------------------------------------ --- 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------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 19.2 MB/s eta 0:00:09\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 19.3 MB/s eta 0:00:09\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 19.9 MB/s eta 0:00:09\n", " ------------------------------------- -- 2.3/2.4 GB 23.4 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 23.4 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 23.4 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 17.7 MB/s eta 0:00:09\n", " ------------------------------------- -- 2.3/2.4 GB 25.1 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 25.1 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 25.1 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 18.2 MB/s eta 0:00:09\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 25.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 27.3 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 19.3 MB/s eta 0:00:08\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 26.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 23.4 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 23.4 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 23.4 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 18.2 MB/s eta 0:00:08\n", " ------------------------------------- -- 2.3/2.4 GB 25.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 25.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 25.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 18.7 MB/s eta 0:00:07\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 24.2 MB/s eta 0:00:06\n", " ------------------------------------- -- 2.3/2.4 GB 18.7 MB/s eta 0:00:07\n", " -------------------------------------- - 2.3/2.4 GB 26.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 26.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 26.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 25.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 26.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 26.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 26.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 25.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 25.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 25.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 25.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 26.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 25.1 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 25.1 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 25.1 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 26.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 24.3 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 24.3 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 24.3 MB/s eta 0:00:05\n", " -------------------------------------- - 2.3/2.4 GB 25.1 MB/s eta 0:00:04\n", " -------------------------------------- - 2.3/2.4 GB 25.1 MB/s eta 0:00:04\n", " -------------------------------------- - 2.3/2.4 GB 25.1 MB/s eta 0:00:04\n", " -------------------------------------- - 2.3/2.4 GB 25.1 MB/s eta 0:00:04\n", " -------------------------------------- - 2.3/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.3/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.3/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.3/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.1 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 23.4 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 23.4 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 23.4 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 26.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 18.7 MB/s eta 0:00:05\n", " -------------------------------------- - 2.4/2.4 GB 18.7 MB/s eta 0:00:05\n", " -------------------------------------- - 2.4/2.4 GB 18.7 MB/s eta 0:00:05\n", " -------------------------------------- - 2.4/2.4 GB 18.7 MB/s eta 0:00:05\n", " -------------------------------------- - 2.4/2.4 GB 18.2 MB/s eta 0:00:05\n", " -------------------------------------- - 2.4/2.4 GB 25.1 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.1 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.1 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 18.7 MB/s eta 0:00:05\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:03\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:03\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:03\n", " -------------------------------------- - 2.4/2.4 GB 18.7 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:03\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:03\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:03\n", " -------------------------------------- - 2.4/2.4 GB 18.2 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 25.2 MB/s eta 0:00:03\n", " -------------------------------------- - 2.4/2.4 GB 23.4 MB/s eta 0:00:03\n", " -------------------------------------- - 2.4/2.4 GB 21.1 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 19.3 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 17.7 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 16.4 MB/s eta 0:00:04\n", " -------------------------------------- - 2.4/2.4 GB 20.5 MB/s eta 0:00:04\n", " --------------------------------------- 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--------------------------------------- 2.4/2.4 GB 24.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 24.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 24.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 18.7 MB/s eta 0:00:03\n", " --------------------------------------- 2.4/2.4 GB 24.3 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 24.3 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 24.3 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 26.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 26.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 26.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 26.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 27.3 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 26.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 26.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 26.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 19.8 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:02\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 27.3 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 27.3 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 27.3 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 24.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 24.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 24.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 25.2 MB/s eta 0:00:01\n", " --------------------------------------- 2.4/2.4 GB 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"output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 25.0.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "%pip install torch==2.4.1+cu121 --index-url https://download.pytorch.org/whl/cu121\n", "\n", "%pip install torch_geometric==2.3.1\n", "\n", "%pip install torch-scatter torch-sparse torch-cluster torch-spline-conv -f https://data.pyg.org/whl/torch-2.4.1+cu121.html\n", "\n", "%pip install torch-geometric -f https://data.pyg.org/whl/torch-2.4.1+cu121.html" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T13:32:24.196512Z", "iopub.status.busy": "2025-03-18T13:32:24.196264Z", "iopub.status.idle": "2025-03-18T13:32:43.697807Z", "shell.execute_reply": "2025-03-18T13:32:43.696958Z", "shell.execute_reply.started": "2025-03-18T13:32:24.196491Z" }, "executionInfo": { "elapsed": 4325, 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scikit-learn->torch_geometric==2.3.1) (1.4.2)\n", "Requirement already satisfied: threadpoolctl>=3.1.0 in d:\\mlsp\\mlsp-project\\project\\lib\\site-packages (from scikit-learn->torch_geometric==2.3.1) (3.6.0)\n", "Requirement already satisfied: colorama in d:\\mlsp\\mlsp-project\\project\\lib\\site-packages (from tqdm->torch_geometric==2.3.1) (0.4.6)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 25.0.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Looking in links: https://data.pyg.org/whl/torch-2.4.1+cu121.html\n", "Requirement already satisfied: torch-scatter in d:\\mlsp\\mlsp-project\\project\\lib\\site-packages (2.1.2+pt24cu121)\n", "Requirement already satisfied: torch-sparse in d:\\mlsp\\mlsp-project\\project\\lib\\site-packages 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requests->torch-geometric) (2.4.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in d:\\mlsp\\mlsp-project\\project\\lib\\site-packages (from requests->torch-geometric) (2025.1.31)\n", "Requirement already satisfied: joblib>=1.2.0 in d:\\mlsp\\mlsp-project\\project\\lib\\site-packages (from scikit-learn->torch-geometric) (1.4.2)\n", "Requirement already satisfied: threadpoolctl>=3.1.0 in d:\\mlsp\\mlsp-project\\project\\lib\\site-packages (from scikit-learn->torch-geometric) (3.6.0)\n", "Requirement already satisfied: colorama in d:\\mlsp\\mlsp-project\\project\\lib\\site-packages (from tqdm->torch-geometric) (0.4.6)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 25.0.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "%pip install torch==2.4.1\n", "%pip install torch_geometric==2.3.1\n", "%pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-2.4.1+cu121.html" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PyTorch version: 2.4.1+cu121\n", "CUDA available: True\n", "CUDA version: 12.1\n" ] } ], "source": [ "import torch\n", "print(f\"PyTorch version: {torch.__version__}\")\n", "print(f\"CUDA available: {torch.cuda.is_available()}\")\n", "if torch.cuda.is_available():\n", " print(f\"CUDA version: {torch.version.cuda}\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T13:32:43.699482Z", "iopub.status.busy": "2025-03-18T13:32:43.699175Z", "iopub.status.idle": "2025-03-18T13:32:48.777459Z", "shell.execute_reply": "2025-03-18T13:32:48.776713Z", "shell.execute_reply.started": "2025-03-18T13:32:43.699442Z" }, "executionInfo": { "elapsed": 20202, "status": "ok", "timestamp": 1742037293334, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "N4fWLl3xZfng", "outputId": "a5ec8a20-cdc0-441d-ce39-31a526e5596a", "trusted": true }, "outputs": [], "source": [ "import torch\n", "from torch_geometric.data import Data\n", "import torch_geometric.transforms as T\n", "from torch_geometric.data import InMemoryDataset\n", "from torch_geometric.loader import DataLoader\n", "import numpy as np\n", "import time" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "nCPfEeWKZjzq" }, "outputs": [], "source": [ "# headDir = \"/content/drive/MyDrive/BARC_study_project/Task_4/\"\n", "#headDir = \"../\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T13:32:48.778984Z", "iopub.status.busy": "2025-03-18T13:32:48.778537Z", "iopub.status.idle": "2025-03-18T13:32:52.176677Z", "shell.execute_reply": "2025-03-18T13:32:52.175952Z", "shell.execute_reply.started": "2025-03-18T13:32:48.778957Z" }, "executionInfo": { "elapsed": 472, "status": "ok", "timestamp": 1742037397891, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "8QsPXHgQZn6q", "trusted": true }, "outputs": [], "source": [ "path =\"Copy of andrea-dd-dataLte100.npy\"\n", "# path=\"/content/drive/MyDrive/Barc Datasets/andrea-juslin-dataGt100.npy\"\n", "data = np.load(path, allow_pickle=True)\n", "data = data[None][0]\n", "dataXorig = []\n", "dataY = []\n", "dataPe = []\n", "for x, y, z in zip(data['x'], data['y'], data['z']):\n", " if y > 3: continue # We only want 0 and 1 labels for this activity\n", " dataXorig.append(x)\n", " dataY.append(y)\n", " dataPe.append(z['pe'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T13:33:45.961080Z", "iopub.status.busy": "2025-03-18T13:33:45.960716Z", "iopub.status.idle": "2025-03-18T13:33:46.107857Z", "shell.execute_reply": "2025-03-18T13:33:46.107151Z", "shell.execute_reply.started": "2025-03-18T13:33:45.961052Z" }, "executionInfo": { "elapsed": 47, "status": "ok", "timestamp": 1742037397935, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "QB-ijJ-UcqJU", "trusted": true }, "outputs": [], "source": [ "featLen = 30 # we are restricting to first thirty nearest neighbours\n", "dataAtoms = torch.tensor(np.array([x[0][:featLen+1] for x in dataXorig]), dtype=torch.float)\n", "dataX = torch.tensor(np.array([x[1][:featLen+1] for x in dataXorig]).reshape(len(dataAtoms),featLen+1,1), dtype=torch.float)\n", "dataY = torch.tensor(dataY, dtype=torch.long)\n", "#dataX = dataAtoms" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T13:33:48.542460Z", "iopub.status.busy": "2025-03-18T13:33:48.542165Z", "iopub.status.idle": "2025-03-18T13:33:48.575845Z", "shell.execute_reply": "2025-03-18T13:33:48.574909Z", "shell.execute_reply.started": "2025-03-18T13:33:48.542438Z" }, "executionInfo": { "elapsed": 64, "status": "ok", "timestamp": 1742037398163, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "JqB2c-aYQ2qY", "trusted": true }, "outputs": [], "source": [ "dataXAll = torch.concat((dataX, dataAtoms), axis=2)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T13:33:50.368881Z", "iopub.status.busy": "2025-03-18T13:33:50.368535Z", "iopub.status.idle": "2025-03-18T13:33:50.374749Z", "shell.execute_reply": "2025-03-18T13:33:50.373957Z", "shell.execute_reply.started": "2025-03-18T13:33:50.368837Z" }, "executionInfo": { "elapsed": 30, "status": "ok", "timestamp": 1742037398904, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "avfljoELQpsV", "outputId": "921214e7-cd2e-4489-e38d-96903881baef", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "(torch.Size([42699, 31, 4]),\n", " torch.Size([42699, 31, 1]),\n", " torch.Size([42699]),\n", " torch.Size([42699, 31, 3]))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataXAll.shape, dataX.shape, dataY.shape, dataAtoms.shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "31" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataAtoms[0])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:28:46.359929Z", "iopub.status.busy": "2025-03-18T01:28:46.359648Z", "iopub.status.idle": "2025-03-18T01:28:48.607938Z", "shell.execute_reply": "2025-03-18T01:28:48.607292Z", "shell.execute_reply.started": "2025-03-18T01:28:46.359907Z" }, "executionInfo": { "elapsed": 366, "status": "ok", "timestamp": 1742037400745, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "4RXmuuP3Q5eD", "trusted": true }, "outputs": [], "source": [ "posData = []\n", "for dt, y in zip(dataAtoms, dataY):\n", " data = Data(x= dt, pos=dt, y = y)#, pre_transform=T.RadiusGraph(r=4.0), transform=T.Distance())\n", " data.validate(raise_on_error=True)\n", " posData.append(data)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:28:48.609119Z", "iopub.status.busy": "2025-03-18T01:28:48.608821Z", "iopub.status.idle": "2025-03-18T01:28:49.927719Z", "shell.execute_reply": "2025-03-18T01:28:49.927026Z", "shell.execute_reply.started": "2025-03-18T01:28:48.609095Z" }, "executionInfo": { "elapsed": 194, "status": "ok", "timestamp": 1742037401684, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "ynke0Q0CQ7hL", "trusted": true }, "outputs": [], "source": [ "distData = []\n", "for dt, x, y in zip(dataAtoms, dataX, dataY):\n", " data = Data(x= x, pos=dt, y = y)#, pre_transform=T.RadiusGraph(r=4.0), transform=T.Distance())\n", " data.validate(raise_on_error=True)\n", " distData.append(data)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:28:49.929233Z", "iopub.status.busy": "2025-03-18T01:28:49.928899Z", "iopub.status.idle": "2025-03-18T01:28:52.282097Z", "shell.execute_reply": "2025-03-18T01:28:52.281167Z", "shell.execute_reply.started": "2025-03-18T01:28:49.929195Z" }, "executionInfo": { "elapsed": 373, "status": "ok", "timestamp": 1742037402787, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "mFNL2g19Q_KC", "trusted": true }, "outputs": [], "source": [ "allData = []\n", "for dt, x, y in zip(dataAtoms, dataXAll, dataY):\n", " data = Data(x= x, pos=dt, y = y)#, pre_transform=T.RadiusGraph(r=4.0), transform=T.Distance())\n", " data.validate(raise_on_error=True)\n", " allData.append(data)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:28:54.806534Z", "iopub.status.busy": "2025-03-18T01:28:54.806201Z", "iopub.status.idle": "2025-03-18T01:28:54.856656Z", "shell.execute_reply": "2025-03-18T01:28:54.856012Z", "shell.execute_reply.started": "2025-03-18T01:28:54.806504Z" }, "executionInfo": { "elapsed": 32, "status": "ok", "timestamp": 1742037415685, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "GbylGfrORCDe", "trusted": true }, "outputs": [], "source": [ "allData = posData" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:28:58.084342Z", "iopub.status.busy": "2025-03-18T01:28:58.084016Z", "iopub.status.idle": "2025-03-18T01:28:58.090346Z", "shell.execute_reply": "2025-03-18T01:28:58.089522Z", "shell.execute_reply.started": "2025-03-18T01:28:58.084316Z" }, "executionInfo": { "elapsed": 20, "status": "ok", "timestamp": 1742037415834, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "CK6Hgz_NRHBL", "trusted": true }, "outputs": [], "source": [ "class MyDataset(InMemoryDataset):\n", " def __init__(self, root, data_list, transform=None, pre_transform=None, pre_filter=None):\n", " self.data_list = data_list\n", " super().__init__(root, transform, pre_transform, pre_filter)\n", " self.data, self.slices = torch.load(self.processed_paths[0])\n", "\n", " @property\n", " def raw_file_names(self):\n", " return ['mydata']\n", "\n", " @property\n", " def processed_file_names(self):\n", " return ['data.pt']\n", "\n", " def download(self):\n", " # Download to `self.raw_dir`.\n", " pass\n", "\n", " def process(self):\n", " # Read data into huge `Data` list.\n", " data_list = self.data_list\n", "\n", " if self.pre_filter is not None:\n", " data_list = [data for data in data_list if self.pre_filter(data)]\n", "\n", " if self.pre_transform is not None:\n", " data_list = [self.pre_transform(data) for data in data_list]\n", "\n", " data, slices = self.collate(data_list)\n", " torch.save((data, slices), self.processed_paths[0])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:29:00.484160Z", "iopub.status.busy": "2025-03-18T01:29:00.483815Z", "iopub.status.idle": "2025-03-18T01:29:00.487993Z", "shell.execute_reply": "2025-03-18T01:29:00.487059Z", "shell.execute_reply.started": "2025-03-18T01:29:00.484132Z" }, "executionInfo": { "elapsed": 3, "status": "ok", "timestamp": 1742037417901, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "ynO4uda8RJC9", "trusted": true }, "outputs": [], "source": [ "dir(data)\n", "data.edge_weight" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 513 }, "execution": { "iopub.execute_input": "2025-03-18T01:29:02.943372Z", "iopub.status.busy": "2025-03-18T01:29:02.943076Z", "iopub.status.idle": "2025-03-18T01:29:16.812135Z", "shell.execute_reply": "2025-03-18T01:29:16.811150Z", "shell.execute_reply.started": "2025-03-18T01:29:02.943351Z" }, "executionInfo": { "elapsed": 367, "status": "error", "timestamp": 1742037418996, "user": { "displayName": "Srinadh Vura", "userId": "10847572504220496293" }, "user_tz": -330 }, "id": "rXiZrcOXRL8d", "outputId": "75d74e19-3a98-467c-93eb-aa0a79f3b160", "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "'rm' is not recognized as an internal or external command,\n", "operable program or batch file.\n", "d:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch_geometric\\data\\dataset.py:208: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " if osp.exists(f) and torch.load(f) != _repr(self.pre_transform):\n", "d:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch_geometric\\data\\dataset.py:216: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " if osp.exists(f) and torch.load(f) != _repr(self.pre_filter):\n", "C:\\Users\\SrinadhVura\\AppData\\Local\\Temp\\ipykernel_6976\\1359331624.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " self.data, self.slices = torch.load(self.processed_paths[0])\n" ] } ], "source": [ "!rm -rf ./data/processed/\n", "dataset = MyDataset(\"./data\",allData, pre_transform=T.Compose([T.RadiusGraph(r=2.0), T.Distance()]))\n", "#dataset = MyDataset(\"./data\",allData, pre_transform=T.Compose([T.RadiusGraph(r=2.0), T.Distance()]))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T01:29:21.394489Z", "iopub.status.busy": "2025-03-18T01:29:21.394157Z", "iopub.status.idle": "2025-03-18T01:29:21.401708Z", "shell.execute_reply": "2025-03-18T01:29:21.401028Z", "shell.execute_reply.started": "2025-03-18T01:29:21.394463Z" }, "executionInfo": { "elapsed": 29, "status": "ok", "timestamp": 1691998517864, "user": { "displayName": "Gowri Vinay Kamalakar S", "userId": "03382600267171878090" }, "user_tz": -330 }, "id": "my5BI9wKRN_Z", "outputId": "36d929c5-18bb-4a74-e03f-199670de8c5e", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "(torch.Size([31, 3]),\n", " torch.Size([31, 3]),\n", " torch.Size([650, 1]),\n", " torch.Size([2, 650]),\n", " 3)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = dataset[0]\n", "data.x.shape, data.pos.shape, data.edge_attr.shape, data.edge_index.shape, dataset.num_node_features" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T01:29:23.151366Z", "iopub.status.busy": "2025-03-18T01:29:23.151074Z", "iopub.status.idle": "2025-03-18T01:29:23.158134Z", "shell.execute_reply": "2025-03-18T01:29:23.157327Z", "shell.execute_reply.started": "2025-03-18T01:29:23.151345Z" }, "executionInfo": { "elapsed": 23, "status": "ok", "timestamp": 1691998517865, "user": { "displayName": "Gowri Vinay Kamalakar S", "userId": "03382600267171878090" }, "user_tz": -330 }, "id": "wfTgkFjARX7G", "outputId": "1c19d772-59c5-4a9a-e766-ba39135f93ba", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "(torch.Size([31, 3]),\n", " torch.Size([31, 3]),\n", " torch.Size([644, 1]),\n", " torch.Size([2, 644]),\n", " 3)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = dataset[1]\n", "data.x.shape, data.pos.shape, data.edge_attr.shape, data.edge_index.shape, dataset.num_node_features" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T01:29:28.209514Z", "iopub.status.busy": "2025-03-18T01:29:28.209158Z", "iopub.status.idle": "2025-03-18T01:29:28.220800Z", "shell.execute_reply": "2025-03-18T01:29:28.219826Z", "shell.execute_reply.started": "2025-03-18T01:29:28.209476Z" }, "executionInfo": { "elapsed": 19, "status": "ok", "timestamp": 1691998517865, "user": { "displayName": "Gowri Vinay Kamalakar S", "userId": "03382600267171878090" }, "user_tz": -330 }, "id": "baCxnVo_RitL", "outputId": "a2c10d53-21a0-4cef-9faf-9e48573678c5", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "tensor([[ 5, 16, 22, ..., 20, 0, 14],\n", " [ 0, 0, 0, ..., 30, 30, 30]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.edge_index" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[0.4332],\n", " [0.7063],\n", " [0.7078],\n", " [0.5009],\n", " [0.7082],\n", " [0.5009],\n", " [0.5014],\n", " [0.7084],\n", " [0.5011],\n", " [0.4305],\n", " [0.7069],\n", " [0.4287],\n", " [0.5011],\n", " [0.8276],\n", " [0.8272],\n", " [0.7080],\n", " [0.7070],\n", " [0.7064],\n", " [0.4330],\n", " [0.7068],\n", " [0.7066],\n", " [0.8280],\n", " [0.4341],\n", " [0.4357],\n", " [0.5005],\n", " 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"metadata": {}, "output_type": "execute_result" } ], "source": [ "data.edge_attr" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T01:29:29.280151Z", "iopub.status.busy": "2025-03-18T01:29:29.279814Z", "iopub.status.idle": "2025-03-18T01:29:29.284569Z", "shell.execute_reply": "2025-03-18T01:29:29.283641Z", "shell.execute_reply.started": "2025-03-18T01:29:29.280124Z" }, "executionInfo": { "elapsed": 16, "status": "ok", "timestamp": 1691998517866, "user": { "displayName": "Gowri Vinay Kamalakar S", "userId": "03382600267171878090" }, "user_tz": -330 }, "id": "LL-DD5dxUqyW", "outputId": "2b1ddb54-17b7-4eee-b653-8f0334d81eef", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data(x=[31, 3], y=[1], pos=[31, 3], edge_index=[2, 644], edge_attr=[644, 1])\n" ] } ], "source": [ "print(data)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:29:31.757537Z", "iopub.status.busy": "2025-03-18T01:29:31.757184Z", "iopub.status.idle": "2025-03-18T01:29:31.761505Z", "shell.execute_reply": "2025-03-18T01:29:31.760640Z", "shell.execute_reply.started": "2025-03-18T01:29:31.757507Z" }, "id": "z4tlLkVTQqS1", "trusted": true }, "outputs": [], "source": [ "import torch.nn.functional as F\n", "from torch.nn import Linear, Dropout\n", "from torch_geometric.nn import GCNConv, GATConv\n", "from torch_geometric.nn.glob import global_mean_pool\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T01:29:34.639111Z", "iopub.status.busy": "2025-03-18T01:29:34.638741Z", "iopub.status.idle": "2025-03-18T01:29:34.644211Z", "shell.execute_reply": "2025-03-18T01:29:34.643135Z", "shell.execute_reply.started": "2025-03-18T01:29:34.639080Z" }, "executionInfo": { "elapsed": 12, "status": "ok", "timestamp": 1691998517866, "user": { "displayName": "Gowri Vinay Kamalakar S", "userId": "03382600267171878090" }, "user_tz": -330 }, "id": "90XhdBSwR8gJ", "outputId": "e6841c7e-6ee4-4624-9971-2a204dd46823", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MyDataset(42699)\n" ] } ], "source": [ "print(dataset)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T01:29:36.454153Z", "iopub.status.busy": "2025-03-18T01:29:36.453814Z", "iopub.status.idle": "2025-03-18T01:29:36.459122Z", "shell.execute_reply": "2025-03-18T01:29:36.458354Z", "shell.execute_reply.started": "2025-03-18T01:29:36.454123Z" }, "executionInfo": { "elapsed": 947, "status": "ok", "timestamp": 1691998518804, "user": { "displayName": "Gowri Vinay Kamalakar S", "userId": "03382600267171878090" }, "user_tz": -330 }, "id": "UAdtK2pIWRYn", "outputId": "5731893b-2206-4d2b-f330-7897391a7a4b", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "(42699, 32024.25)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataset), (len(dataset) * 0.75)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T01:29:40.348824Z", "iopub.status.busy": "2025-03-18T01:29:40.348535Z", "iopub.status.idle": "2025-03-18T01:29:40.365551Z", "shell.execute_reply": "2025-03-18T01:29:40.364760Z", "shell.execute_reply.started": "2025-03-18T01:29:40.348802Z" }, "executionInfo": { "elapsed": 24, "status": "ok", "timestamp": 1691998518806, "user": { "displayName": "Gowri Vinay Kamalakar S", "userId": "03382600267171878090" }, "user_tz": -330 }, "id": "AlVCCMxwWRyj", "outputId": "e4a1b0f8-12bf-4a3c-b053-521d107e7e12", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of training graphs: 32024\n", "Number of test graphs: 10675\n" ] } ], "source": [ "torch.manual_seed(12345)\n", "dataset = dataset.shuffle()\n", "\n", "n_train = int((len(dataset) * 0.75)//1.0)\n", "train_dataset = dataset[:n_train]\n", "test_dataset = dataset[n_train:]\n", "\n", "print(f'Number of training graphs: {len(train_dataset)}')\n", "print(f'Number of test graphs: {len(test_dataset)}')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:29:43.901004Z", "iopub.status.busy": "2025-03-18T01:29:43.900589Z", "iopub.status.idle": "2025-03-18T01:29:44.848329Z", "shell.execute_reply": "2025-03-18T01:29:44.847516Z", "shell.execute_reply.started": "2025-03-18T01:29:43.900966Z" }, "id": "sahRSJL5WPcz", "trusted": true }, "outputs": [], "source": [ "from torch_geometric.loader import DataLoader\n", "\n", "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n", "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:32:16.537965Z", "iopub.status.busy": "2025-03-18T01:32:16.537645Z", "iopub.status.idle": "2025-03-18T01:32:16.543789Z", "shell.execute_reply": "2025-03-18T01:32:16.542979Z", "shell.execute_reply.started": "2025-03-18T01:32:16.537918Z" }, "trusted": true }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.num_classes" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2025-03-18T01:29:50.752724Z", "iopub.status.busy": "2025-03-18T01:29:50.752410Z", "iopub.status.idle": "2025-03-18T01:29:50.759104Z", "shell.execute_reply": "2025-03-18T01:29:50.758226Z", "shell.execute_reply.started": "2025-03-18T01:29:50.752698Z" }, "id": "9fW_X1THQYIe", "trusted": true }, "outputs": [], "source": [ "from torch_geometric.nn.glob import global_mean_pool\n", "class GAT(torch.nn.Module):\n", " def __init__(self,dim_h,heads=1):\n", " super().__init__()\n", " torch.manual_seed(55)\n", " self.gat1=GATConv(dataset.num_node_features,dim_h,heads=heads,concat=False)\n", " self.gat2=GATConv(dim_h,dim_h*2,heads=1)\n", " self.gat3=GATConv(dim_h*2,dim_h*2,heads=1)\n", " self.gat4=GATConv(dim_h*2,dim_h,heads=1)\n", " self.lin=torch.nn.Linear(dim_h,dataset.num_classes)\n", " def forward(self,data):\n", " x,edge_index,edge_attr,batch=data.x,data.edge_index,data.edge_attr,data.batch\n", " x=self.gat1(x,edge_index,edge_attr)\n", " x=F.elu(x)\n", " x=self.gat2(x,edge_index,edge_attr)\n", " x=F.elu(x)\n", " x=self.gat3(x,edge_index,edge_attr)\n", " x=F.elu(x)\n", " x=self.gat4(x,edge_index,edge_attr)\n", "\n", " x=global_mean_pool(x,batch)\n", " x=F.dropout(x,0.5,training=self.training)\n", " x=self.lin(x)\n", " return x\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "from torch_geometric.nn import GATv2Conv\n", "from torch_geometric.nn.glob import global_mean_pool\n", "class GATv2(torch.nn.Module):\n", " def __init__(self,dim_h,heads=1):\n", " super().__init__()\n", " torch.manual_seed(55)\n", " self.gat1=GATv2Conv(dataset.num_node_features,dim_h*2,heads=heads,concat=False)\n", " self.gat2=GATv2Conv(dim_h*2,dim_h*2,heads=1)\n", " self.gat3=GATv2Conv(dim_h*2,dim_h,heads=1)\n", " self.gat4=GATv2Conv(dim_h,dim_h,heads=1)\n", " self.lin=Linear(dim_h,dataset.num_classes)\n", " def forward(self,data):\n", " x,edge_index,edge_attr,batch=data.x,data.edge_index,data.edge_attr,data.batch\n", " x=self.gat1(x,edge_index)\n", " x=F.elu(x)\n", " x=self.gat2(x,edge_index)\n", " x=F.elu(x)\n", " x=self.gat3(x,edge_index)\n", " x=F.elu(x)\n", " x=self.gat4(x,edge_index)\n", " x=global_mean_pool(x,batch)\n", " x=F.dropout(x,0.5,training=self.training)\n", " x=self.lin(x)\n", " return x\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# class GraphTransformer(torch.nn.Module):\n", "# def __init__(self,dim_h,dropout=0.25,num_layers=4,num_heads=8):\n", "# super().__init__()\n", "# torch.manual_seed(42)\n", "# self.embedd=GATConv(dataset.num_node_features,dim_h,heads=num_heads,concat=True,dropout=dropout)\n", "# # self.transformer_layers=torch.nn.ModuleList([\n", "# # torch.nn.TransformerEncoderLayer(\n", "# # d_model=dim_h*num_heads,\n", "# # nhead=num_heads,\n", "# # dim_feedforward=dim_h*num_heads*4,\n", "# # dropout=dropout,\n", "# # activation='gelu'\n", "\n", "# # ) for i in range(num_layers)\n", "# # ])\n", "# self.transformer_layer = torch.nn.TransformerEncoderLayer(\n", "# d_model=dim_h * num_heads,\n", "# nhead=num_heads,\n", "# dim_feedforward=dim_h * num_heads * 4,\n", "# dropout=dropout,\n", "# activation='gelu'\n", "# )\n", "# self.encoder=torch.nn.TransformerEncoder(self.transformer_layer,num_layers)\n", "# self.classify=torch.nn.Sequential(\n", "# torch.nn.Linear(dim_h*num_heads,dim_h),\n", "# torch.nn.ELU(),\n", "# torch.nn.Dropout(dropout),\n", "# torch.nn.Linear(dim_h,dataset.num_classes)\n", "# )\n", "# def forward(self,data):\n", "# x,edge_index,edge_attr,batch=data.x,data.edge_index,data.edge_attr,data.batch \n", "# x=self.embedd(x,edge_index,edge_attr)\n", "# x=F.elu(x)\n", "# x = x.unsqueeze(1)\n", "# x=self.encoder(x)\n", "# x = x.squeeze(1)\n", "# x=global_mean_pool(x,batch)\n", "# x=self.classify(x)\n", "# return x\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "from torch_geometric.nn import GATConv, global_mean_pool\n", "\n", "class GraphTransformer(torch.nn.Module):\n", " def __init__(self, dim_h, dropout=0.25, num_layers=4, num_heads=1):\n", " super().__init__()\n", " torch.manual_seed(42)\n", "\n", " # Graph Attention Layers (Stacked for better expressiveness)\n", " self.gat1 = GATConv(dataset.num_node_features, dim_h, heads=num_heads, concat=True, dropout=dropout)\n", " self.gat2 = GATConv(dim_h * num_heads, dim_h, heads=num_heads, concat=True, dropout=dropout)\n", "\n", " # Transformer Encoder\n", " self.encoder_layer = torch.nn.TransformerEncoderLayer(\n", " d_model=dim_h * num_heads,\n", " nhead=num_heads,\n", " dim_feedforward=dim_h * num_heads * 4,\n", " dropout=dropout,\n", " activation='gelu',\n", " batch_first=True # Ensure batch-first format\n", " )\n", " self.encoder = torch.nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)\n", "\n", " # Classification Head\n", " self.classify = torch.nn.Sequential(\n", " torch.nn.Linear(dim_h * num_heads, dim_h),\n", " torch.nn.LayerNorm(dim_h),\n", " torch.nn.ELU(),\n", " torch.nn.Dropout(dropout),\n", " torch.nn.Linear(dim_h, dataset.num_classes)\n", " )\n", "\n", " def forward(self, data):\n", " x, edge_index, edge_attr, batch = data.x, data.edge_index, data.edge_attr, data.batch\n", "\n", " # Graph Attention Layers\n", " x = self.gat1(x, edge_index, edge_attr)\n", " x = F.elu(x)\n", " x = self.gat2(x, edge_index, edge_attr)\n", " x = F.elu(x)\n", "\n", " # Transformer Encoder (Ensure proper shape)\n", " x = x.view(x.size(0), 1, -1) # Reshape for Transformer: (batch, seq_len=1, dim)\n", " x = self.encoder(x)\n", " x = x.squeeze(1) # Remove sequence dimension\n", "\n", " # Global Pooling\n", " x = global_mean_pool(x, batch)\n", "\n", " # Classification Head\n", " x = self.classify(x)\n", "\n", " return x" ] }, { "cell_type": "markdown", "metadata": { "id": "0H6OlxadS5Ek" }, "source": [ "## On Cuda" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T01:29:53.592840Z", "iopub.status.busy": "2025-03-18T01:29:53.592544Z", "iopub.status.idle": "2025-03-18T01:29:53.597993Z", "shell.execute_reply": "2025-03-18T01:29:53.597259Z", "shell.execute_reply.started": "2025-03-18T01:29:53.592819Z" }, "executionInfo": { "elapsed": 737, "status": "ok", "timestamp": 1691999288537, "user": { "displayName": "Gowri Vinay Kamalakar S", "userId": "03382600267171878090" }, "user_tz": -330 }, "id": "kJCdrc_lJCPM", "outputId": "980a0374-9d94-4127-bfe3-1c001c330ca8", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "device(type='cuda', index=2)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "device=torch.device(\"cuda:2\" if torch.cuda.is_available() else 'cpu')\n", "device" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-03-18T01:33:13.809496Z", "iopub.status.busy": "2025-03-18T01:33:13.809141Z", "iopub.status.idle": "2025-03-18T01:41:03.745520Z", "shell.execute_reply": "2025-03-18T01:41:03.744658Z", "shell.execute_reply.started": "2025-03-18T01:33:13.809470Z" }, "executionInfo": { "elapsed": 53705, "status": "ok", "timestamp": 1691198799662, "user": { "displayName": "Gowri Vinay Kamalakar S", "userId": "03382600267171878090" }, "user_tz": -330 }, "id": "gN6JVVeKoW4n", "outputId": "eb81e418-9cd1-4f14-99b9-1993aef438fa", "trusted": true }, "outputs": [ { "ename": "RuntimeError", "evalue": "CUDA error: invalid device ordinal\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[32]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mIPython\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdisplay\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Javascript\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m model = \u001b[43mGAT\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m256\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m optimizer = torch.optim.Adam(model.parameters(), lr=\u001b[32m0.001\u001b[39m)\n\u001b[32m 5\u001b[39m criterion = torch.nn.CrossEntropyLoss()\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1174\u001b[39m, in \u001b[36mModule.to\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1171\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1172\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1174\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:780\u001b[39m, in \u001b[36mModule._apply\u001b[39m\u001b[34m(self, fn, recurse)\u001b[39m\n\u001b[32m 778\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m recurse:\n\u001b[32m 779\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.children():\n\u001b[32m--> \u001b[39m\u001b[32m780\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 782\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[32m 783\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[32m 784\u001b[39m \u001b[38;5;66;03m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[32m 785\u001b[39m \u001b[38;5;66;03m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 790\u001b[39m \u001b[38;5;66;03m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[32m 791\u001b[39m \u001b[38;5;66;03m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:780\u001b[39m, in \u001b[36mModule._apply\u001b[39m\u001b[34m(self, fn, recurse)\u001b[39m\n\u001b[32m 778\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m recurse:\n\u001b[32m 779\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.children():\n\u001b[32m--> \u001b[39m\u001b[32m780\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 782\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[32m 783\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[32m 784\u001b[39m \u001b[38;5;66;03m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[32m 785\u001b[39m \u001b[38;5;66;03m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 790\u001b[39m \u001b[38;5;66;03m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[32m 791\u001b[39m \u001b[38;5;66;03m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:805\u001b[39m, in \u001b[36mModule._apply\u001b[39m\u001b[34m(self, fn, recurse)\u001b[39m\n\u001b[32m 801\u001b[39m \u001b[38;5;66;03m# Tensors stored in modules are graph leaves, and we don't want to\u001b[39;00m\n\u001b[32m 802\u001b[39m \u001b[38;5;66;03m# track autograd history of `param_applied`, so we have to use\u001b[39;00m\n\u001b[32m 803\u001b[39m \u001b[38;5;66;03m# `with torch.no_grad():`\u001b[39;00m\n\u001b[32m 804\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m torch.no_grad():\n\u001b[32m--> \u001b[39m\u001b[32m805\u001b[39m param_applied = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 806\u001b[39m p_should_use_set_data = compute_should_use_set_data(param, param_applied)\n\u001b[32m 808\u001b[39m \u001b[38;5;66;03m# subclasses may have multiple child tensors so we need to use swap_tensors\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1160\u001b[39m, in \u001b[36mModule.to..convert\u001b[39m\u001b[34m(t)\u001b[39m\n\u001b[32m 1153\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m convert_to_format \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m t.dim() \u001b[38;5;129;01min\u001b[39;00m (\u001b[32m4\u001b[39m, \u001b[32m5\u001b[39m):\n\u001b[32m 1154\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m t.to(\n\u001b[32m 1155\u001b[39m device,\n\u001b[32m 1156\u001b[39m dtype \u001b[38;5;28;01mif\u001b[39;00m t.is_floating_point() \u001b[38;5;129;01mor\u001b[39;00m t.is_complex() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1157\u001b[39m non_blocking,\n\u001b[32m 1158\u001b[39m memory_format=convert_to_format,\n\u001b[32m 1159\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1160\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1161\u001b[39m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1162\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mis_floating_point\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mis_complex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 1163\u001b[39m \u001b[43m \u001b[49m\u001b[43mnon_blocking\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1164\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1165\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 1166\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(e) == \u001b[33m\"\u001b[39m\u001b[33mCannot copy out of meta tensor; no data!\u001b[39m\u001b[33m\"\u001b[39m:\n", "\u001b[31mRuntimeError\u001b[39m: CUDA error: invalid device ordinal\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n" ] } ], "source": [ "from IPython.display import Javascript\n", "\n", "model = GAT(256).to(device)\n", "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n", "criterion = torch.nn.CrossEntropyLoss()\n", "\n", "def train():\n", " model.train()\n", "\n", " for data in train_loader: # Iterate in batches over the training dataset.\n", " out = model(data.to(device)) # Perform a single forward pass.\n", " loss = criterion(out, data.y) # Compute the loss.\n", " loss.backward() # Derive gradients.\n", " optimizer.step() # Update parameters based on gradients.\n", " optimizer.zero_grad() # Clear gradients.\n", "\n", "def test(loader):\n", " model.eval()\n", "\n", " correct = 0\n", " for data in loader: # Iterate in batches over the training/test dataset.\n", " out = model(data.to(device))\n", " pred = out.argmax(dim=1) # Use the class with highest probability.\n", " correct += int((pred == data.y).sum()) # Check against ground-truth labels.\n", " return correct / len(loader.dataset) # Derive ratio of correct predictions.\n", "\n", "\n", "acc,t=[],[]\n", "a=time.time()\n", "for epoch in range(1, 25):\n", " x=time.time()\n", " train()\n", " train_acc = test(train_loader)\n", " test_acc = test(test_loader)\n", " y=time.time()\n", " acc.append(test_acc)\n", " t.append(y-x)\n", " print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}, Time: {(y-x):.4f} seconds')\n", "b=time.time()\n", "print(f'TOtal time taken:{(b-a):.4f} seconds')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "d:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\transformer.py:307: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.num_heads is odd\n", " warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n" ] }, { "ename": "RuntimeError", "evalue": "CUDA error: invalid device ordinal\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[33]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mIPython\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdisplay\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Javascript\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m model = \u001b[43mGraphTransformer\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m256\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m optimizer = torch.optim.Adam(model.parameters(), lr=\u001b[32m0.001\u001b[39m)\n\u001b[32m 5\u001b[39m criterion = torch.nn.CrossEntropyLoss()\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1174\u001b[39m, in \u001b[36mModule.to\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1171\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1172\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1174\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:780\u001b[39m, in \u001b[36mModule._apply\u001b[39m\u001b[34m(self, fn, recurse)\u001b[39m\n\u001b[32m 778\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m recurse:\n\u001b[32m 779\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.children():\n\u001b[32m--> \u001b[39m\u001b[32m780\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 782\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[32m 783\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[32m 784\u001b[39m \u001b[38;5;66;03m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[32m 785\u001b[39m \u001b[38;5;66;03m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 790\u001b[39m \u001b[38;5;66;03m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[32m 791\u001b[39m \u001b[38;5;66;03m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:780\u001b[39m, in \u001b[36mModule._apply\u001b[39m\u001b[34m(self, fn, recurse)\u001b[39m\n\u001b[32m 778\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m recurse:\n\u001b[32m 779\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.children():\n\u001b[32m--> \u001b[39m\u001b[32m780\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 782\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[32m 783\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[32m 784\u001b[39m \u001b[38;5;66;03m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[32m 785\u001b[39m \u001b[38;5;66;03m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 790\u001b[39m \u001b[38;5;66;03m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[32m 791\u001b[39m \u001b[38;5;66;03m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:805\u001b[39m, in \u001b[36mModule._apply\u001b[39m\u001b[34m(self, fn, recurse)\u001b[39m\n\u001b[32m 801\u001b[39m \u001b[38;5;66;03m# Tensors stored in modules are graph leaves, and we don't want to\u001b[39;00m\n\u001b[32m 802\u001b[39m \u001b[38;5;66;03m# track autograd history of `param_applied`, so we have to use\u001b[39;00m\n\u001b[32m 803\u001b[39m \u001b[38;5;66;03m# `with torch.no_grad():`\u001b[39;00m\n\u001b[32m 804\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m torch.no_grad():\n\u001b[32m--> \u001b[39m\u001b[32m805\u001b[39m param_applied = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 806\u001b[39m p_should_use_set_data = compute_should_use_set_data(param, param_applied)\n\u001b[32m 808\u001b[39m \u001b[38;5;66;03m# subclasses may have multiple child tensors so we need to use swap_tensors\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1160\u001b[39m, in \u001b[36mModule.to..convert\u001b[39m\u001b[34m(t)\u001b[39m\n\u001b[32m 1153\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m convert_to_format \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m t.dim() \u001b[38;5;129;01min\u001b[39;00m (\u001b[32m4\u001b[39m, \u001b[32m5\u001b[39m):\n\u001b[32m 1154\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m t.to(\n\u001b[32m 1155\u001b[39m device,\n\u001b[32m 1156\u001b[39m dtype \u001b[38;5;28;01mif\u001b[39;00m t.is_floating_point() \u001b[38;5;129;01mor\u001b[39;00m t.is_complex() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1157\u001b[39m non_blocking,\n\u001b[32m 1158\u001b[39m memory_format=convert_to_format,\n\u001b[32m 1159\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1160\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1161\u001b[39m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1162\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mis_floating_point\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mis_complex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 1163\u001b[39m \u001b[43m \u001b[49m\u001b[43mnon_blocking\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1164\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1165\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 1166\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(e) == \u001b[33m\"\u001b[39m\u001b[33mCannot copy out of meta tensor; no data!\u001b[39m\u001b[33m\"\u001b[39m:\n", "\u001b[31mRuntimeError\u001b[39m: CUDA error: invalid device ordinal\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n" ] } ], "source": [ "from IPython.display import Javascript\n", "\n", "model = GraphTransformer(256).to(device)\n", "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n", "criterion = torch.nn.CrossEntropyLoss()\n", "\n", "def train():\n", " model.train()\n", "\n", " for data in train_loader: # Iterate in batches over the training dataset.\n", " out = model(data.to(device)) # Perform a single forward pass.\n", " loss = criterion(out, data.y) # Compute the loss.\n", " loss.backward() # Derive gradients.\n", " optimizer.step() # Update parameters based on gradients.\n", " optimizer.zero_grad() # Clear gradients.\n", "\n", "def test(loader):\n", " model.eval()\n", "\n", " correct = 0\n", " for data in loader: # Iterate in batches over the training/test dataset.\n", " out = model(data.to(device))\n", " pred = out.argmax(dim=1) # Use the class with highest probability.\n", " correct += int((pred == data.y).sum()) # Check against ground-truth labels.\n", " return correct / len(loader.dataset) # Derive ratio of correct predictions.\n", "\n", "\n", "acc,t=[],[]\n", "a=time.time()\n", "for epoch in range(1, 25):\n", " x=time.time()\n", " train()\n", " train_acc = test(train_loader)\n", " test_acc = test(test_loader)\n", " y=time.time()\n", " acc.append(test_acc)\n", " t.append(y-x)\n", " print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}, Time: {(y-x):.4f} seconds')\n", "b=time.time()\n", "print(f'TOtal time taken:{(b-a):.4f} seconds')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "ename": "RuntimeError", "evalue": "CUDA error: invalid device ordinal\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[34]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mIPython\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdisplay\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Javascript\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m model = \u001b[43mGATv2\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m256\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m optimizer = torch.optim.Adam(model.parameters(), lr=\u001b[32m0.001\u001b[39m)\n\u001b[32m 5\u001b[39m criterion = torch.nn.CrossEntropyLoss()\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1174\u001b[39m, in \u001b[36mModule.to\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1171\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1172\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1174\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:780\u001b[39m, in \u001b[36mModule._apply\u001b[39m\u001b[34m(self, fn, recurse)\u001b[39m\n\u001b[32m 778\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m recurse:\n\u001b[32m 779\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.children():\n\u001b[32m--> \u001b[39m\u001b[32m780\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 782\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[32m 783\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[32m 784\u001b[39m \u001b[38;5;66;03m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[32m 785\u001b[39m \u001b[38;5;66;03m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 790\u001b[39m \u001b[38;5;66;03m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[32m 791\u001b[39m \u001b[38;5;66;03m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:780\u001b[39m, in \u001b[36mModule._apply\u001b[39m\u001b[34m(self, fn, recurse)\u001b[39m\n\u001b[32m 778\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m recurse:\n\u001b[32m 779\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.children():\n\u001b[32m--> \u001b[39m\u001b[32m780\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 782\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[32m 783\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[32m 784\u001b[39m \u001b[38;5;66;03m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[32m 785\u001b[39m \u001b[38;5;66;03m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 790\u001b[39m \u001b[38;5;66;03m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[32m 791\u001b[39m \u001b[38;5;66;03m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:805\u001b[39m, in \u001b[36mModule._apply\u001b[39m\u001b[34m(self, fn, recurse)\u001b[39m\n\u001b[32m 801\u001b[39m \u001b[38;5;66;03m# Tensors stored in modules are graph leaves, and we don't want to\u001b[39;00m\n\u001b[32m 802\u001b[39m \u001b[38;5;66;03m# track autograd history of `param_applied`, so we have to use\u001b[39;00m\n\u001b[32m 803\u001b[39m \u001b[38;5;66;03m# `with torch.no_grad():`\u001b[39;00m\n\u001b[32m 804\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m torch.no_grad():\n\u001b[32m--> \u001b[39m\u001b[32m805\u001b[39m param_applied = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 806\u001b[39m p_should_use_set_data = compute_should_use_set_data(param, param_applied)\n\u001b[32m 808\u001b[39m \u001b[38;5;66;03m# subclasses may have multiple child tensors so we need to use swap_tensors\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MLSP\\MLSP-Project\\Project\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1160\u001b[39m, in \u001b[36mModule.to..convert\u001b[39m\u001b[34m(t)\u001b[39m\n\u001b[32m 1153\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m convert_to_format \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m t.dim() \u001b[38;5;129;01min\u001b[39;00m (\u001b[32m4\u001b[39m, \u001b[32m5\u001b[39m):\n\u001b[32m 1154\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m t.to(\n\u001b[32m 1155\u001b[39m device,\n\u001b[32m 1156\u001b[39m dtype \u001b[38;5;28;01mif\u001b[39;00m t.is_floating_point() \u001b[38;5;129;01mor\u001b[39;00m t.is_complex() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1157\u001b[39m non_blocking,\n\u001b[32m 1158\u001b[39m memory_format=convert_to_format,\n\u001b[32m 1159\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1160\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1161\u001b[39m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1162\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mis_floating_point\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mis_complex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 1163\u001b[39m \u001b[43m \u001b[49m\u001b[43mnon_blocking\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1164\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1165\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 1166\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(e) == \u001b[33m\"\u001b[39m\u001b[33mCannot copy out of meta tensor; no data!\u001b[39m\u001b[33m\"\u001b[39m:\n", "\u001b[31mRuntimeError\u001b[39m: CUDA error: invalid device ordinal\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n" ] } ], "source": [ "from IPython.display import Javascript\n", "\n", "model = GATv2(256).to(device)\n", "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n", "criterion = torch.nn.CrossEntropyLoss()\n", "\n", "def train():\n", " model.train()\n", "\n", " for data in train_loader: # Iterate in batches over the training dataset.\n", " out = model(data.to(device)) # Perform a single forward pass.\n", " loss = criterion(out, data.y) # Compute the loss.\n", " loss.backward() # Derive gradients.\n", " optimizer.step() # Update parameters based on gradients.\n", " optimizer.zero_grad() # Clear gradients.\n", "\n", "def test(loader):\n", " model.eval()\n", "\n", " correct = 0\n", " for data in loader: # Iterate in batches over the training/test dataset.\n", " out = model(data.to(device))\n", " pred = out.argmax(dim=1) # Use the class with highest probability.\n", " correct += int((pred == data.y).sum()) # Check against ground-truth labels.\n", " return correct / len(loader.dataset) # Derive ratio of correct predictions.\n", "\n", "\n", "acc,t=[],[]\n", "a=time.time()\n", "for epoch in range(1, 70):\n", " x=time.time()\n", " train()\n", " train_acc = test(train_loader)\n", " test_acc = test(test_loader)\n", " y=time.time()\n", " acc.append(test_acc)\n", " t.append(y-x)\n", " print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}, Time: {(y-x):.4f} seconds')\n", "b=time.time()\n", "print(f'TOtal time taken:{(b-a):.4f} seconds')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kaggle": { "accelerator": "gpu", "dataSources": [ { "datasetId": 6876192, "sourceId": 11039420, "sourceType": "datasetVersion" }, { "datasetId": 6896628, "sourceId": 11067551, "sourceType": "datasetVersion" } ], "dockerImageVersionId": 30919, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Project", "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 4 }