From 76896af96a12d36bb530eebc4309c29c8eae66b0 Mon Sep 17 00:00:00 2001
From: chunhuizhang <zch921005@126.com>
Date: Fri, 10 Feb 2023 22:52:10 +0800
Subject: test jupyter

---
 dl/tutorials/test_jupyter_notebook.ipynb | 176 +++++++++++++++++++++++++++++++
 1 file changed, 176 insertions(+)
 create mode 100644 dl/tutorials/test_jupyter_notebook.ipynb

(limited to 'dl/tutorials')

diff --git a/dl/tutorials/test_jupyter_notebook.ipynb b/dl/tutorials/test_jupyter_notebook.ipynb
new file mode 100644
index 0000000..cfe5de8
--- /dev/null
+++ b/dl/tutorials/test_jupyter_notebook.ipynb
@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4764eedc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/chzhang/anaconda3/envs/ldm/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "664bf7de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.11.0'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1a845e6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.is_available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1749f11f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchvision import models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b6fd37e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['__name__', '__doc__', '__package__', '__loader__', '__spec__', '__path__', '__file__', '__cached__', '__builtins__', 'alexnet', 'AlexNet', 'convnext', 'ConvNeXt', 'convnext_tiny', 'convnext_small', 'convnext_base', 'convnext_large', 'resnet', 'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2', 'wide_resnet101_2', 'vgg', 'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19_bn', 'vgg19', 'squeezenet', 'SqueezeNet', 'squeezenet1_0', 'squeezenet1_1', 'inception', 'Inception3', 'inception_v3', 'InceptionOutputs', '_InceptionOutputs', 'densenet', 'DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161', 'googlenet', 'GoogLeNet', 'GoogLeNetOutputs', '_GoogLeNetOutputs', '_utils', 'mobilenetv2', 'mobilenetv3', 'mobilenet', 'MobileNetV2', 'mobilenet_v2', 'MobileNetV3', 'mobilenet_v3_large', 'mobilenet_v3_small', 'mnasnet', 'MNASNet', 'mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3', 'shufflenetv2', 'ShuffleNetV2', 'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0', 'efficientnet', 'EfficientNet', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'efficientnet_b5', 'efficientnet_b6', 'efficientnet_b7', 'regnet', 'RegNet', 'regnet_y_400mf', 'regnet_y_800mf', 'regnet_y_1_6gf', 'regnet_y_3_2gf', 'regnet_y_8gf', 'regnet_y_16gf', 'regnet_y_32gf', 'regnet_y_128gf', 'regnet_x_400mf', 'regnet_x_800mf', 'regnet_x_1_6gf', 'regnet_x_3_2gf', 'regnet_x_8gf', 'regnet_x_16gf', 'regnet_x_32gf', 'vision_transformer', 'VisionTransformer', 'vit_b_16', 'vit_b_32', 'vit_l_16', 'vit_l_32', 'detection', 'feature_extraction', 'optical_flow', 'quantization', 'segmentation', 'video'])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models.__dict__.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aec9cafa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = models.__dict__['resnet152']()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "29409f63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b11e424b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cuda')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "252ac26f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = models.__dict__['resnet152']().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65262e8e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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