Develop

.gitignore

@@ -1,3 +1,6 @@
 .DS_Store
 tools
 *_old
+
+.fake
+Copilot/

.vscode/settings.json

@@ -0,0 +1,14 @@
+{
+    "python.languageServer": "Pylance",
+    "python.analysis.typeCheckingMode": "basic",
+    "typescript.tsserver.maxTsServerMemory": 3072,
+    "typescript.tsserver.watchOptions": {
+        "watchFile": "dynamicPriorityPolling"
+    },
+    "javascript.suggest.includeAutomaticOptionalChainCompletions": false,
+    "debug.saveBeforeStart": "none",
+    "vscode-as-git-mergetool.settingsAssistantOnStartup": false,
+    "c3.welcome.showFeatureHighlight": false,
+    "git-blame.gitWebUrl": "",
+    "workbench.view.alwaysShowHeaderActions": true
+}

p1ch2/2_pre_trained_networks.ipynb

@@ -3,7 +3,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from torchvision import models"
@@ -12,7 +16,11 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -72,7 +80,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "alexnet = models.AlexNet()"
@@ -81,7 +93,11 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "resnet = models.resnet101(pretrained=True)"
@@ -90,7 +106,11 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -438,7 +458,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from torchvision import transforms\n",
@@ -455,7 +479,11 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from PIL import Image\n",
@@ -465,7 +493,11 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -486,7 +518,11 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "img_t = preprocess(img)"
@@ -495,7 +531,11 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import torch\n",
@@ -505,7 +545,11 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -853,7 +897,11 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1074,7 +1122,11 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "with open('../data/p1ch2/imagenet_classes.txt') as f:\n",
@@ -1084,7 +1136,11 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "_, index = torch.max(out, 1)"
@@ -1093,7 +1149,11 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1114,7 +1174,11 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1139,7 +1203,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": []
   }
@@ -1165,4 +1233,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

requirements.txt

@@ -4,9 +4,9 @@
 #
 # And then the virtual environment can be created by running:
 #
-# python3 -m venv .venv
-# .venv/bin/pip install --upgrade pip
-# .venv/bin/pip install -r requirements.txt
+python3 -m venv .venv
+.venv/bin/pip install --upgrade pip
+.venv/bin/pip install -r requirements.txt
 #
 # Then use .venv/bin/python to run the various training scripts.