DCO Remediation Commit for Pedro Borges <[email protected]…

…c.uk> I, Pedro Borges <[email protected]>, hereby add my Signed-off-by to this commit: 347a2c0 I, Pedro Borges <[email protected]>, hereby add my Signed-off-by to this commit: 74341ed I, Pedro Borges <[email protected]>, hereby add my Signed-off-by to this commit: 83b986d Signed-off-by: Pedro Borges <[email protected]>
Project-MONAI · Sep 12, 2024 · 1cc68ce · 1cc68ce
1 parent 83b986d
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diff --git a/generation/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb b/generation/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb
@@ -38,6 +38,24 @@
    ],
    "id": "63d95da6"
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Setup environment",
+   "id": "3e054c148b8aceca"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
+    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
+    "%matplotlib inline"
+   ],
+   "id": "130611664cf10de6"
+  },
   {
    "cell_type": "markdown",
    "id": "6b766027",
@@ -81,7 +99,6 @@
     "from monai.networks.schedulers.ddim import DDIMScheduler\n",
     "\n",
     "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
     "\n",
     "print_config()"
    ],
@@ -409,7 +426,7 @@
    },
    "source": [
     "condition_dropout = 0.15\n",
-    "n_iterations = 2e4\n",
+    "max_epochs = 2e4\n",
     "batch_size = 64\n",
     "val_interval = 100\n",
     "iter_loss_list = []\n",
@@ -428,7 +445,7 @@
     "scaler = GradScaler()\n",
     "total_start = time.time()\n",
     "\n",
-    "while iteration < n_iterations:\n",
+    "while iteration < max_epochs:\n",
     "    for batch in train_loader:\n",
     "        iteration += 1\n",
     "        model.train()\n",
@@ -454,7 +471,7 @@
     "        scaler.step(optimizer)\n",
     "        scaler.update()\n",
     "        iter_loss += loss.item()\n",
-    "        sys.stdout.write(f\"Iteration {iteration}/{n_iterations} - train Loss {loss.item():.4f}\" + \"\\r\")\n",
+    "        sys.stdout.write(f\"Iteration {iteration}/{max_epochs} - train Loss {loss.item():.4f}\" + \"\\r\")\n",
     "        sys.stdout.flush()\n",
     "\n",
     "        if iteration % val_interval == 0:\n",
@@ -498,7 +515,7 @@
     "plt.plot(iterations, iter_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
     "plt.plot(\n",
     "    iterations, val_iter_loss_list, color=\"C1\", linewidth=2.0, label=\"Validation\"\n",
-    ")  # np.linspace(1, n_iterations, len(val_iter_loss_list))\n",
+    ")\n",
     "plt.yticks(fontsize=12)\n",
     "plt.xticks(fontsize=12)\n",
     "plt.xlabel(\"Iterations\", fontsize=16)\n",
@@ -513,202 +530,7 @@
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+      "...\n",
       "Train Loss 0.0140, Interval Loss 0.0161, Interval Loss Val 0.0210\n",
       "Train Loss 0.0168, Interval Loss 0.0167, Interval Loss Val 0.0176\n",
       "train diffusion completed, total time: 4280.638695478439.\n"