From b85ee9d64a536937912544c7bbd5b98b635b7e8d Mon Sep 17 00:00:00 2001
From: Christian C <cc@localhost>
Date: Mon, 11 Nov 2024 12:29:32 -0800
Subject: Initial commit

---
 old_notebooks/TensorflowToPyTorch.ipynb | 120 ++++++++++++++++++++++++++++++++
 1 file changed, 120 insertions(+)
 create mode 100644 old_notebooks/TensorflowToPyTorch.ipynb

(limited to 'old_notebooks/TensorflowToPyTorch.ipynb')

diff --git a/old_notebooks/TensorflowToPyTorch.ipynb b/old_notebooks/TensorflowToPyTorch.ipynb
new file mode 100644
index 0000000..76d588c
--- /dev/null
+++ b/old_notebooks/TensorflowToPyTorch.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0dfb6ada-ed98-48c9-8610-cadb7493e138",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sunlab.environment.base.cpu import *\n",
+    "from sunlab.environment.base.extras import *\n",
+    "from sunlab.globals import FILES\n",
+    "from sunlab.sunflow import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4136a260-bb40-47ec-8aad-d2a6ac31f1f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model, dataset = load_aae_and_dataset(FILES['TRAINING_DATASET'], FILES['PRETRAINED_MODEL_DIR'], MaxAbsScaler)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00694ce2",
+   "metadata": {},
+   "source": [
+    "# Save for PyTorch!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "23ae3a97",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Already Saved the model Weights!",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAlready Saved the model Weights!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m names_tup \u001b[38;5;241m=\u001b[39m [(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLAYER_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_WEIGHTS\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLAYER_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_BIAS\u001b[39m\u001b[38;5;124m\"\u001b[39m,) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)]\n\u001b[1;32m      3\u001b[0m names \u001b[38;5;241m=\u001b[39m []\n",
+      "\u001b[0;31mValueError\u001b[0m: Already Saved the model Weights!"
+     ]
+    }
+   ],
+   "source": [
+    "raise ValueError(\"Already Saved the model Weights!\")\n",
+    "names_tup = [(f\"LAYER_{i}_WEIGHTS\",f\"LAYER_{i}_BIAS\",) for i in range(1,3+1)]\n",
+    "names = []\n",
+    "for name_tup in names_tup:\n",
+    "    names.extend(name_tup)\n",
+    "ENCODER_DICT = {}\n",
+    "for idx, name in enumerate(names):\n",
+    "    trainable_variable = model.encoder.model.trainable_variables[idx].numpy()\n",
+    "    ENCODER_DICT[name] = trainable_variable\n",
+    "\n",
+    "names_tup = [(f\"LAYER_{i}_WEIGHTS\",f\"LAYER_{i}_BIAS\",) for i in range(1,3+1)]\n",
+    "names = []\n",
+    "for name_tup in names_tup:\n",
+    "    names.extend(name_tup)\n",
+    "DECODER_DICT = {}\n",
+    "for idx, name in enumerate(names):\n",
+    "    trainable_variable = model.decoder.model.trainable_variables[idx].numpy()\n",
+    "    DECODER_DICT[name] = trainable_variable\n",
+    "\n",
+    "names_tup = [(f\"LAYER_{i}_WEIGHTS\",f\"LAYER_{i}_BIAS\",) for i in range(1,3+1)]\n",
+    "names = []\n",
+    "for name_tup in names_tup:\n",
+    "    names.extend(name_tup)\n",
+    "DISCRIMINATOR_DICT = {}\n",
+    "for idx, name in enumerate(names):\n",
+    "    trainable_variable = model.discriminator.model.trainable_variables[idx].numpy()\n",
+    "    DISCRIMINATOR_DICT[name] = trainable_variable\n",
+    "\n",
+    "AAE_DICT = {\n",
+    "    \"ENCODER\": ENCODER_DICT,\n",
+    "    \"DECODER\": DECODER_DICT,\n",
+    "    \"DISCRIMINATOR\": DISCRIMINATOR_DICT,\n",
+    "}\n",
+    "\n",
+    "np.save(DIR_ROOT + \"models/current_model/portable/trainable_variables.npy\", AAE_DICT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3332ec53",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tfnb",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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