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{
"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
}
|
