{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": [ "# Diffractive Autoencoder for MNIST classification task\n", "\n", "This notebook is based on the article \"All-optical autoencoder machine learning framework using diffractive processors\" [[1]](https://arxiv.org/pdf/2409.20346)." ], "id": "e5d6c95dca20557a" }, { "cell_type": "markdown", "id": "a5b9bab8-8d17-4b86-8548-f3edd43ca1c4", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "id": "d15a31da-c6c3-427d-a070-afdc48a01305", "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import random" ] }, { "cell_type": "code", "execution_count": 2, "id": "8f0f6378-5bc3-477d-bfd3-a08a9123b091", "metadata": {}, "outputs": [], "source": [ "import time\n", "import json" ] }, { "cell_type": "code", "execution_count": 3, "id": "2c831434-5730-415f-93c6-e9909364ff89", "metadata": {}, "outputs": [], "source": [ "# import warnings\n", "# warnings.simplefilter(\"always\") # always show warnings!" ] }, { "cell_type": "code", "execution_count": 4, "id": "f309cc84-f338-409f-b1a3-6ac36b945622", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 5, "id": "a0b67b5f-5ada-42c3-963f-27dee6c298ca", "metadata": {}, "outputs": [], "source": [ "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 6, "id": "c39328b9-9fa8-4937-9a53-905ea59276c5", "metadata": {}, "outputs": [], "source": [ "import torch\n", "from torch.utils.data import Dataset" ] }, { "cell_type": "code", "execution_count": 7, "id": "59041be7-8d30-4981-a3da-e5d090a32339", "metadata": {}, "outputs": [], "source": [ "from torch import nn" ] }, { "cell_type": "code", "execution_count": 8, "id": "72e92418-e8e7-4baa-882f-92b87f2c68ea", "metadata": {}, "outputs": [], "source": [ "from torch.nn import functional" ] }, { "cell_type": "code", "execution_count": 9, "id": "4ba00109-7b62-4f32-b685-17f437630230", "metadata": {}, "outputs": [], "source": [ "import torchvision\n", "import torchvision.transforms as transforms" ] }, { "cell_type": "code", "execution_count": 10, "id": "a39c467c-b066-4afb-b470-4265088d5301", "metadata": {}, "outputs": [], "source": [ "from torchvision.transforms import InterpolationMode" ] }, { "cell_type": "code", "execution_count": 11, "id": "902804f6-c104-4b4b-9b9f-f43ac9b4c1e1", "metadata": {}, "outputs": [], "source": [ "# our library\n", "from svetlanna import SimulationParameters\n", "from svetlanna.parameters import ConstrainedParameter" ] }, { "cell_type": "code", "execution_count": 12, "id": "9b7826e8-9de7-4539-b16a-60827a25959f", "metadata": {}, "outputs": [], "source": [ "# our library\n", "from svetlanna import Wavefront\n", "from svetlanna import elements\n", "from svetlanna.detector import Detector, DetectorProcessorClf" ] }, { "cell_type": "code", "execution_count": 13, "id": "d5a64cdd-5ed6-4fe1-ab77-d95120a45f72", "metadata": {}, "outputs": [], "source": [ "from svetlanna.transforms import ToWavefront" ] }, { "cell_type": "code", "execution_count": 14, "id": "f45236b9-f74d-46ae-9829-b7f2a08480d2", "metadata": {}, "outputs": [], "source": [ "# dataset\n", "from src.wf_datasets import DatasetOfWavefronts" ] }, { "cell_type": "code", "execution_count": 15, "id": "8603ed7b-af59-4d79-b6a7-dcb599e28e1b", "metadata": {}, "outputs": [], "source": [ "from tqdm import tqdm" ] }, { "cell_type": "code", "execution_count": 16, "id": "b216854c-ca48-43e5-8672-070d0ac78392", "metadata": {}, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 17, "id": "02414ffa-fc8b-4cd7-a6fa-1f744a9a68a7", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.patches as patches\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", "plt.style.use('dark_background')\n", "%matplotlib inline\n", "# %config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "markdown", "id": "a89c1e3b-c615-4f7d-822b-bb3866773dd0", "metadata": {}, "source": "" }, { "cell_type": "code", "execution_count": 18, "id": "b68ebffd-4f98-45d8-9bac-35adb3371377", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'26-03-2025_23-38'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "today_date = datetime.today().strftime('%d-%m-%Y_%H-%M') # date for a results folder name\n", "today_date" ] }, { "cell_type": "code", "execution_count": 19, "id": "a3e5807e-cb16-46ac-89a0-848d2f75529e", "metadata": {}, "outputs": [], "source": [ "# Define all necessery variables for that notebook\n", "VARIABLES = {\n", " # FILEPATHES\n", " 'data_path': './data', # folder which will be created (if not exists) to load/store Weizmann dataset\n", " 'results_path': f'models/autoencoder/ae_exp_{today_date}', # filepath to save results!\n", " \n", " # GENERAL SETTINGS - SECTION 1 of the notebook\n", " 'wavelength': 0.435 * 1e-3, # working wavelength, in [m]\n", " # Comment: Value from the article [1] - 0.435 * 1e-3 # in [m]\n", " 'neuron_size': 0.435 * 1e-3, # size of a pixel for DiffractiveLayers, in [m]\n", " # Comment: Value from the article [1] - 64 neurons ~ 27.84 * 1e-3 # in [m]\n", " 'mesh_size': (160, 160), # full size of a layer = numerical mesh\n", " # Comment: value from the article [1] - (160, 160)\n", "\n", " 'use_apertures': False, # if we need to add apertures before each Diffractie layer\n", " # Comment: value from the article [1] - unknown\n", " 'aperture_size': (64, 64), # size of each aperture = a detector square for classes zones\n", " # Comment: value from the article [1] - unknown\n", " \n", " # DATASET OF SUBSEQUENCES SETTINGS - SECTION 2 of the notebook\n", " 'resize': (64, 64), # size to resize pictures to add 0th padding then (up to the mesh size)\n", " 'modulation': 'amp', # modulation type to make a wavefront from each picture mask (see 2.3.2.)\n", " # Comment: can be equal to `phase`, `amp` or `both`\n", "\n", " # NETWORK - SECTION 3 of the notebook\n", " 'max_phase': 2 * torch.pi, # maximal possible phase for each DiffractiveLayer\n", " 'free_space_method': 'AS', # propagation method\n", " # Comment: can be 'AS' or 'fresnel'\n", " 'distance': 34.8 * 1e-3, # distance between diffractive layers \n", " # Comment: distances between two successive layers were all 34.8mm (80 * wavelength)\n", "\n", " # ENCODER\n", " 'encoder_use_slm': True, # use SLM (if True) or DiffractiveLayers (if False)\n", " 'encoder_num_layers': 5,\n", " 'encoder_init_phases': torch.pi,\n", " # value or a list of initial constant phases for DiffractiveLayers OR SLM\n", " # SLM settings - if 'use_slm' == True\n", " # Comment: a size of each SLM is equal to SimulationParameters!\n", " 'encoder_slm_shapes': [(160, 160), (160, 160), (80, 80), (80, 80), (40, 40)],\n", " # list of size 'encoder_num_layers'\n", " 'encoder_slm_levels': 256, \n", " # value OR a list (len = 'encoder_num_layers') of numbers of levels for each SLM\n", " 'encoder_slm_step_funcs': 'linear', # value OR a list of step function names\n", " # Comment: available stp functions names - 'linear'\n", "\n", " # PRESERVE PHASE OR NOT?!\n", " 'preserve_phase': False, # by default - resets phases after encoding (before decoding)\n", "\n", " # DECODER - same params!\n", " 'decoder_use_slm': False,\n", " 'decoder_num_layers': 5,\n", " 'decoder_init_phases': torch.pi,\n", " # SLM settings - if 'use_slm' == True\n", " 'decoder_slm_shapes': (160, 160),\n", " 'decoder_slm_levels': 256, \n", " 'decoder_slm_step_funcs': 'linear',\n", "\n", " # NETWORK LEARNING - SECTION 4 of the notebook\n", " # FOR CUSTOM LOSS\n", " 'eta_th': 0.04, # value for loss (see 4.1.2.)\n", " 'encoding_region_size': (20, 20), # a shape of encoding image\n", " # Comment: value from the article [1] - (8, 8)\n", " 'gamma_rec': 1e3, # coeff before MSE between Input and Encoded\n", " 'gamma_en': 2e-5,\n", " 'gamma_eff': 0.1, # coefficients for a loss function (see 4.1.2.)\n", " # Comment: values from [1] - 5e-5 and 0.1 respectively\n", "\n", " # Comment: it is also influence on validation/training loops!!!\n", " 'DEVICE': 'cpu', # if `cuda` - we will check if it is available (see first cells of Sec. 4)\n", " 'train_batch_size': 128, # batch sizes for training (see 4.1.1.)\n", " 'val_batch_size': 128,\n", " # Comment: value from the article [1] - 20 # for both train and test?\n", " 'adam_lr': 0.01, # learning rate for Adam optimizer (see 4.1.2.)\n", " # Comment: value from the article [1] - 0.01\n", " 'number_of_epochs': 80, # number of epochs to train\n", " # Comment: value from the article [1] - 100-300 ?!\n", "}" ] }, { "cell_type": "code", "execution_count": 20, "id": "abcc961c-3ad1-464c-801e-9e4b49c288d9", "metadata": {}, "outputs": [], "source": [ "# functions for SLM step (look documentation of SLM)\n", "SLM_STEPS = {\n", " 'linear': lambda x: x,\n", "}" ] }, { "cell_type": "code", "execution_count": 21, "id": "ee011e83-a29a-402e-8bfd-be812f50e6e3", "metadata": {}, "outputs": [], "source": [ "RESULTS_FOLDER = VARIABLES['results_path']\n", "\n", "if not os.path.exists(RESULTS_FOLDER):\n", " os.makedirs(RESULTS_FOLDER)" ] }, { "cell_type": "code", "execution_count": 22, "id": "f08a30ac-c6de-480b-9ad9-59a3584a4242", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'models/autoencoder/ae_exp_26-03-2025_23-38'" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RESULTS_FOLDER" ] }, { "cell_type": "code", "execution_count": 23, "id": "7b059ab6-d167-4530-984f-f1bdb0381396", "metadata": {}, "outputs": [], "source": [ "# save experiment conditions (VARIABLES dictionary)\n", "with open(f'{RESULTS_FOLDER}/conditions.json', 'w', encoding ='utf8') as json_file:\n", " json.dump(VARIABLES, json_file, ensure_ascii = True)" ] }, { "cell_type": "code", "execution_count": null, "id": "2708cbb4-b4bd-4d93-88fe-ff49c2bddb44", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "a32cbdf9-97b3-4865-9827-95e97deefded", "metadata": {}, "source": [ "## 1. Simulation parameters\n", "\n", "Citations from **Methods and Materials** (_Parameter details of diffractive processors_) of [[1]](https://arxiv.org/pdf/2409.20346):\n", "\n", "> ... i.e., $\\lambda = 0.435$ mm for $f_0 = 0.69$ THz ...\n", "\n", "> The diffractive layer sets of numerical models were all $69.6$ mm $\\times$ $69.6$ mm in size ($160 \\times 160$ pixels)" ] }, { "cell_type": "code", "execution_count": 24, "id": "d815d886-8ffb-4a79-b488-6b207cd05b64", "metadata": {}, "outputs": [], "source": [ "working_wavelength = VARIABLES['wavelength'] # [m] - like for MNIST\n", "\n", "c_const = 299_792_458 # [m / s]\n", "working_frequency = c_const / working_wavelength # [Hz]" ] }, { "cell_type": "code", "execution_count": 25, "id": "e389f46c-f2ca-4204-a30f-c270e5987163", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lambda = 0.435 mm\n", "frequency = 0.689 THz\n" ] } ], "source": [ "print(f'lambda = {working_wavelength * 1e3:.3f} mm')\n", "print(f'frequency = {working_frequency / 1e12:.3f} THz')" ] }, { "cell_type": "code", "execution_count": 26, "id": "257fcf62-f897-4b81-af45-96326d47ab5e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "neuron size = 0.435 mm\n" ] } ], "source": [ "# neuron size (square)\n", "neuron_size = VARIABLES['neuron_size'] # [m] - like for MNIST\n", "print(f'neuron size = {neuron_size * 1e3:.3f} mm')" ] }, { "cell_type": "code", "execution_count": 27, "id": "4a35ef33-9209-48a7-8380-dbd183c45461", "metadata": {}, "outputs": [], "source": [ "APERTURES = VARIABLES['use_apertures'] # add apertures BEFORE each diffractive layer or not" ] }, { "cell_type": "code", "execution_count": 28, "id": "6618b4ec-9fe2-45c7-8019-1f3b63c2e0f2", "metadata": {}, "outputs": [], "source": [ "LAYER_SIZE = VARIABLES['mesh_size'] # mesh size\n", "DETECTOR_SIZE = VARIABLES['aperture_size']" ] }, { "cell_type": "code", "execution_count": 29, "id": "1da8d905-67e4-4df9-8686-ce14f1b71b25", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Layer size (in neurons): 160 x 160 = 25600\n" ] } ], "source": [ "# number of neurons in simulation\n", "x_layer_nodes = LAYER_SIZE[1]\n", "y_layer_nodes = LAYER_SIZE[0]\n", "# Comment: Same size as proposed!\n", "\n", "print(f'Layer size (in neurons): {x_layer_nodes} x {y_layer_nodes} = {x_layer_nodes * y_layer_nodes}')" ] }, { "cell_type": "code", "execution_count": 30, "id": "d74aeced-a6b6-4335-bb08-db138373441b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Layer size (in mm): 69.600 x 69.600\n" ] } ], "source": [ "# physical size of each layer\n", "x_layer_size_m = x_layer_nodes * neuron_size # [m]\n", "y_layer_size_m = y_layer_nodes * neuron_size\n", "\n", "print(f'Layer size (in mm): {x_layer_size_m * 1e3 :.3f} x {y_layer_size_m * 1e3 :.3f}')" ] }, { "cell_type": "code", "execution_count": 31, "id": "884df414-9277-4fbe-835e-29a010e5d814", "metadata": {}, "outputs": [], "source": [ "X_LAYER_SIZE_M = x_layer_size_m\n", "Y_LAYER_SIZE_M = y_layer_size_m" ] }, { "cell_type": "code", "execution_count": null, "id": "37057e7b-8ba8-49d4-a9e3-c4b9ece0c7ba", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 32, "id": "6c3d95cc-633a-481a-bfa6-fe25a1d5b1a4", "metadata": {}, "outputs": [], "source": [ "# simulation parameters for the rest of the notebook\n", "\n", "SIM_PARAMS = SimulationParameters(\n", " axes={\n", " 'W': torch.linspace(-x_layer_size_m / 2, x_layer_size_m / 2, x_layer_nodes),\n", " 'H': torch.linspace(-y_layer_size_m / 2, y_layer_size_m / 2, y_layer_nodes),\n", " 'wavelength': working_wavelength, # only one wavelength!\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "b44384b6-0e40-463b-ba3c-94251f920e26", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "caa288cb-e021-424c-9ed0-2bb3d1f30a2d", "metadata": {}, "source": "## 2. Dataset preparation" }, { "cell_type": "markdown", "id": "985fb370-38fa-4b2f-9223-77897ad4f6f2", "metadata": {}, "source": "### 2.1. [MNIST Dataset](https://www.kaggle.com/datasets/hojjatk/mnist-dataset)" }, { "cell_type": "code", "execution_count": 33, "id": "0de278d4-1f7a-4651-8fce-74c41567c08f", "metadata": {}, "outputs": [], "source": [ "# initialize a directory for a dataset\n", "MNIST_DATA_FOLDER = VARIABLES['data_path'] # folder to store data" ] }, { "cell_type": "markdown", "id": "e459cb17-1178-4c58-ac73-ac45e14ca7dd", "metadata": {}, "source": "#### 2.1.1. Load Train and Test datasets of images" }, { "cell_type": "code", "execution_count": 34, "id": "c1d740b2-dfd1-4bf3-a86c-6fb13a831df0", "metadata": {}, "outputs": [], "source": [ "# TRAIN (images)\n", "mnist_train_ds = torchvision.datasets.MNIST(\n", " root=MNIST_DATA_FOLDER,\n", " train=True, # for train dataset\n", " download=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 35, "id": "14fb6dc4-3849-467c-957d-26617d8c5214", "metadata": {}, "outputs": [], "source": [ "# TEST (images)\n", "mnist_test_ds = torchvision.datasets.MNIST(\n", " root=MNIST_DATA_FOLDER,\n", " train=False, # for test dataset\n", " download=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 36, "id": "72f00e8c-0624-499f-9176-8820274bf05f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data: 60000\n", "Test data : 10000\n" ] } ], "source": [ "print(f'Train data: {len(mnist_train_ds)}')\n", "print(f'Test data : {len(mnist_test_ds)}')" ] }, { "cell_type": "markdown", "id": "016863e7-b697-4557-94a7-eb0c896a3ee1", "metadata": {}, "source": [ "#### 2.1.2. Create Train and Test datasets of wavefronts\n", "\n", "From [[1]](https://arxiv.org/pdf/2409.20346):\n", "\n", "> $FOV_I$ and $FOV_{II}$ were both $27.84$ mm $\\times$ $27.84$ mm in size ($64\\times64$ pixels)" ] }, { "cell_type": "code", "execution_count": 37, "id": "8c37007c-0731-4b58-9059-e2f2bb792b52", "metadata": {}, "outputs": [], "source": [ "# select modulation type\n", "MODULATION_TYPE = VARIABLES['modulation'] # using ONLY amplitude to encode each picture in a Wavefront!\n", "RESIZE_SHAPE = VARIABLES['resize'] # size to resize pictures to add 0th padding then (up to the mesh size)" ] }, { "cell_type": "code", "execution_count": 38, "id": "88113581-f819-4554-8d6a-db075f713a22", "metadata": {}, "outputs": [], "source": [ "resize_y = RESIZE_SHAPE[0]\n", "resize_x = RESIZE_SHAPE[1] # shape for transforms.Resize\n", "\n", "# paddings along OY\n", "pad_top = int((y_layer_nodes - resize_y) / 2)\n", "pad_bottom = y_layer_nodes - pad_top - resize_y\n", "# paddings along OX\n", "pad_left = int((x_layer_nodes - resize_x) / 2)\n", "pad_right = x_layer_nodes - pad_left - resize_x # params for transforms.Pad" ] }, { "cell_type": "code", "execution_count": 39, "id": "47e20e55-53f7-4f92-95eb-3ef75d5dddeb", "metadata": {}, "outputs": [], "source": [ "# compose all transforms!\n", "image_transform_for_ds = transforms.Compose(\n", " [\n", " transforms.ToTensor(),\n", " transforms.Resize(\n", " size=(resize_y, resize_x),\n", " interpolation=InterpolationMode.NEAREST,\n", " ),\n", " transforms.Pad(\n", " padding=(\n", " pad_left, # left padding\n", " pad_top, # top padding\n", " pad_right, # right padding\n", " pad_bottom # bottom padding\n", " ),\n", " fill=0,\n", " ), # padding to match sizes!\n", " ToWavefront(modulation_type=MODULATION_TYPE) # <- selected modulation type here!!!\n", " ] \n", ")" ] }, { "cell_type": "code", "execution_count": 40, "id": "7309365a-2986-45e8-a259-97f378fcccef", "metadata": {}, "outputs": [], "source": [ "# TRAIN dataset of WAVEFRONTS\n", "mnist_wf_train_ds = DatasetOfWavefronts(\n", " init_ds=mnist_train_ds, # dataset of images\n", " transformations=image_transform_for_ds, # image transformation\n", " sim_params=SIM_PARAMS, # simulation parameters\n", ")" ] }, { "cell_type": "code", "execution_count": 41, "id": "873dfbe6-81a1-47ab-9dfd-2f1513044fe7", "metadata": {}, "outputs": [], "source": [ "# TEST dataset of WAVEFRONTS\n", "mnist_wf_test_ds = DatasetOfWavefronts(\n", " init_ds=mnist_test_ds, # dataset of images\n", " transformations=image_transform_for_ds, # image transformation\n", " sim_params=SIM_PARAMS, # simulation parameters\n", ")" ] }, { "cell_type": "code", "execution_count": 42, "id": "038b5a54-175a-433b-98b6-8e37565d19b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data: 60000\n", "Test data : 10000\n" ] } ], "source": [ "print(f'Train data: {len(mnist_train_ds)}')\n", "print(f'Test data : {len(mnist_test_ds)}')" ] }, { "cell_type": "code", "execution_count": null, "id": "bc64e303-24da-49c9-9d3c-13e2c54bacbb", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "5e8bb35f-56df-401f-bdd5-296b6cd5dc11", "metadata": {}, "source": "## 3. Autoencoder" }, { "cell_type": "markdown", "id": "f5d30d21-bd1b-463d-8279-ef6f4e84b1c7", "metadata": {}, "source": [ "> distances between two successive layers, $d_l$, were all $34.8$ mm ($80\\lambda$)\n", "\n", "> model consists of five equally spaced phase-only modulation diffractive layers ($N=5$)" ] }, { "cell_type": "code", "execution_count": 43, "id": "076325f0-614e-4be2-ba62-421300e34b54", "metadata": {}, "outputs": [], "source": [ "if 'preserve_phase' not in VARIABLES.keys():\n", " PRESERVE_PHASE = False\n", "else:\n", " PRESERVE_PHASE = VARIABLES['preserve_phase']" ] }, { "cell_type": "code", "execution_count": 44, "id": "6f206a9d-3f7d-4b29-9caa-b8995ae20bf9", "metadata": {}, "outputs": [], "source": [ "NUM_ENCODER_LAYERS = VARIABLES['encoder_num_layers'] # number of diffractive layers\n", "NUM_DECODER_LAYERS = VARIABLES['decoder_num_layers'] # number of diffractive layers\n", "\n", "ENCODER_USE_SLM = VARIABLES['encoder_use_slm'] # if we use SLM or DiffractiveLayer as a main element for encoder/decoder\n", "DECODER_USE_SLM = VARIABLES['decoder_use_slm']\n", "\n", "MAX_PHASE = VARIABLES['max_phase']\n", "\n", "FS_METHOD = VARIABLES['free_space_method']\n", "FS_DISTANCE = VARIABLES['distance'] # [m] - distance between difractive layers" ] }, { "cell_type": "code", "execution_count": 45, "id": "5095d714-215a-4e48-825a-3e343c432da2", "metadata": {}, "outputs": [], "source": [ "if isinstance(VARIABLES['encoder_init_phases'], list):\n", " ENCODER_INIT_PHASES = VARIABLES['encoder_init_phases']\n", "else:\n", " ENCODER_INIT_PHASES = [VARIABLES['encoder_init_phases'] for _ in range(NUM_ENCODER_LAYERS)]\n", "\n", "if isinstance(VARIABLES['decoder_init_phases'], list):\n", " DECODER_INIT_PHASES = VARIABLES['decoder_init_phases']\n", "else:\n", " DECODER_INIT_PHASES = [VARIABLES['decoder_init_phases'] for _ in range(NUM_DECODER_LAYERS)]\n", " \n", "assert len(ENCODER_INIT_PHASES) == NUM_ENCODER_LAYERS\n", "assert len(DECODER_INIT_PHASES) == NUM_DECODER_LAYERS" ] }, { "cell_type": "markdown", "id": "46297d26-07a6-43e3-a7b3-ea6e6dd1b2b3", "metadata": {}, "source": [ "#### SLM settings if needed (for encoder/decoder)" ] }, { "cell_type": "code", "execution_count": 46, "id": "51f5ea29-ba40-42d6-82c5-399ef0bd91cc", "metadata": {}, "outputs": [], "source": [ "if ENCODER_USE_SLM:\n", " ENCODER_SLM_VARIABLES = {}\n", " \n", " for key in ['encoder_slm_shapes', 'encoder_slm_levels', 'encoder_slm_step_funcs']:\n", " if key != 'encoder_slm_step_funcs':\n", " \n", " if isinstance(VARIABLES[key], list):\n", " ENCODER_SLM_VARIABLES[key] = VARIABLES[key]\n", " else: # all SLM's have the same parameter\n", " ENCODER_SLM_VARIABLES[key] = [VARIABLES[key] for _ in range(NUM_ENCODER_LAYERS)]\n", " \n", " else: # for step functions!\n", " \n", " if isinstance(VARIABLES[key], list):\n", " ENCODER_SLM_VARIABLES[key] = [SLM_STEPS[name] for name in VARIABLES[key]]\n", " else: # all SLM's have the same parameter\n", " ENCODER_SLM_VARIABLES[key] = [SLM_STEPS[VARIABLES[key]] for _ in range(NUM_ENCODER_LAYERS)]\n", " \n", " assert len(ENCODER_SLM_VARIABLES[key]) == NUM_ENCODER_LAYERS" ] }, { "cell_type": "code", "execution_count": 47, "id": "c0f4b76c-786a-4b4b-ac8c-43ea676de78d", "metadata": {}, "outputs": [], "source": [ "if DECODER_USE_SLM:\n", " DECODER_SLM_VARIABLES = {}\n", " \n", " for key in ['decoder_slm_shapes', 'decoder_slm_levels', 'decoder_slm_step_funcs']:\n", " if key != 'decoder_slm_step_funcs':\n", " \n", " if isinstance(VARIABLES[key], list):\n", " DECODER_SLM_VARIABLES[key] = VARIABLES[key]\n", " else: # all SLM's have the same parameter\n", " DECODER_SLM_VARIABLES[key] = [VARIABLES[key] for _ in range(NUM_DECODER_LAYERS)]\n", " \n", " else: # for step functions!\n", " \n", " if isinstance(VARIABLES[key], list):\n", " DECODER_SLM_VARIABLES[key] = [SLM_STEPS[name] for name in VARIABLES[key]]\n", " else: # all SLM's have the same parameter\n", " DECODER_SLM_VARIABLES[key] = [SLM_STEPS[VARIABLES[key]] for _ in range(NUM_DECODER_LAYERS)]\n", " \n", " assert len(DECODER_SLM_VARIABLES[key]) == NUM_DECODER_LAYERS" ] }, { "cell_type": "markdown", "id": "0333993e-7c02-4cb6-b1c8-9d5ab4565811", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "## 3.1. Architecture" ] }, { "cell_type": "markdown", "id": "c74a48af-bb0f-4f7a-ad6d-def59245e4c7", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "### 3.1.1. Functions to get new elements" ] }, { "cell_type": "code", "execution_count": 48, "id": "cbf4a62c-c06a-42a9-968b-2820818ef1e3", "metadata": {}, "outputs": [], "source": [ "# functions that return single elements for further architecture\n", "\n", "def get_free_space(\n", " freespace_sim_params,\n", " freespace_distance, # in [m]!\n", " freespace_method='AS',\n", "):\n", " \"\"\"\n", " Returns FreeSpace layer with a bounded distance parameter.\n", " \"\"\"\n", " return elements.FreeSpace(\n", " simulation_parameters=freespace_sim_params,\n", " distance=freespace_distance, # distance is not learnable!\n", " method=freespace_method\n", " )\n", "\n", "\n", "def get_const_phase_layer(\n", " sim_params: SimulationParameters,\n", " value, max_phase=2 * torch.pi\n", "):\n", " \"\"\"\n", " Returns DiffractiveLayer with a constant phase mask.\n", " \"\"\"\n", " x_nodes, y_nodes = sim_params.axes_size(axs=('W', 'H'))\n", "\n", " const_mask = torch.ones(size=(y_nodes, x_nodes)) * value\n", " \n", " return elements.DiffractiveLayer(\n", " simulation_parameters=sim_params,\n", " mask=ConstrainedParameter(\n", " const_mask,\n", " min_value=0,\n", " max_value=max_phase\n", " ), # HERE WE ARE USING CONSTRAINED PARAMETER!\n", " ) # ATTENTION TO DOCUMENTATION!\n", "\n", "\n", "# CHANGE ACCORDING TO THE DOCUMENTATION OF SLM!\n", "def get_const_slm_layer(\n", " sim_params: SimulationParameters,\n", " mask_shape, \n", " phase_value,\n", " num_levels, \n", " step_func,\n", " height_m=Y_LAYER_SIZE_M,\n", " width_m=X_LAYER_SIZE_M,\n", " max_phase=2 * torch.pi\n", "):\n", " \"\"\"\n", " Returns SpatialLightModulator with a constant phase mask.\n", " \"\"\"\n", " y_nodes, x_nodes = mask_shape\n", " const_mask = torch.ones(size=(y_nodes, x_nodes)) * phase_value\n", " \n", " return elements.SpatialLightModulator(\n", " simulation_parameters=sim_params,\n", " mask=ConstrainedParameter(\n", " const_mask,\n", " min_value=0,\n", " max_value=max_phase\n", " ), # HERE WE ARE USING CONSTRAINED PARAMETER!\n", " height=height_m,\n", " width=width_m,\n", " # location=(0., 0.), # by default\n", " number_of_levels=num_levels,\n", " step_function=step_func,\n", " # mode='nearest', # by default it is 'nearest'\n", " ) # ATTENTION TO DOCUMENTATION!" ] }, { "cell_type": "markdown", "id": "2db85395-7ff2-4d71-9d15-d6849a9780af", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "#### 3.1.2. Elements list\n", "Function to get a list of elements to reproduce an architecture:" ] }, { "cell_type": "code", "execution_count": 49, "id": "f4e3cf49-5e7f-4fe1-81c9-dd90021c9406", "metadata": {}, "outputs": [], "source": [ "def get_elements_list(\n", " num_layers,\n", " simulation_parameters,\n", " freespace_distance,\n", " freespace_method,\n", " apertures=False,\n", " aperture_size=(100, 100),\n", " mode='encoder'\n", "):\n", " \"\"\"\n", " Composes a list of elements for an encoder (with no Detector).\n", " ...\n", "\n", " Parameters\n", " ----------\n", " num_layers : int\n", " Number of layers in the system.\n", " simulation_parameters : SimulationParameters()\n", " A simulation parameters for a task.\n", " freespace_distance : float,\n", " A distance between phase layers in [m].\n", " freespace_method : str\n", " Propagation method for free spaces in a setup.\n", " \n", " apertures : bool\n", " If True, than before each DiffractiveLayer (and detector) we add a square aperture.\n", " Comment: there are strickt square apertures!\n", " aperture_size : tuple\n", " A size of square apertures.\n", "\n", " mode : str\n", " Takes values: 'encoder' or 'decoder'.\n", " \n", " Returns\n", " -------\n", " elements_list : list(Element)\n", " List of Elements for an encoder/decoder.\n", " \"\"\"\n", " elements_list = [] # list of elements\n", "\n", " if mode == 'encoder':\n", " use_slm = ENCODER_USE_SLM\n", " init_phases = ENCODER_INIT_PHASES\n", " if use_slm:\n", " slm_masks_shape = ENCODER_SLM_VARIABLES['encoder_slm_shapes']\n", " slm_levels = ENCODER_SLM_VARIABLES['encoder_slm_levels']\n", " slm_funcs = ENCODER_SLM_VARIABLES['encoder_slm_step_funcs']\n", "\n", " if mode == 'decoder':\n", " use_slm = DECODER_USE_SLM\n", " init_phases = DECODER_INIT_PHASES\n", " if use_slm:\n", " slm_masks_shape = DECODER_SLM_VARIABLES['decoder_slm_shapes']\n", " slm_levels = DECODER_SLM_VARIABLES['decoder_slm_levels']\n", " slm_funcs = DECODER_SLM_VARIABLES['decoder_slm_step_funcs']\n", " \n", " if apertures: # equal masks for all apertures (select a part in the middle)\n", " aperture_mask = torch.ones(size=aperture_size)\n", "\n", " y_nodes, x_nodes = simulation_parameters.axes_size(axs=('H', 'W'))\n", " y_mask, x_mask = aperture_mask.size()\n", " pad_top = int((y_nodes - y_mask) / 2)\n", " pad_bottom = y_nodes - pad_top - y_mask\n", " pad_left = int((x_nodes - x_mask) / 2)\n", " pad_right = x_nodes - pad_left - x_mask # params for transforms.Pad\n", " \n", " # padding transform to match aperture size with simulation parameters \n", " aperture_mask = functional.pad(\n", " input=aperture_mask,\n", " pad=(pad_left, pad_right, pad_top, pad_bottom),\n", " mode='constant',\n", " value=0\n", " )\n", "\n", " # first FreeSpace layer before first DiffractiveLayer\n", " elements_list.append(\n", " get_free_space(\n", " simulation_parameters, # simulation parameters for the notebook\n", " freespace_distance, # in [m]\n", " freespace_method=freespace_method,\n", " )\n", " )\n", "\n", " # compose the architecture\n", " for ind_layer in range(num_layers):\n", "\n", " # add strickt square Aperture\n", " if apertures:\n", " elements_list.append(\n", " elements.Aperture(\n", " simulation_parameters=simulation_parameters,\n", " mask=aperture_mask\n", " )\n", " )\n", " \n", " # ------------------------------------------------------------PHASE LAYER\n", " if use_slm: # add a phase layer (SLM or DiffractiveLayer)\n", " # add SLM (learnable phase mask)\n", " elements_list.append(\n", " get_const_slm_layer(\n", " simulation_parameters,\n", " mask_shape=slm_masks_shape[ind_layer], \n", " phase_value=init_phases[ind_layer],\n", " num_levels=slm_levels[ind_layer], \n", " step_func=slm_funcs[ind_layer],\n", " max_phase=MAX_PHASE\n", " )\n", " )\n", " else:\n", " # add DiffractiveLayer (learnable phase mask)\n", " elements_list.append(\n", " get_const_phase_layer(\n", " simulation_parameters,\n", " value=init_phases[ind_layer],\n", " max_phase=MAX_PHASE\n", " )\n", " )\n", " # -----------------------------------------------------------------------\n", " \n", " # add FreeSpace\n", " elements_list.append(\n", " get_free_space(\n", " simulation_parameters, # simulation parameters for the notebook\n", " freespace_distance, # in [m]\n", " freespace_method=freespace_method,\n", " )\n", " )\n", "\n", " return elements_list" ] }, { "cell_type": "code", "execution_count": null, "id": "39e8b941-4f5a-4f19-b712-e20ea5b21b73", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "fc401562-b61d-4606-93f8-30a65ac6b027", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "### 3.1.3. Autoencoder (bidirectional encoder-decoder) class" ] }, { "cell_type": "code", "execution_count": 50, "id": "07759f1b-8e4a-4539-af81-2263e42edd8d", "metadata": {}, "outputs": [], "source": [ "class AutoencoderSymmetrical(nn.Module):\n", " \"\"\"\n", " An autoencoder:\n", " .encode() - forward propagation through encoder elements\n", " .decode() - forward propagation through decoder elements\n", " \"\"\"\n", "\n", " def __init__(\n", " self,\n", " sim_params: SimulationParameters,\n", " encoder_num_layers: int,\n", " decoder_num_layers: int,\n", " fs_distance: float,\n", " fs_method: str = 'AS',\n", " encoder_elements_list: list = None,\n", " decoder_elements_list: list = None,\n", " device: str | torch.device = torch.get_default_device(),\n", " ):\n", " \"\"\"\n", " Parameters\n", " ----------\n", " sim_params : SimulationParameters\n", " Simulation parameters for the task.\n", " encoder_num_layers, decoder_num_layers : int\n", " Number of layers in encoder/decoder.\n", " fs_distance : float,\n", " A distance between phase layers in [m].\n", " fs_method : str\n", " Propagation method for free spaces in a setup.\n", " elements_list : list\n", " List of elements to compose an autoencoder.\n", " \"\"\"\n", " super().__init__()\n", "\n", " self.sim_params = sim_params\n", "\n", " self.h, self.w = self.sim_params.axes_size(\n", " axs=('H', 'W')\n", " ) # height and width for a wavefronts\n", " \n", " self.__device = device\n", " self.fs_method = fs_method\n", "\n", " # ENCODER\n", " if encoder_elements_list is None:\n", " encoder_elements_list = get_elements_list(\n", " encoder_num_layers,\n", " self.sim_params,\n", " fs_distance,\n", " fs_method,\n", " apertures=VARIABLES['use_apertures'],\n", " aperture_size=VARIABLES['aperture_size'],\n", " mode='encoder'\n", " ) # no Detector here!\n", "\n", " # self.encoder_elements = encoder_elements_list\n", " self.encoder = nn.Sequential(*encoder_elements_list).to(self.__device)\n", "\n", " # DECODER\n", " if decoder_elements_list is None:\n", " decoder_elements_list = get_elements_list(\n", " decoder_num_layers,\n", " self.sim_params,\n", " fs_distance,\n", " fs_method,\n", " apertures=VARIABLES['use_apertures'],\n", " aperture_size=VARIABLES['aperture_size'],\n", " mode='decoder', # DECODER is a mirror image of ENCODER!\n", " ) # no Detector here!\n", "\n", " # self.decoder_elements = decoder_elements_list\n", " self.decoder = nn.Sequential(*decoder_elements_list).to(self.__device)\n", "\n", " def encode(self, wavefront_in):\n", " \"\"\"\n", " Forward propagation through the autoencoder - encode an image wavefront (input).\n", " \n", " Returns\n", " -------\n", " wavefront_encoded : Wavefront\n", " An encoded input wavefront.\n", " \"\"\"\n", " return self.encoder(wavefront_in)\n", "\n", " def decode(self, wavefront_encoded):\n", " \"\"\"\n", " Backward propagation through the autoencoder - decode an wncoded image.\n", " \n", " Returns\n", " -------\n", " wavefront_decoded : Wavefront\n", " A decoded wavefront.\n", " \"\"\"\n", " return self.decoder(wavefront_encoded)\n", "\n", " def stepwise_propagation(self, input_wavefront: Wavefront, mode: str='encode'):\n", " \"\"\"\n", " Function that consistently applies forward method of each element of ENCODER/DECODER\n", " to an input wavefront.\n", "\n", " Parameters\n", " ----------\n", " input_wavefront : torch.Tensor\n", " A wavefront that enters the optical network.\n", " mode : str\n", " Specify a mode 'encode' or 'decode'.\n", "\n", " Returns\n", " -------\n", " str\n", " A string that represents a scheme of a propagation through a setup.\n", " list(torch.Tensor)\n", " A list of an input wavefront evolution\n", " during a propagation through a setup.\n", " \"\"\"\n", " this_wavefront = input_wavefront\n", " # list of wavefronts while propagation of an initial wavefront through the system\n", " steps_wavefront = [this_wavefront] # input wavefront is a zeroth step\n", "\n", " optical_scheme = '' # string that represents a linear optical setup (schematic)\n", "\n", " if mode == 'encode':\n", " net = self.encoder\n", " if mode == 'decode':\n", " net = self.decoder\n", " \n", " net.eval()\n", " for ind_element, element in enumerate(net):\n", " # for visualization in a console\n", " element_name = type(element).__name__\n", " optical_scheme += f'-({ind_element})-> [{ind_element + 1}. {element_name}] '\n", "\n", " if ind_element == len(net) - 1:\n", " optical_scheme += f'-({ind_element + 1})->'\n", " \n", " # element forward\n", " with torch.no_grad():\n", " this_wavefront = element.forward(this_wavefront)\n", " \n", " steps_wavefront.append(this_wavefront) # add a wavefront to list of steps\n", "\n", " return optical_scheme, steps_wavefront\n", "\n", " def forward(self, wavefront_in):\n", " \"\"\"\n", " Parameters\n", " ----------\n", " wavefront_in: Wavefront('bs', 'H', 'W')\n", "\n", " Returns\n", " -------\n", " amplitudes_encoded, amplitudes_decoded : torch.Tensor\n", " Amplitudes of encoded and decoded wavefronts.\n", " \"\"\"\n", " if len(wavefront_in.shape) > 2: # if a batch is an input\n", " batch_flag = True\n", " bs = wavefront_in.shape[0]\n", " else:\n", " batch_flag = False\n", "\n", " # encode\n", " wavefront_encoded = self.encode(wavefront_in)\n", " # decode from intencity!\n", " if PRESERVE_PHASE:\n", " wavefront_decoded = self.decode(wavefront_encoded)\n", " else:\n", " wavefront_encoded_no_phase = wavefront_encoded.abs() + 0j\n", " wavefront_decoded = self.decode(wavefront_encoded_no_phase)\n", " \n", " # results to calculate loss\n", " amplitudes_encoded = wavefront_encoded.abs()\n", " amplitudes_decoded = wavefront_decoded.abs()\n", " \n", " return amplitudes_encoded, amplitudes_decoded" ] }, { "cell_type": "code", "execution_count": null, "id": "8f5c3c3c-bf0a-4460-a2a2-878c44fbbec6", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "29d6e5f1-fdb3-4e6d-adf6-d9c32d0eabc7", "metadata": {}, "source": [ "## 3.2. An untrained autoencoder" ] }, { "cell_type": "code", "execution_count": 51, "id": "df23c960-d7b8-4f00-9e8a-09c24cb776e3", "metadata": {}, "outputs": [], "source": [ "def get_autoencoder():\n", " return AutoencoderSymmetrical(\n", " sim_params=SIM_PARAMS,\n", " encoder_num_layers=NUM_ENCODER_LAYERS,\n", " decoder_num_layers=NUM_DECODER_LAYERS,\n", " fs_distance=FS_DISTANCE,\n", " fs_method=FS_METHOD,\n", " )" ] }, { "cell_type": "code", "execution_count": null, "id": "cd38899c-a20c-47cc-9694-2ef8e67ed1c5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "bb8fcccb-50f2-42db-8ce2-914c30baeefb", "metadata": {}, "source": "## 4. Training of the network" }, { "cell_type": "code", "execution_count": 52, "id": "4c10c255-7453-4141-aeda-cf5478a34846", "metadata": {}, "outputs": [], "source": [ "DEVICE = VARIABLES['DEVICE'] # 'mps' is not support a CrossEntropyLoss" ] }, { "cell_type": "code", "execution_count": 53, "id": "efd3095b-872f-4cc1-a644-252fa62fe00b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'cpu'" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "if DEVICE == 'cuda':\n", " DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "DEVICE" ] }, { "cell_type": "markdown", "id": "8eca038b-1006-42fe-be21-1a9a1eb19dc3", "metadata": {}, "source": "### 4.1. Prepare some stuff for training" }, { "cell_type": "markdown", "id": "72ae6be9-65cd-4ed7-9747-32c76ff37e3d", "metadata": {}, "source": [ "#### 4.1.1. `DataLoader`'s\n", "\n", "Citation from [[1]](https://arxiv.org/pdf/2409.20346):\n", "\n", "> During each training iteration, a mini-batch composed of $128$ randomly selected images was input to the model." ] }, { "cell_type": "code", "execution_count": 54, "id": "1e01a654-e506-4afc-a367-c67722b772ee", "metadata": {}, "outputs": [], "source": [ "train_bs = VARIABLES['train_batch_size'] # a batch size for training set\n", "val_bs = VARIABLES['val_batch_size']" ] }, { "cell_type": "code", "execution_count": 55, "id": "4684d63a-bb24-4b82-bced-fd93e937fbfb", "metadata": {}, "outputs": [], "source": [ "train_wf_loader = torch.utils.data.DataLoader(\n", " mnist_wf_train_ds,\n", " batch_size=train_bs,\n", " shuffle=True,\n", " drop_last=False,\n", ")\n", "\n", "test_wf_loader = torch.utils.data.DataLoader(\n", " mnist_wf_test_ds,\n", " batch_size=val_bs,\n", " shuffle=True,\n", " drop_last=False,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "0d0c48fb-91fa-49b1-857c-509411ee0cac", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "ea115342-07ee-45dc-8873-ecbbd80f5619", "metadata": {}, "source": [ "#### 4.1.2. Optimizer and loss function\n", "\n", "Citations from methods of [[1]](https://www.nature.com/articles/s41566-021-00796-w#Sec4):\n", "\n", "> For all three architectures, the learning rate was set to $0.01$, with a training batch size of ... $20$ for the D-RNN.\n", "\n", "> We adopt a stochastic gradient descent algorithm, that is, the adaptive moment estimation (Adam) optimizer ..." ] }, { "cell_type": "code", "execution_count": 56, "id": "4c550973-3e7b-42e0-bbaf-9b9d9ac49da7", "metadata": {}, "outputs": [], "source": [ "LR = VARIABLES['adam_lr']" ] }, { "cell_type": "code", "execution_count": 57, "id": "0388df2a-78f2-4648-8c90-e69cf9dc3574", "metadata": {}, "outputs": [], "source": [ "def get_adam_optimizer(net):\n", " return torch.optim.Adam(\n", " params=net.parameters(), # NETWORK PARAMETERS!\n", " lr=LR\n", " )" ] }, { "cell_type": "markdown", "id": "882bcd48-1fa4-4d15-b153-b403ad30d040", "metadata": {}, "source": [ "#### Loss function\n", "From [[1]](https://www.nature.com/articles/s41566-021-00796-w#Sec4):\n", " \n", ">$$\n", "\\mathcal{L}_\\text{OAE} = \\mathcal{L}_\\text{Rec}+\\gamma_{En} \\mathcal{L}_{En} +\\gamma_{eff}\\mathcal{L}_{eff}\n", "$$\n", "Here, $\\mathcal{L}_\\text{Rec}$ represents the reconstruction error between input images and corresponding decoded images, and is defined as:\n", "$$\n", "\\mathcal{L}_\\text{Rec} = \\mathcal{L}_\\text{MSE} \\left( I(x,y), O_{De}(x,y) \\right)\n", "=E \\left[ \\left| \\sigma_1 I(x,y) - \\sigma_2 O_{De}(x,y) \\right|^2 \\right],\n", "$$\n", "$$\n", "\\sigma_1=\\frac{1}{\\sum\\limits_{(x,y)} I(x,y)},\n", "$$\n", "$$\n", "\\sigma_2 = \\sigma_1 \\frac{\\sum\\limits_{(x,y)} I(x,y) O_{De}(x,y) }{\\sum\\limits_{(x,y)} |O_{De}(x,y)|^2 },\n", "$$\n", "where $E[\\cdot]$ is the average operator, $I(x,y)$ stands for input image and $O_{De}(x,y)$ stands for decoded image. $\\sigma_1$ and $\\sigma_2$ are parameters that eliminate the error due to diffractive energy loss.\n", "\n", ">$\\mathcal{L}_{En}$ is a regularization term designed to maximize the concentration of energy in the target encoding region, which can be expressed as:\n", "$$\n", "\\mathcal{L}_{En} = \n", "-\\ln\\left( \n", "\\frac{\\sum\\limits_{(x,y)} |M(x,y)O_{En}(x,y)|^2 }{\\sum\\limits_{(x,y)} |O_{En}(x,y)|^2 }\n", "\\right),\n", "$$\n", "$$\n", "M(x,y)=\n", "\\begin{cases}\n", "1,\\;(x,y) \\in \\text{encoding region} \\\\\n", "0,\\; \\text{otherwise}\n", "\\end{cases}\n", "$$\n", ">where $O_{En}(x,y)$ represents the encoder output. $M(x,y)$, a region mask function that defines the shape of encoding patterns, guides the output to align with the target shape by providing a prior shape distribution, similar to the prior probability distribution in the electronic VAE model.\n", "\n", "> $\\mathcal{L}_{Eff}$ is another regulation term that controls the bidirectional diffraction efficiency. For SOAE and its extended models, we simply used the\n", "reconstruction efficiency to constrain the model weights. The equation can be expressed as follows:\n", "$$\n", "\\mathcal{L}_{Eff} = \n", "\\begin{cases}\n", "-\\ln \\left( \\frac{\\eta_R}{\\eta_{Th}} \\right), \\; \\eta_R < \\eta_{Th} \\\\\n", "0,\\; \\eta_R \\geq \\eta_{Th}\n", "\\end{cases}\n", "$$\n", "$$\n", "\\eta_R=\\frac{ \\sum\\limits_{(x,y)} |O_{De}(x,y)|^2 }{ \\sum\\limits_{(x,y)} |I(x,y)|^2 }\n", "$$" ] }, { "cell_type": "markdown", "id": "4b6e34c9-91f1-4e65-bb3e-26c7ee581c07", "metadata": {}, "source": [ "> ... with $\\eta_{Th}$ in equation ... being $4\\%$" ] }, { "cell_type": "code", "execution_count": 58, "id": "c84e1144-2750-44e8-9126-1fa62cb8fcc4", "metadata": {}, "outputs": [], "source": [ "ETA_TH = VARIABLES['eta_th']" ] }, { "cell_type": "markdown", "id": "8240a958-0a42-4425-a387-9eab64b7c68c", "metadata": {}, "source": [ "> The encoding region of tiny SOAE and MOAE models are both a square with a side length of $8$, resulting\n", "in a compression ratio of $16$." ] }, { "cell_type": "code", "execution_count": 59, "id": "73dbcbe0-e54e-48da-8f97-2767ef5facb9", "metadata": {}, "outputs": [], "source": [ "REGION_MASK_SIZE = VARIABLES['encoding_region_size']" ] }, { "cell_type": "code", "execution_count": 60, "id": "f790b054-ab70-4fd5-b40e-1e02f1207db9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "M(x,y) shape: (160, 160)\n", "Number of nonzero (ones) elements: 400\n" ] } ], "source": [ "REGION_MASK = torch.ones(size=REGION_MASK_SIZE) # M(x,y)\n", "\n", "y_nodes, x_nodes = SIM_PARAMS.axes_size(axs=('H', 'W'))\n", "y_mask, x_mask = REGION_MASK_SIZE\n", "pad_top = int((y_nodes - y_mask) / 2)\n", "pad_bottom = y_nodes - pad_top - y_mask\n", "pad_left = int((x_nodes - x_mask) / 2)\n", "pad_right = x_nodes - pad_left - x_mask # params for transforms.Pad\n", "\n", "# padding transform to match aperture size with simulation parameters \n", "REGION_MASK = functional.pad(\n", " input=REGION_MASK,\n", " pad=(pad_left, pad_right, pad_top, pad_bottom),\n", " mode='constant',\n", " value=0\n", ")\n", "\n", "print(f'M(x,y) shape: ({REGION_MASK.shape[0]}, {REGION_MASK.shape[1]})')\n", "print(f'Number of nonzero (ones) elements: {int(REGION_MASK.sum().item())}')" ] }, { "cell_type": "markdown", "id": "248f4e14-95f5-4d83-8899-72c23f948656", "metadata": {}, "source": [ "> ... $\\gamma_{En}$ and $\\gamma_{Eff}$ in equation (4) for training SOAE model and its extensions, as well as DSOAE model were respectively set to $5\\times 10^{-5}$ and $0.1$ ..." ] }, { "cell_type": "code", "execution_count": 61, "id": "c6e317df-660b-43b2-8da6-519c3c1f995e", "metadata": {}, "outputs": [], "source": [ "GAMMA_EN = VARIABLES['gamma_en']\n", "GAMMA_EFF = VARIABLES['gamma_eff']" ] }, { "cell_type": "code", "execution_count": 62, "id": "b98d4d92-013c-4071-a7c9-1db2a6609e10", "metadata": {}, "outputs": [], "source": [ "if 'gamma_rec' in VARIABLES.keys():\n", " GAMMA_REC = VARIABLES['gamma_rec'] # to make losses of the same order\n", "else:\n", " GAMMA_REC = 1.0" ] }, { "cell_type": "markdown", "id": "3cc45745-5e11-42fa-abcf-6d65e5e2b421", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "#### Function" ] }, { "cell_type": "code", "execution_count": 63, "id": "0220d2a8-98fb-4c59-a757-448c12c1147c", "metadata": {}, "outputs": [], "source": [ "def CustomLossFunc(input_image, encoded_image_amp, decoded_image_amp):\n", " \"\"\"\n", " Calculates an error function as was used in the article [1].\n", " \n", " Parameters:\n", " -----------\n", " input_image : Wavefront\n", " Input wavefront constructed from an image (or a batch of images!).\n", " encoded_image_amp : Wavefront (~torch.Tensor)\n", " A result of encoding (forward propagation through autoencoder) of an input image - amplitudes!\n", " decoded_image_amp : Wavefront (~ torch.Tensor)\n", " A result of decoding (backward propagation through autoencoder) of an encoded image - amplitudes! \n", "\n", " Comment: first dimension is responsible for a batch size!\n", " \"\"\"\n", " if len(input_image.shape) > 2: # if a batch is an input\n", " batch_flag = True\n", " bs = input_image.shape[0]\n", " else:\n", " batch_flag = False\n", " \n", " h, w = SIM_PARAMS.axes_size(axs=('H', 'W'))\n", " number_of_layer_neurons = h * w\n", " # TODO: is the error calculates by amplitudes or by intensities?\n", " input_image_amp = input_image.abs()\n", " \n", " # the reconstruction error - L_Rec\n", " sigma_1 = 1 / (input_image_amp.sum(dim=(-2, -1)))\n", " sigma_2 = sigma_1 * (\n", " (input_image_amp * decoded_image_amp).sum(dim=(-2, -1)) /\n", " (decoded_image_amp ** 2).sum(dim=(-2, -1))\n", " )\n", "\n", " if batch_flag:\n", " sigma_1 = sigma_1.view(-1, 1, 1)\n", " sigma_2 = sigma_2.view(-1, 1, 1)\n", "\n", " l_rec = (\n", " (\n", " (\n", " sigma_1 * input_image_amp - \n", " sigma_2 * decoded_image_amp\n", " ) ** 2\n", " ).sum(dim=(-2, -1)) / number_of_layer_neurons # DO WE NEED THIS DIVISION?!\n", " )\n", " \n", " # regularization term - L_En\n", " l_en = -torch.log(\n", " ((REGION_MASK * encoded_image_amp) ** 2).sum(dim=(-2, -1)) /\n", " (encoded_image_amp ** 2).sum(dim=(-2, -1))\n", " )\n", "\n", " # another regulation term - L_Eff\n", " eta_r = (\n", " (decoded_image_amp ** 2).sum(dim=(-2, -1)) /\n", " (input_image_amp ** 2).sum(dim=(-2, -1))\n", " )\n", " if batch_flag:\n", " l_eff = torch.where(eta_r < ETA_TH, -torch.log(eta_r / ETA_TH), 0.0)\n", " else:\n", " l_eff = -torch.log(eta_r / ETA_TH) if eta_r < ETA_TH else 0.0\n", "\n", " # complete loss\n", " loss = GAMMA_REC * l_rec + GAMMA_EN * l_en + GAMMA_EFF * l_eff\n", " \n", " return (l_rec.mean(), l_en.mean(), l_eff.mean()), loss.mean()" ] }, { "cell_type": "code", "execution_count": null, "id": "2dcd8433-40cc-4ae4-9616-39960de3f59b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "9caca2da-d3a4-447f-a9dd-99248231fd21", "metadata": {}, "source": [ "#### Object" ] }, { "cell_type": "code", "execution_count": 64, "id": "b9126a0c-da26-47aa-bc1a-a4a56cee8e3e", "metadata": {}, "outputs": [], "source": [ "loss_func = CustomLossFunc\n", "loss_func_name = 'custom loss'" ] }, { "cell_type": "code", "execution_count": null, "id": "cdb57a04-76ed-4170-9157-8fc7be53ca9e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "e63c095a-91b2-4eb8-91e3-9f06506f60d3", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": "#### 4.1.3. Training and evaluation loops" }, { "cell_type": "code", "execution_count": 65, "id": "3c533d7b-e54c-402f-b8bc-aa7260aeafbe", "metadata": {}, "outputs": [], "source": [ "def autoencoder_train(\n", " autoencoder, wavefronts_dataloader,\n", " loss_func, optimizer,\n", " device='cpu', show_process=False\n", "):\n", " \"\"\"\n", " Function to train `AutoencoderBidirectional`\n", " ...\n", " \n", " Parameters\n", " ----------\n", " autoencoder : torch.nn.Module\n", " Autoencoder which is an encoder in one direction and a decoder in another.\n", " wavefronts_dataloader : torch.utils.data.DataLoader\n", " A loader (by batches) for the train dataset of wavefronts.\n", " loss_func :\n", " Loss function for such an autoencoder.\n", " optimizer: torch.optim\n", " Optimizer...\n", " device : str\n", " Device to computate on...\n", " show_process : bool\n", " Flag to show (or not) a progress bar.\n", " \n", " Returns\n", " -------\n", " batches_loss_parts : list[list]\n", " Parts of the custom loss!\n", " batches_losses : list[float]\n", " Losses for each batch in an epoch.\n", " \"\"\"\n", " autoencoder.train() # activate 'train' mode of a model\n", " batches_losses = [] # to store loss for each batch\n", " batches_loss_parts = [] # to store accuracy for each batch\n", " \n", " correct_preds = 0\n", " size = 0\n", " ind_batch = 1\n", " \n", " for batch_wavefronts, batch_labels in tqdm(\n", " wavefronts_dataloader,\n", " total=len(wavefronts_dataloader),\n", " desc='train', position=0,\n", " leave=True, disable=not show_process\n", " ): # go by batches\n", " # batch_wavefronts - input wavefronts, batch_labels - labels\n", " batch_size = batch_wavefronts.size()[0]\n", " \n", " batch_wavefronts = batch_wavefronts.to(device)\n", " # batch_labels = batch_labels.to(device)\n", " \n", " optimizer.zero_grad()\n", "\n", " # forward of an autoencoder\n", " encoded_image_amp, decoded_image_amp = autoencoder(batch_wavefronts)\n", " \n", " loss_parts, loss = loss_func(batch_wavefronts, encoded_image_amp, decoded_image_amp)\n", " \n", " loss.backward()\n", " optimizer.step()\n", " \n", " # accumulate losses and accuracies for batches\n", " batches_losses.append(loss.item())\n", " batches_loss_parts.append([part.item() for part in loss_parts])\n", "\n", " return batches_loss_parts, batches_losses" ] }, { "cell_type": "code", "execution_count": 66, "id": "7668db55-2104-4706-aa26-3acd07d92063", "metadata": {}, "outputs": [], "source": [ "def autoencoder_validate(\n", " autoencoder, wavefronts_dataloader,\n", " loss_func,\n", " device='cpu', show_process=False\n", " ):\n", " \"\"\"\n", " Function to validate `AutoencoderBidirectional`\n", " ...\n", " \n", " Parameters\n", " ----------\n", " autoencoder : torch.nn.Module\n", " Autoencoder which is an encoder in one direction and a decoder in another.\n", " wavefronts_dataloader : torch.utils.data.DataLoader\n", " A loader (by batches) for the train dataset of wavefronts.\n", " loss_func :\n", " Loss function for such an autoencoder.\n", " device : str\n", " Device to computate on...\n", " show_process : bool\n", " Flag to show (or not) a progress bar.\n", " \n", " Returns\n", " -------\n", " batches_loss_parts : list[list]\n", " Parts of the custom loss!\n", " batches_losses : list[float]\n", " Losses for each batch in an epoch.\n", " \"\"\"\n", " autoencoder.eval() # activate 'eval' mode of a model\n", " batches_losses = [] # to store loss for each batch\n", " batches_loss_parts = [] # to store accuracy for each batch\n", " \n", " correct_preds = 0\n", " size = 0\n", "\n", " for batch_wavefronts, batch_labels in tqdm(\n", " wavefronts_dataloader,\n", " total=len(wavefronts_dataloader),\n", " desc='validation', position=0,\n", " leave=True, disable=not show_process\n", " ): # go by batches\n", " # batch_wavefronts - input wavefronts, batch_labels - labels\n", " batch_size = batch_wavefronts.size()[0]\n", " \n", " batch_wavefronts = batch_wavefronts.to(device)\n", " # batch_labels = batch_labels.to(device)\n", " \n", " with torch.no_grad():\n", " encoded_image_amp, decoded_image_amp = autoencoder(batch_wavefronts)\n", " loss_parts, loss = loss_func(batch_wavefronts, encoded_image_amp, decoded_image_amp)\n", " \n", " # accumulate losses and accuracies for batches\n", " batches_losses.append(loss.item())\n", " batches_loss_parts.append([part.item() for part in loss_parts])\n", " \n", " return batches_loss_parts, batches_losses" ] }, { "cell_type": "markdown", "id": "b5224615-a38f-45c3-bd61-bb5caa4ab1a7", "metadata": {}, "source": [ "## 4.2. Training of the optical network" ] }, { "cell_type": "markdown", "id": "957eb51d-a808-438d-b0c8-50996e654b4c", "metadata": {}, "source": [ "### 4.2.1. Before training" ] }, { "cell_type": "markdown", "id": "fab3fc20-7aa3-42d5-91d2-32b7f0215f3f", "metadata": {}, "source": [ "#### Metrics for Test dataset" ] }, { "cell_type": "code", "execution_count": 67, "id": "e46ec479-6293-433a-9a5f-dbd62018cf6f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/giyuu/science-phd/git-projects/SVETlANNa/svetlanna/elements/free_space.py:152: UserWarning: Aliasing problems may occur in the AS method. Consider reducing the distance or increasing the Nx*dx product.\n", " warn(\n", "/Users/giyuu/science-phd/git-projects/SVETlANNa/svetlanna/elements/free_space.py:158: UserWarning: Aliasing problems may occur in the AS method. Consider reducing the distance or increasing the Ny*dy product.\n", " warn(\n" ] } ], "source": [ "# Comment: not tested for `cuda`!\n", "SIM_PARAMS = SIM_PARAMS.to(DEVICE)\n", "autoencoder_no_train = get_autoencoder().to(DEVICE)" ] }, { "cell_type": "code", "execution_count": 68, "id": "84107b6b-c5db-45ea-a6b0-d94efcba4a95", "metadata": {}, "outputs": [], "source": [ "loss_func_power = 5 # FOR OUTPUT!" ] }, { "cell_type": "code", "execution_count": 69, "id": "2b195605-7089-43af-ac1f-9ec3d7b4acc0", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:34<00:00, 2.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Results before training on TEST set:\n", "\tcustom loss : 8.371190 * 1e-5\n", "\t\tg_rec * L_rec = 5.337000 * 1e-5\n", "\t\tg_en * L_en = 3.034190 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "test_loss_parts_0, test_losses_0 = autoencoder_validate(\n", " autoencoder_no_train, # optical recurrent network composed in 3.\n", " test_wf_loader, # dataloader of training set\n", " loss_func,\n", " device=DEVICE,\n", " show_process=True,\n", ") # evaluate the model\n", "\n", "print(\n", " 'Results before training on TEST set:\\n' + \n", " f'\\t{loss_func_name} : {np.mean(test_losses_0) * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}'\n", ")\n", "\n", "# LOSS PARTS\n", "l_rec_0, l_en_0, l_eff_0 = np.mean(test_loss_parts_0, axis=0)\n", "print(f'\\t\\tg_rec * L_rec = {GAMMA_REC * l_rec_0 * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}') \n", "print(f'\\t\\tg_en * L_en = {GAMMA_EN * l_en_0 * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}')\n", "print(f'\\t\\tg_eff * L_eff = {GAMMA_EFF * l_eff_0 * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}')" ] }, { "cell_type": "code", "execution_count": null, "id": "0d9ddc15-0ffc-42c3-8e5d-91dcd8c8b539", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "d92a5571-ae2d-43b8-ac31-edd5b97cb3bf", "metadata": {}, "source": [ "### 4.2.2. Training" ] }, { "cell_type": "code", "execution_count": 70, "id": "692dab48-ddfa-4ac4-9630-4378a0e365f6", "metadata": {}, "outputs": [], "source": [ "n_epochs = VARIABLES['number_of_epochs']\n", "print_each = 5 # print each n'th epoch info\n", "\n", "loss_func_power = 5 # for good output loss multiplies by 10 ** (n)" ] }, { "cell_type": "code", "execution_count": null, "id": "f9a82cda-32f7-422d-8118-7585f03465ec", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 71, "id": "b7265a03-7275-4326-9eda-601887e98d93", "metadata": {}, "outputs": [], "source": [ "# Recreate a system to restart training!\n", "# Comment: not tested for `cuda`!\n", "SIM_PARAMS = SIM_PARAMS.to(DEVICE)\n", "autoencoder_to_train = get_autoencoder().to(DEVICE)\n", "\n", "# Linc optimizer to a recreated net!\n", "optimizer_train = get_adam_optimizer(autoencoder_to_train)" ] }, { "cell_type": "code", "execution_count": 72, "id": "39ddf8e8-e039-4321-8bea-c765edcc4573", "metadata": {}, "outputs": [], "source": [ "scheduler = None # sheduler for a lr tuning during training " ] }, { "cell_type": "code", "execution_count": null, "id": "de9e28b3-c29f-452c-b4c4-6bccfccb49d7", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 73, "id": "4954a61f-b629-4e67-b75b-47d67a314e75", "metadata": {}, "outputs": [], "source": [ "losses_parts_names = ['l_rec', 'l_en', 'l_eff'] # ORDER IS SPECIFIED IN CustomFunc return!\n", "losses_coeffs = {'l_rec': GAMMA_REC, 'l_en': GAMMA_EN, 'l_eff': GAMMA_EFF}" ] }, { "cell_type": "code", "execution_count": null, "id": "8124fcf3-a171-4493-9629-68a58ea09cb2", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 74, "id": "bdfb2f64-784d-4412-9788-7041c119f7df", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch #1: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [09:13<00:00, 1.18s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 5.676445 * 1e-5\n", "\t\tg_rec * L_rec = 4.524684 * 1e-5\n", "\t\tg_en * L_en = 1.151761 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 553.78 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:44<00:00, 1.77it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 4.826706\n", "\t\tg_rec * L_rec = 3.981955 * 1e-5\n", "\t\tg_en * L_en = 0.844751 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 44.75 s\n", "Epoch #5: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [09:22<00:00, 1.20s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 4.030463 * 1e-5\n", "\t\tg_rec * L_rec = 3.315819 * 1e-5\n", "\t\tg_en * L_en = 0.714645 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 562.91 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:43<00:00, 1.82it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.856242\n", "\t\tg_rec * L_rec = 3.168086 * 1e-5\n", "\t\tg_en * L_en = 0.688156 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 43.35 s\n", "Epoch #10: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [09:41<00:00, 1.24s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.756154 * 1e-5\n", "\t\tg_rec * L_rec = 3.074222 * 1e-5\n", "\t\tg_en * L_en = 0.681933 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 582.00 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:45<00:00, 1.75it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.625069\n", "\t\tg_rec * L_rec = 2.961636 * 1e-5\n", "\t\tg_en * L_en = 0.663434 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 45.20 s\n", "Epoch #15: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [08:50<00:00, 1.13s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.652713 * 1e-5\n", "\t\tg_rec * L_rec = 2.982698 * 1e-5\n", "\t\tg_en * L_en = 0.670015 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 530.13 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:42<00:00, 1.87it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.530050\n", "\t\tg_rec * L_rec = 2.876574 * 1e-5\n", "\t\tg_en * L_en = 0.653476 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 42.32 s\n", "Epoch #20: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [08:35<00:00, 1.10s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.593646 * 1e-5\n", "\t\tg_rec * L_rec = 2.930112 * 1e-5\n", "\t\tg_en * L_en = 0.663534 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 515.93 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:35<00:00, 2.25it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.472647\n", "\t\tg_rec * L_rec = 2.827161 * 1e-5\n", "\t\tg_en * L_en = 0.645485 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 35.11 s\n", "Epoch #25: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:48<00:00, 1.00it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.554895 * 1e-5\n", "\t\tg_rec * L_rec = 2.895163 * 1e-5\n", "\t\tg_en * L_en = 0.659732 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 468.38 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.16it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.441596\n", "\t\tg_rec * L_rec = 2.797544 * 1e-5\n", "\t\tg_en * L_en = 0.644052 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.56 s\n", "Epoch #30: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:47<00:00, 1.00it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.527783 * 1e-5\n", "\t\tg_rec * L_rec = 2.870117 * 1e-5\n", "\t\tg_en * L_en = 0.657666 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 467.56 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.16it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.414059\n", "\t\tg_rec * L_rec = 2.773360 * 1e-5\n", "\t\tg_en * L_en = 0.640698 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.55 s\n", "Epoch #35: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:46<00:00, 1.01it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.506301 * 1e-5\n", "\t\tg_rec * L_rec = 2.850291 * 1e-5\n", "\t\tg_en * L_en = 0.656011 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 466.44 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.17it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.388355\n", "\t\tg_rec * L_rec = 2.749171 * 1e-5\n", "\t\tg_en * L_en = 0.639184 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.43 s\n", "Epoch #40: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:47<00:00, 1.00it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.489515 * 1e-5\n", "\t\tg_rec * L_rec = 2.834854 * 1e-5\n", "\t\tg_en * L_en = 0.654661 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 467.65 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.16it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.379840\n", "\t\tg_rec * L_rec = 2.739617 * 1e-5\n", "\t\tg_en * L_en = 0.640223 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.57 s\n", "Epoch #45: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:46<00:00, 1.00it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.475550 * 1e-5\n", "\t\tg_rec * L_rec = 2.821671 * 1e-5\n", "\t\tg_en * L_en = 0.653878 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 466.79 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.16it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.362566\n", "\t\tg_rec * L_rec = 2.725667 * 1e-5\n", "\t\tg_en * L_en = 0.636899 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.50 s\n", "Epoch #50: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:49<00:00, 1.00s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.463909 * 1e-5\n", "\t\tg_rec * L_rec = 2.810879 * 1e-5\n", "\t\tg_en * L_en = 0.653030 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 469.12 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.15it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.357945\n", "\t\tg_rec * L_rec = 2.720305 * 1e-5\n", "\t\tg_en * L_en = 0.637640 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.81 s\n", "Epoch #55: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:45<00:00, 1.01it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.453883 * 1e-5\n", "\t\tg_rec * L_rec = 2.801488 * 1e-5\n", "\t\tg_en * L_en = 0.652395 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 465.95 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.16it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.342818\n", "\t\tg_rec * L_rec = 2.705650 * 1e-5\n", "\t\tg_en * L_en = 0.637168 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.50 s\n", "Epoch #60: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:46<00:00, 1.01it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.444665 * 1e-5\n", "\t\tg_rec * L_rec = 2.792452 * 1e-5\n", "\t\tg_en * L_en = 0.652213 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 466.61 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.17it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.333549\n", "\t\tg_rec * L_rec = 2.696184 * 1e-5\n", "\t\tg_en * L_en = 0.637365 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.48 s\n", "Epoch #65: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:48<00:00, 1.00it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.436591 * 1e-5\n", "\t\tg_rec * L_rec = 2.784726 * 1e-5\n", "\t\tg_en * L_en = 0.651865 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 468.73 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.16it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.330884\n", "\t\tg_rec * L_rec = 2.692881 * 1e-5\n", "\t\tg_en * L_en = 0.638003 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.60 s\n", "Epoch #70: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:49<00:00, 1.00s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.429498 * 1e-5\n", "\t\tg_rec * L_rec = 2.777646 * 1e-5\n", "\t\tg_en * L_en = 0.651852 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 469.53 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:37<00:00, 2.13it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.320256\n", "\t\tg_rec * L_rec = 2.683382 * 1e-5\n", "\t\tg_en * L_en = 0.636874 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 37.14 s\n", "Epoch #75: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [07:52<00:00, 1.01s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.422733 * 1e-5\n", "\t\tg_rec * L_rec = 2.771069 * 1e-5\n", "\t\tg_en * L_en = 0.651663 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 472.83 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:36<00:00, 2.15it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.323554\n", "\t\tg_rec * L_rec = 2.684794 * 1e-5\n", "\t\tg_en * L_en = 0.638760 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 36.82 s\n", "Epoch #80: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|███████████████████████████████████████████████| 469/469 [09:11<00:00, 1.18s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training results\n", "\tcustom loss: 3.416787 * 1e-5\n", "\t\tg_rec * L_rec = 2.765276 * 1e-5\n", "\t\tg_en * L_en = 0.651512 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 551.17 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "validation: 100%|████████████████████████████████████████████| 79/79 [00:42<00:00, 1.88it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation results\n", "\tcustom loss * 1e5: 3.306052\n", "\t\tg_rec * L_rec = 2.669409 * 1e-5\n", "\t\tg_en * L_en = 0.636643 * 1e-5\n", "\t\tg_eff * L_eff = 0.000000 * 1e-5\n", "\t------------ 42.07 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "train_epochs_losses = []\n", "train_epochs_loss_parts = {key: [] for key in losses_parts_names}\n", "\n", "val_epochs_losses = [] # to store losses of each epoch\n", "val_epochs_loss_parts = {key: [] for key in losses_parts_names}\n", "\n", "torch.manual_seed(98) # for reproducability?\n", "\n", "for epoch in range(n_epochs):\n", " if (epoch == 0) or ((epoch + 1) % print_each == 0) or (epoch == n_epochs - 1):\n", " print(f'Epoch #{epoch + 1}: ', end='')\n", " show_progress = True\n", " else:\n", " show_progress = False\n", "\n", " # TRAIN\n", " start_train_time = time.time() # start time of the epoch (train)\n", " train_loss_parts, train_losses = autoencoder_train(\n", " autoencoder_to_train, # optical network composed in 3.\n", " train_wf_loader, # dataloader of training set\n", " loss_func,\n", " optimizer_train,\n", " device=DEVICE,\n", " show_process=show_progress,\n", " ) # train the model\n", " mean_train_loss = np.mean(train_losses)\n", " train_l_rec, train_l_en, train_l_eff = np.mean(train_loss_parts, axis=0)\n", " \n", " if (epoch == 0) or ((epoch + 1) % print_each == 0) or (epoch == n_epochs - 1): # train info\n", " print('Training results')\n", " print(f'\\t{loss_func_name}: {mean_train_loss * 10 ** (loss_func_power):.6f} * 1e-{loss_func_power}')\n", " \n", " print(f'\\t\\tg_rec * L_rec = {GAMMA_REC * train_l_rec * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}') \n", " print(f'\\t\\tg_en * L_en = {GAMMA_EN * train_l_en * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}')\n", " print(f'\\t\\tg_eff * L_eff = {GAMMA_EFF * train_l_eff * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}')\n", " \n", " print(f'\\t------------ {time.time() - start_train_time:.2f} s')\n", "\n", " # VALIDATION\n", " start_val_time = time.time() # start time of the epoch (validation)\n", " val_loss_parts, val_losses = autoencoder_validate(\n", " autoencoder_to_train, # optical network composed in 3.\n", " test_wf_loader, # dataloader of validation set\n", " loss_func,\n", " device=DEVICE,\n", " show_process=show_progress,\n", " ) # evaluate the model\n", " mean_val_loss = np.mean(val_losses)\n", " val_l_rec, val_l_en, val_l_eff = np.mean(val_loss_parts, axis=0)\n", " \n", " if (epoch == 0) or ((epoch + 1) % print_each == 0) or (epoch == n_epochs - 1): # validation info\n", " print('Validation results')\n", " print(f'\\t{loss_func_name} * 1e{loss_func_power}: {mean_val_loss * 10 ** (loss_func_power):.6f}')\n", " \n", " print(f'\\t\\tg_rec * L_rec = {GAMMA_REC * val_l_rec * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}') \n", " print(f'\\t\\tg_en * L_en = {GAMMA_EN * val_l_en * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}')\n", " print(f'\\t\\tg_eff * L_eff = {GAMMA_EFF * val_l_eff * 10 ** loss_func_power:.6f} * 1e-{loss_func_power}')\n", " \n", " print(f'\\t------------ {time.time() - start_val_time:.2f} s')\n", " \n", " if scheduler:\n", " scheduler.step(mean_val_loss)\n", "\n", " # save losses\n", " train_epochs_losses.append(mean_train_loss)\n", " for key, val in zip(losses_parts_names, [train_l_rec, train_l_en, train_l_eff]):\n", " train_epochs_loss_parts[key].append(val)\n", "\n", " val_epochs_losses.append(mean_val_loss)\n", " for key, val in zip(losses_parts_names, [val_l_rec, val_l_en, val_l_eff]):\n", " val_epochs_loss_parts[key].append(val)" ] }, { "cell_type": "code", "execution_count": null, "id": "399a3b00-27ce-47ea-b749-3c21d5035759", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "9775bea3-8104-4089-9af3-4ec412b463fb", "metadata": {}, "source": [ "#### Learning curves" ] }, { "cell_type": "code", "execution_count": 75, "id": "540a4c28-76ca-4821-ada5-c23bd04cbf96", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# learning curve\n", "fig, ax0 = plt.subplots(1, 1, figsize=(5, 3))\n", "\n", "ax0.plot(range(1, n_epochs + 1), np.array(train_epochs_losses), label='train')\n", "ax0.plot(range(1, n_epochs + 1), np.array(val_epochs_losses), linestyle='dashed', label='validation')\n", "\n", "ax0.set_ylabel(loss_func_name)\n", "ax0.set_xlabel('Epoch')\n", "# ax0.set_ylim([0.42 * 1e-5, 0.9 * 1e-5])\n", "\n", "ax0.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "77690231-be09-476a-94b5-1c4bba6610f8", "metadata": {}, "source": [ "#### Losses parts" ] }, { "cell_type": "code", "execution_count": 76, "id": "af1c7c3d-abd8-4325-9ba5-a92456f6f637", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# learning curve\n", "fig, axs = plt.subplots(1, 3, figsize=(12, 3))\n", "\n", "for ax, key in zip(axs, losses_parts_names):\n", " ax.plot(range(1, n_epochs + 1), np.array(train_epochs_loss_parts[key]), label='train')\n", " ax.plot(range(1, n_epochs + 1), np.array(val_epochs_loss_parts[key]), linestyle='dashed', label='validation')\n", "\n", " ax.set_title(key)\n", " ax.set_xlabel('Epoch')\n", " ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "92d51c2e-d6bc-474b-a54b-20658b9a91d0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "6958a129-1b49-47d3-8503-0cf8aeee2667", "metadata": {}, "source": [ "#### Trained masks (encoder)" ] }, { "cell_type": "code", "execution_count": 82, "id": "ec5cdd6a-4464-49da-a6dc-09dc90d5c043", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_cols = NUM_ENCODER_LAYERS # number of columns for DiffractiveLayer's masks visualization\n", "n_rows = 1\n", "\n", "# plot wavefronts phase\n", "fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols * 3 + 2, n_rows * 3.2))\n", "ind_diff_layer = 0\n", "\n", "net_type = 'encoder'\n", "cmap = 'gist_stern' # 'gist_stern' 'rainbow'\n", "\n", "net_to_plot = autoencoder_to_train.encoder if net_type == 'encoder' else autoencoder_to_train.decoder\n", "\n", "for ind_layer, layer in enumerate(net_to_plot):\n", " if isinstance(layer, elements.DiffractiveLayer) or isinstance(layer, elements.SpatialLightModulator): \n", " # plot masks for Diffractive layers\n", " if n_rows > 1:\n", " ax_this = axs[ind_diff_layer // n_cols][ind_diff_layer % n_cols]\n", " else:\n", " ax_this = axs[ind_diff_layer % n_cols]\n", "\n", " # ax_this.set_title(titles[ind_module])\n", "\n", " trained_mask = layer.mask.detach()\n", " \n", " phase_mask_this = ax_this.imshow( \n", " trained_mask, cmap=cmap,\n", " # vmin=0, vmax=MAX_PHASE\n", " )\n", " ind_diff_layer += 1\n", "\n", " if APERTURES: # select only a part within apertures!\n", " x_frame = (x_layer_nodes - DETECTOR_SIZE[1]) / 2\n", " y_frame = (y_layer_nodes - DETECTOR_SIZE[0]) / 2\n", " ax_this.axis([x_frame, x_layer_nodes - x_frame, y_layer_nodes - y_frame, y_frame])\n", "\n", "fig.subplots_adjust(right=0.85)\n", "cbar_ax = fig.add_axes([0.87, 0.15, 0.01, 0.7])\n", "plt.colorbar(phase_mask_this, cax=cbar_ax)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "71888bc5-c174-4052-8d95-fc80f4a1967a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "564ecf97-a459-4a59-a93d-d2a6c66cbe0a", "metadata": {}, "source": [ "#### Saving results" ] }, { "cell_type": "code", "execution_count": 78, "id": "58179684-64d1-46d7-87e3-1c28a2c20f63", "metadata": {}, "outputs": [], "source": [ "# array with all losses\n", "all_lasses_header = ','.join([\n", " 'train_l_rec', 'train_l_en', 'train_l_eff', 'loss_train', \n", " 'val_l_rec', 'val_l_en', 'val_l_eff', 'loss_val',\n", "])\n", "all_losses_array = np.array(\n", " [\n", " train_epochs_loss_parts['l_rec'], train_epochs_loss_parts['l_en'], train_epochs_loss_parts['l_eff'], train_epochs_losses, \n", " val_epochs_loss_parts['l_rec'], val_epochs_loss_parts['l_en'], val_epochs_loss_parts['l_eff'], val_epochs_losses\n", " ]\n", ").T" ] }, { "cell_type": "code", "execution_count": 79, "id": "5bcb69ea-f12f-4cf8-8f03-adee8aeca8b8", "metadata": {}, "outputs": [], "source": [ "# filepath to save the model\n", "model_filepath = f'{RESULTS_FOLDER}/autoencoder_net.pth'\n", "# filepath to save losses\n", "losses_filepath = f'{RESULTS_FOLDER}/training_curves.csv'" ] }, { "cell_type": "markdown", "id": "28d65cb8-b0fd-455b-b85d-e585ceff818a", "metadata": {}, "source": [ "#### Saving model weights and learning curves" ] }, { "cell_type": "code", "execution_count": 80, "id": "1120ac98-80a7-41e7-80b9-302c34c39054", "metadata": {}, "outputs": [], "source": [ "# saving model\n", "torch.save(autoencoder_to_train.state_dict(), model_filepath)" ] }, { "cell_type": "code", "execution_count": 81, "id": "08a63ea3-daa6-4e6f-891e-f2293ede0cb4", "metadata": {}, "outputs": [], "source": [ "# saving losses\n", "np.savetxt(\n", " losses_filepath, all_losses_array,\n", " delimiter=',', header=all_lasses_header, comments=\"\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "3fa6704b-8587-423c-99d3-090898e9f566", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "68588fd9-8729-4ad6-9885-8642c0bc0197", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "b635c05d-bb86-4a17-8e7d-9dfc586ae51e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "1f4808e2-073f-453a-bb59-4b54fb7ef729", "metadata": {}, "source": [ "## 4.4. Examples of encoding/decoding" ] }, { "cell_type": "markdown", "id": "f4222c1a-6313-46e7-b1fc-0b026dbb132e", "metadata": {}, "source": [ "### 4.4.1. Select a sample to encode/decode" ] }, { "cell_type": "code", "execution_count": 108, "id": "11eb363c-fe14-4bef-80d4-5a47c7aec895", "metadata": {}, "outputs": [], "source": [ "n_test_examples = 5\n", "\n", "random.seed(78)\n", "test_examples_ids = random.sample(range(len(mnist_wf_test_ds)), n_test_examples)" ] }, { "cell_type": "code", "execution_count": null, "id": "1eb9dffe-c998-4f8e-b433-0b64b6809158", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 109, "id": "bb0dc643-0d10-424a-8bdf-c9b6211561af", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1, n_test_examples, figsize=(n_test_examples * 3, 3))\n", "cmap = 'grey'\n", "\n", "for ax, ind_test in zip(axs, test_examples_ids):\n", " test_wavefront, test_target = mnist_wf_test_ds[ind_test]\n", "\n", " ax.set_title(f'{ind_test}')\n", " ax.imshow(test_wavefront.intensity, cmap=cmap)\n", "\n", " # ax.set_xticks([])\n", " # ax.set_yticks([])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "2bb64e6c-baa2-4b0e-aec5-2bc4df7a71a3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "abf62efe-0481-42cc-b25e-a0814e734111", "metadata": {}, "source": [ "### 4.4.2. Encode/decode an example" ] }, { "cell_type": "markdown", "id": "fe6dcfb2-4be6-46d5-b002-2b964dde9431", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "#### Function to encode and decode" ] }, { "cell_type": "code", "execution_count": 93, "id": "a7f2efab-3562-4e85-9893-a73edef021c2", "metadata": {}, "outputs": [], "source": [ "def encode_and_decode(autoencoder, input_wf, use_encoder_aperture=False):\n", " # if use_encoder_aperture is True - apply strickt square aperture to encoded image\n", " # aperture is defined as REGION_MASK, that was used in loss!\n", " \n", " with torch.no_grad():\n", " # ENCODE\n", " encoded_image = autoencoder.encode(input_wf)\n", " if not PRESERVE_PHASE:\n", " encoded_image = encoded_image.abs() + 0j # reset phases before decoding!\n", " if use_encoder_aperture:\n", " encoded_image = encoded_image * REGION_MASK # apply aperture for encoded image\n", " \n", " # DECODE\n", " decoded_image = autoencoder.decode(encoded_image)\n", " if not PRESERVE_PHASE:\n", " decoded_image = decoded_image.abs() + 0j # reset phases before decoding!\n", "\n", " return encoded_image, decoded_image" ] }, { "cell_type": "code", "execution_count": null, "id": "6f926395-b92f-4481-a1a2-a4e4382d0786", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "9d193b02-0a3f-452a-80ed-e04c0da31243", "metadata": {}, "source": [ "#### Plot all examples" ] }, { "cell_type": "code", "execution_count": 110, "id": "20594fd5-0279-4ad4-bd02-4199629959aa", "metadata": {}, "outputs": [ { "data": { "image/png": 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ddJLS0tK077776txzz43LqSDr1q3TokWL9NRTT6msrEynnHKKrrvuOpWUlPCLObpdT9TvgAED9Ktf/Uq33nrrDo8sefnll7Vu3Tr9/ve/V319vZYuXapJkybp9ttvV0tLS4c7xNqqr6/XqlWrtGnTJr344osKBoP6+c9/rqefflpTpkzR119/3eV5GmN033336ZprrtEdd9yhhx56SFlZWfrtb3/r7FfsanuBrkimGu7MPffco/32208nn3zydn+s2pF+/fpJahtUOPvss/Xvf/9bkvTkk09q+fLluuGGGxgsg2slYh/6nHPOUXNzs1NLUTv7Hty+T0tPT9/p9+X2ysvLNWPGDKWlpemQQw7R6aef7tT2rvrPf/7j3PxKkp577jktWLBAixYt0vXXX68f//jHuzX/vojBsiQUPcrqq6++ipm+detWlZeXx0xramrarVOeVq1a5Yxg//Of/9SCBQv0wgsvaNKkSZL+dzpF9PSK9qJHk2zv+ibnnHOOli9f3uE22sFgUHPmzNHvfve7bhvhbr9BfOSRR/Txxx/r4Ycf1plnntktrwe015M1vKvuuusu3XrrrZoxY8Z2B8u2V8PtRQ8Xf+WVV/Tcc89pxYoVqq2t3eVBrbPPPlv333+/xowZo8LCQklt13bxer36zW9+o8cff7zDewjEU0/U72233aby8vIOXwa21dTUpFNOOUX//ve/9fTTT0tq2/G+6qqrdP3112/3Dpo785///Eetra361re+5Ux77rnn9NVXX+n222/f5euL3XTTTc5RAddee62ktqNFH3zwQf34xz/e5fYCXZFMNbytK6+8UhdeeKFuuOEG526SXRXd725ubtaTTz7pTDfG6IknntCvfvUrjRw5MuZaw4Bb9PQ+dGZmpk477TQtWLCgw/x39j24/XfghoaGLn1fbmlpcdr+0ksv6fXXX9d7772nzZs366WXXtqtZWrv3Xff1ZIlS7p8BCzaMFjmcl6vV4MGDbLKLS8vj7nobmeefPJJ54vq6tWrnUNHo4ehtjd06FCVlZV1elTZkUceqT322EPXXHNNh8euvPJKpaSk6IknnnA2iNGLHubk5Cg/P19FRUU7bautlpYWPf/887rmmmuUlpbmjPADySDeNWyrsbFRZWVl273Ww45qeHvWrl2rZcuWaebMmbs8WHbJJZdo2bJlzkBZ1PPPP6/zzz9fhxxySEIGF4HO7Er97rPPPrrwwgt1+eWXx5yWkZaWpkAgoPz8fFVXV6uiokKS9MUXX2j//ffX+PHjlZOToy+++EINDQ266667dum07D333FMnnXSSLrjggpjpFRUVeueddzqcRtkVLS0tuuCCC3T99ddrzJgxKi0t1VdffaVHH31UoVCIa6Yg6fREDUfNmjVLv/nNb3Tvvffu1nV0y8vL1dDQoMrKSoXD4ZjHojfuyMnJYbAMvV489qG//e1vKzMzs9OzKHb2PbioqCgmt7NLI0Sf2z63M4sXL1ZRUZFmzpwZ18Eyqe0Uz7Fjx8Z1nn0Fg2VJKHphwdGjR6ugoMCZnpub2+GL7ciRI+N6vaPoIaLZ2dmS2gp78+bNOvTQQzvkfuMb39Ann3zS6XxmzpzZ4Y4iUaNGjdKAAQM6vTvQ9ddfr+uvv14HH3zwLp9y1pn09HTnToAMlqG7JbKGbfXr10+5ubnasmVLp4/vqIZ3JD09vdNf1mwNGTKkwxcMqe0aL1LbRViB7tTd9Tt8+HD5fL6Y64W1t27dOt199936+c9/HjO9fZ950kknyefz7dL1AYcMGSJJ8vl8HR4LBAJxqbHNmzc7X9q9Xq+mTZumJUuW7PSGIkA8JGMNf+tb39Lf/vY3Pf3007r00kt3Yan+xxijTz75RIcddpgCgUDMAEB08G57fTuQ7Hp6H3rmzJmqqanR888/3+GxFStWqKWlRYceemjM6Y2BQEAHH3xwzGmbn3zyiY455hgFg8GYi/xHz9ba3nfm9tLS0pzv4PG01157sU3YRXzrSEILFy5Uc3OzLrvsMr366qvO9M5uS72r52oPGjSoQ9H4/X6dd955qq+vj9kpf+qppzRr1iyNGDHCOW3y2GOP1dixY3XXXXd1eB2/368zzzxT77zzTqe/av3pT3/Ss88+GzNt8ODBuv/++zVv3jw999xzMRvHruhsubKzs3XGGWdow4YNbCjQI3qihm2lpqYqEAh0OP3pxhtvlNfr1SuvvNLhOTurYZ/Pp2AwqMrKypjphx12mA444IAuD7C1t3r1ah1//PEaPXp0zCH43/ve9xQKhfTZZ5/t8rwBG91dvytWrNC3v/3tDo/fdtttCgaD+tnPfrbDa4alpaXp1ltvVVFRkR5//HGr125vzZo1CoVCOvvss3Xfffc504cPH64pU6bonXfe6fI8d+TKK6/UsGHDdNlll8V1vsD2JFsNT5kyRf/617+0aNEizZw5My53gH/iiSc0efJkzZo1S3/7298ktfX3M2fO1Oeff97phcYBN+jJfejc3FzNmDFDjz/+eKeXFaqurtbChQt17rnn6tZbb3X2pf/v//5PwWAwZgDtySef1C9/+UtdeOGF+sMf/iBJSklJ0fnnn6/333/f+Q6dkZEhY0yH1zv99NM1YMCADnfe7Irc3NwO11A86aSTdOihh+7yna77OgbLktDWrVv1+9//Xtddd51efPFFvfzyyzrkkEN00kkndRjs2dVzte+77z5lZWVp0aJFKiwsVF5enmbOnKl9991Xv/jFL2J+/b3jjjt05pln6o033tAf//hH9evXT7/85S/12Wefad68eR3mvb07ikQtW7ZMy5Yti5kWPR3z888/13PPPdfl5YmaP3++Nm3apCVLlmjz5s0aNWqUzj//fA0bNkxnn332Ls8X6IqeqGFJuvTSS9W/f3/nl+RTTz3VOaV57ty5qq6uVl5enpYtW6bHH39cX375paS2Gj3llFM0f/78TuttZzXcr18/bdy4UU888YQ+//xz1dXV6YADDtD555+vqqqq3brz7O9+9zuddNJJevvtt3XPPfeorKxM3/zmN3XyySfrgQce4AsAul13129ZWVmndRf9IrDtY0888YSKior0xRdfKCsrSz/4wQ+011576ZRTTukwCG6zTdi6daseeughXXDBBXr99df19NNPKxgM6pJLLlF6errmzJnTpeVpb+bMmTrjjDO0aNEi1dbWasaMGTr77LP1wAMPONdcA7pbMtXwqFGj9Pzzz8sYoyeffLLDtXM/++yzmOuCnnvuucrPz1dGRoYk6eijj3Yuav7Pf/5TGzZskNS2H/+jH/1If/7znzVmzBht2LBB//d//6f8/PyYOwjuCpvtCNBdemofWmq7Tm4gENjhjayuv/56vffee3rrrbd0//33a8SIEbriiiu0YMECLViwwMn74IMP9O9//1tz5szR4MGDtWbNGs2aNUt77LGHfvjDHzp5o0eP1sKFC/XEE0/oyy+/VDgc1qGHHqpzzz1XBQUFuzWo9d5772nZsmX66KOPVFVVpQkTJugHP/iBNmzYoDvuuGOX59vXGSKxMWvWLGOMMfn5+c40j8djbrzxRlNYWGjq6urMf//7XzN+/HhTUFBg5s2bt9uvefbZZ5tXX33VFBcXm+bmZlNWVmZeffVVc+qpp3aaP378ePPKK6+Y2tpaU15ebv75z3+awYMHd5r72GOPmaamJpOTk2Pdnvz8fGOMMVdcccVOc6dOnWqMMWbq1KkdHrvkkkvMokWLzObNm01zc7MpLS01zz33nDnqqKO2Oz9jjJk7d27C1wPCvZGIGpZkCgoKzPZE25KdnW3+8Y9/mNWrV5va2lrT0NBgli9fbq655hrj9/s7ne/OajgQCJi77rrLfPLJJ6aystI0NTWZgoIC88ADD8S8B9uLaL3PmjWr08cPO+ww89JLL5mioiLT1NRkvvzyS3Pttdcan8/XaT41TOxOJKp+t4033njDLF++vMP0X/7yl+aLL74w9fX1pqyszDz77LPmoIMO6nQeNtsEScbn85lLL73UfPzxx6a6utpUV1eb119/3UybNm2n7dxRH3zYYYeZN99805SVlZn6+nqzbNkyc+GFF253Xl3p+wlie5HMNRytl+2ZPXt2h3lsz7Y1N2jQIDNv3jyzdetW09DQYBYvXmyOP/74nbZzZ32w7Xakff4LL7yQ8PWAcGcksn7fe+89U1JSYrxe7w7zjjzySPPOO++Y+vp6U1paaubOnWv69evXIS81NdX89re/NUVFRaahocEsWbKkQ00OHDjQ/PWvfzVffPGFqampMY2NjWbVqlXmzjvvNAMHDtxpm3fUB996663m448/NhUVFaapqcmsW7fO/PnPf97ud3b6YKtIeAMIoksR3Uh861vfMgMHDtzuF+idRU5Ojhk4cKAxhi/aBNGTEe2cL730UjNw4EATCAR2aT7UMEH0fMSrDx44cKA5+OCD2VEniB6OePXB2dnZZuDAgWb9+vUMlhFEDwV9cM+GV4BLPffcc9q6dasOPvjgXXr+2rVrO5zXDaDn3HPPPdq6dau+9a1v7dLzqWEgcXanD87OztbWrVs7XJIBQM/Z3T74zTff1NatWzVq1Kg4twzAztAH9wyuWQbX+fTTT2Mu5rhq1apdms9pp53m3GGP22sDPWfbC7Lu6kX7qWGg58WjD45ezyxq9erVcWkbgJ2LVx980UUXKRgMSuLum0BPoQ/uWR61HWIGAAAAAAAA9HkJPQ3zkksuUUFBgRoaGvT+++/rsMMOS2RzAHQB9Qu4GzUMuBf1C7gbNQy4Q0IulnbWWWeZxsZG8/3vf9/su+++5r777jPl5eVm0KBBCb+QG0EQOw7qlyDcHdQwQbg3qF+CcHdQwwThmkjMC7///vsxdy/zeDxm06ZN5uqrr070G0IQxE6C+iUIdwc1TBDuDeqXINwd1DBBuCMScoH/QCCgiRMnas6cOc40Y4wWLlyoyZMnW81j2LBhqqmp6a4mAq4XDAZVVFQU9/nGo34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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_lines = 3 # image / encoded / decoded\n", "\n", "fig, axs = plt.subplots(n_lines, n_test_examples, figsize=(n_test_examples * 3, n_lines * 3.2))\n", "\n", "to_plot = 'amp' # <--- chose what to plot\n", "cmap = 'grey' # choose colormaps\n", "use_encoder_aperture = True\n", "\n", "max_amp = 1 # upper limits for colorplots\n", "max_phase = 2 * torch.pi\n", "\n", "for ind_ex, ind_test in enumerate(test_examples_ids):\n", " ax_image, ax_encoded, ax_decoded = axs[0][ind_ex], axs[1][ind_ex], axs[2][ind_ex]\n", " \n", " test_wavefront, test_target = mnist_wf_test_ds[ind_test]\n", "\n", " ax_image.set_title(f'id={ind_test} [{test_target}]')\n", " if to_plot == 'amp':\n", " ax_image.imshow(\n", " test_wavefront.intensity, cmap=cmap,\n", " # vmin=0, vmax=max_amp\n", " )\n", " if to_plot == 'phase':\n", " ax_image.imshow(\n", " test_wavefront.phase, cmap=cmap,\n", " # vmin=0, vmax=max_phase\n", " )\n", "\n", " encoded_this, decoded_this = encode_and_decode(\n", " autoencoder_to_train, test_wavefront, use_encoder_aperture\n", " )\n", "\n", " ax_encoded.set_title('encoded')\n", " if to_plot == 'amp':\n", " ax_encoded.imshow(\n", " encoded_this.intensity, cmap=cmap,\n", " # vmin=0, vmax=max_amp\n", " )\n", " if to_plot == 'phase':\n", " ax_encoded.imshow(\n", " encoded_this.phase, cmap=cmap,\n", " # vmin=0, vmax=max_phase\n", " )\n", " if use_encoder_aperture: # select only a part within apertures!\n", " x_frame = (x_layer_nodes - REGION_MASK_SIZE[1]) / 2\n", " y_frame = (y_layer_nodes - REGION_MASK_SIZE[0]) / 2\n", " ax_encoded.set_xlim([x_frame, x_layer_nodes - x_frame])\n", " ax_encoded.set_ylim([y_layer_nodes - y_frame, y_frame])\n", "\n", " ax_decoded.set_title('decoded')\n", " if to_plot == 'amp':\n", " ax_decoded.imshow(\n", " decoded_this.intensity, cmap=cmap, \n", " # vmin=0, vmax=max_amp\n", " )\n", " if to_plot == 'phase':\n", " ax_decoded.imshow(\n", " decoded_this.phase, cmap=cmap, \n", " # vmin=0, vmax=max_phase\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "0a160fa8-517f-4fd3-81f7-4900bdddc319", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "d96a4425-2ffc-4630-87cb-d282e391284b", "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }