Datasets
Models
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch.utils.data import Dataset, DataLoader\n",
"import numpy as np\n",
"import os\n",
"import clip\n",
"from torch.nn import functional as F\n",
"import torch.nn as nn\n",
"from torchvision import transforms\n",
"from PIL import Image\n",
"train = False\n",
"classes = None\n",
"pictures= None\n",
"\n",
"def load_data():\n",
" data_list = []\n",
" label_list = []\n",
" texts = []\n",
" images = []\n",
" \n",
" if train:\n",
" text_directory = \"/home/ldy/Workspace/THINGS/images_set/training_images\" \n",
" else:\n",
" text_directory = \"/home/ldy/Workspace/THINGS/images_set/test_images\"\n",
"\n",
" dirnames = [d for d in os.listdir(text_directory) if os.path.isdir(os.path.join(text_directory, d))]\n",
" dirnames.sort()\n",
" \n",
" if classes is not None:\n",
" dirnames = [dirnames[i] for i in classes]\n",
"\n",
" for dir in dirnames:\n",
"\n",
" try:\n",
" idx = dir.index('_')\n",
" description = dir[idx+1:]\n",
" except ValueError:\n",
" print(f\"Skipped: {dir} due to no '_' found.\")\n",
" continue\n",
" \n",
" new_description = f\"{description}\"\n",
" texts.append(new_description)\n",
"\n",
" if train:\n",
" img_directory = \"/home/ldy/Workspace/THINGS/images_set/training_images\"\n",
" else:\n",
" img_directory =\"/home/ldy/Workspace/THINGS/images_set/test_images\"\n",
" \n",
" all_folders = [d for d in os.listdir(img_directory) if os.path.isdir(os.path.join(img_directory, d))]\n",
" all_folders.sort()\n",
"\n",
" if classes is not None and pictures is not None:\n",
" images = []\n",
" for i in range(len(classes)):\n",
" class_idx = classes[i]\n",
" pic_idx = pictures[i]\n",
" if class_idx < len(all_folders):\n",
" folder = all_folders[class_idx]\n",
" folder_path = os.path.join(img_directory, folder)\n",
" all_images = [img for img in os.listdir(folder_path) if img.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
" all_images.sort()\n",
" if pic_idx < len(all_images):\n",
" images.append(os.path.join(folder_path, all_images[pic_idx]))\n",
" elif classes is not None and pictures is None:\n",
" images = []\n",
" for i in range(len(classes)):\n",
" class_idx = classes[i]\n",
" if class_idx < len(all_folders):\n",
" folder = all_folders[class_idx]\n",
" folder_path = os.path.join(img_directory, folder)\n",
" all_images = [img for img in os.listdir(folder_path) if img.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
" all_images.sort()\n",
" images.extend(os.path.join(folder_path, img) for img in all_images)\n",
" elif classes is None:\n",
" images = []\n",
" for folder in all_folders:\n",
" folder_path = os.path.join(img_directory, folder)\n",
" all_images = [img for img in os.listdir(folder_path) if img.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
" all_images.sort() \n",
" images.extend(os.path.join(folder_path, img) for img in all_images)\n",
" else:\n",
"\n",
" print(\"Error\")\n",
" return texts, images\n",
"texts, images = load_data()\n",
"# images"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['aircraft_carrier',\n",
" 'antelope',\n",
" 'backscratcher',\n",
" 'balance_beam',\n",
" 'banana',\n",
" 'baseball_bat',\n",
" 'basil',\n",
" 'basketball',\n",
" 'bassoon',\n",
" 'baton4',\n",
" 'batter',\n",
" 'beaver',\n",
" 'bench',\n",
" 'bike',\n",
" 'birthday_cake',\n",
" 'blowtorch',\n",
" 'boat',\n",
" 'bok_choy',\n",
" 'bonnet',\n",
" 'bottle_opener',\n",
" 'brace',\n",
" 'bread',\n",
" 'breadbox',\n",
" 'bug',\n",
" 'buggy',\n",
" 'bullet',\n",
" 'bun',\n",
" 'bush',\n",
" 'calamari',\n",
" 'candlestick',\n",
" 'cart',\n",
" 'cashew',\n",
" 'cat',\n",
" 'caterpillar',\n",
" 'cd_player',\n",
" 'chain',\n",
" 'chaps',\n",
" 'cheese',\n",
" 'cheetah',\n",
" 'chest2',\n",
" 'chime',\n",
" 'chopsticks',\n",
" 'cleat',\n",
" 'cleaver',\n",
" 'coat',\n",
" 'cobra',\n",
" 'coconut',\n",
" 'coffee_bean',\n",
" 'coffeemaker',\n",
" 'cookie',\n",
" 'cordon_bleu',\n",
" 'coverall',\n",
" 'crab',\n",
" 'creme_brulee',\n",
" 'crepe',\n",
" 'crib',\n",
" 'croissant',\n",
" 'crow',\n",
" 'cruise_ship',\n",
" 'crumb',\n",
" 'cupcake',\n",
" 'dagger',\n",
" 'dalmatian',\n",
" 'dessert',\n",
" 'dragonfly',\n",
" 'dreidel',\n",
" 'drum',\n",
" 'duffel_bag',\n",
" 'eagle',\n",
" 'eel',\n",
" 'egg',\n",
" 'elephant',\n",
" 'espresso',\n",
" 'face_mask',\n",
" 'ferry',\n",
" 'flamingo',\n",
" 'folder',\n",
" 'fork',\n",
" 'freezer',\n",
" 'french_horn',\n",
" 'fruit',\n",
" 'garlic',\n",
" 'glove',\n",
" 'golf_cart',\n",
" 'gondola',\n",
" 'goose',\n",
" 'gopher',\n",
" 'gorilla',\n",
" 'grasshopper',\n",
" 'grenade',\n",
" 'hamburger',\n",
" 'hammer',\n",
" 'handbrake',\n",
" 'headscarf',\n",
" 'highchair',\n",
" 'hoodie',\n",
" 'hummingbird',\n",
" 'ice_cube',\n",
" 'ice_pack',\n",
" 'jeep',\n",
" 'jelly_bean',\n",
" 'jukebox',\n",
" 'kettle',\n",
" 'kneepad',\n",
" 'ladle',\n",
" 'lamb',\n",
" 'lampshade',\n",
" 'laundry_basket',\n",
" 'lettuce',\n",
" 'lightning_bug',\n",
" 'manatee',\n",
" 'marijuana',\n",
" 'meatloaf',\n",
" 'metal_detector',\n",
" 'minivan',\n",
" 'modem',\n",
" 'mosquito',\n",
" 'muff',\n",
" 'music_box',\n",
" 'mussel',\n",
" 'nightstand',\n",
" 'okra',\n",
" 'omelet',\n",
" 'onion',\n",
" 'orange',\n",
" 'orchid',\n",
" 'ostrich',\n",
" 'pajamas',\n",
" 'panther',\n",
" 'paperweight',\n",
" 'pear',\n",
" 'pepper1',\n",
" 'pheasant',\n",
" 'pickax',\n",
" 'pie',\n",
" 'pigeon',\n",
" 'piglet',\n",
" 'pocket',\n",
" 'pocketknife',\n",
" 'popcorn',\n",
" 'popsicle',\n",
" 'possum',\n",
" 'pretzel',\n",
" 'pug',\n",
" 'punch2',\n",
" 'purse',\n",
" 'radish',\n",
" 'raspberry',\n",
" 'recorder',\n",
" 'rhinoceros',\n",
" 'robot',\n",
" 'rooster',\n",
" 'rug',\n",
" 'sailboat',\n",
" 'sandal',\n",
" 'sandpaper',\n",
" 'sausage',\n",
" 'scallion',\n",
" 'scallop',\n",
" 'scooter',\n",
" 'seagull',\n",
" 'seaweed',\n",
" 'seed',\n",
" 'skateboard',\n",
" 'sled',\n",
" 'sleeping_bag',\n",
" 'slide',\n",
" 'slingshot',\n",
" 'snowshoe',\n",
" 'spatula',\n",
" 'spoon',\n",
" 'station_wagon',\n",
" 'stethoscope',\n",
" 'strawberry',\n",
" 'submarine',\n",
" 'suit',\n",
" 't-shirt',\n",
" 'table',\n",
" 'taillight',\n",
" 'tape_recorder',\n",
" 'television',\n",
" 'tiara',\n",
" 'tick',\n",
" 'tomato_sauce',\n",
" 'tongs',\n",
" 'tool',\n",
" 'top_hat',\n",
" 'treadmill',\n",
" 'tube_top',\n",
" 'turkey',\n",
" 'unicycle',\n",
" 'vise',\n",
" 'volleyball',\n",
" 'wallpaper',\n",
" 'walnut',\n",
" 'wheat',\n",
" 'wheelchair',\n",
" 'windshield',\n",
" 'wine',\n",
" 'wok']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"texts"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of parameters: 2630913\n",
"self.subjects ['sub-08']\n",
"exclude_subject None\n",
"Data tensor shape: torch.Size([200, 63, 250]), label tensor shape: torch.Size([200]), text length: 200, image length: 200\n",
"features_tensor torch.Size([200, 1024])\n",
" - Test Loss: 17.3687, Test Accuracy: 0.2900\n"
]
}
],
"source": [
"import os\n",
"\n",
"import torch\n",
"import torch.optim as optim\n",
"from torch.nn import CrossEntropyLoss\n",
"from torch.nn import functional as F\n",
"from torch.optim import Adam\n",
"from torch.utils.data import DataLoader\n",
"# os.environ['http_proxy'] = 'http://10.16.35.10:13390' \n",
"# os.environ['https_proxy'] = 'http://10.16.35.10:13390' \n",
"\n",
"os.environ[\"WANDB_API_KEY\"] = \"KEY\"\n",
"os.environ[\"WANDB_MODE\"] = 'offline'\n",
"from itertools import combinations\n",
"\n",
"import clip\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import torch.nn as nn\n",
"import torchvision.transforms as transforms\n",
"import tqdm\n",
"from eegdatasets_leaveone import EEGDataset\n",
"from eegencoder import eeg_encoder\n",
"from einops.layers.torch import Rearrange, Reduce\n",
"from lavis.models.clip_models.loss import ClipLoss\n",
"from sklearn.metrics import confusion_matrix\n",
"from torch.utils.data import DataLoader, Dataset\n",
"import random\n",
"from utils import wandb_logger\n",
"from torch import Tensor\n",
"import math\n",
"\n",
"\n",
"class PatchEmbedding(nn.Module):\n",
" def __init__(self, emb_size=40):\n",
" super().__init__()\n",
" # revised from shallownet\n",
" self.tsconv = nn.Sequential(\n",
" nn.Conv2d(1, 40, (1, 25), (1, 1)),\n",
" nn.AvgPool2d((1, 51), (1, 5)),\n",
" nn.BatchNorm2d(40),\n",
" nn.ELU(),\n",
" nn.Conv2d(40, 40, (63, 1), (1, 1)),\n",
" nn.BatchNorm2d(40),\n",
" nn.ELU(),\n",
" nn.Dropout(0.5),\n",
" )\n",
"\n",
" self.projection = nn.Sequential(\n",
" nn.Conv2d(40, emb_size, (1, 1), stride=(1, 1)), \n",
" Rearrange('b e (h) (w) -> b (h w) e'),\n",
" )\n",
"\n",
" def forward(self, x: Tensor) -> Tensor:\n",
" # b, _, _, _ = x.shape\n",
" x = x.unsqueeze(1) \n",
" # print(\"x\", x.shape) \n",
" x = self.tsconv(x)\n",
" # print(\"tsconv\", x.shape) \n",
" x = self.projection(x)\n",
" # print(\"projection\", x.shape) \n",
" return x\n",
"\n",
"\n",
"class ResidualAdd(nn.Module):\n",
" def __init__(self, fn):\n",
" super().__init__()\n",
" self.fn = fn\n",
"\n",
" def forward(self, x, **kwargs):\n",
" res = x\n",
" x = self.fn(x, **kwargs)\n",
" x += res\n",
" return x\n",
"\n",
"\n",
"class FlattenHead(nn.Sequential):\n",
" def __init__(self):\n",
" super().__init__()\n",
"\n",
" def forward(self, x):\n",
" x = x.contiguous().view(x.size(0), -1)\n",
" return x\n",
"\n",
"\n",
"class Enc_eeg(nn.Sequential):\n",
" def __init__(self, emb_size=40, **kwargs):\n",
" super().__init__(\n",
" PatchEmbedding(emb_size),\n",
" FlattenHead()\n",
" )\n",
"\n",
" \n",
"class Proj_eeg(nn.Sequential):\n",
" def __init__(self, embedding_dim=1440, proj_dim=1024, drop_proj=0.5):\n",
" super().__init__(\n",
" nn.Linear(embedding_dim, proj_dim),\n",
" ResidualAdd(nn.Sequential(\n",
" nn.GELU(),\n",
" nn.Linear(proj_dim, proj_dim),\n",
" nn.Dropout(drop_proj),\n",
" )),\n",
" nn.LayerNorm(proj_dim),\n",
" )\n",
"\n",
"\n",
"class Proj_img(nn.Sequential):\n",
" def __init__(self, embedding_dim=1024, proj_dim=1024, drop_proj=0.3):\n",
" super().__init__(\n",
" nn.Linear(embedding_dim, proj_dim),\n",
" ResidualAdd(nn.Sequential(\n",
" nn.GELU(),\n",
" nn.Linear(proj_dim, proj_dim),\n",
" nn.Dropout(drop_proj),\n",
" )),\n",
" nn.LayerNorm(proj_dim),\n",
" )\n",
" def forward(self, x):\n",
" return x \n",
"\n",
"class NICE(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.enc_eeg = Enc_eeg()\n",
" self.proj_eeg = Proj_eeg()\n",
" self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))\n",
" self.loss_func = ClipLoss() \n",
" def forward(self, data):\n",
" eeg_embedding = self.enc_eeg(data)\n",
" out = self.proj_eeg(eeg_embedding)\n",
"\n",
" return out \n",
" \n",
"\n",
"def get_eegfeatures(sub, eegmodel, dataloader, device, text_features_all, img_features_all, k, mode):\n",
" eegmodel.eval()\n",
" text_features_all = text_features_all.to(device).float()\n",
" img_features_all = img_features_all.to(device).float()\n",
" total_loss = 0\n",
" correct = 0\n",
" total = 0\n",
" alpha =0.9\n",
" top5_correct = 0\n",
" top5_correct_count = 0\n",
"\n",
" all_labels = set(range(text_features_all.size(0)))\n",
" top5_acc = 0\n",
" mse_loss_fn = nn.MSELoss()\n",
" ridge_lambda = 0.1\n",
" save_features = True\n",
" features_list = [] # List to store features \n",
" with torch.no_grad():\n",
" for batch_idx, (eeg_data, labels, text, text_features, img, img_features) in enumerate(dataloader):\n",
" eeg_data = eeg_data.to(device)\n",
" eeg_data = eeg_data[:, :, :250]\n",
" # print(\"eeg_data\", eeg_data.shape)\n",
" text_features = text_features.to(device).float()\n",
" labels = labels.to(device)\n",
" img_features = img_features.to(device).float()\n",
" eeg_features = eegmodel(eeg_data).float()\n",
" features_list.append(eeg_features)\n",
" logit_scale = eegmodel.logit_scale \n",
" \n",
" regress_loss = mse_loss_fn(eeg_features, img_features)\n",
" # print(\"eeg_features\", eeg_features.shape)\n",
" # print(torch.std(eeg_features, dim=-1))\n",
" # print(torch.std(img_features, dim=-1))\n",
" # l2_norm = sum(p.pow(2.0).sum() for p in model.parameters())\n",
" # loss = (regress_loss + ridge_lambda * l2_norm) \n",
" img_loss = eegmodel.loss_func(eeg_features, img_features, logit_scale)\n",
" text_loss = eegmodel.loss_func(eeg_features, text_features, logit_scale)\n",
" contrastive_loss = img_loss\n",
" # loss = img_loss + text_loss\n",
"\n",
" regress_loss = mse_loss_fn(eeg_features, img_features)\n",
" # print(\"text_loss\", text_loss)\n",
" # print(\"img_loss\", img_loss)\n",
" # print(\"regress_loss\", regress_loss) \n",
" # l2_norm = sum(p.pow(2.0).sum() for p in model.parameters())\n",
" # loss = (regress_loss + ridge_lambda * l2_norm) \n",
" loss = alpha * regress_loss *10 + (1 - alpha) * contrastive_loss*10\n",
" # print(\"loss\", loss)\n",
" total_loss += loss.item()\n",
" \n",
" for idx, label in enumerate(labels):\n",
"\n",
" possible_classes = list(all_labels - {label.item()})\n",
" selected_classes = random.sample(possible_classes, k-1) + [label.item()]\n",
" selected_img_features = img_features_all[selected_classes]\n",
" \n",
"\n",
" logits_img = logit_scale * eeg_features[idx] @ selected_img_features.T\n",
" # logits_text = logit_scale * eeg_features[idx] @ selected_text_features.T\n",
" # logits_single = (logits_text + logits_img) / 2.0\n",
" logits_single = logits_img\n",
" # print(\"logits_single\", logits_single.shape)\n",
"\n",
" # predicted_label = selected_classes[torch.argmax(logits_single).item()]\n",
" predicted_label = selected_classes[torch.argmax(logits_single).item()] # (n_batch, ) \\in {0, 1, ..., n_cls-1}\n",
" if predicted_label == label.item():\n",
" correct += 1 \n",
" total += 1\n",
"\n",
" if save_features:\n",
" features_tensor = torch.cat(features_list, dim=0)\n",
" print(\"features_tensor\", features_tensor.shape)\n",
" torch.save(features_tensor.cpu(), f\"emb_eeg/{config['encoder_type']}_eeg_features_{sub}_{mode}.pt\") # Save features as .pt file\n",
" average_loss = total_loss / (batch_idx+1)\n",
" accuracy = correct / total\n",
" return average_loss, accuracy, labels, features_tensor.cpu()\n",
"\n",
"from IPython.display import Image, display\n",
"config = {\n",
"\"data_path\": \"/home/ldy/Workspace/THINGS/Preprocessed_data_250Hz\",\n",
"\"project\": \"train_pos_img_text_rep\",\n",
"\"entity\": \"sustech_rethinkingbci\",\n",
"\"name\": \"lr=3e-4_img_pos_pro_eeg\",\n",
"\"lr\": 3e-4,\n",
"\"epochs\": 50,\n",
"\"batch_size\": 1024,\n",
"\"logger\": True,\n",
"\"encoder_type\":'NICE',\n",
"}\n",
"\n",
"device = torch.device(\"cuda:6\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"data_path = config['data_path']\n",
"emb_img_test = torch.load('variables/ViT-H-14_features_test.pt')\n",
"emb_img_train = torch.load('variables/ViT-H-14_features_train.pt')\n",
"\n",
"eeg_model = NICE()\n",
"print('number of parameters:', sum([p.numel() for p in eeg_model.parameters()]))\n",
"\n",
"#####################################################################################\n",
"\n",
"# eeg_model.load_state_dict(torch.load(\"/home/ldy/Workspace/Reconstruction/models/contrast/sub-08/01-30_00-44/40.pth\"))\n",
"eeg_model.load_state_dict(torch.load(\"/home/ldy/Workspace/BrainAligning_retrieval/models/contrast/NICE/sub-08/02-10_01-58/best.pth\"))\n",
"eeg_model = eeg_model.to(device)\n",
"sub = 'sub-08'\n",
"\n",
"#####################################################################################\n",
"\n",
"test_dataset = EEGDataset(data_path, subjects= [sub], train=False)\n",
"test_loader = DataLoader(test_dataset, batch_size=config[\"batch_size\"], shuffle=False, num_workers=0)\n",
"text_features_test_all = test_dataset.text_features\n",
"img_features_test_all = test_dataset.img_features\n",
"test_loss, test_accuracy,labels, eeg_features_test = get_eegfeatures(sub, eeg_model, test_loader, device, text_features_test_all, img_features_test_all,k=200, mode=\"test\")\n",
"print(f\" - Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"self.subjects ['sub-08']\n",
"exclude_subject None\n",
"data_tensor torch.Size([66160, 63, 250])\n",
"Data tensor shape: torch.Size([66160, 63, 250]), label tensor shape: torch.Size([66160]), text length: 1654, image length: 16540\n",
"features_tensor torch.Size([66160, 1024])\n",
" - Test Loss: 13.3945, Test Accuracy: 0.0049\n"
]
}
],
"source": [
"#####################################################################################\n",
"train_dataset = EEGDataset(data_path, subjects= [sub], train=True)\n",
"train_loader = DataLoader(train_dataset, batch_size=config[\"batch_size\"], shuffle=False, num_workers=0)\n",
"text_features_test_all = train_dataset.text_features\n",
"img_features_test_all = train_dataset.img_features\n",
"\n",
"train_loss, train_accuracy, labels, eeg_features_train = get_eegfeatures(sub, eeg_model, train_loader, device, text_features_test_all, img_features_test_all,k=200, mode=\"train\")\n",
"print(f\" - Test Loss: {train_loss:.4f}, Test Accuracy: {train_accuracy:.4f}\")\n",
"#####################################################################################"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch import nn\n",
"import torch.nn.functional as F\n",
"import torchvision.transforms as transforms\n",
"import matplotlib.pyplot as plt\n",
"import open_clip\n",
"from matplotlib.font_manager import FontProperties\n",
"\n",
"import sys\n",
"from diffusion_prior import *\n",
"from custom_pipeline import *\n",
"# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"5\" \n",
"device = torch.device('cuda:3' if torch.cuda.is_available() else 'cpu')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"emb_img_train_4 = emb_img_train.view(1654,10,1,1024).repeat(1,1,4,1).view(-1,1024)\n",
"emb_eeg = torch.load(f\"/home/ldy/Workspace/Generation/emb_eeg/{config['encoder_type']}_eeg_features_{sub}_train.pt\")\n",
"emb_eeg_test = torch.load(f\"/home/ldy/Workspace/Generation/emb_eeg/{config['encoder_type']}_eeg_features_{sub}_test.pt\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([66160, 1024]), torch.Size([200, 1024]))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"emb_eeg.shape, emb_eeg_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[-0.4268, -0.6550, -0.3994, ..., -0.0573, -1.2790, 1.7172],\n",
" [-2.5021, 3.2850, -0.1402, ..., -0.4890, -0.6232, 0.1757],\n",
" [-1.8498, -0.1019, -1.6115, ..., -0.4604, 0.1076, 0.9637],\n",
" ...,\n",
" [-2.1374, 0.4877, -0.5477, ..., -1.2601, 0.0205, 2.3434],\n",
" [-1.7127, 1.2059, 0.3979, ..., -1.9886, -1.5540, 1.5707],\n",
" [ 1.2487, 1.1401, -0.4919, ..., -1.6411, -0.8680, 0.6948]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eeg_features_train"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from diffusion_prior import *\n",
"class EmbeddingDataset(Dataset):\n",
"\n",
" def __init__(self, c_embeddings=None, h_embeddings=None, h_embeds_uncond=None, cond_sampling_rate=0.5):\n",
" self.c_embeddings = c_embeddings\n",
" self.h_embeddings = h_embeddings\n",
" self.N_cond = 0 if self.h_embeddings is None else len(self.h_embeddings)\n",
" self.h_embeds_uncond = h_embeds_uncond\n",
" self.N_uncond = 0 if self.h_embeds_uncond is None else len(self.h_embeds_uncond)\n",
" self.cond_sampling_rate = cond_sampling_rate\n",
"\n",
" def __len__(self):\n",
" return self.N_cond\n",
"\n",
" def __getitem__(self, idx):\n",
" return {\n",
" \"c_embedding\": self.c_embeddings[idx],\n",
" \"h_embedding\": self.h_embeddings[idx]\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9675648\n"
]
}
],
"source": [
"dataset = EmbeddingDataset(\n",
" c_embeddings=eeg_features_train, h_embeddings=emb_img_train_4, \n",
" # h_embeds_uncond=h_embeds_imgnet\n",
")\n",
"dl = DataLoader(dataset, batch_size=1024, shuffle=True, num_workers=64)\n",
"diffusion_prior = DiffusionPriorUNet(cond_dim=1024, dropout=0.1)\n",
"# number of parameters\n",
"print(sum(p.numel() for p in diffusion_prior.parameters() if p.requires_grad))\n",
"pipe = Pipe(diffusion_prior, device=device)\n",
"\n",
"# load pretrained model\n",
"model_name = 'diffusion_prior' # 'diffusion_prior_vice_pre_imagenet' or 'diffusion_prior_vice_pre'\n",
"# pipe.train(dl, num_epochs=150, learning_rate=1e-3) # to 0.142 "
]
},
{
"cell_type": "code",
"execution_count": 11,
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],
"source": [
"\n",
"pipe.diffusion_prior.load_state_dict(torch.load(f\"./fintune_ckpts/{config['encoder_type']}/{sub}/{model_name}.pt\", map_location=device))\n",
"# save_path = f'./fintune_ckpts/{config[\"encoder_type\"]}/{sub}/{model_name}.pt'\n",
"\n",
"# directory = os.path.dirname(save_path)\n",
"\n",
"# Create the directory if it doesn't exist\n",
"# os.makedirs(directory, exist_ok=True)\n",
"# torch.save(pipe.diffusion_prior.state_dict(), save_path)\n",
"from PIL import Image\n",
"import os\n",
"\n",
"# Assuming generator.generate returns a PIL Image\n",
"generator = Generator4Embeds(num_inference_steps=4, device=device)\n",
"\n",
"directory = f\"Generation/{config['encoder_type']}/generated_imgs/{sub}\"\n",
"for k in range(200):\n",
" eeg_embeds = emb_eeg_test[k:k+1]\n",
" h = pipe.generate(c_embeds=eeg_embeds, num_inference_steps=50, guidance_scale=5.0)\n",
" for j in range(1):\n",
" image = generator.generate(h.to(dtype=torch.float16))\n",
" # Construct the save path for each image\n",
" path = f'{directory}/{texts[k]}/{j}.png'\n",
" # Ensure the directory exists\n",
" os.makedirs(os.path.dirname(path), exist_ok=True)\n",
" # Save the PIL Image\n",
" image.save(path)\n",
" # print(f'Image saved to {path}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# import os\n",
"# # os.environ['http_proxy'] = 'http://10.16.35.10:13390' \n",
"# # os.environ['https_proxy'] = 'http://10.16.35.10:13390' \n",
"# # os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"5\" \n",
"# # os.environ['PATH'] += os.pathsep + '/usr/local/texlive/2023/bin/x86_64-linux'\n",
"\n",
"# import torch\n",
"# from torch import nn\n",
"# import torch.nn.functional as F\n",
"# import torchvision.transforms as transforms\n",
"# import matplotlib.pyplot as plt\n",
"# import open_clip\n",
"# from matplotlib.font_manager import FontProperties\n",
"\n",
"# import sys\n",
"# from diffusion_prior import *\n",
"# from custom_pipeline import *\n",
"\n",
"# device = torch.device('cuda:5' if torch.cuda.is_available() else 'cpu')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load eeg and image embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # load image embeddings\n",
"# data = torch.load('/home/ldy/Workspace/THINGS/CLIP/ViT-H-14_features_test.pt', map_location='cuda:3')\n",
"# emb_img_test = data['img_features']\n",
"\n",
"# # load image embeddings\n",
"# data = torch.load('/home/ldy/Workspace/THINGS/CLIP/ViT-H-14_features_train.pt', map_location='cuda:3')\n",
"# emb_img_train = data['img_features']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# emb_img_test = torch.load('variables/ViT-H-14_features_test.pt')\n",
"# emb_img_train = torch.load('variables/ViT-H-14_features_train.pt')\n",
"\n",
"# torch.save(emb_img_test.cpu().detach(), 'variables/ViT-H-14_features_test.pt')\n",
"# torch.save(emb_img_train.cpu().detach(), 'variables/ViT-H-14_features_train.pt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# emb_img_test.shape, emb_img_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 1654clsx10imgsx4trials=66160\n",
"# emb_eeg = torch.load('/home/ldy/Workspace/Reconstruction/ATM_S_eeg_features_sub-08.pt')\n",
"\n",
"# emb_eeg_test = torch.load('/home/ldy/Workspace/Reconstruction/ATM_S_eeg_features_sub-08_test.pt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# emb_eeg.shape, emb_eeg_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Training prior diffusion"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"class EmbeddingDataset(Dataset):\n",
"\n",
" def __init__(self, c_embeddings=None, h_embeddings=None, h_embeds_uncond=None, cond_sampling_rate=0.5):\n",
" self.c_embeddings = c_embeddings\n",
" self.h_embeddings = h_embeddings\n",
" self.N_cond = 0 if self.h_embeddings is None else len(self.h_embeddings)\n",
" self.h_embeds_uncond = h_embeds_uncond\n",
" self.N_uncond = 0 if self.h_embeds_uncond is None else len(self.h_embeds_uncond)\n",
" self.cond_sampling_rate = cond_sampling_rate\n",
"\n",
" def __len__(self):\n",
" return self.N_cond\n",
"\n",
" def __getitem__(self, idx):\n",
" return {\n",
" \"c_embedding\": self.c_embeddings[idx],\n",
" \"h_embedding\": self.h_embeddings[idx]\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"emb_img_train_4 = emb_img_train.view(1654,10,1,1024).repeat(1,1,4,1).view(-1,1024)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"emb_img_train_4.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# path_data = '/mnt/dataset0/weichen/projects/visobj/proposals/mise/data'\n",
"# image_features = torch.load(os.path.join(path_data, 'openclip_emb/emb_imgnet.pt')) # 'emb_imgnet' or 'image_features'\n",
"# h_embeds_imgnet = image_features['image_features']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import DataLoader\n",
"dataset = EmbeddingDataset(\n",
" c_embeddings=emb_eeg, h_embeddings=emb_img_train_4, \n",
" # h_embeds_uncond=h_embeds_imgnet\n",
")\n",
"print(len(dataset))\n",
"dataloader = DataLoader(dataset, batch_size=1024, shuffle=True, num_workers=64)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# diffusion_prior = DiffusionPrior(dropout=0.1)\n",
"diffusion_prior = DiffusionPriorUNet(cond_dim=1024, dropout=0.1)\n",
"# number of parameters\n",
"print(sum(p.numel() for p in diffusion_prior.parameters() if p.requires_grad))\n",
"pipe = Pipe(diffusion_prior, device=device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load pretrained model\n",
"model_name = 'diffusion_prior' # 'diffusion_prior_vice_pre_imagenet' or 'diffusion_prior_vice_pre'\n",
"pipe.diffusion_prior.load_state_dict(torch.load(f'./ckpts/{model_name}.pt', map_location=device))\n",
"# pipe.train(dataloader, num_epochs=150, learning_rate=1e-3) # to 0.142 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# save model\n",
"# torch.save(pipe.diffusion_prior.state_dict(), f'./ckpts/{model_name}.pt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generating by eeg embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# save model\n",
"# torch.save(pipe.diffusion_prior.state_dict(), f'./ckpts/{model_name}.pt')\n",
"from IPython.display import Image, display\n",
"generator = Generator4Embeds(num_inference_steps=4, device=device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# path of ground truth: /home/ldy/Workspace/THINGS/images_set/test_images\n",
"k = 99\n",
"image_embeds = emb_img_test[k:k+1]\n",
"print(\"image_embeds\", image_embeds.shape)\n",
"image = generator.generate(image_embeds)\n",
"display(image)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# image_embeds = emb_eeg_test[k:k+1]\n",
"# print(\"image_embeds\", image_embeds.shape)\n",
"# image = generator.generate(image_embeds)\n",
"# display(image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generating by eeg informed image embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# k = 0\n",
"eeg_embeds = emb_eeg_test[k:k+1]\n",
"print(\"image_embeds\", eeg_embeds.shape)\n",
"h = pipe.generate(c_embeds=eeg_embeds, num_inference_steps=50, guidance_scale=5.0)\n",
"image = generator.generate(h.to(dtype=torch.float16))\n",
"display(image)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "BCI",
"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": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}