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null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ed83845aa50b42fc97b4a63e2ff8a0e2": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Setup the repo" ], "metadata": { "id": "Ks8v56Tei8Tu" } }, { "cell_type": "code", "source": [ "try:\n", " !pip uninstall -qy geometricvocab geofractal\n", "except:\n", " pass\n", "\n", "!pip install -q git+https://github.com/AbstractEyes/geofractal.git" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "expZq_6Zi7xz", "outputId": "d90373b1-d412-498b-a6a9-b0781856901a" }, "execution_count": 30, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for geofractal (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for geometricvocab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ] }, { "cell_type": "markdown", "source": [ "# test the factory" ], "metadata": { "id": "4nhokFtki-Kt" } }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NtENgWYYiecb", "outputId": "9f5a2f24-8f28-4184-9a68-4445b5ee816d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Device: cuda\n", "Factory imports successful!\n" ] } ], "source": [ "# Cell 2: Imports and Setup\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from torch.utils.data import DataLoader\n", "from torchvision import datasets, transforms\n", "from tqdm.auto import tqdm\n", "\n", "# Check device\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(f\"Device: {device}\")\n", "\n", "# Import our factory system\n", "from geofractal.router.factory import (\n", " PrototypeBuilder,\n", " StreamSpec,\n", " HeadSpec,\n", " FusionSpec,\n", " get_prototype_registry,\n", ")\n", "\n", "print(\"Factory imports successful!\")" ] }, { "cell_type": "code", "source": [ "# Cell 3: Dataset Setup (CIFAR-10 for quick validation)\n", "transform = transforms.Compose([\n", " transforms.Resize((224, 224)), # CLIP expects 224x224\n", " transforms.ToTensor(),\n", " transforms.Normalize(\n", " mean=[0.485, 0.456, 0.406],\n", " std=[0.229, 0.224, 0.225]\n", " ),\n", "])\n", "\n", "train_dataset = datasets.CIFAR10(\n", " root='./data',\n", " train=True,\n", " download=True,\n", " transform=transform\n", ")\n", "val_dataset = datasets.CIFAR10(\n", " root='./data',\n", " train=False,\n", " download=True,\n", " transform=transform\n", ")\n", "\n", "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=2)\n", "val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=2)\n", "\n", "print(f\"Train: {len(train_dataset)}, Val: {len(val_dataset)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xcBIl5eLmhsM", "outputId": "8c1b86eb-e489-46b3-8121-684e70daf614" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 170M/170M [00:13<00:00, 12.2MB/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Train: 50000, Val: 10000\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell 4: Direct Component Test (bypass factory for now)\n", "from geofractal.router.head import HeadBuilder, HeadConfig, build_standard_head\n", "from geofractal.router.fusion import ConcatFusion, FusionConfig\n", "from transformers import CLIPModel, CLIPProcessor\n", "\n", "# Load CLIP\n", "clip = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\").to(device)\n", "clip.eval()\n", "for p in clip.parameters():\n", " p.requires_grad = False\n", "\n", "# Build head\n", "head_config = HeadConfig(feature_dim=512, fingerprint_dim=64, num_anchors=16, num_routes=4)\n", "head = build_standard_head(head_config).to(device)\n", "\n", "# Simple classifier\n", "classifier = nn.Sequential(\n", " nn.LayerNorm(512),\n", " nn.Dropout(0.1),\n", " nn.Linear(512, 512),\n", " nn.GELU(),\n", " nn.Dropout(0.1),\n", " nn.Linear(512, 10),\n", ").to(device)\n", "\n", "print(f\"Head params: {sum(p.numel() for p in head.parameters()):,}\")\n", "print(f\"Classifier params: {sum(p.numel() for p in classifier.parameters()):,}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 116, "referenced_widgets": [ "a75cc9f85df04d45a47a3ec012865cba", "c7cc3fa753f749748ea8a76eb42e7733", "cc23f0fe3987421b8a9ede38ae8c779d", "724abda314d240768e3717171628f1e9", "2e8da54447354519b6871e65aa4becd3", "938e1c2462a347cd84feb9226fcf4934", "d1c99f4661984ece880b72107ef64428", "9c568e0dba4e4ed3b8cb6dc7adc84a20", "f68d580ef4ca40aab8d3d6f635093ac1", "d2d2b328174548738cc21361df78afe7", "3324bf8211164ef9ac44293e172295bf", "34581411c6b14adfa3eab871f891d200", "8ad75514bc6f40dc8e14f9baecffc32d", "65d0833934844926b405c01cca45e293", "b38f0da2091c4d05b4d61ca14e5b7586", "c3c561e778714cda85d83cc29235fd04", "f0a22d7ab36548f8af31b1183a0e55d6", "0fd4b1de13d34cf393b68674933563ad", "273adec3908c451c9641132f3a083564", "1bb6c3d063c04b69b1e9399e268327ed", "3e039a5d01514204b6bf54f88d830742", "d5fbd15305984a9d80396f4a544ca5ad" ] }, "id": "5t595zC3mj8a", "outputId": "9b552d95-b3d9-46ec-aa97-36751dfb5228" }, "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a75cc9f85df04d45a47a3ec012865cba" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "pytorch_model.bin: 0%| | 0.00/605M [00:00 0 else 0\n", "\n", " history.append({\n", " 'epoch': epoch + 1,\n", " 'collective': coll_acc,\n", " 'ind_a': ind_a_acc,\n", " 'ind_b': ind_b_acc,\n", " 'rho': rho,\n", " })\n", "\n", " print(f\"Epoch {epoch+1}: Collective {coll_acc:.1%}, A {ind_a_acc:.1%}, B {ind_b_acc:.1%}, ρ={rho:.3f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 264, "referenced_widgets": [ 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"5c5c12b3cf5e446eb02163577f44fb10", "b17b1dfd3e724feb8a76695a48bf47e2", "34f583a4ba334dc7a54e7c9641453db8", "85c52e4a3fcd4703b10a1e900916b7a3", "026eb791b5194be1b6c38a8b70dcd2ae", "ecb8ff4602804777b2af9dd9b109ad3e", "f5be7c5d0bfe4ae2ae78da306756ca5e", "d00b17390b184d0baaf74a95e888f554", "c73f22d82e1a4816b7c1271f76d1340d", "9d336822319f4786af1c68bc469f4ab3", "adc3f9655b554296ab0d46e0f76502d3", "d1bfe8a687cf4e49b62cc85d8d884087", "94531e0bfa444b3f9c3ac2f5f6147796", "8705cbd3dc1349daa6f29370e01823d5", "3b43d5b5855c4130ae42680dc5e72599", "f3a6bf3bb4964132bfcda1204903a062", "fd27e5c96e8e40c8ac439def3ea487df", "485ef9d058874fe984a896716f680077", "6c0ae9c37c0a44ada0f828ff49c5c8b5", "5c03e85271564333b3485a2615bb5d5d", "11746560484548dfaa2736ff71340c68", "e0522902809b4834a1c73f3c6fab800c", "d4a2011e644c46f09cd35ec3ace41c88", "e4f28c9823c34daeaf759f264470bb1b", "7c32812664d54e51b9b385a71f5aea90", "dbec58a90c6d4b31b1dfbfe4e8af8bbb", "31e59212561148079a4179149f79ca33", "0f9db337b219483ab3d597eb6ca8243d" ] }, "id": "6nipPVkr5e2o", "outputId": "a8e0d52f-a9c9-451a-bff1-6dc721562048" }, "execution_count": 11, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Epoch 1: 0%| | 0/782 [00:00 {h}\" for p, h in zip(batch['premise'], batch['hypothesis'])]\n", "\n", " # Stream A: summarization framing\n", " texts_summarize = [f\"summarize: {t}\" for t in combined]\n", " enc_a = tokenizer(texts_summarize, return_tensors=\"pt\",\n", " padding=True, truncation=True, max_length=max_length)\n", "\n", " # Stream B: raw\n", " enc_b = tokenizer(combined, return_tensors=\"pt\",\n", " padding=True, truncation=True, max_length=max_length)\n", "\n", " return enc_a, enc_b, torch.tensor(batch['label'])\n", "\n", "def forward_batch(enc_a, enc_b):\n", " \"\"\"Forward through both streams and fuse.\"\"\"\n", " with torch.no_grad():\n", " hidden_a = t5(enc_a.input_ids.to(device),\n", " attention_mask=enc_a.attention_mask.to(device)).last_hidden_state\n", " hidden_b = t5(enc_b.input_ids.to(device),\n", " attention_mask=enc_b.attention_mask.to(device)).last_hidden_state\n", "\n", " # Through heads\n", " out_a = head_summarize(hidden_a)\n", " out_b = head_raw(hidden_b)\n", "\n", " # Mean pooling\n", " mask_a = enc_a.attention_mask.to(device).unsqueeze(-1)\n", " mask_b = enc_b.attention_mask.to(device).unsqueeze(-1)\n", "\n", " pooled_a = (out_a * mask_a).sum(1) / mask_a.sum(1)\n", " pooled_b = (out_b * mask_b).sum(1) / mask_b.sum(1)\n", "\n", " # Fuse\n", " fused = fusion(torch.cat([pooled_a, pooled_b], dim=-1))\n", "\n", " return fused, pooled_a, pooled_b" ], "metadata": { "id": "zMdImZxkB2DV" }, "execution_count": 18, "outputs": [] }, { "cell_type": "code", "source": [ "# Cell: Training loop\n", "from torch.utils.data import DataLoader\n", "\n", "# Subset for faster iteration (MNLI is huge)\n", "train_subset = dataset['train'].shuffle(seed=42).select(range(50000))\n", "val_subset = dataset['validation_matched'].shuffle(seed=42).select(range(5000))\n", "\n", "BATCH_SIZE = 32\n", "EPOCHS = 3\n", "\n", "def collate_fn(examples):\n", " return {\n", " 'premise': [ex['premise'] for ex in examples],\n", " 'hypothesis': [ex['hypothesis'] for ex in examples],\n", " 'label': [ex['label'] for ex in examples],\n", " }\n", "\n", "train_loader = DataLoader(train_subset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", "val_loader = DataLoader(val_subset, batch_size=BATCH_SIZE, shuffle=False, collate_fn=collate_fn)\n", "\n", "history = []\n", "\n", "for epoch in range(EPOCHS):\n", " # Train\n", " head_summarize.train(); head_raw.train(); fusion.train(); classifier.train()\n", " classifier_summarize.train(); classifier_raw.train()\n", "\n", " total_loss, correct, total = 0, 0, 0\n", " pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}\")\n", "\n", " for batch in pbar:\n", " enc_a, enc_b, labels = encode_entailment_batch(batch)\n", " labels = labels.to(device)\n", "\n", " optimizer.zero_grad()\n", "\n", " fused, pooled_a, pooled_b = forward_batch(enc_a, enc_b)\n", "\n", " # Collective prediction\n", " logits = classifier(fused)\n", " loss = criterion(logits, labels)\n", "\n", " # Individual supervision (light)\n", " loss += 0.1 * criterion(classifier_summarize(pooled_a), labels)\n", " loss += 0.1 * criterion(classifier_raw(pooled_b), labels)\n", "\n", " loss.backward()\n", " optimizer.step()\n", "\n", " correct += (logits.argmax(-1) == labels).sum().item()\n", " total += labels.size(0)\n", " pbar.set_postfix({'loss': f'{loss.item():.3f}', 'acc': f'{correct/total:.1%}'})\n", "\n", " # Eval\n", " head_summarize.eval(); head_raw.eval(); fusion.eval(); classifier.eval()\n", " classifier_summarize.eval(); classifier_raw.eval()\n", "\n", " coll_correct, sum_correct, raw_correct, val_total = 0, 0, 0, 0\n", "\n", " with torch.no_grad():\n", " for batch in tqdm(val_loader, desc=\"Validating\"):\n", " enc_a, enc_b, labels = encode_entailment_batch(batch)\n", " labels = labels.to(device)\n", "\n", " fused, pooled_a, pooled_b = forward_batch(enc_a, enc_b)\n", "\n", " coll_correct += (classifier(fused).argmax(-1) == labels).sum().item()\n", " sum_correct += (classifier_summarize(pooled_a).argmax(-1) == labels).sum().item()\n", " raw_correct += (classifier_raw(pooled_b).argmax(-1) == labels).sum().item()\n", " val_total += labels.size(0)\n", "\n", " coll_acc = coll_correct / val_total\n", " sum_acc = sum_correct / val_total\n", " raw_acc = raw_correct / val_total\n", " max_ind = max(sum_acc, raw_acc)\n", " rho = coll_acc / max_ind if max_ind > 0 else 0\n", "\n", " history.append({\n", " 'epoch': epoch + 1,\n", " 'collective': coll_acc,\n", " 'summarize': sum_acc,\n", " 'raw': raw_acc,\n", " 'rho': rho,\n", " })\n", "\n", " print(f\"Epoch {epoch+1}: Collective {coll_acc:.1%}, Summarize {sum_acc:.1%}, Raw {raw_acc:.1%}, ρ={rho:.3f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 316, "referenced_widgets": [ "3d9acc8ffbe340a898540ed2f1e48089", "306b1f4fe4e0479d8485ce8dd991b08d", "e8544bfa70c049f9b5b8730905b632a2", "4a2c091690bf430cb95a77c89956b3f6", "423ebb9da5624612ad2a206be9d9cfd3", "c69016a2f7f345cb8a6182f919dc36aa", "26ba333edef14701966f665938b0947a", "1f03887b123e44d19c229d605edf5e38", "b40a66325ba641019a10fd560d07a4b5", "33a282d1f26d4f9faecad2cc100bd254", "2c241d31afcc40c6a57ebee01ecc099e", "3c7853e17b22443c9207c40e2ddb63f4", "2efa50ac868f42258499ee6f2fc704da", "daa6bf6fe76e4928a65fe5fd56a44bba", "61bf54105b364d448ad0fe4dfa2d78f7", "4780dd7f77e2445aabcb2d607bf30baa", "551738e492c34657a826bcb31f1b56c7", "ea02af5216c64690bccc59cbe201d44f", "b946fe981d704925b4ec403060bcba3c", "a22061fb1f1943a6b0f72c24f3d5179e", "3529638dd05448ed8f5e4c9d34bdc17f", "6c87c734d97845e49dddad6846b8f674", "861af31c51d5467a86389d6b66b2a4c9", "c163a8f4ad4b4e908c0c2a0873ae62d8", "cb404a476e6449a5acf83703697ef33d", "18851a9e73c645528a94e942a4cca380", "b6abd5ffd8f74a238de743eb5f8fe54f", "b8c2f0b9281f49ad814967babb8dd7bf", "5668e9d1b0ee42c39ed82291d77c95ba", "63fbe41c177f4a3c83bcbe805bc96682", "35c8c9f267064b039b5036cccd8dba48", "0be01697aa494b71b509110b811f76c8", "f224c46f0af9401fb0e66133499cb76b", "033320a924924af6b64f9862cc5a6352", "a042319409bd41288b1220fda69fc01c", "8e6f04cf3976402bb39ef590c0883738", "eeda206cc1b14b90a304cd3ed7156c89", "e631800cd7884a56bf715002a567df79", "6960964f456c4d819cbf8b02dae9711e", "a714ef36b94849f9994a47066d5349c0", "71dafa6deefa43c99d8ceec7ad70dadc", "3fe3111ce6144822bca2f84b691b17fb", "2b05c17ed24c441f95c19f6463091497", "f76f53783c5f4c2592cbb7ebfb3bd961", "ad614a71b8f74b88a02de454e2d4afe1", "6c7e91dc340f4a63bb2bd1ef20f087bc", "d759f3c0d0554531ba117e12df07349e", "5f8f0891dd0b4a8f8f52c61d86dde9ab", "f2a8ea16ed0b475196e93f77d2a31bc6", "b7de25850cb04034bed6407dce59fa33", "03b75048714842559cf4a2996cfd2ce1", "5428e43bea194e6d84542beff0b80207", "ddd19f80a7584b7f98b11bc970f6de4b", "0c12fa2f32e145f4a99c27088e3a862a", "5407b5e62a3948a8b24e47a5b2a2d087", "1e79e553205040ef86213c09e4a475e8", "b4632244f0e14245b4f8f225c83f74a6", "2d3b4260860442daaaf299801f081921", "9226a1cd9164497ba0069b91da90d754", "7f8a3a0eebab41d2a17d510de9b7714c", "e94902d1c00e4c078366217ed0ba0cde", "c7fc166bfc694d6a8af38fe195890759", "5e5cfda389464d368dd0eaf1ce96a03c", "9a087a9980af434dac45e8839764eec3", "f55736e1e4784ce8a4d4c796e1250cac", "8ffeb6c437f144e795fd52bb0df441d5" ] }, "id": "hzFGofeCB46k", "outputId": "6697819c-652e-422f-adbd-a1756666999e" }, "execution_count": 19, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Epoch 1: 0%| | 0/1563 [00:00. In future versions this behavior may lead to duplicated eos tokens being added.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Validating: 0%| | 0/157 [00:00 hypothesis' → What's essential?\")\n", "print(f\" Head B: 'premise hypothesis' → What's there?\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "P2n6sTDsCcP9", "outputId": "8bafc62b-ae99-4afc-d371-f329c24dcb06" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=== T5 Entailment Results ===\n", "\n", "| Epoch | Collective | Summarize | Raw | ρ |\n", "|-------|------------|-----------|-----|-------|\n", "| 1 | 74.5% | 71.7% | 71.6% | 1.040 |\n", "| 2 | 76.5% | 74.6% | 73.8% | 1.025 |\n", "| 3 | 77.5% | 74.6% | 75.2% | 1.031 |\n", "\n", "Task framing divergence:\n", " Head A: 'summarize: premise hypothesis' → What's essential?\n", " Head B: 'premise hypothesis' → What's there?\n" ] } ] }, { "cell_type": "markdown", "source": [ "# entailment dual stream t5-base + bert" ], "metadata": { "id": "t-QHTLvzFh3z" } }, { "cell_type": "code", "source": [ "# Cell: T5 + BERT Dual Architecture System\n", "from transformers import T5Tokenizer, T5EncoderModel, BertTokenizer, BertModel\n", "import torch\n", "import torch.nn as nn\n", "from torch.utils.data import DataLoader\n", "from datasets import load_dataset\n", "from geofractal.router.head import build_standard_head, HeadConfig\n", "from tqdm.auto import tqdm\n", "\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "# ============ FROZEN BACKBONES ============\n", "\n", "# T5-base encoder\n", "t5_tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\n", "t5 = T5EncoderModel.from_pretrained(\"t5-base\").to(device)\n", "t5.eval()\n", "for p in t5.parameters():\n", " p.requires_grad = False\n", "\n", "# BERT-base encoder\n", "bert_tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n", "bert = BertModel.from_pretrained(\"bert-base-uncased\").to(device)\n", "bert.eval()\n", "for p in bert.parameters():\n", " p.requires_grad = False\n", "\n", "print(f\"T5 hidden: {t5.config.d_model}, BERT hidden: {bert.config.hidden_size}\")\n", "print(f\"T5 params: {sum(p.numel() for p in t5.parameters()):,} (frozen)\")\n", "print(f\"BERT params: {sum(p.numel() for p in bert.parameters()):,} (frozen)\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 229, "referenced_widgets": [ "9aea5a5dd7c4476c9e90aca5691dc145", "e27b6de67b164546b2ba0608db96b050", "91e78d4a2ecd4ed3be54405f493c867f", "5a9e393cf460457698ef2d3c07e88776", "5e11e6894f0e445fadc2953c59d3fa21", "c245a7aad11e4d8fa8f4990e7eea3524", "681537e40eae4a808fb7d3ebf6a88cdf", "c7b09a93f98a44aea0fa5ba170077dff", "35405a31f4da429288ab824de8173f67", "e2e87b4d865d4a7db0af267c0276892d", "241d0b6086ab4330809e41c322658481", "389d95910cae42a4a2d1b43c6831fdbb", "e7e5dfe974ed411a987778212ed04f72", "dd25845ea6db4fd3973270920898d8c5", "1ff1e4f9b8534d79b2eada46937cb5a9", "82c52e71d5c042f2b177ea1d80d7e0fb", "0d0e8a3061704b3e9d1e18e6b3a2774c", "7f8a74f4395d4cd88c6db42aa0484f73", "1c601cd0e3ca42eb87397c010f770a93", "f2a9af1c76534a45ac32bcc5117d3f67", "e8e5dbfe63ea431f8e08dc86b9223852", "454ccc81f718447aa19a5d287f3c493f", "089c3b67b7fa4493ae2d5a5a7e46dd0c", "54ff26ad684f4a6e8e3735789a59c543", "73891e400395448e89d774e2e1716217", "ee71958645a346fc94c4ef39c72d649c", "b37b919d405549e7ab1a6a18d7d18117", "d8d58ff74da441b7ab35ca5b78fc330f", "9075d3132ca84446afddba935225401c", "cc0a079533dd4f25838d78d007838b4a", "51b4947253a947d2a769caf0151ddc44", "e80d500e96c84adca8ad5bae6b4553a5", "4617a75e044b4e89b6d5f45f638ac706", "7e0f989aecd2420a9955b0301e798f65", "27fee63e55b441d2801fcb515fa46878", "500bf9bf18cd47a8a956e38d869df34c", "acef65b366e44fba9457ee17bef9428e", "36a795db44d34282912d1392f02bd022", "cdd9287eebdb4a479e5af5dc5865b79e", "74fc070a050c4e08a48106d753907962", "2627fdacd2904ff58f6f7d725eab4d55", "2b798a130d9e440783434602637059eb", "511020882e75476d8f4b26162ed160ae", "5b0bd1d03aa5436baa64aaa40de82268", "62a52e6c78074dd69c773273ba52759a", "543ef05f6f4a4dd386c8bc6e0b80311b", "9e13a33bd6c04f988250f1b45036b798", "8cb93bd5dee746e7988d5a543845b76d", "0b72987d796f4e45a9580f1bc1ce6dd8", "fe3b6c49eb574abf8d0ba7dbeb2eee91", "9b5e702c253f43f28866ed0990ee1b52", "be04d058506349cdb36c2321f7bcb691", "8f7e34003f034b3695208cc66e21e75a", "5d9c7f108eb0499782685e063b1b0f0d", "7b77d113492e45c0968c207db476f54f" ] }, "id": "tjhgHuh-FlKm", "outputId": "0015dea1-ecca-48d3-f638-0ae737fb7dca" }, "execution_count": 21, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/48.0 [00:00 hypothesis'\"\"\"\n", " texts = [f\"{p} {h}\" for p, h in zip(premises, hypotheses)]\n", " enc = t5_tokenizer(texts, return_tensors=\"pt\", padding=True,\n", " truncation=True, max_length=max_length)\n", " return enc\n", "\n", "def encode_bert(premises, hypotheses, max_length=128):\n", " \"\"\"BERT style: '[CLS] premise [SEP] hypothesis [SEP]'\"\"\"\n", " enc = bert_tokenizer(premises, hypotheses, return_tensors=\"pt\",\n", " padding=True, truncation=True, max_length=max_length)\n", " return enc\n", "\n", "def forward_dual_arch(premises, hypotheses):\n", " \"\"\"Forward through both architectures.\"\"\"\n", " # Encode\n", " enc_t5 = encode_t5(premises, hypotheses)\n", " enc_bert = encode_bert(premises, hypotheses)\n", "\n", " # Through frozen backbones\n", " with torch.no_grad():\n", " hidden_t5 = t5(\n", " enc_t5.input_ids.to(device),\n", " attention_mask=enc_t5.attention_mask.to(device)\n", " ).last_hidden_state\n", "\n", " hidden_bert = bert(\n", " enc_bert.input_ids.to(device),\n", " attention_mask=enc_bert.attention_mask.to(device)\n", " ).last_hidden_state\n", "\n", " # Through routing heads\n", " out_t5 = head_t5(hidden_t5)\n", " out_bert = head_bert(hidden_bert)\n", "\n", " # Pool (mean over sequence)\n", " mask_t5 = enc_t5.attention_mask.to(device).unsqueeze(-1)\n", " mask_bert = enc_bert.attention_mask.to(device).unsqueeze(-1)\n", "\n", " pooled_t5 = (out_t5 * mask_t5).sum(1) / mask_t5.sum(1)\n", " pooled_bert = (out_bert * mask_bert).sum(1) / mask_bert.sum(1)\n", "\n", " # Fuse\n", " fused = fusion(torch.cat([pooled_t5, pooled_bert], dim=-1))\n", "\n", " return fused, pooled_t5, pooled_bert\n", "\n", "# Test forward\n", "test_p = [\"A man is playing guitar.\", \"The cat sat on the mat.\"]\n", "test_h = [\"Someone is making music.\", \"An animal is on furniture.\"]\n", "fused, pt5, pbert = forward_dual_arch(test_p, test_h)\n", "print(f\"T5 pooled: {pt5.shape}, BERT pooled: {pbert.shape}, Fused: {fused.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-aOzwcikFtoi", "outputId": "a913600a-96d3-4d22-a72a-0f8b5921146d" }, "execution_count": 23, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "T5 pooled: torch.Size([2, 768]), BERT pooled: torch.Size([2, 768]), Fused: torch.Size([2, 768])\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Load data and train\n", "dataset = load_dataset(\"glue\", \"mnli\")\n", "train_subset = dataset['train'].shuffle(seed=42).select(range(50000))\n", "val_subset = dataset['validation_matched'].shuffle(seed=42).select(range(5000))\n", "\n", "BATCH_SIZE = 32\n", "EPOCHS = 3\n", "\n", "def collate_fn(examples):\n", " return {\n", " 'premise': [ex['premise'] for ex in examples],\n", " 'hypothesis': [ex['hypothesis'] for ex in examples],\n", " 'label': [ex['label'] for ex in examples],\n", " }\n", "\n", "train_loader = DataLoader(train_subset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", "val_loader = DataLoader(val_subset, batch_size=BATCH_SIZE, shuffle=False, collate_fn=collate_fn)\n", "\n", "history = []\n", "\n", "for epoch in range(EPOCHS):\n", " # Train\n", " head_t5.train(); head_bert.train(); fusion.train(); classifier.train()\n", " classifier_t5.train(); classifier_bert.train()\n", "\n", " total_loss, correct, total = 0, 0, 0\n", " pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}\")\n", "\n", " for batch in pbar:\n", " labels = torch.tensor(batch['label']).to(device)\n", "\n", " optimizer.zero_grad()\n", "\n", " fused, pooled_t5, pooled_bert = forward_dual_arch(batch['premise'], batch['hypothesis'])\n", "\n", " # Collective\n", " logits = classifier(fused)\n", " loss = criterion(logits, labels)\n", "\n", " # Individual supervision\n", " loss += 0.1 * criterion(classifier_t5(pooled_t5), labels)\n", " loss += 0.1 * criterion(classifier_bert(pooled_bert), labels)\n", "\n", " loss.backward()\n", " optimizer.step()\n", "\n", " correct += (logits.argmax(-1) == labels).sum().item()\n", " total += labels.size(0)\n", " pbar.set_postfix({'loss': f'{loss.item():.3f}', 'acc': f'{correct/total:.1%}'})\n", "\n", " # Eval\n", " head_t5.eval(); head_bert.eval(); fusion.eval(); classifier.eval()\n", " classifier_t5.eval(); classifier_bert.eval()\n", "\n", " coll_correct, t5_correct, bert_correct, val_total = 0, 0, 0, 0\n", "\n", " with torch.no_grad():\n", " for batch in tqdm(val_loader, desc=\"Validating\"):\n", " labels = torch.tensor(batch['label']).to(device)\n", "\n", " fused, pooled_t5, pooled_bert = forward_dual_arch(batch['premise'], batch['hypothesis'])\n", "\n", " coll_correct += (classifier(fused).argmax(-1) == labels).sum().item()\n", " t5_correct += (classifier_t5(pooled_t5).argmax(-1) == labels).sum().item()\n", " bert_correct += (classifier_bert(pooled_bert).argmax(-1) == labels).sum().item()\n", " val_total += labels.size(0)\n", "\n", " coll_acc = coll_correct / val_total\n", " t5_acc = t5_correct / val_total\n", " bert_acc = bert_correct / val_total\n", " max_ind = max(t5_acc, bert_acc)\n", " rho = coll_acc / max_ind if max_ind > 0 else 0\n", "\n", " history.append({\n", " 'epoch': epoch + 1,\n", " 'collective': coll_acc,\n", " 't5': t5_acc,\n", " 'bert': bert_acc,\n", " 'rho': rho,\n", " })\n", "\n", " print(f\"Epoch {epoch+1}: Collective {coll_acc:.1%}, T5 {t5_acc:.1%}, BERT {bert_acc:.1%}, ρ={rho:.3f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "bdcb878747da484189bc4934504e0617", "7de966a4268d4361950b42393d90f72d", "baa3de07d1c0489bbc15fe81aa9ce494", "f013448ac6394f0180093e0049c78d2a", "e723925ebe164c7fa50c2e583f9e35a1", "87d7e5d34bd24580bc073f358a5c4dc7", "117901aa658a4b0986769cfc4865a298", "5df14f88e0dc4dffb5fcce2bde47b46c", "c5ef5165dffd40cdbaaa7969811102e3", "7d7421abb05c44f7816be4c9b4d18e08", "9ad11ed6f2b14dec8a70a06f5c195aa0", "f041192ba07d43099d12678eca247b42", "9076a63e5bdf468f9db525dc5a57c442", "d291faa28cc6453992a3feb5c43af418", "ff6ef9d40c364ad9b60976dc7f11323a", "c606f7d7ace74f48a1fb630ab8e2f820", "be38f0cb41dd4b93983ad56c36c5d5c2", "f1c68db47d6147a8bc4ff2cf9a2a7408", "7ff416b941384e21a960fb1d532f81ed", "13fca4be2f884255b44de98aca2b2f7a", "7ec83f19968945109716a08f7665711a", "16cb117b7d5849cf9f23511af023781e", "012c17880ad247ca86393ae65db418f5", "7a863ec21cef41209fa04574e76cbdf3", "0bff2ca87a314dbebdea95a40124cf23", "973b70e5f09a4f1b89a4c2fdece521cc", "a963d4cc5a7c49279cf109fb2572644c", "509b062e881f4b38a0de8f83b7f4ddf2", "cfe995a0f111471a8111d8d6a915f0da", "16d4f117ff6845a4923b529a97497211", "84c4da9e75f5412cb506c4f83cbb35b9", "a2e223e278274e84b346565bee7940fa", "94564754940f4a7cad312efad26a8b2f", "301fb81f9ec64cd2a44d68b0b6e19831", "305580aacc214c6da5a1f1016d6e53d0", "51c5d12ca4c34440acf6acf8a984f405", "7351c60a384449dca3c3731bfa3a788a", "f27747cb715e44e6b261a9d39a857da1", "dbb286d83b7a4340aa559808f0549f03", "12cbc537af1549c48c57c92b91a35ac1", "c9d087b7c36f447bb1e332524e474414", "69d037f0eb6d4705b64169cc3208b971", "0fec93f99772477a98a62b4680ee96a9", "2f0d956732fb4ca7bd57d3cc50e3ac1f", "3f80fe00f5cf4feca71a921505ec7e65", "238601e0d1634b9185f7ff15ec46cea2", "43181c95bce745aa839059a454430078", "ed000bc7fec84db6b0766fc4a392d1b4", "c4129c0e2f0a4945a061cb1bb6b7408e", "2e5f8f3ec9e34990a1ce848e8ab735ce", "99fed36365724cb0b6ddea4d06921a1c", "a4b3dd09cb704290a33fa50cfdf75d86", "ee3be5c198534791a5610951e9d4cf48", "d6610f18fb904aa288293df2d1deaec3", "4bb675db89ab484b8779f389991dbfde", "ebf746ea62d74e818d37ac28fd74a717", "08d6306a57d142f184f7ca86ce598c8c", "b3b5ae130a3b4fb6a4c12342c2e93d54", "43f9aed4aca044669cfdb1fbf2a903b6", "4d4782aed6b34601bac230759059926f", "1abe1bccfeb04d8abe5bc37bd2fa3d0d", "399601abf2f1432aa271f4d35d05d6cf", "aee2f0d21a2a45b1a960a69a49daec57", "743da364bb5647e8ba3ca25bee9b416a", "fb9c96e21a7d4ecca9097f4c35274e38", "02efea1094de4f6e93bb74f5c72fb9f5" ] }, "id": "8a-16wRaFyES", "outputId": "7c91a2e8-344a-43af-8ce2-b709224e2f26" }, "execution_count": 24, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Epoch 1: 0%| | 0/1563 [00:00 supervised and unsupervised dual-stream dual head" ], "metadata": { "id": "27d2M2YeFlbr" } }, { "cell_type": "code", "source": [ "!pip install -q git+https://github.com/mlfoundations/open_clip.git" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zQHe7Nk5Kp7D", "outputId": "a26b239f-56ce-4776-9b54-ca59dc8b9b51" }, "execution_count": 28, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.8/44.8 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for open_clip_torch (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Load MaxViT-CLIP and DINO\n", "import torch\n", "import torch.nn as nn\n", "import open_clip\n", "from transformers import Dinov2Model, AutoImageProcessor\n", "from torchvision import datasets, transforms\n", "from torch.utils.data import DataLoader\n", "from geofractal.router.head import build_standard_head, HeadConfig\n", "from tqdm.auto import tqdm\n", "\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "# MaxViT CLIP (open_clip)\n", "maxvit_clip, _, preprocess_clip = open_clip.create_model_and_transforms(\n", " 'ViT-SO400M-14-SigLIP-384', # Strong SigLIP variant\n", " pretrained='webli'\n", ")\n", "maxvit_clip = maxvit_clip.visual.to(device)\n", "maxvit_clip.eval()\n", "for p in maxvit_clip.parameters():\n", " p.requires_grad = False\n", "\n", "# DINOv2\n", "dino = Dinov2Model.from_pretrained(\"facebook/dinov2-base\").to(device)\n", "dino.eval()\n", "for p in dino.parameters():\n", " p.requires_grad = False\n", "\n", "# Check dimensions\n", "print(f\"MaxViT-CLIP output: {maxvit_clip.output_dim if hasattr(maxvit_clip, 'output_dim') else 'check'}\")\n", "print(f\"DINOv2 hidden: {dino.config.hidden_size}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 148, "referenced_widgets": [ "4100dbea2e3448d28b7213ef79028056", "9a11f3c60f39485d8232d8ea875f66be", "cb8d0b7cff63460c8c15a6f70bf96a5d", "6f7ffda75ed245ef9c1a5f2aeff20d4f", "0d4999ad7b3e4def8693bbe260fd98da", "5a6276a29d92457eb5779008a7445ea7", "849ec7fda5ed4684a16769f8777b08cf", "3a799d40710746c49658d7a87bafe4d4", "77763189002b4c56b8564fee5f0add7b", "034568fad98640c68edc0ca82750c4c4", "45f52c1cc5d3466fac6391bdc8212d6b", "c84c39fb6744474fb81d6ef85a4f9618", "eee71f57b3e848a3a55d5f0687a42673", "5f768fbab06e4419aec8b2b1466ec55d", "612bda44451941c59d9bd189b7438dd7", "a0132e12606f4db086fdd081f02dcec3", "4a9e9df6e61d4d39a9424c54fb03bd7b", "76b3bfa03d1040b08a9a23d39be6dedb", "c16064d57db14ba2baf5c22941075282", "cf8bfbf86b0e42e6b5c114bc4511fcd3", "cd3b9ace1b494d97ba8700bb4c4f6975", "a7b92658cd1f4a5691dec03dbae2816a", "6754bd08b660449d8c323b99ec374bdd", "1b0c8172830d45e1ba3d19322ab1defb", "ead668f6f9b74792bd9c94937f78df70", "3b087e00fe15448e9832df7b3bbc8873", "e477c9c474534ac49a7715f91cc4cb03", "ceb6edda6e344d2587750cd1b291fdf0", "fff3c97f51de4d88b1288680c9cd30b3", "85b7894c4e064998bb79a4c8dad70465", "c9311bf3d9a940d9aa1a516e514104aa", "2fa9d58dfffd4c1f8c169a9d77b111f8", "eba22e85eff44060a9b747811f9d4adb" ] }, "id": "OxWJwS4KKKBj", "outputId": "9edebbec-c0ae-41ac-a878-387af55c105a" }, "execution_count": 30, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "open_clip_model.safetensors: 0%| | 0.00/3.51G [00:00 2 else clip_out.shape[-1]\n", "DINO_DIM = dino_out.shape[-1]\n", "ROUTE_DIM = 512\n", "\n", "print(f\"CLIP dim: {CLIP_DIM}, DINO dim: {DINO_DIM}\")\n", "\n", "# Projections to common space\n", "proj_clip = nn.Linear(CLIP_DIM, ROUTE_DIM).to(device)\n", "proj_dino = nn.Linear(DINO_DIM, ROUTE_DIM).to(device)\n", "\n", "# Routing heads\n", "head_config = HeadConfig(feature_dim=ROUTE_DIM, fingerprint_dim=64, num_anchors=16, num_routes=4)\n", "head_clip = build_standard_head(head_config).to(device) # \"What is this called?\"\n", "head_dino = build_standard_head(head_config).to(device) # \"What does this look like?\"\n", "\n", "# Fusion\n", "fusion = nn.Sequential(\n", " nn.Linear(ROUTE_DIM * 2, ROUTE_DIM),\n", " nn.LayerNorm(ROUTE_DIM),\n", " nn.GELU(),\n", " nn.Dropout(0.1),\n", ").to(device)\n", "\n", "# Classifier (CIFAR-10)\n", "classifier = nn.Sequential(\n", " nn.Linear(ROUTE_DIM, 256),\n", " nn.GELU(),\n", " nn.Dropout(0.1),\n", " nn.Linear(256, 10),\n", ").to(device)\n", "\n", "# Individual classifiers\n", "classifier_clip = nn.Linear(ROUTE_DIM, 10).to(device)\n", "classifier_dino = nn.Linear(ROUTE_DIM, 10).to(device)\n", "\n", "trainable = (\n", " list(proj_clip.parameters()) + list(proj_dino.parameters()) +\n", " list(head_clip.parameters()) + list(head_dino.parameters()) +\n", " list(fusion.parameters()) + list(classifier.parameters()) +\n", " list(classifier_clip.parameters()) + list(classifier_dino.parameters())\n", ")\n", "\n", "optimizer = torch.optim.AdamW(trainable, lr=3e-4, weight_decay=0.01)\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "print(f\"Trainable params: {sum(p.numel() for p in trainable):,}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ayJaazh9KPJr", "outputId": "aee93442-6889-45f0-8f91-ca49da99c4d3" }, "execution_count": 32, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "CLIP dim: 1152, DINO dim: 768\n", "Trainable params: 9,182,998\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Data loaders\n", "transform = T.Compose([\n", " T.Resize((384, 384)),\n", " T.ToTensor(),\n", " T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n", "])\n", "\n", "train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n", "val_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n", "\n", "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=2)\n", "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=2)\n", "\n", "print(f\"Train: {len(train_dataset)}, Val: {len(val_dataset)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X-KcBA-1KS1H", "outputId": "0cc24733-11d8-4ef7-d9e9-72e73c6fa2d4" }, "execution_count": 33, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train: 50000, Val: 10000\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Forward function\n", "def forward_all(images):\n", " with torch.no_grad():\n", " # CLIP - may be [B, D] or [B, S, D]\n", " clip_feat = maxvit_clip(images)\n", " if len(clip_feat.shape) == 2:\n", " clip_feat = clip_feat.unsqueeze(1) # [B, 1, D]\n", "\n", " # DINO - [B, S, D] with CLS + patches\n", " dino_feat = dino(images).last_hidden_state\n", "\n", " # Project\n", " clip_proj = proj_clip(clip_feat) # [B, S, 512]\n", " dino_proj = proj_dino(dino_feat) # [B, S, 512]\n", "\n", " # Route\n", " clip_routed = head_clip(clip_proj)\n", " dino_routed = head_dino(dino_proj)\n", "\n", " # Pool (CLS token or mean)\n", " clip_pooled = clip_routed[:, 0] if clip_routed.shape[1] > 1 else clip_routed.squeeze(1)\n", " dino_pooled = dino_routed[:, 0] # CLS token\n", "\n", " # Fuse\n", " fused = fusion(torch.cat([clip_pooled, dino_pooled], dim=-1))\n", "\n", " # Classify\n", " logits = classifier(fused)\n", " logits_clip = classifier_clip(clip_pooled)\n", " logits_dino = classifier_dino(dino_pooled)\n", "\n", " return logits, logits_clip, logits_dino" ], "metadata": { "id": "Q_PowD-wKO3S" }, "execution_count": 34, "outputs": [] }, { "cell_type": "code", "source": [ "# Cell: Train\n", "EPOCHS = 5\n", "history = []\n", "\n", "for epoch in range(EPOCHS):\n", " # Train\n", " head_clip.train(); head_dino.train(); fusion.train(); classifier.train()\n", " proj_clip.train(); proj_dino.train()\n", " classifier_clip.train(); classifier_dino.train()\n", "\n", " correct, total = 0, 0\n", " pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}\")\n", "\n", " for images, labels in pbar:\n", " images, labels = images.to(device), labels.to(device)\n", "\n", " optimizer.zero_grad()\n", " logits, logits_clip, logits_dino = forward_all(images)\n", "\n", " loss = criterion(logits, labels)\n", " loss += 0.1 * criterion(logits_clip, labels)\n", " loss += 0.1 * criterion(logits_dino, labels)\n", "\n", " loss.backward()\n", " optimizer.step()\n", "\n", " correct += (logits.argmax(-1) == labels).sum().item()\n", " total += labels.size(0)\n", " pbar.set_postfix({'acc': f'{correct/total:.1%}'})\n", "\n", " # Eval\n", " head_clip.eval(); head_dino.eval(); fusion.eval(); classifier.eval()\n", " classifier_clip.eval(); classifier_dino.eval()\n", "\n", " coll_correct, clip_correct, dino_correct, val_total = 0, 0, 0, 0\n", "\n", " with torch.no_grad():\n", " for images, labels in val_loader:\n", " images, labels = images.to(device), labels.to(device)\n", " logits, logits_clip, logits_dino = forward_all(images)\n", "\n", " coll_correct += (logits.argmax(-1) == labels).sum().item()\n", " clip_correct += (logits_clip.argmax(-1) == labels).sum().item()\n", " dino_correct += (logits_dino.argmax(-1) == labels).sum().item()\n", " val_total += labels.size(0)\n", "\n", " coll_acc = coll_correct / val_total\n", " clip_acc = clip_correct / val_total\n", " dino_acc = dino_correct / val_total\n", " rho = coll_acc / max(clip_acc, dino_acc)\n", "\n", " history.append({'epoch': epoch+1, 'collective': coll_acc, 'clip': clip_acc, 'dino': dino_acc, 'rho': rho})\n", " print(f\"Epoch {epoch+1}: Collective {coll_acc:.1%}, CLIP {clip_acc:.1%}, DINO {dino_acc:.1%}, ρ={rho:.3f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "8380866c18ea4bfaaae1b56b6e628731", "3a8f2ed289b4446fbded11ff204cfb26", "f55500b2ae094f0ba0957600b963598a", "edac6249b4144cd0ab5111ae037fdc8c", "4c4ce124a71f49afbb8413ba7e17999d", "f04129fc61be493c9ff7228b6e61e309", "adedf607636947d3bcad52baf15ab040", "9c544d4b7a3047c3b4f40983f9c3e41b", "b6cf5385becf47fb88d2c56fee6a30a3", "363966739088438a931da3733717d834", "ed83845aa50b42fc97b4a63e2ff8a0e2" ] }, "id": "x8kxu3-lLCOr", "outputId": "8d245b04-669d-4ff3-f47b-fa1520c708b3" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Epoch 1: 0%| | 0/1563 [00:00