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"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": [ + "dac1a6a433574b4a9c0af1ffbd682c20", + "b0228f7c26614de1b5fb0b6cb7adefbc", + "9e8ffacb25da46b8983dbaff545b0e36", + "bde43559a6a84179a2e7d2515b090afb", + "e14f5d1a326f409abda05c092e4d2dd3", + "8b8170382c434a6cae8bd93bf6436074", + "87255e74378a404a97a3cd1b62705182", + "803a37f6bef84ab292b33e90b8cb76f2", + "7ca52f569c824a589feba598c0332858", + "d304e708ac13425482fdaa6b1437723b", + "5e368cce751a473b996341a9d4af4957", + "0f7d4e8ad15649508092384bc619d380", + "91061fb34e31427cae7eba20b0b8ddd7", + "7634d5cee7424772badf44c1abe5aebd", + "9598fac5c7de44278d631d3c0d091152", + "c4c3f3d7884b4ce897d9fcdc7cea45f6", + "096c61b128264cf69d56135006724506", + "ee132acd1a934239b6b24d6d24e78264", + "616bbf9663e6416f9d26cb33a04c9112", + "3f570e2268cc443195736d780c01e566", + "dec8cbc81da44b39b468003ec34177c5", + "afcd82c72760404ca20e34dee8c7b1fe", + "0e64dee122de460cb72f56b298171925", + "68dd87ed820d462193e0321b67bb8340", + "377b2d4b4cd145a8b92a8d14298a4537", + "c8161b295d9247de8e59f14afd11b91c", + "c1a4b6310d5f4d009d35a944e17433f9", + "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", + 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"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", + 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"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