Generate en docs

localization/v2.5.x/site/en/integrations/integrate_with_pytorch.json

@@ -1 +1 @@
-{"codeList":["pip install pymilvus torch gdown torchvision tqdm\n","import gdown\nimport zipfile\n\nurl = 'https://drive.google.com/uc?id=1OYDHLEy992qu5C4C8HV5uDIkOWRTAR1_'\noutput = './paintings.zip'\ngdown.download(url, output)\n\nwith zipfile.ZipFile(\"./paintings.zip\",\"r\") as zip_ref:\n    zip_ref.extractall(\"./paintings\")\n","# Milvus Setup Arguments\nCOLLECTION_NAME = 'image_search'  # Collection name\nDIMENSION = 2048  # Embedding vector size in this example\nMILVUS_HOST = \"localhost\"\nMILVUS_PORT = \"19530\"\n\n# Inference Arguments\nBATCH_SIZE = 128\nTOP_K = 3\n","from pymilvus import connections\n\n# Connect to the instance\nconnections.connect(host=MILVUS_HOST, port=MILVUS_PORT)\n","from pymilvus import utility\n\n# Remove any previous collections with the same name\nif utility.has_collection(COLLECTION_NAME):\n    utility.drop_collection(COLLECTION_NAME)\n","from pymilvus import FieldSchema, CollectionSchema, DataType, Collection\n\n# Create collection which includes the id, filepath of the image, and image embedding\nfields = [\n    FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=True),\n    FieldSchema(name='filepath', dtype=DataType.VARCHAR, max_length=200),  # VARCHARS need a maximum length, so for this example they are set to 200 characters\n    FieldSchema(name='image_embedding', dtype=DataType.FLOAT_VECTOR, dim=DIMENSION)\n]\nschema = CollectionSchema(fields=fields)\ncollection = Collection(name=COLLECTION_NAME, schema=schema)\n","# Create an AutoIndex index for collection\nindex_params = {\n'metric_type':'L2',\n'index_type':\"IVF_FLAT\",\n'params':{'nlist': 16384}\n}\ncollection.create_index(field_name=\"image_embedding\", index_params=index_params)\ncollection.load()\n","import glob\n\n# Get the filepaths of the images\npaths = glob.glob('./paintings/paintings/**/*.jpg', recursive=True)\nlen(paths)\n","import torch\n\n# Load the embedding model with the last layer removed\nmodel = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)\nmodel = torch.nn.Sequential(*(list(model.children())[:-1]))\nmodel.eval()\n","from torchvision import transforms\n\n# Preprocessing for images\npreprocess = transforms.Compose([\n    transforms.Resize(256),\n    transforms.CenterCrop(224),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])\n","from PIL import Image\nfrom tqdm import tqdm\n\n# Embed function that embeds the batch and inserts it\ndef embed(data):\n    with torch.no_grad():\n        output = model(torch.stack(data[0])).squeeze()\n        collection.insert([data[1], output.tolist()])\n\ndata_batch = [[],[]]\n\n# Read the images into batches for embedding and insertion\nfor path in tqdm(paths):\n    im = Image.open(path).convert('RGB')\n    data_batch[0].append(preprocess(im))\n    data_batch[1].append(path)\n    if len(data_batch[0]) % BATCH_SIZE == 0:\n        embed(data_batch)\n        data_batch = [[],[]]\n\n# Embed and insert the remainder\nif len(data_batch[0]) != 0:\n    embed(data_batch)\n\n# Call a flush to index any unsealed segments.\ncollection.flush()\n","import glob\n\n# Get the filepaths of the search images\nsearch_paths = glob.glob('./paintings/test_paintings/**/*.jpg', recursive=True)\nlen(search_paths)\n","import time\nfrom matplotlib import pyplot as plt\n\n# Embed the search images\ndef embed(data):\n    with torch.no_grad():\n        ret = model(torch.stack(data))\n        # If more than one image, use squeeze\n        if len(ret) > 1:\n            return ret.squeeze().tolist()\n        # Squeeze would remove batch for single image, so using flatten\n        else:\n            return torch.flatten(ret, start_dim=1).tolist()\n\ndata_batch = [[],[]]\n\nfor path in search_paths:\n    im = Image.open(path).convert('RGB')\n    data_batch[0].append(preprocess(im))\n    data_batch[1].append(path)\n\nembeds = embed(data_batch[0])\nstart = time.time()\nres = collection.search(embeds, anns_field='image_embedding', param={'nprobe': 128}, limit=TOP_K, output_fields=['filepath'])\nfinish = time.time()\n","# Show the image results\nf, axarr = plt.subplots(len(data_batch[1]), TOP_K + 1, figsize=(20, 10), squeeze=False)\n\nfor hits_i, hits in enumerate(res):\n    axarr[hits_i][0].imshow(Image.open(data_batch[1][hits_i]))\n    axarr[hits_i][0].set_axis_off()\n    axarr[hits_i][0].set_title('Search Time: ' + str(finish - start))\n    for hit_i, hit in enumerate(hits):\n        axarr[hits_i][hit_i + 1].imshow(Image.open(hit.entity.get('filepath')))\n        axarr[hits_i][hit_i + 1].set_axis_off()\n        axarr[hits_i][hit_i + 1].set_title('Distance: ' + str(hit.distance))\n\n# Save the search result in a separate image file alongside your script.\nplt.savefig('search_result.png')\n"],"headingContent":"Image Search with Milvus","anchorList":[{"label":"Image Search with Milvus","href":"Image-Search-with-Milvus","type":1,"isActive":false},{"label":"Installing the requirements","href":"Installing-the-requirements","type":2,"isActive":false},{"label":"Grabbing the data","href":"Grabbing-the-data","type":2,"isActive":false},{"label":"Global Arguments","href":"Global-Arguments","type":2,"isActive":false},{"label":"Setting up Milvus","href":"Setting-up-Milvus","type":2,"isActive":false},{"label":"Inserting the data","href":"Inserting-the-data","type":2,"isActive":false},{"label":"Performing the search","href":"Performing-the-search","type":2,"isActive":false}]}
+{"codeList":["pip install pymilvus torch gdown torchvision tqdm\n","import gdown\nimport zipfile\n\nurl = 'https://drive.google.com/uc?id=1OYDHLEy992qu5C4C8HV5uDIkOWRTAR1_'\noutput = './paintings.zip'\ngdown.download(url, output)\n\nwith zipfile.ZipFile(\"./paintings.zip\",\"r\") as zip_ref:\n    zip_ref.extractall(\"./paintings\")\n","# Milvus Setup Arguments\nCOLLECTION_NAME = 'image_search'  # Collection name\nDIMENSION = 2048  # Embedding vector size in this example\nMILVUS_HOST = \"localhost\"\nMILVUS_PORT = \"19530\"\n\n# Inference Arguments\nBATCH_SIZE = 128\nTOP_K = 3\n","from pymilvus import connections\n\n# Connect to the instance\nconnections.connect(host=MILVUS_HOST, port=MILVUS_PORT)\n","from pymilvus import utility\n\n# Remove any previous collections with the same name\nif utility.has_collection(COLLECTION_NAME):\n    utility.drop_collection(COLLECTION_NAME)\n","from pymilvus import FieldSchema, CollectionSchema, DataType, Collection\n\n# Create collection which includes the id, filepath of the image, and image embedding\nfields = [\n    FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=True),\n    FieldSchema(name='filepath', dtype=DataType.VARCHAR, max_length=200),  # VARCHARS need a maximum length, so for this example they are set to 200 characters\n    FieldSchema(name='image_embedding', dtype=DataType.FLOAT_VECTOR, dim=DIMENSION)\n]\nschema = CollectionSchema(fields=fields)\ncollection = Collection(name=COLLECTION_NAME, schema=schema)\n","# Create an AutoIndex index for collection\nindex_params = {\n'metric_type':'L2',\n'index_type':\"IVF_FLAT\",\n'params':{'nlist': 16384}\n}\ncollection.create_index(field_name=\"image_embedding\", index_params=index_params)\ncollection.load()\n","import glob\n\n# Get the filepaths of the images\npaths = glob.glob('./paintings/paintings/**/*.jpg', recursive=True)\nlen(paths)\n","import torch\n\n# Load the embedding model with the last layer removed\nmodel = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)\nmodel = torch.nn.Sequential(*(list(model.children())[:-1]))\nmodel.eval()\n","from torchvision import transforms\n\n# Preprocessing for images\npreprocess = transforms.Compose([\n    transforms.Resize(256),\n    transforms.CenterCrop(224),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])\n","from PIL import Image\nfrom tqdm import tqdm\n\n# Embed function that embeds the batch and inserts it\ndef embed(data):\n    with torch.no_grad():\n        output = model(torch.stack(data[0])).squeeze()\n        collection.insert([data[1], output.tolist()])\n\ndata_batch = [[],[]]\n\n# Read the images into batches for embedding and insertion\nfor path in tqdm(paths):\n    im = Image.open(path).convert('RGB')\n    data_batch[0].append(preprocess(im))\n    data_batch[1].append(path)\n    if len(data_batch[0]) % BATCH_SIZE == 0:\n        embed(data_batch)\n        data_batch = [[],[]]\n\n# Embed and insert the remainder\nif len(data_batch[0]) != 0:\n    embed(data_batch)\n\n# Call a flush to index any unsealed segments.\ncollection.flush()\n","import glob\n\n# Get the filepaths of the search images\nsearch_paths = glob.glob('./paintings/test_paintings/**/*.jpg', recursive=True)\nlen(search_paths)\n","import time\nfrom matplotlib import pyplot as plt\n\n# Embed the search images\ndef embed(data):\n    with torch.no_grad():\n        ret = model(torch.stack(data))\n        # If more than one image, use squeeze\n        if len(ret) > 1:\n            return ret.squeeze().tolist()\n        # Squeeze would remove batch for single image, so using flatten\n        else:\n            return torch.flatten(ret, start_dim=1).tolist()\n\ndata_batch = [[],[]]\n\nfor path in search_paths:\n    im = Image.open(path).convert('RGB')\n    data_batch[0].append(preprocess(im))\n    data_batch[1].append(path)\n\nembeds = embed(data_batch[0])\nstart = time.time()\nres = collection.search(embeds, anns_field='image_embedding', param={'nprobe': 128}, limit=TOP_K, output_fields=['filepath'])\nfinish = time.time()\n","# Show the image results\nf, axarr = plt.subplots(len(data_batch[1]), TOP_K + 1, figsize=(20, 10), squeeze=False)\n\nfor hits_i, hits in enumerate(res):\n    axarr[hits_i][0].imshow(Image.open(data_batch[1][hits_i]))\n    axarr[hits_i][0].set_axis_off()\n    axarr[hits_i][0].set_title('Search Time: ' + str(finish - start))\n    for hit_i, hit in enumerate(hits):\n        axarr[hits_i][hit_i + 1].imshow(Image.open(hit.entity.get('filepath')))\n        axarr[hits_i][hit_i + 1].set_axis_off()\n        axarr[hits_i][hit_i + 1].set_title('Distance: ' + str(hit.distance))\n\n# Save the search result in a separate image file alongside your script.\nplt.savefig('search_result.png')\n"],"headingContent":"Image Search with PyTorch and Milvus","anchorList":[{"label":"Image Search with PyTorch and Milvus","href":"Image-Search-with-PyTorch-and-Milvus","type":1,"isActive":false},{"label":"Installing the requirements","href":"Installing-the-requirements","type":2,"isActive":false},{"label":"Grabbing the data","href":"Grabbing-the-data","type":2,"isActive":false},{"label":"Global Arguments","href":"Global-Arguments","type":2,"isActive":false},{"label":"Setting up Milvus","href":"Setting-up-Milvus","type":2,"isActive":false},{"label":"Inserting the data","href":"Inserting-the-data","type":2,"isActive":false},{"label":"Performing the search","href":"Performing-the-search","type":2,"isActive":false}]}

localization/v2.5.x/site/en/integrations/integrate_with_pytorch.md

@@ -1,9 +1,9 @@
 ---
 id: integrate_with_pytorch.md
-summary: This page discusses image search using Milvus
-title: Image Search with Milvus - Integration
+summary: This page demostrates how to build image search with PyTorch and Milvus
+title: Image Search with PyTorch and Milvus
 ---
-<h1 id="Image-Search-with-Milvus" class="common-anchor-header">Image Search with Milvus<button data-href="#Image-Search-with-Milvus" class="anchor-icon" translate="no">
+<h1 id="Image-Search-with-PyTorch-and-Milvus" class="common-anchor-header">Image Search with PyTorch and Milvus<button data-href="#Image-Search-with-PyTorch-and-Milvus" class="anchor-icon" translate="no">
       <svg translate="no"
         aria-hidden="true"
         focusable="false"
@@ -18,8 +18,8 @@ title: Image Search with Milvus - Integration
           d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"
         ></path>
       </svg>
-    </button></h1><p>On this page, we are going to be going over a simple image search example using Milvus. The dataset we are searching through is the Impressionist-Classifier Dataset found on <a href="https://www.kaggle.com/datasets/delayedkarma/impressionist-classifier-data">Kaggle</a>. For this example, we have rehosted the data in a public google drive.</p>
-<p>For this example, we are just using the Torchvision pre-trained Resnet50 model for embeddings. Let’s get started!</p>
+    </button></h1><p>This guide introduces an example of integrating PyTorch and Milvus to perform image search using embeddings. PyTorch is a powerful open-source deep learning framework widely used for building and deploying machine learning models. In this example, we’ll leverage its Torchvision library and a pre-trained ResNet50 model to generate feature vectors (embeddings) that represent image content. These embeddings will be stored in Milvus, a high-performance vector database, to enable efficient similarity search. The dataset used is the Impressionist-Classifier Dataset from <a href="https://www.kaggle.com/datasets/delayedkarma/impressionist-classifier-data">Kaggle</a>. By combining the deep learning capabilities of PyTorch with the scalable search functionality of Milvus, this example demonstrates how to build a robust and efficient image retrieval system.</p>
+<p>Let’s get started!</p>
 <h2 id="Installing-the-requirements" class="common-anchor-header">Installing the requirements<button data-href="#Installing-the-requirements" class="anchor-icon" translate="no">
       <svg translate="no"
         aria-hidden="true"