Update README.md (#25)

- Update README.md (1f0515555b87f739d1ad02dcc7580e764c540171)

README.md

@@ -46,7 +46,190 @@ BiomedCLIP establishes new state of the art in a wide range of standard datasets
 
 ## Model Use
 
-### How to use
+### 1. Environment
+
+```bash
+conda create -n biomedclip python=3.10 -y
+conda activate biomedclip
+pip install open_clip_torch==2.23.0 transformers==4.35.2 matplotlib
+```
+
+### 2.1 Load from HF hub
+
+```python
+import torch
+from urllib.request import urlopen
+from PIL import Image
+from open_clip import create_model_from_pretrained, get_tokenizer
+
+# Load the model and config files from the Hugging Face Hub
+model, preprocess = create_model_from_pretrained('hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224')
+tokenizer = get_tokenizer('hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224')
+
+
+# Zero-shot image classification
+template = 'this is a photo of '
+labels = [
+    'adenocarcinoma histopathology',
+    'brain MRI',
+    'covid line chart',
+    'squamous cell carcinoma histopathology',
+    'immunohistochemistry histopathology',
+    'bone X-ray',
+    'chest X-ray',
+    'pie chart',
+    'hematoxylin and eosin histopathology'
+]
+
+dataset_url = 'https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224/resolve/main/example_data/biomed_image_classification_example_data/'
+test_imgs = [
+    'squamous_cell_carcinoma_histopathology.jpeg',
+    'H_and_E_histopathology.jpg',
+    'bone_X-ray.jpg',
+    'adenocarcinoma_histopathology.jpg',
+    'covid_line_chart.png',
+    'IHC_histopathology.jpg',
+    'chest_X-ray.jpg',
+    'brain_MRI.jpg',
+    'pie_chart.png'
+]
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+model.to(device)
+model.eval()
+
+context_length = 256
+
+images = torch.stack([preprocess(Image.open(urlopen(dataset_url + img))) for img in test_imgs]).to(device)
+texts = tokenizer([template + l for l in labels], context_length=context_length).to(device)
+with torch.no_grad():
+    image_features, text_features, logit_scale = model(images, texts)
+
+    logits = (logit_scale * image_features @ text_features.t()).detach().softmax(dim=-1)
+    sorted_indices = torch.argsort(logits, dim=-1, descending=True)
+
+    logits = logits.cpu().numpy()
+    sorted_indices = sorted_indices.cpu().numpy()
+
+top_k = -1
+
+for i, img in enumerate(test_imgs):
+    pred = labels[sorted_indices[i][0]]
+
+    top_k = len(labels) if top_k == -1 else top_k
+    print(img.split('/')[-1] + ':')
+    for j in range(top_k):
+        jth_index = sorted_indices[i][j]
+        print(f'{labels[jth_index]}: {logits[i][jth_index]}')
+    print('\n')
+```
+
+### 2.2 Load from local files
+
+```python
+import json
+
+from urllib.request import urlopen
+from PIL import Image
+import torch
+from huggingface_hub import hf_hub_download
+from open_clip import create_model_and_transforms, get_tokenizer
+from open_clip.factory import HF_HUB_PREFIX, _MODEL_CONFIGS
+
+
+# Download the model and config files
+hf_hub_download(
+    repo_id="microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224",
+    filename="open_clip_pytorch_model.bin",
+    local_dir="checkpoints"
+)
+hf_hub_download(
+    repo_id="microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224",
+    filename="open_clip_config.json",
+    local_dir="checkpoints"
+)
+
+
+# Load the model and config files
+model_name = "biomedclip_local"
+
+with open("checkpoints/open_clip_config.json", "r") as f:
+    config = json.load(f)
+    model_cfg = config["model_cfg"]
+    preprocess_cfg = config["preprocess_cfg"]
+
+
+if (not model_name.startswith(HF_HUB_PREFIX)
+    and model_name not in _MODEL_CONFIGS
+    and config is not None):
+    _MODEL_CONFIGS[model_name] = model_cfg
+
+tokenizer = get_tokenizer(model_name)
+
+model, _, preprocess = create_model_and_transforms(
+    model_name=model_name,
+    pretrained="checkpoints/open_clip_pytorch_model.bin",
+    **{f"image_{k}": v for k, v in preprocess_cfg.items()},
+)
+
+
+# Zero-shot image classification
+template = 'this is a photo of '
+labels = [
+    'adenocarcinoma histopathology',
+    'brain MRI',
+    'covid line chart',
+    'squamous cell carcinoma histopathology',
+    'immunohistochemistry histopathology',
+    'bone X-ray',
+    'chest X-ray',
+    'pie chart',
+    'hematoxylin and eosin histopathology'
+]
+
+dataset_url = 'https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224/resolve/main/example_data/biomed_image_classification_example_data/'
+test_imgs = [
+    'squamous_cell_carcinoma_histopathology.jpeg',
+    'H_and_E_histopathology.jpg',
+    'bone_X-ray.jpg',
+    'adenocarcinoma_histopathology.jpg',
+    'covid_line_chart.png',
+    'IHC_histopathology.jpg',
+    'chest_X-ray.jpg',
+    'brain_MRI.jpg',
+    'pie_chart.png'
+]
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+model.to(device)
+model.eval()
+
+context_length = 256
+
+images = torch.stack([preprocess(Image.open(urlopen(dataset_url + img))) for img in test_imgs]).to(device)
+texts = tokenizer([template + l for l in labels], context_length=context_length).to(device)
+with torch.no_grad():
+    image_features, text_features, logit_scale = model(images, texts)
+
+    logits = (logit_scale * image_features @ text_features.t()).detach().softmax(dim=-1)
+    sorted_indices = torch.argsort(logits, dim=-1, descending=True)
+
+    logits = logits.cpu().numpy()
+    sorted_indices = sorted_indices.cpu().numpy()
+
+top_k = -1
+
+for i, img in enumerate(test_imgs):
+    pred = labels[sorted_indices[i][0]]
+
+    top_k = len(labels) if top_k == -1 else top_k
+    print(img.split('/')[-1] + ':')
+    for j in range(top_k):
+        jth_index = sorted_indices[i][j]
+        print(f'{labels[jth_index]}: {logits[i][jth_index]}')
+    print('\n')
+
+```
+
+### Use in Jupyter Notebook
 
 Please refer to this [example notebook](https://aka.ms/biomedclip-example-notebook).