Update README.md

README.md

@@ -239,132 +239,25 @@ pipeline_tag: image-text-to-text
 
 ## 4. Usage
 
-```python
-import math
-import torch
-import torchvision.transforms as T
-from PIL import Image
-from torchvision.transforms.functional import InterpolationMode
-from transformers import AutoConfig, AutoModel, AutoTokenizer
+### step1. Clone the Repository
+First, clone the repository to your local machine:
 
-IMAGENET_MEAN = (0.485, 0.456, 0.406)
-IMAGENET_STD = (0.229, 0.224, 0.225)
-
-def build_transform(input_size):
-    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
-    transform = T.Compose([
-        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
-        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
-        T.ToTensor(),
-        T.Normalize(mean=MEAN, std=STD)
-    ])
-    return transform
-
-def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
-    best_ratio_diff = float('inf')
-    best_ratio = (1, 1)
-    area = width * height
-    for ratio in target_ratios:
-        target_aspect_ratio = ratio[0] / ratio[1]
-        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
-        if ratio_diff < best_ratio_diff:
-            best_ratio_diff = ratio_diff
-            best_ratio = ratio
-        elif ratio_diff == best_ratio_diff:
-            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
-                best_ratio = ratio
-    return best_ratio
-
-def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
-    orig_width, orig_height = image.size
-    aspect_ratio = orig_width / orig_height
-    target_ratios = set(
-        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
-        i * j <= max_num and i * j >= min_num)
-    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
-    target_aspect_ratio = find_closest_aspect_ratio(
-        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
-    target_width = image_size * target_aspect_ratio[0]
-    target_height = image_size * target_aspect_ratio[1]
-    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
-    resized_img = image.resize((target_width, target_height))
-    processed_images = []
-    for i in range(blocks):
-        box = (
-            (i % (target_width // image_size)) * image_size,
-            (i // (target_width // image_size)) * image_size,
-            ((i % (target_width // image_size)) + 1) * image_size,
-            ((i // (target_width // image_size)) + 1) * image_size
-        )
-        split_img = resized_img.crop(box)
-        processed_images.append(split_img)
-    assert len(processed_images) == blocks
-    if use_thumbnail and len(processed_images) != 1:
-        thumbnail_img = image.resize((image_size, image_size))
-        processed_images.append(thumbnail_img)
-    return processed_images
-
-def load_image(image_file, input_size=448, max_num=12):
-    image = Image.open(image_file).convert('RGB')
-    transform = build_transform(input_size=input_size)
-    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
-    pixel_values = [transform(image) for image in images]
-    pixel_values = torch.stack(pixel_values)
-    return pixel_values
+```shell
+git clone https://github.com/SkyworkAI/Skywork-R1V.git
+cd skywork-r1v/inference
+```
+### step2. Set Up the Environment
 
-def split_model(model_path):
-    device_map = {}
-    world_size = torch.cuda.device_count()
-    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
-    num_layers = config.llm_config.num_hidden_layers
-    num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5))
-    num_layers_per_gpu = [num_layers_per_gpu] * world_size
-    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
-    layer_cnt = 0
-    for i, num_layer in enumerate(num_layers_per_gpu):
-        for j in range(num_layer):
-            device_map[f'language_model.model.layers.{layer_cnt}'] = i
-            layer_cnt += 1
-    device_map['vision_model'] = 0
-    device_map['mlp1'] = 0
-    device_map['language_model.model.tok_embeddings'] = 0
-    device_map['language_model.model.embed_tokens'] = 0
-    device_map['language_model.output'] = 0
-    device_map['language_model.model.norm'] = 0
-    device_map['language_model.model.rotary_emb'] = 0
-    device_map['language_model.lm_head'] = 0
-    device_map[f'language_model.model.layers.{num_layers - 1}'] = 0
-    return device_map
+```shell
+pip install -r requirements.txt
+```
 
-path = 'Skywork/Skywork-R1V-38B'
-device_map = split_model(path)
-model = AutoModel.from_pretrained(
-    path,
-    torch_dtype=torch.bfloat16,
-    load_in_8bit=False,
-    low_cpu_mem_usage=True,
-    use_flash_attn=True,
-    trust_remote_code=True,
-    device_map=device_map).eval()
-tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
-generation_config = dict(max_new_tokens=64000, do_sample=True, temperature=0.6, top_p=0.95, repetition_penalty=1.05)
+#### step3. Run the Inference Script
 
-# single-image conversation
-pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
-question = '<image>\nSelect the correct option from this question.'
-response = model.chat(tokenizer, pixel_values, question, generation_config)
-print(f'User: {question}\nAssistant: {response}')
+Prepare your images and questions, and update them to inference_with_transformers.py
 
-# multi-image conversation 
-pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
-pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
-pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
-num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
-question = '<image>\n<image>\nSelect the correct option from this question.'
-response, history = model.chat(tokenizer, pixel_values, question, generation_config,
-                               num_patches_list=num_patches_list,
-                               history=None, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+```shell
+python inference_with_transformers.py
 ```
 
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