add examples

examples/image_chat.py

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+
+import torch
+from transformers import AutoModel, AutoTokenizer, AutoConfig
+from utils import load_image
+
+if __name__ == "__main__":
+
+    dir, rev = 'morpheushoc/InternVL2_5-2B', 'main'
+    # path = 'OpenGVLab/InternVL2_5-2B'
+    model = AutoModel.from_pretrained(dir,
+                                      torch_dtype=torch.bfloat16,
+                                      load_in_8bit=False,
+                                      low_cpu_mem_usage=True,
+                                      use_flash_attn=True,
+                                      trust_remote_code=True,
+                                      revision=rev).eval().cuda()
+    tokenizer = AutoTokenizer.from_pretrained(dir, trust_remote_code=True, use_fast=False)
+    generation_config = dict(max_new_tokens=1024, do_sample=False)
+
+    fp, question = 'image1.jpg', 'Describe this image in great details'
+
+    pixel_values = load_image(fp, max_num=12).to(torch.bfloat16).cuda()
+    num_patches_list = [len(pixel_values)]
+    prefix = '<image>\n'
+    
+    question = prefix + question
+    pixel_values = pixel_values.to(torch.bfloat16).cuda()
+    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}')
+

examples/image_chat_batch.py

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+
+import torch
+from transformers import AutoModel, AutoTokenizer
+from utils import load_image
+
+if __name__ == "__main__":
+
+    dir, rev = 'morpheushoc/InternVL2_5-2B', 'main'
+    model = AutoModel.from_pretrained(dir,
+                                      torch_dtype=torch.bfloat16,
+                                      load_in_8bit=False,
+                                      low_cpu_mem_usage=True,
+                                      use_flash_attn=True,
+                                      trust_remote_code=True,
+                                      revision=rev).eval().cuda()
+    tokenizer = AutoTokenizer.from_pretrained(dir, trust_remote_code=True, use_fast=False)
+    generation_config = dict(max_new_tokens=1024, do_sample=False)
+    paths = [
+        'image1.jpg', 
+        'image1.jpg'
+        ]
+    
+    questions = [
+        'Describe this image in great details',
+        'Describe this image in great details'
+        ]
+
+    pixel_values, num_patches_list, l_questions = [], [], []
+    for path, q in zip(paths, questions):
+        pxl_val = load_image(path, max_num=12).to(torch.bfloat16).cuda()
+        pixel_values.append(pxl_val)
+        num_patches_list.append(len(pxl_val))
+        l_questions.append('<image>\n{}'.format(q)) 
+    pixel_values = torch.cat(pixel_values)
+        
+    responses = model.batch_chat(tokenizer, pixel_values, num_patches_list=num_patches_list,questions=questions,generation_config=generation_config)
+    for question, response in zip(questions, responses):
+        print(f'User: {question}\nAssistant: {response}')

examples/utils.py

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+import numpy as np
+import torch
+import torchvision.transforms as T
+from decord import VideoReader, cpu
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+
+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
+
+    # calculate the existing image aspect ratio
+    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])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    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]
+
+    # resize the image
+    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 the image
+        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
+
+# video multi-round conversation (视频多轮对话)
+def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
+    if bound:
+        start, end = bound[0], bound[1]
+    else:
+        start, end = -100000, 100000
+    start_idx = max(first_idx, round(start * fps))
+    end_idx = min(round(end * fps), max_frame)
+    seg_size = float(end_idx - start_idx) / num_segments
+    frame_indices = np.array([
+        int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
+        for idx in range(num_segments)
+    ])
+    return frame_indices
+
+def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
+    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+    max_frame = len(vr) - 1
+    fps = float(vr.get_avg_fps())
+
+    pixel_values_list, num_patches_list = [], []
+    transform = build_transform(input_size=input_size)
+    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
+    for frame_index in frame_indices:
+        img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
+        img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
+        pixel_values = [transform(tile) for tile in img]
+        pixel_values = torch.stack(pixel_values)
+        num_patches_list.append(pixel_values.shape[0])
+        pixel_values_list.append(pixel_values)
+    pixel_values = torch.cat(pixel_values_list)
+    return pixel_values, num_patches_list

examples/video_chat.py

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+
+import torch
+from transformers import AutoModel, AutoTokenizer
+from utils import load_video
+
+if __name__ == "__main__":
+
+    dir, rev = 'morpheushoc/InternVL2_5-2B', 'main'
+    model = AutoModel.from_pretrained(dir,
+                                      torch_dtype=torch.bfloat16,
+                                      load_in_8bit=False,
+                                      low_cpu_mem_usage=True,
+                                      use_flash_attn=True,
+                                      trust_remote_code=True,
+                                      revision=rev).eval().cuda()
+    tokenizer = AutoTokenizer.from_pretrained(dir, trust_remote_code=True, use_fast=False)
+    generation_config = dict(max_new_tokens=1024, do_sample=False)
+
+    fp, question = 'red-panda.mp4', 'Describe this video in great details'
+
+    pixel_values, num_patches_list = load_video(fp, num_segments=8, max_num=1)
+    prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
+    question = prefix + question
+    pixel_values = pixel_values.to(torch.bfloat16).cuda()
+    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}')