Update app.py

app.py

@@ -6,6 +6,9 @@ from PIL import Image
 from torchvision.transforms.functional import InterpolationMode
 from transformers import AutoModel, AutoTokenizer
 
+# Device Configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
 IMAGENET_MEAN = (0.485, 0.456, 0.406)
 IMAGENET_STD = (0.229, 0.224, 0.225)
 
@@ -38,22 +41,18 @@ def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbna
     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):
@@ -63,7 +62,6 @@ def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbna
             ((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
@@ -77,108 +75,9 @@ def load_image(image_file, input_size=448, max_num=12):
     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)
+    pixel_values = torch.stack(pixel_values).to(device)
     return pixel_values
 
-# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
-path = 'OpenGVLab/InternVL2_5-1B'
-model = AutoModel.from_pretrained(
-    path,
-    torch_dtype=torch.bfloat16,
-    low_cpu_mem_usage=True,
-    use_flash_attn=True,
-    trust_remote_code=True).eval().cuda()
-tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
-
-# set the max number of tiles in `max_num`
-pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
-generation_config = dict(max_new_tokens=1024, do_sample=True)
-
-# pure-text conversation (纯文本对话)
-question = 'Hello, who are you?'
-response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
-
-question = 'Can you tell me a story?'
-response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
-
-# single-image single-round conversation (单图单轮对话)
-question = '<image>\nPlease describe the image shortly.'
-response = model.chat(tokenizer, pixel_values, question, generation_config)
-print(f'User: {question}\nAssistant: {response}')
-
-# single-image multi-round conversation (单图多轮对话)
-question = '<image>\nPlease describe the image in detail.'
-response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
-
-question = 'Please write a poem according to the image.'
-response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
-
-# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
-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)
-
-question = '<image>\nDescribe the two images in detail.'
-response, history = model.chat(tokenizer, pixel_values, question, generation_config,
-                               history=None, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
-
-question = 'What are the similarities and differences between these two images.'
-response, history = model.chat(tokenizer, pixel_values, question, generation_config,
-                               history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
-
-# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
-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-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'
-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}')
-
-question = 'What are the similarities and differences between these two images.'
-response, history = model.chat(tokenizer, pixel_values, question, generation_config,
-                               num_patches_list=num_patches_list,
-                               history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
-
-# batch inference, single image per sample (单图批处理)
-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()
-num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
-pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
-
-questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
-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}')
-
-# 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
@@ -197,17 +96,26 @@ def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=3
     pixel_values = torch.cat(pixel_values_list)
     return pixel_values, num_patches_list
 
-video_path = './examples/red-panda.mp4'
-pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=1)
-pixel_values = pixel_values.to(torch.bfloat16).cuda()
-video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
-question = video_prefix + 'What is the red panda doing?'
-# Frame1: <image>\nFrame2: <image>\n...\nFrame8: <image>\n{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}')
+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
 
-question = 'Describe this video in detail.'
-response, history = model.chat(tokenizer, pixel_values, question, generation_config,
-                               num_patches_list=num_patches_list, history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+# Load Model
+path = 'OpenGVLab/InternVL2_5-1B'
+model = AutoModel.from_pretrained(
+    path,
+    low_cpu_mem_usage=True,
+    use_flash_attn=False,
+    trust_remote_code=True
+).eval().to(device)
+tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)