The inference code provided does not work

import sys
sys.path.append('./')
from videollama2 import model_init, mm_infer
from videollama2.utils import disable_torch_init

def inference():
    disable_torch_init()

    # Video Inference
    modal = 'video'
    modal_path = 'assets/cat_and_chicken.mp4' 
    instruct = 'What animals are in the video, what are they doing, and how does the video feel?'
    # Reply:
    # The video features a kitten and a baby chick playing together. The kitten is seen laying on the floor while the baby chick hops around. The two animals interact playfully with each other, and the video has a cute and heartwarming feel to it.

    model_path = 'DAMO-NLP-SG/VideoLLaMA2.1-7B-16F'
    # Base model inference (only need to replace model_path)
    # model_path = 'DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base'
    model, processor, tokenizer = model_init(model_path)
    output = mm_infer(processor[modal](modal_path), instruct, model=model, tokenizer=tokenizer, do_sample=False, modal=modal)

    print(output)

if __name__ == "__main__":
    inference()

following error occur

I also get this problem by using the old inference code. Image inference is OK,but video inference don't work, please help me to solve this issue.

My inference code: import torch import transformers

import sys sys.path.append('./') from videollama2.conversation import conv_templates from videollama2.constants import DEFAULT_MMODAL_TOKEN, MMODAL_TOKEN_INDEX from videollama2.mm_utils import get_model_name_from_path, tokenizer_MMODAL_token, process_video, process_image from videollama2.model.builder import load_pretrained_model

def inference():

Video Inference

paths = ['assets/cat_and_chicken.mp4']
questions = ['What animals are in the video, what are they doing, and how does the video feel?']
# Reply:
# The video features a kitten and a baby chick playing together. The kitten is seen laying on the floor while the baby chick hops around. The two animals interact playfully with each other, and the video has a cute and heartwarming feel to it.
modal_list = ['video']

# Image Inference
paths = ['assets/sora.png']
questions = ['What is the woman wearing, what is she doing, and how does the image feel?']
# Reply:
# The woman in the image is wearing a black coat and sunglasses, and she is walking down a rain-soaked city street. The image feels vibrant and lively, with the bright city lights reflecting off the wet pavement, creating a visually appealing atmosphere. The woman's presence adds a sense of style and confidence to the scene, as she navigates the bustling urban environment.
modal_list = ['image']

# 1. Initialize the model.
model_path = './checkpoints/VideoLLaMA2-7B'   # DAMO-NLP-SG/VideoLLaMA2-7B
# Base model inference (only need to replace model_path)
# model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-Base'
model_name = get_model_name_from_path(model_path)
tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name)
model = model.to('cuda:0')
conv_mode = 'llama2'

# 2. Visual preprocess (load & transform image or video).
if modal_list[0] == 'video':
    tensor = process_video(paths[0], processor, model.config.image_aspect_ratio).to(dtype=torch.float16, device='cuda', non_blocking=True)
    default_mm_token = DEFAULT_MMODAL_TOKEN["VIDEO"]
    modal_token_index = MMODAL_TOKEN_INDEX["VIDEO"]
else:
    tensor = process_image(paths[0], processor, model.config.image_aspect_ratio)[0].to(dtype=torch.float16, device='cuda', non_blocking=True)
    default_mm_token = DEFAULT_MMODAL_TOKEN["IMAGE"]
    modal_token_index = MMODAL_TOKEN_INDEX["IMAGE"]
tensor = [tensor]

# 3. text preprocess (tag process & generate prompt).
question = default_mm_token + "\n" + questions[0]
conv = conv_templates[conv_mode].copy()
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_MMODAL_token(prompt, tokenizer, modal_token_index, return_tensors='pt').unsqueeze(0).to('cuda:0')

with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        images_or_videos=tensor,
        modal_list=modal_list,
        do_sample=True,
        temperature=0.2,
        max_new_tokens=1024,
        use_cache=True,
    )

outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
print(outputs[0])

if name == "main": inference()

2.Terminal show: (videollama2) (base) lm@SR6430G23:~/videollama2/VideoLLaMA2$ python inference.py 200 2024-10-29 00:03:33.555533: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. You are using a model of type videollama2_mistral to instantiate a model of type . This is not supported for all configurations of models and can yield errors. Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 4/4 [00:05<00:00, 1.37s/it] Some weights of the model checkpoint at ./checkpoints/VideoLLaMA2-7B were not used when initializing Videollama2MistralForCausalLM: ['model.vision_tower.vision_tower.vision_model.embeddings.class_embedding', 'model.vision_tower.vision_tower.vision_model.embeddings.patch_embedding.weight', 'model.vision_tower.vision_tower.vision_model.embeddings.position_embedding.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.weight', 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This IS expected if you are initializing Videollama2MistralForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
This IS NOT expected if you are initializing Videollama2MistralForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's attention_mask to obtain reliable results. Setting pad_token_id to eos_token_id:2 for open-end generation. The woman is wearing a black coat and sunglasses, and she is walking down a busy city street at night. The image feels vibrant and lively, with the bright lights and neon signs illuminating the scene. The woman appears confident and stylish as she navigates the bustling urban environment.

I also get this problem by using the old inference code. Image inference is OK,but video inference don't work, please help me to solve this issue.
1. My inference code:
   import torch
   import transformers
import sys sys.path.append('./') from videollama2.conversation import conv_templates from videollama2.constants import DEFAULT_MMODAL_TOKEN, MMODAL_TOKEN_INDEX from videollama2.mm_utils import get_model_name_from_path, tokenizer_MMODAL_token, process_video, process_image from videollama2.model.builder import load_pretrained_model

def inference(): # Video Inference paths = ['assets/cat_and_chicken.mp4'] questions = ['What animals are in the video, what are they doing, and how does the video feel?'] # Reply: # The video features a kitten and a baby chick playing together. The kitten is seen laying on the floor while the baby chick hops around. The two animals interact playfully with each other, and the video has a cute and heartwarming feel to it. modal_list = ['video']
# Image Inference
paths = ['assets/sora.png']
questions = ['What is the woman wearing, what is she doing, and how does the image feel?']
# Reply:
# The woman in the image is wearing a black coat and sunglasses, and she is walking down a rain-soaked city street. The image feels vibrant and lively, with the bright city lights reflecting off the wet pavement, creating a visually appealing atmosphere. The woman's presence adds a sense of style and confidence to the scene, as she navigates the bustling urban environment.
modal_list = ['image']

# 1. Initialize the model.
model_path = './checkpoints/VideoLLaMA2-7B'   # DAMO-NLP-SG/VideoLLaMA2-7B
# Base model inference (only need to replace model_path)
# model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-Base'
model_name = get_model_name_from_path(model_path)
tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name)
model = model.to('cuda:0')
conv_mode = 'llama2'

# 2. Visual preprocess (load & transform image or video).
if modal_list[0] == 'video':
    tensor = process_video(paths[0], processor, model.config.image_aspect_ratio).to(dtype=torch.float16, device='cuda', non_blocking=True)
    default_mm_token = DEFAULT_MMODAL_TOKEN["VIDEO"]
    modal_token_index = MMODAL_TOKEN_INDEX["VIDEO"]
else:
    tensor = process_image(paths[0], processor, model.config.image_aspect_ratio)[0].to(dtype=torch.float16, device='cuda', non_blocking=True)
    default_mm_token = DEFAULT_MMODAL_TOKEN["IMAGE"]
    modal_token_index = MMODAL_TOKEN_INDEX["IMAGE"]
tensor = [tensor]

# 3. text preprocess (tag process & generate prompt).
question = default_mm_token + "\n" + questions[0]
conv = conv_templates[conv_mode].copy()
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_MMODAL_token(prompt, tokenizer, modal_token_index, return_tensors='pt').unsqueeze(0).to('cuda:0')

with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        images_or_videos=tensor,
        modal_list=modal_list,
        do_sample=True,
        temperature=0.2,
        max_new_tokens=1024,
        use_cache=True,
    )

outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
print(outputs[0])
if name == "main": inference()

2.Terminal show: (videollama2) (base) lm@SR6430G23:~/videollama2/VideoLLaMA2$ python inference.py 200 2024-10-29 00:03:33.555533: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. You are using a model of type videollama2_mistral to instantiate a model of type . This is not supported for all configurations of models and can yield errors. Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 4/4 [00:05<00:00, 1.37s/it] Some weights of the model checkpoint at ./checkpoints/VideoLLaMA2-7B were not used when initializing Videollama2MistralForCausalLM: ['model.vision_tower.vision_tower.vision_model.embeddings.class_embedding', 'model.vision_tower.vision_tower.vision_model.embeddings.patch_embedding.weight', 'model.vision_tower.vision_tower.vision_model.embeddings.position_embedding.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.weight', 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* This IS expected if you are initializing Videollama2MistralForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).

* This IS NOT expected if you are initializing Videollama2MistralForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
  Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
  The woman is wearing a black coat and sunglasses, and she is walking down a busy city street at night. The image feels vibrant and lively, with the bright lights and neon signs illuminating the scene. The woman appears confident and stylish as she navigates the bustling urban environment.

First,I used my qlore checkpoints to do the video inference,it didn't work, then I came back to use the orignal code and data which you show in the README file.

I also get this problem by using the old inference code. Image inference is OK,but video inference don't work, please help me to solve this issue.
1. My inference code:
   import torch
   import transformers
import sys sys.path.append('./') from videollama2.conversation import conv_templates from videollama2.constants import DEFAULT_MMODAL_TOKEN, MMODAL_TOKEN_INDEX from videollama2.mm_utils import get_model_name_from_path, tokenizer_MMODAL_token, process_video, process_image from videollama2.model.builder import load_pretrained_model def inference(): # Video Inference paths = ['assets/cat_and_chicken.mp4'] questions = ['What animals are in the video, what are they doing, and how does the video feel?'] # Reply: # The video features a kitten and a baby chick playing together. The kitten is seen laying on the floor while the baby chick hops around. The two animals interact playfully with each other, and the video has a cute and heartwarming feel to it. modal_list = ['video']
# Image Inference
paths = ['assets/sora.png']
questions = ['What is the woman wearing, what is she doing, and how does the image feel?']
# Reply:
# The woman in the image is wearing a black coat and sunglasses, and she is walking down a rain-soaked city street. The image feels vibrant and lively, with the bright city lights reflecting off the wet pavement, creating a visually appealing atmosphere. The woman's presence adds a sense of style and confidence to the scene, as she navigates the bustling urban environment.
modal_list = ['image']

# 1. Initialize the model.
model_path = './checkpoints/VideoLLaMA2-7B'   # DAMO-NLP-SG/VideoLLaMA2-7B
# Base model inference (only need to replace model_path)
# model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-Base'
model_name = get_model_name_from_path(model_path)
tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name)
model = model.to('cuda:0')
conv_mode = 'llama2'

# 2. Visual preprocess (load & transform image or video).
if modal_list[0] == 'video':
    tensor = process_video(paths[0], processor, model.config.image_aspect_ratio).to(dtype=torch.float16, device='cuda', non_blocking=True)
    default_mm_token = DEFAULT_MMODAL_TOKEN["VIDEO"]
    modal_token_index = MMODAL_TOKEN_INDEX["VIDEO"]
else:
    tensor = process_image(paths[0], processor, model.config.image_aspect_ratio)[0].to(dtype=torch.float16, device='cuda', non_blocking=True)
    default_mm_token = DEFAULT_MMODAL_TOKEN["IMAGE"]
    modal_token_index = MMODAL_TOKEN_INDEX["IMAGE"]
tensor = [tensor]

# 3. text preprocess (tag process & generate prompt).
question = default_mm_token + "\n" + questions[0]
conv = conv_templates[conv_mode].copy()
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_MMODAL_token(prompt, tokenizer, modal_token_index, return_tensors='pt').unsqueeze(0).to('cuda:0')

with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        images_or_videos=tensor,
        modal_list=modal_list,
        do_sample=True,
        temperature=0.2,
        max_new_tokens=1024,
        use_cache=True,
    )

outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
print(outputs[0])
if name == "main": inference() 2.Terminal show: (videollama2) (base) lm@SR6430G23:~/videollama2/VideoLLaMA2$ python inference.py 200 2024-10-29 00:03:33.555533: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. You are using a model of type videollama2_mistral to instantiate a model of type . This is not supported for all configurations of models and can yield errors. Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 4/4 [00:05<00:00, 1.37s/it] Some weights of the model checkpoint at ./checkpoints/VideoLLaMA2-7B were not used when initializing Videollama2MistralForCausalLM: ['model.vision_tower.vision_tower.vision_model.embeddings.class_embedding', 'model.vision_tower.vision_tower.vision_model.embeddings.patch_embedding.weight', 'model.vision_tower.vision_tower.vision_model.embeddings.position_embedding.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.weight', 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* This IS expected if you are initializing Videollama2MistralForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).

* This IS NOT expected if you are initializing Videollama2MistralForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
  Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
  The woman is wearing a black coat and sunglasses, and she is walking down a busy city street at night. The image feels vibrant and lively, with the bright lights and neon signs illuminating the scene. The woman appears confident and stylish as she navigates the bustling urban environment.
First,I used my qlore checkpoints to do the video inference,it didn't work, then I came back to use the orignal code and data which you show in the README file.

It's working now,but using my data's qlore checkpoints to inference new test video data is don't work.

DAMO-NLP-SG / VideoLLaMA2

Inference code does not work for videos #114

Video Inference