diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..5febc5f8c6599b10f1132897925c305eee622d5e 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
+data/IT_data/T-T+X_data/audio_t2x.json filter=lfs diff=lfs merge=lfs -text
+data/IT_data/T-T+X_data/image_t2x.json filter=lfs diff=lfs merge=lfs -text
+data/IT_data/T-T+X_data/video_t2x.json filter=lfs diff=lfs merge=lfs -text
+figures/demo.png filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a03164857dacfceb0de215a15e40fa2612cd9fcd
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+__pycache__/
+.idea
\ No newline at end of file
diff --git a/LICENSE.md b/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..7059d4adb9115845347d574009a98ecc914cb7ee
--- /dev/null
+++ b/LICENSE.md
@@ -0,0 +1,13 @@
+BSD 3-Clause License
+
+Copyright 2023 Shengqiong Wu All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/README.md b/README.md
index e7d90ddb789e264c5ea4a99dccd0b911883df4c5..69259f3750de7bd025535229075aba1c20992db5 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,417 @@
----
-license: unknown
----
+#
+
+
+- **Multimodal Encoding Stage.** Leveraging established encoders to encode inputs in various modalities, where these representations are projected into language-like representations comprehensible to the LLM through a projection layer.
+- **LLM Understanding and Reasoning Stage.** Harnessing an existing open-sourced LLM as the core to process input information for semantic understanding and reasoning. The LLM not only directly generates text tokens but also produces unique “modality signal” tokens that serve as instructions to dictate the decoding layers whether & what modal content to output correspondingly.
+- **Multimodal Generation Stage.** Receiving the multimodal signals with specific instructions from LLM (if any), the Transformer-based output projection layers map the signal token representations into the ones that are understandable to following multimodal decoders.
+
+
+For more technical details, kindly refer to the [paper](https://arxiv.org/pdf/2309.05519.pdf).
+
+
+-----------
+
+
+1. Code Structure
+* 2. Environment Preparation
+* 3. Training/Adapting NExt-GPT on Your Own
+ * 3.1. Preparing Pre-trained Checkpoint
+ * 3.2. Preparing Dataset
+ * 3.3. Precomputing Embeddings
+ * 3.4. Training NExT-GPT
+* 4. Running NExT-GPT System
+ * 4.1. Preparing checkpoints
+ * 4.2. Deploying Demo System
+
+****
+
+
+
+
+
+[Back to Top]
+Please first clone the repo and install the required environment, which can be done by running the following commands:
+```
+conda env create -n nextgpt python=3.8
+
+conda activate nextgpt
+
+# CUDA 11.6
+conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
+
+git clone https://github.com/NExT-GPT/NExT-GPT.git
+cd NExT-GPT
+
+pip install -r requirements.txt
+```
+
+[Back to Top]
+NExT-GPT is trained based on following excellent existing models.
+Please follow the instructions to prepare the checkpoints.
+
+- `ImageBind`
+is the unified image/video/audio encoder. The pre-trained checkpoint can be downloaded from [here](https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) with version `huge`. Afterward, put the `imagebind_huge.pth` file at [[./ckpt/pretrained_ckpt/imagebind_ckpt/huge]](ckpt/pretrained_ckpt/imagebind_ckpt/).
+- `Vicuna`:
+first prepare the LLaMA by following the instructions [[here]](ckpt/pretrained_ckpt/prepare_vicuna.md). Then put the pre-trained model at [[./ckpt/pretrained_ckpt/vicuna_ckpt/]](ckpt/pretrained_ckpt/vicuna_ckpt/).
+- `Image Diffusion`
+is used to generate images. NExT-GPT uses [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5) with version `
+v1-5`. (_will be automatically downloaded_)
+- `Audio Diffusion`
+for producing audio content. NExT-GPT employs [AudioLDM](https://github.com/haoheliu/AudioLDM) with version `l-full`. (_will be automatically downloaded_)
+- `Video Diffusion`
+for the video generation. We employ [ZeroScope](https://huggingface.co/cerspense/zeroscope_v2_576w) with version `v2_576w`. (_will be automatically downloaded_)
+
+
+
+[Back to Top]
+Please download the following datasets used for model training:
+
+A) T-X pairs data
+ - `CC3M` of ***text-image*** pairs, please follow this instruction [[here]](./data/T-X_pair_data/cc3m/prepare.md). Then put the data at [[./data/T-X_pair_data/cc3m]](./data/T-X_pair_data/cc3m).
+ - `WebVid` of ***text-video*** pairs, see the [[instruction]](./data/T-X_pair_data/webvid/prepare.md). The file should be saved at [[./data/T-X_pair_data/webvid]](./data/T-X_pair_data/webvid).
+ - `AudioCap` of ***text-audio*** pairs, see the [[instruction]](./data/T-X_pair_data/audiocap/prepare.md). Save the data in [[./data/T-X_pair_data/audiocap]](./data/T-X_pair_data/audiocap).
+
+B) Instruction data
+ - T+X-T
+ - `LLaVA` of the ***visual instruction data***, download it from [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md), and then put it at [[./data/IT_data/T+X-T_data/llava]](./data/IT_data/T+X-T_data/llava/).
+ - `Alpaca` of the ***textual instruction data***, download it from [here](https://github.com/tatsu-lab/stanford_alpaca), and then put it at [[./data/IT_data/T+X-T_data/alpaca/]](data/IT_data/T+X-T_data/alpaca/).
+ - `VideoChat`, download the ***video instruction data*** [here](https://github.com/OpenGVLab/InternVideo/tree/main/Data/instruction_data), and then put it at [[./data/IT_data/T+X-T_data/videochat/]](data/IT_data/T+X-T_data/videochat/).
+
+ Side note：After downloading dataset, please run `preprocess_dataset.py` to preprocess the dataset into a unified format.
+ - T-X+T (T2M)
+ - The `T-X+T` instruction datasets (T2M) are saved at [[./data/IT_data/T-T+X_data]](./data/IT_data/T-T+X_data).
+
+ - MosIT
+ - Download the file from [here](), put them in [[./data/IT_data/MosIT_data/]](./data/IT_data/MosIT_data/). (_We are in the process of finalizing the data and handling the copyright issue. Will release later._)
+
+
+[Back to Top]
+In decoding-side alignment training, we minimize the distance between the representation of signal tokens and captions.
+To save costs of time and memory, we precompute the text embeddings for image, audio and video captions using the text encoder within the respective diffusion models.
+
+Please run this command before the following training of NExT-GPT, where the produced `embedding` file will be saved at [[./data/embed]](./data/embed).
+```angular2html
+cd ./code/
+python process_embeddings.py ../data/T-X_pair_data/cc3m/cc3m.json image ../data/embed/ runwayml/stable-diffusion-v1-5
+```
+
+Note of arguments:
+- args[1]: path of caption file;
+- args[2]: modality, which can be `image`, `video`, and `audio`;
+- args[3]: saving path of embedding file;
+- args[4]: corresponding pre-trained diffusion model name.
+
+
+
+[Back to Top]
+
+First of all, please refer to the base configuration file [[./code/config/base.yaml]](./code/config/base.yaml) for the basic system setting of overall modules.
+
+Then, the training of NExT-GPT starts with this script:
+```angular2html
+cd ./code
+bash scripts/train.sh
+```
+Specifying the command:
+```angular2html
+deepspeed --include localhost:0 --master_addr 127.0.0.1 --master_port 28459 train.py \
+ --model nextgpt \
+ --stage 1\
+ --save_path ../ckpt/delta_ckpt/nextgpt/7b_tiva_v0/\
+ --log_path ../ckpt/delta_ckpt/nextgpt/7b_tiva_v0/log/
+```
+where the key arguments are:
+- `--include`: `localhost:0` indicating the GPT cuda number `0` of deepspeed.
+- `--stage`: training stage.
+- `--save_path`: the directory which saves the trained delta weights. This directory will be automatically created.
+- `--log_path`: the directory which saves the log file.
+
+
+
+
+
+
+The whole NExT-GPT training involves 3 steps:
+
+- **Step-1**: Encoding-side LLM-centric Multimodal Alignment. This stage trains the ***input projection layer*** while freezing the ImageBind, LLM, output projection layer.
+
+ Just run the above `train.sh` script by setting: `--stage 1`
+
+ Also refer to the running config file [[./code/config/stage_1.yaml]](./code/config/stage_1.yaml) and deepspeed config file [[./code/dsconfig/stage_1.yaml]](./code/dsconfig/stage_1.yaml) for more step-wise configurations.
+
+ Note that the dataset used for training in this step is included `dataset_name_list` and the dataset name must precisely match the definition in [[./code/dataset/catalog.py]](./code/dataset/catalog.py)
+
+
+
+- **Step-2**: Decoding-side Instruction-following Alignment. This stage trains the ***output projection layers*** while freezing the ImageBind, LLM, input projection layers.
+
+ Just run the above `train.sh` script by setting: `--stage 2`
+
+ Also refer to the running config file [[./code/config/stage_2.yaml]](./code/config/stage_2.yaml) and deepspeed config file [[./code/dsconfig/stage_2.yaml]](./code/dsconfig/stage_2.yaml) for more step-wise configurations.
+
+
+
+
+
+- **Step-3**: Instruction Tuning. This stage instruction-tune 1) the ***LLM*** via LoRA, 2) ***input projection layer*** and 3) ***output projection layer*** on the instruction dataset.
+
+ Just run the above `train.sh` script by setting: `--stage 3`
+
+ Also refer to the running config file [[./code/config/stage_3.yaml]](./code/config/stage_3.yaml) and deepspeed config file [[./code/dsconfig/stage_3.yaml]](./code/dsconfig/stage_3.yaml) for more step-wise configurations.
+
+
+
+
+[Back to Top]
+
+
+3.1 Preparing Pre-trained Checkpoint .
+
+- **Step-2**: load `Tunable parameters`. Please put the NExT-GPT system at [[./ckpt/delta_ckpt/nextgpt/7b_tiva_v0]](./ckpt/delta_ckpt/nextgpt/7b_tiva_v0). You may either 1) use the params trained yourselves, or 2) download our checkpoints from [Huggingface](https://huggingface.co/ChocoWu/nextgpt_7b_tiva_v0).
+
+
+'
+ else:
+ lines[i] = f''):
+ pattern = r'Image>(.*?)<\/Image'
+ matches = re.findall(pattern, text)
+ for m in matches:
+ image_path_list.append(m)
+ elif st.startswith(''):
+ pattern = r'Audio>(.*?)<\/Audio'
+ matches = re.findall(pattern, text)
+ for m in matches:
+ audio_path_list.append(m)
+ elif st.startswith(''):
+ pattern = r'Video>(.*?)<\/Video'
+ matches = re.findall(pattern, text)
+ for m in matches:
+ video_path_list.append(m)
+ else:
+ res_text += st
+ text = res_text
+ if image_path is not None:
+ text += f'{image_path} ' + outputs
+ if len(image_path_list) > 0:
+ for i in image_path_list:
+ text += f'{i} ' + outputs
+ if video_path is not None:
+ text += f' {video_path} ' + outputs
+ if len(video_path_list) > 0:
+ for i in video_path_list:
+ text += f' {i} ' + outputs
+ if audio_path is not None:
+ text += f'{audio_path} ' + outputs
+ if len(audio_path_list) > 0:
+ for i in audio_path_list:
+ text += f'{i} ' + outputs
+ # text = text[::-1].replace(">rb<", "", 1)[::-1]
+ text = text[:-len("{filename} ' + _temp_output
+ response += f'{filename} ' + _temp_output
+ response += f'{filename} ' + _temp_output
+ response += f'{image_path} '
+ if audio_path is not None:
+ prompt_text += f'{audio_path} '
+ if video_path is not None:
+ prompt_text += f'{video_path} '
+ prompt_text += f' {prompt_input}'
+ else:
+ for idx, (q, a) in enumerate(history):
+ if idx == 0:
+ prompt_text += f'### Human: {q}\n### Assistant: {a}\n###'
+ else:
+ prompt_text += f' Human: {q}\n### Assistant: {a}\n###'
+ prompt_text += ' Human: '
+ if image_path is not None:
+ prompt_text += f'{image_path} '
+ if audio_path is not None:
+ prompt_text += f'{audio_path} '
+ if video_path is not None:
+ prompt_text += f'{video_path} '
+ prompt_text += f' {prompt_input}'
+ print('prompt_text: ', prompt_text)
+ print('image_path: ', image_path)
+ print('audio_path: ', audio_path)
+ print('video_path: ', video_path)
+ response = model.generate({
+ 'prompt': prompt_text,
+ 'image_paths': [image_path] if image_path else [],
+ 'audio_paths': [audio_path] if audio_path else [],
+ 'video_paths': [video_path] if video_path else [],
+ # 'thermal_paths': [thermal_path] if thermal_path else [],
+ 'top_p': top_p,
+ 'temperature': temperature,
+ 'max_tgt_len': max_length,
+ 'modality_embeds': modality_cache,
+ 'filter_value': filter_value, 'min_word_tokens': min_word_tokens,
+ 'gen_scale_factor': gen_scale_factor, 'max_num_imgs': max_num_imgs,
+ 'stops_id': stops_id,
+ 'load_sd': load_sd,
+ 'generator': generator,
+ 'guidance_scale_for_img': guidance_scale_for_img,
+ 'num_inference_steps_for_img': num_inference_steps_for_img,
+
+ 'guidance_scale_for_vid': guidance_scale_for_vid,
+ 'num_inference_steps_for_vid': num_inference_steps_for_vid,
+ 'max_num_vids': max_num_vids,
+ 'height': height,
+ 'width': width,
+ 'num_frames': num_frames,
+
+ 'guidance_scale_for_aud': guidance_scale_for_aud,
+ 'num_inference_steps_for_aud': num_inference_steps_for_aud,
+ 'max_num_auds': max_num_auds,
+ 'audio_length_in_s': audio_length_in_s,
+ 'ENCOUNTERS': ENCOUNTERS,
+ })
+ response_chat, response_outputs = parse_reponse(response)
+ print('text_outputs: ', response_outputs)
+ user_chat, user_outputs = parse_text(prompt_input, image_path, video_path, audio_path)
+ chatbot.append((user_chat, response_chat))
+ history.append((user_outputs, ''.join(response_outputs).replace('\n###', '')))
+ return chatbot, history, modality_cache, None, None, None,
+
+
+def reset_user_input():
+ return gr.update(value='')
+
+
+def reset_dialog():
+ return [], []
+
+
+def reset_state():
+ return None, None, None, None, [], [], []
+
+
+def upload_image(conversation, chat_history, image_input):
+ input_image = Image.open(image_input.name).resize(
+ (224, 224)).convert('RGB')
+ input_image.save(image_input.name) # Overwrite with smaller image.
+ conversation += [(f'This is the demo page of NExT-GPT, an any-to-any multimodal LLM that allows for seamless conversion and generation among text, image, video and audio!
+ The current initial version of NExT-GPT, limited by the quantity of fine-tuning data and the quality of the base models, may generate some low-quality or hallucinated content. Please interpret the results with caution. We will continue to update the model to enhance its performance. Thank you for trying the demo! If you have any questions or feedback, feel free to contact us.
+
+ """)
+
+ with gr.Row():
+ with gr.Column(scale=0.7, min_width=500):
+ with gr.Row():
+ chatbot = gr.Chatbot(label='NExT-GPT Chatbot', avatar_images=((os.path.join(os.path.dirname(__file__), 'user.png')), (os.path.join(os.path.dirname(__file__), "bot.png")))).style(height=440)
+
+ with gr.Tab("User Input"):
+ with gr.Row(scale=3):
+ user_input = gr.Textbox(label="Text", placeholder="Key in something here...", lines=3)
+ with gr.Row(scale=3):
+ with gr.Column(scale=1):
+ # image_btn = gr.UploadButton("🖼️ Upload Image", file_types=["image"])
+ image_path = gr.Image(type="filepath", label="Image") # .style(height=200) #
+ with gr.Column(scale=1):
+ audio_path = gr.Audio(type='filepath') #.style(height=200)
+ with gr.Column(scale=1):
+ video_path = gr.Video() #.style(height=200) # , value=None, interactive=True
+ with gr.Column(scale=0.3, min_width=300):
+ with gr.Group():
+ with gr.Accordion('Text Advanced Options', open=True):
+ top_p = gr.Slider(0, 1, value=0.01, step=0.01, label="Top P", interactive=True)
+ temperature = gr.Slider(0, 1, value=1.0, step=0.01, label="Temperature", interactive=True)
+ with gr.Accordion('Image Advanced Options', open=True):
+ guidance_scale_for_img = gr.Slider(1, 10, value=7.5, step=0.5, label="Guidance scale",
+ interactive=True)
+ num_inference_steps_for_img = gr.Slider(10, 50, value=50, step=1, label="Number of inference steps",
+ interactive=True)
+ with gr.Accordion('Video Advanced Options', open=False):
+ guidance_scale_for_vid = gr.Slider(1, 10, value=7.5, step=0.5, label="Guidance scale",
+ interactive=True)
+ num_inference_steps_for_vid = gr.Slider(10, 50, value=50, step=1, label="Number of inference steps",
+ interactive=True)
+ num_frames = gr.Slider(label='Number of frames', minimum=16, maximum=32, step=8, value=24,
+ interactive=True,
+ info='Note that the content of the video also changes when you change the number of frames.')
+ with gr.Accordion('Audio Advanced Options', open=False):
+ guidance_scale_for_aud = gr.Slider(1, 10, value=7.5, step=0.5, label="Guidance scale",
+ interactive=True)
+ num_inference_steps_for_aud = gr.Slider(10, 50, value=50, step=1, label="Number of inference steps",
+ interactive=True)
+ audio_length_in_s = gr.Slider(1, 9, value=9, step=1, label="The audio length in seconds",
+ interactive=True)
+ with gr.Tab("Operation"):
+ with gr.Row(scale=1):
+ submitBtn = gr.Button(value="Submit & Run", variant="primary")
+ with gr.Row(scale=1):
+ resubmitBtn = gr.Button("Rerun")
+ with gr.Row(scale=1):
+ emptyBtn = gr.Button("Clear History")
+
+ history = gr.State([])
+ modality_cache = gr.State([])
+
+ submitBtn.click(
+ predict, [
+ user_input,
+ image_path,
+ audio_path,
+ video_path,
+ chatbot,
+ # max_length,
+ top_p,
+ temperature,
+ history,
+ modality_cache,
+ guidance_scale_for_img,
+ num_inference_steps_for_img,
+ guidance_scale_for_vid,
+ num_inference_steps_for_vid,
+ num_frames,
+ guidance_scale_for_aud,
+ num_inference_steps_for_aud,
+ audio_length_in_s
+ ], [
+ chatbot,
+ history,
+ modality_cache,
+ image_path,
+ audio_path,
+ video_path
+ ],
+ show_progress=True
+ )
+
+ resubmitBtn.click(
+ re_predict, [
+ user_input,
+ image_path,
+ audio_path,
+ video_path,
+ chatbot,
+ # max_length,
+ top_p,
+ temperature,
+ history,
+ modality_cache,
+ guidance_scale_for_img,
+ num_inference_steps_for_img,
+ guidance_scale_for_vid,
+ num_inference_steps_for_vid,
+ num_frames,
+ guidance_scale_for_aud,
+ num_inference_steps_for_aud,
+ audio_length_in_s
+ ], [
+ chatbot,
+ history,
+ modality_cache,
+ image_path,
+ audio_path,
+ video_path
+ ],
+ show_progress=True
+ )
+
+ submitBtn.click(reset_user_input, [], [user_input])
+ emptyBtn.click(reset_state, outputs=[
+ image_path,
+ audio_path,
+ video_path,
+ chatbot,
+ history,
+ modality_cache
+ ], show_progress=True)
+
+demo.queue().launch(share=True, inbrowser=True, server_name='0.0.0.0', server_port=24004)
diff --git a/code/dsconfig/stage_1.json b/code/dsconfig/stage_1.json
new file mode 100644
index 0000000000000000000000000000000000000000..b1afb1e05ec26172bd0a03c845d32e33d599a4b8
--- /dev/null
+++ b/code/dsconfig/stage_1.json
@@ -0,0 +1,58 @@
+{
+ "train_batch_size": 2,
+ "train_micro_batch_size_per_gpu": 1,
+ "gradient_accumulation_steps": 1,
+ "steps_per_print": 1,
+ "gradient_clipping": 1.0,
+ "zero_optimization": {
+ "stage": 2,
+ "offload_optimizer": {
+ "device": "cpu"
+ },
+ "contiguous_gradients": true,
+ "allgather_bucket_size": 500000000,
+ "allgather_partitions": true
+ },
+ "fp16": {
+ "enabled": true,
+ "opt_level": "O2",
+ "loss_scale": 64,
+ "loss_scale_window": 1000,
+ "initial_scale_power": 16,
+ "hysteresis": 2,
+ "min_loss_scale": 1
+ },
+ "bf16": {
+ "enable": true
+ },
+ "optimizer": {
+ "type": "Adam",
+ "params": {
+ "lr": 0.0004,
+ "betas": [
+ 0.9,
+ 0.95
+ ],
+ "eps": 1e-8,
+ "weight_decay": 0.001
+ }
+ },
+ "scheduler": {
+ "type": "WarmupDecayLR",
+ "params": {
+ "warmup_min_lr": 0,
+ "warmup_max_lr": 0.0005,
+ "warmup_num_steps": 10,
+ "total_num_steps": 10000
+ }
+ },
+ "activation_checkpointing": {
+ "partition_activations": true,
+ "cpu_checkpointing": true,
+ "contiguous_memory_optimization": false,
+ "number_checkpoints": null,
+ "synchronize_checkpoint_boundary": false,
+ "profile": false
+ }
+
+}
diff --git a/code/dsconfig/stage_2.json b/code/dsconfig/stage_2.json
new file mode 100644
index 0000000000000000000000000000000000000000..b1afb1e05ec26172bd0a03c845d32e33d599a4b8
--- /dev/null
+++ b/code/dsconfig/stage_2.json
@@ -0,0 +1,58 @@
+{
+ "train_batch_size": 2,
+ "train_micro_batch_size_per_gpu": 1,
+ "gradient_accumulation_steps": 1,
+ "steps_per_print": 1,
+ "gradient_clipping": 1.0,
+ "zero_optimization": {
+ "stage": 2,
+ "offload_optimizer": {
+ "device": "cpu"
+ },
+ "contiguous_gradients": true,
+ "allgather_bucket_size": 500000000,
+ "allgather_partitions": true
+ },
+ "fp16": {
+ "enabled": true,
+ "opt_level": "O2",
+ "loss_scale": 64,
+ "loss_scale_window": 1000,
+ "initial_scale_power": 16,
+ "hysteresis": 2,
+ "min_loss_scale": 1
+ },
+ "bf16": {
+ "enable": true
+ },
+ "optimizer": {
+ "type": "Adam",
+ "params": {
+ "lr": 0.0004,
+ "betas": [
+ 0.9,
+ 0.95
+ ],
+ "eps": 1e-8,
+ "weight_decay": 0.001
+ }
+ },
+ "scheduler": {
+ "type": "WarmupDecayLR",
+ "params": {
+ "warmup_min_lr": 0,
+ "warmup_max_lr": 0.0005,
+ "warmup_num_steps": 10,
+ "total_num_steps": 10000
+ }
+ },
+ "activation_checkpointing": {
+ "partition_activations": true,
+ "cpu_checkpointing": true,
+ "contiguous_memory_optimization": false,
+ "number_checkpoints": null,
+ "synchronize_checkpoint_boundary": false,
+ "profile": false
+ }
+
+}
diff --git a/code/dsconfig/stage_3.json b/code/dsconfig/stage_3.json
new file mode 100644
index 0000000000000000000000000000000000000000..036ccb8f8e041782ac95d28e2d746531539ba24a
--- /dev/null
+++ b/code/dsconfig/stage_3.json
@@ -0,0 +1,58 @@
+{
+ "train_batch_size": 2,
+ "train_micro_batch_size_per_gpu": 1,
+ "gradient_accumulation_steps": 1,
+ "steps_per_print": 1,
+ "gradient_clipping": 1.0,
+ "zero_optimization": {
+ "stage": 2,
+ "offload_optimizer": {
+ "device": "cpu"
+ },
+ "contiguous_gradients": true,
+ "allgather_bucket_size": 500000000,
+ "allgather_partitions": true
+ },
+ "fp16": {
+ "enabled": true,
+ "opt_level": "O2",
+ "loss_scale": 64,
+ "loss_scale_window": 1000,
+ "initial_scale_power": 16,
+ "hysteresis": 2,
+ "min_loss_scale": 1
+ },
+ "bf16": {
+ "enable": true
+ },
+ "optimizer": {
+ "type": "Adam",
+ "params": {
+ "lr": 0.0005,
+ "betas": [
+ 0.9,
+ 0.95
+ ],
+ "eps": 1e-8,
+ "weight_decay": 0.001
+ }
+ },
+ "scheduler": {
+ "type": "WarmupDecayLR",
+ "params": {
+ "warmup_min_lr": 0,
+ "warmup_max_lr": 0.0005,
+ "warmup_num_steps": 10,
+ "total_num_steps": 10000
+ }
+ },
+ "activation_checkpointing": {
+ "partition_activations": true,
+ "cpu_checkpointing": true,
+ "contiguous_memory_optimization": false,
+ "number_checkpoints": null,
+ "synchronize_checkpoint_boundary": false,
+ "profile": false
+ }
+
+}
diff --git a/code/header.py b/code/header.py
new file mode 100644
index 0000000000000000000000000000000000000000..01df0b9a5b7a25152c683068af9647e0c4738b02
--- /dev/null
+++ b/code/header.py
@@ -0,0 +1,39 @@
+import torch
+import datetime
+import types
+import deepspeed
+from transformers.deepspeed import HfDeepSpeedConfig
+import transformers
+import numpy as np
+from collections import OrderedDict
+from torch.utils.data import Dataset, DataLoader
+from torch.nn.utils import clip_grad_norm_
+from torch.cuda.amp import autocast, GradScaler
+from torch.nn import DataParallel
+from torch.optim import lr_scheduler
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+import os
+import re
+import math
+import random
+import json
+import time
+import logging
+from omegaconf import OmegaConf
+from copy import deepcopy
+import ipdb
+import argparse
+import data
+from transformers import LlamaTokenizer, LlamaForCausalLM, LlamaConfig
+from torch.nn.utils.rnn import pad_sequence
+from peft import LoraConfig, TaskType, get_peft_model
+from diffusers.utils import export_to_video
+import scipy
+from torch.utils.tensorboard import SummaryWriter
+
+logging.getLogger("transformers").setLevel(logging.WARNING)
+logging.getLogger("transformers.tokenization_utils").setLevel(logging.ERROR)
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
diff --git a/code/inference.py b/code/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..211866480186edeee7e5bd6523b84ea5b14af20f
--- /dev/null
+++ b/code/inference.py
@@ -0,0 +1,175 @@
+import os
+from model.anyToImageVideoAudio import NextGPTModel
+import torch
+import json
+from config import *
+import matplotlib.pyplot as plt
+from diffusers.utils import export_to_video
+import scipy
+
+
+def predict(
+ input,
+ image_path=None,
+ audio_path=None,
+ video_path=None,
+ thermal_path=None,
+ max_tgt_len=200,
+ top_p=10.0,
+ temperature=0.1,
+ history=None,
+ modality_cache=None,
+ filter_value=-float('Inf'), min_word_tokens=0,
+ gen_scale_factor=10.0, max_num_imgs=1,
+ stops_id=None,
+ load_sd=True,
+ generator=None,
+ guidance_scale_for_img=7.5,
+ num_inference_steps_for_img=50,
+ guidance_scale_for_vid=7.5,
+ num_inference_steps_for_vid=50,
+ max_num_vids=1,
+ height=320,
+ width=576,
+ num_frames=24,
+ guidance_scale_for_aud=7.5,
+ num_inference_steps_for_aud=50,
+ max_num_auds=1,
+ audio_length_in_s=9,
+ ENCOUNTERS=1,
+):
+ if image_path is None and audio_path is None and video_path is None and thermal_path is None:
+ # return [(input, "There is no input data provided! Please upload your data and start the conversation.")]
+ print('no image, audio, video, and thermal are input')
+ else:
+ print(
+ f'[!] image path: {image_path}\n[!] audio path: {audio_path}\n[!] video path: {video_path}\n[!] thermal path: {thermal_path}')
+
+ # prepare the prompt
+ prompt_text = ''
+ if history != None:
+ for idx, (q, a) in enumerate(history):
+ if idx == 0:
+ prompt_text += f'{q}\n### Assistant: {a}\n###'
+ else:
+ prompt_text += f' Human: {q}\n### Assistant: {a}\n###'
+ prompt_text += f'### Human: {input}'
+ else:
+ prompt_text += f'### Human: {input}'
+
+ print('prompt_text: ', prompt_text)
+
+ response = model.generate({
+ 'prompt': prompt_text,
+ 'image_paths': [image_path] if image_path else [],
+ 'audio_paths': [audio_path] if audio_path else [],
+ 'video_paths': [video_path] if video_path else [],
+ 'thermal_paths': [thermal_path] if thermal_path else [],
+ 'top_p': top_p,
+ 'temperature': temperature,
+ 'max_tgt_len': max_tgt_len,
+ 'modality_embeds': modality_cache,
+ 'filter_value': filter_value, 'min_word_tokens': min_word_tokens,
+ 'gen_scale_factor': gen_scale_factor, 'max_num_imgs': max_num_imgs,
+ 'stops_id': stops_id,
+ 'load_sd': load_sd,
+ 'generator': generator,
+ 'guidance_scale_for_img': guidance_scale_for_img,
+ 'num_inference_steps_for_img': num_inference_steps_for_img,
+
+ 'guidance_scale_for_vid': guidance_scale_for_vid,
+ 'num_inference_steps_for_vid': num_inference_steps_for_vid,
+ 'max_num_vids': max_num_vids,
+ 'height': height,
+ 'width': width,
+ 'num_frames': num_frames,
+
+ 'guidance_scale_for_aud': guidance_scale_for_aud,
+ 'num_inference_steps_for_aud': num_inference_steps_for_aud,
+ 'max_num_auds': max_num_auds,
+ 'audio_length_in_s': audio_length_in_s,
+ 'ENCOUNTERS': ENCOUNTERS,
+
+ })
+ return response
+
+
+if __name__ == '__main__':
+ # init the model
+
+ g_cuda = torch.Generator(device='cuda').manual_seed(1337)
+ args = {'model': 'nextgpt',
+ 'nextgpt_ckpt_path': '../ckpt/delta_ckpt/nextgpt/7b_tiva_v0/',
+ 'max_length': 128,
+ 'stage': 3,
+ 'root_dir': '../',
+ 'mode': 'validate',
+ }
+ args.update(load_config(args))
+
+ model = NextGPTModel(**args)
+ delta_ckpt = torch.load(os.path.join(args['nextgpt_ckpt_path'], 'pytorch_model.pt'), map_location=torch.device('cuda'))
+ # print(delta_ckpt)
+ model.load_state_dict(delta_ckpt, strict=False)
+ model = model.eval().half().cuda()
+ # model = model.eval().cuda()
+ print(f'[!] init the 7b model over ...')
+
+ """Override Chatbot.postprocess"""
+ max_tgt_length = 150
+ top_p = 1.0
+ temperature = 0.4
+ modality_cache = None
+
+ prompt = 'show me a video. a woman walk a dop in the park.'
+
+ history = []
+
+ output = predict(input=prompt, history=history,
+ max_tgt_len=max_tgt_length, top_p=top_p,
+ temperature=temperature, modality_cache=modality_cache,
+ filter_value=-float('Inf'), min_word_tokens=10,
+ gen_scale_factor=4.0, max_num_imgs=1,
+ stops_id=[[835]],
+ load_sd=True,
+ generator=g_cuda,
+ guidance_scale_for_img=7.5,
+ num_inference_steps_for_img=50,
+ guidance_scale_for_vid=7.5,
+ num_inference_steps_for_vid=50,
+ max_num_vids=1,
+ height=320,
+ width=576,
+ num_frames=24,
+ ENCOUNTERS=1
+ )
+
+ # print("output: ", output)
+
+ for i in output:
+ if isinstance(i, str):
+ print(i)
+ elif 'img' in i.keys():
+ for m in i['img']:
+ if isinstance(m, str):
+ print(m)
+ else:
+ m[0].save(f'./assets/images/{prompt}.jpg')
+
+ elif 'vid' in i.keys():
+ for idx, m in enumerate(i['vid']):
+ if isinstance(m, str):
+ print(m)
+ else:
+ video_path = export_to_video(video_frames=m, output_video_path=f'./assets/videos/{prompt}.mp4')
+ print("video_path: ", video_path)
+ elif 'aud' in i.keys():
+ for idx, m in enumerate(i['aud']):
+ if isinstance(m, str):
+ print(m)
+ else:
+ audio_path = f'./assets/audios/{prompt}.wav'
+ scipy.io.wavfile.write(audio_path, rate=16000, data=m)
+ print("video_path: ", audio_path)
+ else:
+ pass
diff --git a/code/model/ImageBind/CODE_OF_CONDUCT.md b/code/model/ImageBind/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000000000000000000000000000000000..f913b6a55a6c5ab6e1224e11fc039c3d4c3b6283
--- /dev/null
+++ b/code/model/ImageBind/CODE_OF_CONDUCT.md
@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at . All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq
\ No newline at end of file
diff --git a/code/model/ImageBind/CONTRIBUTING.md b/code/model/ImageBind/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..63d0b751e8a00b606ddff92e2524faa3c90a63b0
--- /dev/null
+++ b/code/model/ImageBind/CONTRIBUTING.md
@@ -0,0 +1,31 @@
+# Contributing to ImageBind
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here:
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to Omnivore, you agree that your contributions will be licensed
+under the [LICENSE](LICENSE) file in the root directory of this source tree.
diff --git a/code/model/ImageBind/LICENSE b/code/model/ImageBind/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..bfef380bf7d9cb74ec9ba533b37c3fbeef3bdc09
--- /dev/null
+++ b/code/model/ImageBind/LICENSE
@@ -0,0 +1,437 @@
+Attribution-NonCommercial-ShareAlike 4.0 International
+
+=======================================================================
+
+Creative Commons Corporation ("Creative Commons") is not a law firm and
+does not provide legal services or legal advice. Distribution of
+Creative Commons public licenses does not create a lawyer-client or
+other relationship. Creative Commons makes its licenses and related
+information available on an "as-is" basis. Creative Commons gives no
+warranties regarding its licenses, any material licensed under their
+terms and conditions, or any related information. Creative Commons
+disclaims all liability for damages resulting from their use to the
+fullest extent possible.
+
+Using Creative Commons Public Licenses
+
+Creative Commons public licenses provide a standard set of terms and
+conditions that creators and other rights holders may use to share
+original works of authorship and other material subject to copyright
+and certain other rights specified in the public license below. The
+following considerations are for informational purposes only, are not
+exhaustive, and do not form part of our licenses.
+
+ Considerations for licensors: Our public licenses are
+ intended for use by those authorized to give the public
+ permission to use material in ways otherwise restricted by
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+ irrevocable. Licensors should read and understand the terms
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+ Licensors should also secure all rights necessary before
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+
+ Considerations for the public: By using one of our public
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+=======================================================================
+
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
+Public License
+
+By exercising the Licensed Rights (defined below), You accept and agree
+to be bound by the terms and conditions of this Creative Commons
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+
+
+Section 1 -- Definitions.
+
+ a. Adapted Material means material subject to Copyright and Similar
+ Rights that is derived from or based upon the Licensed Material
+ and in which the Licensed Material is translated, altered,
+ arranged, transformed, or otherwise modified in a manner requiring
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+
+ b. Adapter's License means the license You apply to Your Copyright
+ and Similar Rights in Your contributions to Adapted Material in
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+ closely related to copyright including, without limitation,
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+
+ k. NonCommercial means not primarily intended for or directed towards
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+ a. License grant.
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+ b. produce, reproduce, and Share Adapted Material for
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diff --git a/code/model/ImageBind/README.md b/code/model/ImageBind/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..028fa988bb6cd9843aec9454636e1541b53680e7
--- /dev/null
+++ b/code/model/ImageBind/README.md
@@ -0,0 +1,155 @@
+# ImageBind: One Embedding Space To Bind Them All
+
+**[FAIR, Meta AI](https://ai.facebook.com/research/)**
+
+Rohit Girdhar*,
+Alaaeldin El-Nouby*,
+Zhuang Liu,
+Mannat Singh,
+Kalyan Vasudev Alwala,
+Armand Joulin,
+Ishan Misra*
+
+To appear at CVPR 2023 (*Highlighted paper*)
+
+[[`Paper`](https://facebookresearch.github.io/ImageBind/paper)] [[`Blog`](https://ai.facebook.com/blog/imagebind-six-modalities-binding-ai/)] [[`Demo`](https://imagebind.metademolab.com/)] [[`Supplementary Video`](https://dl.fbaipublicfiles.com/imagebind/imagebind_video.mp4)] [[`BibTex`](#citing-imagebind)]
+
+PyTorch implementation and pretrained models for ImageBind. For details, see the paper: **[ImageBind: One Embedding Space To Bind Them All](https://facebookresearch.github.io/ImageBind/paper)**.
+
+ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.
+
+
+
+
+
+## ImageBind model
+
+Emergent zero-shot classification performance.
+
+
+
+ Model
+ IN1k K400 NYU-D ESC LLVIP Ego4D download
+
+
+ imagebind_huge
+ 77.7
+ 50.0
+ 54.0
+ 66.9
+ 63.4
+ 25.0
+ checkpoint
+
+
+
+## Usage
+
+Install pytorch 1.13+ and other 3rd party dependencies.
+
+```shell
+conda create --name imagebind python=3.8 -y
+conda activate imagebind
+
+pip install -r requirements.txt
+```
+
+For windows users, you might need to install `soundfile` for reading/writing audio files. (Thanks @congyue1977)
+
+```
+pip install soundfile
+```
+
+
+Extract and compare features across modalities (e.g. Image, Text and Audio).
+
+```python
+import data
+import torch
+from models import imagebind_model
+from models.imagebind_model import ModalityType
+
+text_list=["A dog.", "A car", "A bird"]
+image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
+audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Instantiate model
+model = imagebind_model.imagebind_huge(pretrained=True)
+model.eval()
+model.to(device)
+
+# Load data
+inputs = {
+ ModalityType.TEXT: data.load_and_transform_text(text_list, device),
+ ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device),
+ ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, device),
+}
+
+with torch.no_grad():
+ embeddings = model(inputs)
+
+print(
+ "Vision x Text: ",
+ torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1),
+)
+print(
+ "Audio x Text: ",
+ torch.softmax(embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, dim=-1),
+)
+print(
+ "Vision x Audio: ",
+ torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, dim=-1),
+)
+
+# Expected output:
+#
+# Vision x Text:
+# tensor([[9.9761e-01, 2.3694e-03, 1.8612e-05],
+# [3.3836e-05, 9.9994e-01, 2.4118e-05],
+# [4.7997e-05, 1.3496e-02, 9.8646e-01]])
+#
+# Audio x Text:
+# tensor([[1., 0., 0.],
+# [0., 1., 0.],
+# [0., 0., 1.]])
+#
+# Vision x Audio:
+# tensor([[0.8070, 0.1088, 0.0842],
+# [0.1036, 0.7884, 0.1079],
+# [0.0018, 0.0022, 0.9960]])
+
+```
+
+## Model card
+Please see the [model card](model_card.md) for details.
+
+## License
+
+ImageBind code and model weights are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details.
+
+## Contributing
+
+See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
+
+## Citing ImageBind
+
+If you find this repository useful, please consider giving a star :star: and citation
+
+```
+@inproceedings{girdhar2023imagebind,
+ title={ImageBind: One Embedding Space To Bind Them All},
+ author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
+and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
+ booktitle={CVPR},
+ year={2023}
+}
+```
diff --git a/code/model/ImageBind/__init__.py b/code/model/ImageBind/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d872d0725710d6dde3af3b6e05382922f074338b
--- /dev/null
+++ b/code/model/ImageBind/__init__.py
@@ -0,0 +1,2 @@
+from .models import imagebind_model
+from .models.imagebind_model import ModalityType
diff --git a/code/model/ImageBind/bpe/bpe_simple_vocab_16e6.txt.gz b/code/model/ImageBind/bpe/bpe_simple_vocab_16e6.txt.gz
new file mode 100644
index 0000000000000000000000000000000000000000..36a15856e00a06a9fbed8cdd34d2393fea4a3113
--- /dev/null
+++ b/code/model/ImageBind/bpe/bpe_simple_vocab_16e6.txt.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
+size 1356917
diff --git a/code/model/ImageBind/data.py b/code/model/ImageBind/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bc32bf17bc95a280051817d0d7bd379f1416509
--- /dev/null
+++ b/code/model/ImageBind/data.py
@@ -0,0 +1,375 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+import torch.nn as nn
+import torchaudio
+import logging
+
+from .models.multimodal_preprocessors import SimpleTokenizer
+from PIL import Image
+from pytorchvideo import transforms as pv_transforms
+from pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler
+from pytorchvideo.data.encoded_video import EncodedVideo
+
+from torchvision import transforms
+from torchvision.transforms._transforms_video import NormalizeVideo
+
+DEFAULT_AUDIO_FRAME_SHIFT_MS = 10 # in milliseconds
+
+BPE_PATH = "bpe/bpe_simple_vocab_16e6.txt.gz"
+
+
+def waveform2melspec(waveform, sample_rate, num_mel_bins, target_length):
+ # Based on https://github.com/YuanGongND/ast/blob/d7d8b4b8e06cdaeb6c843cdb38794c1c7692234c/src/dataloader.py#L102
+ waveform -= waveform.mean()
+ fbank = torchaudio.compliance.kaldi.fbank(
+ waveform,
+ htk_compat=True,
+ sample_frequency=sample_rate,
+ use_energy=False,
+ window_type="hanning",
+ num_mel_bins=num_mel_bins,
+ dither=0.0,
+ frame_length=25,
+ frame_shift=DEFAULT_AUDIO_FRAME_SHIFT_MS,
+ )
+ # Convert to [mel_bins, num_frames] shape
+ fbank = fbank.transpose(0, 1)
+ # Pad to target_length
+ n_frames = fbank.size(1)
+ p = target_length - n_frames
+ # if p is too large (say >20%), flash a warning
+ if abs(p) / n_frames > 0.2:
+ logging.warning(
+ "Large gap between audio n_frames(%d) and "
+ "target_length (%d). Is the audio_target_length "
+ "setting correct?",
+ n_frames,
+ target_length,
+ )
+ # cut and pad
+ if p > 0:
+ fbank = torch.nn.functional.pad(fbank, (0, p), mode="constant", value=0)
+ elif p < 0:
+ fbank = fbank[:, 0:target_length]
+ # Convert to [1, mel_bins, num_frames] shape, essentially like a 1
+ # channel image
+ fbank = fbank.unsqueeze(0)
+ return fbank
+
+
+def get_clip_timepoints(clip_sampler, duration):
+ # Read out all clips in this video
+ all_clips_timepoints = []
+ is_last_clip = False
+ end = 0.0
+ while not is_last_clip:
+ start, end, _, _, is_last_clip = clip_sampler(end, duration, annotation=None)
+ all_clips_timepoints.append((start, end))
+ return all_clips_timepoints
+
+
+def load_and_transform_vision_data(image_paths, device):
+ if image_paths is None:
+ return None
+
+ image_ouputs = []
+ for image_path in image_paths:
+ data_transform = transforms.Compose(
+ [
+ transforms.Resize(
+ 224, interpolation=transforms.InterpolationMode.BICUBIC
+ ),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ transforms.Normalize(
+ mean=(0.48145466, 0.4578275, 0.40821073),
+ std=(0.26862954, 0.26130258, 0.27577711),
+ ),
+ ]
+ )
+ if isinstance(image_path, Image.Image):
+ image = image_path
+ else:
+ with open(image_path, "rb") as fopen:
+ image = Image.open(fopen).convert("RGB")
+
+ image = data_transform(image).to(device)
+ image_ouputs.append(image)
+ return torch.stack(image_ouputs, dim=0)
+
+
+def load_and_transform_thermal_data(thermal_paths, device):
+ if thermal_paths is None:
+ return None
+
+ thermal_ouputs = []
+ for thermal_path in thermal_paths:
+ data_transform = transforms.Compose(
+ [
+ transforms.Resize(
+ 224, interpolation=transforms.InterpolationMode.BICUBIC
+ ),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ ]
+ )
+ with open(thermal_path, "rb") as fopen:
+ thermal = Image.open(fopen).convert("L")
+ thermal = data_transform(thermal).to(device)
+ thermal_ouputs.append(thermal)
+ return torch.stack(thermal_ouputs, dim=0)
+
+
+def load_and_transform_text(text, device):
+ if text is None:
+ return None
+ tokenizer = SimpleTokenizer(bpe_path=BPE_PATH)
+ tokens = [tokenizer(t).unsqueeze(0).to(device) for t in text]
+ tokens = torch.cat(tokens, dim=0)
+ return tokens
+
+
+def load_and_transform_audio_data(
+ audio_paths,
+ device,
+ num_mel_bins=128,
+ target_length=204,
+ sample_rate=16000,
+ clip_duration=2,
+ clips_per_video=3,
+ mean=-4.268,
+ std=9.138,
+):
+ if audio_paths is None:
+ return None
+
+ audio_outputs = []
+ clip_sampler = ConstantClipsPerVideoSampler(
+ clip_duration=clip_duration, clips_per_video=clips_per_video
+ )
+
+ for audio_path in audio_paths:
+ waveform, sr = torchaudio.load(audio_path)
+ if sample_rate != sr:
+ waveform = torchaudio.functional.resample(
+ waveform, orig_freq=sr, new_freq=sample_rate
+ )
+ all_clips_timepoints = get_clip_timepoints(
+ clip_sampler, waveform.size(1) / sample_rate
+ )
+ all_clips = []
+ for clip_timepoints in all_clips_timepoints:
+ waveform_clip = waveform[
+ :,
+ int(clip_timepoints[0] * sample_rate): int(
+ clip_timepoints[1] * sample_rate
+ ),
+ ]
+ waveform_melspec = waveform2melspec(
+ waveform_clip, sample_rate, num_mel_bins, target_length
+ )
+ all_clips.append(waveform_melspec)
+
+ normalize = transforms.Normalize(mean=mean, std=std)
+ all_clips = [normalize(ac).to(device) for ac in all_clips]
+
+ all_clips = torch.stack(all_clips, dim=0)
+ audio_outputs.append(all_clips)
+
+ return torch.stack(audio_outputs, dim=0)
+
+
+def get_clip_timepoints(clip_sampler, duration):
+ # Read out all clips in this video
+ all_clips_timepoints = []
+ is_last_clip = False
+ end = 0.0
+ while not is_last_clip:
+ start, end, _, _, is_last_clip = clip_sampler(end, duration, annotation=None)
+ all_clips_timepoints.append((start, end))
+ return all_clips_timepoints
+
+
+def crop_boxes(boxes, x_offset, y_offset):
+ """
+ Perform crop on the bounding boxes given the offsets.
+ Args:
+ boxes (ndarray or None): bounding boxes to perform crop. The dimension
+ is `num boxes` x 4.
+ x_offset (int): cropping offset in the x axis.
+ y_offset (int): cropping offset in the y axis.
+ Returns:
+ cropped_boxes (ndarray or None): the cropped boxes with dimension of
+ `num boxes` x 4.
+ """
+ cropped_boxes = boxes.copy()
+ cropped_boxes[:, [0, 2]] = boxes[:, [0, 2]] - x_offset
+ cropped_boxes[:, [1, 3]] = boxes[:, [1, 3]] - y_offset
+
+ return cropped_boxes
+
+
+def uniform_crop(images, size, spatial_idx, boxes=None, scale_size=None):
+ """
+ Perform uniform spatial sampling on the images and corresponding boxes.
+ Args:
+ images (tensor): images to perform uniform crop. The dimension is
+ `num frames` x `channel` x `height` x `width`.
+ size (int): size of height and weight to crop the images.
+ spatial_idx (int): 0, 1, or 2 for left, center, and right crop if width
+ is larger than height. Or 0, 1, or 2 for top, center, and bottom
+ crop if height is larger than width.
+ boxes (ndarray or None): optional. Corresponding boxes to images.
+ Dimension is `num boxes` x 4.
+ scale_size (int): optinal. If not None, resize the images to scale_size before
+ performing any crop.
+ Returns:
+ cropped (tensor): images with dimension of
+ `num frames` x `channel` x `size` x `size`.
+ cropped_boxes (ndarray or None): the cropped boxes with dimension of
+ `num boxes` x 4.
+ """
+ assert spatial_idx in [0, 1, 2]
+ ndim = len(images.shape)
+ if ndim == 3:
+ images = images.unsqueeze(0)
+ height = images.shape[2]
+ width = images.shape[3]
+
+ if scale_size is not None:
+ if width <= height:
+ width, height = scale_size, int(height / width * scale_size)
+ else:
+ width, height = int(width / height * scale_size), scale_size
+ images = torch.nn.functional.interpolate(
+ images,
+ size=(height, width),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ y_offset = int(math.ceil((height - size) / 2))
+ x_offset = int(math.ceil((width - size) / 2))
+
+ if height > width:
+ if spatial_idx == 0:
+ y_offset = 0
+ elif spatial_idx == 2:
+ y_offset = height - size
+ else:
+ if spatial_idx == 0:
+ x_offset = 0
+ elif spatial_idx == 2:
+ x_offset = width - size
+ cropped = images[:, :, y_offset : y_offset + size, x_offset : x_offset + size]
+ cropped_boxes = crop_boxes(boxes, x_offset, y_offset) if boxes is not None else None
+ if ndim == 3:
+ cropped = cropped.squeeze(0)
+ return cropped, cropped_boxes
+
+
+class SpatialCrop(nn.Module):
+ """
+ Convert the video into 3 smaller clips spatially. Must be used after the
+ temporal crops to get spatial crops, and should be used with
+ -2 in the spatial crop at the slowfast augmentation stage (so full
+ frames are passed in here). Will return a larger list with the
+ 3x spatial crops as well.
+ """
+
+ def __init__(self, crop_size: int = 224, num_crops: int = 3):
+ super().__init__()
+ self.crop_size = crop_size
+ if num_crops == 3:
+ self.crops_to_ext = [0, 1, 2]
+ self.flipped_crops_to_ext = []
+ elif num_crops == 1:
+ self.crops_to_ext = [1]
+ self.flipped_crops_to_ext = []
+ else:
+ raise NotImplementedError("Nothing else supported yet")
+
+ def forward(self, videos):
+ """
+ Args:
+ videos: A list of C, T_I_V_A.txt, H, W videos.
+ Returns:
+ videos: A list with 3x the number of elements. Each video converted
+ to C, T_I_V_A.txt, H', W' by spatial cropping.
+ """
+ assert isinstance(videos, list), "Must be a list of videos after temporal crops"
+ assert all([video.ndim == 4 for video in videos]), "Must be (C,T_I_V_A.txt,H,W)"
+ res = []
+ for video in videos:
+ for spatial_idx in self.crops_to_ext:
+ res.append(uniform_crop(video, self.crop_size, spatial_idx)[0])
+ if not self.flipped_crops_to_ext:
+ continue
+ flipped_video = transforms.functional.hflip(video)
+ for spatial_idx in self.flipped_crops_to_ext:
+ res.append(uniform_crop(flipped_video, self.crop_size, spatial_idx)[0])
+ return res
+
+
+def load_and_transform_video_data(
+ video_paths,
+ device,
+ clip_duration=2,
+ clips_per_video=5,
+ sample_rate=16000,
+):
+ if video_paths is None:
+ return None
+
+ video_outputs = []
+ video_transform = transforms.Compose(
+ [
+ pv_transforms.ShortSideScale(224),
+ NormalizeVideo(
+ mean=(0.48145466, 0.4578275, 0.40821073),
+ std=(0.26862954, 0.26130258, 0.27577711),
+ ),
+ ]
+ )
+
+ clip_sampler = ConstantClipsPerVideoSampler(
+ clip_duration=clip_duration, clips_per_video=clips_per_video
+ )
+ frame_sampler = pv_transforms.UniformTemporalSubsample(num_samples=clip_duration)
+
+ for video_path in video_paths:
+ video = EncodedVideo.from_path(
+ video_path,
+ decoder="decord",
+ decode_audio=False,
+ # **{"sample_rate": sample_rate},
+ )
+
+ all_clips_timepoints = get_clip_timepoints(clip_sampler, video.duration)
+
+ all_video = []
+ for clip_timepoints in all_clips_timepoints:
+ # Read the clip, get frames
+ clip = video.get_clip(clip_timepoints[0], clip_timepoints[1])
+ if clip is None:
+ raise ValueError("No clip found")
+ video_clip = frame_sampler(clip["video"])
+ video_clip = video_clip / 255.0 # since this is float, need 0-1
+
+ all_video.append(video_clip)
+
+ all_video = [video_transform(clip) for clip in all_video]
+ all_video = SpatialCrop(224, num_crops=3)(all_video)
+
+ all_video = torch.stack(all_video, dim=0)
+ video_outputs.append(all_video)
+
+ return torch.stack(video_outputs, dim=0).to(device)
diff --git a/code/model/ImageBind/model_card.md b/code/model/ImageBind/model_card.md
new file mode 100644
index 0000000000000000000000000000000000000000..c7bb26500b6590b64ffa6350f37be80dc88612d8
--- /dev/null
+++ b/code/model/ImageBind/model_card.md
@@ -0,0 +1,94 @@
+# Model Card for ImageBind
+
+Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images.
+Input any of the six modalities and get the same sized embedding that can be used for cross-modal and multimodal tasks.
+
+# Model Details
+
+## Model Description
+
+
+Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images
+
+- **Developed by:** Meta AI
+- **Model type:** Multimodal model
+- **Language(s) (NLP):** en
+- **License:** CC BY-NC-SA 4.0
+- **Resources for more information:**
+ - [GitHub Repo](https://github.com/facebookresearch/ImageBind)
+
+
+# Uses
+
+
+This model is intended only for research purposes. It provides a joint embedding space for different modalities -- image/video, text, audio, depth, IMU and thermal images.
+We hope that these joint embeddings can be used for a variety of different cross-modal research, e.g., cross-modal retrieval and combining embeddings from different modalities.
+
+## Out-of-Scope Use
+
+
+
+
+This model is *NOT* intended to be used in any real world application -- commercial or otherwise.
+It may produce harmful associations with different inputs.
+The model needs to be investigated and likely re-trained on specific data for any such application.
+The model is expected to work better on web-based visual data since it was trained on such data.
+The text encoder is likely to work only on English language text because of the underlying training datasets.
+
+# Bias, Risks, and Limitations
+
+
+Open-domain joint embedding models are prone to producing specific biases, e.g., study from [CLIP](https://github.com/openai/CLIP/blob/main/model-card.md#bias-and-fairness).
+Since our model uses such models as initialization, it will exhibit such biases too.
+Moreover, for learning joint embeddings for other modalities such as audio, thermal, depth, and IMU we leverage datasets that are relatively small. These joint embeddings are thus limited to the concepts present in the datasets. For example, the thermal datasets we used are limited to outdoor street scenes, while the depth datasets are limited to indoor scenes.
+
+
+
+# Training Details
+
+## Training Data
+
+
+
+ImageBind uses image-paired data for training -- (image, X) where X is one of text, audio, depth, IMU or thermal data.
+In particular, we initialize and freeze the image and text encoders using an OpenCLIP ViT-H encoder.
+We train audio embeddings using Audioset, depth embeddings using the SUN RGB-D dataset, IMU using the Ego4D dataset and thermal embeddings using the LLVIP dataset.
+We provide the exact training data details in the paper.
+
+
+## Training Procedure
+
+
+Please refer to the research paper and github repo for exact details on this.
+
+# Evaluation
+
+## Testing Data, Factors & Metrics
+
+We evaluate the model on a variety of different classification benchmarks for each modality.
+The evaluation details are presented in the paper.
+The models performance is measured using standard classification metrics such as accuracy and mAP.
+
+# Citation
+
+
+
+**BibTeX:**
+```
+@inproceedings{girdhar2023imagebind,
+ title={ImageBind: One Embedding Space To Bind Them All},
+ author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
+and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
+ booktitle={CVPR},
+ year={2023}
+}
+```
+
+
+# Model Card Contact
+
+Please reach out to the authors at: rgirdhar@meta.com imisra@meta.com alaaelnouby@gmail.com
+
+# How to Get Started with the Model
+
+Our github repo provides a simple example to extract embeddings from images, audio etc.
diff --git a/code/model/ImageBind/models/__init__.py b/code/model/ImageBind/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/model/ImageBind/models/helpers.py b/code/model/ImageBind/models/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..049e1f1b0580832e8574350991bf347b6da81482
--- /dev/null
+++ b/code/model/ImageBind/models/helpers.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import einops
+import numpy as np
+import torch
+
+import torch.nn as nn
+
+
+class Normalize(nn.Module):
+ def __init__(self, dim: int) -> None:
+ super().__init__()
+ self.dim = dim
+
+ def forward(self, x):
+ return torch.nn.functional.normalize(x, dim=self.dim, p=2)
+
+
+class LearnableLogitScaling(nn.Module):
+ def __init__(
+ self,
+ logit_scale_init: float = 1 / 0.07,
+ learnable: bool = True,
+ max_logit_scale: float = 100,
+ ) -> None:
+ super().__init__()
+ self.max_logit_scale = max_logit_scale
+ self.logit_scale_init = logit_scale_init
+ self.learnable = learnable
+ log_logit_scale = torch.ones([]) * np.log(self.logit_scale_init)
+ if learnable:
+ self.log_logit_scale = nn.Parameter(log_logit_scale)
+ else:
+ self.register_buffer("log_logit_scale", log_logit_scale)
+
+ def forward(self, x):
+ return torch.clip(self.log_logit_scale.exp(), max=self.max_logit_scale) * x
+
+ def extra_repr(self):
+ st = f"logit_scale_init={self.logit_scale_init},learnable={self.learnable}, max_logit_scale={self.max_logit_scale}"
+ return st
+
+
+class EinOpsRearrange(nn.Module):
+ def __init__(self, rearrange_expr: str, **kwargs) -> None:
+ super().__init__()
+ self.rearrange_expr = rearrange_expr
+ self.kwargs = kwargs
+
+ def forward(self, x):
+ assert isinstance(x, torch.Tensor)
+ return einops.rearrange(x, self.rearrange_expr, **self.kwargs)
+
+
+class VerboseNNModule(nn.Module):
+ """
+ Wrapper around nn.Module that prints registered buffers and parameter names.
+ """
+
+ @staticmethod
+ def get_readable_tensor_repr(name: str, tensor: torch.Tensor) -> str:
+ st = (
+ "("
+ + name
+ + "): "
+ + "tensor("
+ + str(tuple(tensor[1].shape))
+ + ", requires_grad="
+ + str(tensor[1].requires_grad)
+ + ")\n"
+ )
+ return st
+
+ def extra_repr(self) -> str:
+ named_modules = set()
+ for p in self.named_modules():
+ named_modules.update([p[0]])
+ named_modules = list(named_modules)
+
+ string_repr = ""
+ for p in self.named_parameters():
+ name = p[0].split(".")[0]
+ if name not in named_modules:
+ string_repr += self.get_readable_tensor_repr(name, p)
+
+ for p in self.named_buffers():
+ name = p[0].split(".")[0]
+ string_repr += self.get_readable_tensor_repr(name, p)
+
+ return string_repr
+
+
+def cast_if_src_dtype(
+ tensor: torch.Tensor, src_dtype: torch.dtype, tgt_dtype: torch.dtype
+):
+ updated = False
+ if tensor.dtype == src_dtype:
+ tensor = tensor.to(dtype=tgt_dtype)
+ updated = True
+ return tensor, updated
+
+
+class QuickGELU(nn.Module):
+ # From https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/clip/model.py#L166
+ def forward(self, x: torch.Tensor):
+ return x * torch.sigmoid(1.702 * x)
+
+
+class SelectElement(nn.Module):
+ def __init__(self, index) -> None:
+ super().__init__()
+ self.index = index
+
+ def forward(self, x):
+ assert x.ndim >= 3
+ return x[:, self.index, ...]
+
+
+class SelectEOSAndProject(nn.Module):
+ """
+ Text Pooling used in OpenCLIP
+ """
+
+ def __init__(self, proj: nn.Module) -> None:
+ super().__init__()
+ self.proj = proj
+
+ def forward(self, x, seq_len):
+ assert x.ndim == 3
+ # x is of shape B x L x D
+ # take features from the eot embedding (eot_token is the highest number in each sequence)
+ x = x[torch.arange(x.shape[0]), seq_len]
+ x = self.proj(x)
+ return x
diff --git a/code/model/ImageBind/models/imagebind_model.py b/code/model/ImageBind/models/imagebind_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba1981e8790b98131e2a89388142a79c6de94628
--- /dev/null
+++ b/code/model/ImageBind/models/imagebind_model.py
@@ -0,0 +1,521 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import os
+import urllib
+from functools import partial
+from types import SimpleNamespace
+
+import torch
+import torch.nn as nn
+
+from .helpers import (
+ EinOpsRearrange,
+ LearnableLogitScaling,
+ Normalize,
+ SelectElement,
+ SelectEOSAndProject,
+)
+from .multimodal_preprocessors import (
+ AudioPreprocessor,
+ IMUPreprocessor,
+ PadIm2Video,
+ PatchEmbedGeneric,
+ RGBDTPreprocessor,
+ SpatioTemporalPosEmbeddingHelper,
+ TextPreprocessor,
+ ThermalPreprocessor,
+)
+
+from .transformer import MultiheadAttention, SimpleTransformer
+
+
+ModalityType = SimpleNamespace(
+ VISION="vision",
+ TEXT="text",
+ AUDIO="audio",
+ THERMAL="thermal",
+ DEPTH="depth",
+ IMU="imu",
+)
+
+
+class ImageBindModel(nn.Module):
+ def __init__(
+ self,
+ video_frames=2,
+ kernel_size=(2, 14, 14),
+ audio_kernel_size=16,
+ audio_stride=10,
+ out_embed_dim=768,
+ vision_embed_dim=1024,
+ vision_num_blocks=24,
+ vision_num_heads=16,
+ audio_embed_dim=768,
+ audio_num_blocks=12,
+ audio_num_heads=12,
+ audio_num_mel_bins=128,
+ audio_target_len=204,
+ audio_drop_path=0.1,
+ text_embed_dim=768,
+ text_num_blocks=12,
+ text_num_heads=12,
+ depth_embed_dim=384,
+ depth_kernel_size=16,
+ depth_num_blocks=12,
+ depth_num_heads=8,
+ depth_drop_path=0.0,
+ thermal_embed_dim=768,
+ thermal_kernel_size=16,
+ thermal_num_blocks=12,
+ thermal_num_heads=12,
+ thermal_drop_path=0.0,
+ imu_embed_dim=512,
+ imu_kernel_size=8,
+ imu_num_blocks=6,
+ imu_num_heads=8,
+ imu_drop_path=0.7,
+ ):
+ super().__init__()
+
+ self.modality_preprocessors = self._create_modality_preprocessors(
+ video_frames,
+ vision_embed_dim,
+ kernel_size,
+ text_embed_dim,
+ audio_embed_dim,
+ audio_kernel_size,
+ audio_stride,
+ audio_num_mel_bins,
+ audio_target_len,
+ depth_embed_dim,
+ depth_kernel_size,
+ thermal_embed_dim,
+ thermal_kernel_size,
+ imu_embed_dim,
+ )
+
+ self.modality_trunks = self._create_modality_trunks(
+ vision_embed_dim,
+ vision_num_blocks,
+ vision_num_heads,
+ text_embed_dim,
+ text_num_blocks,
+ text_num_heads,
+ audio_embed_dim,
+ audio_num_blocks,
+ audio_num_heads,
+ audio_drop_path,
+ depth_embed_dim,
+ depth_num_blocks,
+ depth_num_heads,
+ depth_drop_path,
+ thermal_embed_dim,
+ thermal_num_blocks,
+ thermal_num_heads,
+ thermal_drop_path,
+ imu_embed_dim,
+ imu_num_blocks,
+ imu_num_heads,
+ imu_drop_path,
+ )
+
+ self.modality_heads = self._create_modality_heads(
+ out_embed_dim,
+ vision_embed_dim,
+ text_embed_dim,
+ audio_embed_dim,
+ depth_embed_dim,
+ thermal_embed_dim,
+ imu_embed_dim,
+ )
+
+ self.modality_postprocessors = self._create_modality_postprocessors(
+ out_embed_dim
+ )
+
+ def _create_modality_preprocessors(
+ self,
+ video_frames=2,
+ vision_embed_dim=1024,
+ kernel_size=(2, 14, 14),
+ text_embed_dim=768,
+ audio_embed_dim=768,
+ audio_kernel_size=16,
+ audio_stride=10,
+ audio_num_mel_bins=128,
+ audio_target_len=204,
+ depth_embed_dim=768,
+ depth_kernel_size=16,
+ thermal_embed_dim=768,
+ thermal_kernel_size=16,
+ imu_embed_dim=512,
+ ):
+ rgbt_stem = PatchEmbedGeneric(
+ proj_stem=[
+ PadIm2Video(pad_type="repeat", ntimes=2),
+ nn.Conv3d(
+ in_channels=3,
+ kernel_size=kernel_size,
+ out_channels=vision_embed_dim,
+ stride=kernel_size,
+ bias=False,
+ ),
+ ]
+ )
+ rgbt_preprocessor = RGBDTPreprocessor(
+ img_size=[3, video_frames, 224, 224],
+ num_cls_tokens=1,
+ pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+ rgbt_stem=rgbt_stem,
+ depth_stem=None,
+ )
+
+ text_preprocessor = TextPreprocessor(
+ context_length=77,
+ vocab_size=49408,
+ embed_dim=text_embed_dim,
+ causal_masking=True,
+ )
+
+ audio_stem = PatchEmbedGeneric(
+ proj_stem=[
+ nn.Conv2d(
+ in_channels=1,
+ kernel_size=audio_kernel_size,
+ stride=audio_stride,
+ out_channels=audio_embed_dim,
+ bias=False,
+ ),
+ ],
+ norm_layer=nn.LayerNorm(normalized_shape=audio_embed_dim),
+ )
+ audio_preprocessor = AudioPreprocessor(
+ img_size=[1, audio_num_mel_bins, audio_target_len],
+ num_cls_tokens=1,
+ pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+ audio_stem=audio_stem,
+ )
+
+ depth_stem = PatchEmbedGeneric(
+ [
+ nn.Conv2d(
+ kernel_size=depth_kernel_size,
+ in_channels=1,
+ out_channels=depth_embed_dim,
+ stride=depth_kernel_size,
+ bias=False,
+ ),
+ ],
+ norm_layer=nn.LayerNorm(normalized_shape=depth_embed_dim),
+ )
+
+ depth_preprocessor = RGBDTPreprocessor(
+ img_size=[1, 224, 224],
+ num_cls_tokens=1,
+ pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+ rgbt_stem=None,
+ depth_stem=depth_stem,
+ )
+
+ thermal_stem = PatchEmbedGeneric(
+ [
+ nn.Conv2d(
+ kernel_size=thermal_kernel_size,
+ in_channels=1,
+ out_channels=thermal_embed_dim,
+ stride=thermal_kernel_size,
+ bias=False,
+ ),
+ ],
+ norm_layer=nn.LayerNorm(normalized_shape=thermal_embed_dim),
+ )
+ thermal_preprocessor = ThermalPreprocessor(
+ img_size=[1, 224, 224],
+ num_cls_tokens=1,
+ pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+ thermal_stem=thermal_stem,
+ )
+
+ imu_stem = PatchEmbedGeneric(
+ [
+ nn.Linear(
+ in_features=48,
+ out_features=imu_embed_dim,
+ bias=False,
+ ),
+ ],
+ norm_layer=nn.LayerNorm(normalized_shape=imu_embed_dim),
+ )
+
+ imu_preprocessor = IMUPreprocessor(
+ img_size=[6, 2000],
+ num_cls_tokens=1,
+ kernel_size=8,
+ embed_dim=imu_embed_dim,
+ pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+ imu_stem=imu_stem,
+ )
+
+ modality_preprocessors = {
+ ModalityType.VISION: rgbt_preprocessor,
+ ModalityType.TEXT: text_preprocessor,
+ ModalityType.AUDIO: audio_preprocessor,
+ ModalityType.DEPTH: depth_preprocessor,
+ ModalityType.THERMAL: thermal_preprocessor,
+ ModalityType.IMU: imu_preprocessor,
+ }
+
+ return nn.ModuleDict(modality_preprocessors)
+
+ def _create_modality_trunks(
+ self,
+ vision_embed_dim=1024,
+ vision_num_blocks=24,
+ vision_num_heads=16,
+ text_embed_dim=768,
+ text_num_blocks=12,
+ text_num_heads=12,
+ audio_embed_dim=768,
+ audio_num_blocks=12,
+ audio_num_heads=12,
+ audio_drop_path=0.0,
+ depth_embed_dim=768,
+ depth_num_blocks=12,
+ depth_num_heads=12,
+ depth_drop_path=0.0,
+ thermal_embed_dim=768,
+ thermal_num_blocks=12,
+ thermal_num_heads=12,
+ thermal_drop_path=0.0,
+ imu_embed_dim=512,
+ imu_num_blocks=6,
+ imu_num_heads=8,
+ imu_drop_path=0.7,
+ ):
+ def instantiate_trunk(
+ embed_dim, num_blocks, num_heads, pre_transformer_ln, add_bias_kv, drop_path
+ ):
+ return SimpleTransformer(
+ embed_dim=embed_dim,
+ num_blocks=num_blocks,
+ ffn_dropout_rate=0.0,
+ drop_path_rate=drop_path,
+ attn_target=partial(
+ MultiheadAttention,
+ embed_dim=embed_dim,
+ num_heads=num_heads,
+ bias=True,
+ add_bias_kv=add_bias_kv,
+ ),
+ pre_transformer_layer=nn.Sequential(
+ nn.LayerNorm(embed_dim, eps=1e-6)
+ if pre_transformer_ln
+ else nn.Identity(),
+ EinOpsRearrange("b l d -> l b d"),
+ ),
+ post_transformer_layer=EinOpsRearrange("l b d -> b l d"),
+ )
+
+ modality_trunks = {}
+ modality_trunks[ModalityType.VISION] = instantiate_trunk(
+ vision_embed_dim,
+ vision_num_blocks,
+ vision_num_heads,
+ pre_transformer_ln=True,
+ add_bias_kv=False,
+ drop_path=0.0,
+ )
+ modality_trunks[ModalityType.TEXT] = instantiate_trunk(
+ text_embed_dim,
+ text_num_blocks,
+ text_num_heads,
+ pre_transformer_ln=False,
+ add_bias_kv=False,
+ drop_path=0.0,
+ )
+ modality_trunks[ModalityType.AUDIO] = instantiate_trunk(
+ audio_embed_dim,
+ audio_num_blocks,
+ audio_num_heads,
+ pre_transformer_ln=False,
+ add_bias_kv=True,
+ drop_path=audio_drop_path,
+ )
+ modality_trunks[ModalityType.DEPTH] = instantiate_trunk(
+ depth_embed_dim,
+ depth_num_blocks,
+ depth_num_heads,
+ pre_transformer_ln=False,
+ add_bias_kv=True,
+ drop_path=depth_drop_path,
+ )
+ modality_trunks[ModalityType.THERMAL] = instantiate_trunk(
+ thermal_embed_dim,
+ thermal_num_blocks,
+ thermal_num_heads,
+ pre_transformer_ln=False,
+ add_bias_kv=True,
+ drop_path=thermal_drop_path,
+ )
+ modality_trunks[ModalityType.IMU] = instantiate_trunk(
+ imu_embed_dim,
+ imu_num_blocks,
+ imu_num_heads,
+ pre_transformer_ln=False,
+ add_bias_kv=True,
+ drop_path=imu_drop_path,
+ )
+
+ return nn.ModuleDict(modality_trunks)
+
+ def _create_modality_heads(
+ self,
+ out_embed_dim,
+ vision_embed_dim,
+ text_embed_dim,
+ audio_embed_dim,
+ depth_embed_dim,
+ thermal_embed_dim,
+ imu_embed_dim,
+ ):
+ modality_heads = {}
+
+ modality_heads[ModalityType.VISION] = nn.Sequential(
+ nn.LayerNorm(normalized_shape=vision_embed_dim, eps=1e-6),
+ SelectElement(index=0),
+ nn.Linear(vision_embed_dim, out_embed_dim, bias=False),
+ )
+
+ modality_heads[ModalityType.TEXT] = SelectEOSAndProject(
+ proj=nn.Sequential(
+ nn.LayerNorm(normalized_shape=text_embed_dim, eps=1e-6),
+ nn.Linear(text_embed_dim, out_embed_dim, bias=False),
+ )
+ )
+
+ modality_heads[ModalityType.AUDIO] = nn.Sequential(
+ nn.LayerNorm(normalized_shape=audio_embed_dim, eps=1e-6),
+ SelectElement(index=0),
+ nn.Linear(audio_embed_dim, out_embed_dim, bias=False),
+ )
+
+ modality_heads[ModalityType.DEPTH] = nn.Sequential(
+ nn.LayerNorm(normalized_shape=depth_embed_dim, eps=1e-6),
+ SelectElement(index=0),
+ nn.Linear(depth_embed_dim, out_embed_dim, bias=False),
+ )
+
+ modality_heads[ModalityType.THERMAL] = nn.Sequential(
+ nn.LayerNorm(normalized_shape=thermal_embed_dim, eps=1e-6),
+ SelectElement(index=0),
+ nn.Linear(thermal_embed_dim, out_embed_dim, bias=False),
+ )
+
+ modality_heads[ModalityType.IMU] = nn.Sequential(
+ nn.LayerNorm(normalized_shape=imu_embed_dim, eps=1e-6),
+ SelectElement(index=0),
+ nn.Dropout(p=0.5),
+ nn.Linear(imu_embed_dim, out_embed_dim, bias=False),
+ )
+
+ return nn.ModuleDict(modality_heads)
+
+ def _create_modality_postprocessors(self, out_embed_dim):
+ modality_postprocessors = {}
+
+ modality_postprocessors[ModalityType.VISION] = Normalize(dim=-1)
+ modality_postprocessors[ModalityType.TEXT] = nn.Sequential(
+ Normalize(dim=-1), LearnableLogitScaling(learnable=True)
+ )
+ modality_postprocessors[ModalityType.AUDIO] = nn.Sequential(
+ Normalize(dim=-1),
+ LearnableLogitScaling(logit_scale_init=20.0, learnable=False),
+ )
+ modality_postprocessors[ModalityType.DEPTH] = nn.Sequential(
+ Normalize(dim=-1),
+ LearnableLogitScaling(logit_scale_init=5.0, learnable=False),
+ )
+ modality_postprocessors[ModalityType.THERMAL] = nn.Sequential(
+ Normalize(dim=-1),
+ LearnableLogitScaling(logit_scale_init=10.0, learnable=False),
+ )
+ modality_postprocessors[ModalityType.IMU] = nn.Sequential(
+ Normalize(dim=-1),
+ LearnableLogitScaling(logit_scale_init=5.0, learnable=False),
+ )
+ return nn.ModuleDict(modality_postprocessors)
+
+ def forward(self, inputs):
+ outputs = {}
+ for modality_key, modality_value in inputs.items():
+ reduce_list = (
+ modality_value.ndim >= 5
+ ) # Audio and Video inputs consist of multiple clips
+ if reduce_list:
+ B, S = modality_value.shape[:2]
+ modality_value = modality_value.reshape(
+ B * S, *modality_value.shape[2:]
+ )
+
+ if modality_value is not None:
+ modality_value = self.modality_preprocessors[modality_key](
+ **{modality_key: modality_value}
+ )
+ trunk_inputs = modality_value["trunk"]
+ head_inputs = modality_value["head"]
+ modality_value = self.modality_trunks[modality_key](**trunk_inputs)
+ modality_value = self.modality_heads[modality_key](
+ modality_value, **head_inputs
+ )
+ if modality_key in [ModalityType.AUDIO]:
+ modality_value = self.modality_postprocessors[modality_key][0](
+ modality_value
+ )
+ else:
+ modality_value = self.modality_postprocessors[modality_key](
+ modality_value
+ )
+
+ if reduce_list:
+ modality_value = modality_value.reshape(B, S, -1)
+ modality_value = modality_value.mean(dim=1)
+
+ outputs[modality_key] = modality_value
+
+ return outputs
+
+
+def imagebind_huge(pretrained=False, store_path=r'.checkpoints'):
+ model = ImageBindModel(
+ vision_embed_dim=1280,
+ vision_num_blocks=32,
+ vision_num_heads=16,
+ text_embed_dim=1024,
+ text_num_blocks=24,
+ text_num_heads=16,
+ out_embed_dim=1024,
+ audio_drop_path=0.1,
+ imu_drop_path=0.7,
+ )
+
+ if pretrained:
+ if not os.path.exists("{}/imagebind_huge.pth".format(store_path)):
+ print(
+ "Downloading imagebind weights to {}/imagebind_huge.pth ...".format(store_path)
+ )
+ os.makedirs(store_path, exist_ok=True)
+ torch.hub.download_url_to_file(
+ "https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth",
+ "{}/imagebind_huge.pth".format(store_path),
+ progress=True,
+ )
+
+ model.load_state_dict(torch.load("{}/imagebind_huge.pth".format(store_path)))
+
+ return model, 1024
diff --git a/code/model/ImageBind/models/multimodal_preprocessors.py b/code/model/ImageBind/models/multimodal_preprocessors.py
new file mode 100644
index 0000000000000000000000000000000000000000..d16fa8bca2a3edba6d24dd5e16b8965cc004b577
--- /dev/null
+++ b/code/model/ImageBind/models/multimodal_preprocessors.py
@@ -0,0 +1,687 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import gzip
+import html
+import io
+import math
+from functools import lru_cache
+from typing import Callable, List, Optional
+
+import ftfy
+
+import numpy as np
+import regex as re
+import torch
+import torch.nn as nn
+from iopath.common.file_io import g_pathmgr
+from timm.models.layers import trunc_normal_
+
+from .helpers import cast_if_src_dtype, VerboseNNModule
+
+
+def get_sinusoid_encoding_table(n_position, d_hid):
+ """Sinusoid position encoding table"""
+
+ # TODO: make it with torch instead of numpy
+ def get_position_angle_vec(position):
+ return [
+ position / np.power(10000, 2 * (hid_j // 2) / d_hid)
+ for hid_j in range(d_hid)
+ ]
+
+ sinusoid_table = np.array(
+ [get_position_angle_vec(pos_i) for pos_i in range(n_position)]
+ )
+ sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
+ sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
+
+ return torch.FloatTensor(sinusoid_table).unsqueeze(0)
+
+
+def interpolate_pos_encoding_2d(target_spatial_size, pos_embed):
+ N = pos_embed.shape[1]
+ if N == target_spatial_size:
+ return pos_embed
+ dim = pos_embed.shape[-1]
+ # nn.functional.interpolate doesn't work with bfloat16 so we cast to float32
+ pos_embed, updated = cast_if_src_dtype(pos_embed, torch.bfloat16, torch.float32)
+ pos_embed = nn.functional.interpolate(
+ pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(
+ 0, 3, 1, 2
+ ),
+ scale_factor=math.sqrt(target_spatial_size / N),
+ mode="bicubic",
+ )
+ if updated:
+ pos_embed, _ = cast_if_src_dtype(pos_embed, torch.float32, torch.bfloat16)
+ pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+ return pos_embed
+
+
+def interpolate_pos_encoding(
+ npatch_per_img,
+ pos_embed,
+ patches_layout,
+ input_shape=None,
+ first_patch_idx=1,
+):
+ assert first_patch_idx == 0 or first_patch_idx == 1, "there is 1 CLS token or none"
+ N = pos_embed.shape[1] - first_patch_idx # since it's 1 if cls_token exists
+ if npatch_per_img == N:
+ return pos_embed
+
+ assert (
+ patches_layout[-1] == patches_layout[-2]
+ ), "Interpolation of pos embed not supported for non-square layouts"
+
+ class_emb = pos_embed[:, :first_patch_idx]
+ pos_embed = pos_embed[:, first_patch_idx:]
+
+ if input_shape is None or patches_layout[0] == 1:
+ # simple 2D pos embedding, no temporal component
+ pos_embed = interpolate_pos_encoding_2d(npatch_per_img, pos_embed)
+ elif patches_layout[0] > 1:
+ # pos embed has a temporal component
+ assert len(input_shape) == 4, "temporal interpolation not supported"
+ # we only support 2D interpolation in this case
+ num_frames = patches_layout[0]
+ num_spatial_tokens = patches_layout[1] * patches_layout[2]
+ pos_embed = pos_embed.view(1, num_frames, num_spatial_tokens, -1)
+ # interpolate embedding for zeroth frame
+ pos_embed = interpolate_pos_encoding_2d(
+ npatch_per_img, pos_embed[0, 0, ...].unsqueeze(0)
+ )
+ else:
+ raise ValueError("This type of interpolation isn't implemented")
+
+ return torch.cat((class_emb, pos_embed), dim=1)
+
+
+def _get_pos_embedding(
+ npatch_per_img,
+ pos_embed,
+ patches_layout,
+ input_shape,
+ first_patch_idx=1,
+):
+ pos_embed = interpolate_pos_encoding(
+ npatch_per_img,
+ pos_embed,
+ patches_layout,
+ input_shape=input_shape,
+ first_patch_idx=first_patch_idx,
+ )
+ return pos_embed
+
+
+class PatchEmbedGeneric(nn.Module):
+ """
+ PatchEmbed from Hydra
+ """
+
+ def __init__(self, proj_stem, norm_layer: Optional[nn.Module] = None):
+ super().__init__()
+
+ if len(proj_stem) > 1:
+ self.proj = nn.Sequential(*proj_stem)
+ else:
+ # Special case to be able to load pre-trained models that were
+ # trained with a standard stem
+ self.proj = proj_stem[0]
+ self.norm_layer = norm_layer
+
+ def get_patch_layout(self, img_size):
+ with torch.no_grad():
+ dummy_img = torch.zeros(
+ [
+ 1,
+ ]
+ + img_size
+ )
+ dummy_out = self.proj(dummy_img)
+ embed_dim = dummy_out.shape[1]
+ patches_layout = tuple(dummy_out.shape[2:])
+ num_patches = np.prod(patches_layout)
+ return patches_layout, num_patches, embed_dim
+
+ def forward(self, x):
+ x = self.proj(x)
+ # B C (T_I_V_A.txt) H W -> B (T_I_V_A.txt)HW C
+ x = x.flatten(2).transpose(1, 2)
+ if self.norm_layer is not None:
+ x = self.norm_layer(x)
+ return x
+
+
+class SpatioTemporalPosEmbeddingHelper(VerboseNNModule):
+ def __init__(
+ self,
+ patches_layout: List,
+ num_patches: int,
+ num_cls_tokens: int,
+ embed_dim: int,
+ learnable: bool,
+ ) -> None:
+ super().__init__()
+ self.num_cls_tokens = num_cls_tokens
+ self.patches_layout = patches_layout
+ self.num_patches = num_patches
+ self.num_tokens = num_cls_tokens + num_patches
+ self.learnable = learnable
+ if self.learnable:
+ self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim))
+ trunc_normal_(self.pos_embed, std=0.02)
+ else:
+ self.register_buffer(
+ "pos_embed", get_sinusoid_encoding_table(self.num_tokens, embed_dim)
+ )
+
+ def get_pos_embedding(self, vision_input, all_vision_tokens):
+ input_shape = vision_input.shape
+ pos_embed = _get_pos_embedding(
+ all_vision_tokens.size(1) - self.num_cls_tokens,
+ pos_embed=self.pos_embed,
+ patches_layout=self.patches_layout,
+ input_shape=input_shape,
+ first_patch_idx=self.num_cls_tokens,
+ )
+ return pos_embed
+
+
+class RGBDTPreprocessor(VerboseNNModule):
+ def __init__(
+ self,
+ rgbt_stem: PatchEmbedGeneric,
+ depth_stem: PatchEmbedGeneric,
+ img_size: List = (3, 224, 224),
+ num_cls_tokens: int = 1,
+ pos_embed_fn: Callable = None,
+ use_type_embed: bool = False,
+ init_param_style: str = "openclip",
+ ) -> None:
+ super().__init__()
+ stem = rgbt_stem if rgbt_stem is not None else depth_stem
+ (
+ self.patches_layout,
+ self.num_patches,
+ self.embed_dim,
+ ) = stem.get_patch_layout(img_size)
+ self.rgbt_stem = rgbt_stem
+ self.depth_stem = depth_stem
+ self.use_pos_embed = pos_embed_fn is not None
+ self.use_type_embed = use_type_embed
+ self.num_cls_tokens = num_cls_tokens
+
+ if self.use_pos_embed:
+ self.pos_embedding_helper = pos_embed_fn(
+ patches_layout=self.patches_layout,
+ num_cls_tokens=num_cls_tokens,
+ num_patches=self.num_patches,
+ embed_dim=self.embed_dim,
+ )
+ if self.num_cls_tokens > 0:
+ self.cls_token = nn.Parameter(
+ torch.zeros(1, self.num_cls_tokens, self.embed_dim)
+ )
+ if self.use_type_embed:
+ self.type_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
+
+ self.init_parameters(init_param_style)
+
+ @torch.no_grad()
+ def init_parameters(self, init_param_style):
+ if init_param_style == "openclip":
+ # OpenCLIP style initialization
+ scale = self.embed_dim**-0.5
+ if self.use_pos_embed:
+ nn.init.normal_(self.pos_embedding_helper.pos_embed)
+ self.pos_embedding_helper.pos_embed *= scale
+
+ if self.num_cls_tokens > 0:
+ nn.init.normal_(self.cls_token)
+ self.cls_token *= scale
+ elif init_param_style == "vit":
+ self.cls_token.data.fill_(0)
+ else:
+ raise ValueError(f"Unknown init {init_param_style}")
+
+ if self.use_type_embed:
+ nn.init.normal_(self.type_embed)
+
+ def tokenize_input_and_cls_pos(self, input, stem, mask):
+ # tokens is of shape B x L x D
+ tokens = stem(input)
+ assert tokens.ndim == 3
+ assert tokens.shape[2] == self.embed_dim
+ B = tokens.shape[0]
+ if self.num_cls_tokens > 0:
+ class_tokens = self.cls_token.expand(
+ B, -1, -1
+ ) # stole class_tokens impl from Phil Wang, thanks
+ tokens = torch.cat((class_tokens, tokens), dim=1)
+ if self.use_pos_embed:
+ pos_embed = self.pos_embedding_helper.get_pos_embedding(input, tokens)
+ tokens = tokens + pos_embed
+ if self.use_type_embed:
+ tokens = tokens + self.type_embed.expand(B, -1, -1)
+ return tokens
+
+ def forward(self, vision=None, depth=None, patch_mask=None):
+ if patch_mask is not None:
+ raise NotImplementedError()
+
+ if vision is not None:
+ vision_tokens = self.tokenize_input_and_cls_pos(
+ vision, self.rgbt_stem, patch_mask
+ )
+
+ if depth is not None:
+ depth_tokens = self.tokenize_input_and_cls_pos(
+ depth, self.depth_stem, patch_mask
+ )
+
+ # aggregate tokens
+ if vision is not None and depth is not None:
+ final_tokens = vision_tokens + depth_tokens
+ else:
+ final_tokens = vision_tokens if vision is not None else depth_tokens
+ return_dict = {
+ "trunk": {
+ "tokens": final_tokens,
+ },
+ "head": {},
+ }
+ return return_dict
+
+
+class AudioPreprocessor(RGBDTPreprocessor):
+ def __init__(self, audio_stem: PatchEmbedGeneric, **kwargs) -> None:
+ super().__init__(rgbt_stem=audio_stem, depth_stem=None, **kwargs)
+
+ def forward(self, audio=None):
+ return super().forward(vision=audio)
+
+
+class ThermalPreprocessor(RGBDTPreprocessor):
+ def __init__(self, thermal_stem: PatchEmbedGeneric, **kwargs) -> None:
+ super().__init__(rgbt_stem=thermal_stem, depth_stem=None, **kwargs)
+
+ def forward(self, thermal=None):
+ return super().forward(vision=thermal)
+
+
+def build_causal_attention_mask(context_length):
+ # lazily create causal attention mask, with full attention between the vision tokens
+ # pytorch uses additive attention mask; fill with -inf
+ mask = torch.empty(context_length, context_length, requires_grad=False)
+ mask.fill_(float("-inf"))
+ mask.triu_(1) # zero out the lower diagonal
+ return mask
+
+
+class TextPreprocessor(VerboseNNModule):
+ def __init__(
+ self,
+ vocab_size: int,
+ context_length: int,
+ embed_dim: int,
+ causal_masking: bool,
+ supply_seq_len_to_head: bool = True,
+ num_cls_tokens: int = 0,
+ init_param_style: str = "openclip",
+ ) -> None:
+ super().__init__()
+ self.vocab_size = vocab_size
+ self.context_length = context_length
+ self.token_embedding = nn.Embedding(vocab_size, embed_dim)
+ self.pos_embed = nn.Parameter(
+ torch.empty(1, self.context_length + num_cls_tokens, embed_dim)
+ )
+ self.causal_masking = causal_masking
+ if self.causal_masking:
+ mask = build_causal_attention_mask(self.context_length)
+ # register the mask as a buffer so it can be moved to the right device
+ self.register_buffer("mask", mask)
+
+ self.supply_seq_len_to_head = supply_seq_len_to_head
+ self.num_cls_tokens = num_cls_tokens
+ self.embed_dim = embed_dim
+ if num_cls_tokens > 0:
+ assert self.causal_masking is False, "Masking + CLS token isn't implemented"
+ self.cls_token = nn.Parameter(
+ torch.zeros(1, self.num_cls_tokens, embed_dim)
+ )
+
+ self.init_parameters(init_param_style)
+
+ @torch.no_grad()
+ def init_parameters(self, init_param_style="openclip"):
+ # OpenCLIP style initialization
+ nn.init.normal_(self.token_embedding.weight, std=0.02)
+ nn.init.normal_(self.pos_embed, std=0.01)
+
+ if init_param_style == "openclip":
+ # OpenCLIP style initialization
+ scale = self.embed_dim**-0.5
+ if self.num_cls_tokens > 0:
+ nn.init.normal_(self.cls_token)
+ self.cls_token *= scale
+ elif init_param_style == "vit":
+ self.cls_token.data.fill_(0)
+ else:
+ raise ValueError(f"Unknown init {init_param_style}")
+
+ def forward(self, text):
+ # text tokens are of shape B x L x D
+ text_tokens = self.token_embedding(text)
+ # concat CLS tokens if any
+ if self.num_cls_tokens > 0:
+ B = text_tokens.shape[0]
+ class_tokens = self.cls_token.expand(
+ B, -1, -1
+ ) # stole class_tokens impl from Phil Wang, thanks
+ text_tokens = torch.cat((class_tokens, text_tokens), dim=1)
+ text_tokens = text_tokens + self.pos_embed
+ return_dict = {
+ "trunk": {
+ "tokens": text_tokens,
+ },
+ "head": {},
+ }
+ # Compute sequence length after adding CLS tokens
+ if self.supply_seq_len_to_head:
+ text_lengths = text.argmax(dim=-1)
+ return_dict["head"] = {
+ "seq_len": text_lengths,
+ }
+ if self.causal_masking:
+ return_dict["trunk"].update({"attn_mask": self.mask})
+ return return_dict
+
+
+class Im2Video(nn.Module):
+ """Convert an image into a trivial video."""
+
+ def __init__(self, time_dim=2):
+ super().__init__()
+ self.time_dim = time_dim
+
+ def forward(self, x):
+ if x.ndim == 4:
+ # B, C, H, W -> B, C, T_I_V_A.txt, H, W
+ return x.unsqueeze(self.time_dim)
+ elif x.ndim == 5:
+ return x
+ else:
+ raise ValueError(f"Dimension incorrect {x.shape}")
+
+
+class PadIm2Video(Im2Video):
+ def __init__(self, ntimes, pad_type, time_dim=2):
+ super().__init__(time_dim=time_dim)
+ assert ntimes > 0
+ assert pad_type in ["zero", "repeat"]
+ self.ntimes = ntimes
+ self.pad_type = pad_type
+
+ def forward(self, x):
+ x = super().forward(x)
+ if x.shape[self.time_dim] == 1:
+ if self.pad_type == "repeat":
+ new_shape = [1] * len(x.shape)
+ new_shape[self.time_dim] = self.ntimes
+ x = x.repeat(new_shape)
+ elif self.pad_type == "zero":
+ padarg = [0, 0] * len(x.shape)
+ padarg[2 * self.time_dim + 1] = self.ntimes - x.shape[self.time_dim]
+ x = nn.functional.pad(x, padarg)
+ return x
+
+
+# Modified from github.com/openai/CLIP
+@lru_cache()
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a corresponding list of unicode strings.
+ The reversible bpe codes work on unicode strings.
+ This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+ When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+ This is a signficant percentage of your normal, say, 32K bpe vocab.
+ To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+ And avoids mapping to whitespace/control characters the bpe code barfs on.
+ """
+ bs = (
+ list(range(ord("!"), ord("~") + 1))
+ + list(range(ord("¡"), ord("¬") + 1))
+ + list(range(ord("®"), ord("ÿ") + 1))
+ )
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8 + n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+ """Return set of symbol pairs in a word.
+ Word is represented as tuple of symbols (symbols being variable-length strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+def basic_clean(text):
+ text = ftfy.fix_text(text)
+ text = html.unescape(html.unescape(text))
+ return text.strip()
+
+
+def whitespace_clean(text):
+ text = re.sub(r"\s+", " ", text)
+ text = text.strip()
+ return text
+
+
+class SimpleTokenizer(object):
+ def __init__(self, bpe_path: str, context_length=77):
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+
+ with g_pathmgr.open(bpe_path, "rb") as fh:
+ bpe_bytes = io.BytesIO(fh.read())
+ merges = gzip.open(bpe_bytes).read().decode("utf-8").split("\n")
+ merges = merges[1 : 49152 - 256 - 2 + 1]
+ merges = [tuple(merge.split()) for merge in merges]
+ vocab = list(bytes_to_unicode().values())
+ vocab = vocab + [v + "" for v in vocab]
+ for merge in merges:
+ vocab.append("".join(merge))
+ vocab.extend(["<|startoftext|>", "<|endoftext|>"])
+ self.encoder = dict(zip(vocab, range(len(vocab))))
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.bpe_ranks = dict(zip(merges, range(len(merges))))
+ self.cache = {
+ "<|startoftext|>": "<|startoftext|>",
+ "<|endoftext|>": "<|endoftext|>",
+ }
+ self.pat = re.compile(
+ r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
+ re.IGNORECASE,
+ )
+ self.context_length = context_length
+
+ def bpe(self, token):
+ if token in self.cache:
+ return self.cache[token]
+ word = tuple(token[:-1]) + (token[-1] + "",)
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token + ""
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ new_word.extend(word[i:j])
+ i = j
+ except:
+ new_word.extend(word[i:])
+ break
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = " ".join(word)
+ self.cache[token] = word
+ return word
+
+ def encode(self, text):
+ bpe_tokens = []
+ text = whitespace_clean(basic_clean(text)).lower()
+ for token in re.findall(self.pat, text):
+ token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
+ bpe_tokens.extend(
+ self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
+ )
+ return bpe_tokens
+
+ def decode(self, tokens):
+ text = "".join([self.decoder[token] for token in tokens])
+ text = (
+ bytearray([self.byte_decoder[c] for c in text])
+ .decode("utf-8", errors="replace")
+ .replace("", " ")
+ )
+ return text
+
+ def __call__(self, texts, context_length=None):
+ if not context_length:
+ context_length = self.context_length
+
+ if isinstance(texts, str):
+ texts = [texts]
+
+ sot_token = self.encoder["<|startoftext|>"]
+ eot_token = self.encoder["<|endoftext|>"]
+ all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]
+ result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
+
+ for i, tokens in enumerate(all_tokens):
+ tokens = tokens[:context_length]
+ result[i, : len(tokens)] = torch.tensor(tokens)
+
+ if len(result) == 1:
+ return result[0]
+ return result
+
+
+class IMUPreprocessor(VerboseNNModule):
+ def __init__(
+ self,
+ kernel_size: int,
+ imu_stem: PatchEmbedGeneric,
+ embed_dim: int,
+ img_size: List = (6, 2000),
+ num_cls_tokens: int = 1,
+ pos_embed_fn: Callable = None,
+ init_param_style: str = "openclip",
+ ) -> None:
+ super().__init__()
+ stem = imu_stem
+ self.imu_stem = imu_stem
+ self.embed_dim = embed_dim
+ self.use_pos_embed = pos_embed_fn is not None
+ self.num_cls_tokens = num_cls_tokens
+ self.kernel_size = kernel_size
+ self.pos_embed = nn.Parameter(
+ torch.empty(1, (img_size[1] // kernel_size) + num_cls_tokens, embed_dim)
+ )
+
+ if self.num_cls_tokens > 0:
+ self.cls_token = nn.Parameter(
+ torch.zeros(1, self.num_cls_tokens, self.embed_dim)
+ )
+
+ self.init_parameters(init_param_style)
+
+ @torch.no_grad()
+ def init_parameters(self, init_param_style):
+ nn.init.normal_(self.pos_embed, std=0.01)
+
+ if init_param_style == "openclip":
+ # OpenCLIP style initialization
+ scale = self.embed_dim**-0.5
+
+ if self.num_cls_tokens > 0:
+ nn.init.normal_(self.cls_token)
+ self.cls_token *= scale
+ elif init_param_style == "vit":
+ self.cls_token.data.fill_(0)
+ else:
+ raise ValueError(f"Unknown init {init_param_style}")
+
+ def tokenize_input_and_cls_pos(self, input, stem):
+ # tokens is of shape B x L x D
+ tokens = stem.norm_layer(stem.proj(input))
+ assert tokens.ndim == 3
+ assert tokens.shape[2] == self.embed_dim
+ B = tokens.shape[0]
+ if self.num_cls_tokens > 0:
+ class_tokens = self.cls_token.expand(
+ B, -1, -1
+ ) # stole class_tokens impl from Phil Wang, thanks
+ tokens = torch.cat((class_tokens, tokens), dim=1)
+ if self.use_pos_embed:
+ tokens = tokens + self.pos_embed
+ return tokens
+
+ def forward(self, imu):
+ # Patchify
+ imu = imu.unfold(
+ -1,
+ self.kernel_size,
+ self.kernel_size,
+ ).permute(0, 2, 1, 3)
+ imu = imu.reshape(imu.size(0), imu.size(1), -1)
+
+ imu_tokens = self.tokenize_input_and_cls_pos(
+ imu,
+ self.imu_stem,
+ )
+
+ return_dict = {
+ "trunk": {
+ "tokens": imu_tokens,
+ },
+ "head": {},
+ }
+ return return_dict
diff --git a/code/model/ImageBind/models/transformer.py b/code/model/ImageBind/models/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..98902ac8f08868c486a7c74781e952bee444c2e6
--- /dev/null
+++ b/code/model/ImageBind/models/transformer.py
@@ -0,0 +1,284 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Code modified from
+# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py ;
+# https://github.com/facebookresearch/deit/blob/main/models.py
+# and https://github.com/facebookresearch/vissl/blob/main/vissl/models/trunks/vision_transformer.py
+
+
+import copy
+import fnmatch
+import logging
+from functools import partial
+from typing import Callable, List
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+
+from timm.models.layers import DropPath, trunc_normal_
+
+
+class Attention(nn.Module):
+ def __init__(
+ self,
+ dim,
+ num_heads=8,
+ qkv_bias=False,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ ):
+ super().__init__()
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ # NOTE scale factor was wrong in my original version,
+ # can set manually to be compat with prev weights
+ self.scale = qk_scale or head_dim**-0.5
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ def forward(self, x):
+ B, N, C = x.shape
+ qkv = (
+ self.qkv(x)
+ .reshape(B, N, 3, self.num_heads, C // self.num_heads)
+ .permute(2, 0, 3, 1, 4)
+ )
+ q, k, v = (
+ qkv[0],
+ qkv[1],
+ qkv[2],
+ ) # make torchscript happy (cannot use tensor as tuple)
+
+ attn = (q @ k.transpose(-2, -1)) * self.scale
+ attn = attn.softmax(dim=-1)
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+
+class Mlp(nn.Module):
+ def __init__(
+ self,
+ in_features,
+ hidden_features=None,
+ out_features=None,
+ act_layer=nn.GELU,
+ drop=0.0,
+ ):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class MultiheadAttention(nn.MultiheadAttention):
+ def forward(self, x: torch.Tensor, attn_mask: torch.Tensor):
+ return super().forward(x, x, x, need_weights=False, attn_mask=attn_mask)[0]
+
+
+class ViTAttention(Attention):
+ def forward(self, x: torch.Tensor, attn_mask: torch.Tensor):
+ assert attn_mask is None
+ return super().forward(x)
+
+
+class BlockWithMasking(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ attn_target: Callable,
+ mlp_ratio: int = 4,
+ act_layer: Callable = nn.GELU,
+ norm_layer: Callable = nn.LayerNorm,
+ ffn_dropout_rate: float = 0.0,
+ drop_path: float = 0.0,
+ layer_scale_type: str = None,
+ layer_scale_init_value: float = 1e-4,
+ ):
+ super().__init__()
+
+ assert not isinstance(
+ attn_target, nn.Module
+ ), "attn_target should be a Callable. Otherwise attn_target is shared across blocks!"
+ self.attn = attn_target()
+ if drop_path > 0.0:
+ self.drop_path = DropPath(drop_path)
+ else:
+ self.drop_path = nn.Identity()
+ self.norm_1 = norm_layer(dim)
+ mlp_hidden_dim = int(mlp_ratio * dim)
+ self.mlp = Mlp(
+ in_features=dim,
+ hidden_features=mlp_hidden_dim,
+ act_layer=act_layer,
+ drop=ffn_dropout_rate,
+ )
+ self.norm_2 = norm_layer(dim)
+ self.layer_scale_type = layer_scale_type
+ if self.layer_scale_type is not None:
+ assert self.layer_scale_type in [
+ "per_channel",
+ "scalar",
+ ], f"Found Layer scale type {self.layer_scale_type}"
+ if self.layer_scale_type == "per_channel":
+ # one gamma value per channel
+ gamma_shape = [1, 1, dim]
+ elif self.layer_scale_type == "scalar":
+ # single gamma value for all channels
+ gamma_shape = [1, 1, 1]
+ # two gammas: for each part of the fwd in the encoder
+ self.layer_scale_gamma1 = nn.Parameter(
+ torch.ones(size=gamma_shape) * layer_scale_init_value,
+ requires_grad=True,
+ )
+ self.layer_scale_gamma2 = nn.Parameter(
+ torch.ones(size=gamma_shape) * layer_scale_init_value,
+ requires_grad=True,
+ )
+
+ def forward(self, x: torch.Tensor, attn_mask: torch.Tensor):
+ if self.layer_scale_type is None:
+ x = x + self.drop_path(self.attn(self.norm_1(x), attn_mask))
+ x = x + self.drop_path(self.mlp(self.norm_2(x)))
+ else:
+ x = (
+ x
+ + self.drop_path(self.attn(self.norm_1(x), attn_mask))
+ * self.layer_scale_gamma1
+ )
+ x = x + self.drop_path(self.mlp(self.norm_2(x))) * self.layer_scale_gamma2
+ return x
+
+
+_LAYER_NORM = partial(nn.LayerNorm, eps=1e-6)
+
+
+class SimpleTransformer(nn.Module):
+ def __init__(
+ self,
+ attn_target: Callable,
+ embed_dim: int,
+ num_blocks: int,
+ block: Callable = BlockWithMasking,
+ pre_transformer_layer: Callable = None,
+ post_transformer_layer: Callable = None,
+ drop_path_rate: float = 0.0,
+ drop_path_type: str = "progressive",
+ norm_layer: Callable = _LAYER_NORM,
+ mlp_ratio: int = 4,
+ ffn_dropout_rate: float = 0.0,
+ layer_scale_type: str = None, # from cait; possible values are None, "per_channel", "scalar"
+ layer_scale_init_value: float = 1e-4, # from cait; float
+ weight_init_style: str = "jax", # possible values jax or pytorch
+ ):
+ """
+ Simple Transformer with the following features
+ 1. Supports masked attention
+ 2. Supports DropPath
+ 3. Supports LayerScale
+ 4. Supports Dropout in Attention and FFN
+ 5. Makes few assumptions about the input except that it is a Tensor
+ """
+ super().__init__()
+ self.pre_transformer_layer = pre_transformer_layer
+ if drop_path_type == "progressive":
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, num_blocks)]
+ elif drop_path_type == "uniform":
+ dpr = [drop_path_rate for i in range(num_blocks)]
+ else:
+ raise ValueError(f"Unknown drop_path_type: {drop_path_type}")
+
+ self.blocks = nn.Sequential(
+ *[
+ block(
+ dim=embed_dim,
+ attn_target=attn_target,
+ mlp_ratio=mlp_ratio,
+ ffn_dropout_rate=ffn_dropout_rate,
+ drop_path=dpr[i],
+ norm_layer=norm_layer,
+ layer_scale_type=layer_scale_type,
+ layer_scale_init_value=layer_scale_init_value,
+ )
+ for i in range(num_blocks)
+ ]
+ )
+ self.post_transformer_layer = post_transformer_layer
+ self.weight_init_style = weight_init_style
+ self.apply(self._init_weights)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ if self.weight_init_style == "jax":
+ # Based on MAE and official Jax ViT implementation
+ torch.nn.init.xavier_uniform_(m.weight)
+ elif self.weight_init_style == "pytorch":
+ # PyTorch ViT uses trunc_normal_
+ trunc_normal_(m.weight, std=0.02)
+
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, (nn.LayerNorm)):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ def forward(
+ self,
+ tokens: torch.Tensor,
+ attn_mask: torch.Tensor = None,
+ use_checkpoint: bool = False,
+ checkpoint_every_n: int = 1,
+ checkpoint_blk_ids: List[int] = None,
+ ):
+ """
+ Inputs
+ - tokens: data of shape N x L x D (or L x N x D depending on the attention implementation)
+ - attn: mask of shape L x L
+
+ Output
+ - x: data of shape N x L x D (or L x N x D depending on the attention implementation)
+ """
+ if self.pre_transformer_layer:
+ tokens = self.pre_transformer_layer(tokens)
+ if use_checkpoint and checkpoint_blk_ids is None:
+ checkpoint_blk_ids = [
+ blk_id
+ for blk_id in range(len(self.blocks))
+ if blk_id % checkpoint_every_n == 0
+ ]
+ if checkpoint_blk_ids:
+ checkpoint_blk_ids = set(checkpoint_blk_ids)
+ for blk_id, blk in enumerate(self.blocks):
+ if use_checkpoint and blk_id in checkpoint_blk_ids:
+ tokens = checkpoint.checkpoint(
+ blk, tokens, attn_mask, use_reentrant=False
+ )
+ else:
+ tokens = blk(tokens, attn_mask=attn_mask)
+ if self.post_transformer_layer:
+ tokens = self.post_transformer_layer(tokens)
+ return tokens
diff --git a/code/model/ImageBind/requirements.txt b/code/model/ImageBind/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..572ae079a6cc3592552d93b8ca08c3ec7fd4efc9
--- /dev/null
+++ b/code/model/ImageBind/requirements.txt
@@ -0,0 +1,10 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+torchvision==0.14.0
+torchaudio==0.13.0
+pytorchvideo @ git+https://github.com/facebookresearch/pytorchvideo.git@28fe037d212663c6a24f373b94cc5d478c8c1a1d
+timm==0.6.7
+ftfy
+regex
+einops
+fvcore
+decord==0.6.0
diff --git a/code/model/__init__.py b/code/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..469bdf5b9c3f3e52f7ee61e990f5a27a2052ec15
--- /dev/null
+++ b/code/model/__init__.py
@@ -0,0 +1,15 @@
+import os.path
+from collections import OrderedDict
+
+from .agent import DeepSpeedAgent
+from .anyToImageVideoAudio import NextGPTModel
+import torch
+
+
+def load_model(args):
+ agent_name = args['models'][args['model']]['agent_name']
+ model_name = args['models'][args['model']]['model_name']
+ model = globals()[model_name](**args)
+
+ agent = globals()[agent_name](model, args)
+ return agent
diff --git a/code/model/agent.py b/code/model/agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc2af973a4ad7beb185737612205a088a06e55ba
--- /dev/null
+++ b/code/model/agent.py
@@ -0,0 +1,161 @@
+import os.path
+
+import torch
+from header import *
+
+
+class DeepSpeedAgent:
+
+ def __init__(self, model, args):
+ super(DeepSpeedAgent, self).__init__()
+ self.args = args
+ self.model = model
+
+ self.print_model_parameters()
+ self.writer = SummaryWriter(args['log_path'])
+
+ self.load_parameters(self.args['save_path'], self.args['stage'])
+
+ # load config parameters of deepspeed
+ ds_params = json.load(open(self.args['ds_config_path']))
+ ds_params['scheduler']['params']['total_num_steps'] = self.args['total_steps']
+ ds_params['scheduler']['params']['warmup_num_steps'] = max(10, int(
+ self.args['total_steps'] * self.args['warmup_rate']))
+ self.ds_engine, self.optimizer, _, _ = deepspeed.initialize(
+ model=self.model,
+ model_parameters=self.model.parameters(),
+ config_params=ds_params,
+ dist_init_required=True,
+ args=types.SimpleNamespace(**args)
+ )
+
+ @torch.no_grad()
+ def predict(self):
+ self.ds_engine.module.eval()
+ output = self.ds_engine.generate(self.args)
+ return output
+
+ def train_model(self, batch, current_step=0, pbar=None):
+ self.ds_engine.module.train()
+
+ loss, mle_acc, mse_loss = self.ds_engine(batch)
+
+ self.writer.add_scalar('loss', loss, current_step)
+ self.writer.add_scalar('mle_acc', mle_acc, current_step)
+ # if isinstance(mse_loss, list):
+ # self.writer.add_scalar('img_mse_loss', mse_loss[0], current_step)
+ # self.writer.add_scalar('vid_mse_loss', mse_loss[1], current_step)
+ # self.writer.add_scalar('aud_mse_loss', mse_loss[2], current_step)
+ if isinstance(mse_loss, torch.Tensor):
+ self.writer.add_scalar('mse_loss', mse_loss, current_step)
+ else:
+ pass
+ # self.writer.add_scalar('mse_loss', mse_loss, current_step)
+
+ self.ds_engine.backward(loss)
+ self.ds_engine.step()
+ # pbar.set_description(f'[!] loss: {round(loss.item(), 4)}; token_acc: {round(mle_acc * 100, 2)}; mse_loss: {round(mse_loss[0].item(), 4)} ')
+ pbar.set_description(f'[!] loss: {round(loss.item(), 4)}; token_acc: {round(mle_acc * 100, 2)}')
+ pbar.update(1)
+ if self.args['local_rank'] == 0 and self.args['log_path'] and current_step % self.args['logging_step'] == 0:
+ elapsed = pbar.format_dict['elapsed']
+ rate = pbar.format_dict['rate']
+ remaining = (pbar.total - pbar.n) / rate if rate and pbar.total else 0
+ remaining = str(datetime.timedelta(seconds=remaining))
+ logging.info(
+ f'[!] progress: {round(pbar.n / pbar.total, 5)}; remaining time: {remaining}; loss: {round(loss.item(), 4)}; token_acc: {round(mle_acc * 100, 2)}')
+ # ; mse_loss: {round(mse_loss[0].item(), 4)}
+ mle_acc *= 100
+ return mle_acc
+
+ def save_model(self, path, current_step):
+ """
+ this function also save the trainable parameters and specific name parameters
+ """
+ param_grad_dic = {
+ k: v.requires_grad for (k, v) in self.ds_engine.module.named_parameters()
+ }
+ state_dict = self.ds_engine.module.state_dict()
+ checkpoint = OrderedDict()
+ for k, v in self.ds_engine.module.named_parameters():
+ if v.requires_grad:
+ checkpoint[k] = v
+ if 'gen_text_hidden_fcs' in k:
+ checkpoint[k] = v
+ if 'gen_text_hidden_fcs_video' in k:
+ checkpoint[k] = v
+ if 'gen_text_hidden_fcs_audio' in k:
+ checkpoint[k] = v
+ if 'llama_proj' in k:
+ checkpoint[k] = v
+ torch.save(checkpoint, f'{path}/pytorch_model.pt')
+ # save tokenizer
+ self.model.llama_tokenizer.save_pretrained(path)
+ # save configuration
+ self.model.llama_model.config.save_pretrained(path)
+ print(f'[!] save model into {path}')
+
+ def print_model_parameters(self, use_4bit=False):
+ """
+ Prints the number of trainable parameters in the model.
+ """
+ trainable_params = 0
+ all_param = 0
+ lora = 0
+ image = 0
+ video = 0
+ audio = 0
+ linear = 0
+ llama = 0
+ imagebind = 0
+ for name, param in self.model.named_parameters():
+ num_params = param.numel()
+ # if using DS Zero 3 and the weights are initialized empty
+ if num_params == 0 and hasattr(param, "ds_numel"):
+ num_params = param.ds_numel
+
+ if 'lora' in name:
+ lora += num_params
+ elif 'gen_text_hidden_fcs_video' in name:
+ video += num_params
+ elif 'gen_text_hidden_fcs_audio' in name:
+ audio += num_params
+ elif 'gen_text_hidden_fcs' in name:
+ image += num_params
+ elif 'llama_proj' in name:
+ linear += num_params
+ elif 'llama_model' in name:
+ llama += num_params
+ elif 'visual_encoder' in name:
+ imagebind += num_params
+ else:
+ pass
+
+ all_param += num_params
+ if param.requires_grad:
+ trainable_params += num_params
+ if use_4bit:
+ trainable_params /= 2
+ print(
+ f"all params: {all_param:,d} || trainable params: {trainable_params:,d} || trainable%: {100 * trainable_params / all_param}"
+ )
+ print(f'lora params: {lora:,d} || video params: {video:,d} || audio params: {audio:,d} || image params: {image:,d}')
+ print(f'linear params: {linear:,d} || imagebind params: {imagebind:,d} || llama params: {llama:,d}')
+
+ def load_parameters(self, path, stage=3):
+ if os.path.exists(os.path.join(path, 'pytorch_model.pt')):
+ print('loading parameters from {}'.format(self.args['save_path']))
+ delta_ckpt = torch.load(f'{path}/pytorch_model.pt', map_location=torch.device('cuda'))
+ checkpoint = OrderedDict()
+ if stage == 3:
+ for k, v in delta_ckpt.items():
+ if 'llama_model.model.embed_tokens.weight' in k:
+ checkpoint['llama_model.base_model.model.model.embed_tokens.weight'] = v
+ elif 'llama_model.lm_head.weight' in k:
+ checkpoint['llama_model.base_model.model.lm_head.weight'] = v
+ else:
+ checkpoint[k] = v
+ else:
+ checkpoint = delta_ckpt
+ self.model.load_state_dict(checkpoint, strict=False)
+
diff --git a/code/model/anyToImageVideoAudio.py b/code/model/anyToImageVideoAudio.py
new file mode 100644
index 0000000000000000000000000000000000000000..95c556a2a7364061b5c10e8a14e7035a279ef315
--- /dev/null
+++ b/code/model/anyToImageVideoAudio.py
@@ -0,0 +1,993 @@
+import logging
+import os.path
+from typing import List
+
+import torch
+from header import *
+import torch.nn.functional as F
+from .ImageBind import *
+from .ImageBind import data
+from .modeling_llama import LlamaForCausalLM
+from transformers import StoppingCriteria, StoppingCriteriaList
+# from diffusers import StableDiffusionPipeline
+from .custom_sd import StableDiffusionPipeline
+from .custom_vd import TextToVideoSDPipeline
+from .custom_ad import AudioLDMPipeline
+from .layers import *
+from .common.utils import *
+
+
+class StoppingCriteriaSub(StoppingCriteria):
+
+ def __init__(self, stops: List = None, encounters: int = 1):
+ super().__init__()
+ self.stops = stops
+ self.ENCOUNTERS = encounters
+
+ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
+ stop_count = 0
+ for stop in self.stops:
+ _stop = torch.tensor(stop).to(input_ids[0].device)
+ indices = torch.where(_stop[0] == input_ids)
+ for i in indices:
+ if len(i) > 0:
+ if torch.all(input_ids[0][i:i + len(_stop)] == _stop):
+ stop_count += 1
+ if stop_count >= self.ENCOUNTERS:
+ return True
+ return False
+
+
+class NextGPTModel(nn.Module):
+ """LoRA for LLaMa model"""
+
+ def __init__(self, **args):
+ super(NextGPTModel, self).__init__()
+ self.args = args
+
+ self.max_length = args['max_length']
+ self.device = torch.cuda.current_device()
+ self.stage = args['stage']
+ print('args max_length', args['max_length'])
+
+ imagebind_ckpt_path = os.path.join(self.args['pretrained_ckpt_path'], 'imagebind_ckpt',
+ self.args['imagebind_version'])
+ print(f'Initializing visual encoder from {imagebind_ckpt_path} ...')
+ self.visual_encoder, self.visual_hidden_size = \
+ imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path)
+ # free vision encoder
+ for name, param in self.visual_encoder.named_parameters():
+ param.requires_grad = False
+ self.visual_encoder.eval()
+ print('Visual encoder initialized.')
+
+ self.vicuna_ckpt_path = os.path.join(self.args['pretrained_ckpt_path'], 'vicuna_ckpt',
+ self.args['vicuna_version'])
+ print(f'Initializing language decoder from {self.vicuna_ckpt_path} ...')
+
+ self.llama_model = LlamaForCausalLM.from_pretrained(self.vicuna_ckpt_path)
+ if self.args.get('freeze_lm'):
+ print("Freezing the LLaMa ...")
+ for param in self.llama_model.parameters():
+ param.requires_grad = False
+ self.llama_model.eval()
+ else:
+ print("Instruct tuning the LLaMa ...")
+ # add the lora module
+ peft_config = LoraConfig(
+ task_type=TaskType.CAUSAL_LM,
+ inference_mode=False,
+ r=self.args['lora_r'],
+ lora_alpha=self.args['lora_alpha'],
+ lora_dropout=self.args['lora_dropout'],
+ target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj']
+ )
+
+ self.llama_model = get_peft_model(self.llama_model, peft_config)
+ self.llama_model.print_trainable_parameters()
+ print('Language decoder initialized.')
+
+ # use the new trained tokenizer
+ tokenizer_path = self.vicuna_ckpt_path
+ print(f'Initializing tokenizer from {tokenizer_path} ...')
+ self.llama_tokenizer = LlamaTokenizer.from_pretrained(tokenizer_path, use_fast=False)
+ self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token
+ self.llama_tokenizer.padding_side = "right"
+ # self.llama_tokenizer.add_special_tokens({"mask_token": "[MASK]"})
+ self._add_image_token()
+ self._add_video_token()
+ self._add_audio_token()
+ self.llama_model.resize_token_embeddings(len(self.llama_tokenizer))
+ print('Tokenizer initialized.')
+
+ self.llama_proj = nn.Linear(
+ self.visual_hidden_size, self.llama_model.config.hidden_size
+ )
+ if self.args.get('freeze_input_proj'):
+ for param in self.llama_proj.parameters():
+ param.requires_grad = False
+
+ self.input_embeddings = self.llama_model.get_input_embeddings()
+
+ # the alignment module for LLM-TO-IMAGE
+ self.sd_ckpt_path = self.args['image_diffusion']
+ self.gen_text_hidden_fcs = nn.ModuleList([])
+ for layer_idx in self.args['text_emb_to_img_layers']:
+ if layer_idx == -1 or layer_idx == self.llama_model.config.num_hidden_layers:
+ in_dim = self.llama_model.config.hidden_size
+
+ self.gen_text_hidden_fcs.append(
+ TextFcLayer(in_dim, 768, num_input_tokens=self.args['num_gen_img_tokens'],
+ num_output_tokens=self.args['num_clip_tokens'],
+ mode=self.args['text_fc_to_img_mode']))
+ # self.sd_pipe.text_encoder.config.hidden_size
+ elif layer_idx < self.llama_model.config.num_hidden_layers:
+ self.gen_text_hidden_fcs.append(
+ TextFcLayer(self.llama_model.config.hidden_size, 768,
+ num_input_tokens=self.args['num_gen_img_tokens'],
+ num_output_tokens=self.args['num_clip_tokens'],
+ mode=self.args['text_fc_to_img_mode']))
+ else:
+ raise ValueError(
+ f'Embedding of layer {layer_idx} was requested but model only has {self.llama_model.config.num_hidden_layers} layers.')
+
+ # the alignment module for LLM-TO-VIDEO
+ self.vd_ckpt_path = self.args['video_diffusion']
+ self.gen_text_hidden_fcs_video = nn.ModuleList([])
+ for layer_idx in self.args['text_emb_to_video_layers']:
+ if layer_idx == -1 or layer_idx == self.llama_model.config.num_hidden_layers:
+ in_dim = self.llama_model.config.hidden_size # 4096
+
+ self.gen_text_hidden_fcs_video.append(
+ TextFcLayer(in_dim, 1024, num_input_tokens=self.args['num_gen_video_tokens'],
+ num_output_tokens=self.args['num_clip_tokens'],
+ mode=self.args['text_fc_to_video_mode']))
+ # self.vd_pipe.text_encoder.config.hidden_size
+ elif layer_idx < self.llama_model.config.num_hidden_layers:
+ self.gen_text_hidden_fcs_video.append(
+ TextFcLayer(self.llama_model.config.hidden_size, 1024,
+ num_input_tokens=self.args['num_gen_video_tokens'],
+ num_output_tokens=self.args['num_clip_tokens'],
+ mode=self.args['text_fc_to_video_mode']))
+ else:
+ raise ValueError(
+ f'Embedding of layer {layer_idx} was requested but model only has {self.llama_model.config.num_hidden_layers} layers.')
+
+ # the alignment module for LLM-TO-AUDIO
+ self.ad_ckpt_path = self.args['audio_diffusion']
+ self.gen_text_hidden_fcs_audio = nn.ModuleList([])
+ for layer_idx in self.args['text_emb_to_audio_layers']:
+ if layer_idx == -1 or layer_idx == self.llama_model.config.num_hidden_layers:
+ in_dim = self.llama_model.config.hidden_size
+
+ self.gen_text_hidden_fcs_audio.append(
+ TextFcLayer(in_dim, 512,
+ num_input_tokens=self.args['num_gen_audio_tokens'],
+ num_output_tokens=1,
+ mode=self.args['text_fc_to_audio_mode']))
+ # self.ad_pipe.text_encoder.config.projection_dim
+ elif layer_idx < self.llama_model.config.num_hidden_layers:
+ self.gen_text_hidden_fcs_audio.append(
+ TextFcLayer(self.llama_model.config.hidden_size, 512,
+ num_input_tokens=self.args['num_gen_audio_tokens'],
+ num_output_tokens=1,
+ mode=self.args['text_fc_to_audio_mode']))
+ else:
+ raise ValueError(
+ f'Embedding of layer {layer_idx} was requested but model only has {self.llama_model.config.num_hidden_layers} layers.')
+
+ if self.args.get('freeze_output_proj'):
+ for name, param in self.gen_text_hidden_fcs.named_parameters():
+ param.requires_grad = False
+ for name, param in self.gen_text_hidden_fcs_video.named_parameters():
+ param.requires_grad = False
+ for name, param in self.gen_text_hidden_fcs_audio.named_parameters():
+ param.requires_grad = False
+
+ def _add_image_token(self):
+ # Add an image token for loss masking (and visualization) purposes.
+ self.llama_tokenizer.add_tokens([""}) # add special video token to tokenizer
+ # self.llama_tokenizer.add_tokens({" "}) # add special video token to tokenizer
+
+ # Add [VID] tokens to the vocabulary.
+ self.args['gen_video_token_idx'] = []
+ for i in range(self.args['num_gen_video_tokens']):
+ print(f'Adding [VID{i}] token to vocabulary.')
+ print(f'Before adding new token, tokenizer("[VID{i}]") =',
+ self.llama_tokenizer(f'[VID{i}]', add_special_tokens=False))
+ num_added_tokens = self.llama_tokenizer.add_tokens(f'[VID{i}]')
+ print(f'After adding {num_added_tokens} new tokens, tokenizer("[VID{i}]") =',
+ self.llama_tokenizer(f'[VID{i}]', add_special_tokens=False))
+ gen_token_idx = self.llama_tokenizer(f'[VID{i}]', add_special_tokens=False).input_ids
+ assert len(gen_token_idx) == 1, gen_token_idx
+ self.args['gen_video_token_idx'].append(gen_token_idx[0])
+
+ def _add_audio_token(self):
+ # self.llama_tokenizer.add_tokens({""}) # add special audio token to tokenizer
+ # self.llama_tokenizer.add_tokens({" "}) # add special audio token to tokenizer
+
+ # Add [AUD] tokens to the vocabulary.
+ self.args['gen_audio_token_idx'] = []
+ for i in range(self.args['num_gen_audio_tokens']):
+ print(f'Adding [AUD{i}] token to vocabulary.')
+ print(f'Before adding new token, tokenizer("[AUD{i}]") =',
+ self.llama_tokenizer(f'[AUD{i}]', add_special_tokens=False))
+ num_added_tokens = self.llama_tokenizer.add_tokens(f'[AUD{i}]')
+ print(f'After adding {num_added_tokens} new tokens, tokenizer("[AUD{i}]") =',
+ self.llama_tokenizer(f'[AUD{i}]', add_special_tokens=False))
+ gen_token_idx = self.llama_tokenizer(f'[AUD{i}]', add_special_tokens=False).input_ids
+ assert len(gen_token_idx) == 1, gen_token_idx
+ self.args['gen_audio_token_idx'].append(gen_token_idx[0])
+
+ def encode_video(self, video_paths):
+ inputs = {ModalityType.VISION: data.load_and_transform_video_data(video_paths, self.device)}
+ # convert into visual dtype
+ inputs = {key: inputs[key].to(self.llama_model.dtype) for key in inputs}
+ with torch.no_grad():
+ embeddings = self.visual_encoder(inputs)
+ video_embeds = embeddings[ModalityType.VISION] # bsz x 1024
+ inputs_llama = self.llama_proj(video_embeds).unsqueeze(1) # bsz x 1 x llama_size
+ atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1
+ return inputs_llama, atts_llama
+
+ def encode_audio(self, audio_paths):
+ inputs = {ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, self.device)}
+ # convert into visual dtype
+ inputs = {key: inputs[key].to(self.llama_model.dtype) for key in inputs}
+ with torch.no_grad():
+ embeddings = self.visual_encoder(inputs)
+ audio_embeds = embeddings[ModalityType.AUDIO] # bsz x 1024
+ inputs_llama = self.llama_proj(audio_embeds).unsqueeze(1) # bsz x 1 x llama_size
+ atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1
+ return inputs_llama, atts_llama
+
+ def encode_image(self, image_paths):
+ inputs = {ModalityType.VISION: data.load_and_transform_vision_data(image_paths, self.device)}
+ # convert into visual dtype
+ inputs = {key: inputs[key].to(self.llama_model.dtype) for key in inputs}
+ with torch.no_grad():
+ embeddings = self.visual_encoder(inputs)
+ image_embeds = embeddings['vision'] # bsz x 1024
+ inputs_llama = self.llama_proj(image_embeds).unsqueeze(1) # bsz x 1 x llama_size
+ atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1
+ return inputs_llama, atts_llama
+
+ def prompt_wrap(self, img_embeds, input_ids, target_ids, attention_mask):
+ '''
+ input_ids, target_ids, attention_mask: bsz x s2
+ '''
+ input_ids = input_ids.to(self.device) # bsz x s2
+ target_ids = target_ids.to(self.device) # bsz x s2
+ attention_mask = attention_mask.to(self.device) # bsz x s2
+
+ batch_size = input_ids.shape[0]
+
+ bos = torch.ones([batch_size, 1], dtype=input_ids.dtype,
+ device=input_ids.device) * self.llama_tokenizer.bos_token_id # bsz x 1
+ if self.args['freeze_lm']:
+ p_after_embeds = self.llama_model.model.embed_tokens(input_ids).expand(batch_size, -1,
+ -1) # bsz x s2 x embed_dim
+ bos_embeds = self.llama_model.model.embed_tokens(bos) # bsz x 1 x embed_dim
+ else:
+ p_after_embeds = self.llama_model.model.model.embed_tokens(input_ids).expand(batch_size, -1,
+ -1) # bsz x s2 x embed_dim
+ bos_embeds = self.llama_model.model.model.embed_tokens(bos) # bsz x 1 x embed_dim
+ if img_embeds is not None:
+ p_before = '### Human: `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+ Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+ argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
+ input to the forward pass.
+ """
+
+ def __init__(self, config, add_pooling_layer=False):
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = BertEmbeddings(config)
+
+ self.encoder = BertEncoder(config)
+
+ self.pooler = BertPooler(config) if add_pooling_layer else None
+
+ self.init_weights()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ def get_extended_attention_mask(
+ self,
+ attention_mask: Tensor,
+ input_shape: Tuple[int],
+ device: device,
+ is_decoder: bool,
+ has_query: bool = False,
+ ) -> Tensor:
+ """
+ Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
+
+ Arguments:
+ attention_mask (:obj:`torch.Tensor`):
+ Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
+ input_shape (:obj:`Tuple[int]`):
+ The shape of the input to the model.
+ device: (:obj:`torch.device`):
+ The device of the input to the model.
+
+ Returns:
+ :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
+ """
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ if attention_mask.dim() == 3:
+ extended_attention_mask = attention_mask[:, None, :, :]
+ elif attention_mask.dim() == 2:
+ # Provided a padding mask of dimensions [batch_size, seq_length]
+ # - if the model is a decoder, apply a causal mask in addition to the padding mask
+ # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+ if is_decoder:
+ batch_size, seq_length = input_shape
+
+ seq_ids = torch.arange(seq_length, device=device)
+ causal_mask = (
+ seq_ids[None, None, :].repeat(batch_size, seq_length, 1)
+ <= seq_ids[None, :, None]
+ )
+
+ # add a prefix ones mask to the causal mask
+ # causal and attention masks must have same type with pytorch version < 1.3
+ causal_mask = causal_mask.to(attention_mask.dtype)
+
+ if causal_mask.shape[1] < attention_mask.shape[1]:
+ prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
+ if has_query: # UniLM style attention mask
+ causal_mask = torch.cat(
+ [
+ torch.zeros(
+ (batch_size, prefix_seq_len, seq_length),
+ device=device,
+ dtype=causal_mask.dtype,
+ ),
+ causal_mask,
+ ],
+ axis=1,
+ )
+ causal_mask = torch.cat(
+ [
+ torch.ones(
+ (batch_size, causal_mask.shape[1], prefix_seq_len),
+ device=device,
+ dtype=causal_mask.dtype,
+ ),
+ causal_mask,
+ ],
+ axis=-1,
+ )
+ extended_attention_mask = (
+ causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
+ )
+ else:
+ extended_attention_mask = attention_mask[:, None, None, :]
+ else:
+ raise ValueError(
+ "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
+ input_shape, attention_mask.shape
+ )
+ )
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ extended_attention_mask = extended_attention_mask.to(
+ dtype=self.dtype
+ ) # fp16 compatibility
+ extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+ return extended_attention_mask
+
+ def forward(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ position_ids=None,
+ head_mask=None,
+ query_embeds=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_values=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ is_decoder=False,
+ ):
+ r"""
+ encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+ the model is configured as a decoder.
+ encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+ the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+ If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+ (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+ instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+ use_cache (:obj:`bool`, `optional`):
+ If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+ decoding (see :obj:`past_key_values`).
+ """
+ output_attentions = (
+ output_attentions
+ if output_attentions is not None
+ else self.config.output_attentions
+ )
+ output_hidden_states = (
+ output_hidden_states
+ if output_hidden_states is not None
+ else self.config.output_hidden_states
+ )
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+
+ # use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ if input_ids is None:
+ assert (
+ query_embeds is not None
+ ), "You have to specify query_embeds when input_ids is None"
+
+ # past_key_values_length
+ past_key_values_length = (
+ past_key_values[0][0].shape[2] - self.config.query_length
+ if past_key_values is not None
+ else 0
+ )
+
+ query_length = query_embeds.shape[1] if query_embeds is not None else 0
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ query_embeds=query_embeds,
+ past_key_values_length=past_key_values_length,
+ )
+
+ input_shape = embedding_output.size()[:-1]
+ batch_size, seq_length = input_shape
+ device = embedding_output.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones(
+ ((batch_size, seq_length + past_key_values_length)), device=device
+ )
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ if is_decoder:
+ extended_attention_mask = self.get_extended_attention_mask(
+ attention_mask,
+ input_ids.shape,
+ device,
+ is_decoder,
+ has_query=(query_embeds is not None),
+ )
+ else:
+ extended_attention_mask = self.get_extended_attention_mask(
+ attention_mask, input_shape, device, is_decoder
+ )
+
+ # If a 2D or 3D attention mask is provided for the cross-attention
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+ if encoder_hidden_states is not None:
+ if type(encoder_hidden_states) == list:
+ encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[
+ 0
+ ].size()
+ else:
+ (
+ encoder_batch_size,
+ encoder_sequence_length,
+ _,
+ ) = encoder_hidden_states.size()
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+
+ if type(encoder_attention_mask) == list:
+ encoder_extended_attention_mask = [
+ self.invert_attention_mask(mask) for mask in encoder_attention_mask
+ ]
+ elif encoder_attention_mask is None:
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+ encoder_extended_attention_mask = self.invert_attention_mask(
+ encoder_attention_mask
+ )
+ else:
+ encoder_extended_attention_mask = self.invert_attention_mask(
+ encoder_attention_mask
+ )
+ else:
+ encoder_extended_attention_mask = None
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_extended_attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ query_length=query_length,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = (
+ self.pooler(sequence_output) if self.pooler is not None else None
+ )
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPoolingAndCrossAttentions(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ past_key_values=encoder_outputs.past_key_values,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ cross_attentions=encoder_outputs.cross_attentions,
+ )
+
+
+class BertLMHeadModel(BertPreTrainedModel):
+
+ _keys_to_ignore_on_load_unexpected = [r"pooler"]
+ _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.cls = BertOnlyMLMHead(config)
+
+ self.init_weights()
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+
+ def forward(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ position_ids=None,
+ head_mask=None,
+ query_embeds=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ labels=None,
+ past_key_values=None,
+ use_cache=True,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ return_logits=False,
+ is_decoder=True,
+ reduction="mean",
+ ):
+ r"""
+ encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+ the model is configured as a decoder.
+ encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+ the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+ Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+ ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
+ ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
+ past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+ If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+ (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+ instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+ use_cache (:obj:`bool`, `optional`):
+ If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+ decoding (see :obj:`past_key_values`).
+ Returns:
+ Example::
+ >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
+ >>> import torch
+ >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+ >>> config = BertConfig.from_pretrained("bert-base-cased")
+ >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
+ >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+ >>> outputs = model(**inputs)
+ >>> prediction_logits = outputs.logits
+ """
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+ if labels is not None:
+ use_cache = False
+ if past_key_values is not None:
+ query_embeds = None
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ query_embeds=query_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ is_decoder=is_decoder,
+ )
+
+ sequence_output = outputs[0]
+ if query_embeds is not None:
+ sequence_output = outputs[0][:, query_embeds.shape[1] :, :]
+
+ prediction_scores = self.cls(sequence_output)
+
+ if return_logits:
+ return prediction_scores[:, :-1, :].contiguous()
+
+ lm_loss = None
+ if labels is not None:
+ # we are doing next-token prediction; shift prediction scores and input ids by one
+ shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
+ labels = labels[:, 1:].contiguous()
+ loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
+ lm_loss = loss_fct(
+ shifted_prediction_scores.view(-1, self.config.vocab_size),
+ labels.view(-1),
+ )
+ if reduction == "none":
+ lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1)
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((lm_loss,) + output) if lm_loss is not None else output
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=lm_loss,
+ logits=prediction_scores,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, query_embeds, past=None, attention_mask=None, **model_kwargs
+ ):
+ # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+ if attention_mask is None:
+ attention_mask = input_ids.new_ones(input_ids.shape)
+ query_mask = input_ids.new_ones(query_embeds.shape[:-1])
+ attention_mask = torch.cat([query_mask, attention_mask], dim=-1)
+
+ # cut decoder_input_ids if past is used
+ if past is not None:
+ input_ids = input_ids[:, -1:]
+
+ return {
+ "input_ids": input_ids,
+ "query_embeds": query_embeds,
+ "attention_mask": attention_mask,
+ "past_key_values": past,
+ "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
+ "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
+ "is_decoder": True,
+ }
+
+ def _reorder_cache(self, past, beam_idx):
+ reordered_past = ()
+ for layer_past in past:
+ reordered_past += (
+ tuple(
+ past_state.index_select(0, beam_idx) for past_state in layer_past
+ ),
+ )
+ return reordered_past
+
+
+class BertForMaskedLM(BertPreTrainedModel):
+
+ _keys_to_ignore_on_load_unexpected = [r"pooler"]
+ _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.cls = BertOnlyMLMHead(config)
+
+ self.init_weights()
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+
+ def forward(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ position_ids=None,
+ head_mask=None,
+ query_embeds=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ labels=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ return_logits=False,
+ is_decoder=False,
+ ):
+ r"""
+ labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+ Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
+ config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
+ (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
+ """
+
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ query_embeds=query_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ is_decoder=is_decoder,
+ )
+
+ if query_embeds is not None:
+ sequence_output = outputs[0][:, query_embeds.shape[1] :, :]
+ prediction_scores = self.cls(sequence_output)
+
+ if return_logits:
+ return prediction_scores
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss() # -100 index = padding token
+ masked_lm_loss = loss_fct(
+ prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+ )
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return (
+ ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+ )
+
+ return MaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
diff --git a/code/nextgpt.png b/code/nextgpt.png
new file mode 100644
index 0000000000000000000000000000000000000000..7d28abeb07171731def6090854d402941c58e901
Binary files /dev/null and b/code/nextgpt.png differ
diff --git a/code/process_embeddings.py b/code/process_embeddings.py
new file mode 100644
index 0000000000000000000000000000000000000000..648043f2dcbaa9bc34f342b096b17a77b42e993b
--- /dev/null
+++ b/code/process_embeddings.py
@@ -0,0 +1,115 @@
+
+import numpy as np
+import os
+import sys
+from joblib import Parallel, delayed
+from tqdm import tqdm
+import torch
+import json
+import pandas as pd
+import argparse
+
+# Load a slightly modified version of the Stable Diffusion pipeline.
+# This allows us to extract text embeddings directly (without generating images).
+from model.custom_sd import StableDiffusionPipeline
+from model.custom_vd import TextToVideoSDPipeline
+from model.custom_ad import AudioLDMPipeline
+
+
+
+def save_to_path(emb, path):
+ """Save embeddings to disk."""
+ try:
+ with open(path, 'wb') as wf:
+ np.save(wf, emb)
+ except:
+ print("Error with", path)
+ return path
+
+
+if __name__ == '__main__':
+
+ batch_size = 128
+
+ dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+ # clip_output_dir = './embed/'
+ # synthesize_path = '../data/synthesize_data/synthesize_data.json'
+
+ # video_path = '../data/T-X_pair_data/webvid/webvid.json'
+ # audio_path = '../data/T-X_pair_data/audiocap/audiocap.json'
+ # img_path = '../data/T-X_pair_data/cc3m/cc3m.json'
+
+ # image_generation_ckpt_path = 'runwayml/stable-diffusion-v1-5'
+ # video_generation_ckpt_path = 'cerspense/zeroscope_v2_576w'
+ # audio_generation_ckpt_path = 'cvssp/audioldm-l-full'
+
+ data_path = sys.argv[1]
+ modality = sys.argv[2]
+ clip_output_dir = sys.argv[3]
+ ckpt_path = sys.argv[4]
+
+ if not os.path.exists(clip_output_dir):
+ os.makedirs(clip_output_dir, exist_ok=True)
+
+ # Get existing files, so that we don't recompute them.
+ existing_files = set([f.strip('.npy') for f in os.listdir(clip_output_dir)])
+
+ caption_list = []
+ name_list = []
+ if modality == 'audio':
+ print('extract audio caption embedding')
+ with open(data_path, 'r', encoding='utf-8') as f:
+ data = json.load(f)
+
+ for row in tqdm(data, total=len(data)):
+ one_audio_name, one_caption = row["audio_name"], row["caption"]
+ if one_audio_name not in existing_files:
+ caption_list.append(one_caption)
+ name_list.append(one_audio_name)
+ pipe = AudioLDMPipeline.from_pretrained(ckpt_path, torch_dtype=dtype)
+ if not torch.cuda.is_available():
+ print('WARNING: using CPU, this will be slow!')
+ else:
+ pipe = pipe.to("cuda")
+ elif modality == 'image':
+ print('extract image caption embedding')
+ with open(data_path, 'r', encoding='utf-8') as f:
+ data = json.load(f)
+ for row in tqdm(data, total=len(data)):
+ one_image_name, one_caption = row["image_name"], row["caption"]
+ if one_image_name not in existing_files:
+ caption_list.append(one_caption)
+ name_list.append(one_image_name)
+ pipe = StableDiffusionPipeline.from_pretrained(ckpt_path, torch_dtype=dtype)
+ if not torch.cuda.is_available():
+ print('WARNING: using CPU, this will be slow!')
+ else:
+ pipe = pipe.to("cuda")
+ elif modality == 'video':
+ print('extract video caption embedding')
+ with open(data_path, 'r', encoding='utf-8') as f:
+ data = json.load(f)
+ for row in tqdm(data, total=len(data)):
+ one_video_name, one_caption = row["video_name"], row["caption"]
+ if one_video_name not in existing_files:
+ caption_list.append(one_caption)
+ name_list.append(one_video_name)
+ pipe = TextToVideoSDPipeline.from_pretrained(ckpt_path, torch_dtype=dtype)
+ if not torch.cuda.is_available():
+ print('WARNING: using CPU, this will be slow!')
+ else:
+ pipe = pipe.to("cuda")
+
+ print('Extract embeddings in batches.')
+ num_batches = int(np.ceil(len(caption_list) / batch_size))
+ for i in tqdm(range(num_batches)):
+ start_idx = i * batch_size
+ end_idx = start_idx + batch_size
+ batch_captions = caption_list[start_idx:end_idx]
+ batch_ids = name_list[start_idx:end_idx]
+ prompt_embeds = pipe(batch_captions, return_prompts_only=True).detach().cpu().numpy()
+
+ # Save embeddings to disk in parallel.
+ Parallel(n_jobs=8)(delayed(save_to_path)(
+ prompt_embeds[j, :, ...], os.path.join(clip_output_dir, f'{batch_ids[j]}.npy')
+ ) for j in range(prompt_embeds.shape[0]))
diff --git a/code/scripts/app.sh b/code/scripts/app.sh
new file mode 100644
index 0000000000000000000000000000000000000000..77242be57128fd43a54a79b053d83ab0a54d750b
--- /dev/null
+++ b/code/scripts/app.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python demo_app.py --nextgpt_ckpt_path ../ckpt/delta_ckpt/nextgpt/7b_tiva_v0
diff --git a/code/scripts/train.sh b/code/scripts/train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a196fccb02a214a7e860327b8b031030bec77666
--- /dev/null
+++ b/code/scripts/train.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+
+deepspeed --include localhost:6 --master_addr 127.0.0.1 --master_port 28459 train.py \
+ --model nextgpt \
+ --stage 1\
+ --save_path ../ckpt/delta_ckpt/nextgpt/7b_tiva_v0\
+ --log_path ../ckpt/delta_ckpt/nextgpt/7b_tiva_v0/log
+
diff --git a/code/train.py b/code/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..479eea68de89a38facf87852dc1744bab6bff9b8
--- /dev/null
+++ b/code/train.py
@@ -0,0 +1,109 @@
+from header import *
+from dataset import load_dataset
+from model import *
+from config import *
+
+
+def parser_args():
+ parser = argparse.ArgumentParser(description='train parameters')
+ parser.add_argument('--model', type=str, default='nextgpt')
+ parser.add_argument('--mode', type=str, default='train', help='train or test or validation')
+ parser.add_argument('--local_rank', default=0, type=int)
+ parser.add_argument('--save_path', type=str, default='../ckpt/delta_ckpt/nextgpt/7b_tiva_v0/')
+ parser.add_argument('--log_path', type=str, default='../ckpt/delta_ckpt/nextgpt/7b_tiva_v0/log/')
+ parser.add_argument('--assets_path', type=str, default='./assets/')
+
+ # model configurations
+ parser.add_argument('--max_length', type=int, default=512) # the maximum input sequence length for LLMs
+ parser.add_argument('--stage', type=int, default=1) # the training stage
+ parser.add_argument('--modality', type=list, default=['image', 'video', 'audio', 'text'])
+ return parser.parse_args()
+
+
+def initialize_distributed(args):
+ args['master_ip'] = os.getenv('MASTER_ADDR', 'localhost')
+ args['master_port'] = os.getenv('MASTER_PORT', '6000')
+ args['world_size'] = int(os.getenv('WORLD_SIZE', '1'))
+ args['local_rank'] = int(os.getenv('RANK', '0')) % torch.cuda.device_count()
+ device = args['local_rank'] % torch.cuda.device_count()
+ torch.cuda.set_device(device)
+ deepspeed.init_distributed(dist_backend='nccl')
+
+
+def set_random_seed(seed):
+ if seed is not None and seed > 0:
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.random.manual_seed(seed)
+ torch.cuda.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+
+def config_env(args):
+ args['root_dir'] = '../'
+ # args['mode'] = 'train'
+ config = load_config(args)
+ args.update(config)
+ initialize_distributed(args)
+ set_random_seed(args['seed'])
+
+
+def build_directory(path):
+ if os.path.exists(path):
+ pass
+ else: # recursively construct directory
+ os.makedirs(path, exist_ok=True)
+
+
+def main(**args):
+ config_env(args)
+ print(args)
+ args['ds_config_path'] = f'dsconfig/stage_{args["stage"]}.json'
+ dschf = HfDeepSpeedConfig(args['ds_config_path'])
+ args['dschf'] = dschf
+
+ build_directory(args['save_path'])
+ build_directory(args['log_path'])
+
+ if args['log_path']:
+ logging.basicConfig(
+ format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s',
+ level=logging.DEBUG,
+ filename=f'{args["log_path"]}/train_{time.asctime()}.log',
+ filemode='w'
+ )
+ train_data, train_iter, sampler = load_dataset(args, args['dataset_name_list'])
+
+ train_num = max([_cur_dataset.__len__() for _cur_dataset in train_data.datasets.datasets]) * len(train_data.datasets.datasets)
+ length = args['epochs'] * train_num // args['world_size'] // dschf.config[
+ 'train_micro_batch_size_per_gpu']
+ total_steps = args['epochs'] * train_num // dschf.config['train_batch_size']
+ args['total_steps'] = total_steps
+ agent = load_model(args)
+ torch.distributed.barrier()
+
+ # begin to train
+ pbar = tqdm(total=length) # maximum total number
+ current_step = 0
+ for epoch_i in tqdm(range(args['epochs'])):
+ # for train_iter in train_iter_list:
+ for batch in train_iter:
+ agent.train_model(
+ batch,
+ current_step=current_step,
+ pbar=pbar
+ )
+ current_step += 1
+ # if current_step % 2000 == 0:
+ # torch.distributed.barrier()
+ # agent.save_model(args['save_path'], current_step)
+ # save at the end of the training
+ torch.distributed.barrier()
+ agent.save_model(args['save_path'], current_step)
+
+
+if __name__ == "__main__":
+ args = parser_args()
+ args = vars(args)
+ main(**args)
diff --git a/code/user.png b/code/user.png
new file mode 100644
index 0000000000000000000000000000000000000000..5dfdd89145ba278a582e454d011b524c2696e0ab
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diff --git a/data/IT_data/MosIT_data/__init__.py b/data/IT_data/MosIT_data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/IT_data/T+X-T_data/__init__.py b/data/IT_data/T+X-T_data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/IT_data/T+X-T_data/alpaca/__init__.py b/data/IT_data/T+X-T_data/alpaca/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/IT_data/T+X-T_data/llava/prapare.md b/data/IT_data/T+X-T_data/llava/prapare.md
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/IT_data/T+X-T_data/videochat/__init__.py b/data/IT_data/T+X-T_data/videochat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/IT_data/T-T+X_data/__init__.py b/data/IT_data/T-T+X_data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/IT_data/T-T+X_data/audio_t2x.json b/data/IT_data/T-T+X_data/audio_t2x.json
new file mode 100644
index 0000000000000000000000000000000000000000..ae82958f801da60e9bd9f484b884b50c8d0925a8
--- /dev/null
+++ b/data/IT_data/T-T+X_data/audio_t2x.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03c37ba9b284752bcb6847bc0ccf7b600f07875b38efed4b7cd1b786825a9d70
+size 10824519
diff --git a/data/IT_data/T-T+X_data/image_t2x.json b/data/IT_data/T-T+X_data/image_t2x.json
new file mode 100644
index 0000000000000000000000000000000000000000..b186183fb8a99ca2214dd083c496214cb3a857cb
--- /dev/null
+++ b/data/IT_data/T-T+X_data/image_t2x.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42c7c61a4540c20787602941979cc978b02431f02222af36ad599d0821ad4a3a
+size 11353446
diff --git a/data/IT_data/T-T+X_data/video_t2x.json b/data/IT_data/T-T+X_data/video_t2x.json
new file mode 100644
index 0000000000000000000000000000000000000000..01ba3e7a020c889ed19d718926eaea270feb5ace
--- /dev/null
+++ b/data/IT_data/T-T+X_data/video_t2x.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c923c80b2c1db3926343bef054bdf58a3fb167bf2cd09726a932b540d3bc5a9
+size 14333305
diff --git a/data/T-X_pair_data/audiocap/prepare.md b/data/T-X_pair_data/audiocap/prepare.md
new file mode 100644
index 0000000000000000000000000000000000000000..58370c32e21d0bbdc8a0f47d5fc4b8e84aa05305
--- /dev/null
+++ b/data/T-X_pair_data/audiocap/prepare.md
@@ -0,0 +1,75 @@
+
+## Preparation
+
+
+### Requirements
+
+Python 3.9 (it may work with other versions, but it has not been tested)
+
+### Installation
+
+```angular2html
+# Install ffmpeg
+sudo apt install ffmpeg
+# Install audiocaps-download
+pip install audiocaps-download
+```
+
+### Usage
+- Download `csv` file in [here](https://audiocaps.github.io/). The header of the CSV file are:
+```angular2html
+audiocap_id,youtube_id,start_time,caption
+```
+
+- Download audio by following codes:
+```angular2html
+from audiocaps_download import Downloader
+d = Downloader(root_path='data/T-X_pair_data/audiocap/', n_jobs=16)
+d.download(format = 'wav')
+```
+
+
+The main class is `audiocaps_download.Downloader`. It is initialized using the following parameters:
+
+- `root_path`: the path to the directory where the dataset will be downloaded.
+- `n_jobs`: the number of parallel downloads. Default is 1.
+The methods of the class are:
+
+- `download(format='vorbis', quality=5)`: downloads the dataset.
+- The format can be one of the following (supported by `yt-dlp` `--audio-format parameter`):
+ - `vorbis`: downloads the dataset in Ogg Vorbis format. This is the default.
+ - `wav`: downloads the dataset in WAV format.
+ - `mp3`: downloads the dataset in MP3 format.
+ - `m4a`: downloads the dataset in M4A format.
+ - `flac`: downloads the dataset in FLAC format.
+ - `opus`: downloads the dataset in Opus format.
+ - `webm`: downloads the dataset in WebM format.
+ - ... and many more.
+ - The quality can be an integer between 0 and 10. Default is 5.
+- `load_dataset()`: reads the csv files from the original repository. It is not used externally.
+- `download_file(...)`: downloads a single file. It is not used externally.
+
+### Postprocess
+Once you've downloaded the dataset, please verify the download status, as some audio files may not have been successfully downloaded. Afterward, organize the dataset into a json file with the following format:
+```angular2html
+[
+ {
+ "caption": "A woman talks nearby as water pours",
+ "audio_name": "91139.wav"
+ },
+ {
+ "caption": "The wind is blowing and rustling occurs",
+ "audio_name": "11543.wav"
+ },
+ ...
+]
+```
+The data file structure should be:
+```angular2html
+data/T-X_pair_data/audiocap
+├── audiocap.json
+├── audios
+| ├── 91139.wav
+| ├── 11543.wav
+| └── ...
+```
diff --git a/data/T-X_pair_data/cc3m/prepare.md b/data/T-X_pair_data/cc3m/prepare.md
new file mode 100644
index 0000000000000000000000000000000000000000..ecc3d78d31ef49065c6cb1ebc738d658009e3a39
--- /dev/null
+++ b/data/T-X_pair_data/cc3m/prepare.md
@@ -0,0 +1,56 @@
+
+
+## Preparation
+
+1. Download `Train_GCC-training.tsv` dataset from [here](https://ai.google.com/research/ConceptualCaptions/download)
+
+
+2. Download images via the following commands:
+```commandline
+pip install img2dataset
+
+img2dataset --url_list Train_GCC-training.tsv --input_format "tsv" --url_col "url" --caption_col "caption" --output_format webdataset --output_folder cc3m --processes_count 16 --thread_count 64 --image_size 256 --enable_wandb True
+```
+Note that:
+
+- `url_list` A file with the list of url of images to download. It can be a folder of such files. (required)
+- `image_size` The size to resize image to (default 256)
+- `output_folder` The path to the output folder. (default "images")
+- `processes_count` The number of processes used for downloading the pictures. This is important to be high for performance. (default 1)
+- `thread_count` The number of threads used for downloading the pictures. This is important to be high for performance. (default 256)
+- `output_format` decides how to save pictures (default files)
+ - `files saves` as a set of subfolder containing pictures
+ - `webdataset` saves as tars containing pictures
+ - ...
+- `url_col` the name of the url column for parquet and csv (default url)
+- `caption_col` the name of the caption column for parquet and csv (default None)
+- `enable_wandb` whether to enable wandb logging (default False)
+
+For more details, please refer to [img2dataset](https://github.com/rom1504/img2dataset/blob/main/README.md)
+
+
+
+3. Once you've downloaded the dataset, please verify the download status, as some image files may not have been successfully downloaded. Afterward, organize the dataset into a json file with the following format:
+
+```angular2html
+[
+ {
+ "caption": "pitcher pitches against sports team",
+ "image_name": "000002362.jpg"
+ },
+ {
+ "caption": "sea beach with mountains on the horizon , yellow umbrella and sand",
+ "image_name": "000007816.jpg"
+ },
+ ...
+]
+```
+The data file structure should be:
+```angular2html
+data/T-X_pair_data/cc3m
+├── cc3m.json
+├── images
+| ├── 000002362.jpg
+| ├── 000007816.jpg
+| └── ...
+```
\ No newline at end of file
diff --git a/data/T-X_pair_data/webvid/prepare.md b/data/T-X_pair_data/webvid/prepare.md
new file mode 100644
index 0000000000000000000000000000000000000000..b67f5686f365f2972295f91c5f533cfaca72987d
--- /dev/null
+++ b/data/T-X_pair_data/webvid/prepare.md
@@ -0,0 +1,46 @@
+## Preparation
+
+WebVid is a large-scale text-video dataset, containing 10 million video-text pairs scraped from the stock footage sites.
+To download the dataset, run the following command:
+
+```angular2html
+wget -nc http://www.robots.ox.ac.uk/~maxbain/webvid/results_2M_train.csv
+
+video2dataset --url_list="results_10M_train.csv" \
+ --input_format="csv" \
+ --output-format="webdataset" \
+ --output_folder="dataset" \
+ --url_col="contentUrl" \
+ --caption_col="name" \
+ --save_additional_columns='[videoid,page_idx,page_dir,duration]' \
+ --enable_wandb=True \
+ --config="path/to/config.yaml" \
+```
+For more datails, please refer to [video2dataset](https://github.com/iejMac/video2dataset/blob/main/dataset_examples/WebVid.md).
+
+
+### Postprocess
+Once you've downloaded the dataset, please verify the download status, as some video files may not have been successfully downloaded. Afterward, organize the dataset into a json file with the following format:
+```angular2html
+[
+ {
+ "caption": "Merida, mexico - may 23, 2017: tourists are walking on a roadside near catholic church in the street of mexico at sunny summer day.",
+ "video_name": "31353427.mp4"
+ },
+ {
+ "caption": "Happy family using laptop on bed at home",
+ "video_name": "14781349.mp4"
+ },
+ ...
+]
+```
+
+The data file structure should be:
+```angular2html
+data/T-X_pair_data/webvid
+├── webvid.json
+├── videos
+| ├── 31353427.mp4
+| ├── 14781349.mp4
+| └── ...
+```
\ No newline at end of file
diff --git a/figures/demo.png b/figures/demo.png
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index 0000000000000000000000000000000000000000..1780305ba22ade8c25afc03afcf12ba0d8676d81
--- /dev/null
+++ b/figures/demo.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dcfde50a0ee172b181c88ec045ac782f09c37c5ed577272d907b8aa2ef44e6d3
+size 2613099
diff --git a/figures/framework.png b/figures/framework.png
new file mode 100644
index 0000000000000000000000000000000000000000..15a854bdd5caf552f0ffd4d803a0fc1c36921622
Binary files /dev/null and b/figures/framework.png differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6171ebf2a44e7674dd784c6773673dbdb9b44992
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,31 @@
+accelerate==0.19.0
+datasets==2.12.0
+decord==0.6.0
+deepspeed==0.9.3
+diffusers==0.17.0
+einops==0.6.1
+ftfy==6.1.1
+gradio==3.44.0
+imageio==2.31.1
+iopath==0.1.10
+ipdb==0.13.13
+joblib==1.3.1
+matplotlib==3.7.1
+mdtex2html==1.2.0
+numpy==1.24.3
+packaging==23.1
+pandas==2.0.2
+peft==0.3.0
+Pillow==9.5.0
+pytorchvideo==0.1.5
+PyYAML==6.0
+regex==2023.6.3
+scipy==1.11.2
+timm==0.9.2
+torch==1.13.1
+torchaudio==0.13.1
+torchvision==0.14.1
+tqdm==4.65.0
+transformers==4.29.2
+omegaconf==2.3.0
+tensorboard==2.13.0
NExT-GPT: Any-to-Any Multimodal LLM
+[Shengqiong Wu](https://chocowu.github.io/), [Hao Fei](http://haofei.vip/)*, [Leigang Qu](#), [Wei Ji](https://jiwei0523.github.io/), and [Tat-Seng Chua](https://www.chuatatseng.com/).
+(*Correspondence )
+
+**[NExT++](https://www.nextcenter.org/), School of Computing, National University of Singapore**
+
+-----
+
+
+
+
+[](https://www.youtube.com/watch?v=aqw2SCWeWD0)
+
+
+This repository hosts the code, data and model weight of **NExT-GPT**, the first end-to-end MM-LLM that perceives input and generates output in arbitrary combinations (any-to-any) of text, image, video, and audio and beyond.
+
+
+
+-----------
+
+## 🎉 News
+
+- [x] [2023.09.15] 🚀🚀 Release the code of NExT-GPT in version `7b_tiva_v0`.
+- [x] [2023.09.27] 🔨🧩 Added modality-blended batch sampler .
+- [x] [2023.10.01] 📢📢 Release the T2M instruction dataset.
+- [x] [2023.10.04] 👏👏 Release the checkpoint of NExT-GPT in version [7b_tiva_v0](https://huggingface.co/ChocoWu/nextgpt_7b_tiva_v0) .
+- [x] [2023.10.15] 🔨🚀 Update of NExT-GPT in version [7b_tiva_v0](https://huggingface.co/ChocoWu/nextgpt_7b_tiva_v0) .
+
+
+## 👉 TODO
+- [ ] Release MosIT data.
+- [ ] Updating NExT-GPT in more types&sizes of LLMs.
+- [ ] Empowering NExT-GPT with more modalities of inputs&outputs.
+- [ ] ...
+
+
+
+-----------
+
+## Example Demos
+Here we showcase examples generated from NExT-GPT.
+For more examples, kindly visit the [webpage](https://next-gpt.github.io/), or the online live [demo](https://acc414b22d6839d28f.gradio.live).
+
+
+https://github.com/NExT-GPT/NExT-GPT/assets/18722770/0c2b3d88-a533-4899-ab44-65580fe54538
+
+
+https://github.com/NExT-GPT/NExT-GPT/assets/18722770/eb1319a6-38aa-4546-a96e-163207e7de93
+
+
+https://github.com/NExT-GPT/NExT-GPT/assets/18722770/36bec0ad-9bad-4bcf-bc37-92b028f1bc6a
+
+
+
+
+
+## Brief Introduction
+
+
+NExt-GPT is built on top of existing pre-trained LLM, multimodal encoder and SoTA diffusion models, with sufficient end-to-end instruction tuning.
+
+
+