diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbf6c454b0dc228f3a27a304d1b28047f539a952
--- /dev/null
+++ b/app.py
@@ -0,0 +1,249 @@
+"""
+Adapted from: https://github.com/Vision-CAIR/MiniGPT-4/blob/main/demo.py
+"""
+import argparse
+import os
+import sys
+import random
+
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+import gradio as gr
+
+from global_local.common.config import Config
+from global_local.common.dist_utils import get_rank
+from global_local.common.registry import registry
+from global_local.conversation.conversation_video import Chat, Conversation, default_conversation,SeparatorStyle,conv_llava_llama_2
+import decord
+decord.bridge.set_bridge('torch')
+
+#%%
+# imports modules for registration
+from global_local.datasets.builders import *
+from global_local.models import *
+from global_local.processors import *
+from global_local.runners import *
+from global_local.tasks import *
+
+#%%
+def parse_args():
+ parser = argparse.ArgumentParser(description="Demo")
+ #parser.add_argument("--cfg-path", required=True, help="path to configuration file.")
+ parser.add_argument("--cfg-path", type=str, default='./eval_configs/conversation_demo.yaml', help="path to configuration file.")
+ parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
+ parser.add_argument("--model_type", type=str, default='llama_v2', help="specify LLM")
+ parser.add_argument('--pretrained_weight_path', type=str, default="./ckpt/finetuned_model.pth", metavar='PWP',
+ help='path to pretrained weight path')
+ parser.add_argument('--num_frames_per_clip', type=int, default=16, metavar='NPPC',
+ help='specify how frames to use per clip')
+ parser.add_argument('--num_segments', type=int, default=4, metavar='NS',
+ help='specify number of video segments')
+ parser.add_argument('--hierarchical_agg_function', type=str, default="without-top-final-global-prompts-region-segment-full-dis-spatiotemporal-prompts-attn-early-attn-linear-learned", metavar='HAF',
+ help='specify function to merge global and clip visual representations')
+
+ parser.add_argument(
+ "--options",
+ nargs="+",
+ help="override some settings in the used config, the key-value pair "
+ "in xxx=yyy format will be merged into config file (deprecate), "
+ "change to --cfg-options instead.",
+ )
+ args = parser.parse_args()
+ return args
+
+
+def setup_seeds(config):
+ seed = config.run_cfg.seed + get_rank()
+
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+
+ cudnn.benchmark = False
+ cudnn.deterministic = True
+
+
+# ========================================
+# Model Initialization
+# ========================================
+
+print('Initializing Chat')
+args = parse_args()
+cfg = Config(args)
+
+model_config = cfg.model_cfg
+model_config.device_8bit = args.gpu_id
+model_cls = registry.get_model_class(model_config.arch)
+model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id))
+
+model.num_frames_per_clip = args.num_frames_per_clip
+model.num_segments = args.num_segments
+model.hierarchical_agg_function = args.hierarchical_agg_function
+model.global_region_embed_weight = None
+
+model.initialize_visual_agg_function()
+
+best_checkpoint = torch.load(args.pretrained_weight_path, map_location='cpu')['model_state_dict']
+pretrained_dict = {}
+for k, v in best_checkpoint.items():
+ pretrained_dict[k.replace('module.', '')] = v
+
+model_dict = model.state_dict()
+model_dict.update(pretrained_dict)
+model.load_state_dict(model_dict)
+model.cuda().eval()
+
+#vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train
+vis_processor_cfg = cfg.datasets_cfg.webvid.vis_processor.train
+vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
+chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id))
+print('Initialization Finished')
+
+# ========================================
+# Gradio Setting
+# ========================================
+
+def gradio_reset(chat_state, img_list):
+ if chat_state is not None:
+ chat_state.messages = []
+ if img_list is not None:
+ img_list = []
+ return None, gr.update(value=None, interactive=True), gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your video first', interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list
+
+def upload_imgorvideo(gr_video, gr_img, text_input, chat_state,chatbot):
+ if args.model_type == 'vicuna':
+ chat_state = default_conversation.copy()
+ else:
+ chat_state = conv_llava_llama_2.copy()
+ if gr_img is None and gr_video is None:
+ return None, None, None, gr.update(interactive=True), chat_state, None
+ elif gr_img is not None and gr_video is None:
+ print(gr_img)
+ chatbot = chatbot + [((gr_img,), None)]
+ chat_state.system = "You are able to understand the visual content that the user provides. Follow the instructions carefully and explain your answers in detail."
+ img_list = []
+ llm_message = chat.upload_img(gr_img, chat_state, img_list)
+ return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list,chatbot
+ elif gr_video is not None and gr_img is None:
+ print(gr_video)
+ chatbot = chatbot + [((gr_video,), None)]
+ chat_state.system = "You are able to understand the visual content that the user provides. Follow the instructions carefully and explain your answers in detail."
+ img_list = []
+ llm_message = chat.upload_video_without_audio(gr_video, chat_state, img_list)
+ return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list,chatbot
+ else:
+ # img_list = []
+ return gr.update(interactive=False), gr.update(interactive=False, placeholder='Currently, only one input is supported'), gr.update(value="Currently, only one input is supported", interactive=False), chat_state, None,chatbot
+
+def gradio_ask(user_message, chatbot, chat_state):
+ if len(user_message) == 0:
+ return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
+ chat.ask(user_message, chat_state)
+ chatbot = chatbot + [[user_message, None]]
+ return '', chatbot, chat_state
+
+
+def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
+ llm_message = chat.answer(conv=chat_state,
+ img_list=img_list,
+ num_beams=num_beams,
+ temperature=temperature,
+ max_new_tokens=300,
+ max_length=2000)[0]
+ chatbot[-1][1] = llm_message
+ print(chat_state.get_prompt())
+ print(chat_state)
+ return chatbot, chat_state, img_list
+
+title = """
+Global-Local QFormer for Long Video Understanding with LLMs
+
+ Introduction: We introduce a Global-Local QFormer video model that is connected with a Large Language Model to understand \
+ and answer questions about long videos.
+
+
+
+
+
Global-Local QFormer for Long Video Understanding with LLMs
+
+ Introduction: We introduce a Global-Local QFormer video model that is connected with a Large Language Model to understand \
+ and answer questions about long videos.
+
+
+
+
+
![]()
ImageContent. "
+ "You will be able to see the image once I provide it to you. Please answer my questions.",
+ roles=("Human", "Assistant"),
+ messages=[],
+ offset=0,
+ sep_style=SeparatorStyle.SINGLE,
+ sep="###",
+)
+
+default_conversation = Conversation(
+ system="",
+ roles=("Human", "Assistant"),
+ messages=[],
+ offset=0,
+ sep_style=SeparatorStyle.SINGLE,
+ sep="###",
+)
+conv_llava_llama_2 = Conversation(
+ system="You are a helpful language and vision assistant. "
+ "You are able to understand the visual content that the user provides, "
+ "and assist the user with a variety of tasks using natural language.",
+ roles=("USER", "ASSISTANT"),
+ messages=(),
+ offset=0,
+ sep_style=SeparatorStyle.LLAMA_2,
+ sep="",
+ sep2="",
+)
+class Chat:
+ def __init__(self, model, vis_processor, device='cuda:0'):
+ self.device = device
+ self.model = model
+ self.vis_processor = vis_processor
+ self.image_vis_processor = Blip2ImageEvalProcessor()
+ # stop_words_ids = [torch.tensor([835]).to(self.device),
+ # torch.tensor([2277, 29937]).to(self.device)] # '###' can be encoded in two different ways.
+ # self.stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
+
+ self.num_frames_per_clip = 16
+ self.num_segments = 4
+
+ def ask(self, text, conv):
+ if len(conv.messages) > 0 and conv.messages[-1][0] == conv.roles[0] \
+ and ('' in conv.messages[-1][1] or '' in conv.messages[-1][1]): # last message is image.
+ conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text])
+ else:
+ conv.append_message(conv.roles[0], text)
+
+ def answer(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9,
+ repetition_penalty=1.0, length_penalty=1, temperature=1.0, max_length=2000):
+ conv.append_message(conv.roles[1], None)
+ embs = self.get_context_emb(conv, img_list)
+
+ current_max_len = embs.shape[1] + max_new_tokens
+ if current_max_len - max_length > 0:
+ print('Warning: The number of tokens in current conversation exceeds the max length. '
+ 'The model will not see the contexts outside the range.')
+ begin_idx = max(0, current_max_len - max_length)
+
+ embs = embs[:, begin_idx:]
+ if conv.sep =="###":
+ stop_words_ids = [torch.tensor([835]).to(self.device),
+ torch.tensor([2277, 29937]).to(self.device)] # '###' can be encoded in two different ways.
+ stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
+ else:
+ stop_words_ids = [torch.tensor([2]).to(self.device)]
+ stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
+
+ # stopping_criteria
+ outputs = self.model.llama_model.generate(
+ inputs_embeds=embs,
+ max_new_tokens=max_new_tokens,
+ stopping_criteria=stopping_criteria,
+ num_beams=num_beams,
+ do_sample=True,
+ min_length=min_length,
+ top_p=top_p,
+ repetition_penalty=repetition_penalty,
+ length_penalty=length_penalty,
+ temperature=temperature,
+ )
+ output_token = outputs[0]
+ if output_token[0] == 0: # the model might output a unknow token at the beginning. remove it
+ output_token = output_token[1:]
+ if output_token[0] == 1: # some users find that there is a start token at the beginning. remove it
+ output_token = output_token[1:]
+ output_text = self.model.llama_tokenizer.decode(output_token, add_special_tokens=False)
+ if conv.sep =="###":
+ output_text = output_text.split('###')[0] # remove the stop sign '###'
+ output_text = output_text.split('Assistant:')[-1].strip()
+ else:
+ output_text = output_text.split(conv.sep2)[0] # remove the stop sign '###'
+ output_text = output_text.split(conv.roles[1]+':')[-1].strip()
+ conv.messages[-1][1] = output_text
+ return output_text, output_token.cpu().numpy()
+
+ def upload_video(self, video_path, conv, img_list):
+
+ msg = ""
+ if isinstance(video_path, str): # is a video path
+ ext = os.path.splitext(video_path)[-1].lower()
+ print(video_path)
+ # image = self.vis_processor(image).unsqueeze(0).to(self.device)
+ video, msg = load_video(
+ video_path=video_path,
+ n_frms=8,
+ height=224,
+ width=224,
+ sampling ="uniform", return_msg = True
+ )
+ video = self.vis_processor.transform(video)
+ video = video.unsqueeze(0).to(self.device)
+ # print(image)
+ else:
+ raise NotImplementedError
+
+ try:
+ audio_flag = 1
+ audio = load_and_transform_audio_data([video_path],"cpu", clips_per_video=8)
+ audio = audio.to(self.device)
+ except :
+ print('no audio is found')
+ audio_flag = 0
+ finally:
+ if audio_flag == 1:
+ # image_emb, _ = self.model.encode_videoQformer_audiovideo(video,audio)
+ image_emb, _ = self.model.encode_videoQformer_visual(video)
+ audio_emb,_ = self.model.encode_audioQformer(audio)
+ img_list.append(audio_emb)
+ img_list.append(image_emb)
+ conv.system = ""
+ # conv.append_message(conv.roles[0], "The audio of this video is ")
+ conv.append_message(conv.roles[0], "Close your eyes, open your ears and you imagine only based on the sound that: . \
+ Close your ears, open your eyes and you see that . \
+ Now answer my question based on what you have just seen and heard.")
+
+ else: # only vison no audio
+ # conv.system = "You can understand the video that the user provides. Follow the instructions carefully and explain your answers in detail."
+ image_emb, _ = self.model.encode_videoQformer_visual(video)
+ img_list.append(image_emb)
+ conv.append_message(conv.roles[0], " "+ msg)
+ return "Received."
+
+ def upload_video_without_audio(self, video_path, conv, img_list):
+ msg = ""
+ if isinstance(video_path, str): # is a video path
+ ext = os.path.splitext(video_path)[-1].lower()
+ print(video_path)
+ # image = self.vis_processor(image).unsqueeze(0).to(self.device)
+ video, msg = load_video(
+ video_path=video_path,
+ n_frms=self.num_frames_per_clip*self.num_segments,
+ height=224,
+ width=224,
+ sampling ="uniform", return_msg = True
+ )
+
+ video = self.vis_processor.transform(video)
+ video = video.unsqueeze(0).to(self.device)
+ else:
+ raise NotImplementedError
+
+ # conv.system = "You can understand the video that the user provides. Follow the instructions carefully and explain your answers in detail."
+ #image_emb, _ = self.model.encode_videoQformer_visual(video)
+ image_emb, _ = self.process_video_frames(video)
+ img_list.append(image_emb)
+ conv.append_message(conv.roles[0], " "+ msg)
+
+ return "Received."
+
+ def process_video_frames(self, all_frames):
+ total_num_frames = self.num_frames_per_clip * self.num_segments
+ global_clip_indices = torch.linspace(0, total_num_frames-1, steps=self.num_frames_per_clip)
+ short_window_indices = torch.linspace(0, total_num_frames-1, steps=self.num_frames_per_clip * self.num_segments)
+
+ global_processed_frames = []
+ for i in global_clip_indices:
+ i = int(i)
+ curr = all_frames[:, :, i]
+ #curr = np.uint8(all_frames[i])
+ #curr = frame_transform(Image.fromarray(curr))
+ global_processed_frames.append(curr)
+ global_processed_frames = torch.stack(global_processed_frames, dim=2)
+
+ '''if len(global_processed_frames) < args.num_frames_per_clip:
+ diff = args.num_frames_per_clip - len(global_processed_frames)
+ pad = global_processed_frames[-1].unsqueeze(0).repeat(diff, 1, 1, 1)
+ global_processed_frames = torch.cat((global_processed_frames, pad), dim=0)'''
+
+ short_window_processed_frames = []
+ for i in short_window_indices:
+ i = int(i)
+ curr = all_frames[:, :, i]
+ #curr = np.uint8(all_frames[i])
+ #curr = frame_transform(Image.fromarray(curr))
+ short_window_processed_frames.append(curr)
+ short_window_processed_frames = torch.stack(short_window_processed_frames, dim=2)
+
+ '''if len(short_window_processed_frames) < args.num_frames_per_clip * args.num_segments:
+ diff = args.num_frames_per_clip * args.num_segments - len(short_window_processed_frames)
+ pad = short_window_processed_frames[-1].unsqueeze(0).repeat(diff, 1, 1, 1)
+ short_window_processed_frames = torch.cat((short_window_processed_frames, pad), dim=0)'''
+
+ global_attn_mask = torch.zeros((self.num_frames_per_clip))
+ global_attn_mask[:global_processed_frames.size(2)] = True
+
+ short_window_attn_mask = torch.zeros((self.num_frames_per_clip * self.num_segments))
+ short_window_attn_mask[:short_window_processed_frames.size(2)] = True
+
+ global_processed_frames = global_processed_frames.permute((0, 2, 1, 3, 4)).cuda()
+ short_window_processed_frames = short_window_processed_frames.permute((0, 2, 1, 3, 4)).cuda()
+ global_frame_attn_mask = global_attn_mask.unsqueeze(0).cuda()
+ segments_frame_attn_mask = short_window_attn_mask.unsqueeze(0).cuda()
+
+ with torch.no_grad():
+ samples = {'global_video': global_processed_frames, 'global_frame_attn_mask': global_frame_attn_mask, 'segments_video': short_window_processed_frames, 'segments_frame_attn_mask': segments_frame_attn_mask}
+ merged_video_embeds, merged_video_embeds_mask = self.model.compute_merged_video_embeds(samples)
+
+ return merged_video_embeds, merged_video_embeds_mask
+
+ def upload_img(self, image, conv, img_list):
+
+ msg = ""
+ if isinstance(image, str): # is a image path
+ raw_image = Image.open(image).convert('RGB') # 增加一个时间维度
+ image = self.image_vis_processor(raw_image).unsqueeze(0).unsqueeze(2).to(self.device)
+ elif isinstance(image, Image.Image):
+ raw_image = image
+ image = self.image_vis_processor(raw_image).unsqueeze(0).unsqueeze(2).to(self.device)
+ elif isinstance(image, torch.Tensor):
+ if len(image.shape) == 3:
+ image = image.unsqueeze(0)
+ image = image.to(self.device)
+ else:
+ raise NotImplementedError
+
+ image_emb, _ = self.model.encode_videoQformer_visual(image)
+ img_list.append(image_emb)
+ # Todo msg=""
+ conv.append_message(conv.roles[0], " "+ msg)
+
+ return "Received."
+
+ def get_context_emb(self, conv, img_list):
+ prompt = conv.get_prompt()
+ prompt_segs = prompt.split('')
+ assert len(prompt_segs) == len(img_list) + 1, "Unmatched numbers of image placeholders and images."
+ seg_tokens = [
+ self.model.llama_tokenizer(
+ seg, return_tensors="pt", add_special_tokens=i == 0).to(self.device).input_ids
+ # only add bos to the first seg
+ for i, seg in enumerate(prompt_segs)
+ ]
+ seg_embs = [self.model.llama_model.model.embed_tokens(seg_t) for seg_t in seg_tokens]
+ mixed_embs = [emb for pair in zip(seg_embs[:-1], img_list) for emb in pair] + [seg_embs[-1]]
+ mixed_embs = torch.cat(mixed_embs, dim=1)
+
+ return mixed_embs
+
+if __name__ =='__main__':
+ video_path = '/mnt/workspace/videoGPT/Video-LLaMA/examples/applausing.mp4'
+ # import torch.classes.torchaudio.ffmpeg_StreamReader
+ # ffmpeg_StreamReader(video_path)
+ load_and_transform_audio_data([video_path],"cpu", clips_per_video=8)
diff --git a/global_local/datasets/__init__.py b/global_local/datasets/__init__.py
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diff --git a/global_local/datasets/builders/__init__.py b/global_local/datasets/builders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..14b373a8c1ea80464da7b34e2e4b7851ce0c3b5e
--- /dev/null
+++ b/global_local/datasets/builders/__init__.py
@@ -0,0 +1,77 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from global_local.datasets.builders.base_dataset_builder import load_dataset_config
+from global_local.datasets.builders.image_text_pair_builder import (
+ CCSBUBuilder,
+ LaionBuilder,
+ CCSBUAlignBuilder
+)
+from global_local.datasets.builders.video_caption_builder import WebvidBuilder
+from global_local.common.registry import registry
+from global_local.datasets.builders.instruct_builder import WebvidInstruct_Builder,LlavaInstruct_Builder
+__all__ = [
+ "CCSBUBuilder",
+ "LaionBuilder",
+ "CCSBUAlignBuilder",
+ "WebvidBuilder",
+ "LlavaInstruct_Builder",
+ "WebvidInstruct_Builder"
+
+]
+
+
+def load_dataset(name, cfg_path=None, vis_path=None, data_type=None):
+ """
+ Example
+
+ >>> dataset = load_dataset("coco_caption", cfg=None)
+ >>> splits = dataset.keys()
+ >>> print([len(dataset[split]) for split in splits])
+
+ """
+ if cfg_path is None:
+ cfg = None
+ else:
+ cfg = load_dataset_config(cfg_path)
+
+ try:
+ builder = registry.get_builder_class(name)(cfg)
+ except TypeError:
+ print(
+ f"Dataset {name} not found. Available datasets:\n"
+ + ", ".join([str(k) for k in dataset_zoo.get_names()])
+ )
+ exit(1)
+
+ if vis_path is not None:
+ if data_type is None:
+ # use default data type in the config
+ data_type = builder.config.data_type
+
+ assert (
+ data_type in builder.config.build_info
+ ), f"Invalid data_type {data_type} for {name}."
+
+ builder.config.build_info.get(data_type).storage = vis_path
+
+ dataset = builder.build_datasets()
+ return dataset
+
+
+class DatasetZoo:
+ def __init__(self) -> None:
+ self.dataset_zoo = {
+ k: list(v.DATASET_CONFIG_DICT.keys())
+ for k, v in sorted(registry.mapping["builder_name_mapping"].items())
+ }
+
+ def get_names(self):
+ return list(self.dataset_zoo.keys())
+
+
+dataset_zoo = DatasetZoo()
diff --git a/global_local/datasets/builders/__pycache__/__init__.cpython-39.pyc b/global_local/datasets/builders/__pycache__/__init__.cpython-39.pyc
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diff --git a/global_local/datasets/builders/__pycache__/video_caption_builder.cpython-39.pyc b/global_local/datasets/builders/__pycache__/video_caption_builder.cpython-39.pyc
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diff --git a/global_local/datasets/builders/base_dataset_builder.py b/global_local/datasets/builders/base_dataset_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd8759f2636cf2b7f6be75b80526d017eaa30c61
--- /dev/null
+++ b/global_local/datasets/builders/base_dataset_builder.py
@@ -0,0 +1,236 @@
+"""
+ This file is from
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import logging
+import os
+import shutil
+import warnings
+
+from omegaconf import OmegaConf
+import torch.distributed as dist
+from torchvision.datasets.utils import download_url
+
+import global_local.common.utils as utils
+from global_local.common.dist_utils import is_dist_avail_and_initialized, is_main_process
+from global_local.common.registry import registry
+from global_local.processors.base_processor import BaseProcessor
+
+
+
+class BaseDatasetBuilder:
+ train_dataset_cls, eval_dataset_cls = None, None
+
+ def __init__(self, cfg=None):
+ super().__init__()
+
+ if cfg is None:
+ # help to create datasets from default config.
+ self.config = load_dataset_config(self.default_config_path())
+ elif isinstance(cfg, str):
+ self.config = load_dataset_config(cfg)
+ else:
+ # when called from task.build_dataset()
+ self.config = cfg
+
+ self.data_type = self.config.data_type
+
+ self.vis_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
+ self.text_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
+
+ def build_datasets(self):
+ # download, split, etc...
+ # only called on 1 GPU/TPU in distributed
+
+ if is_main_process():
+ self._download_data()
+
+ if is_dist_avail_and_initialized():
+ dist.barrier()
+
+ # at this point, all the annotations and image/videos should be all downloaded to the specified locations.
+ logging.info("Building datasets...")
+ datasets = self.build() # dataset['train'/'val'/'test']
+
+ return datasets
+
+ def build_processors(self):
+ vis_proc_cfg = self.config.get("vis_processor")
+ txt_proc_cfg = self.config.get("text_processor")
+
+ if vis_proc_cfg is not None:
+ vis_train_cfg = vis_proc_cfg.get("train")
+ vis_eval_cfg = vis_proc_cfg.get("eval")
+
+ self.vis_processors["train"] = self._build_proc_from_cfg(vis_train_cfg)
+ self.vis_processors["eval"] = self._build_proc_from_cfg(vis_eval_cfg)
+
+ if txt_proc_cfg is not None:
+ txt_train_cfg = txt_proc_cfg.get("train")
+ txt_eval_cfg = txt_proc_cfg.get("eval")
+
+ self.text_processors["train"] = self._build_proc_from_cfg(txt_train_cfg)
+ self.text_processors["eval"] = self._build_proc_from_cfg(txt_eval_cfg)
+
+ @staticmethod
+ def _build_proc_from_cfg(cfg):
+ return (
+ registry.get_processor_class(cfg.name).from_config(cfg)
+ if cfg is not None
+ else None
+ )
+
+ @classmethod
+ def default_config_path(cls, type="default"):
+ return utils.get_abs_path(cls.DATASET_CONFIG_DICT[type])
+
+ def _download_data(self):
+ self._download_ann()
+ self._download_vis()
+
+ def _download_ann(self):
+ """
+ Download annotation files if necessary.
+ All the vision-language datasets should have annotations of unified format.
+
+ storage_path can be:
+ (1) relative/absolute: will be prefixed with env.cache_root to make full path if relative.
+ (2) basename/dirname: will be suffixed with base name of URL if dirname is provided.
+
+ Local annotation paths should be relative.
+ """
+ anns = self.config.build_info.annotations
+
+ splits = anns.keys()
+
+ cache_root = registry.get_path("cache_root")
+
+ for split in splits:
+ info = anns[split]
+
+ urls, storage_paths = info.get("url", None), info.storage
+
+ if isinstance(urls, str):
+ urls = [urls]
+ if isinstance(storage_paths, str):
+ storage_paths = [storage_paths]
+
+ assert len(urls) == len(storage_paths)
+
+ for url_or_filename, storage_path in zip(urls, storage_paths):
+ # if storage_path is relative, make it full by prefixing with cache_root.
+ if not os.path.isabs(storage_path):
+ storage_path = os.path.join(cache_root, storage_path)
+
+ dirname = os.path.dirname(storage_path)
+ if not os.path.exists(dirname):
+ os.makedirs(dirname)
+
+ if os.path.isfile(url_or_filename):
+ src, dst = url_or_filename, storage_path
+ if not os.path.exists(dst):
+ shutil.copyfile(src=src, dst=dst)
+ else:
+ logging.info("Using existing file {}.".format(dst))
+ else:
+ if os.path.isdir(storage_path):
+ # if only dirname is provided, suffix with basename of URL.
+ raise ValueError(
+ "Expecting storage_path to be a file path, got directory {}".format(
+ storage_path
+ )
+ )
+ else:
+ filename = os.path.basename(storage_path)
+
+ download_url(url=url_or_filename, root=dirname, filename=filename)
+
+ def _download_vis(self):
+
+ storage_path = self.config.build_info.get(self.data_type).storage
+ storage_path = utils.get_cache_path(storage_path)
+
+ if not os.path.exists(storage_path):
+ warnings.warn(
+ f"""
+ The specified path {storage_path} for visual inputs does not exist.
+ Please provide a correct path to the visual inputs or
+ refer to datasets/download_scripts/README.md for downloading instructions.
+ """
+ )
+
+ def build(self):
+ """
+ Create by split datasets inheriting torch.utils.data.Datasets.
+
+ # build() can be dataset-specific. Overwrite to customize.
+ """
+ self.build_processors()
+
+ build_info = self.config.build_info
+
+ ann_info = build_info.annotations
+ vis_info = build_info.get(self.data_type)
+
+ datasets = dict()
+ for split in ann_info.keys():
+ if split not in ["train", "val", "test"]:
+ continue
+
+ is_train = split == "train"
+
+ # processors
+ vis_processor = (
+ self.vis_processors["train"]
+ if is_train
+ else self.vis_processors["eval"]
+ )
+ text_processor = (
+ self.text_processors["train"]
+ if is_train
+ else self.text_processors["eval"]
+ )
+
+ # annotation path
+ ann_paths = ann_info.get(split).storage
+ if isinstance(ann_paths, str):
+ ann_paths = [ann_paths]
+
+ abs_ann_paths = []
+ for ann_path in ann_paths:
+ if not os.path.isabs(ann_path):
+ ann_path = utils.get_cache_path(ann_path)
+ abs_ann_paths.append(ann_path)
+ ann_paths = abs_ann_paths
+
+ # visual data storage path
+ vis_path = os.path.join(vis_info.storage, split)
+
+ if not os.path.isabs(vis_path):
+ # vis_path = os.path.join(utils.get_cache_path(), vis_path)
+ vis_path = utils.get_cache_path(vis_path)
+
+ if not os.path.exists(vis_path):
+ warnings.warn("storage path {} does not exist.".format(vis_path))
+
+ # create datasets
+ dataset_cls = self.train_dataset_cls if is_train else self.eval_dataset_cls
+ datasets[split] = dataset_cls(
+ vis_processor=vis_processor,
+ text_processor=text_processor,
+ ann_paths=ann_paths,
+ vis_root=vis_path,
+ )
+
+ return datasets
+
+
+def load_dataset_config(cfg_path):
+ cfg = OmegaConf.load(cfg_path).datasets
+ cfg = cfg[list(cfg.keys())[0]]
+
+ return cfg
diff --git a/global_local/datasets/builders/image_text_pair_builder.py b/global_local/datasets/builders/image_text_pair_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..11d2887706e6ade56c8040095630f8a266566f1f
--- /dev/null
+++ b/global_local/datasets/builders/image_text_pair_builder.py
@@ -0,0 +1,106 @@
+import os
+import logging
+import warnings
+
+from global_local.common.registry import registry
+from global_local.datasets.builders.base_dataset_builder import BaseDatasetBuilder
+from global_local.datasets.datasets.laion_dataset import LaionDataset
+from global_local.datasets.datasets.cc_sbu_dataset import CCSBUDataset, CCSBUAlignDataset
+
+
+@registry.register_builder("cc_sbu")
+class CCSBUBuilder(BaseDatasetBuilder):
+ train_dataset_cls = CCSBUDataset
+
+ DATASET_CONFIG_DICT = {"default": "configs/datasets/cc_sbu/defaults.yaml"}
+
+ def _download_ann(self):
+ pass
+
+ def _download_vis(self):
+ pass
+
+ def build(self):
+ self.build_processors()
+
+ build_info = self.config.build_info
+
+ datasets = dict()
+ split = "train"
+
+ # create datasets
+ # [NOTE] return inner_datasets (wds.DataPipeline)
+ dataset_cls = self.train_dataset_cls
+ datasets[split] = dataset_cls(
+ vis_processor=self.vis_processors[split],
+ text_processor=self.text_processors[split],
+ location=build_info.storage,
+ ).inner_dataset
+
+ return datasets
+
+
+@registry.register_builder("laion")
+class LaionBuilder(BaseDatasetBuilder):
+ train_dataset_cls = LaionDataset
+
+ DATASET_CONFIG_DICT = {"default": "configs/datasets/laion/defaults.yaml"}
+
+ def _download_ann(self):
+ pass
+
+ def _download_vis(self):
+ pass
+
+ def build(self):
+ self.build_processors()
+
+ build_info = self.config.build_info
+
+ datasets = dict()
+ split = "train"
+
+ # create datasets
+ # [NOTE] return inner_datasets (wds.DataPipeline)
+ dataset_cls = self.train_dataset_cls
+ datasets[split] = dataset_cls(
+ vis_processor=self.vis_processors[split],
+ text_processor=self.text_processors[split],
+ location=build_info.storage,
+ ).inner_dataset
+
+ return datasets
+
+
+@registry.register_builder("cc_sbu_align")
+class CCSBUAlignBuilder(BaseDatasetBuilder):
+ train_dataset_cls = CCSBUAlignDataset
+
+ DATASET_CONFIG_DICT = {
+ "default": "configs/datasets/cc_sbu/align.yaml",
+ }
+
+ def build_datasets(self):
+ # at this point, all the annotations and image/videos should be all downloaded to the specified locations.
+ logging.info("Building datasets...")
+ self.build_processors()
+
+ build_info = self.config.build_info
+ storage_path = build_info.storage
+
+ datasets = dict()
+
+ if not os.path.exists(storage_path):
+ warnings.warn("storage path {} does not exist.".format(storage_path))
+
+ # create datasets
+ dataset_cls = self.train_dataset_cls
+ datasets['train'] = dataset_cls(
+ vis_processor=self.vis_processors["train"],
+ text_processor=self.text_processors["train"],
+ ann_paths=[os.path.join(storage_path, 'filter_cap.json')],
+ vis_root=os.path.join(storage_path, 'image'),
+ )
+
+ return datasets
+
diff --git a/global_local/datasets/builders/instruct_builder.py b/global_local/datasets/builders/instruct_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b963512da50c7c4de2e1f4c3ce4e09217d56529
--- /dev/null
+++ b/global_local/datasets/builders/instruct_builder.py
@@ -0,0 +1,78 @@
+import os
+import logging
+import warnings
+
+from global_local.common.registry import registry
+from global_local.datasets.builders.base_dataset_builder import BaseDatasetBuilder
+from global_local.datasets.datasets.laion_dataset import LaionDataset
+from global_local.datasets.datasets.llava_instruct_dataset import Instruct_Dataset
+from global_local.datasets.datasets.video_instruct_dataset import Video_Instruct_Dataset
+
+@registry.register_builder("instruct")
+class Instruct_Builder(BaseDatasetBuilder):
+ train_dataset_cls = Instruct_Dataset
+
+ DATASET_CONFIG_DICT = {"default": "configs/datasets/instruct/defaults.yaml"}
+
+ def _download_ann(self):
+ pass
+
+ def _download_vis(self):
+ pass
+
+ def build(self):
+ self.build_processors()
+ datasets = dict()
+ split = "train"
+
+ build_info = self.config.build_info
+ dataset_cls = self.train_dataset_cls
+ if self.config.num_video_query_token:
+ num_video_query_token = self.config.num_video_query_token
+ else:
+ num_video_query_token = 32
+
+ if self.config.tokenizer_name:
+ tokenizer_name = self.config.tokenizer_name
+ else:
+ tokenizer_name = '/mnt/workspace/ckpt/vicuna-13b/'
+
+
+ datasets[split] = dataset_cls(
+ vis_processor=self.vis_processors[split],
+ text_processor=self.text_processors[split],
+ vis_root=build_info.videos_dir,
+ ann_root=build_info.anno_dir,
+ num_video_query_token = num_video_query_token,
+ tokenizer_name = tokenizer_name,
+ data_type = self.config.data_type
+ )
+
+ return datasets
+
+@registry.register_builder("webvid_instruct")
+class WebvidInstruct_Builder(Instruct_Builder):
+ train_dataset_cls = Video_Instruct_Dataset
+
+ DATASET_CONFIG_DICT = {
+ "default": "configs/datasets/instruct/webvid_instruct.yaml",
+ }
+
+@registry.register_builder("webvid_instruct_zh")
+class WebvidInstruct_zh_Builder(Instruct_Builder):
+ train_dataset_cls = Video_Instruct_Dataset
+
+ DATASET_CONFIG_DICT = {
+ "default": "configs/datasets/instruct/webvid_instruct.yaml",
+ }
+
+
+
+@registry.register_builder("llava_instruct")
+class LlavaInstruct_Builder(Instruct_Builder):
+ train_dataset_cls = Instruct_Dataset
+
+ DATASET_CONFIG_DICT = {
+ "default": "configs/datasets/instruct/llava_instruct.yaml",
+ }
+
diff --git a/global_local/datasets/builders/video_caption_builder.py b/global_local/datasets/builders/video_caption_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..79654e317b81863536514db7fe9670ca36285d9b
--- /dev/null
+++ b/global_local/datasets/builders/video_caption_builder.py
@@ -0,0 +1,34 @@
+import os
+import logging
+import warnings
+
+from global_local.common.registry import registry
+from global_local.datasets.builders.base_dataset_builder import BaseDatasetBuilder
+from global_local.datasets.datasets.webvid_datasets import WebvidDataset
+
+@registry.register_builder("webvid")
+class WebvidBuilder(BaseDatasetBuilder):
+ train_dataset_cls = WebvidDataset
+ DATASET_CONFIG_DICT = {"default": "configs/datasets/webvid/defaults.yaml"}
+
+ def _download_ann(self):
+ pass
+
+ def _download_vis(self):
+ pass
+
+ def build(self):
+ self.build_processors()
+ datasets = dict()
+ split = "train"
+
+ build_info = self.config.build_info
+ dataset_cls = self.train_dataset_cls
+ datasets[split] = dataset_cls(
+ vis_processor=self.vis_processors[split],
+ text_processor=self.text_processors[split],
+ vis_root=build_info.videos_dir,
+ ann_root=build_info.anno_dir
+ )
+
+ return datasets
\ No newline at end of file
diff --git a/global_local/datasets/data_utils.py b/global_local/datasets/data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6781ca7f8a4772246c40cc24a5e323b21a94938a
--- /dev/null
+++ b/global_local/datasets/data_utils.py
@@ -0,0 +1,196 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import gzip
+import logging
+import os
+import random as rnd
+import tarfile
+import zipfile
+import random
+from typing import List
+from tqdm import tqdm
+
+import decord
+from decord import VideoReader
+import webdataset as wds
+import numpy as np
+import torch
+from torch.utils.data.dataset import IterableDataset
+
+from global_local.common.registry import registry
+from global_local.datasets.datasets.base_dataset import ConcatDataset
+
+
+decord.bridge.set_bridge("torch")
+MAX_INT = registry.get("MAX_INT")
+
+
+class ChainDataset(wds.DataPipeline):
+ r"""Dataset for chaining multiple :class:`DataPipeline` s.
+
+ This class is useful to assemble different existing dataset streams. The
+ chaining operation is done on-the-fly, so concatenating large-scale
+ datasets with this class will be efficient.
+
+ Args:
+ datasets (iterable of IterableDataset): datasets to be chained together
+ """
+ def __init__(self, datasets: List[wds.DataPipeline]) -> None:
+ super().__init__()
+ self.datasets = datasets
+ self.prob = []
+ self.names = []
+ for dataset in self.datasets:
+ if hasattr(dataset, 'name'):
+ self.names.append(dataset.name)
+ else:
+ self.names.append('Unknown')
+ if hasattr(dataset, 'sample_ratio'):
+ self.prob.append(dataset.sample_ratio)
+ else:
+ self.prob.append(1)
+ logging.info("One of the datapipeline doesn't define ratio and set to 1 automatically.")
+
+ def __iter__(self):
+ datastreams = [iter(dataset) for dataset in self.datasets]
+ while True:
+ select_datastream = random.choices(datastreams, weights=self.prob, k=1)[0]
+ yield next(select_datastream)
+
+
+def apply_to_sample(f, sample):
+ if len(sample) == 0:
+ return {}
+
+ def _apply(x):
+ if torch.is_tensor(x):
+ return f(x)
+ elif isinstance(x, dict):
+ return {key: _apply(value) for key, value in x.items()}
+ elif isinstance(x, list):
+ return [_apply(x) for x in x]
+ else:
+ return x
+
+ return _apply(sample)
+
+
+def move_to_cuda(sample):
+ def _move_to_cuda(tensor):
+ return tensor.cuda()
+
+ return apply_to_sample(_move_to_cuda, sample)
+
+
+def prepare_sample(samples, cuda_enabled=True):
+ if cuda_enabled:
+ samples = move_to_cuda(samples)
+
+ # TODO fp16 support
+
+ return samples
+
+
+def reorg_datasets_by_split(datasets):
+ """
+ Organizes datasets by split.
+
+ Args:
+ datasets: dict of torch.utils.data.Dataset objects by name.
+
+ Returns:
+ Dict of datasets by split {split_name: List[Datasets]}.
+ """
+ # if len(datasets) == 1:
+ # return datasets[list(datasets.keys())[0]]
+ # else:
+ reorg_datasets = dict()
+
+ # reorganize by split
+ for _, dataset in datasets.items():
+ for split_name, dataset_split in dataset.items():
+ if split_name not in reorg_datasets:
+ reorg_datasets[split_name] = [dataset_split]
+ else:
+ reorg_datasets[split_name].append(dataset_split)
+
+ return reorg_datasets
+
+
+def concat_datasets(datasets):
+ """
+ Concatenates multiple datasets into a single dataset.
+
+ It supports may-style datasets and DataPipeline from WebDataset. Currently, does not support
+ generic IterableDataset because it requires creating separate samplers.
+
+ Now only supports conctenating training datasets and assuming validation and testing
+ have only a single dataset. This is because metrics should not be computed on the concatenated
+ datasets.
+
+ Args:
+ datasets: dict of torch.utils.data.Dataset objects by split.
+
+ Returns:
+ Dict of concatenated datasets by split, "train" is the concatenation of multiple datasets,
+ "val" and "test" remain the same.
+
+ If the input training datasets contain both map-style and DataPipeline datasets, returns
+ a tuple, where the first element is a concatenated map-style dataset and the second
+ element is a chained DataPipeline dataset.
+
+ """
+ # concatenate datasets in the same split
+ for split_name in datasets:
+ if split_name != "train":
+ assert (
+ len(datasets[split_name]) == 1
+ ), "Do not support multiple {} datasets.".format(split_name)
+ datasets[split_name] = datasets[split_name][0]
+ else:
+ iterable_datasets, map_datasets = [], []
+ for dataset in datasets[split_name]:
+ if isinstance(dataset, wds.DataPipeline):
+ logging.info(
+ "Dataset {} is IterableDataset, can't be concatenated.".format(
+ dataset
+ )
+ )
+ iterable_datasets.append(dataset)
+ elif isinstance(dataset, IterableDataset):
+ raise NotImplementedError(
+ "Do not support concatenation of generic IterableDataset."
+ )
+ else:
+ map_datasets.append(dataset)
+
+ # if len(iterable_datasets) > 0:
+ # concatenate map-style datasets and iterable-style datasets separately
+ if len(iterable_datasets) > 1:
+ chained_datasets = (
+ ChainDataset(iterable_datasets)
+ )
+ elif len(iterable_datasets) == 1:
+ chained_datasets = iterable_datasets[0]
+ else:
+ chained_datasets = None
+
+ concat_datasets = (
+ ConcatDataset(map_datasets) if len(map_datasets) > 0 else None
+ )
+
+ train_datasets = concat_datasets, chained_datasets
+ train_datasets = tuple([x for x in train_datasets if x is not None])
+ train_datasets = (
+ train_datasets[0] if len(train_datasets) == 1 else train_datasets
+ )
+
+ datasets[split_name] = train_datasets
+
+ return datasets
+
diff --git a/global_local/datasets/datasets/__init__.py b/global_local/datasets/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/global_local/datasets/datasets/__pycache__/webvid_datasets.cpython-39.pyc b/global_local/datasets/datasets/__pycache__/webvid_datasets.cpython-39.pyc
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diff --git a/global_local/datasets/datasets/base_dataset.py b/global_local/datasets/datasets/base_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae2a8d0e21370129c0182cddc427eb293bbe5982
--- /dev/null
+++ b/global_local/datasets/datasets/base_dataset.py
@@ -0,0 +1,68 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import json
+from typing import Iterable
+
+from torch.utils.data import Dataset, ConcatDataset
+from torch.utils.data.dataloader import default_collate
+
+
+class BaseDataset(Dataset):
+ def __init__(
+ self, vis_processor=None, text_processor=None, vis_root=None, ann_paths=[]
+ ):
+ """
+ vis_root (string): Root directory of images (e.g. coco/images/)
+ ann_root (string): directory to store the annotation file
+ """
+ self.vis_root = vis_root
+
+ self.annotation = []
+ for ann_path in ann_paths:
+ self.annotation.extend(json.load(open(ann_path, "r"))['annotations'])
+
+ self.vis_processor = vis_processor
+ self.text_processor = text_processor
+
+ self._add_instance_ids()
+
+ def __len__(self):
+ return len(self.annotation)
+
+ def collater(self, samples):
+ return default_collate(samples)
+
+ def set_processors(self, vis_processor, text_processor):
+ self.vis_processor = vis_processor
+ self.text_processor = text_processor
+
+ def _add_instance_ids(self, key="instance_id"):
+ for idx, ann in enumerate(self.annotation):
+ ann[key] = str(idx)
+
+
+class ConcatDataset(ConcatDataset):
+ def __init__(self, datasets: Iterable[Dataset]) -> None:
+ super().__init__(datasets)
+
+ def collater(self, samples):
+ # TODO For now only supports datasets with same underlying collater implementations
+
+ all_keys = set()
+ for s in samples:
+ all_keys.update(s)
+
+ shared_keys = all_keys
+ for s in samples:
+ shared_keys = shared_keys & set(s.keys())
+
+ samples_shared_keys = []
+ for s in samples:
+ samples_shared_keys.append({k: s[k] for k in s.keys() if k in shared_keys})
+
+ return self.datasets[0].collater(samples_shared_keys)
diff --git a/global_local/datasets/datasets/caption_datasets.py b/global_local/datasets/datasets/caption_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..235f5e3710034996fbb6a0f9c582b66c199b58cb
--- /dev/null
+++ b/global_local/datasets/datasets/caption_datasets.py
@@ -0,0 +1,85 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import os
+from collections import OrderedDict
+
+from global_local.datasets.datasets.base_dataset import BaseDataset
+from PIL import Image
+
+
+class __DisplMixin:
+ def displ_item(self, index):
+ sample, ann = self.__getitem__(index), self.annotation[index]
+
+ return OrderedDict(
+ {
+ "file": ann["image"],
+ "caption": ann["caption"],
+ "image": sample["image"],
+ }
+ )
+
+
+class CaptionDataset(BaseDataset, __DisplMixin):
+ def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
+ """
+ vis_root (string): Root directory of images (e.g. coco/images/)
+ ann_root (string): directory to store the annotation file
+ """
+ super().__init__(vis_processor, text_processor, vis_root, ann_paths)
+
+ self.img_ids = {}
+ n = 0
+ for ann in self.annotation:
+ img_id = ann["image_id"]
+ if img_id not in self.img_ids.keys():
+ self.img_ids[img_id] = n
+ n += 1
+
+ def __getitem__(self, index):
+
+ # TODO this assumes image input, not general enough
+ ann = self.annotation[index]
+
+ img_file = '{:0>12}.jpg'.format(ann["image_id"])
+ image_path = os.path.join(self.vis_root, img_file)
+ image = Image.open(image_path).convert("RGB")
+
+ image = self.vis_processor(image)
+ caption = self.text_processor(ann["caption"])
+
+ return {
+ "image": image,
+ "text_input": caption,
+ "image_id": self.img_ids[ann["image_id"]],
+ }
+
+
+class CaptionEvalDataset(BaseDataset, __DisplMixin):
+ def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
+ """
+ vis_root (string): Root directory of images (e.g. coco/images/)
+ ann_root (string): directory to store the annotation file
+ split (string): val or test
+ """
+ super().__init__(vis_processor, text_processor, vis_root, ann_paths)
+
+ def __getitem__(self, index):
+
+ ann = self.annotation[index]
+
+ image_path = os.path.join(self.vis_root, ann["image"])
+ image = Image.open(image_path).convert("RGB")
+
+ image = self.vis_processor(image)
+
+ return {
+ "image": image,
+ "image_id": ann["image_id"],
+ "instance_id": ann["instance_id"],
+ }
diff --git a/global_local/datasets/datasets/cc_sbu_dataset.py b/global_local/datasets/datasets/cc_sbu_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc9f64fc4819d64ce1f1b2b970b8c00c2c254a2e
--- /dev/null
+++ b/global_local/datasets/datasets/cc_sbu_dataset.py
@@ -0,0 +1,49 @@
+import os
+from PIL import Image
+import webdataset as wds
+from global_local.datasets.datasets.base_dataset import BaseDataset
+from global_local.datasets.datasets.caption_datasets import CaptionDataset
+
+
+class CCSBUDataset(BaseDataset):
+ def __init__(self, vis_processor, text_processor, location):
+ super().__init__(vis_processor=vis_processor, text_processor=text_processor)
+
+ self.inner_dataset = wds.DataPipeline(
+ wds.ResampledShards(location),
+ wds.tarfile_to_samples(handler=wds.warn_and_continue),
+ wds.shuffle(1000, handler=wds.warn_and_continue),
+ wds.decode("pilrgb", handler=wds.warn_and_continue),
+ wds.to_tuple("jpg", "json", handler=wds.warn_and_continue),
+ wds.map_tuple(self.vis_processor, handler=wds.warn_and_continue),
+ wds.map(self.to_dict, handler=wds.warn_and_continue),
+ )
+
+ def to_dict(self, sample):
+ return {
+ "image": sample[0],
+ "text_input": self.text_processor(sample[1]["caption"]),
+ "type":'image',
+ }
+
+
+class CCSBUAlignDataset(CaptionDataset):
+
+ def __getitem__(self, index):
+
+ # TODO this assumes image input, not general enough
+ ann = self.annotation[index]
+
+ img_file = '{}.jpg'.format(ann["image_id"])
+ image_path = os.path.join(self.vis_root, img_file)
+ image = Image.open(image_path).convert("RGB")
+
+ image = self.vis_processor(image)
+ caption = ann["caption"]
+
+ return {
+ "image": image,
+ "text_input": caption,
+ "image_id": self.img_ids[ann["image_id"]],
+ "type":'image',
+ }
\ No newline at end of file
diff --git a/global_local/datasets/datasets/dataloader_utils.py b/global_local/datasets/datasets/dataloader_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdff93597ba9add53a06a7cd30b62bd5bb971e8e
--- /dev/null
+++ b/global_local/datasets/datasets/dataloader_utils.py
@@ -0,0 +1,162 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import time
+import random
+import torch
+from global_local.datasets.data_utils import move_to_cuda
+from torch.utils.data import DataLoader
+
+
+class MultiIterLoader:
+ """
+ A simple wrapper for iterating over multiple iterators.
+
+ Args:
+ loaders (List[Loader]): List of Iterator loaders.
+ ratios (List[float]): List of ratios to sample from each loader. If None, all loaders are sampled uniformly.
+ """
+
+ def __init__(self, loaders, ratios=None):
+ # assert all loaders has __next__ method
+ for loader in loaders:
+ assert hasattr(
+ loader, "__next__"
+ ), "Loader {} has no __next__ method.".format(loader)
+
+ if ratios is None:
+ ratios = [1.0] * len(loaders)
+ else:
+ assert len(ratios) == len(loaders)
+ ratios = [float(ratio) / sum(ratios) for ratio in ratios]
+
+ self.loaders = loaders
+ self.ratios = ratios
+
+ def __next__(self):
+ # random sample from each loader by ratio
+ loader_idx = random.choices(range(len(self.loaders)), self.ratios, k=1)[0]
+ return next(self.loaders[loader_idx])
+
+
+class PrefetchLoader(object):
+ """
+ Modified from https://github.com/ChenRocks/UNITER.
+
+ overlap compute and cuda data transfer
+ (copied and then modified from nvidia apex)
+ """
+
+ def __init__(self, loader):
+ self.loader = loader
+ self.stream = torch.cuda.Stream()
+
+ def __iter__(self):
+ loader_it = iter(self.loader)
+ self.preload(loader_it)
+ batch = self.next(loader_it)
+ while batch is not None:
+ is_tuple = isinstance(batch, tuple)
+ if is_tuple:
+ task, batch = batch
+
+ if is_tuple:
+ yield task, batch
+ else:
+ yield batch
+ batch = self.next(loader_it)
+
+ def __len__(self):
+ return len(self.loader)
+
+ def preload(self, it):
+ try:
+ self.batch = next(it)
+ except StopIteration:
+ self.batch = None
+ return
+ # if record_stream() doesn't work, another option is to make sure
+ # device inputs are created on the main stream.
+ # self.next_input_gpu = torch.empty_like(self.next_input,
+ # device='cuda')
+ # self.next_target_gpu = torch.empty_like(self.next_target,
+ # device='cuda')
+ # Need to make sure the memory allocated for next_* is not still in use
+ # by the main stream at the time we start copying to next_*:
+ # self.stream.wait_stream(torch.cuda.current_stream())
+ with torch.cuda.stream(self.stream):
+ self.batch = move_to_cuda(self.batch)
+ # more code for the alternative if record_stream() doesn't work:
+ # copy_ will record the use of the pinned source tensor in this
+ # side stream.
+ # self.next_input_gpu.copy_(self.next_input, non_blocking=True)
+ # self.next_target_gpu.copy_(self.next_target, non_blocking=True)
+ # self.next_input = self.next_input_gpu
+ # self.next_target = self.next_target_gpu
+
+ def next(self, it):
+ torch.cuda.current_stream().wait_stream(self.stream)
+ batch = self.batch
+ if batch is not None:
+ record_cuda_stream(batch)
+ self.preload(it)
+ return batch
+
+ def __getattr__(self, name):
+ method = self.loader.__getattribute__(name)
+ return method
+
+
+def record_cuda_stream(batch):
+ if isinstance(batch, torch.Tensor):
+ batch.record_stream(torch.cuda.current_stream())
+ elif isinstance(batch, list) or isinstance(batch, tuple):
+ for t in batch:
+ record_cuda_stream(t)
+ elif isinstance(batch, dict):
+ for t in batch.values():
+ record_cuda_stream(t)
+ else:
+ pass
+
+
+class IterLoader:
+ """
+ A wrapper to convert DataLoader as an infinite iterator.
+
+ Modified from:
+ https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/iter_based_runner.py
+ """
+
+ def __init__(self, dataloader: DataLoader, use_distributed: bool = False):
+ self._dataloader = dataloader
+ self.iter_loader = iter(self._dataloader)
+ self._use_distributed = use_distributed
+ self._epoch = 0
+
+ @property
+ def epoch(self) -> int:
+ return self._epoch
+
+ def __next__(self):
+ try:
+ data = next(self.iter_loader)
+ except StopIteration:
+ self._epoch += 1
+ if hasattr(self._dataloader.sampler, "set_epoch") and self._use_distributed:
+ self._dataloader.sampler.set_epoch(self._epoch)
+ time.sleep(2) # Prevent possible deadlock during epoch transition
+ self.iter_loader = iter(self._dataloader)
+ data = next(self.iter_loader)
+
+ return data
+
+ def __iter__(self):
+ return self
+
+ def __len__(self):
+ return len(self._dataloader)
diff --git a/global_local/datasets/datasets/laion_dataset.py b/global_local/datasets/datasets/laion_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f76d4926f1352ef68ade942e62382fc9b35bcc3
--- /dev/null
+++ b/global_local/datasets/datasets/laion_dataset.py
@@ -0,0 +1,31 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import webdataset as wds
+from global_local.datasets.datasets.base_dataset import BaseDataset
+
+
+class LaionDataset(BaseDataset):
+ def __init__(self, vis_processor, text_processor, location):
+ super().__init__(vis_processor=vis_processor, text_processor=text_processor)
+
+ self.inner_dataset = wds.DataPipeline(
+ wds.ResampledShards(location),
+ wds.tarfile_to_samples(handler=wds.warn_and_continue),
+ wds.shuffle(1000, handler=wds.warn_and_continue),
+ wds.decode("pilrgb", handler=wds.warn_and_continue),
+ wds.to_tuple("jpg", "json", handler=wds.warn_and_continue),
+ wds.map_tuple(self.vis_processor, handler=wds.warn_and_continue),
+ wds.map(self.to_dict, handler=wds.warn_and_continue),
+ )
+
+ def to_dict(self, sample):
+ return {
+ "image": sample[0],
+ "text_input": self.text_processor(sample[1]["caption"]),
+ }
+
diff --git a/global_local/datasets/datasets/llava_instruct_dataset.py b/global_local/datasets/datasets/llava_instruct_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9bea8f9d6639563d433da6ed767ed6c7183167f
--- /dev/null
+++ b/global_local/datasets/datasets/llava_instruct_dataset.py
@@ -0,0 +1,312 @@
+import os
+from global_local.datasets.datasets.base_dataset import BaseDataset
+from global_local.datasets.datasets.caption_datasets import CaptionDataset
+import pandas as pd
+import decord
+from decord import VideoReader
+import random
+import torch
+from torch.utils.data.dataloader import default_collate
+from PIL import Image
+from typing import Dict, Optional, Sequence
+import transformers
+import pathlib
+import json
+from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
+from global_local.conversation.conversation_video import Conversation,SeparatorStyle
+DEFAULT_IMAGE_PATCH_TOKEN = ''
+DEFAULT_IMAGE_TOKEN = ""
+import copy
+from global_local.processors import transforms_video,AlproVideoTrainProcessor
+IGNORE_INDEX = -100
+image_conversation = Conversation(
+ system="",
+ roles=("Human", "Assistant"),
+ messages=[],
+ offset=0,
+ sep_style=SeparatorStyle.SINGLE,
+ sep="###",
+)
+llama_v2_image_conversation = Conversation(
+ system=" ",
+ roles=("USER", "ASSISTANT"),
+ messages=(),
+ offset=0,
+ sep_style=SeparatorStyle.LLAMA_2,
+ sep="",
+ sep2="",
+)
+IGNORE_INDEX = -100
+
+class Instruct_Dataset(BaseDataset):
+ def __init__(self, vis_processor, text_processor, vis_root, ann_root,num_video_query_token=32,tokenizer_name = '/mnt/workspace/ckpt/vicuna-13b/',data_type = 'image', model_type='vicuna'):
+ """
+ vis_root (string): Root directory of Llava images (e.g. webvid_eval/video/)
+ ann_root (string): Root directory of video (e.g. webvid_eval/annotations/)
+ split (string): val or test
+ """
+ super().__init__(vis_processor=vis_processor, text_processor=text_processor)
+
+ data_path = pathlib.Path(ann_root)
+ with data_path.open(encoding='utf-8') as f:
+ self.annotation = json.load(f)
+
+ self.vis_root = vis_root
+ self.resize_size = 224
+ self.num_frm = 8
+ self.tokenizer = LlamaTokenizer.from_pretrained(tokenizer_name, use_fast=False)
+ self.tokenizer.pad_token = self.tokenizer.unk_token
+ self.tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+ self.num_video_query_token = num_video_query_token
+ self.IMAGE_PATCH_TOKEN_ID = self.tokenizer.get_vocab()[DEFAULT_IMAGE_PATCH_TOKEN]
+
+ self.transform = AlproVideoTrainProcessor(
+ image_size=self.resize_size, n_frms = self.num_frm
+ ).transform
+ self.data_type = data_type
+ self.model_type = model_type
+
+ def _get_image_path(self, sample):
+ rel_video_fp ='COCO_train2014_' + sample['image']
+ full_video_fp = os.path.join(self.vis_root, rel_video_fp)
+ return full_video_fp
+
+ def __getitem__(self, index):
+ num_retries = 10 # skip error videos
+ for _ in range(num_retries):
+ try:
+ sample = self.annotation[index]
+
+ image_path = self._get_image_path(sample)
+ conversation_list = sample['conversations']
+ image = Image.open(image_path).convert("RGB")
+
+ image = self.vis_processor(image)
+ # text = self.text_processor(text)
+ sources = preprocess_multimodal(copy.deepcopy(conversation_list), None, cur_token_len=self.num_video_query_token)
+ if self.model_type =='vicuna':
+ data_dict = preprocess(
+ sources,
+ self.tokenizer)
+ elif self.model_type =='llama_v2':
+ data_dict = preprocess_for_llama_v2(
+ sources,
+ self.tokenizer)
+ else:
+ print('not support')
+ raise('not support')
+ data_dict = dict(input_ids=data_dict["input_ids"][0],
+ labels=data_dict["labels"][0])
+
+ # image exist in the data
+ data_dict['image'] = image
+ except:
+ print(f"Failed to load examples with image: {image_path}. "
+ f"Will randomly sample an example as a replacement.")
+ index = random.randint(0, len(self) - 1)
+ continue
+ break
+ else:
+ raise RuntimeError(f"Failed to fetch image after {num_retries} retries.")
+ # "image_id" is kept to stay compatible with the COCO evaluation format
+ return {
+ "image": image,
+ "text_input": data_dict["input_ids"],
+ "labels": data_dict["labels"],
+ "type":'image',
+ }
+
+ def __len__(self):
+ return len(self.annotation)
+
+ def collater(self, instances):
+ input_ids, labels = tuple([instance[key] for instance in instances]
+ for key in ("text_input", "labels"))
+ input_ids = torch.nn.utils.rnn.pad_sequence(
+ input_ids,
+ batch_first=True,
+ padding_value=self.tokenizer.pad_token_id)
+ labels = torch.nn.utils.rnn.pad_sequence(labels,
+ batch_first=True,
+ padding_value=IGNORE_INDEX)
+ batch = dict(
+ input_ids=input_ids,
+ labels=labels,
+ attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
+ )
+
+ if 'image' in instances[0]:
+ images = [instance['image'] for instance in instances]
+ if all(x is not None and x.shape == images[0].shape for x in images):
+ batch['images'] = torch.stack(images)
+ else:
+ batch['images'] = images
+ batch['conv_type'] = 'multi'
+ return batch
+
+
+def preprocess_multimodal(
+ conversation_list: Sequence[str],
+ multimodal_cfg: dict,
+ cur_token_len: int,
+) -> Dict:
+ # 将conversational list中
+ is_multimodal = True
+ # image_token_len = multimodal_cfg['image_token_len']
+ image_token_len = cur_token_len
+
+ for sentence in conversation_list:
+ replace_token = ''+DEFAULT_IMAGE_PATCH_TOKEN * image_token_len+''
+ sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token)
+
+ return [conversation_list]
+
+def _add_speaker_and_signal(header, source, get_conversation=True):
+ """Add speaker and start/end signal on each round."""
+ BEGIN_SIGNAL = "###"
+ END_SIGNAL = "\n"
+ conversation = header
+ for sentence in source:
+ from_str = sentence["from"]
+ if from_str.lower() == "human":
+ from_str = image_conversation.roles[0]
+ elif from_str.lower() == "gpt":
+ from_str = image_conversation.roles[1]
+ else:
+ from_str = 'unknown'
+ sentence["value"] = (BEGIN_SIGNAL + from_str + ": " +
+ sentence["value"] + END_SIGNAL)
+ if get_conversation:
+ conversation += sentence["value"]
+ conversation += BEGIN_SIGNAL
+ return conversation
+
+def _tokenize_fn(strings: Sequence[str],
+ tokenizer: transformers.PreTrainedTokenizer) -> Dict:
+ """Tokenize a list of strings."""
+ tokenized_list = [
+ tokenizer(
+ text,
+ return_tensors="pt",
+ padding="longest",
+ max_length=512,
+ truncation=True,
+ ) for text in strings
+ ]
+ input_ids = labels = [
+ tokenized.input_ids[0] for tokenized in tokenized_list
+ ]
+ input_ids_lens = labels_lens = [
+ tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item()
+ for tokenized in tokenized_list
+ ]
+ return dict(
+ input_ids=input_ids,
+ labels=labels,
+ input_ids_lens=input_ids_lens,
+ labels_lens=labels_lens,
+ )
+
+def preprocess(
+ sources: Sequence[str],
+ tokenizer: transformers.PreTrainedTokenizer,
+) -> Dict:
+ """
+ Given a list of sources, each is a conversation list. This transform:
+ 1. Add signal '### ' at the beginning each sentence, with end signal '\n';
+ 2. Concatenate conversations together;
+ 3. Tokenize the concatenated conversation;
+ 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
+ """
+ # add end signal and concatenate together
+ conversations = []
+ for source in sources:
+ header = f"{image_conversation.system}\n\n"
+ conversation = _add_speaker_and_signal(header, source)
+ conversations.append(conversation)
+ # tokenize conversations
+ conversations_tokenized = _tokenize_fn(conversations, tokenizer)
+ input_ids = conversations_tokenized["input_ids"]
+ targets = copy.deepcopy(input_ids)
+ for target, source in zip(targets, sources):
+ tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source],
+ tokenizer)["input_ids_lens"]
+ speakers = [sentence["from"] for sentence in source]
+ _mask_targets(target, tokenized_lens, speakers)
+
+ return dict(input_ids=input_ids, labels=targets)
+
+def preprocess_for_llama_v2(
+ sources: Sequence[str],
+ tokenizer: transformers.PreTrainedTokenizer,
+) -> Dict:
+ """
+ Given a list of sources, each is a conversation list. This transform:
+ 1. Add signal '### ' at the beginning each sentence, with end signal '\n';
+ 2. Concatenate conversations together;
+ 3. Tokenize the concatenated conversation;
+ 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
+ """
+ # add end signal and concatenate together
+ conversations = []
+ conv = copy.deepcopy(llama_v2_image_conversation.copy())
+ roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+ for source in sources:
+ # [INST] <>\n{system_prompt}\n<>\n\n
+ header = f"[INST] <>\n{conv.system}\n>\n\n"
+
+ if roles[source[0]["from"]] != conv.roles[0]:
+ # Skip the first one if it is not from human
+ source = source[1:]
+ conv.messages = []
+ for j, sentence in enumerate(source):
+ role = roles[sentence["from"]]
+ assert role == conv.roles[j % 2]
+ conv.append_message(role, sentence["value"])
+ conversations.append(conv.get_prompt())
+
+ input_ids = tokenizer(
+ conversations,
+ return_tensors="pt",
+ padding="longest",
+ max_length=512,
+ truncation=True,
+ ).input_ids
+ targets = copy.deepcopy(input_ids)
+
+
+ sep = "[/INST] "
+ for conversation, target in zip(conversations, targets):
+ # total_len = int(target.ne(tokenizer.pad_token_id).sum())
+ rounds = conversation.split(conv.sep2)
+ cur_len = 1
+ target[:cur_len] = IGNORE_INDEX
+ for i, rou in enumerate(rounds):
+ if rou == "":
+ break
+
+ parts = rou.split(sep)
+ if len(parts) != 2:
+ break
+ parts[0] += sep
+
+
+ round_len = len(tokenizer(rou).input_ids)
+ instruction_len = len(tokenizer(parts[0]).input_ids) - 2 # 为什么减去2,speical token 的数目
+
+ target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
+
+ cur_len += round_len
+ target[cur_len:] = IGNORE_INDEX
+
+ return dict(input_ids=input_ids, labels=targets)
+
+def _mask_targets(target, tokenized_lens, speakers):
+ # cur_idx = 0
+ cur_idx = tokenized_lens[0]
+ tokenized_lens = tokenized_lens[1:]
+ target[:cur_idx] = IGNORE_INDEX
+ for tokenized_len, speaker in zip(tokenized_lens, speakers):
+ if speaker == "human":
+ target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX
+ cur_idx += tokenized_len
diff --git a/global_local/datasets/datasets/video_instruct_dataset.py b/global_local/datasets/datasets/video_instruct_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..655df9dcda0097bed6c4842846cc505eb7ba1447
--- /dev/null
+++ b/global_local/datasets/datasets/video_instruct_dataset.py
@@ -0,0 +1,335 @@
+import os
+from global_local.datasets.datasets.base_dataset import BaseDataset
+from global_local.datasets.datasets.caption_datasets import CaptionDataset
+import pandas as pd
+import decord
+from decord import VideoReader
+import random
+import torch
+from torch.utils.data.dataloader import default_collate
+from PIL import Image
+from typing import Dict, Optional, Sequence
+import transformers
+import pathlib
+import json
+from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
+import copy
+from global_local.processors import transforms_video,AlproVideoTrainProcessor
+from torchvision import transforms
+from global_local.processors.video_processor import ToTHWC,ToUint8,load_video
+from global_local.conversation.conversation_video import Conversation,SeparatorStyle
+
+DEFAULT_IMAGE_PATCH_TOKEN = ''
+video_conversation = Conversation(
+ system="",
+ roles=("Human", "Assistant"),
+ messages=[],
+ offset=0,
+ sep_style=SeparatorStyle.SINGLE,
+ sep="###",
+)
+llama_v2_video_conversation = Conversation(
+ system=" ",
+ roles=("USER", "ASSISTANT"),
+ messages=(),
+ offset=0,
+ sep_style=SeparatorStyle.LLAMA_2,
+ sep="",
+ sep2="",
+)
+IGNORE_INDEX = -100
+
+class Video_Instruct_Dataset(BaseDataset):
+ def __init__(self, vis_processor, text_processor, vis_root, ann_root,num_video_query_token=32,tokenizer_name = '/mnt/workspace/ckpt/vicuna-13b/',data_type = 'video', model_type='vicuna'):
+ """
+ vis_root (string): Root directory of Llava images (e.g. webvid_eval/video/)
+ ann_root (string): Root directory of video (e.g. webvid_eval/annotations/)
+ split (string): val or test
+ """
+ super().__init__(vis_processor=vis_processor, text_processor=text_processor)
+
+ data_path = pathlib.Path(ann_root)
+ with data_path.open(encoding='utf-8') as f:
+ self.annotation = json.load(f)
+
+ self.num_video_query_token = num_video_query_token
+ self.vis_root = vis_root
+ self.resize_size = 224
+ self.num_frm = 8
+ self.tokenizer = LlamaTokenizer.from_pretrained(tokenizer_name, use_fast=False)
+ self.tokenizer.pad_token = self.tokenizer.unk_token
+ self.tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+ self.IMAGE_PATCH_TOKEN_ID = self.tokenizer.get_vocab()[DEFAULT_IMAGE_PATCH_TOKEN]
+
+ self.transform = AlproVideoTrainProcessor(
+ image_size=self.resize_size, n_frms = self.num_frm
+ ).transform
+ self.data_type = data_type
+ self.model_type = model_type
+
+ def _get_video_path(self, sample):
+ rel_video_fp = sample['video']
+ full_video_fp = os.path.join(self.vis_root, rel_video_fp)
+ return full_video_fp
+
+ def __getitem__(self, index):
+ num_retries = 10 # skip error videos
+ for _ in range(num_retries):
+ try:
+ sample = self.annotation[index]
+
+ video_path = self._get_video_path(sample)
+ conversation_list = sample['QA']
+
+ video, msg = load_video(
+ video_path=video_path,
+ n_frms=self.num_frm,
+ height=self.resize_size,
+ width=self.resize_size,
+ sampling ="uniform", return_msg = True
+ )
+ video = self.transform(video)
+ if 'cn' in self.data_type:
+ msg = ""
+ # 添加视频,以及msg到convsation list 0
+ sources = preprocess_multimodal(copy.deepcopy(conversation_list), None, cur_token_len=self.num_video_query_token,msg = msg)
+ new_sources = convert_source_vicuna_format(sources)
+
+ if self.model_type =='vicuna':
+ data_dict = preprocess(
+ new_sources,
+ self.tokenizer)
+ elif self.model_type =='llama_v2':
+ data_dict = preprocess_for_llama_v2(
+ new_sources,
+ self.tokenizer)
+ else:
+ print('not support')
+ raise('not support')
+ data_dict = dict(input_ids=data_dict["input_ids"][0],
+ labels=data_dict["labels"][0])
+ # image exist in the data
+ data_dict['image'] = video
+ except:
+ print(f"Failed to load examples with video: {video_path}. "
+ f"Will randomly sample an example as a replacement.")
+ index = random.randint(0, len(self) - 1)
+ continue
+ break
+ else:
+ raise RuntimeError(f"Failed to fetch video after {num_retries} retries.")
+ # "image_id" is kept to stay compatible with the COCO evaluation format
+ return {
+ "image": video,
+ "text_input": data_dict["input_ids"],
+ "labels": data_dict["labels"],
+ "type":'video',
+ }
+
+ def __len__(self):
+ return len(self.annotation)
+
+ def collater(self, instances):
+ input_ids, labels = tuple([instance[key] for instance in instances]
+ for key in ("text_input", "labels"))
+ input_ids = torch.nn.utils.rnn.pad_sequence(
+ input_ids,
+ batch_first=True,
+ padding_value=self.tokenizer.pad_token_id)
+ labels = torch.nn.utils.rnn.pad_sequence(labels,
+ batch_first=True,
+ padding_value=IGNORE_INDEX)
+ batch = dict(
+ input_ids=input_ids,
+ labels=labels,
+ attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
+ )
+
+ if 'image' in instances[0]:
+ images = [instance['image'] for instance in instances]
+ if all(x is not None and x.shape == images[0].shape for x in images):
+ batch['images'] = torch.stack(images)
+ else:
+ batch['images'] = images
+ batch['conv_type'] = 'multi'
+ return batch
+
+def convert_source_vicuna_format(sources):
+ new_sources = []
+ for source in sources:
+ new_source = []
+ for i, sentence in enumerate(source):
+ role_0_msg = sentence['q']
+ role_1_msg = sentence['a']
+ new_source.append({
+ 'from':'human',
+ 'value': role_0_msg,
+ })
+ new_source.append({
+ 'from':'gpt',
+ 'value': role_1_msg,
+ })
+ new_sources.append(new_source)
+ return new_sources
+
+def preprocess_multimodal(
+ conversation_list: Sequence[str],
+ multimodal_cfg: dict,
+ cur_token_len: int,
+ msg=''
+) -> Dict:
+ # 将conversational list中
+ is_multimodal = True
+ # image_token_len = multimodal_cfg['image_token_len']
+ image_token_len = cur_token_len
+ conversation_list[0]["q"] = " " + msg + conversation_list[0]["q"]
+ return [conversation_list]
+
+def _add_speaker_and_signal(header, source, get_conversation=True):
+ """Add speaker and start/end signal on each round."""
+ BEGIN_SIGNAL = "###"
+ END_SIGNAL = "\n"
+ conversation = header
+ for sentence in source:
+ from_str = sentence["from"]
+ if from_str.lower() == "human":
+ from_str = video_conversation.roles[0]
+ elif from_str.lower() == "gpt":
+ from_str = video_conversation.roles[1]
+ else:
+ from_str = 'unknown'
+ sentence["value"] = (BEGIN_SIGNAL + from_str + ": " +
+ sentence["value"] + END_SIGNAL)
+ if get_conversation:
+ conversation += sentence["value"]
+ conversation += BEGIN_SIGNAL
+ return conversation
+
+def _tokenize_fn(strings: Sequence[str],
+ tokenizer: transformers.PreTrainedTokenizer) -> Dict:
+ """Tokenize a list of strings."""
+ tokenized_list = [
+ tokenizer(
+ text,
+ return_tensors="pt",
+ padding="longest",
+ max_length=512,
+ truncation=True,
+ ) for text in strings
+ ]
+ input_ids = labels = [
+ tokenized.input_ids[0] for tokenized in tokenized_list
+ ]
+ input_ids_lens = labels_lens = [
+ tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item()
+ for tokenized in tokenized_list
+ ]
+ return dict(
+ input_ids=input_ids,
+ labels=labels,
+ input_ids_lens=input_ids_lens,
+ labels_lens=labels_lens,
+ )
+
+def preprocess(
+ sources: Sequence[str],
+ tokenizer: transformers.PreTrainedTokenizer,
+) -> Dict:
+ """
+ Given a list of sources, each is a conversation list. This transform:
+ 1. Add signal '### ' at the beginning each sentence, with end signal '\n';
+ 2. Concatenate conversations together;
+ 3. Tokenize the concatenated conversation;
+ 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
+ """
+ # add end signal and concatenate together
+ conversations = []
+ for source in sources:
+ header = f"{video_conversation.system}\n\n"
+ conversation = _add_speaker_and_signal(header, source)
+ conversations.append(conversation)
+ # tokenize conversations
+ conversations_tokenized = _tokenize_fn(conversations, tokenizer)
+ input_ids = conversations_tokenized["input_ids"]
+ targets = copy.deepcopy(input_ids)
+ for target, source in zip(targets, sources):
+ tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source],
+ tokenizer)["input_ids_lens"]
+ speakers = [sentence["from"] for sentence in source]
+ _mask_targets(target, tokenized_lens, speakers)
+
+ return dict(input_ids=input_ids, labels=targets)
+
+def preprocess_for_llama_v2(
+ sources: Sequence[str],
+ tokenizer: transformers.PreTrainedTokenizer,
+) -> Dict:
+ """
+ Given a list of sources, each is a conversation list. This transform:
+ 1. Add signal '### ' at the beginning each sentence, with end signal '\n';
+ 2. Concatenate conversations together;
+ 3. Tokenize the concatenated conversation;
+ 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
+ """
+ # add end signal and concatenate together
+ conversations = []
+ conv = copy.deepcopy(video_conversation.copy())
+ roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+ for source in sources:
+ # [INST] <>\n{system_prompt}\n<>\n\n
+ header = f"[INST] <>\n{conv.system}\n>\n\n"
+
+ if roles[source[0]["from"]] != conv.roles[0]:
+ # Skip the first one if it is not from human
+ source = source[1:]
+ conv.messages = []
+ for j, sentence in enumerate(source):
+ role = roles[sentence["from"]]
+ assert role == conv.roles[j % 2]
+ conv.append_message(role, sentence["value"])
+ conversations.append(conv.get_prompt())
+
+ input_ids = tokenizer(
+ conversations,
+ return_tensors="pt",
+ padding="longest",
+ max_length=512,
+ truncation=True,
+ ).input_ids
+ targets = copy.deepcopy(input_ids)
+
+
+ sep = "[/INST] "
+ for conversation, target in zip(conversations, targets):
+ # total_len = int(target.ne(tokenizer.pad_token_id).sum())
+ rounds = conversation.split(conv.sep2)
+ cur_len = 1
+ target[:cur_len] = IGNORE_INDEX
+ for i, rou in enumerate(rounds):
+ if rou == "":
+ break
+
+ parts = rou.split(sep)
+ if len(parts) != 2:
+ break
+ parts[0] += sep
+
+
+ round_len = len(tokenizer(rou).input_ids)
+ instruction_len = len(tokenizer(parts[0]).input_ids) - 2 # 为什么减去2,speical token 的数目
+
+ target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
+
+ cur_len += round_len
+ target[cur_len:] = IGNORE_INDEX
+
+ return dict(input_ids=input_ids, labels=targets)
+def _mask_targets(target, tokenized_lens, speakers):
+ # cur_idx = 0
+ cur_idx = tokenized_lens[0]
+ tokenized_lens = tokenized_lens[1:]
+ target[:cur_idx] = IGNORE_INDEX
+ for tokenized_len, speaker in zip(tokenized_lens, speakers):
+ if speaker == "human":
+ target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX
+ cur_idx += tokenized_len
diff --git a/global_local/datasets/datasets/webvid_datasets.py b/global_local/datasets/datasets/webvid_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..21df4c3764847f097b6111f286da915d7486e6d9
--- /dev/null
+++ b/global_local/datasets/datasets/webvid_datasets.py
@@ -0,0 +1,122 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import os
+from global_local.datasets.datasets.base_dataset import BaseDataset
+from global_local.datasets.datasets.caption_datasets import CaptionDataset
+import pandas as pd
+import decord
+from decord import VideoReader
+import random
+import torch
+from torch.utils.data.dataloader import default_collate
+class WebvidDataset(BaseDataset):
+ def __init__(self, vis_processor, text_processor, vis_root, ann_root):
+ """
+ vis_root (string): Root directory of video (e.g. webvid_eval/video/)
+ ann_root (string): Root directory of video (e.g. webvid_eval/annotations/)
+ split (string): val or test
+ """
+ super().__init__(vis_processor=vis_processor, text_processor=text_processor)
+
+
+ # 读取一个路径下所有的
+
+ ts_df = []
+ for file_name in os.listdir(ann_root):
+ if file_name.endswith('.csv'):
+ df = pd.read_csv(os.path.join(ann_root, file_name))
+ ts_df.append(df)
+
+ merged_df = pd.concat(ts_df)
+ self.annotation = merged_df
+ self.vis_root = vis_root
+ self.resize_size = 224
+ self.num_frm = 8
+ self.frm_sampling_strategy = 'headtail'
+
+ def _get_video_path(self, sample):
+ rel_video_fp = os.path.join(sample['page_dir'], str(sample['videoid']) + '.mp4')
+ full_video_fp = os.path.join(self.vis_root, rel_video_fp)
+ return full_video_fp
+
+ def __getitem__(self, index):
+ num_retries = 10 # skip error videos
+ for _ in range(num_retries):
+ sample = self.annotation.iloc[index]
+ sample_dict = sample.to_dict()
+ video_id = sample_dict['videoid']
+
+ if 'name' in sample_dict.keys():
+ text = sample_dict['name'].strip()
+ else:
+ raise NotImplementedError("Un-supported text annotation format.")
+
+ # fetch video
+ video_path = self._get_video_path(sample_dict)
+ # if os.path.exists(video_path):
+ try:
+ video = self.vis_processor(video_path)
+ except:
+ print(f"Failed to load examples with video: {video_path}. "
+ f"Will randomly sample an example as a replacement.")
+ index = random.randint(0, len(self) - 1)
+ continue
+ caption = self.text_processor(text)
+
+ # print(video.size())
+ if video is None or caption is None \
+ or video.size()!=torch.Size([3,self.vis_processor.n_frms,224,224]):
+ print(f"Failed to load examples with video: {video_path}. "
+ f"Will randomly sample an example as a replacement.")
+ index = random.randint(0, len(self) - 1)
+ continue
+ else:
+ break
+ else:
+ raise RuntimeError(f"Failed to fetch video after {num_retries} retries.")
+ # "image_id" is kept to stay compatible with the COCO evaluation format
+ return {
+ "image": video,
+ "text_input": caption,
+ "type":'video',
+ }
+
+ def __len__(self):
+ return len(self.annotation)
+
+ # def collater(self, samples):
+ # new_result = {}
+ # new_result['image'] = default_collate( [sample["image"] for sample in samples])
+ # new_result['text_input'] = default_collate( [sample["text_input"] for sample in samples])
+ # return new_result
+
+class WebvidDatasetEvalDataset(BaseDataset):
+ def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
+ """
+ vis_root (string): Root directory of images (e.g. coco/images/)
+ ann_root (string): directory to store the annotation file
+ split (string): val or test
+ """
+ super().__init__(vis_processor, text_processor, vis_root, ann_paths)
+
+ def __getitem__(self, index):
+
+ ann = self.annotation[index]
+
+ vname = ann["video"]
+ video_path = os.path.join(self.vis_root, vname)
+
+ video = self.vis_processor(video_path)
+
+ return {
+ "video": video,
+ "image_id": ann["image_id"],
+ "instance_id": ann["instance_id"],
+ }
+
+
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diff --git a/global_local/models/ImageBind/CODE_OF_CONDUCT.md b/global_local/models/ImageBind/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000000000000000000000000000000000..f913b6a55a6c5ab6e1224e11fc039c3d4c3b6283
--- /dev/null
+++ b/global_local/models/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
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+## Our Responsibilities
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+## Attribution
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+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
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\ No newline at end of file
diff --git a/global_local/models/ImageBind/CONTRIBUTING.md b/global_local/models/ImageBind/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..63d0b751e8a00b606ddff92e2524faa3c90a63b0
--- /dev/null
+++ b/global_local/models/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
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+
+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/global_local/models/ImageBind/LICENSE b/global_local/models/ImageBind/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..bfef380bf7d9cb74ec9ba533b37c3fbeef3bdc09
--- /dev/null
+++ b/global_local/models/ImageBind/LICENSE
@@ -0,0 +1,437 @@
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diff --git a/global_local/models/ImageBind/README.md b/global_local/models/ImageBind/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..028fa988bb6cd9843aec9454636e1541b53680e7
--- /dev/null
+++ b/global_local/models/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/global_local/models/ImageBind/__pycache__/data.cpython-39.pyc b/global_local/models/ImageBind/__pycache__/data.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..563b024ba8f422a535f4a4fa8dc6249194b78b22
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diff --git a/global_local/models/ImageBind/bpe/bpe_simple_vocab_16e6.txt.gz b/global_local/models/ImageBind/bpe/bpe_simple_vocab_16e6.txt.gz
new file mode 100644
index 0000000000000000000000000000000000000000..36a15856e00a06a9fbed8cdd34d2393fea4a3113
--- /dev/null
+++ b/global_local/models/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/global_local/models/ImageBind/data.py b/global_local/models/ImageBind/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..993ff696bd98f1b380f2e9537b3f70ca38501f22
--- /dev/null
+++ b/global_local/models/ImageBind/data.py
@@ -0,0 +1,338 @@
+#!/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 logging
+import math
+
+import torch
+import torch.nn as nn
+import torchaudio
+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
+
+from .models.multimodal_preprocessors import SimpleTokenizer
+
+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),
+ ),
+ ]
+ )
+ 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_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 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, H, W videos.
+ Returns:
+ videos: A list with 3x the number of elements. Each video converted
+ to C, T, 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,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/global_local/models/ImageBind/model_card.md b/global_local/models/ImageBind/model_card.md
new file mode 100644
index 0000000000000000000000000000000000000000..c7bb26500b6590b64ffa6350f37be80dc88612d8
--- /dev/null
+++ b/global_local/models/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/global_local/models/ImageBind/models/__init__.py b/global_local/models/ImageBind/models/__init__.py
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diff --git a/global_local/models/ImageBind/models/helpers.py b/global_local/models/ImageBind/models/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..71abe9b1fc32ed22ba46ea89ae7439d4ea49afca
--- /dev/null
+++ b/global_local/models/ImageBind/models/helpers.py
@@ -0,0 +1,140 @@
+#!/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 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}," \
+ f" 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/global_local/models/ImageBind/models/imagebind_model.py b/global_local/models/ImageBind/models/imagebind_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..4430d2ea7a0acb19ca0bdf16dfebfc252164cccd
--- /dev/null
+++ b/global_local/models/ImageBind/models/imagebind_model.py
@@ -0,0 +1,541 @@
+#!/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
+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
+ )
+ 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
+ # modality_heads normalize 后768->linear 1024 ->
+ return outputs
+ def get_audio_feature(self, inputs, modality_type):
+ modality_value = inputs
+ modality_key = modality_type
+ 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)
+
+ audio_feature = self.modality_heads[modality_key][:-1](
+ modality_value, **head_inputs
+ )
+ modality_value = self.modality_heads[modality_key][-1:](
+ audio_feature, **head_inputs
+ )
+ modality_value = self.modality_postprocessors[modality_key](
+ modality_value
+ )
+
+ if reduce_list:
+ audio_feature = audio_feature.reshape(B, S, -1)
+ modality_value = modality_value.reshape(B, S, -1)
+ # modality_heads
+ return audio_feature, modality_value
+
+
+def imagebind_huge(pretrained=False):
+ 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(".checkpoints/imagebind_huge.pth"):
+ print(
+ "Downloading imagebind weights to .checkpoints/imagebind_huge.pth ..."
+ )
+ os.makedirs(".checkpoints", exist_ok=True)
+ torch.hub.download_url_to_file(
+ "https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth",
+ ".checkpoints/imagebind_huge.pth",
+ progress=True,
+ )
+
+ model.load_state_dict(torch.load(".checkpoints/imagebind_huge.pth"))
+
+ return model,1024
diff --git a/global_local/models/ImageBind/models/multimodal_preprocessors.py b/global_local/models/ImageBind/models/multimodal_preprocessors.py
new file mode 100644
index 0000000000000000000000000000000000000000..768c5b9c4f3f9b17b04ee41fec7ca2d99c15335e
--- /dev/null
+++ b/global_local/models/ImageBind/models/multimodal_preprocessors.py
@@ -0,0 +1,685 @@
+#!/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, Tuple
+
+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 VerboseNNModule, cast_if_src_dtype
+
+
+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) H W -> B (T)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: Optional[PatchEmbedGeneric],
+ img_size: Tuple = (3, 224, 224),
+ num_cls_tokens: int = 1,
+ pos_embed_fn: Optional[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, 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: List[str] = gzip.open(bpe_bytes).read().decode("utf-8").split("\n")
+ merges = merges[1 : 49152 - 256 - 2 + 1]
+ merges: List[Tuple[str, ...]] = [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: Tuple = (6, 2000),
+ num_cls_tokens: int = 1,
+ pos_embed_fn: Optional[Callable] = None,
+ init_param_style: str = "openclip",
+ ) -> None:
+ super().__init__()
+ 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/global_local/models/ImageBind/models/transformer.py b/global_local/models/ImageBind/models/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6224faf89d620de010d148bd50dae85176995031
--- /dev/null
+++ b/global_local/models/ImageBind/models/transformer.py
@@ -0,0 +1,280 @@
+#!/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
+
+
+from functools import partial
+from typing import Callable, List, Optional
+
+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: Optional[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: Optional[Callable] = None,
+ post_transformer_layer: Optional[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: Optional[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: Optional[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/global_local/models/ImageBind/requirements.txt b/global_local/models/ImageBind/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d35cb65aedcc46b805aac9328ca2e0e246a76dae
--- /dev/null
+++ b/global_local/models/ImageBind/requirements.txt
@@ -0,0 +1,17 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+torch==1.13.0
+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
+iopath
+numpy
+matplotlib
+types-regex
+mayavi
+cartopy
diff --git a/global_local/models/Qformer.py b/global_local/models/Qformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..4902165ec6574d89f04cbeb2141b018278324ca6
--- /dev/null
+++ b/global_local/models/Qformer.py
@@ -0,0 +1,1217 @@
+"""
+Adapted from salesforce@LAVIS. Below is the original copyright:
+ * Copyright (c) 2023, salesforce.com, inc.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+ * By Junnan Li
+ * Based on huggingface code base
+ * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
+"""
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple, Dict, Any
+
+import torch
+from torch import Tensor, device, dtype, nn
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.file_utils import (
+ ModelOutput,
+)
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPastAndCrossAttentions,
+ BaseModelOutputWithPoolingAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+ MaskedLMOutput,
+ MultipleChoiceModelOutput,
+ NextSentencePredictorOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from transformers.modeling_utils import (
+ PreTrainedModel,
+ apply_chunking_to_forward,
+ find_pruneable_heads_and_indices,
+ prune_linear_layer,
+)
+from transformers.utils import logging
+from transformers.models.bert.configuration_bert import BertConfig
+
+logger = logging.get_logger(__name__)
+
+
+class BertEmbeddings(nn.Module):
+ """Construct the embeddings from word and position embeddings."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(
+ config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+ )
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size
+ )
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))
+ )
+ self.position_embedding_type = getattr(
+ config, "position_embedding_type", "absolute"
+ )
+
+ self.config = config
+
+ def forward(
+ self,
+ input_ids=None,
+ position_ids=None,
+ query_embeds=None,
+ past_key_values_length=0,
+ ):
+ if input_ids is not None:
+ seq_length = input_ids.size()[1]
+ else:
+ seq_length = 0
+
+ if position_ids is None:
+ position_ids = self.position_ids[
+ :, past_key_values_length : seq_length + past_key_values_length
+ ].clone()
+
+ if input_ids is not None:
+ embeddings = self.word_embeddings(input_ids)
+ if self.position_embedding_type == "absolute":
+ position_embeddings = self.position_embeddings(position_ids)
+ embeddings = embeddings + position_embeddings
+
+ if query_embeds is not None:
+ embeddings = torch.cat((query_embeds, embeddings), dim=1)
+ else:
+ embeddings = query_embeds
+
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class BertSelfAttention(nn.Module):
+ def __init__(self, config, is_cross_attention):
+ super().__init__()
+ self.config = config
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
+ config, "embedding_size"
+ ):
+ raise ValueError(
+ "The hidden size (%d) is not a multiple of the number of attention "
+ "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ if is_cross_attention:
+ self.key = nn.Linear(config.encoder_width, self.all_head_size)
+ self.value = nn.Linear(config.encoder_width, self.all_head_size)
+ else:
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+ self.position_embedding_type = getattr(
+ config, "position_embedding_type", "absolute"
+ )
+ if (
+ self.position_embedding_type == "relative_key"
+ or self.position_embedding_type == "relative_key_query"
+ ):
+ self.max_position_embeddings = config.max_position_embeddings
+ self.distance_embedding = nn.Embedding(
+ 2 * config.max_position_embeddings - 1, self.attention_head_size
+ )
+ self.save_attention = False
+
+ def save_attn_gradients(self, attn_gradients):
+ self.attn_gradients = attn_gradients
+
+ def get_attn_gradients(self):
+ return self.attn_gradients
+
+ def save_attention_map(self, attention_map):
+ self.attention_map = attention_map
+
+ def get_attention_map(self):
+ return self.attention_map
+
+ def transpose_for_scores(self, x):
+ new_x_shape = x.size()[:-1] + (
+ self.num_attention_heads,
+ self.attention_head_size,
+ )
+ x = x.view(*new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_value=None,
+ output_attentions=False,
+ ):
+
+ # If this is instantiated as a cross-attention module, the keys
+ # and values come from an encoder; the attention mask needs to be
+ # such that the encoder's padding tokens are not attended to.
+ is_cross_attention = encoder_hidden_states is not None
+
+ if is_cross_attention:
+ key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+ value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+ attention_mask = encoder_attention_mask
+ elif past_key_value is not None:
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
+ key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+ value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+ else:
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+ mixed_query_layer = self.query(hidden_states)
+
+ query_layer = self.transpose_for_scores(mixed_query_layer)
+
+ past_key_value = (key_layer, value_layer)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+ if (
+ self.position_embedding_type == "relative_key"
+ or self.position_embedding_type == "relative_key_query"
+ ):
+ seq_length = hidden_states.size()[1]
+ position_ids_l = torch.arange(
+ seq_length, dtype=torch.long, device=hidden_states.device
+ ).view(-1, 1)
+ position_ids_r = torch.arange(
+ seq_length, dtype=torch.long, device=hidden_states.device
+ ).view(1, -1)
+ distance = position_ids_l - position_ids_r
+ positional_embedding = self.distance_embedding(
+ distance + self.max_position_embeddings - 1
+ )
+ positional_embedding = positional_embedding.to(
+ dtype=query_layer.dtype
+ ) # fp16 compatibility
+
+ if self.position_embedding_type == "relative_key":
+ relative_position_scores = torch.einsum(
+ "bhld,lrd->bhlr", query_layer, positional_embedding
+ )
+ attention_scores = attention_scores + relative_position_scores
+ elif self.position_embedding_type == "relative_key_query":
+ relative_position_scores_query = torch.einsum(
+ "bhld,lrd->bhlr", query_layer, positional_embedding
+ )
+ relative_position_scores_key = torch.einsum(
+ "bhrd,lrd->bhlr", key_layer, positional_embedding
+ )
+ attention_scores = (
+ attention_scores
+ + relative_position_scores_query
+ + relative_position_scores_key
+ )
+
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+ if is_cross_attention and self.save_attention:
+ self.save_attention_map(attention_probs)
+ attention_probs.register_hook(self.save_attn_gradients)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs_dropped = self.dropout(attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs_dropped = attention_probs_dropped * head_mask
+
+ context_layer = torch.matmul(attention_probs_dropped, value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+
+ outputs = (
+ (context_layer, attention_probs) if output_attentions else (context_layer,)
+ )
+
+ outputs = outputs + (past_key_value,)
+ return outputs
+
+
+class BertSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class BertAttention(nn.Module):
+ def __init__(self, config, is_cross_attention=False):
+ super().__init__()
+ self.self = BertSelfAttention(config, is_cross_attention)
+ self.output = BertSelfOutput(config)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads,
+ self.self.num_attention_heads,
+ self.self.attention_head_size,
+ self.pruned_heads,
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = (
+ self.self.attention_head_size * self.self.num_attention_heads
+ )
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_value=None,
+ output_attentions=False,
+ ):
+ self_outputs = self.self(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ past_key_value,
+ output_attentions,
+ )
+ attention_output = self.output(self_outputs[0], hidden_states)
+
+ outputs = (attention_output,) + self_outputs[
+ 1:
+ ] # add attentions if we output them
+ return outputs
+
+
+class BertIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+class BertOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class BertLayer(nn.Module):
+ def __init__(self, config, layer_num):
+ super().__init__()
+ self.config = config
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = BertAttention(config)
+ self.layer_num = layer_num
+ if (
+ self.config.add_cross_attention
+ and layer_num % self.config.cross_attention_freq == 0
+ ):
+ self.crossattention = BertAttention(
+ config, is_cross_attention=self.config.add_cross_attention
+ )
+ self.has_cross_attention = True
+ else:
+ self.has_cross_attention = False
+ self.intermediate = BertIntermediate(config)
+ self.output = BertOutput(config)
+
+ self.intermediate_query = BertIntermediate(config)
+ self.output_query = BertOutput(config)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_value=None,
+ output_attentions=False,
+ query_length=0,
+ ):
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+ self_attn_past_key_value = (
+ past_key_value[:2] if past_key_value is not None else None
+ )
+ self_attention_outputs = self.attention(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions=output_attentions,
+ past_key_value=self_attn_past_key_value,
+ )
+ attention_output = self_attention_outputs[0]
+ outputs = self_attention_outputs[1:-1]
+
+ present_key_value = self_attention_outputs[-1]
+
+ if query_length > 0:
+ query_attention_output = attention_output[:, :query_length, :]
+
+ if self.has_cross_attention:
+ assert (
+ encoder_hidden_states is not None
+ ), "encoder_hidden_states must be given for cross-attention layers"
+ cross_attention_outputs = self.crossattention(
+ query_attention_output,
+ attention_mask,
+ head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ output_attentions=output_attentions,
+ )
+ query_attention_output = cross_attention_outputs[0]
+ outputs = (
+ outputs + cross_attention_outputs[1:-1]
+ ) # add cross attentions if we output attention weights
+
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk_query,
+ self.chunk_size_feed_forward,
+ self.seq_len_dim,
+ query_attention_output,
+ )
+ if attention_output.shape[1] > query_length:
+ layer_output_text = apply_chunking_to_forward(
+ self.feed_forward_chunk,
+ self.chunk_size_feed_forward,
+ self.seq_len_dim,
+ attention_output[:, query_length:, :],
+ )
+ layer_output = torch.cat([layer_output, layer_output_text], dim=1)
+ else:
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk,
+ self.chunk_size_feed_forward,
+ self.seq_len_dim,
+ attention_output,
+ )
+ outputs = (layer_output,) + outputs
+
+ outputs = outputs + (present_key_value,)
+
+ return outputs
+
+ def feed_forward_chunk(self, attention_output):
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+ def feed_forward_chunk_query(self, attention_output):
+ intermediate_output = self.intermediate_query(attention_output)
+ layer_output = self.output_query(intermediate_output, attention_output)
+ return layer_output
+
+
+class BertEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList(
+ [BertLayer(config, i) for i in range(config.num_hidden_layers)]
+ )
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_values=None,
+ use_cache=None,
+ output_attentions=False,
+ output_hidden_states=False,
+ return_dict=True,
+ query_length=0,
+ ):
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+ all_cross_attentions = (
+ () if output_attentions and self.config.add_cross_attention else None
+ )
+
+ next_decoder_cache = () if use_cache else None
+
+ for i in range(self.config.num_hidden_layers):
+ layer_module = self.layer[i]
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+ past_key_value = past_key_values[i] if past_key_values is not None else None
+
+ if getattr(self.config, "gradient_checkpointing", False) and self.training:
+
+ if use_cache:
+ logger.warn(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ return module(
+ *inputs, past_key_value, output_attentions, query_length
+ )
+
+ return custom_forward
+
+ layer_outputs = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(layer_module),
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ )
+ else:
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ past_key_value,
+ output_attentions,
+ query_length,
+ )
+
+ hidden_states = layer_outputs[0]
+ if use_cache:
+ next_decoder_cache += (layer_outputs[-1],)
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+ all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ next_decoder_cache,
+ all_hidden_states,
+ all_self_attentions,
+ all_cross_attentions,
+ ]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=next_decoder_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+class BertPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states):
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.transform = BertPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+ self.decoder.bias = self.bias
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states)
+ return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = BertLMPredictionHead(config)
+
+ def forward(self, sequence_output):
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+class BertPreTrainedModel(PreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = BertConfig
+ base_model_prefix = "bert"
+ _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Embedding)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ if isinstance(module, nn.Linear) and module.bias is not None:
+ module.bias.data.zero_()
+
+
+class BertModel(BertPreTrainedModel):
+ """
+ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+ cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+ all you need `__ 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/global_local/models/__init__.py b/global_local/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac338d1edce580b3ca4f52fdd8a65a0ebbb2b32d
--- /dev/null
+++ b/global_local/models/__init__.py
@@ -0,0 +1,201 @@
+"""
+Adapted from salesforce@LAVIS Vision-CAIR@MiniGPT-4. Below is the original copyright:
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import logging
+import torch
+from omegaconf import OmegaConf
+
+from global_local.common.registry import registry
+from global_local.models.base_model import BaseModel
+from global_local.models.blip2 import Blip2Base
+from global_local.models.video_instruction_ft_model import VideoInstructionFTLLAMA
+from global_local.processors.base_processor import BaseProcessor
+
+
+__all__ = [
+ "load_model",
+ "BaseModel",
+ "Blip2Base",
+ "VideoInstructionFTLLAMA"
+]
+
+
+def load_model(name, model_type, is_eval=False, device="cpu", checkpoint=None):
+ """
+ Load supported models.
+
+ To list all available models and types in registry:
+ >>> from video_llama.models import model_zoo
+ >>> print(model_zoo)
+
+ Args:
+ name (str): name of the model.
+ model_type (str): type of the model.
+ is_eval (bool): whether the model is in eval mode. Default: False.
+ device (str): device to use. Default: "cpu".
+ checkpoint (str): path or to checkpoint. Default: None.
+ Note that expecting the checkpoint to have the same keys in state_dict as the model.
+
+ Returns:
+ model (torch.nn.Module): model.
+ """
+
+ model = registry.get_model_class(name).from_pretrained(model_type=model_type)
+
+ if checkpoint is not None:
+ model.load_checkpoint(checkpoint)
+
+ if is_eval:
+ model.eval()
+
+ if device == "cpu":
+ model = model.float()
+
+ return model.to(device)
+
+
+def load_preprocess(config):
+ """
+ Load preprocessor configs and construct preprocessors.
+
+ If no preprocessor is specified, return BaseProcessor, which does not do any preprocessing.
+
+ Args:
+ config (dict): preprocessor configs.
+
+ Returns:
+ vis_processors (dict): preprocessors for visual inputs.
+ txt_processors (dict): preprocessors for text inputs.
+
+ Key is "train" or "eval" for processors used in training and evaluation respectively.
+ """
+
+ def _build_proc_from_cfg(cfg):
+ return (
+ registry.get_processor_class(cfg.name).from_config(cfg)
+ if cfg is not None
+ else BaseProcessor()
+ )
+
+ vis_processors = dict()
+ txt_processors = dict()
+
+ vis_proc_cfg = config.get("vis_processor")
+ txt_proc_cfg = config.get("text_processor")
+
+ if vis_proc_cfg is not None:
+ vis_train_cfg = vis_proc_cfg.get("train")
+ vis_eval_cfg = vis_proc_cfg.get("eval")
+ else:
+ vis_train_cfg = None
+ vis_eval_cfg = None
+
+ vis_processors["train"] = _build_proc_from_cfg(vis_train_cfg)
+ vis_processors["eval"] = _build_proc_from_cfg(vis_eval_cfg)
+
+ if txt_proc_cfg is not None:
+ txt_train_cfg = txt_proc_cfg.get("train")
+ txt_eval_cfg = txt_proc_cfg.get("eval")
+ else:
+ txt_train_cfg = None
+ txt_eval_cfg = None
+
+ txt_processors["train"] = _build_proc_from_cfg(txt_train_cfg)
+ txt_processors["eval"] = _build_proc_from_cfg(txt_eval_cfg)
+
+ return vis_processors, txt_processors
+
+
+def load_model_and_preprocess(name, model_type, is_eval=False, device="cpu"):
+ """
+ Load model and its related preprocessors.
+
+ List all available models and types in registry:
+ >>> from video_llama.models import model_zoo
+ >>> print(model_zoo)
+
+ Args:
+ name (str): name of the model.
+ model_type (str): type of the model.
+ is_eval (bool): whether the model is in eval mode. Default: False.
+ device (str): device to use. Default: "cpu".
+
+ Returns:
+ model (torch.nn.Module): model.
+ vis_processors (dict): preprocessors for visual inputs.
+ txt_processors (dict): preprocessors for text inputs.
+ """
+ model_cls = registry.get_model_class(name)
+
+ # load model
+ model = model_cls.from_pretrained(model_type=model_type)
+
+ if is_eval:
+ model.eval()
+
+ # load preprocess
+ cfg = OmegaConf.load(model_cls.default_config_path(model_type))
+ if cfg is not None:
+ preprocess_cfg = cfg.preprocess
+
+ vis_processors, txt_processors = load_preprocess(preprocess_cfg)
+ else:
+ vis_processors, txt_processors = None, None
+ logging.info(
+ f"""No default preprocess for model {name} ({model_type}).
+ This can happen if the model is not finetuned on downstream datasets,
+ or it is not intended for direct use without finetuning.
+ """
+ )
+
+ if device == "cpu" or device == torch.device("cpu"):
+ model = model.float()
+
+ return model.to(device), vis_processors, txt_processors
+
+
+class ModelZoo:
+ """
+ A utility class to create string representation of available model architectures and types.
+
+ >>> from video_llama.models import model_zoo
+ >>> # list all available models
+ >>> print(model_zoo)
+ >>> # show total number of models
+ >>> print(len(model_zoo))
+ """
+
+ def __init__(self) -> None:
+ self.model_zoo = {
+ k: list(v.PRETRAINED_MODEL_CONFIG_DICT.keys())
+ for k, v in registry.mapping["model_name_mapping"].items()
+ }
+
+ def __str__(self) -> str:
+ return (
+ "=" * 50
+ + "\n"
+ + f"{'Architectures':<30} {'Types'}\n"
+ + "=" * 50
+ + "\n"
+ + "\n".join(
+ [
+ f"{name:<30} {', '.join(types)}"
+ for name, types in self.model_zoo.items()
+ ]
+ )
+ )
+
+ def __iter__(self):
+ return iter(self.model_zoo.items())
+
+ def __len__(self):
+ return sum([len(v) for v in self.model_zoo.values()])
+
+
+model_zoo = ModelZoo()
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diff --git a/global_local/models/base_model.py b/global_local/models/base_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..31068e52b4ec5343114d98a6c07584ae14890a2d
--- /dev/null
+++ b/global_local/models/base_model.py
@@ -0,0 +1,248 @@
+"""
+Adapted from salesforce@LAVIS. Below is the original copyright:
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import logging
+import os
+
+import numpy as np
+import torch
+import torch.nn as nn
+from global_local.common.dist_utils import download_cached_file, is_dist_avail_and_initialized
+from global_local.common.utils import get_abs_path, is_url
+from omegaconf import OmegaConf
+
+
+class BaseModel(nn.Module):
+ """Base class for models."""
+
+ def __init__(self):
+ super().__init__()
+
+ @property
+ def device(self):
+ return list(self.parameters())[0].device
+
+ def load_checkpoint(self, url_or_filename):
+ """
+ Load from a finetuned checkpoint.
+
+ This should expect no mismatch in the model keys and the checkpoint keys.
+ """
+
+ if is_url(url_or_filename):
+ cached_file = download_cached_file(
+ url_or_filename, check_hash=False, progress=True
+ )
+ checkpoint = torch.load(cached_file, map_location="cpu")
+ elif os.path.isfile(url_or_filename):
+ checkpoint = torch.load(url_or_filename, map_location="cpu")
+ else:
+ raise RuntimeError("checkpoint url or path is invalid")
+
+ if "model" in checkpoint.keys():
+ state_dict = checkpoint["model"]
+ else:
+ state_dict = checkpoint
+
+ msg = self.load_state_dict(state_dict, strict=False)
+
+ logging.info("Missing keys {}".format(msg.missing_keys))
+ logging.info("load checkpoint from %s" % url_or_filename)
+
+ return msg
+
+ @classmethod
+ def from_pretrained(cls, model_type):
+ """
+ Build a pretrained model from default configuration file, specified by model_type.
+
+ Args:
+ - model_type (str): model type, specifying architecture and checkpoints.
+
+ Returns:
+ - model (nn.Module): pretrained or finetuned model, depending on the configuration.
+ """
+ model_cfg = OmegaConf.load(cls.default_config_path(model_type)).model
+ model = cls.from_config(model_cfg)
+
+ return model
+
+ @classmethod
+ def default_config_path(cls, model_type):
+ assert (
+ model_type in cls.PRETRAINED_MODEL_CONFIG_DICT
+ ), "Unknown model type {}".format(model_type)
+ return get_abs_path(cls.PRETRAINED_MODEL_CONFIG_DICT[model_type])
+
+ def load_checkpoint_from_config(self, cfg, **kwargs):
+ """
+ Load checkpoint as specified in the config file.
+
+ If load_finetuned is True, load the finetuned model; otherwise, load the pretrained model.
+ When loading the pretrained model, each task-specific architecture may define their
+ own load_from_pretrained() method.
+ """
+ load_finetuned = cfg.get("load_finetuned", True)
+ if load_finetuned:
+ finetune_path = cfg.get("finetuned", None)
+ assert (
+ finetune_path is not None
+ ), "Found load_finetuned is True, but finetune_path is None."
+ self.load_checkpoint(url_or_filename=finetune_path)
+ else:
+ # load pre-trained weights
+ pretrain_path = cfg.get("pretrained", None)
+ assert "Found load_finetuned is False, but pretrain_path is None."
+ self.load_from_pretrained(url_or_filename=pretrain_path, **kwargs)
+
+ def before_evaluation(self, **kwargs):
+ pass
+
+ def show_n_params(self, return_str=True):
+ tot = 0
+ for p in self.parameters():
+ w = 1
+ for x in p.shape:
+ w *= x
+ tot += w
+ if return_str:
+ if tot >= 1e6:
+ return "{:.1f}M".format(tot / 1e6)
+ else:
+ return "{:.1f}K".format(tot / 1e3)
+ else:
+ return tot
+
+
+class BaseEncoder(nn.Module):
+ """
+ Base class for primitive encoders, such as ViT, TimeSformer, etc.
+ """
+
+ def __init__(self):
+ super().__init__()
+
+ def forward_features(self, samples, **kwargs):
+ raise NotImplementedError
+
+ @property
+ def device(self):
+ return list(self.parameters())[0].device
+
+
+class SharedQueueMixin:
+ @torch.no_grad()
+ def _dequeue_and_enqueue(self, image_feat, text_feat, idxs=None):
+ # gather keys before updating queue
+ image_feats = concat_all_gather(image_feat)
+ text_feats = concat_all_gather(text_feat)
+
+ batch_size = image_feats.shape[0]
+
+ ptr = int(self.queue_ptr)
+ assert self.queue_size % batch_size == 0 # for simplicity
+
+ # replace the keys at ptr (dequeue and enqueue)
+ self.image_queue[:, ptr : ptr + batch_size] = image_feats.T
+ self.text_queue[:, ptr : ptr + batch_size] = text_feats.T
+
+ if idxs is not None:
+ idxs = concat_all_gather(idxs)
+ self.idx_queue[:, ptr : ptr + batch_size] = idxs.T
+
+ ptr = (ptr + batch_size) % self.queue_size # move pointer
+ self.queue_ptr[0] = ptr
+
+
+class MomentumDistilationMixin:
+ @torch.no_grad()
+ def copy_params(self):
+ for model_pair in self.model_pairs:
+ for param, param_m in zip(
+ model_pair[0].parameters(), model_pair[1].parameters()
+ ):
+ param_m.data.copy_(param.data) # initialize
+ param_m.requires_grad = False # not update by gradient
+
+ @torch.no_grad()
+ def _momentum_update(self):
+ for model_pair in self.model_pairs:
+ for param, param_m in zip(
+ model_pair[0].parameters(), model_pair[1].parameters()
+ ):
+ param_m.data = param_m.data * self.momentum + param.data * (
+ 1.0 - self.momentum
+ )
+
+
+class GatherLayer(torch.autograd.Function):
+ """
+ Gather tensors from all workers with support for backward propagation:
+ This implementation does not cut the gradients as torch.distributed.all_gather does.
+ """
+
+ @staticmethod
+ def forward(ctx, x):
+ output = [
+ torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())
+ ]
+ torch.distributed.all_gather(output, x)
+ return tuple(output)
+
+ @staticmethod
+ def backward(ctx, *grads):
+ all_gradients = torch.stack(grads)
+ torch.distributed.all_reduce(all_gradients)
+ return all_gradients[torch.distributed.get_rank()]
+
+
+def all_gather_with_grad(tensors):
+ """
+ Performs all_gather operation on the provided tensors.
+ Graph remains connected for backward grad computation.
+ """
+ # Queue the gathered tensors
+ world_size = torch.distributed.get_world_size()
+ # There is no need for reduction in the single-proc case
+ if world_size == 1:
+ return tensors
+
+ # tensor_all = GatherLayer.apply(tensors)
+ tensor_all = GatherLayer.apply(tensors)
+
+ return torch.cat(tensor_all, dim=0)
+
+
+@torch.no_grad()
+def concat_all_gather(tensor):
+ """
+ Performs all_gather operation on the provided tensors.
+ *** Warning ***: torch.distributed.all_gather has no gradient.
+ """
+ # if use distributed training
+ if not is_dist_avail_and_initialized():
+ return tensor
+
+ tensors_gather = [
+ torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())
+ ]
+ torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
+
+ output = torch.cat(tensors_gather, dim=0)
+ return output
+
+
+def tile(x, dim, n_tile):
+ init_dim = x.size(dim)
+ repeat_idx = [1] * x.dim()
+ repeat_idx[dim] = n_tile
+ x = x.repeat(*(repeat_idx))
+ order_index = torch.LongTensor(
+ np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])
+ )
+ return torch.index_select(x, dim, order_index.to(x.device))
diff --git a/global_local/models/blip2.py b/global_local/models/blip2.py
new file mode 100644
index 0000000000000000000000000000000000000000..1935c584cbc4508de5e85bf891d882a42ff054ea
--- /dev/null
+++ b/global_local/models/blip2.py
@@ -0,0 +1,222 @@
+"""
+Adapted from salesforce@LAVIS. Below is the original copyright:
+ Copyright (c) 2023, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+import contextlib
+import logging
+import os
+import time
+import datetime
+
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+import torch.nn.functional as F
+
+import global_local.common.dist_utils as dist_utils
+from global_local.common.dist_utils import download_cached_file
+from global_local.common.utils import is_url
+from global_local.common.logger import MetricLogger
+from global_local.models.base_model import BaseModel
+from global_local.models.Qformer import BertConfig, BertLMHeadModel
+from global_local.models.eva_vit import create_eva_vit_g
+from transformers import BertTokenizer
+
+
+class Blip2Base(BaseModel):
+ @classmethod
+ def init_tokenizer(cls):
+ tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+ tokenizer.add_special_tokens({"bos_token": "[DEC]"})
+ return tokenizer
+
+ def maybe_autocast(self, dtype=torch.float16):
+ # if on cpu, don't use autocast
+ # if on gpu, use autocast with dtype if provided, otherwise use torch.float16
+ enable_autocast = self.device != torch.device("cpu")
+
+ if enable_autocast:
+ return torch.cuda.amp.autocast(dtype=dtype)
+ else:
+ return contextlib.nullcontext()
+
+ @classmethod
+ def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2):
+ encoder_config = BertConfig.from_pretrained("bert-base-uncased")
+ encoder_config.encoder_width = vision_width
+ # insert cross-attention layer every other block
+ encoder_config.add_cross_attention = True
+ encoder_config.cross_attention_freq = cross_attention_freq
+ encoder_config.query_length = num_query_token
+ Qformer = BertLMHeadModel(config=encoder_config)
+ query_tokens = nn.Parameter(
+ torch.zeros(1, num_query_token, encoder_config.hidden_size)
+ )
+ query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
+ return Qformer, query_tokens
+
+ @classmethod
+ def init_vision_encoder(
+ cls, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision
+ ):
+ assert model_name == "eva_clip_g", "vit model must be eva_clip_g for current version of MiniGPT-4"
+ visual_encoder = create_eva_vit_g(
+ img_size, drop_path_rate, use_grad_checkpoint, precision
+ )
+
+ ln_vision = LayerNorm(visual_encoder.num_features)
+ return visual_encoder, ln_vision
+
+ def load_from_pretrained(self, url_or_filename):
+ if is_url(url_or_filename):
+ cached_file = download_cached_file(
+ url_or_filename, check_hash=False, progress=True
+ )
+ checkpoint = torch.load(cached_file, map_location="cpu")
+ elif os.path.isfile(url_or_filename):
+ checkpoint = torch.load(url_or_filename, map_location="cpu")
+ else:
+ raise RuntimeError("checkpoint url or path is invalid")
+
+ state_dict = checkpoint["model"]
+
+ msg = self.load_state_dict(state_dict, strict=False)
+
+ # logging.info("Missing keys {}".format(msg.missing_keys))
+ logging.info("load checkpoint from %s" % url_or_filename)
+
+ return msg
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+class LayerNorm(nn.LayerNorm):
+ """Subclass torch's LayerNorm to handle fp16."""
+
+ def forward(self, x: torch.Tensor):
+ orig_type = x.dtype
+ ret = super().forward(x.type(torch.float32))
+ return ret.type(orig_type)
+
+
+def compute_sim_matrix(model, data_loader, **kwargs):
+ k_test = kwargs.pop("k_test")
+
+ metric_logger = MetricLogger(delimiter=" ")
+ header = "Evaluation:"
+
+ logging.info("Computing features for evaluation...")
+ start_time = time.time()
+
+ texts = data_loader.dataset.text
+ num_text = len(texts)
+ text_bs = 256
+ text_ids = []
+ text_embeds = []
+ text_atts = []
+ for i in range(0, num_text, text_bs):
+ text = texts[i : min(num_text, i + text_bs)]
+ text_input = model.tokenizer(
+ text,
+ padding="max_length",
+ truncation=True,
+ max_length=35,
+ return_tensors="pt",
+ ).to(model.device)
+ text_feat = model.forward_text(text_input)
+ text_embed = F.normalize(model.text_proj(text_feat))
+ text_embeds.append(text_embed)
+ text_ids.append(text_input.input_ids)
+ text_atts.append(text_input.attention_mask)
+
+ text_embeds = torch.cat(text_embeds, dim=0)
+ text_ids = torch.cat(text_ids, dim=0)
+ text_atts = torch.cat(text_atts, dim=0)
+
+ vit_feats = []
+ image_embeds = []
+ for samples in data_loader:
+ image = samples["image"]
+
+ image = image.to(model.device)
+ image_feat, vit_feat = model.forward_image(image)
+ image_embed = model.vision_proj(image_feat)
+ image_embed = F.normalize(image_embed, dim=-1)
+
+ vit_feats.append(vit_feat.cpu())
+ image_embeds.append(image_embed)
+
+ vit_feats = torch.cat(vit_feats, dim=0)
+ image_embeds = torch.cat(image_embeds, dim=0)
+
+ sims_matrix = []
+ for image_embed in image_embeds:
+ sim_q2t = image_embed @ text_embeds.t()
+ sim_i2t, _ = sim_q2t.max(0)
+ sims_matrix.append(sim_i2t)
+ sims_matrix = torch.stack(sims_matrix, dim=0)
+
+ score_matrix_i2t = torch.full(
+ (len(data_loader.dataset.image), len(texts)), -100.0
+ ).to(model.device)
+
+ num_tasks = dist_utils.get_world_size()
+ rank = dist_utils.get_rank()
+ step = sims_matrix.size(0) // num_tasks + 1
+ start = rank * step
+ end = min(sims_matrix.size(0), start + step)
+
+ for i, sims in enumerate(
+ metric_logger.log_every(sims_matrix[start:end], 50, header)
+ ):
+ topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
+ image_inputs = vit_feats[start + i].repeat(k_test, 1, 1).to(model.device)
+ score = model.compute_itm(
+ image_inputs=image_inputs,
+ text_ids=text_ids[topk_idx],
+ text_atts=text_atts[topk_idx],
+ ).float()
+ score_matrix_i2t[start + i, topk_idx] = score + topk_sim
+
+ sims_matrix = sims_matrix.t()
+ score_matrix_t2i = torch.full(
+ (len(texts), len(data_loader.dataset.image)), -100.0
+ ).to(model.device)
+
+ step = sims_matrix.size(0) // num_tasks + 1
+ start = rank * step
+ end = min(sims_matrix.size(0), start + step)
+
+ for i, sims in enumerate(
+ metric_logger.log_every(sims_matrix[start:end], 50, header)
+ ):
+ topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
+ image_inputs = vit_feats[topk_idx.cpu()].to(model.device)
+ score = model.compute_itm(
+ image_inputs=image_inputs,
+ text_ids=text_ids[start + i].repeat(k_test, 1),
+ text_atts=text_atts[start + i].repeat(k_test, 1),
+ ).float()
+ score_matrix_t2i[start + i, topk_idx] = score + topk_sim
+
+ if dist_utils.is_dist_avail_and_initialized():
+ dist.barrier()
+ torch.distributed.all_reduce(
+ score_matrix_i2t, op=torch.distributed.ReduceOp.SUM
+ )
+ torch.distributed.all_reduce(
+ score_matrix_t2i, op=torch.distributed.ReduceOp.SUM
+ )
+
+ total_time = time.time() - start_time
+ total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+ logging.info("Evaluation time {}".format(total_time_str))
+
+ return score_matrix_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy()
diff --git a/global_local/models/blip2_outputs.py b/global_local/models/blip2_outputs.py
new file mode 100644
index 0000000000000000000000000000000000000000..92d83a0556e6c5c3c0a603279f318605ae25d6d5
--- /dev/null
+++ b/global_local/models/blip2_outputs.py
@@ -0,0 +1,111 @@
+"""
+Adapted from salesforce@LAVIS. Below is the original copyright:
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+from transformers.modeling_outputs import (
+ ModelOutput,
+ BaseModelOutputWithPoolingAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+)
+
+
+@dataclass
+class BlipSimilarity(ModelOutput):
+ sim_i2t: torch.FloatTensor = None
+ sim_t2i: torch.FloatTensor = None
+
+ sim_i2t_m: Optional[torch.FloatTensor] = None
+ sim_t2i_m: Optional[torch.FloatTensor] = None
+
+ sim_i2t_targets: Optional[torch.FloatTensor] = None
+ sim_t2i_targets: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+class BlipIntermediateOutput(ModelOutput):
+ """
+ Data class for intermediate outputs of BLIP models.
+
+ image_embeds (torch.FloatTensor): Image embeddings, shape (batch_size, num_patches, embed_dim).
+ text_embeds (torch.FloatTensor): Text embeddings, shape (batch_size, seq_len, embed_dim).
+
+ image_embeds_m (torch.FloatTensor): Image embeddings from momentum visual encoder, shape (batch_size, num_patches, embed_dim).
+ text_embeds_m (torch.FloatTensor): Text embeddings from momentum text encoder, shape (batch_size, seq_len, embed_dim).
+
+ encoder_output (BaseModelOutputWithPoolingAndCrossAttentions): output from the image-grounded text encoder.
+ encoder_output_neg (BaseModelOutputWithPoolingAndCrossAttentions): output from the image-grounded text encoder for negative pairs.
+
+ decoder_output (CausalLMOutputWithCrossAttentions): output from the image-grounded text decoder.
+ decoder_labels (torch.LongTensor): labels for the captioning loss.
+
+ itm_logits (torch.FloatTensor): logits for the image-text matching loss, shape (batch_size * 3, 2).
+ itm_labels (torch.LongTensor): labels for the image-text matching loss, shape (batch_size * 3,)
+
+ """
+
+ # uni-modal features
+ image_embeds: torch.FloatTensor = None
+ text_embeds: Optional[torch.FloatTensor] = None
+
+ image_embeds_m: Optional[torch.FloatTensor] = None
+ text_embeds_m: Optional[torch.FloatTensor] = None
+
+ # intermediate outputs of multimodal encoder
+ encoder_output: Optional[BaseModelOutputWithPoolingAndCrossAttentions] = None
+ encoder_output_neg: Optional[BaseModelOutputWithPoolingAndCrossAttentions] = None
+
+ itm_logits: Optional[torch.FloatTensor] = None
+ itm_labels: Optional[torch.LongTensor] = None
+
+ # intermediate outputs of multimodal decoder
+ decoder_output: Optional[CausalLMOutputWithCrossAttentions] = None
+ decoder_labels: Optional[torch.LongTensor] = None
+
+
+@dataclass
+class BlipOutput(ModelOutput):
+ # some finetuned models (e.g. BlipVQA) do not compute similarity, thus optional.
+ sims: Optional[BlipSimilarity] = None
+
+ intermediate_output: BlipIntermediateOutput = None
+
+ loss: Optional[torch.FloatTensor] = None
+
+ loss_itc: Optional[torch.FloatTensor] = None
+
+ loss_itm: Optional[torch.FloatTensor] = None
+
+ loss_lm: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+class BlipOutputFeatures(ModelOutput):
+ """
+ Data class of features from BlipFeatureExtractor.
+
+ Args:
+ image_embeds: (torch.FloatTensor) of shape (batch_size, num_patches+1, embed_dim), optional
+ image_features: (torch.FloatTensor) of shape (batch_size, num_patches+1, feature_dim), optional
+ text_embeds: (torch.FloatTensor) of shape (batch_size, sequence_length+1, embed_dim), optional
+ text_features: (torch.FloatTensor) of shape (batch_size, sequence_length+1, feature_dim), optional
+
+ The first embedding or feature is for the [CLS] token.
+
+ Features are obtained by projecting the corresponding embedding into a normalized low-dimensional space.
+ """
+
+ image_embeds: Optional[torch.FloatTensor] = None
+ image_embeds_proj: Optional[torch.FloatTensor] = None
+
+ text_embeds: Optional[torch.FloatTensor] = None
+ text_embeds_proj: Optional[torch.FloatTensor] = None
+
+ multimodal_embeds: Optional[torch.FloatTensor] = None
diff --git a/global_local/models/eva_vit.py b/global_local/models/eva_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..abc1cda9fe26c4c184a627e8b4835d3860343f95
--- /dev/null
+++ b/global_local/models/eva_vit.py
@@ -0,0 +1,505 @@
+# Based on EVA, BEIT, timm and DeiT code bases
+# https://github.com/baaivision/EVA
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/facebookresearch/deit/
+# https://github.com/facebookresearch/dino
+# --------------------------------------------------------'
+import math
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import drop_path, to_2tuple, trunc_normal_
+from timm.models.registry import register_model
+
+from global_local.common.dist_utils import download_cached_file
+
+def _cfg(url='', **kwargs):
+ return {
+ 'url': url,
+ 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+ 'crop_pct': .9, 'interpolation': 'bicubic',
+ 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
+ **kwargs
+ }
+
+
+class DropPath(nn.Module):
+ """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+ """
+ def __init__(self, drop_prob=None):
+ super(DropPath, self).__init__()
+ self.drop_prob = drop_prob
+
+ def forward(self, x):
+ return drop_path(x, self.drop_prob, self.training)
+
+ def extra_repr(self) -> str:
+ return 'p={}'.format(self.drop_prob)
+
+
+class Mlp(nn.Module):
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=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)
+ # commit this for the orignal BERT implement
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class Attention(nn.Module):
+ def __init__(
+ self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
+ proj_drop=0., window_size=None, attn_head_dim=None):
+ super().__init__()
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ if attn_head_dim is not None:
+ head_dim = attn_head_dim
+ all_head_dim = head_dim * self.num_heads
+ self.scale = qk_scale or head_dim ** -0.5
+
+ self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+ if qkv_bias:
+ self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+ self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
+ else:
+ self.q_bias = None
+ self.v_bias = None
+
+ if window_size:
+ self.window_size = window_size
+ self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+ self.relative_position_bias_table = nn.Parameter(
+ torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
+ # cls to token & token 2 cls & cls to cls
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(window_size[0])
+ coords_w = torch.arange(window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+ relative_position_index = \
+ torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype)
+ relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ relative_position_index[0, 0:] = self.num_relative_distance - 3
+ relative_position_index[0:, 0] = self.num_relative_distance - 2
+ relative_position_index[0, 0] = self.num_relative_distance - 1
+
+ self.register_buffer("relative_position_index", relative_position_index)
+ else:
+ self.window_size = None
+ self.relative_position_bias_table = None
+ self.relative_position_index = None
+
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(all_head_dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ def forward(self, x, rel_pos_bias=None):
+ B, N, C = x.shape
+ qkv_bias = None
+ if self.q_bias is not None:
+ qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+ # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+ qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+ qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ q = q * self.scale
+ attn = (q @ k.transpose(-2, -1))
+
+ if self.relative_position_bias_table is not None:
+ relative_position_bias = \
+ self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+ self.window_size[0] * self.window_size[1] + 1,
+ self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if rel_pos_bias is not None:
+ attn = attn + rel_pos_bias
+
+ attn = attn.softmax(dim=-1)
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+ def get_attn_weights(self, x, rel_pos_bias=None):
+ B, N, C = x.shape
+ qkv_bias = None
+ if self.q_bias is not None:
+ qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+ # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+ qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+ qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ q = q * self.scale
+ attn = (q @ k.transpose(-2, -1))
+
+ if self.relative_position_bias_table is not None:
+ relative_position_bias = \
+ self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+ self.window_size[0] * self.window_size[1] + 1,
+ self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if rel_pos_bias is not None:
+ attn = attn + rel_pos_bias
+
+ normalized_attn = attn.softmax(dim=-1)
+ attn = self.attn_drop(normalized_attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x, normalized_attn
+
+
+class Block(nn.Module):
+
+ def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+ drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
+ window_size=None, attn_head_dim=None):
+ super().__init__()
+ self.norm1 = norm_layer(dim)
+ self.attn = Attention(
+ dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
+ attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim)
+ # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+ if init_values is not None and init_values > 0:
+ self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
+ self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
+ else:
+ self.gamma_1, self.gamma_2 = None, None
+
+ def forward(self, x, rel_pos_bias=None):
+ if self.gamma_1 is None:
+ x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
+ else:
+ x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
+ x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
+ return x
+
+ def get_attn_weights(self, x, rel_pos_bias=None):
+ if self.gamma_1 is None:
+ tmp, tmp_attn = self.attn.get_attn_weights(self.norm1(x), rel_pos_bias=rel_pos_bias)
+ x = x + self.drop_path(tmp)
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
+ else:
+ tmp, tmp_attn = self.attn.get_attn_weights(self.norm1(x), rel_pos_bias=rel_pos_bias)
+ x = x + self.drop_path(self.gamma_1 * tmp)
+ x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
+ return x, tmp_attn
+
+
+class PatchEmbed(nn.Module):
+ """ Image to Patch Embedding
+ """
+ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
+ self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.num_patches = num_patches
+
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+ def forward(self, x, **kwargs):
+ B, C, H, W = x.shape
+ # FIXME look at relaxing size constraints
+ assert H == self.img_size[0] and W == self.img_size[1], \
+ f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+ x = self.proj(x).flatten(2).transpose(1, 2)
+ return x
+
+
+class RelativePositionBias(nn.Module):
+
+ def __init__(self, window_size, num_heads):
+ super().__init__()
+ self.window_size = window_size
+ self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+ self.relative_position_bias_table = nn.Parameter(
+ torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
+ # cls to token & token 2 cls & cls to cls
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(window_size[0])
+ coords_w = torch.arange(window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+ relative_position_index = \
+ torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
+ relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ relative_position_index[0, 0:] = self.num_relative_distance - 3
+ relative_position_index[0:, 0] = self.num_relative_distance - 2
+ relative_position_index[0, 0] = self.num_relative_distance - 1
+
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ # trunc_normal_(self.relative_position_bias_table, std=.02)
+
+ def forward(self):
+ relative_position_bias = \
+ self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+ self.window_size[0] * self.window_size[1] + 1,
+ self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
+ return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+
+
+class VisionTransformer(nn.Module):
+ """ Vision Transformer with support for patch or hybrid CNN input stage
+ """
+ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+ num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+ drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None,
+ use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False,
+ use_mean_pooling=True, init_scale=0.001, use_checkpoint=False):
+ super().__init__()
+ self.image_size = img_size
+ self.num_classes = num_classes
+ self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
+
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+ num_patches = self.patch_embed.num_patches
+
+ self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+ if use_abs_pos_emb:
+ self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+ else:
+ self.pos_embed = None
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ if use_shared_rel_pos_bias:
+ self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
+ else:
+ self.rel_pos_bias = None
+ self.use_checkpoint = use_checkpoint
+
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
+ self.use_rel_pos_bias = use_rel_pos_bias
+ self.blocks = nn.ModuleList([
+ Block(
+ dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
+ init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None)
+ for i in range(depth)])
+# self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
+# self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
+# self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+ if self.pos_embed is not None:
+ trunc_normal_(self.pos_embed, std=.02)
+ trunc_normal_(self.cls_token, std=.02)
+ # trunc_normal_(self.mask_token, std=.02)
+# if isinstance(self.head, nn.Linear):
+# trunc_normal_(self.head.weight, std=.02)
+ self.apply(self._init_weights)
+ self.fix_init_weight()
+# if isinstance(self.head, nn.Linear):
+# self.head.weight.data.mul_(init_scale)
+# self.head.bias.data.mul_(init_scale)
+
+ def fix_init_weight(self):
+ def rescale(param, layer_id):
+ param.div_(math.sqrt(2.0 * layer_id))
+
+ for layer_id, layer in enumerate(self.blocks):
+ rescale(layer.attn.proj.weight.data, layer_id + 1)
+ rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if isinstance(m, nn.Linear) and 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 get_classifier(self):
+ return self.head
+
+ def reset_classifier(self, num_classes, global_pool=''):
+ self.num_classes = num_classes
+ self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+ def forward_features(self, x):
+ x = self.patch_embed(x)
+ batch_size, seq_len, _ = x.size()
+
+ cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
+ x = torch.cat((cls_tokens, x), dim=1)
+ if self.pos_embed is not None:
+ x = x + self.pos_embed
+ x = self.pos_drop(x)
+
+ rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
+ for blk in self.blocks:
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, rel_pos_bias)
+ else:
+ x = blk(x, rel_pos_bias)
+ return x
+# x = self.norm(x)
+
+# if self.fc_norm is not None:
+# t = x[:, 1:, :]
+# return self.fc_norm(t.mean(1))
+# else:
+# return x[:, 0]
+
+ def forward(self, x):
+ x = self.forward_features(x)
+# x = self.head(x)
+ return x
+
+ def get_intermediate_layers(self, x):
+ x = self.patch_embed(x)
+ batch_size, seq_len, _ = x.size()
+
+ cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
+ x = torch.cat((cls_tokens, x), dim=1)
+ if self.pos_embed is not None:
+ x = x + self.pos_embed
+ x = self.pos_drop(x)
+
+ features = []
+ rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
+ for blk in self.blocks:
+ x = blk(x, rel_pos_bias)
+ features.append(x)
+
+ return features
+
+ def get_attn_weights(self, x):
+ x = self.patch_embed(x)
+ batch_size, seq_len, _ = x.size()
+
+ cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
+ x = torch.cat((cls_tokens, x), dim=1)
+ if self.pos_embed is not None:
+ x = x + self.pos_embed
+ x = self.pos_drop(x)
+
+ rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
+ all_attn = []
+ for blk in self.blocks:
+ if self.use_checkpoint:
+ x, x_attn = checkpoint.checkpoint(blk, x, rel_pos_bias)
+ else:
+ x, x_attn = blk.get_attn_weights(x, rel_pos_bias)
+ all_attn.append(x_attn)
+
+ return x, all_attn
+
+def interpolate_pos_embed(model, checkpoint_model):
+ if 'pos_embed' in checkpoint_model:
+ pos_embed_checkpoint = checkpoint_model['pos_embed'].float()
+ embedding_size = pos_embed_checkpoint.shape[-1]
+ num_patches = model.patch_embed.num_patches
+ num_extra_tokens = model.pos_embed.shape[-2] - num_patches
+ # height (== width) for the checkpoint position embedding
+ orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+ # height (== width) for the new position embedding
+ new_size = int(num_patches ** 0.5)
+ # class_token and dist_token are kept unchanged
+ if orig_size != new_size:
+ print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
+ extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+ # only the position tokens are interpolated
+ pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+ pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+ pos_tokens = torch.nn.functional.interpolate(
+ pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+ pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+ new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+ checkpoint_model['pos_embed'] = new_pos_embed
+
+
+def convert_weights_to_fp16(model: nn.Module):
+ """Convert applicable model parameters to fp16"""
+
+ def _convert_weights_to_fp16(l):
+ if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+ l.weight.data = l.weight.data.half()
+ if l.bias is not None:
+ l.bias.data = l.bias.data.half()
+
+# if isinstance(l, (nn.MultiheadAttention, Attention)):
+# for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
+# tensor = getattr(l, attr)
+# if tensor is not None:
+# tensor.data = tensor.data.half()
+
+ model.apply(_convert_weights_to_fp16)
+
+
+def create_eva_vit_g(img_size=224,drop_path_rate=0.4,use_checkpoint=False,precision="fp16"):
+ model = VisionTransformer(
+ img_size=img_size,
+ patch_size=14,
+ use_mean_pooling=False,
+ embed_dim=1408,
+ depth=39,
+ num_heads=1408//88,
+ mlp_ratio=4.3637,
+ qkv_bias=True,
+ drop_path_rate=drop_path_rate,
+ norm_layer=partial(nn.LayerNorm, eps=1e-6),
+ use_checkpoint=use_checkpoint,
+ )
+ url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth"
+ cached_file = download_cached_file(
+ url, check_hash=False, progress=True
+ )
+ state_dict = torch.load(cached_file, map_location="cpu")
+ interpolate_pos_embed(model,state_dict)
+
+ incompatible_keys = model.load_state_dict(state_dict, strict=False)
+# print(incompatible_keys)
+
+ if precision == "fp16":
+# model.to("cuda")
+ convert_weights_to_fp16(model)
+ return model
\ No newline at end of file
diff --git a/global_local/models/modeling_llama.py b/global_local/models/modeling_llama.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a608a3f1c04b653d384e3d61974a6a1718cc83e
--- /dev/null
+++ b/global_local/models/modeling_llama.py
@@ -0,0 +1,767 @@
+# This script is based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+
+""" PyTorch LLaMA model."""
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from transformers.models.llama.configuration_llama import LlamaConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaConfig"
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+ """
+ Make causal mask used for bi-directional self-attention.
+ """
+ bsz, tgt_len = input_ids_shape
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+ mask_cond = torch.arange(mask.size(-1), device=device)
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+ mask = mask.to(dtype)
+
+ if past_key_values_length > 0:
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ bsz, src_len = mask.size()
+ tgt_len = tgt_len if tgt_len is not None else src_len
+
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+ inverted_mask = 1.0 - expanded_mask
+
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class LlamaRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ LlamaRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+ # convert into half-precision if necessary
+ if self.weight.dtype in [torch.float16, torch.bfloat16]:
+ hidden_states = hidden_states.to(self.weight.dtype)
+
+ return self.weight * hidden_states
+
+
+class LlamaRotaryEmbedding(torch.nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+ inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+ self.register_buffer("inv_freq", inv_freq)
+
+ # Build here to make `torch.jit.trace` work.
+ self.max_seq_len_cached = max_position_embeddings
+ t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+ def forward(self, x, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
+ if seq_len > self.max_seq_len_cached:
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+ return (
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+ )
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+ gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1]
+ gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
+ cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+ sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class LlamaMLP(nn.Module):
+ def __init__(
+ self,
+ hidden_size: int,
+ intermediate_size: int,
+ hidden_act: str,
+ ):
+ super().__init__()
+ self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+ self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+ self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+ self.act_fn = ACT2FN[hidden_act]
+
+ def forward(self, x):
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class LlamaAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: LlamaConfig):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.max_position_embeddings = config.max_position_embeddings
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+ self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value[0].shape[-2]
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+ # [bsz, nh, t, hd]
+
+ if past_key_value is not None:
+ # reuse k, v, self_attention
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+ past_key_value = (key_states, value_states) if use_cache else None
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+ attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2)
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+
+class LlamaDecoderLayer(nn.Module):
+ def __init__(self, config: LlamaConfig):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.self_attn = LlamaAttention(config=config)
+ self.mlp = LlamaMLP(
+ hidden_size=self.hidden_size,
+ intermediate_size=config.intermediate_size,
+ hidden_act=config.hidden_act,
+ )
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ """
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ return outputs
+
+
+LLAMA_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`LlamaConfig`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+ LLAMA_START_DOCSTRING,
+)
+class LlamaPreTrainedModel(PreTrainedModel):
+ config_class = LlamaConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LlamaDecoderLayer"]
+ _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if isinstance(module, LlamaModel):
+ module.gradient_checkpointing = value
+
+
+LLAMA_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+ LLAMA_START_DOCSTRING,
+)
+class LlamaModel(LlamaPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+ Args:
+ config: LlamaConfig
+ """
+
+ def __init__(self, config: LlamaConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+ # create causal mask
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ combined_attention_mask = None
+ if input_shape[-1] > 1:
+ combined_attention_mask = _make_causal_mask(
+ input_shape,
+ inputs_embeds.dtype,
+ device=inputs_embeds.device,
+ past_key_values_length=past_key_values_length,
+ )
+
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+ inputs_embeds.device
+ )
+ combined_attention_mask = (
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+ )
+
+ return combined_attention_mask
+
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ query_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ 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
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape
+ elif inputs_embeds is not None:
+ batch_size, seq_length, _ = inputs_embeds.shape
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+ if query_embeds is not None:
+ inputs_embeds = torch.cat([query_embeds, inputs_embeds], dim=1)
+ batch_size, seq_length, _ = inputs_embeds.shape
+
+ seq_length_with_past = seq_length
+ past_key_values_length = 0
+
+ if past_key_values is not None:
+ past_key_values_length = past_key_values[0][0].shape[2]
+ seq_length_with_past = seq_length_with_past + past_key_values_length
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+ )
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+ else:
+ position_ids = position_ids.view(-1, seq_length).long()
+
+ # embed positions
+ if attention_mask is None:
+ attention_mask = torch.ones(
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+ )
+ attention_mask = self._prepare_decoder_attention_mask(
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+ )
+
+ hidden_states = inputs_embeds
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = () if use_cache else None
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+ if self.gradient_checkpointing and self.training:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ # None for past_key_value
+ return module(*inputs, output_attentions, None)
+
+ return custom_forward
+
+ layer_outputs = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(decoder_layer),
+ hidden_states,
+ attention_mask,
+ position_ids,
+ None,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = next_decoder_cache if use_cache else None
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class LlamaForCausalLM(LlamaPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = LlamaModel(config)
+
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ query_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ value_forward: Optional[bool] = False,
+ no_average: Optional[bool] = False,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (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]`.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+ >>> prompt = "Hey, are you consciours? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
+ ```"""
+
+ 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
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ query_embeds=query_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+
+ if value_forward:
+ return hidden_states
+
+ logits = self.lm_head(hidden_states)
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ if no_average:
+ loss_fct = CrossEntropyLoss(reduction='none')
+ else:
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if no_average:
+ loss = loss.view(logits[..., :-1, :].size(0), logits[..., :-1, :].size(1))
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, query_embeds=None, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+ ):
+ if past_key_values:
+ input_ids = input_ids[:, -1:]
+
+ position_ids = kwargs.get("position_ids", None)
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ position_ids = position_ids[:, -1].unsqueeze(-1)
+ query_embeds = None
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "query_embeds": query_embeds,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ def _reorder_cache(past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+ return reordered_past
+
diff --git a/global_local/models/video_instruction_ft_model.py b/global_local/models/video_instruction_ft_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2e43e27658cb6ee12c5b1994448564848fdd13c
--- /dev/null
+++ b/global_local/models/video_instruction_ft_model.py
@@ -0,0 +1,1395 @@
+import logging
+import random
+import sys
+
+import torch
+from torch.cuda.amp import autocast as autocast
+import torch.nn as nn
+import torch.nn.functional as F
+
+from global_local.common.registry import registry
+from global_local.models.blip2 import Blip2Base, disabled_train
+from global_local.models.modeling_llama import LlamaForCausalLM
+from transformers import LlamaTokenizer,BertConfig
+import einops
+import copy
+from global_local.models.Qformer import BertConfig, BertLMHeadModel
+from global_local.models.ImageBind.models.imagebind_model import ImageBindModel,ModalityType
+from global_local.models.ImageBind.models import imagebind_model
+# from flamingo_pytorch import PerceiverResampler
+@registry.register_model("video_instruction_llama")
+class VideoInstructionFTLLAMA(Blip2Base):
+ """
+ BLIP2 GPT-LLAMA model.
+ """
+
+ PRETRAINED_MODEL_CONFIG_DICT = {
+ "pretrain_vicuna": "configs/models/video_llama.yaml",
+ "pretrain_llama_v2": "configs/models/video_llama.yaml",
+ }
+
+ @classmethod
+ def init_video_Qformer(cls, num_query_token, vision_width,num_hidden_layers =2):
+ encoder_config = BertConfig.from_pretrained("bert-base-uncased")
+ encoder_config.num_hidden_layers = num_hidden_layers
+ encoder_config.encoder_width = vision_width
+ # insert cross-attention layer every other block
+ encoder_config.add_cross_attention = True
+ encoder_config.cross_attention_freq = 1
+ encoder_config.query_length = num_query_token
+ Qformer = BertLMHeadModel(config=encoder_config)
+ query_tokens = nn.Parameter(
+ torch.zeros(1, num_query_token, encoder_config.hidden_size)
+ )
+ query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
+ return Qformer, query_tokens
+
+ def __init__(
+ self,
+ vit_model="eva_clip_g",
+ q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
+ img_size=224,
+ drop_path_rate=0,
+ use_grad_checkpoint=False,
+ vit_precision="fp16",
+ freeze_vit=True,
+ freeze_qformer=True,
+ num_query_token=32,
+ llama_model="",
+ prompt_path="",
+ prompt_template="",
+ max_txt_len=128,
+ end_sym='\n',
+ low_resource=False, # use 8 bit and put vit in cpu
+ device_8bit=0, # the device of 8bit model should be set when loading and cannot be changed anymore.
+
+ frozen_llama_proj=True,
+ frozen_video_Qformer=True,
+ frozen_audio_Qformer=True,
+
+ llama_proj_model='',
+ fusion_header_type= "seqTransf",
+ max_frame_pos= 32,
+ fusion_head_layers = 2,
+ num_video_query_token = 32,
+ num_audio_query_token = 8,
+ imagebind_ckpt_path = '/mnt/workspace/ckpt',
+ equip_audio_branch = True
+ ):
+ super().__init__()
+
+ self.tokenizer = self.init_tokenizer()
+ self.low_resource = low_resource
+
+ print('Loading VIT')
+ self.visual_encoder, self.ln_vision = self.init_vision_encoder(
+ vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
+ )
+ if freeze_vit:
+ for name, param in self.visual_encoder.named_parameters():
+ param.requires_grad = False
+ self.visual_encoder = self.visual_encoder.eval()
+ self.visual_encoder.train = disabled_train
+ for name, param in self.ln_vision.named_parameters():
+ param.requires_grad = False
+ self.ln_vision = self.ln_vision.eval()
+ self.ln_vision.train = disabled_train
+ logging.info("freeze vision encoder")
+ print('Loading VIT Done')
+
+ print('Loading Q-Former')
+ self.Qformer, self.query_tokens = self.init_Qformer(
+ num_query_token, self.visual_encoder.num_features
+ )
+ self.Qformer.cls = None
+ self.Qformer.bert.embeddings.word_embeddings = None
+ self.Qformer.bert.embeddings.position_embeddings = None
+ for layer in self.Qformer.bert.encoder.layer:
+ layer.output = None
+ layer.intermediate = None
+ self.load_from_pretrained(url_or_filename=q_former_model)
+
+ if freeze_qformer:
+ for name, param in self.Qformer.named_parameters():
+ param.requires_grad = False
+ self.Qformer = self.Qformer.eval()
+ self.Qformer.train = disabled_train
+ self.query_tokens.requires_grad = False
+ logging.info("freeze Qformer")
+ logging.info('Loading Q-Former Done')
+
+ logging.info('Loading LLAMA Tokenizer')
+ self.llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model, use_fast=False)
+ if self.llama_tokenizer.pad_token is None:
+ self.llama_tokenizer.pad_token = self.llama_tokenizer.unk_token
+ DEFAULT_IMAGE_PATCH_TOKEN = ''
+ DEFAULT_AUDIO_PATCH_TOKEN = ''
+ self.llama_tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+ self.llama_tokenizer.add_tokens([DEFAULT_AUDIO_PATCH_TOKEN], special_tokens=True)
+
+ self.IMAGE_PATCH_TOKEN_ID = self.llama_tokenizer.get_vocab()[DEFAULT_IMAGE_PATCH_TOKEN]
+ self.AUDIO_PATCH_TOKEN_ID = self.llama_tokenizer.get_vocab()[DEFAULT_AUDIO_PATCH_TOKEN]
+
+ logging.info('Loading LLAMA Model')
+ if self.low_resource:
+ self.llama_model = LlamaForCausalLM.from_pretrained(
+ llama_model,
+ torch_dtype=torch.bfloat16,
+ load_in_8bit=True,
+ device_map={'': device_8bit}
+ )
+ else:
+ self.llama_model = LlamaForCausalLM.from_pretrained(
+ llama_model,
+ torch_dtype=torch.bfloat16,
+ )
+
+ for name, param in self.llama_model.named_parameters():
+ param.requires_grad = False
+ logging.info('Loading LLAMA Done')
+
+
+ logging.info('Loading LLAMA proj')
+ self.llama_proj = nn.Linear(
+ self.Qformer.config.hidden_size, self.llama_model.config.hidden_size
+ )
+ if llama_proj_model:
+ print("load llama proj weight: {}".format(llama_proj_model))
+ llama_proj_weight = torch.load(llama_proj_model, map_location="cpu")
+ msg = self.load_state_dict(llama_proj_weight['model'], strict=False)
+
+ if frozen_llama_proj:
+ # todo frozen llama_proj
+ for name, param in self.llama_proj.named_parameters():
+ param.requires_grad = False
+ logging.info('LLAMA proj is frozen')
+ else:
+ for name, param in self.llama_proj.named_parameters():
+ param.requires_grad = True
+ logging.info('LLAMA proj is not frozen')
+
+ logging.info('Loading llama_proj Done')
+
+ self.max_txt_len = max_txt_len
+ self.end_sym = end_sym
+
+ if prompt_path:
+ with open(prompt_path, 'r') as f:
+ raw_prompts = f.read().splitlines()
+ filted_prompts = [raw_prompt for raw_prompt in raw_prompts if "" in raw_prompt]
+ self.prompt_list = [prompt_template.format(p) for p in filted_prompts]
+ print('Load {} training prompts'.format(len(self.prompt_list)))
+ print('Prompt Example \n{}'.format(random.choice(self.prompt_list)))
+ else:
+ self.prompt_list = []
+
+ self.video_frame_position_embedding = nn.Embedding(max_frame_pos, self.Qformer.config.hidden_size)
+
+ self.num_video_query_token = num_video_query_token
+ self.video_Qformer,self.video_query_tokens = self.init_video_Qformer(num_query_token = num_video_query_token,\
+ vision_width=self.Qformer.config.hidden_size, num_hidden_layers =2)
+
+ self.video_Qformer.cls = None
+ self.video_Qformer.bert.embeddings.word_embeddings = None
+ self.video_Qformer.bert.embeddings.position_embeddings = None
+ for layer in self.video_Qformer.bert.encoder.layer:
+ layer.output = None
+ layer.intermediate = None
+
+
+ if frozen_video_Qformer:
+ # todo frozen llama_proj
+ for name, param in self.video_Qformer.named_parameters():
+ param.requires_grad = False
+ for name, param in self.video_frame_position_embedding.named_parameters():
+ param.requires_grad = False
+ self.video_query_tokens.requires_grad = False
+
+ logging.info('video_Qformer is frozen')
+ else:
+ for name, param in self.video_Qformer.named_parameters():
+ param.requires_grad = True
+ for name, param in self.video_frame_position_embedding.named_parameters():
+ param.requires_grad = True
+ self.video_query_tokens.requires_grad = True
+ logging.info('video_Qformer is not frozen')
+
+ if frozen_video_Qformer and (not frozen_audio_Qformer):
+ self.train_flag = 1 # 只训练audio_Qformer
+ elif not(frozen_video_Qformer) and frozen_audio_Qformer:
+ self.train_flag = 0 # 训练video_Qformer
+ elif not(frozen_video_Qformer) and not(frozen_audio_Qformer):
+ self.train_flag = 2 # video_Qformer and AL trained
+ else:
+ self.train_flag = 3
+
+ if equip_audio_branch:
+ print (f'Initializing audio encoder from {imagebind_ckpt_path} ...')
+ self.audio_encoder,self.audio_hidden_size = \
+ imagebind_model.imagebind_huge()
+ self.audio_encoder.load_state_dict(torch.load("{}/imagebind_huge.pth".format(imagebind_ckpt_path)))
+ # free vision encoder
+ for name, param in self.audio_encoder.named_parameters():
+ param.requires_grad = False
+ self.audio_encoder.eval()
+ print ('audio encoder initialized.')
+
+ self.num_audio_query_token = num_audio_query_token
+ self.audio_Qformer,self.audio_query_tokens = self.init_video_Qformer(num_query_token = self.num_audio_query_token,\
+ vision_width=self.audio_hidden_size, num_hidden_layers =2)
+ self.audio_Qformer.cls = None
+ self.audio_Qformer.bert.embeddings.word_embeddings = None
+ self.audio_Qformer.bert.embeddings.position_embeddings = None
+ for layer in self.audio_Qformer.bert.encoder.layer:
+ layer.output = None
+ layer.intermediate = None
+ self.audio_llama_proj = nn.Linear(
+ self.audio_Qformer.config.hidden_size, self.llama_model.config.hidden_size
+ )
+ self.audio_position_embedding = nn.Embedding(8, self.audio_hidden_size)
+
+ if frozen_audio_Qformer:
+ # todo frozen llama_proj
+ for name, param in self.audio_Qformer.named_parameters():
+ param.requires_grad = False
+ self.audio_query_tokens.requires_grad = False
+ for name, param in self.audio_llama_proj.named_parameters():
+ param.requires_grad = False
+ for name, param in self.audio_position_embedding.named_parameters():
+ param.requires_grad = False
+ logging.info('audio_Qformer and audio-LLAMA proj is frozen')
+ else:
+ for name, param in self.audio_Qformer.named_parameters():
+ param.requires_grad = True
+ self.audio_query_tokens.requires_grad = True
+ for name, param in self.audio_llama_proj.named_parameters():
+ param.requires_grad = True
+ for name, param in self.audio_position_embedding.named_parameters():
+ param.requires_grad = True
+ logging.info('audio_Qformer is not frozen')
+
+ # initialize additional arguments
+ self.pos_extending_factor = None
+
+ self.prompt = '[INST] %s [/INST]'
+
+ def vit_to_cpu(self):
+ self.ln_vision.to("cpu")
+ self.ln_vision.float()
+ self.visual_encoder.to("cpu")
+ self.visual_encoder.float()
+
+ def initialize_visual_agg_function(self):
+ if self.hierarchical_agg_function == 'without-top-final-global-prompts-region-segment-full-dis-spatiotemporal-prompts-attn-early-attn-linear-learned':
+ self.video_global_proj = nn.Linear(self.Qformer.config.hidden_size, self.llama_model.config.hidden_size)
+ self.video_global_proj.load_state_dict(self.llama_proj.state_dict())
+
+ for name, param in self.video_global_proj.named_parameters():
+ param.requires_grad = True
+
+ if 'without-top' not in self.hierarchical_agg_function:
+ self.global_region_prompts = nn.Parameter(torch.zeros(1, self.video_query_tokens.size(1), self.video_query_tokens.size(-1)))
+ self.global_region_prompts.data = self.video_query_tokens.data.clone()
+ self.global_region_prompts.requires_grad = True
+
+ self.segment_region_prompts = nn.Parameter(torch.zeros(1, self.video_query_tokens.size(1), self.video_query_tokens.size(-1)))
+ self.segment_region_prompts.data = self.video_query_tokens.data.clone()
+ self.segment_region_prompts.requires_grad = True
+
+ if 'region-prompts' in self.hierarchical_agg_function:
+ self.segment_attn_queries = nn.Parameter(torch.zeros(1, self.num_segments, self.video_query_tokens.size(-1)))
+ self.segment_attn_queries.data = self.video_query_tokens.data[:, :self.num_segments].clone()
+ self.segment_attn_queries.requires_grad = True
+
+ if 'spatiotemporal-prompts' in self.hierarchical_agg_function:
+ if 'full-dis-spatiotemporal' in self.hierarchical_agg_function:
+ self.spatial_segment_prompts = nn.Parameter(torch.zeros(1, self.video_query_tokens.size(1), self.video_query_tokens.size(-1)))
+ self.spatial_segment_prompts.data = self.video_query_tokens.data.clone()
+ self.spatial_segment_prompts.requires_grad = True
+
+ self.temporal_segment_prompts = nn.Parameter(torch.zeros(1, self.num_segments, self.video_query_tokens.size(-1)))
+ self.temporal_segment_prompts.data = self.video_query_tokens.data.clone().mean(1, keepdim=True).repeat(1, self.num_segments, 1)
+ self.temporal_segment_prompts.requires_grad = True
+
+ elif 'full-spatiotemporal' not in self.hierarchical_agg_function:
+ self.temporal_segment_prompts = nn.Parameter(torch.zeros(1, self.num_segments, self.video_query_tokens.size(-1)))
+ self.temporal_segment_prompts.data = self.video_query_tokens.data[:, :self.num_segments].clone()
+ self.temporal_segment_prompts.requires_grad = True
+
+ self.spatial_segment_prompts = nn.Parameter(torch.zeros(1, self.video_query_tokens.size(1), self.video_query_tokens.size(-1)))
+ self.spatial_segment_prompts.data = self.video_query_tokens.data.clone()
+ self.spatial_segment_prompts.requires_grad = True
+ else:
+ self.spatial_segment_prompts = nn.Parameter(torch.zeros(self.num_segments, self.video_query_tokens.size(1), self.video_query_tokens.size(-1)))
+ self.spatial_segment_prompts.data = self.video_query_tokens.data.clone().repeat(self.num_segments, 1, 1)
+ self.spatial_segment_prompts.requires_grad = True
+
+ if 'final-global-prompts' in self.hierarchical_agg_function:
+ self.global_to_segment_prompts = nn.Parameter(torch.zeros(1, self.video_query_tokens.size(1), self.video_query_tokens.size(-1)))
+ self.global_to_segment_prompts.data = self.video_query_tokens.data.clone()
+ self.global_to_segment_prompts.requires_grad = True
+
+ if 'proj-' in self.hierarchical_agg_function:
+ self.global_frame_proj = nn.Linear(self.Qformer.config.hidden_size, self.Qformer.config.hidden_size)
+ self.global_segment_proj = nn.Linear(self.Qformer.config.hidden_size, self.Qformer.config.hidden_size)
+
+ if '-learned' in self.hierarchical_agg_function:
+ if self.global_region_embed_weight is None:
+ self.global_region_embed_weight = nn.Parameter(data=torch.rand(1))
+ else:
+ self.global_region_embed_weight = nn.Parameter(data=torch.Tensor([self.global_region_embed_weight]))
+ self.global_region_embed_weight.requires_grad = True
+
+ for k, v in self.named_parameters():
+ if 'video_global_proj' not in k:
+ v.requires_grad = False
+
+ return
+
+ def encode_videoQformer_visual(self, image, frame_attn_mask, global_video=True):
+ device = image.device
+
+ # input shape b,t,c,h,w
+ batch_size, time_length, _, _, _ = image.size()
+
+ image = einops.rearrange(image, 'b t c h w -> (b t) c h w')
+
+ with self.maybe_autocast():
+ # embed image features with blip2, out: (b t) q h
+ image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
+ image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
+
+ query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
+
+ query_output = self.Qformer.bert(
+ query_embeds=query_tokens,
+ encoder_hidden_states=image_embeds,
+ encoder_attention_mask=image_atts,
+ return_dict=True,
+ )
+
+ # add frame_pos embedding
+ position_ids = torch.arange(time_length, dtype=torch.long, device=query_tokens.device)
+ position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
+ frame_position_embeddings = self.video_frame_position_embedding(position_ids)
+ q_hidden_state = query_output.last_hidden_state
+
+ frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
+ frame_hidden_state = einops.rearrange(q_hidden_state, '(b t) q h -> b t q h',b=batch_size,t=time_length)
+ frame_hidden_state = frame_position_embeddings + frame_hidden_state
+
+ # frame attention
+ frame_hidden_state = einops.rearrange(frame_hidden_state, 'b t q h -> b (t q) h',b=batch_size,t=time_length)
+ frame_atts = torch.ones(frame_hidden_state.size()[:-1], dtype=torch.long).to(device)
+ video_query_tokens = self.video_query_tokens.expand(frame_hidden_state.shape[0], -1, -1)
+
+ frame_attn_mask = frame_attn_mask.unsqueeze(-1).repeat(1, 1, q_hidden_state.size(1))
+ frame_attn_mask = frame_attn_mask.view(frame_attn_mask.size(0), -1)
+ frame_atts = frame_atts * frame_attn_mask
+
+ video_query_output = self.video_Qformer.bert(
+ query_embeds=video_query_tokens,
+ encoder_hidden_states=frame_hidden_state,
+ encoder_attention_mask=frame_atts,
+ return_dict=True,
+ )
+ video_hidden = video_query_output.last_hidden_state
+ atts_llama = torch.ones(video_hidden.size()[:-1], dtype=torch.long).to(image_embeds.device)
+ return video_hidden, atts_llama
+
+ def encode_frame_level_visual(self, image, frame_attn_mask, return_attn=False):
+ device = image.device
+
+
+ # input shape b,t,c,h,w
+ batch_size, time_length, _, _, _ = image.size()
+ image = einops.rearrange(image, 'b t c h w -> (b t) c h w')
+
+ with self.maybe_autocast():
+ # embed image features with blip2, out: (b t) q h
+ image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
+ image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
+
+ query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
+ query_output = self.Qformer.bert(
+ query_embeds=query_tokens,
+ encoder_hidden_states=image_embeds,
+ encoder_attention_mask=image_atts,
+ output_attentions=return_attn,
+ return_dict=True,
+ )
+
+ q_hidden_state = query_output.last_hidden_state
+ frame_hidden_state = einops.rearrange(q_hidden_state, '(b t) q h -> b t q h',b=batch_size,t=time_length)
+ frame_atts = torch.ones(frame_hidden_state.size()[:-1], dtype=torch.long).to(device)
+
+ if return_attn:
+ with self.maybe_autocast():
+ _, frame_patch_attn = self.visual_encoder.get_attn_weights(image)
+ return frame_hidden_state, frame_atts, query_output['attentions'], query_output['cross_attentions'], frame_patch_attn
+ else:
+ return frame_hidden_state, frame_atts
+
+
+ def prompt_wrap(self, img_embeds, atts_img, prompt):
+ if prompt:
+ batch_size = img_embeds.shape[0]
+ # print(prompt)
+ p_before, p_after = self.prompt.split('')
+ tmp = p_after.split('%s')
+ p_video_end = tmp[0]
+ p_inst_end = tmp[1]
+ p_before_tokens = self.llama_tokenizer(
+ p_before, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
+ p_video_end_tokens = self.llama_tokenizer(
+ p_video_end, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
+ p_inst_end_tokens = self.llama_tokenizer(
+ p_inst_end, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
+
+ prompt_tokens = self.llama_tokenizer(
+ prompt,
+ return_tensors="pt",
+ padding="longest",
+ truncation=True,
+ max_length=self.max_txt_len,
+ add_special_tokens=False
+ ).to(img_embeds.device)
+
+ p_before_embeds = self.llama_model.model.embed_tokens(p_before_tokens.input_ids).expand(batch_size, -1, -1)
+ p_video_end_embeds = self.llama_model.model.embed_tokens(p_video_end_tokens.input_ids).expand(batch_size, -1, -1)
+ p_inst_end_embeds = self.llama_model.model.embed_tokens(p_inst_end_tokens.input_ids).expand(batch_size, -1, -1)
+ p_tokens_embeds = self.llama_model.model.embed_tokens(prompt_tokens.input_ids)
+ wrapped_img_embeds = torch.cat([p_before_embeds, img_embeds, p_video_end_embeds, p_tokens_embeds, p_inst_end_embeds], dim=1)
+
+ p_before_attn = p_before_tokens.attention_mask.expand(batch_size, -1)
+ p_video_end_attn = p_video_end_tokens.attention_mask.expand(batch_size, -1)
+ p_inst_end_attn = p_inst_end_tokens.attention_mask.expand(batch_size, -1)
+ p_tokens_attn = prompt_tokens.attention_mask
+ wrapped_atts_img = torch.cat([p_before_attn, atts_img, p_video_end_attn, p_tokens_attn, p_inst_end_attn], dim=1)
+ return wrapped_img_embeds, wrapped_atts_img
+ else:
+ return img_embeds, atts_img
+ # input audio shape [b t c h w]
+ def encode_audioQformer(self, audio,modality_type=ModalityType.AUDIO):
+ device = audio.device
+ with self.maybe_autocast():
+ audio_feature, audio_imagebind_finalout = self.audio_encoder.get_audio_feature(audio,modality_type=modality_type)
+ batch_size,time_length = audio.size()[:2]
+
+
+ position_ids = torch.arange(time_length, dtype=torch.long, device=device)
+ position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
+
+ audio_position_embeddings = self.audio_position_embedding(position_ids)
+ audio_imagebind_finalout = audio_imagebind_finalout + audio_position_embeddings
+
+ audio_query_tokens = self.audio_query_tokens.expand(audio_imagebind_finalout.shape[0], -1, -1)
+ frame_atts = torch.ones(audio_imagebind_finalout.size()[:-1], dtype=torch.long).to(device)
+
+ audio_query_output = self.audio_Qformer.bert(
+ query_embeds=audio_query_tokens, #[32,768]
+ encoder_hidden_states=audio_imagebind_finalout,
+ encoder_attention_mask=frame_atts,
+ return_dict=True,
+ )
+ audio_hidden = audio_query_output.last_hidden_state
+
+ inputs_llama = self.audio_llama_proj(audio_hidden)
+ atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(device)
+
+ return inputs_llama, atts_llama
+
+ def encode_videoQformer_audiovideo(self, image, audio):
+ device = image.device
+
+ # input shape b,c,t,h,w
+ batch_size,_,time_length,_,_ = image.size()
+ image = einops.rearrange(image, 'b c t h w -> (b t) c h w')
+ with self.maybe_autocast():
+ # embed image features with blip2, out: (b t) q h
+ image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
+ image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
+
+ query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
+ query_output = self.Qformer.bert(
+ query_embeds=query_tokens,
+ encoder_hidden_states=image_embeds,
+ encoder_attention_mask=image_atts,
+ return_dict=True,
+ )
+
+ # add frame_pos embedding
+ position_ids = torch.arange(time_length, dtype=torch.long, device=query_tokens.device)
+ position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
+ frame_position_embeddings = self.video_frame_position_embedding(position_ids)
+ q_hidden_state = query_output.last_hidden_state
+
+ frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
+ frame_hidden_state = einops.rearrange(q_hidden_state, '(b t) q h -> b t q h',b=batch_size,t=time_length)
+ frame_hidden_state = frame_position_embeddings + frame_hidden_state
+
+ # encode audio
+ audio_feature, audio_imagebind_finalout = self.audio_encoder.get_audio_feature(audio,modality_type=ModalityType.AUDIO) # [batch,8*1,768] 8*32, 768
+ audio_frame_position_embeddings = frame_position_embeddings.squeeze(-2)
+ audio_feature = audio_feature + audio_frame_position_embeddings
+
+ # frame attention a
+ frame_hidden_state = einops.rearrange(frame_hidden_state, 'b t q h -> b (t q) h',b=batch_size,t=time_length)
+ frame_hidden_state = torch.cat([frame_hidden_state,audio_feature],dim = 1)
+ video_query_tokens = self.video_query_tokens.expand(frame_hidden_state.shape[0], -1, -1)
+ frame_atts = torch.ones(frame_hidden_state.size()[:-1], dtype=torch.long).to(device)
+
+ video_query_output = self.video_Qformer.bert(
+ query_embeds=video_query_tokens, #[32,768]
+ encoder_hidden_states=frame_hidden_state,
+ encoder_attention_mask=frame_atts,
+ return_dict=True,
+ )
+ video_hidden = video_query_output.last_hidden_state
+
+ inputs_llama = self.llama_proj(video_hidden)
+ atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image_embeds.device)
+
+ return inputs_llama, atts_llama
+
+ def forward(self, samples):
+
+ global_video = samples['global_video'].cuda()
+ global_frame_attn_mask = samples['global_frame_attn_mask'].cuda()
+ segments_video = samples['segments_video'].cuda()
+ segments_frame_attn_mask = samples['segments_frame_attn_mask'].cuda()
+ #text = samples['text']
+ text_question = samples['text_question']
+ text_answer = samples['text_answer']
+
+ batch_size = global_video.size(0)
+
+ global_video_embeds, global_video_embeds_mask = self.encode_videoQformer_visual(global_video, global_frame_attn_mask)
+ segments_video = segments_video.view(-1, self.num_frames_per_clip, segments_video.size(-3), segments_video.size(-2), segments_video.size(-1))
+ segments_frame_attn_mask = segments_frame_attn_mask.view(-1, self.num_frames_per_clip)
+
+ segments_video_embeds, segments_video_embeds_mask = self.encode_frame_level_visual(segments_video, segments_frame_attn_mask)
+ segments_video_embeds = segments_video_embeds.view(-1, self.num_segments, segments_video_embeds.size(-3), segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(-1, self.num_segments, segments_video_embeds_mask.size(-2), segments_video_embeds_mask.size(-1))
+
+ if self.hierarchical_agg_function == 'without-top-final-global-prompts-region-segment-full-dis-spatiotemporal-prompts-attn-early-attn-linear-learned':
+ # add segment pos embedding
+ position_ids = torch.arange(segments_video_embeds.size(2), dtype=torch.long, device=self.video_query_tokens.device)
+ segments_video_embeds = segments_video_embeds.view(batch_size*self.num_segments, segments_video_embeds.size(-3), segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(batch_size*self.num_segments, segments_video_embeds_mask.size(-2), segments_video_embeds_mask.size(-1))
+
+ position_ids = position_ids.unsqueeze(0).expand(batch_size*self.num_segments, -1)
+ frame_position_embeddings = self.video_frame_position_embedding(position_ids)
+ frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
+ segments_video_embeds = frame_position_embeddings + segments_video_embeds
+ segments_video_embeds_mask = segments_video_embeds_mask * segments_frame_attn_mask.unsqueeze(-1)
+
+ segments_video_embeds = segments_video_embeds.view(segments_video_embeds.size(0), -1, segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+ video_query_tokens = self.video_query_tokens.expand(segments_video_embeds.shape[0], -1, -1)
+ num_region_queries = video_query_tokens.size(1)
+
+ # add short video segment prompts
+ curr_segment_query_tokens = self.segment_region_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_context = global_video_embeds + curr_segment_query_tokens
+
+ global_context = global_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+ global_context = global_context.view(-1, global_context.size(-2), global_context.size(-1))
+
+ video_query_tokens = torch.cat([video_query_tokens, global_context], dim=1)
+
+ global_region_query_output = self.video_Qformer.bert(
+ query_embeds=video_query_tokens,
+ encoder_hidden_states=segments_video_embeds,
+ encoder_attention_mask=segments_video_embeds_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_segment_embeds = global_region_query_output.last_hidden_state[:, :num_region_queries]
+ global_region_segment_embeds = global_region_segment_embeds.view(batch_size, self.num_segments, global_region_segment_embeds.size(-2), global_region_segment_embeds.size(-1))
+
+ # add segment pos embedding
+ position_ids = torch.arange(self.num_segments, dtype=torch.long, device=segments_video_embeds.device)
+ position_ids = position_ids.unsqueeze(0).expand(len(global_video_embeds), -1)
+ segments_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ segments_position_embeddings = segments_position_embeddings.unsqueeze(-2)
+ segments_hidden_state = segments_position_embeddings + global_region_segment_embeds
+
+ segment_temporal_context = segments_hidden_state.mean(1)
+ segment_spatial_context = segments_hidden_state.mean(2)
+
+ if 'spatiotemporal-prompts' in self.hierarchical_agg_function:
+
+ if 'full-dis-spatiotemporal' in self.hierarchical_agg_function:
+ temporal_context_prompts = self.temporal_segment_prompts.unsqueeze(-2).expand(global_video_embeds.shape[0], -1, segments_hidden_state.size(-2), -1)
+ spatial_context_prompts = self.spatial_segment_prompts.unsqueeze(0).expand(global_video_embeds.shape[0], self.num_segments, -1, -1)
+
+ final_context = segments_hidden_state + temporal_context_prompts + spatial_context_prompts
+ final_context = final_context.view(final_context.size(0), -1, final_context.size(-1))
+
+ if 'without' in self.hierarchical_agg_function and 'full-dis-spatiotemporal' in self.hierarchical_agg_function:
+ final_top_down_context = final_context
+
+ final_top_down_context_mask = torch.ones(final_top_down_context.size()[:-1], dtype=torch.long).to(final_top_down_context.device)
+
+
+ if 'without-top' in self.hierarchical_agg_function:
+ merged_query_tokens = self.video_query_tokens.expand(len(global_video_embeds), -1, -1)
+
+ if 'final-global-prompts' in self.hierarchical_agg_function:
+ global_to_segment_prompts = self.global_to_segment_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_to_segment_context = global_video_embeds + global_to_segment_prompts
+
+ merged_query_tokens = torch.cat([merged_query_tokens, global_to_segment_context], dim=1)
+
+ global_region_output = self.video_Qformer.bert(
+ query_embeds=merged_query_tokens,
+ encoder_hidden_states=final_top_down_context,
+ encoder_attention_mask=final_top_down_context_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_merged_embeds = global_region_output.last_hidden_state
+
+ if 'final-global-prompts' in self.hierarchical_agg_function:
+ global_region_merged_embeds = global_region_merged_embeds[:, :num_region_queries]
+
+ global_region_merged_embeds = self.video_global_proj(global_region_merged_embeds)
+
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = global_video_embeds_mask
+
+ merged_video_embeds = self.llama_proj(merged_video_embeds)
+ merged_video_embeds = merged_video_embeds + self.global_region_embed_weight * global_region_merged_embeds
+
+ merged_video_embeds, merged_atts_video = self.prompt_wrap(merged_video_embeds, merged_video_embeds_mask, text_question)
+ to_regress_tokens = self.llama_tokenizer(
+ text_answer,
+ return_tensors="pt",
+ padding="longest",
+ truncation=True,
+ #max_length=self.max_txt_len,
+ add_special_tokens=False
+ ).to(global_video.device)
+
+ targets = to_regress_tokens.input_ids.masked_fill(
+ to_regress_tokens.input_ids == self.llama_tokenizer.pad_token_id, -100
+ )
+
+ empty_targets = (
+ torch.ones([merged_atts_video.shape[0], merged_atts_video.shape[1]+1],
+ dtype=torch.long).to(global_video.device).fill_(-100) # plus one for bos
+ )
+
+ targets = torch.cat([empty_targets, targets], dim=1)
+
+ batch_size = merged_video_embeds.shape[0]
+ bos = torch.ones([batch_size, 1],
+ dtype=to_regress_tokens.input_ids.dtype,
+ device=to_regress_tokens.input_ids.device) * self.llama_tokenizer.bos_token_id
+ bos_embeds = self.llama_model.model.embed_tokens(bos)
+ atts_bos = merged_atts_video[:, :1]
+
+ to_regress_embeds = self.llama_model.model.embed_tokens(to_regress_tokens.input_ids)
+ inputs_embeds = torch.cat([bos_embeds, merged_video_embeds, to_regress_embeds], dim=1)
+ attention_mask = torch.cat([atts_bos, merged_atts_video, to_regress_tokens.attention_mask], dim=1)
+
+ with self.maybe_autocast():
+ outputs = self.llama_model(
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ return_dict=True,
+ labels=targets,
+ )
+ loss = outputs.loss
+
+ return {"loss": loss}
+
+ def compute_merged_video_embeds(self, samples):
+
+ global_video = samples['global_video'].cuda()
+ global_frame_attn_mask = samples['global_frame_attn_mask'].cuda()
+ segments_video = samples['segments_video'].cuda()
+ segments_frame_attn_mask = samples['segments_frame_attn_mask'].cuda()
+
+ global_video_embeds, global_video_embeds_mask = self.encode_videoQformer_visual(global_video, global_frame_attn_mask)
+
+ segments_video = segments_video.view(-1, self.num_frames_per_clip, segments_video.size(-3), segments_video.size(-2), segments_video.size(-1))
+ segments_frame_attn_mask = segments_frame_attn_mask.view(-1, self.num_frames_per_clip)
+
+ if 'early-attn' not in self.hierarchical_agg_function:
+ segments_video_embeds, segments_video_embeds_mask = self.encode_videoQformer_visual(segments_video, segments_frame_attn_mask, global_video=False)
+
+ segments_video_embeds = segments_video_embeds.view(-1, self.num_segments, segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(-1, self.num_segments, segments_video_embeds_mask.size(-1))
+ else:
+ segments_video_embeds, segments_video_embeds_mask = self.encode_frame_level_visual(segments_video, segments_frame_attn_mask)
+ segments_video_embeds = segments_video_embeds.view(-1, self.num_segments, segments_video_embeds.size(-3), segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(-1, self.num_segments, segments_video_embeds_mask.size(-2), segments_video_embeds_mask.size(-1))
+
+ ############################################################################################
+
+ batch_size = global_video_embeds.size(0)
+
+ if self.hierarchical_agg_function == 'average':
+ segments_video_embeds = segments_video_embeds.mean(1, keepdim=True)
+ merged_video_embeds = torch.cat([global_video_embeds.unsqueeze(1), segments_video_embeds], dim=1)
+ merged_video_embeds = merged_video_embeds.mean(1)
+ merged_video_embeds_mask = segments_video_embeds_mask[:, 0]
+ elif self.hierarchical_agg_function == 'global-region':
+ # add segment pos embedding
+ position_ids = torch.arange(self.num_segments, dtype=torch.long, device=segments_video_embeds.device)
+ position_ids = position_ids.unsqueeze(0).expand(len(segments_video_embeds), -1)
+ segments_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ segments_position_embeddings = segments_position_embeddings.unsqueeze(-2)
+ segments_hidden_state = segments_position_embeddings + segments_video_embeds
+ segments_hidden_state = einops.rearrange(segments_hidden_state, 'b t q h -> b (t q) h',b=len(segments_hidden_state),t=self.num_segments)
+
+ merged_query_output = self.video_Qformer.bert(
+ query_embeds=global_video_embeds,
+ encoder_hidden_states=segments_hidden_state,
+ encoder_attention_mask=segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1),
+ #output_attentions=True,
+ return_dict=True,
+ )
+ merged_video_embeds = merged_query_output.last_hidden_state
+ merged_video_embeds_mask = segments_video_embeds_mask[:, 0]
+ elif self.hierarchical_agg_function == 'global-region-prompts':
+ # add segment pos embedding
+ position_ids = torch.arange(self.num_segments, dtype=torch.long, device=segments_video_embeds.device)
+ position_ids = position_ids.unsqueeze(0).expand(len(segments_video_embeds), -1)
+ segments_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ segments_position_embeddings = segments_position_embeddings.unsqueeze(-2)
+ segments_hidden_state = segments_position_embeddings + segments_video_embeds
+ segments_hidden_state = einops.rearrange(segments_hidden_state, 'b t q h -> b (t q) h',b=len(segments_hidden_state),t=self.num_segments)
+ segments_attn_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+
+ merged_query_tokens = self.global_region_prompts.expand(len(global_video_embeds), -1, -1)
+ segments_hidden_state = torch.cat([global_video_embeds, segments_hidden_state], dim=1)
+ segments_attn_mask = torch.cat([global_video_embeds_mask, segments_attn_mask], dim=1)
+
+ merged_query_output = self.video_Qformer.bert(
+ query_embeds=merged_query_tokens,
+ encoder_hidden_states=segments_hidden_state,
+ encoder_attention_mask=segments_attn_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ merged_video_embeds = merged_query_output.last_hidden_state
+ merged_video_embeds_mask = segments_video_embeds_mask[:, 0]
+ elif self.hierarchical_agg_function == 'global-region-prompts-attn':
+ # add segment pos embedding
+ position_ids = torch.arange(self.num_segments, dtype=torch.long, device=segments_video_embeds.device)
+ position_ids = position_ids.unsqueeze(0).expand(len(segments_video_embeds), -1)
+ segments_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ segments_position_embeddings = segments_position_embeddings.unsqueeze(-2)
+ segments_hidden_state = segments_position_embeddings + segments_video_embeds
+ segments_hidden_state = einops.rearrange(segments_hidden_state, 'b t q h -> b (t q) h',b=len(segments_hidden_state),t=self.num_segments)
+ segments_attn_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+
+ merged_query_tokens = self.global_region_prompts.expand(len(global_video_embeds), -1, -1)
+ segments_hidden_state = torch.cat([global_video_embeds, segments_hidden_state], dim=1)
+ segments_attn_mask = torch.cat([global_video_embeds_mask, segments_attn_mask], dim=1)
+
+ merged_query_output = self.global_region_Qformer.bert(
+ query_embeds=merged_query_tokens,
+ encoder_hidden_states=segments_hidden_state,
+ encoder_attention_mask=segments_attn_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+
+ merged_video_embeds = merged_query_output.last_hidden_state
+ merged_video_embeds_mask = segments_video_embeds_mask[:, 0]
+ elif self.hierarchical_agg_function == 'average-linear' or self.hierarchical_agg_function == 'average-linear-learned':
+ segments_video_embeds = segments_video_embeds.mean(1)
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = segments_video_embeds_mask[:, 0]
+
+ global_region_merged_embeds = self.video_global_proj(segments_video_embeds)
+ elif self.hierarchical_agg_function == 'global-region-prompts-linear':
+ # add segment pos embedding
+ position_ids = torch.arange(self.num_segments, dtype=torch.long, device=segments_video_embeds.device)
+ position_ids = position_ids.unsqueeze(0).expand(len(segments_video_embeds), -1)
+ segments_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ segments_position_embeddings = segments_position_embeddings.unsqueeze(-2)
+ segments_hidden_state = segments_position_embeddings + segments_video_embeds
+ segments_hidden_state = einops.rearrange(segments_hidden_state, 'b t q h -> b (t q) h',b=len(segments_hidden_state),t=self.num_segments)
+ segments_attn_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+
+ merged_query_tokens = self.global_region_prompts.expand(len(global_video_embeds), -1, -1)
+ segments_hidden_state = torch.cat([global_video_embeds, segments_hidden_state], dim=1)
+ segments_attn_mask = torch.cat([global_video_embeds_mask, segments_attn_mask], dim=1)
+
+ global_region_output = self.video_Qformer.bert(
+ query_embeds=merged_query_tokens,
+ encoder_hidden_states=segments_hidden_state,
+ encoder_attention_mask=segments_attn_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_merged_embeds = global_region_output.last_hidden_state
+
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = global_video_embeds_mask
+
+ global_region_merged_embeds = self.video_global_proj(global_region_merged_embeds)
+ elif self.hierarchical_agg_function == 'global-to-region-early-attn-linear' or self.hierarchical_agg_function == 'global-to-region-early-attn-linear-learned':
+ position_ids = torch.arange(segments_video_embeds.size(2), dtype=torch.long, device=self.video_query_tokens.device)
+ segments_video_embeds = segments_video_embeds.view(batch_size*self.num_segments, segments_video_embeds.size(-3), segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(batch_size*self.num_segments, segments_video_embeds_mask.size(-2), segments_video_embeds_mask.size(-1))
+
+ position_ids = position_ids.unsqueeze(0).expand(batch_size*self.num_segments, -1)
+ frame_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
+ segments_video_embeds = frame_position_embeddings + segments_video_embeds
+ segments_video_embeds_mask = segments_video_embeds_mask * segments_frame_attn_mask.unsqueeze(-1)
+
+ segments_video_embeds = segments_video_embeds.view(segments_video_embeds.size(0), -1, segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+ video_query_tokens = self.video_query_tokens.expand(segments_video_embeds.shape[0], -1, -1)
+
+ global_context = global_video_embeds.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+ global_context = global_context.view(-1, global_context.size(-2), global_context.size(-1))
+
+ num_region_queries = video_query_tokens.size(1)
+ video_query_tokens = torch.cat([video_query_tokens, global_context], dim=1)
+
+ global_region_query_output = self.video_Qformer.bert(
+ query_embeds=video_query_tokens,
+ encoder_hidden_states=segments_video_embeds,
+ encoder_attention_mask=segments_video_embeds_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_merged_embeds = global_region_query_output.last_hidden_state[:, :num_region_queries]
+ global_region_merged_embeds = global_region_merged_embeds.view(batch_size, self.num_segments, global_region_merged_embeds.size(-2), global_region_merged_embeds.size(-1))
+ global_region_merged_embeds = global_region_merged_embeds.mean(1)
+ global_region_merged_embeds = self.video_global_proj(global_region_merged_embeds)
+
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = global_video_embeds_mask
+ elif self.hierarchical_agg_function == 'global-prompts-region-prompts-early-attn-linear' or self.hierarchical_agg_function == 'global-prompts-region-prompts-early-attn-linear-weighted' or self.hierarchical_agg_function == 'global-prompts-region-prompts-early-attn-linear-learned' or self.hierarchical_agg_function == 'global-prompts-region-prompts-segment-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'global-prompts-region-prompts-region-attn-segment-attn-early-attn-linear-learned':
+ # add segment pos embedding
+ position_ids = torch.arange(segments_video_embeds.size(2), dtype=torch.long, device=self.video_query_tokens.device)
+ segments_video_embeds = segments_video_embeds.view(batch_size*self.num_segments, segments_video_embeds.size(-3), segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(batch_size*self.num_segments, segments_video_embeds_mask.size(-2), segments_video_embeds_mask.size(-1))
+
+ position_ids = position_ids.unsqueeze(0).expand(batch_size*self.num_segments, -1)
+ frame_position_embeddings = self.video_frame_position_embedding(position_ids)
+ frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
+ segments_video_embeds = frame_position_embeddings + segments_video_embeds
+ segments_video_embeds_mask = segments_video_embeds_mask * segments_frame_attn_mask.unsqueeze(-1)
+
+ segments_video_embeds = segments_video_embeds.view(segments_video_embeds.size(0), -1, segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+ video_query_tokens = self.video_query_tokens.expand(segments_video_embeds.shape[0], -1, -1)
+ num_region_queries = video_query_tokens.size(1)
+
+ # add short video segment prompts
+ curr_segment_query_tokens = self.segment_region_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_context = global_video_embeds + curr_segment_query_tokens
+ global_context = global_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+
+ global_context = global_context.view(-1, global_context.size(-2), global_context.size(-1))
+
+ if 'region-attn' in self.hierarchical_agg_function:
+ region_context = segments_video_embeds.view(batch_size, self.num_segments, -1, segments_video_embeds.size(-1)).mean(1)
+ region_context = region_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+ region_context = region_context.view(-1, region_context.size(-2), region_context.size(-1))
+ region_attn_queries = self.region_attn_queries.expand(segments_video_embeds.shape[0], -1, -1, -1)
+ region_attn_queries = region_attn_queries.view(region_attn_queries.size(0), -1, region_attn_queries.size(-1))
+ region_context = region_context + region_attn_queries
+ else:
+ region_context = None
+
+ if 'segment-attn' in self.hierarchical_agg_function:
+ segment_attn_queries = self.segment_attn_queries.expand(global_video_embeds.shape[0], -1, -1)
+ segment_context = segments_video_embeds.mean(1)
+ segment_context = segment_context.view(batch_size, self.num_segments, segment_context.size(-1))
+ segment_context = segment_context + segment_attn_queries
+ segment_context = segment_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+ segment_context = segment_context.view(-1, segment_context.size(-2), segment_context.size(-1))
+
+ if region_context is not None:
+ video_query_tokens = torch.cat([video_query_tokens, region_context, segment_context, global_context], dim=1)
+ else:
+ video_query_tokens = torch.cat([video_query_tokens, segment_context, global_context], dim=1)
+ else:
+ video_query_tokens = torch.cat([video_query_tokens, global_context], dim=1)
+
+ global_region_query_output = self.video_Qformer.bert(
+ query_embeds=video_query_tokens,
+ encoder_hidden_states=segments_video_embeds,
+ encoder_attention_mask=segments_video_embeds_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_segment_embeds = global_region_query_output.last_hidden_state[:, :num_region_queries]
+ global_region_segment_embeds = global_region_segment_embeds.view(batch_size, self.num_segments, global_region_segment_embeds.size(-2), global_region_segment_embeds.size(-1))
+
+ # add segment pos embedding
+ position_ids = torch.arange(self.num_segments, dtype=torch.long, device=segments_video_embeds.device)
+ position_ids = position_ids.unsqueeze(0).expand(len(global_video_embeds), -1)
+ segments_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ segments_position_embeddings = segments_position_embeddings.unsqueeze(-2)
+ segments_hidden_state = segments_position_embeddings + global_region_segment_embeds
+ segments_hidden_state = einops.rearrange(segments_hidden_state, 'b t q h -> b (t q) h',b=len(segments_hidden_state),t=self.num_segments)
+ #segments_attn_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+
+ segments_attn_mask = torch.ones(segments_hidden_state.size()[:-1], dtype=torch.long).to(segments_hidden_state.device)
+
+ merged_query_tokens = self.global_region_prompts.expand(len(global_video_embeds), -1, -1)
+ segments_hidden_state = torch.cat([global_video_embeds, segments_hidden_state], dim=1)
+ segments_attn_mask = torch.cat([global_video_embeds_mask, segments_attn_mask], dim=1)
+
+ global_region_output = self.video_Qformer.bert(
+ query_embeds=merged_query_tokens,
+ encoder_hidden_states=segments_hidden_state,
+ encoder_attention_mask=segments_attn_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_merged_embeds = global_region_output.last_hidden_state
+ global_region_merged_embeds = self.video_global_proj(global_region_merged_embeds)
+
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = global_video_embeds_mask
+ elif self.hierarchical_agg_function == 'global-to-region-prompts-early-attn-linear' or self.hierarchical_agg_function == 'global-to-region-prompts-early-attn-linear-learned':
+ position_ids = torch.arange(segments_video_embeds.size(2), dtype=torch.long, device=self.video_query_tokens.device)
+ segments_video_embeds = segments_video_embeds.view(batch_size*self.num_segments, segments_video_embeds.size(-3), segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(batch_size*self.num_segments, segments_video_embeds_mask.size(-2), segments_video_embeds_mask.size(-1))
+
+ position_ids = position_ids.unsqueeze(0).expand(batch_size*self.num_segments, -1)
+ frame_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
+ segments_video_embeds = frame_position_embeddings + segments_video_embeds
+ segments_video_embeds_mask = segments_video_embeds_mask * segments_frame_attn_mask.unsqueeze(-1)
+
+ segments_video_embeds = segments_video_embeds.view(segments_video_embeds.size(0), -1, segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+ video_query_tokens = self.video_query_tokens.expand(segments_video_embeds.shape[0], -1, -1)
+
+ curr_segment_query_tokens = self.segment_region_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_context = global_video_embeds + curr_segment_query_tokens
+
+ global_context = global_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+ global_context = global_context.view(-1, global_context.size(-2), global_context.size(-1))
+
+ num_region_queries = video_query_tokens.size(1)
+ video_query_tokens = torch.cat([video_query_tokens, global_context], dim=1)
+
+ global_region_query_output = self.video_Qformer.bert(
+ query_embeds=video_query_tokens,
+ encoder_hidden_states=segments_video_embeds,
+ encoder_attention_mask=segments_video_embeds_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_merged_embeds = global_region_query_output.last_hidden_state[:, :num_region_queries]
+ global_region_merged_embeds = global_region_merged_embeds.view(batch_size, self.num_segments, global_region_merged_embeds.size(-2), global_region_merged_embeds.size(-1))
+ global_region_merged_embeds = global_region_merged_embeds.mean(1)
+ global_region_merged_embeds = self.video_global_proj(global_region_merged_embeds)
+
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = global_video_embeds_mask
+
+ elif self.hierarchical_agg_function == 'top-down-context-segment-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'top-down-context-region-attn-segment-attn-early-attn-linear-learned':
+ # add segment pos embedding
+ position_ids = torch.arange(segments_video_embeds.size(2), dtype=torch.long, device=self.video_query_tokens.device)
+ segments_video_embeds = segments_video_embeds.view(batch_size*self.num_segments, segments_video_embeds.size(-3), segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(batch_size*self.num_segments, segments_video_embeds_mask.size(-2), segments_video_embeds_mask.size(-1))
+
+ position_ids = position_ids.unsqueeze(0).expand(batch_size*self.num_segments, -1)
+ frame_position_embeddings = self.video_frame_position_embedding(position_ids)
+ frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
+ segments_video_embeds = frame_position_embeddings + segments_video_embeds
+ segments_video_embeds_mask = segments_video_embeds_mask * segments_frame_attn_mask.unsqueeze(-1)
+
+ segments_video_embeds = segments_video_embeds.view(segments_video_embeds.size(0), -1, segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+ video_query_tokens = self.video_query_tokens.expand(segments_video_embeds.shape[0], -1, -1)
+ num_region_queries = video_query_tokens.size(1)
+
+ # add short video segment prompts
+ curr_segment_query_tokens = self.segment_region_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_context = global_video_embeds + curr_segment_query_tokens
+
+ global_context = global_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+ global_context = global_context.view(-1, global_context.size(-2), global_context.size(-1))
+ global_context_attn_mask = global_video_embeds_mask.unsqueeze(1).repeat(1, self.num_segments, 1)
+ global_context_attn_mask = global_context_attn_mask.view(-1, global_context_attn_mask.size(-1))
+
+ segment_attn_queries = self.segment_attn_queries.expand(global_video_embeds.shape[0], -1, -1)
+ segment_context = segments_video_embeds.mean(1)
+ segment_context = segment_context.view(batch_size, self.num_segments, segment_context.size(-1))
+ segment_context = segment_context + segment_attn_queries
+ segment_context = segment_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+ segment_context = segment_context.view(-1, segment_context.size(-2), segment_context.size(-1))
+ segment_context_attn_mask = torch.ones(segment_context.size()[:-1], dtype=torch.long).to(segment_context.device)
+
+ if 'region-attn' in self.hierarchical_agg_function:
+ region_context = segments_video_embeds.view(batch_size, self.num_segments, -1, segments_video_embeds.size(-1)).mean(1)
+ region_context = region_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+ region_context = region_context.view(-1, region_context.size(-2), region_context.size(-1))
+ region_attn_queries = self.region_attn_queries.expand(segments_video_embeds.shape[0], -1, -1, -1)
+ region_attn_queries = region_attn_queries.view(region_attn_queries.size(0), -1, region_attn_queries.size(-1))
+ region_context = region_context + region_attn_queries
+
+ region_context_attn_mask = segments_video_embeds_mask.clone()
+
+ segments_video_embeds = torch.cat([segments_video_embeds, region_context, segment_context, global_context], dim=1)
+ segments_video_embeds_mask = torch.cat([segments_video_embeds_mask, region_context_attn_mask, segment_context_attn_mask, global_context_attn_mask], dim=1)
+ else:
+ segments_video_embeds = torch.cat([segments_video_embeds, segment_context, global_context], dim=1)
+ segments_video_embeds_mask = torch.cat([segments_video_embeds_mask, segment_context_attn_mask, global_context_attn_mask], dim=1)
+
+ global_region_query_output = self.video_Qformer.bert(
+ query_embeds=video_query_tokens,
+ encoder_hidden_states=segments_video_embeds,
+ encoder_attention_mask=segments_video_embeds_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_segment_embeds = global_region_query_output.last_hidden_state[:, :num_region_queries]
+ global_region_segment_embeds = global_region_segment_embeds.view(batch_size, self.num_segments, global_region_segment_embeds.size(-2), global_region_segment_embeds.size(-1))
+
+ # add segment pos embedding
+ position_ids = torch.arange(self.num_segments, dtype=torch.long, device=segments_video_embeds.device)
+ position_ids = position_ids.unsqueeze(0).expand(len(global_video_embeds), -1)
+ segments_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ segments_position_embeddings = segments_position_embeddings.unsqueeze(-2)
+ segments_hidden_state = segments_position_embeddings + global_region_segment_embeds
+ segments_hidden_state = einops.rearrange(segments_hidden_state, 'b t q h -> b (t q) h',b=len(segments_hidden_state),t=self.num_segments)
+ #segments_attn_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+
+ segments_attn_mask = torch.ones(segments_hidden_state.size()[:-1], dtype=torch.long).to(segments_hidden_state.device)
+
+ merged_query_tokens = self.global_region_prompts.expand(len(global_video_embeds), -1, -1)
+ segments_hidden_state = torch.cat([global_video_embeds, segments_hidden_state], dim=1)
+ segments_attn_mask = torch.cat([global_video_embeds_mask, segments_attn_mask], dim=1)
+
+ global_region_output = self.video_Qformer.bert(
+ query_embeds=merged_query_tokens,
+ encoder_hidden_states=segments_hidden_state,
+ encoder_attention_mask=segments_attn_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_merged_embeds = global_region_output.last_hidden_state
+ global_region_merged_embeds = self.video_global_proj(global_region_merged_embeds)
+
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = global_video_embeds_mask
+ elif self.hierarchical_agg_function == 'global-prompts-region-prompts-segment-spatiotemporal-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'global-prompts-region-prompts-segment-spatiotemporal-prompts-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'global-prompts-region-segment-spatiotemporal-prompts-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'without-global-prompts-region-segment-spatiotemporal-prompts-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'without-top-global-prompts-region-segment-spatiotemporal-prompts-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'without-top-global-prompts-region-segment-full-spatiotemporal-prompts-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'without-top-global-prompts-region-segment-full-dis-spatiotemporal-prompts-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'without-top-final-global-prompts-region-segment-full-dis-spatiotemporal-prompts-attn-early-attn-linear-learned' or self.hierarchical_agg_function == 'proj-without-top-final-global-prompts-region-segment-full-dis-spatiotemporal-prompts-attn-early-attn-linear-learned':
+ # add segment pos embedding
+ position_ids = torch.arange(segments_video_embeds.size(2), dtype=torch.long, device=self.video_query_tokens.device)
+ segments_video_embeds = segments_video_embeds.view(batch_size*self.num_segments, segments_video_embeds.size(-3), segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(batch_size*self.num_segments, segments_video_embeds_mask.size(-2), segments_video_embeds_mask.size(-1))
+
+ position_ids = position_ids.unsqueeze(0).expand(batch_size*self.num_segments, -1)
+ frame_position_embeddings = self.video_frame_position_embedding(position_ids)
+ frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
+ segments_video_embeds = frame_position_embeddings + segments_video_embeds
+ segments_video_embeds_mask = segments_video_embeds_mask * segments_frame_attn_mask.unsqueeze(-1)
+
+ segments_video_embeds = segments_video_embeds.view(segments_video_embeds.size(0), -1, segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+ video_query_tokens = self.video_query_tokens.expand(segments_video_embeds.shape[0], -1, -1)
+ num_region_queries = video_query_tokens.size(1)
+
+ # add short video segment prompts
+ curr_segment_query_tokens = self.segment_region_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_context = global_video_embeds + curr_segment_query_tokens
+
+ global_context = global_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+ global_context = global_context.view(-1, global_context.size(-2), global_context.size(-1))
+
+ if 'proj-' in self.hierarchical_agg_function:
+ global_context = self.global_frame_proj(global_context)
+
+ if 'region-prompts' in self.hierarchical_agg_function:
+ segment_attn_queries = self.segment_attn_queries.expand(global_video_embeds.shape[0], -1, -1)
+ segment_context = segments_video_embeds.mean(1)
+
+ segment_context = segment_context.view(batch_size, self.num_segments, segment_context.size(-1))
+ segment_context = segment_context + segment_attn_queries
+
+ segment_context = segment_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+
+ segment_context = segment_context.view(-1, segment_context.size(-2), segment_context.size(-1))
+
+ video_query_tokens = torch.cat([video_query_tokens, segment_context, global_context], dim=1)
+ else:
+ video_query_tokens = torch.cat([video_query_tokens, global_context], dim=1)
+
+ global_region_query_output = self.video_Qformer.bert(
+ query_embeds=video_query_tokens,
+ encoder_hidden_states=segments_video_embeds,
+ encoder_attention_mask=segments_video_embeds_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_segment_embeds = global_region_query_output.last_hidden_state[:, :num_region_queries]
+ global_region_segment_embeds = global_region_segment_embeds.view(batch_size, self.num_segments, global_region_segment_embeds.size(-2), global_region_segment_embeds.size(-1))
+
+ # add segment pos embedding
+ position_ids = torch.arange(self.num_segments, dtype=torch.long, device=segments_video_embeds.device)
+ position_ids = position_ids.unsqueeze(0).expand(len(global_video_embeds), -1)
+ segments_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ segments_position_embeddings = segments_position_embeddings.unsqueeze(-2)
+ segments_hidden_state = segments_position_embeddings + global_region_segment_embeds
+
+ segment_temporal_context = segments_hidden_state.mean(1)
+ segment_spatial_context = segments_hidden_state.mean(2)
+
+ if 'spatiotemporal-prompts' in self.hierarchical_agg_function:
+
+ if 'full-dis-spatiotemporal' in self.hierarchical_agg_function:
+ temporal_context_prompts = self.temporal_segment_prompts.unsqueeze(-2).expand(global_video_embeds.shape[0], -1, segments_hidden_state.size(-2), -1)
+ spatial_context_prompts = self.spatial_segment_prompts.unsqueeze(0).expand(global_video_embeds.shape[0], self.num_segments, -1, -1)
+
+ final_context = segments_hidden_state + temporal_context_prompts + spatial_context_prompts
+ final_context = final_context.view(final_context.size(0), -1, final_context.size(-1))
+
+ elif 'full-spatiotemporal' not in self.hierarchical_agg_function:
+ temporal_context_prompts = self.temporal_segment_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ spatial_context_prompts = self.spatial_segment_prompts.expand(global_video_embeds.shape[0], -1, -1)
+
+ segment_temporal_context = segment_temporal_context + spatial_context_prompts
+ segment_spatial_context = segment_spatial_context + temporal_context_prompts
+ else:
+ spatial_context_prompts = self.spatial_segment_prompts.unsqueeze(0).expand(global_video_embeds.shape[0], -1, -1, -1)
+ segment_spatial_context = segments_hidden_state + spatial_context_prompts
+ segment_spatial_context = segment_spatial_context.view(segment_spatial_context.size(0), -1, segment_spatial_context.size(-1))
+
+ if 'without' in self.hierarchical_agg_function and 'full-dis-spatiotemporal' in self.hierarchical_agg_function:
+ final_top_down_context = final_context
+ elif 'without' in self.hierarchical_agg_function and 'full-spatiotemporal' not in self.hierarchical_agg_function:
+ final_top_down_context = torch.cat([segment_temporal_context, segment_spatial_context], dim=1)
+ elif 'without' in self.hierarchical_agg_function and 'full-spatiotemporal' in self.hierarchical_agg_function:
+ final_top_down_context = segment_spatial_context
+ else:
+ final_top_down_context = torch.cat([global_video_embeds, segment_temporal_context, segment_spatial_context], dim=1)
+ final_top_down_context_mask = torch.ones(final_top_down_context.size()[:-1], dtype=torch.long).to(final_top_down_context.device)
+
+ if 'without-top' in self.hierarchical_agg_function:
+ merged_query_tokens = self.video_query_tokens.expand(len(global_video_embeds), -1, -1)
+ else:
+ merged_query_tokens = self.global_region_prompts.expand(len(global_video_embeds), -1, -1)
+
+ if 'final-global-prompts' in self.hierarchical_agg_function:
+ global_to_segment_prompts = self.global_to_segment_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_to_segment_context = global_video_embeds + global_to_segment_prompts
+
+ if 'proj-' in self.hierarchical_agg_function:
+ global_to_segment_context = self.global_segment_proj(global_to_segment_context)
+
+ merged_query_tokens = torch.cat([merged_query_tokens, global_to_segment_context], dim=1)
+
+ global_region_output = self.video_Qformer.bert(
+ query_embeds=merged_query_tokens,
+ encoder_hidden_states=final_top_down_context,
+ encoder_attention_mask=final_top_down_context_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_merged_embeds = global_region_output.last_hidden_state
+
+ if 'final-global-prompts' in self.hierarchical_agg_function:
+ global_region_merged_embeds = global_region_merged_embeds[:, :num_region_queries]
+
+ global_region_merged_embeds = self.video_global_proj(global_region_merged_embeds)
+
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = global_video_embeds_mask
+ elif self.hierarchical_agg_function == 'without-context-top-final-global-prompts-region-segment-full-dis-spatiotemporal-prompts-attn-early-attn-linear-learned':
+ # add segment pos embedding
+ position_ids = torch.arange(segments_video_embeds.size(2), dtype=torch.long, device=self.video_query_tokens.device)
+ segments_video_embeds = segments_video_embeds.view(batch_size*self.num_segments, segments_video_embeds.size(-3), segments_video_embeds.size(-2), segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(batch_size*self.num_segments, segments_video_embeds_mask.size(-2), segments_video_embeds_mask.size(-1))
+
+ position_ids = position_ids.unsqueeze(0).expand(batch_size*self.num_segments, -1)
+ frame_position_embeddings = self.video_frame_position_embedding(position_ids)
+ frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
+ segments_video_embeds = frame_position_embeddings + segments_video_embeds
+ segments_video_embeds_mask = segments_video_embeds_mask * segments_frame_attn_mask.unsqueeze(-1)
+
+ segments_video_embeds = segments_video_embeds.view(segments_video_embeds.size(0), -1, segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+ video_query_tokens = self.video_query_tokens.expand(segments_video_embeds.shape[0], -1, -1)
+ num_region_queries = video_query_tokens.size(1)
+
+ # add short video segment prompts
+ curr_segment_query_tokens = self.segment_region_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_context = curr_segment_query_tokens
+
+ global_context = global_context.unsqueeze(1).repeat(1, self.num_segments, 1, 1)
+
+ global_context = global_context.view(-1, global_context.size(-2), global_context.size(-1))
+
+ video_query_tokens = torch.cat([video_query_tokens, global_context], dim=1)
+
+ global_region_query_output = self.video_Qformer.bert(
+ query_embeds=video_query_tokens,
+ encoder_hidden_states=segments_video_embeds,
+ encoder_attention_mask=segments_video_embeds_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_segment_embeds = global_region_query_output.last_hidden_state[:, :num_region_queries]
+ global_region_segment_embeds = global_region_segment_embeds.view(batch_size, self.num_segments, global_region_segment_embeds.size(-2), global_region_segment_embeds.size(-1))
+
+ # add segment pos embedding
+ position_ids = torch.arange(self.num_segments, dtype=torch.long, device=segments_video_embeds.device)
+ position_ids = position_ids.unsqueeze(0).expand(len(global_video_embeds), -1)
+ segments_position_embeddings = self.video_frame_position_embedding(position_ids)
+
+ segments_position_embeddings = segments_position_embeddings.unsqueeze(-2)
+ segments_hidden_state = segments_position_embeddings + global_region_segment_embeds
+
+ segment_temporal_context = segments_hidden_state.mean(1)
+ segment_spatial_context = segments_hidden_state.mean(2)
+
+ temporal_context_prompts = self.temporal_segment_prompts.unsqueeze(-2).expand(global_video_embeds.shape[0], -1, segments_hidden_state.size(-2), -1)
+ spatial_context_prompts = self.spatial_segment_prompts.unsqueeze(0).expand(global_video_embeds.shape[0], self.num_segments, -1, -1)
+ final_context = segments_hidden_state + temporal_context_prompts + spatial_context_prompts
+ final_context = final_context.view(final_context.size(0), -1, final_context.size(-1))
+
+ final_top_down_context = final_context
+ final_top_down_context_mask = torch.ones(final_top_down_context.size()[:-1], dtype=torch.long).to(final_top_down_context.device)
+
+ if 'without-top' in self.hierarchical_agg_function:
+ merged_query_tokens = self.video_query_tokens.expand(len(global_video_embeds), -1, -1)
+ else:
+ merged_query_tokens = self.global_region_prompts.expand(len(global_video_embeds), -1, -1)
+
+ if 'final-global-prompts' in self.hierarchical_agg_function:
+ global_to_segment_prompts = self.global_to_segment_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_to_segment_context = global_to_segment_prompts
+ merged_query_tokens = torch.cat([merged_query_tokens, global_to_segment_context], dim=1)
+
+ global_region_output = self.video_Qformer.bert(
+ query_embeds=merged_query_tokens,
+ encoder_hidden_states=final_top_down_context,
+ encoder_attention_mask=final_top_down_context_mask,
+ #output_attentions=True,
+ return_dict=True,
+ )
+ global_region_merged_embeds = global_region_output.last_hidden_state
+
+ if 'final-global-prompts' in self.hierarchical_agg_function:
+ global_region_merged_embeds = global_region_merged_embeds[:, :num_region_queries]
+
+ global_region_merged_embeds = self.video_global_proj(global_region_merged_embeds)
+
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = global_video_embeds_mask
+
+ elif self.hierarchical_agg_function == 'ablation-concat-linear':
+ #position_ids = torch.arange(segments_video_embeds.size(2), dtype=torch.long, device=self.video_query_tokens.device)
+ segments_video_embeds = segments_video_embeds.view(segments_video_embeds.size(0), -1, segments_video_embeds.size(-1))
+ segments_video_embeds_mask = segments_video_embeds_mask.view(segments_video_embeds_mask.size(0), -1)
+
+ curr_segment_query_tokens = self.segment_region_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ temporal_context_prompts = self.temporal_segment_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ spatial_context_prompts = self.spatial_segment_prompts.expand(global_video_embeds.shape[0], -1, -1)
+ global_to_segment_prompts = self.global_to_segment_prompts.expand(global_video_embeds.shape[0], -1, -1)
+
+ global_region_merged_embeds = torch.cat([curr_segment_query_tokens, temporal_context_prompts, spatial_context_prompts, global_to_segment_prompts], dim=1)
+
+ global_region_merged_embeds = self.video_global_proj(global_region_merged_embeds)
+ merged_video_embeds = global_video_embeds
+ merged_video_embeds_mask = global_video_embeds_mask[:, 0:1].expand(-1, global_region_merged_embeds.size(1)+global_video_embeds_mask.size(1))
+
+ merged_video_embeds = self.llama_proj(merged_video_embeds)
+ merged_video_embeds = merged_video_embeds + self.global_region_embed_weight * global_region_merged_embeds
+
+ return merged_video_embeds, merged_video_embeds_mask
+
+ @classmethod
+ def from_config(cls, cfg):
+ vit_model = cfg.get("vit_model", "eva_clip_g")
+ q_former_model = cfg.get("q_former_model", "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth")
+ img_size = cfg.get("image_size")
+ num_query_token = cfg.get("num_query_token")
+ llama_model = cfg.get("llama_model")
+
+ drop_path_rate = cfg.get("drop_path_rate", 0)
+ use_grad_checkpoint = cfg.get("use_grad_checkpoint", False)
+ vit_precision = cfg.get("vit_precision", "fp16")
+ freeze_vit = cfg.get("freeze_vit", True)
+ freeze_qformer = cfg.get("freeze_qformer", True)
+ low_resource = cfg.get("low_resource", False)
+ device_8bit = cfg.get("device_8bit", 0)
+
+ prompt_path = cfg.get("prompt_path", "")
+ prompt_template = cfg.get("prompt_template", "")
+ max_txt_len = cfg.get("max_txt_len", 32)
+ end_sym = cfg.get("end_sym", '\n')
+
+ frozen_llama_proj = cfg.get("frozen_llama_proj", True)
+ frozen_video_Qformer = cfg.get("frozen_video_Qformer", True)
+ frozen_audio_Qformer = cfg.get("frozen_audio_Qformer", True)
+
+ llama_proj_model = cfg.get("llama_proj_model", '')
+
+ fusion_header_type = cfg.get("fusion_header_type", 'seqTransf')
+ max_frame_pos = cfg.get("max_frame_pos", 32)
+ fusion_head_layers = cfg.get("fusion_head_layers", 2)
+ num_video_query_token = cfg.get("num_video_query_token", 32)
+
+ equip_audio_branch= cfg.get("equip_audio_branch", True)
+ num_audio_query_token = cfg.get("num_audio_query_token", 8)
+ imagebind_ckpt_path = cfg.get("imagebind_ckpt_path", '/mnt/workspace/ckpt')
+ model = cls(
+ vit_model=vit_model,
+ q_former_model=q_former_model,
+ img_size=img_size,
+ drop_path_rate=drop_path_rate,
+ use_grad_checkpoint=use_grad_checkpoint,
+ vit_precision=vit_precision,
+ freeze_vit=freeze_vit,
+ freeze_qformer=freeze_qformer,
+ num_query_token=num_query_token,
+ llama_model=llama_model,
+ prompt_path=prompt_path,
+ prompt_template=prompt_template,
+ max_txt_len=max_txt_len,
+ end_sym=end_sym,
+ low_resource=low_resource,
+ device_8bit=device_8bit,
+ fusion_header_type=fusion_header_type,
+ max_frame_pos=max_frame_pos,
+ fusion_head_layers=fusion_head_layers,
+ frozen_llama_proj=frozen_llama_proj,
+ frozen_video_Qformer=frozen_video_Qformer,
+ frozen_audio_Qformer=frozen_audio_Qformer,
+ num_video_query_token=num_video_query_token,
+ num_audio_query_token = num_audio_query_token,
+ imagebind_ckpt_path = imagebind_ckpt_path,
+ equip_audio_branch = equip_audio_branch,
+ llama_proj_model = llama_proj_model
+ )
+
+ ckpt_path = cfg.get("ckpt", "") # load weights of MiniGPT-4
+ if ckpt_path:
+ print("Load first Checkpoint: {}".format(ckpt_path))
+ ckpt = torch.load(ckpt_path, map_location="cpu")
+ msg = model.load_state_dict(ckpt['model'], strict=False)
+ ckpt_path_2 = cfg.get("ckpt_2", "")
+ if ckpt_path_2:
+ print("Load second Checkpoint: {}".format(ckpt_path_2))
+ ckpt = torch.load(ckpt_path_2, map_location="cpu")
+ msg = model.load_state_dict(ckpt['model'], strict=False)
+ return model
diff --git a/global_local/processors/.ipynb_checkpoints/video_processor-checkpoint.py b/global_local/processors/.ipynb_checkpoints/video_processor-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac0f3a9d56aef53de0ee9f2ee3d6d2cda0ea1674
--- /dev/null
+++ b/global_local/processors/.ipynb_checkpoints/video_processor-checkpoint.py
@@ -0,0 +1,243 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import torch
+from video_llama.common.registry import registry
+from decord import VideoReader
+import decord
+import numpy as np
+from video_llama.processors import transforms_video
+from video_llama.processors.base_processor import BaseProcessor
+from video_llama.processors.randaugment import VideoRandomAugment
+from video_llama.processors import functional_video as F
+from omegaconf import OmegaConf
+from torchvision import transforms
+import random as rnd
+MAX_INT = registry.get("MAX_INT")
+
+def load_video(video_path, n_frms=MAX_INT, height=-1, width=-1, sampling="uniform"):
+ vr = VideoReader(uri=video_path, height=height, width=width)
+
+ vlen = len(vr)
+ start, end = 0, vlen
+
+ n_frms = min(n_frms, vlen)
+
+ if sampling == "uniform":
+ indices = np.arange(start, end, vlen / n_frms).astype(int).tolist()
+ elif sampling == "headtail":
+ indices_h = sorted(rnd.sample(range(vlen // 2), n_frms // 2))
+ indices_t = sorted(rnd.sample(range(vlen // 2, vlen), n_frms // 2))
+ indices = indices_h + indices_t
+ else:
+ raise NotImplementedError
+
+ # get_batch -> T, H, W, C
+ print(video_path)
+ print(indices)
+ print(vr.get_batch(indices))
+
+ frms = vr.get_batch(indices).permute(3, 0, 1, 2).float() # (C, T, H, W)
+ # print(111)
+ return frms
+
+class AlproVideoBaseProcessor(BaseProcessor):
+ def __init__(self, mean=None, std=None, n_frms=MAX_INT):
+ if mean is None:
+ mean = (0.48145466, 0.4578275, 0.40821073)
+ if std is None:
+ std = (0.26862954, 0.26130258, 0.27577711)
+
+ self.normalize = transforms_video.NormalizeVideo(mean, std)
+
+ self.n_frms = n_frms
+
+
+class ToUint8(object):
+ def __init__(self):
+ pass
+
+ def __call__(self, tensor):
+ return tensor.to(torch.uint8)
+
+ def __repr__(self):
+ return self.__class__.__name__
+
+
+class ToTHWC(object):
+ """
+ Args:
+ clip (torch.tensor, dtype=torch.uint8): Size is (C, T, H, W)
+ Return:
+ clip (torch.tensor, dtype=torch.float): Size is (T, H, W, C)
+ """
+
+ def __init__(self):
+ pass
+
+ def __call__(self, tensor):
+ return tensor.permute(1, 2, 3, 0)
+
+ def __repr__(self):
+ return self.__class__.__name__
+
+
+class ResizeVideo(object):
+ def __init__(self, target_size, interpolation_mode="bilinear"):
+ self.target_size = target_size
+ self.interpolation_mode = interpolation_mode
+
+ def __call__(self, clip):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ Returns:
+ torch.tensor: central cropping of video clip. Size is
+ (C, T, crop_size, crop_size)
+ """
+ return F.resize(clip, self.target_size, self.interpolation_mode)
+
+ def __repr__(self):
+ return self.__class__.__name__ + "(resize_size={0})".format(self.target_size)
+
+
+@registry.register_processor("alpro_video_train")
+class AlproVideoTrainProcessor(AlproVideoBaseProcessor):
+ def __init__(
+ self,
+ image_size=384,
+ mean=None,
+ std=None,
+ min_scale=0.5,
+ max_scale=1.0,
+ n_frms=MAX_INT,
+ ):
+ super().__init__(mean=mean, std=std, n_frms=n_frms)
+
+ self.image_size = image_size
+
+ self.transform = transforms.Compose(
+ [
+ # Video size is (C, T, H, W)
+ transforms_video.RandomResizedCropVideo(
+ image_size,
+ scale=(min_scale, max_scale),
+ interpolation_mode="bicubic",
+ ),
+ transforms_video.RandomHorizontalFlipVideo(),
+ ToTHWC(), # C, T, H, W -> T, H, W, C
+ VideoRandomAugment(
+ 2,
+ 5,
+ augs=[
+ "Identity",
+ "AutoContrast",
+ "Brightness",
+ "Sharpness",
+ "Equalize",
+ "ShearX",
+ "ShearY",
+ "TranslateX",
+ "TranslateY",
+ "Rotate",
+ ],
+ ),
+ ToUint8(),
+ transforms_video.ToTensorVideo(), # T, H, W, C -> C, T, H, W
+ self.normalize,
+ ]
+ )
+
+ def __call__(self, vpath):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ Returns:
+ torch.tensor: video clip after transforms. Size is (C, T, size, size).
+ """
+ clip = load_video(
+ video_path=vpath,
+ n_frms=self.n_frms,
+ height=self.image_size,
+ width=self.image_size,
+ sampling="headtail",
+ )
+
+ return self.transform(clip)
+
+ @classmethod
+ def from_config(cls, cfg=None):
+ if cfg is None:
+ cfg = OmegaConf.create()
+
+ image_size = cfg.get("image_size", 256)
+
+ mean = cfg.get("mean", None)
+ std = cfg.get("std", None)
+
+ min_scale = cfg.get("min_scale", 0.5)
+ max_scale = cfg.get("max_scale", 1.0)
+
+ n_frms = cfg.get("n_frms", MAX_INT)
+
+ return cls(
+ image_size=image_size,
+ mean=mean,
+ std=std,
+ min_scale=min_scale,
+ max_scale=max_scale,
+ n_frms=n_frms,
+ )
+
+
+@registry.register_processor("alpro_video_eval")
+class AlproVideoEvalProcessor(AlproVideoBaseProcessor):
+ def __init__(self, image_size=256, mean=None, std=None, n_frms=MAX_INT):
+ super().__init__(mean=mean, std=std, n_frms=n_frms)
+
+ self.image_size = image_size
+
+ # Input video size is (C, T, H, W)
+ self.transform = transforms.Compose(
+ [
+ # frames will be resized during decord loading.
+ ToUint8(), # C, T, H, W
+ ToTHWC(), # T, H, W, C
+ transforms_video.ToTensorVideo(), # C, T, H, W
+ self.normalize, # C, T, H, W
+ ]
+ )
+
+ def __call__(self, vpath):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ Returns:
+ torch.tensor: video clip after transforms. Size is (C, T, size, size).
+ """
+ clip = load_video(
+ video_path=vpath,
+ n_frms=self.n_frms,
+ height=self.image_size,
+ width=self.image_size,
+ )
+
+ return self.transform(clip)
+
+ @classmethod
+ def from_config(cls, cfg=None):
+ if cfg is None:
+ cfg = OmegaConf.create()
+
+ image_size = cfg.get("image_size", 256)
+
+ mean = cfg.get("mean", None)
+ std = cfg.get("std", None)
+
+ n_frms = cfg.get("n_frms", MAX_INT)
+
+ return cls(image_size=image_size, mean=mean, std=std, n_frms=n_frms)
diff --git a/global_local/processors/__init__.py b/global_local/processors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8123a0fb1396d094754b509953aa711baffa8b5e
--- /dev/null
+++ b/global_local/processors/__init__.py
@@ -0,0 +1,38 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from global_local.processors.base_processor import BaseProcessor
+from global_local.processors.blip_processors import (
+ Blip2ImageTrainProcessor,
+ Blip2ImageEvalProcessor,
+ BlipCaptionProcessor,
+)
+from global_local.processors.video_processor import (
+ AlproVideoTrainProcessor,
+ AlproVideoEvalProcessor
+)
+from global_local.common.registry import registry
+
+__all__ = [
+ "BaseProcessor",
+ "Blip2ImageTrainProcessor",
+ "Blip2ImageEvalProcessor",
+ "BlipCaptionProcessor",
+ "AlproVideoTrainProcessor",
+ "AlproVideoEvalProcessor",
+]
+
+
+def load_processor(name, cfg=None):
+ """
+ Example
+
+ >>> processor = load_processor("alpro_video_train", cfg=None)
+ """
+ processor = registry.get_processor_class(name).from_config(cfg)
+
+ return processor
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diff --git a/global_local/processors/base_processor.py b/global_local/processors/base_processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..39b33cdf8fcd97cfd3e4a5fbece6593357af9d41
--- /dev/null
+++ b/global_local/processors/base_processor.py
@@ -0,0 +1,26 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from omegaconf import OmegaConf
+
+
+class BaseProcessor:
+ def __init__(self):
+ self.transform = lambda x: x
+ return
+
+ def __call__(self, item):
+ return self.transform(item)
+
+ @classmethod
+ def from_config(cls, cfg=None):
+ return cls()
+
+ def build(self, **kwargs):
+ cfg = OmegaConf.create(kwargs)
+
+ return self.from_config(cfg)
diff --git a/global_local/processors/blip_processors.py b/global_local/processors/blip_processors.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b26ecb825dea1147320e1e923b5bd38e6102b9c
--- /dev/null
+++ b/global_local/processors/blip_processors.py
@@ -0,0 +1,142 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import re
+
+from global_local.common.registry import registry
+from global_local.processors.base_processor import BaseProcessor
+from global_local.processors.randaugment import RandomAugment
+from omegaconf import OmegaConf
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+
+
+class BlipImageBaseProcessor(BaseProcessor):
+ def __init__(self, mean=None, std=None):
+ if mean is None:
+ mean = (0.48145466, 0.4578275, 0.40821073)
+ if std is None:
+ std = (0.26862954, 0.26130258, 0.27577711)
+
+ self.normalize = transforms.Normalize(mean, std)
+
+
+@registry.register_processor("blip_caption")
+class BlipCaptionProcessor(BaseProcessor):
+ def __init__(self, prompt="", max_words=50):
+ self.prompt = prompt
+ self.max_words = max_words
+
+ def __call__(self, caption):
+ caption = self.prompt + self.pre_caption(caption)
+
+ return caption
+
+ @classmethod
+ def from_config(cls, cfg=None):
+ if cfg is None:
+ cfg = OmegaConf.create()
+
+ prompt = cfg.get("prompt", "")
+ max_words = cfg.get("max_words", 50)
+
+ return cls(prompt=prompt, max_words=max_words)
+
+ def pre_caption(self, caption):
+ caption = re.sub(
+ r"([.!\"()*#:;~])",
+ " ",
+ caption.lower(),
+ )
+ caption = re.sub(
+ r"\s{2,}",
+ " ",
+ caption,
+ )
+ caption = caption.rstrip("\n")
+ caption = caption.strip(" ")
+
+ # truncate caption
+ caption_words = caption.split(" ")
+ if len(caption_words) > self.max_words:
+ caption = " ".join(caption_words[: self.max_words])
+
+ return caption
+
+
+@registry.register_processor("blip2_image_train")
+class Blip2ImageTrainProcessor(BlipImageBaseProcessor):
+ def __init__(self, image_size=224, mean=None, std=None, min_scale=0.5, max_scale=1.0):
+ super().__init__(mean=mean, std=std)
+
+ self.transform = transforms.Compose(
+ [
+ transforms.RandomResizedCrop(
+ image_size,
+ scale=(min_scale, max_scale),
+ interpolation=InterpolationMode.BICUBIC,
+ ),
+ transforms.ToTensor(),
+ self.normalize,
+ ]
+ )
+
+ def __call__(self, item):
+ return self.transform(item)
+
+ @classmethod
+ def from_config(cls, cfg=None):
+ if cfg is None:
+ cfg = OmegaConf.create()
+
+ image_size = cfg.get("image_size", 224)
+
+ mean = cfg.get("mean", None)
+ std = cfg.get("std", None)
+
+ min_scale = cfg.get("min_scale", 0.5)
+ max_scale = cfg.get("max_scale", 1.0)
+
+ return cls(
+ image_size=image_size,
+ mean=mean,
+ std=std,
+ min_scale=min_scale,
+ max_scale=max_scale,
+ )
+
+
+@registry.register_processor("blip2_image_eval")
+class Blip2ImageEvalProcessor(BlipImageBaseProcessor):
+ def __init__(self, image_size=224, mean=None, std=None):
+ super().__init__(mean=mean, std=std)
+
+ self.transform = transforms.Compose(
+ [
+ transforms.Resize(
+ (image_size, image_size), interpolation=InterpolationMode.BICUBIC
+ ),
+ transforms.ToTensor(),
+ self.normalize,
+ ]
+ )
+
+ def __call__(self, item):
+ return self.transform(item)
+
+ @classmethod
+ def from_config(cls, cfg=None):
+ if cfg is None:
+ cfg = OmegaConf.create()
+
+ image_size = cfg.get("image_size", 224)
+
+ mean = cfg.get("mean", None)
+ std = cfg.get("std", None)
+
+ return cls(image_size=image_size, mean=mean, std=std)
+
diff --git a/global_local/processors/functional_video.py b/global_local/processors/functional_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..597a29315d4e1a575e7209edb0618eeaf4fc024a
--- /dev/null
+++ b/global_local/processors/functional_video.py
@@ -0,0 +1,121 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import warnings
+
+import torch
+
+
+def _is_tensor_video_clip(clip):
+ if not torch.is_tensor(clip):
+ raise TypeError("clip should be Tensor. Got %s" % type(clip))
+
+ if not clip.ndimension() == 4:
+ raise ValueError("clip should be 4D. Got %dD" % clip.dim())
+
+ return True
+
+
+def crop(clip, i, j, h, w):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ """
+ if len(clip.size()) != 4:
+ raise ValueError("clip should be a 4D tensor")
+ return clip[..., i : i + h, j : j + w]
+
+
+def resize(clip, target_size, interpolation_mode):
+ if len(target_size) != 2:
+ raise ValueError(
+ f"target size should be tuple (height, width), instead got {target_size}"
+ )
+ return torch.nn.functional.interpolate(
+ clip, size=target_size, mode=interpolation_mode, align_corners=False
+ )
+
+
+def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"):
+ """
+ Do spatial cropping and resizing to the video clip
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ i (int): i in (i,j) i.e coordinates of the upper left corner.
+ j (int): j in (i,j) i.e coordinates of the upper left corner.
+ h (int): Height of the cropped region.
+ w (int): Width of the cropped region.
+ size (tuple(int, int)): height and width of resized clip
+ Returns:
+ clip (torch.tensor): Resized and cropped clip. Size is (C, T, H, W)
+ """
+ if not _is_tensor_video_clip(clip):
+ raise ValueError("clip should be a 4D torch.tensor")
+ clip = crop(clip, i, j, h, w)
+ clip = resize(clip, size, interpolation_mode)
+ return clip
+
+
+def center_crop(clip, crop_size):
+ if not _is_tensor_video_clip(clip):
+ raise ValueError("clip should be a 4D torch.tensor")
+ h, w = clip.size(-2), clip.size(-1)
+ th, tw = crop_size
+ if h < th or w < tw:
+ raise ValueError("height and width must be no smaller than crop_size")
+
+ i = int(round((h - th) / 2.0))
+ j = int(round((w - tw) / 2.0))
+ return crop(clip, i, j, th, tw)
+
+
+def to_tensor(clip):
+ """
+ Convert tensor data type from uint8 to float, divide value by 255.0 and
+ permute the dimensions of clip tensor
+ Args:
+ clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C)
+ Return:
+ clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W)
+ """
+ _is_tensor_video_clip(clip)
+ if not clip.dtype == torch.uint8:
+ raise TypeError(
+ "clip tensor should have data type uint8. Got %s" % str(clip.dtype)
+ )
+ return clip.float().permute(3, 0, 1, 2) / 255.0
+
+
+def normalize(clip, mean, std, inplace=False):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be normalized. Size is (C, T, H, W)
+ mean (tuple): pixel RGB mean. Size is (3)
+ std (tuple): pixel standard deviation. Size is (3)
+ Returns:
+ normalized clip (torch.tensor): Size is (C, T, H, W)
+ """
+ if not _is_tensor_video_clip(clip):
+ raise ValueError("clip should be a 4D torch.tensor")
+ if not inplace:
+ clip = clip.clone()
+ mean = torch.as_tensor(mean, dtype=clip.dtype, device=clip.device)
+ std = torch.as_tensor(std, dtype=clip.dtype, device=clip.device)
+ clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None])
+ return clip
+
+
+def hflip(clip):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be normalized. Size is (C, T, H, W)
+ Returns:
+ flipped clip (torch.tensor): Size is (C, T, H, W)
+ """
+ if not _is_tensor_video_clip(clip):
+ raise ValueError("clip should be a 4D torch.tensor")
+ return clip.flip(-1)
diff --git a/global_local/processors/randaugment.py b/global_local/processors/randaugment.py
new file mode 100644
index 0000000000000000000000000000000000000000..7034a49ad5fc63b97910790017432617ff4c6d7b
--- /dev/null
+++ b/global_local/processors/randaugment.py
@@ -0,0 +1,398 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import cv2
+import numpy as np
+
+import torch
+
+
+## aug functions
+def identity_func(img):
+ return img
+
+
+def autocontrast_func(img, cutoff=0):
+ """
+ same output as PIL.ImageOps.autocontrast
+ """
+ n_bins = 256
+
+ def tune_channel(ch):
+ n = ch.size
+ cut = cutoff * n // 100
+ if cut == 0:
+ high, low = ch.max(), ch.min()
+ else:
+ hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
+ low = np.argwhere(np.cumsum(hist) > cut)
+ low = 0 if low.shape[0] == 0 else low[0]
+ high = np.argwhere(np.cumsum(hist[::-1]) > cut)
+ high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0]
+ if high <= low:
+ table = np.arange(n_bins)
+ else:
+ scale = (n_bins - 1) / (high - low)
+ offset = -low * scale
+ table = np.arange(n_bins) * scale + offset
+ table[table < 0] = 0
+ table[table > n_bins - 1] = n_bins - 1
+ table = table.clip(0, 255).astype(np.uint8)
+ return table[ch]
+
+ channels = [tune_channel(ch) for ch in cv2.split(img)]
+ out = cv2.merge(channels)
+ return out
+
+
+def equalize_func(img):
+ """
+ same output as PIL.ImageOps.equalize
+ PIL's implementation is different from cv2.equalize
+ """
+ n_bins = 256
+
+ def tune_channel(ch):
+ hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
+ non_zero_hist = hist[hist != 0].reshape(-1)
+ step = np.sum(non_zero_hist[:-1]) // (n_bins - 1)
+ if step == 0:
+ return ch
+ n = np.empty_like(hist)
+ n[0] = step // 2
+ n[1:] = hist[:-1]
+ table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8)
+ return table[ch]
+
+ channels = [tune_channel(ch) for ch in cv2.split(img)]
+ out = cv2.merge(channels)
+ return out
+
+
+def rotate_func(img, degree, fill=(0, 0, 0)):
+ """
+ like PIL, rotate by degree, not radians
+ """
+ H, W = img.shape[0], img.shape[1]
+ center = W / 2, H / 2
+ M = cv2.getRotationMatrix2D(center, degree, 1)
+ out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
+ return out
+
+
+def solarize_func(img, thresh=128):
+ """
+ same output as PIL.ImageOps.posterize
+ """
+ table = np.array([el if el < thresh else 255 - el for el in range(256)])
+ table = table.clip(0, 255).astype(np.uint8)
+ out = table[img]
+ return out
+
+
+def color_func(img, factor):
+ """
+ same output as PIL.ImageEnhance.Color
+ """
+ ## implementation according to PIL definition, quite slow
+ # degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
+ # out = blend(degenerate, img, factor)
+ # M = (
+ # np.eye(3) * factor
+ # + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor)
+ # )[np.newaxis, np.newaxis, :]
+ M = np.float32(
+ [[0.886, -0.114, -0.114], [-0.587, 0.413, -0.587], [-0.299, -0.299, 0.701]]
+ ) * factor + np.float32([[0.114], [0.587], [0.299]])
+ out = np.matmul(img, M).clip(0, 255).astype(np.uint8)
+ return out
+
+
+def contrast_func(img, factor):
+ """
+ same output as PIL.ImageEnhance.Contrast
+ """
+ mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299]))
+ table = (
+ np.array([(el - mean) * factor + mean for el in range(256)])
+ .clip(0, 255)
+ .astype(np.uint8)
+ )
+ out = table[img]
+ return out
+
+
+def brightness_func(img, factor):
+ """
+ same output as PIL.ImageEnhance.Contrast
+ """
+ table = (np.arange(256, dtype=np.float32) * factor).clip(0, 255).astype(np.uint8)
+ out = table[img]
+ return out
+
+
+def sharpness_func(img, factor):
+ """
+ The differences the this result and PIL are all on the 4 boundaries, the center
+ areas are same
+ """
+ kernel = np.ones((3, 3), dtype=np.float32)
+ kernel[1][1] = 5
+ kernel /= 13
+ degenerate = cv2.filter2D(img, -1, kernel)
+ if factor == 0.0:
+ out = degenerate
+ elif factor == 1.0:
+ out = img
+ else:
+ out = img.astype(np.float32)
+ degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :]
+ out[1:-1, 1:-1, :] = degenerate + factor * (out[1:-1, 1:-1, :] - degenerate)
+ out = out.astype(np.uint8)
+ return out
+
+
+def shear_x_func(img, factor, fill=(0, 0, 0)):
+ H, W = img.shape[0], img.shape[1]
+ M = np.float32([[1, factor, 0], [0, 1, 0]])
+ out = cv2.warpAffine(
+ img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
+ ).astype(np.uint8)
+ return out
+
+
+def translate_x_func(img, offset, fill=(0, 0, 0)):
+ """
+ same output as PIL.Image.transform
+ """
+ H, W = img.shape[0], img.shape[1]
+ M = np.float32([[1, 0, -offset], [0, 1, 0]])
+ out = cv2.warpAffine(
+ img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
+ ).astype(np.uint8)
+ return out
+
+
+def translate_y_func(img, offset, fill=(0, 0, 0)):
+ """
+ same output as PIL.Image.transform
+ """
+ H, W = img.shape[0], img.shape[1]
+ M = np.float32([[1, 0, 0], [0, 1, -offset]])
+ out = cv2.warpAffine(
+ img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
+ ).astype(np.uint8)
+ return out
+
+
+def posterize_func(img, bits):
+ """
+ same output as PIL.ImageOps.posterize
+ """
+ out = np.bitwise_and(img, np.uint8(255 << (8 - bits)))
+ return out
+
+
+def shear_y_func(img, factor, fill=(0, 0, 0)):
+ H, W = img.shape[0], img.shape[1]
+ M = np.float32([[1, 0, 0], [factor, 1, 0]])
+ out = cv2.warpAffine(
+ img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
+ ).astype(np.uint8)
+ return out
+
+
+def cutout_func(img, pad_size, replace=(0, 0, 0)):
+ replace = np.array(replace, dtype=np.uint8)
+ H, W = img.shape[0], img.shape[1]
+ rh, rw = np.random.random(2)
+ pad_size = pad_size // 2
+ ch, cw = int(rh * H), int(rw * W)
+ x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H)
+ y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W)
+ out = img.copy()
+ out[x1:x2, y1:y2, :] = replace
+ return out
+
+
+### level to args
+def enhance_level_to_args(MAX_LEVEL):
+ def level_to_args(level):
+ return ((level / MAX_LEVEL) * 1.8 + 0.1,)
+
+ return level_to_args
+
+
+def shear_level_to_args(MAX_LEVEL, replace_value):
+ def level_to_args(level):
+ level = (level / MAX_LEVEL) * 0.3
+ if np.random.random() > 0.5:
+ level = -level
+ return (level, replace_value)
+
+ return level_to_args
+
+
+def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
+ def level_to_args(level):
+ level = (level / MAX_LEVEL) * float(translate_const)
+ if np.random.random() > 0.5:
+ level = -level
+ return (level, replace_value)
+
+ return level_to_args
+
+
+def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
+ def level_to_args(level):
+ level = int((level / MAX_LEVEL) * cutout_const)
+ return (level, replace_value)
+
+ return level_to_args
+
+
+def solarize_level_to_args(MAX_LEVEL):
+ def level_to_args(level):
+ level = int((level / MAX_LEVEL) * 256)
+ return (level,)
+
+ return level_to_args
+
+
+def none_level_to_args(level):
+ return ()
+
+
+def posterize_level_to_args(MAX_LEVEL):
+ def level_to_args(level):
+ level = int((level / MAX_LEVEL) * 4)
+ return (level,)
+
+ return level_to_args
+
+
+def rotate_level_to_args(MAX_LEVEL, replace_value):
+ def level_to_args(level):
+ level = (level / MAX_LEVEL) * 30
+ if np.random.random() < 0.5:
+ level = -level
+ return (level, replace_value)
+
+ return level_to_args
+
+
+func_dict = {
+ "Identity": identity_func,
+ "AutoContrast": autocontrast_func,
+ "Equalize": equalize_func,
+ "Rotate": rotate_func,
+ "Solarize": solarize_func,
+ "Color": color_func,
+ "Contrast": contrast_func,
+ "Brightness": brightness_func,
+ "Sharpness": sharpness_func,
+ "ShearX": shear_x_func,
+ "TranslateX": translate_x_func,
+ "TranslateY": translate_y_func,
+ "Posterize": posterize_func,
+ "ShearY": shear_y_func,
+}
+
+translate_const = 10
+MAX_LEVEL = 10
+replace_value = (128, 128, 128)
+arg_dict = {
+ "Identity": none_level_to_args,
+ "AutoContrast": none_level_to_args,
+ "Equalize": none_level_to_args,
+ "Rotate": rotate_level_to_args(MAX_LEVEL, replace_value),
+ "Solarize": solarize_level_to_args(MAX_LEVEL),
+ "Color": enhance_level_to_args(MAX_LEVEL),
+ "Contrast": enhance_level_to_args(MAX_LEVEL),
+ "Brightness": enhance_level_to_args(MAX_LEVEL),
+ "Sharpness": enhance_level_to_args(MAX_LEVEL),
+ "ShearX": shear_level_to_args(MAX_LEVEL, replace_value),
+ "TranslateX": translate_level_to_args(translate_const, MAX_LEVEL, replace_value),
+ "TranslateY": translate_level_to_args(translate_const, MAX_LEVEL, replace_value),
+ "Posterize": posterize_level_to_args(MAX_LEVEL),
+ "ShearY": shear_level_to_args(MAX_LEVEL, replace_value),
+}
+
+
+class RandomAugment(object):
+ def __init__(self, N=2, M=10, isPIL=False, augs=[]):
+ self.N = N
+ self.M = M
+ self.isPIL = isPIL
+ if augs:
+ self.augs = augs
+ else:
+ self.augs = list(arg_dict.keys())
+
+ def get_random_ops(self):
+ sampled_ops = np.random.choice(self.augs, self.N)
+ return [(op, 0.5, self.M) for op in sampled_ops]
+
+ def __call__(self, img):
+ if self.isPIL:
+ img = np.array(img)
+ ops = self.get_random_ops()
+ for name, prob, level in ops:
+ if np.random.random() > prob:
+ continue
+ args = arg_dict[name](level)
+ img = func_dict[name](img, *args)
+ return img
+
+
+class VideoRandomAugment(object):
+ def __init__(self, N=2, M=10, p=0.0, tensor_in_tensor_out=True, augs=[]):
+ self.N = N
+ self.M = M
+ self.p = p
+ self.tensor_in_tensor_out = tensor_in_tensor_out
+ if augs:
+ self.augs = augs
+ else:
+ self.augs = list(arg_dict.keys())
+
+ def get_random_ops(self):
+ sampled_ops = np.random.choice(self.augs, self.N, replace=False)
+ return [(op, self.M) for op in sampled_ops]
+
+ def __call__(self, frames):
+ assert (
+ frames.shape[-1] == 3
+ ), "Expecting last dimension for 3-channels RGB (b, h, w, c)."
+
+ if self.tensor_in_tensor_out:
+ frames = frames.numpy().astype(np.uint8)
+
+ num_frames = frames.shape[0]
+
+ ops = num_frames * [self.get_random_ops()]
+ apply_or_not = num_frames * [np.random.random(size=self.N) > self.p]
+
+ frames = torch.stack(
+ list(map(self._aug, frames, ops, apply_or_not)), dim=0
+ ).float()
+
+ return frames
+
+ def _aug(self, img, ops, apply_or_not):
+ for i, (name, level) in enumerate(ops):
+ if not apply_or_not[i]:
+ continue
+ args = arg_dict[name](level)
+ img = func_dict[name](img, *args)
+ return torch.from_numpy(img)
+
+
+if __name__ == "__main__":
+ a = RandomAugment()
+ img = np.random.randn(32, 32, 3)
+ a(img)
diff --git a/global_local/processors/transforms_video.py b/global_local/processors/transforms_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e737d61624a1593d0092946ff4768a91718e2ef
--- /dev/null
+++ b/global_local/processors/transforms_video.py
@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+
+import numbers
+import random
+
+from torchvision.transforms import (
+ RandomCrop,
+ RandomResizedCrop,
+)
+
+import global_local.processors.functional_video as F
+
+
+__all__ = [
+ "RandomCropVideo",
+ "RandomResizedCropVideo",
+ "CenterCropVideo",
+ "NormalizeVideo",
+ "ToTensorVideo",
+ "RandomHorizontalFlipVideo",
+]
+
+
+class RandomCropVideo(RandomCrop):
+ def __init__(self, size):
+ if isinstance(size, numbers.Number):
+ self.size = (int(size), int(size))
+ else:
+ self.size = size
+
+ def __call__(self, clip):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ Returns:
+ torch.tensor: randomly cropped/resized video clip.
+ size is (C, T, OH, OW)
+ """
+ i, j, h, w = self.get_params(clip, self.size)
+ return F.crop(clip, i, j, h, w)
+
+ def __repr__(self) -> str:
+ return f"{self.__class__.__name__}(size={self.size})"
+
+
+class RandomResizedCropVideo(RandomResizedCrop):
+ def __init__(
+ self,
+ size,
+ scale=(0.08, 1.0),
+ ratio=(3.0 / 4.0, 4.0 / 3.0),
+ interpolation_mode="bilinear",
+ ):
+ if isinstance(size, tuple):
+ if len(size) != 2:
+ raise ValueError(
+ f"size should be tuple (height, width), instead got {size}"
+ )
+ self.size = size
+ else:
+ self.size = (size, size)
+
+ self.interpolation_mode = interpolation_mode
+ self.scale = scale
+ self.ratio = ratio
+
+ def __call__(self, clip):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ Returns:
+ torch.tensor: randomly cropped/resized video clip.
+ size is (C, T, H, W)
+ """
+ i, j, h, w = self.get_params(clip, self.scale, self.ratio)
+ return F.resized_crop(clip, i, j, h, w, self.size, self.interpolation_mode)
+
+ def __repr__(self) -> str:
+ return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}, scale={self.scale}, ratio={self.ratio})"
+
+
+class CenterCropVideo:
+ def __init__(self, crop_size):
+ if isinstance(crop_size, numbers.Number):
+ self.crop_size = (int(crop_size), int(crop_size))
+ else:
+ self.crop_size = crop_size
+
+ def __call__(self, clip):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ Returns:
+ torch.tensor: central cropping of video clip. Size is
+ (C, T, crop_size, crop_size)
+ """
+ return F.center_crop(clip, self.crop_size)
+
+ def __repr__(self) -> str:
+ return f"{self.__class__.__name__}(crop_size={self.crop_size})"
+
+
+class NormalizeVideo:
+ """
+ Normalize the video clip by mean subtraction and division by standard deviation
+ Args:
+ mean (3-tuple): pixel RGB mean
+ std (3-tuple): pixel RGB standard deviation
+ inplace (boolean): whether do in-place normalization
+ """
+
+ def __init__(self, mean, std, inplace=False):
+ self.mean = mean
+ self.std = std
+ self.inplace = inplace
+
+ def __call__(self, clip):
+ """
+ Args:
+ clip (torch.tensor): video clip to be normalized. Size is (C, T, H, W)
+ """
+ return F.normalize(clip, self.mean, self.std, self.inplace)
+
+ def __repr__(self) -> str:
+ return f"{self.__class__.__name__}(mean={self.mean}, std={self.std}, inplace={self.inplace})"
+
+
+class ToTensorVideo:
+ """
+ Convert tensor data type from uint8 to float, divide value by 255.0 and
+ permute the dimensions of clip tensor
+ """
+
+ def __init__(self):
+ pass
+
+ def __call__(self, clip):
+ """
+ Args:
+ clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C)
+ Return:
+ clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W)
+ """
+ return F.to_tensor(clip)
+
+ def __repr__(self) -> str:
+ return self.__class__.__name__
+
+
+class RandomHorizontalFlipVideo:
+ """
+ Flip the video clip along the horizonal direction with a given probability
+ Args:
+ p (float): probability of the clip being flipped. Default value is 0.5
+ """
+
+ def __init__(self, p=0.5):
+ self.p = p
+
+ def __call__(self, clip):
+ """
+ Args:
+ clip (torch.tensor): Size is (C, T, H, W)
+ Return:
+ clip (torch.tensor): Size is (C, T, H, W)
+ """
+ if random.random() < self.p:
+ clip = F.hflip(clip)
+ return clip
+
+ def __repr__(self) -> str:
+ return f"{self.__class__.__name__}(p={self.p})"
diff --git a/global_local/processors/video_processor.py b/global_local/processors/video_processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..80dd863b4c8a87f058e85ff8ec8d0025531ed118
--- /dev/null
+++ b/global_local/processors/video_processor.py
@@ -0,0 +1,237 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import torch
+from global_local.common.registry import registry
+from decord import VideoReader
+import decord
+import numpy as np
+from global_local.processors import transforms_video
+from global_local.processors.base_processor import BaseProcessor
+from global_local.processors.randaugment import VideoRandomAugment
+from global_local.processors import functional_video as F
+from omegaconf import OmegaConf
+from torchvision import transforms
+import random as rnd
+
+
+MAX_INT = registry.get("MAX_INT")
+decord.bridge.set_bridge("torch")
+
+def load_video(video_path, n_frms=MAX_INT, height=-1, width=-1, sampling="uniform", return_msg = False):
+ decord.bridge.set_bridge("torch")
+ vr = VideoReader(uri=video_path, height=height, width=width)
+
+ vlen = len(vr)
+ start, end = 0, vlen
+
+ n_frms = min(n_frms, vlen)
+
+ if sampling == "uniform":
+ indices = np.arange(start, end, vlen / n_frms).astype(int).tolist()
+ elif sampling == "headtail":
+ indices_h = sorted(rnd.sample(range(vlen // 2), n_frms // 2))
+ indices_t = sorted(rnd.sample(range(vlen // 2, vlen), n_frms // 2))
+ indices = indices_h + indices_t
+ else:
+ raise NotImplementedError
+
+ # get_batch -> T, H, W, C
+ temp_frms = vr.get_batch(indices)
+ # print(type(temp_frms))
+ tensor_frms = torch.from_numpy(temp_frms) if type(temp_frms) is not torch.Tensor else temp_frms
+ frms = tensor_frms.permute(3, 0, 1, 2).float() # (C, T, H, W)
+
+ if not return_msg:
+ return frms
+
+ fps = float(vr.get_avg_fps())
+ sec = ", ".join([str(round(f / fps, 1)) for f in indices])
+ # " " should be added in the start and end
+ msg = f"The video contains {len(indices)} frames sampled at {sec} seconds. "
+ return frms, msg
+
+
+class AlproVideoBaseProcessor(BaseProcessor):
+ def __init__(self, mean=None, std=None, n_frms=MAX_INT):
+ if mean is None:
+ mean = (0.48145466, 0.4578275, 0.40821073)
+ if std is None:
+ std = (0.26862954, 0.26130258, 0.27577711)
+
+ self.normalize = transforms_video.NormalizeVideo(mean, std)
+
+ self.n_frms = n_frms
+
+
+class ToUint8(object):
+ def __init__(self):
+ pass
+
+ def __call__(self, tensor):
+ return tensor.to(torch.uint8)
+
+ def __repr__(self):
+ return self.__class__.__name__
+
+
+class ToTHWC(object):
+ """
+ Args:
+ clip (torch.tensor, dtype=torch.uint8): Size is (C, T, H, W)
+ Return:
+ clip (torch.tensor, dtype=torch.float): Size is (T, H, W, C)
+ """
+
+ def __init__(self):
+ pass
+
+ def __call__(self, tensor):
+ return tensor.permute(1, 2, 3, 0)
+
+ def __repr__(self):
+ return self.__class__.__name__
+
+
+class ResizeVideo(object):
+ def __init__(self, target_size, interpolation_mode="bilinear"):
+ self.target_size = target_size
+ self.interpolation_mode = interpolation_mode
+
+ def __call__(self, clip):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ Returns:
+ torch.tensor: central cropping of video clip. Size is
+ (C, T, crop_size, crop_size)
+ """
+ return F.resize(clip, self.target_size, self.interpolation_mode)
+
+ def __repr__(self):
+ return self.__class__.__name__ + "(resize_size={0})".format(self.target_size)
+
+
+@registry.register_processor("alpro_video_train")
+class AlproVideoTrainProcessor(AlproVideoBaseProcessor):
+ def __init__(
+ self,
+ image_size=384,
+ mean=None,
+ std=None,
+ min_scale=0.5,
+ max_scale=1.0,
+ n_frms=MAX_INT,
+ ):
+ super().__init__(mean=mean, std=std, n_frms=n_frms)
+
+ self.image_size = image_size
+
+ self.transform = transforms.Compose(
+ [
+ # Video size is (C, T, H, W)
+ transforms_video.RandomResizedCropVideo(
+ image_size,
+ scale=(min_scale, max_scale),
+ interpolation_mode="bicubic",
+ ),
+ ToTHWC(), # C, T, H, W -> T, H, W, C
+ ToUint8(),
+ transforms_video.ToTensorVideo(), # T, H, W, C -> C, T, H, W
+ self.normalize,
+ ]
+ )
+
+ def __call__(self, vpath):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ Returns:
+ torch.tensor: video clip after transforms. Size is (C, T, size, size).
+ """
+ clip = load_video(
+ video_path=vpath,
+ n_frms=self.n_frms,
+ height=self.image_size,
+ width=self.image_size,
+ sampling="headtail",
+ )
+
+ return self.transform(clip)
+
+ @classmethod
+ def from_config(cls, cfg=None):
+ if cfg is None:
+ cfg = OmegaConf.create()
+
+ image_size = cfg.get("image_size", 256)
+
+ mean = cfg.get("mean", None)
+ std = cfg.get("std", None)
+
+ min_scale = cfg.get("min_scale", 0.5)
+ max_scale = cfg.get("max_scale", 1.0)
+
+ n_frms = cfg.get("n_frms", MAX_INT)
+
+ return cls(
+ image_size=image_size,
+ mean=mean,
+ std=std,
+ min_scale=min_scale,
+ max_scale=max_scale,
+ n_frms=n_frms,
+ )
+
+
+@registry.register_processor("alpro_video_eval")
+class AlproVideoEvalProcessor(AlproVideoBaseProcessor):
+ def __init__(self, image_size=256, mean=None, std=None, n_frms=MAX_INT):
+ super().__init__(mean=mean, std=std, n_frms=n_frms)
+
+ self.image_size = image_size
+
+ # Input video size is (C, T, H, W)
+ self.transform = transforms.Compose(
+ [
+ # frames will be resized during decord loading.
+ ToUint8(), # C, T, H, W
+ ToTHWC(), # T, H, W, C
+ transforms_video.ToTensorVideo(), # C, T, H, W
+ self.normalize, # C, T, H, W
+ ]
+ )
+
+ def __call__(self, vpath):
+ """
+ Args:
+ clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
+ Returns:
+ torch.tensor: video clip after transforms. Size is (C, T, size, size).
+ """
+ clip = load_video(
+ video_path=vpath,
+ n_frms=self.n_frms,
+ height=self.image_size,
+ width=self.image_size,
+ )
+
+ return self.transform(clip)
+
+ @classmethod
+ def from_config(cls, cfg=None):
+ if cfg is None:
+ cfg = OmegaConf.create()
+
+ image_size = cfg.get("image_size", 256)
+
+ mean = cfg.get("mean", None)
+ std = cfg.get("std", None)
+
+ n_frms = cfg.get("n_frms", MAX_INT)
+
+ return cls(image_size=image_size, mean=mean, std=std, n_frms=n_frms)
diff --git a/global_local/runners/__init__.py b/global_local/runners/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7cd263a0bac2fe331e221dee247d79e573d7ca0
--- /dev/null
+++ b/global_local/runners/__init__.py
@@ -0,0 +1,10 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from global_local.runners.runner_base import RunnerBase
+
+__all__ = ["RunnerBase"]
diff --git a/global_local/runners/__pycache__/__init__.cpython-39.pyc b/global_local/runners/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..77e5c2f224fb068d5605f0d43ff4c96c9cd458e8
Binary files /dev/null and b/global_local/runners/__pycache__/__init__.cpython-39.pyc differ
diff --git a/global_local/runners/__pycache__/runner_base.cpython-39.pyc b/global_local/runners/__pycache__/runner_base.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..282221482ef8562736ae6bd1ba143141363a9cf6
Binary files /dev/null and b/global_local/runners/__pycache__/runner_base.cpython-39.pyc differ
diff --git a/global_local/runners/runner_base.py b/global_local/runners/runner_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..378551d887932447a559ae0988b579dd82b764ec
--- /dev/null
+++ b/global_local/runners/runner_base.py
@@ -0,0 +1,658 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import datetime
+import json
+import logging
+import os
+import time
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+import webdataset as wds
+from global_local.common.dist_utils import (
+ download_cached_file,
+ get_rank,
+ get_world_size,
+ is_main_process,
+ main_process,
+)
+from global_local.common.registry import registry
+from global_local.common.utils import is_url
+from global_local.datasets.data_utils import concat_datasets, reorg_datasets_by_split, ChainDataset
+from global_local.datasets.datasets.dataloader_utils import (
+ IterLoader,
+ MultiIterLoader,
+ PrefetchLoader,
+)
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, DistributedSampler
+
+
+@registry.register_runner("runner_base")
+class RunnerBase:
+ """
+ A runner class to train and evaluate a model given a task and datasets.
+
+ The runner uses pytorch distributed data parallel by default. Future release
+ will support other distributed frameworks.
+ """
+
+ def __init__(self, cfg, task, model, datasets, job_id):
+ self.config = cfg
+ self.job_id = job_id
+
+ self.task = task
+ self.datasets = datasets
+
+ self._model = model
+
+ self._wrapped_model = None
+ self._device = None
+ self._optimizer = None
+ self._scaler = None
+ self._dataloaders = None
+ self._lr_sched = None
+
+ self.start_epoch = 0
+
+ # self.setup_seeds()
+ self.setup_output_dir()
+
+ @property
+ def device(self):
+ if self._device is None:
+ self._device = torch.device(self.config.run_cfg.device)
+
+ return self._device
+
+ @property
+ def use_distributed(self):
+ return self.config.run_cfg.distributed
+
+ @property
+ def model(self):
+ """
+ A property to get the DDP-wrapped model on the device.
+ """
+ # move model to device
+ if self._model.device != self.device:
+ self._model = self._model.to(self.device)
+
+ # distributed training wrapper
+ if self.use_distributed:
+ if self._wrapped_model is None:
+ self._wrapped_model = DDP(
+ self._model, device_ids=[self.config.run_cfg.gpu]
+ )
+ else:
+ self._wrapped_model = self._model
+
+ return self._wrapped_model
+
+ @property
+ def optimizer(self):
+ # TODO make optimizer class and configurations
+ if self._optimizer is None:
+ num_parameters = 0
+ p_wd, p_non_wd = [], []
+ for n, p in self.model.named_parameters():
+ if not p.requires_grad:
+ continue # frozen weights
+ print(n)
+ if p.ndim < 2 or "bias" in n or "ln" in n or "bn" in n:
+ p_non_wd.append(p)
+ else:
+ p_wd.append(p)
+ num_parameters += p.data.nelement()
+ logging.info("number of trainable parameters: %d" % num_parameters)
+ optim_params = [
+ {
+ "params": p_wd,
+ "weight_decay": float(self.config.run_cfg.weight_decay),
+ },
+ {"params": p_non_wd, "weight_decay": 0},
+ ]
+ beta2 = self.config.run_cfg.get("beta2", 0.999)
+ self._optimizer = torch.optim.AdamW(
+ optim_params,
+ lr=float(self.config.run_cfg.init_lr),
+ weight_decay=float(self.config.run_cfg.weight_decay),
+ betas=(0.9, beta2),
+ )
+
+ return self._optimizer
+
+ @property
+ def scaler(self):
+ amp = self.config.run_cfg.get("amp", False)
+
+ if amp:
+ if self._scaler is None:
+ self._scaler = torch.cuda.amp.GradScaler()
+
+ return self._scaler
+
+ @property
+ def lr_scheduler(self):
+ """
+ A property to get and create learning rate scheduler by split just in need.
+ """
+ if self._lr_sched is None:
+ lr_sched_cls = registry.get_lr_scheduler_class(self.config.run_cfg.lr_sched)
+
+ # max_epoch = self.config.run_cfg.max_epoch
+ max_epoch = self.max_epoch
+ # min_lr = self.config.run_cfg.min_lr
+ min_lr = self.min_lr
+ # init_lr = self.config.run_cfg.init_lr
+ init_lr = self.init_lr
+
+ # optional parameters
+ decay_rate = self.config.run_cfg.get("lr_decay_rate", None)
+ warmup_start_lr = self.config.run_cfg.get("warmup_lr", -1)
+ warmup_steps = self.config.run_cfg.get("warmup_steps", 0)
+ iters_per_epoch = self.config.run_cfg.get("iters_per_epoch", None)
+
+ if iters_per_epoch is None:
+ try:
+ iters_per_epoch = len(self.dataloaders['train'])
+ except (AttributeError, TypeError):
+ iters_per_epoch = 10000
+
+ self._lr_sched = lr_sched_cls(
+ optimizer=self.optimizer,
+ max_epoch=max_epoch,
+ iters_per_epoch=iters_per_epoch,
+ min_lr=min_lr,
+ init_lr=init_lr,
+ decay_rate=decay_rate,
+ warmup_start_lr=warmup_start_lr,
+ warmup_steps=warmup_steps,
+ )
+
+ return self._lr_sched
+
+ @property
+ def dataloaders(self) -> dict:
+ """
+ A property to get and create dataloaders by split just in need.
+
+ If no train_dataset_ratio is provided, concatenate map-style datasets and
+ chain wds.DataPipe datasets separately. Training set becomes a tuple
+ (ConcatDataset, ChainDataset), both are optional but at least one of them is
+ required. The resultant ConcatDataset and ChainDataset will be sampled evenly.
+
+ If train_dataset_ratio is provided, create a MultiIterLoader to sample
+ each dataset by ratios during training.
+
+ Currently do not support multiple datasets for validation and test.
+
+ Returns:
+ dict: {split_name: (tuples of) dataloader}
+ """
+ if self._dataloaders is None:
+
+ # concatenate map-style datasets and chain wds.DataPipe datasets separately
+ # training set becomes a tuple (ConcatDataset, ChainDataset), both are
+ # optional but at least one of them is required. The resultant ConcatDataset
+ # and ChainDataset will be sampled evenly.
+ logging.info(
+ "dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline)."
+ )
+
+ datasets = reorg_datasets_by_split(self.datasets)
+ self.datasets = datasets
+ # self.datasets = concat_datasets(datasets)
+
+ # print dataset statistics after concatenation/chaining
+ for split_name in self.datasets:
+ if isinstance(self.datasets[split_name], tuple) or isinstance(
+ self.datasets[split_name], list
+ ):
+ # mixed wds.DataPipeline and torch.utils.data.Dataset
+ num_records = sum(
+ [
+ len(d)
+ if not type(d) in [wds.DataPipeline, ChainDataset]
+ else 0
+ for d in self.datasets[split_name]
+ ]
+ )
+
+ else:
+ if hasattr(self.datasets[split_name], "__len__"):
+ # a single map-style dataset
+ num_records = len(self.datasets[split_name])
+ else:
+ # a single wds.DataPipeline
+ num_records = -1
+ logging.info(
+ "Only a single wds.DataPipeline dataset, no __len__ attribute."
+ )
+
+ if num_records >= 0:
+ logging.info(
+ "Loaded {} records for {} split from the dataset.".format(
+ num_records, split_name
+ )
+ )
+
+ # create dataloaders
+ split_names = sorted(self.datasets.keys())
+
+ datasets = [self.datasets[split] for split in split_names]
+ is_trains = [split in self.train_splits for split in split_names]
+
+ batch_sizes = [
+ self.config.run_cfg.batch_size_train
+ if split == "train"
+ else self.config.run_cfg.batch_size_eval
+ for split in split_names
+ ]
+
+ collate_fns = []
+ for dataset in datasets:
+ if isinstance(dataset, tuple) or isinstance(dataset, list):
+ collate_fns.append([getattr(d, "collater", None) for d in dataset])
+ else:
+ collate_fns.append(getattr(dataset, "collater", None))
+
+ dataloaders = self.create_loaders(
+ datasets=datasets,
+ num_workers=self.config.run_cfg.num_workers,
+ batch_sizes=batch_sizes,
+ is_trains=is_trains,
+ collate_fns=collate_fns,
+ )
+
+ self._dataloaders = {k: v for k, v in zip(split_names, dataloaders)}
+
+ return self._dataloaders
+
+ @property
+ def cuda_enabled(self):
+ return self.device.type == "cuda"
+
+ @property
+ def max_epoch(self):
+ return int(self.config.run_cfg.max_epoch)
+
+ @property
+ def log_freq(self):
+ log_freq = self.config.run_cfg.get("log_freq", 50)
+ return int(log_freq)
+
+ @property
+ def init_lr(self):
+ return float(self.config.run_cfg.init_lr)
+
+ @property
+ def min_lr(self):
+ return float(self.config.run_cfg.min_lr)
+
+ @property
+ def accum_grad_iters(self):
+ return int(self.config.run_cfg.get("accum_grad_iters", 1))
+
+ @property
+ def valid_splits(self):
+ valid_splits = self.config.run_cfg.get("valid_splits", [])
+
+ if len(valid_splits) == 0:
+ logging.info("No validation splits found.")
+
+ return valid_splits
+
+ @property
+ def test_splits(self):
+ test_splits = self.config.run_cfg.get("test_splits", [])
+
+ return test_splits
+
+ @property
+ def train_splits(self):
+ train_splits = self.config.run_cfg.get("train_splits", [])
+
+ if len(train_splits) == 0:
+ logging.info("Empty train splits.")
+
+ return train_splits
+
+ @property
+ def evaluate_only(self):
+ """
+ Set to True to skip training.
+ """
+ return self.config.run_cfg.evaluate
+
+ @property
+ def use_dist_eval_sampler(self):
+ return self.config.run_cfg.get("use_dist_eval_sampler", True)
+
+ @property
+ def resume_ckpt_path(self):
+ return self.config.run_cfg.get("resume_ckpt_path", None)
+
+ @property
+ def train_loader(self):
+ train_dataloader = self.dataloaders["train"]
+
+ return train_dataloader
+
+ def setup_output_dir(self):
+ lib_root = Path(registry.get_path("library_root"))
+
+ output_dir = lib_root / self.config.run_cfg.output_dir / self.job_id
+ result_dir = output_dir / "result"
+
+ output_dir.mkdir(parents=True, exist_ok=True)
+ result_dir.mkdir(parents=True, exist_ok=True)
+
+ registry.register_path("result_dir", str(result_dir))
+ registry.register_path("output_dir", str(output_dir))
+
+ self.result_dir = result_dir
+ self.output_dir = output_dir
+
+ def train(self):
+ start_time = time.time()
+ best_agg_metric = 0
+ best_epoch = 0
+
+ self.log_config()
+
+ # resume from checkpoint if specified
+ if not self.evaluate_only and self.resume_ckpt_path is not None:
+ self._load_checkpoint(self.resume_ckpt_path)
+
+ for cur_epoch in range(self.start_epoch, self.max_epoch):
+ # training phase
+ if not self.evaluate_only:
+ logging.info("Start training")
+ train_stats = self.train_epoch(cur_epoch)
+ self.log_stats(split_name="train", stats=train_stats)
+
+ # evaluation phase
+ if len(self.valid_splits) > 0:
+ for split_name in self.valid_splits:
+ logging.info("Evaluating on {}.".format(split_name))
+
+ val_log = self.eval_epoch(
+ split_name=split_name, cur_epoch=cur_epoch
+ )
+ if val_log is not None:
+ if is_main_process():
+ assert (
+ "agg_metrics" in val_log
+ ), "No agg_metrics found in validation log."
+
+ agg_metrics = val_log["agg_metrics"]
+ if agg_metrics > best_agg_metric and split_name == "val":
+ best_epoch, best_agg_metric = cur_epoch, agg_metrics
+
+ self._save_checkpoint(cur_epoch, is_best=True)
+
+ val_log.update({"best_epoch": best_epoch})
+ self.log_stats(val_log, split_name)
+
+ else:
+ # if no validation split is provided, we just save the checkpoint at the end of each epoch.
+ if not self.evaluate_only:
+ self._save_checkpoint(cur_epoch, is_best=False)
+
+ if self.evaluate_only:
+ break
+
+ if self.config.run_cfg.distributed:
+ dist.barrier()
+
+ # testing phase
+ test_epoch = "best" if len(self.valid_splits) > 0 else cur_epoch
+ self.evaluate(cur_epoch=test_epoch, skip_reload=self.evaluate_only)
+
+ total_time = time.time() - start_time
+ total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+ logging.info("Training time {}".format(total_time_str))
+
+ def evaluate(self, cur_epoch="best", skip_reload=False):
+ test_logs = dict()
+
+ if len(self.test_splits) > 0:
+ for split_name in self.test_splits:
+ test_logs[split_name] = self.eval_epoch(
+ split_name=split_name, cur_epoch=cur_epoch, skip_reload=skip_reload
+ )
+
+ return test_logs
+
+ def train_epoch(self, epoch):
+ # train
+ self.model.train()
+
+ return self.task.train_epoch(
+ epoch=epoch,
+ model=self.model,
+ data_loader=self.train_loader,
+ optimizer=self.optimizer,
+ scaler=self.scaler,
+ lr_scheduler=self.lr_scheduler,
+ cuda_enabled=self.cuda_enabled,
+ log_freq=self.log_freq,
+ accum_grad_iters=self.accum_grad_iters,
+ )
+
+ @torch.no_grad()
+ def eval_epoch(self, split_name, cur_epoch, skip_reload=False):
+ """
+ Evaluate the model on a given split.
+
+ Args:
+ split_name (str): name of the split to evaluate on.
+ cur_epoch (int): current epoch.
+ skip_reload_best (bool): whether to skip reloading the best checkpoint.
+ During training, we will reload the best checkpoint for validation.
+ During testing, we will use provided weights and skip reloading the best checkpoint .
+ """
+ data_loader = self.dataloaders.get(split_name, None)
+ assert data_loader, "data_loader for split {} is None.".format(split_name)
+
+ # TODO In validation, you need to compute loss as well as metrics
+ # TODO consider moving to model.before_evaluation()
+ model = self.unwrap_dist_model(self.model)
+ if not skip_reload and cur_epoch == "best":
+ model = self._reload_best_model(model)
+ model.eval()
+
+ self.task.before_evaluation(
+ model=model,
+ dataset=self.datasets[split_name],
+ )
+ results = self.task.evaluation(model, data_loader)
+
+ if results is not None:
+ return self.task.after_evaluation(
+ val_result=results,
+ split_name=split_name,
+ epoch=cur_epoch,
+ )
+
+ def unwrap_dist_model(self, model):
+ if self.use_distributed:
+ return model.module
+ else:
+ return model
+
+ def create_loaders(
+ self,
+ datasets,
+ num_workers,
+ batch_sizes,
+ is_trains,
+ collate_fns,
+ dataset_ratios=None,
+ ):
+ """
+ Create dataloaders for training and validation.
+ """
+
+ def _create_loader(dataset, num_workers, bsz, is_train, collate_fn):
+ # create a single dataloader for each split
+ if isinstance(dataset, ChainDataset) or isinstance(
+ dataset, wds.DataPipeline
+ ):
+ # wds.WebdDataset instance are chained together
+ # webdataset.DataPipeline has its own sampler and collate_fn
+ loader = iter(
+ DataLoader(
+ dataset,
+ batch_size=bsz,
+ num_workers=num_workers,
+ pin_memory=True,
+ )
+ )
+ else:
+ # map-style dataset are concatenated together
+ # setup distributed sampler
+ if self.use_distributed:
+ sampler = DistributedSampler(
+ dataset,
+ shuffle=is_train,
+ num_replicas=get_world_size(),
+ rank=get_rank(),
+ )
+ if not self.use_dist_eval_sampler:
+ # e.g. retrieval evaluation
+ sampler = sampler if is_train else None
+ else:
+ sampler = None
+
+ loader = DataLoader(
+ dataset,
+ batch_size=bsz,
+ num_workers=num_workers,
+ pin_memory=True,
+ sampler=sampler,
+ shuffle=sampler is None and is_train,
+ collate_fn=collate_fn,
+ drop_last=True if is_train else False,
+ )
+ loader = PrefetchLoader(loader)
+
+ if is_train:
+ loader = IterLoader(loader, use_distributed=self.use_distributed)
+
+ return loader
+
+ loaders = []
+
+ for dataset, bsz, is_train, collate_fn in zip(
+ datasets, batch_sizes, is_trains, collate_fns
+ ):
+ if isinstance(dataset, list) or isinstance(dataset, tuple):
+ if hasattr(dataset[0], 'sample_ratio') and dataset_ratios is None:
+ dataset_ratios = [d.sample_ratio for d in dataset]
+ loader = MultiIterLoader(
+ loaders=[
+ _create_loader(d, num_workers, bsz, is_train, collate_fn[i])
+ for i, d in enumerate(dataset)
+ ],
+ ratios=dataset_ratios,
+ )
+ else:
+ loader = _create_loader(dataset, num_workers, bsz, is_train, collate_fn)
+
+ loaders.append(loader)
+
+ return loaders
+
+ @main_process
+ def _save_checkpoint(self, cur_epoch, is_best=False):
+ """
+ Save the checkpoint at the current epoch.
+ """
+ model_no_ddp = self.unwrap_dist_model(self.model)
+ param_grad_dic = {
+ k: v.requires_grad for (k, v) in model_no_ddp.named_parameters()
+ }
+ state_dict = model_no_ddp.state_dict()
+ for k in list(state_dict.keys()):
+ if k in param_grad_dic.keys() and not param_grad_dic[k]:
+ # delete parameters that do not require gradient
+ del state_dict[k]
+ save_obj = {
+ "model": state_dict,
+ "optimizer": self.optimizer.state_dict(),
+ "config": self.config.to_dict(),
+ "scaler": self.scaler.state_dict() if self.scaler else None,
+ "epoch": cur_epoch,
+ }
+ save_to = os.path.join(
+ self.output_dir,
+ "checkpoint_{}.pth".format("best" if is_best else cur_epoch),
+ )
+ logging.info("Saving checkpoint at epoch {} to {}.".format(cur_epoch, save_to))
+ torch.save(save_obj, save_to)
+
+ def _reload_best_model(self, model):
+ """
+ Load the best checkpoint for evaluation.
+ """
+ checkpoint_path = os.path.join(self.output_dir, "checkpoint_best.pth")
+
+ logging.info("Loading checkpoint from {}.".format(checkpoint_path))
+ checkpoint = torch.load(checkpoint_path, map_location="cpu")
+ try:
+ model.load_state_dict(checkpoint["model"])
+ except RuntimeError as e:
+ logging.warning(
+ """
+ Key mismatch when loading checkpoint. This is expected if only part of the model is saved.
+ Trying to load the model with strict=False.
+ """
+ )
+ model.load_state_dict(checkpoint["model"], strict=False)
+ return model
+
+ def _load_checkpoint(self, url_or_filename):
+ """
+ Resume from a checkpoint.
+ """
+ if is_url(url_or_filename):
+ cached_file = download_cached_file(
+ url_or_filename, check_hash=False, progress=True
+ )
+ checkpoint = torch.load(cached_file, map_location=self.device, strict=False)
+ elif os.path.isfile(url_or_filename):
+ checkpoint = torch.load(url_or_filename, map_location=self.device, strict=False)
+ else:
+ raise RuntimeError("checkpoint url or path is invalid")
+
+ state_dict = checkpoint["model"]
+ self.unwrap_dist_model(self.model).load_state_dict(state_dict)
+
+ self.optimizer.load_state_dict(checkpoint["optimizer"])
+ if self.scaler and "scaler" in checkpoint:
+ self.scaler.load_state_dict(checkpoint["scaler"])
+
+ self.start_epoch = checkpoint["epoch"] + 1
+ logging.info("Resume checkpoint from {}".format(url_or_filename))
+
+ @main_process
+ def log_stats(self, stats, split_name):
+ if isinstance(stats, dict):
+ log_stats = {**{f"{split_name}_{k}": v for k, v in stats.items()}}
+ with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
+ f.write(json.dumps(log_stats) + "\n")
+ elif isinstance(stats, list):
+ pass
+
+ @main_process
+ def log_config(self):
+ with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
+ f.write(json.dumps(self.config.to_dict(), indent=4) + "\n")
diff --git a/global_local/runners/test.py b/global_local/runners/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/global_local/tasks/__init__.py b/global_local/tasks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab75be16c5054cc75753a43482a776cc3564026b
--- /dev/null
+++ b/global_local/tasks/__init__.py
@@ -0,0 +1,28 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from global_local.common.registry import registry
+from global_local.tasks.base_task import BaseTask
+from global_local.tasks.image_text_pretrain import ImageTextPretrainTask
+from global_local.tasks.video_text_pretrain import VideoTextPretrainTask
+
+
+def setup_task(cfg):
+ assert "task" in cfg.run_cfg, "Task name must be provided."
+
+ task_name = cfg.run_cfg.task
+ task = registry.get_task_class(task_name).setup_task(cfg=cfg)
+ assert task is not None, "Task {} not properly registered.".format(task_name)
+
+ return task
+
+
+__all__ = [
+ "BaseTask",
+ "ImageTextPretrainTask",
+ "VideoTextPretrainTask"
+]
diff --git a/global_local/tasks/__pycache__/__init__.cpython-39.pyc b/global_local/tasks/__pycache__/__init__.cpython-39.pyc
new file mode 100644
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diff --git a/global_local/tasks/__pycache__/image_text_pretrain.cpython-39.pyc b/global_local/tasks/__pycache__/image_text_pretrain.cpython-39.pyc
new file mode 100644
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diff --git a/global_local/tasks/__pycache__/video_text_pretrain.cpython-39.pyc b/global_local/tasks/__pycache__/video_text_pretrain.cpython-39.pyc
new file mode 100644
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diff --git a/global_local/tasks/base_task.py b/global_local/tasks/base_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f18d48d9e074d3aea37f0bcc473c6ffcf70fa22
--- /dev/null
+++ b/global_local/tasks/base_task.py
@@ -0,0 +1,286 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import logging
+import os
+
+import torch
+import torch.distributed as dist
+from global_local.common.dist_utils import get_rank, get_world_size, is_main_process, is_dist_avail_and_initialized
+from global_local.common.logger import MetricLogger, SmoothedValue
+from global_local.common.registry import registry
+from global_local.datasets.data_utils import prepare_sample
+
+
+class BaseTask:
+ def __init__(self, **kwargs):
+ super().__init__()
+
+ self.inst_id_key = "instance_id"
+
+ @classmethod
+ def setup_task(cls, **kwargs):
+ return cls()
+
+ def build_model(self, cfg):
+ model_config = cfg.model_cfg
+
+ model_cls = registry.get_model_class(model_config.arch)
+ return model_cls.from_config(model_config)
+
+ def build_datasets(self, cfg):
+ """
+ Build a dictionary of datasets, keyed by split 'train', 'valid', 'test'.
+ Download dataset and annotations automatically if not exist.
+
+ Args:
+ cfg (common.config.Config): _description_
+
+ Returns:
+ dict: Dictionary of torch.utils.data.Dataset objects by split.
+ """
+
+ datasets = dict()
+
+ datasets_config = cfg.datasets_cfg
+
+ assert len(datasets_config) > 0, "At least one dataset has to be specified."
+
+ for name in datasets_config:
+ dataset_config = datasets_config[name]
+
+ builder = registry.get_builder_class(name)(dataset_config)
+ dataset = builder.build_datasets()
+
+ dataset['train'].name = name
+ if 'sample_ratio' in dataset_config:
+ dataset['train'].sample_ratio = dataset_config.sample_ratio
+
+ datasets[name] = dataset
+
+ return datasets
+
+ def train_step(self, model, samples):
+ loss = model(samples)["loss"]
+ return loss
+
+ def valid_step(self, model, samples):
+ raise NotImplementedError
+
+ def before_evaluation(self, model, dataset, **kwargs):
+ model.before_evaluation(dataset=dataset, task_type=type(self))
+
+ def after_evaluation(self, **kwargs):
+ pass
+
+ def inference_step(self):
+ raise NotImplementedError
+
+ def evaluation(self, model, data_loader, cuda_enabled=True):
+ metric_logger = MetricLogger(delimiter=" ")
+ header = "Evaluation"
+ # TODO make it configurable
+ print_freq = 10
+
+ results = []
+
+ for samples in metric_logger.log_every(data_loader, print_freq, header):
+ samples = prepare_sample(samples, cuda_enabled=cuda_enabled)
+
+ eval_output = self.valid_step(model=model, samples=samples)
+ results.extend(eval_output)
+
+ if is_dist_avail_and_initialized():
+ dist.barrier()
+
+ return results
+
+ def train_epoch(
+ self,
+ epoch,
+ model,
+ data_loader,
+ optimizer,
+ lr_scheduler,
+ scaler=None,
+ cuda_enabled=False,
+ log_freq=50,
+ accum_grad_iters=1,
+ ):
+ return self._train_inner_loop(
+ epoch=epoch,
+ iters_per_epoch=lr_scheduler.iters_per_epoch,
+ model=model,
+ data_loader=data_loader,
+ optimizer=optimizer,
+ scaler=scaler,
+ lr_scheduler=lr_scheduler,
+ log_freq=log_freq,
+ cuda_enabled=cuda_enabled,
+ accum_grad_iters=accum_grad_iters,
+ )
+
+ def train_iters(
+ self,
+ epoch,
+ start_iters,
+ iters_per_inner_epoch,
+ model,
+ data_loader,
+ optimizer,
+ lr_scheduler,
+ scaler=None,
+ cuda_enabled=False,
+ log_freq=50,
+ accum_grad_iters=1,
+ ):
+ return self._train_inner_loop(
+ epoch=epoch,
+ start_iters=start_iters,
+ iters_per_epoch=iters_per_inner_epoch,
+ model=model,
+ data_loader=data_loader,
+ optimizer=optimizer,
+ scaler=scaler,
+ lr_scheduler=lr_scheduler,
+ log_freq=log_freq,
+ cuda_enabled=cuda_enabled,
+ accum_grad_iters=accum_grad_iters,
+ )
+
+ def _train_inner_loop(
+ self,
+ epoch,
+ iters_per_epoch,
+ model,
+ data_loader,
+ optimizer,
+ lr_scheduler,
+ scaler=None,
+ start_iters=None,
+ log_freq=50,
+ cuda_enabled=False,
+ accum_grad_iters=1,
+ ):
+ """
+ An inner training loop compatible with both epoch-based and iter-based training.
+
+ When using epoch-based, training stops after one epoch; when using iter-based,
+ training stops after #iters_per_epoch iterations.
+ """
+ use_amp = scaler is not None
+
+ if not hasattr(data_loader, "__next__"):
+ # convert to iterator if not already
+ data_loader = iter(data_loader)
+
+ metric_logger = MetricLogger(delimiter=" ")
+ metric_logger.add_meter("lr", SmoothedValue(window_size=1, fmt="{value:.6f}"))
+ metric_logger.add_meter("loss", SmoothedValue(window_size=1, fmt="{value:.4f}"))
+
+ # if iter-based runner, schedule lr based on inner epoch.
+ logging.info(
+ "Start training epoch {}, {} iters per inner epoch.".format(
+ epoch, iters_per_epoch
+ )
+ )
+ header = "Train: data epoch: [{}]".format(epoch)
+ if start_iters is None:
+ # epoch-based runner
+ inner_epoch = epoch
+ else:
+ # In iter-based runner, we schedule the learning rate based on iterations.
+ inner_epoch = start_iters // iters_per_epoch
+ header = header + "; inner epoch [{}]".format(inner_epoch)
+
+ for i in metric_logger.log_every(range(iters_per_epoch), log_freq, header):
+ # if using iter-based runner, we stop after iters_per_epoch iterations.
+ if i >= iters_per_epoch:
+ break
+
+ samples = next(data_loader)
+
+ samples = prepare_sample(samples, cuda_enabled=cuda_enabled)
+ samples.update(
+ {
+ "epoch": inner_epoch,
+ "num_iters_per_epoch": iters_per_epoch,
+ "iters": i,
+ }
+ )
+
+ lr_scheduler.step(cur_epoch=inner_epoch, cur_step=i)
+
+ with torch.cuda.amp.autocast(enabled=use_amp):
+ loss = self.train_step(model=model, samples=samples)
+
+ # after_train_step()
+ if use_amp:
+ scaler.scale(loss).backward()
+ else:
+ loss.backward()
+
+ # update gradients every accum_grad_iters iterations
+ if (i + 1) % accum_grad_iters == 0:
+ if use_amp:
+ scaler.step(optimizer)
+ scaler.update()
+ else:
+ optimizer.step()
+ optimizer.zero_grad()
+
+ metric_logger.update(loss=loss.item())
+ metric_logger.update(lr=optimizer.param_groups[0]["lr"])
+
+ # after train_epoch()
+ # gather the stats from all processes
+ metric_logger.synchronize_between_processes()
+ logging.info("Averaged stats: " + str(metric_logger.global_avg()))
+ return {
+ k: "{:.3f}".format(meter.global_avg)
+ for k, meter in metric_logger.meters.items()
+ }
+
+ @staticmethod
+ def save_result(result, result_dir, filename, remove_duplicate=""):
+ import json
+
+ result_file = os.path.join(
+ result_dir, "%s_rank%d.json" % (filename, get_rank())
+ )
+ final_result_file = os.path.join(result_dir, "%s.json" % filename)
+
+ json.dump(result, open(result_file, "w"))
+
+ if is_dist_avail_and_initialized():
+ dist.barrier()
+
+ if is_main_process():
+ logging.warning("rank %d starts merging results." % get_rank())
+ # combine results from all processes
+ result = []
+
+ for rank in range(get_world_size()):
+ result_file = os.path.join(
+ result_dir, "%s_rank%d.json" % (filename, rank)
+ )
+ res = json.load(open(result_file, "r"))
+ result += res
+
+ if remove_duplicate:
+ result_new = []
+ id_list = []
+ for res in result:
+ if res[remove_duplicate] not in id_list:
+ id_list.append(res[remove_duplicate])
+ result_new.append(res)
+ result = result_new
+
+ json.dump(result, open(final_result_file, "w"))
+ print("result file saved to %s" % final_result_file)
+
+ return final_result_file
diff --git a/global_local/tasks/image_text_pretrain.py b/global_local/tasks/image_text_pretrain.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eb66dba502935f5fc3f48f14e8490a987cc8c40
--- /dev/null
+++ b/global_local/tasks/image_text_pretrain.py
@@ -0,0 +1,18 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from global_local.common.registry import registry
+from global_local.tasks.base_task import BaseTask
+
+
+@registry.register_task("image_text_pretrain")
+class ImageTextPretrainTask(BaseTask):
+ def __init__(self):
+ super().__init__()
+
+ def evaluation(self, model, data_loader, cuda_enabled=True):
+ pass
diff --git a/global_local/tasks/video_text_pretrain.py b/global_local/tasks/video_text_pretrain.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e8f8f7e26bdb6597318ac238a4ddf4c60ec235
--- /dev/null
+++ b/global_local/tasks/video_text_pretrain.py
@@ -0,0 +1,18 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from global_local.common.registry import registry
+from global_local.tasks.base_task import BaseTask
+
+
+@registry.register_task("video_text_pretrain")
+class VideoTextPretrainTask(BaseTask):
+ def __init__(self):
+ super().__init__()
+
+ def evaluation(self, model, data_loader, cuda_enabled=True):
+ pass
diff --git a/prompts/alignment_image.txt b/prompts/alignment_image.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0bc3d22ad8a3ff20180a87bd49d151e5a30553a6
--- /dev/null
+++ b/prompts/alignment_image.txt
@@ -0,0 +1,4 @@
+ Describe this image in detail.
+ Take a look at this image and describe what you notice.
+ Please provide a detailed description of the picture.
+ Could you describe the contents of this image for me?
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9149ac78cda844f0abd40246abd6627116534b91
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,13 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
+torch==2.0.1
+torchvision==0.15.2
+transformers==4.33.3
+tqdm
+decord
+timm
+einops
+opencv_python
+torchvision
+
+salesforce-lavis
+accelerate
\ No newline at end of file