add emu3-chat and emu3-gen

vlmeval/config.py

@@ -36,7 +36,6 @@
     'VisualGLM_6b': partial(VisualGLM, model_path='THUDM/visualglm-6b'),
     'mPLUG-Owl2': partial(mPLUG_Owl2, model_path='MAGAer13/mplug-owl2-llama2-7b'),
     'mPLUG-Owl3': partial(mPLUG_Owl3, model_path='mPLUG/mPLUG-Owl3-7B-240728'),
-    'emu2_chat': partial(Emu, model_path='BAAI/Emu2-Chat'),
     'OmniLMM_12B': partial(OmniLMM12B, model_path='openbmb/OmniLMM-12B', root=OmniLMM_ROOT),
     'MGM_7B': partial(Mini_Gemini, model_path='YanweiLi/MGM-7B-HD', root=Mini_Gemini_ROOT),
     'Bunny-llama3-8B': partial(BunnyLLama3, model_path='BAAI/Bunny-v1_1-Llama-3-8B-V'),
@@ -113,6 +112,11 @@
     'Taichu-VL-2B': partial(TaichuVLAPI, model='Taichu-VL-2B', url='https://platform.wair.ac.cn/api/v1/infer/10381/v1/chat/completions'),
 }
 
+emu_series = {
+    'emu2_chat': partial(Emu, model_path='BAAI/Emu2-Chat'),
+    'Emu3_chat': partial(Emu3_chat, model_path='BAAI/Emu3-Chat'),
+    'Emu3_gen': partial(Emu3_gen, model_path='BAAI/Emu3-Gen')
+}
 mmalaya_series = {
     'MMAlaya': partial(MMAlaya, model_path='DataCanvas/MMAlaya'),
     'MMAlaya2': partial(MMAlaya2, model_path='DataCanvas/MMAlaya2'),
@@ -428,7 +432,7 @@
     mantis_series, mmalaya_series, phi3_series, xgen_mm_series, qwen2vl_series,
     slime_series, eagle_series, moondream_series, llama_series, molmo_series,
     kosmos_series, points_series, nvlm_series, vintern_series, h2ovl_series, aria_series,
-    smolvlm_series, sail_series, valley_series, vita_series, ross_series
+    smolvlm_series, sail_series, valley_series, vita_series, ross_series, emu_series
 ]
 
 for grp in model_groups:

vlmeval/vlm/__init__.py

@@ -5,7 +5,7 @@
 from .aria import Aria
 from .base import BaseModel
 from .cogvlm import CogVlm, GLM4v
-from .emu import Emu
+from .emu import Emu, Emu3_chat, Emu3_gen
 from .eagle_x import Eagle
 from .idefics import IDEFICS, IDEFICS2
 from .instructblip import InstructBLIP

vlmeval/vlm/emu.py

@@ -1,9 +1,22 @@
 import os
+import sys
 import torch
 from PIL import Image
 import os.path as osp
 from .base import BaseModel
 from ..smp import *
+from huggingface_hub import snapshot_download
+
+
+def get_local_root(repo_id):
+    if osp.exists(repo_id) and osp.isdir(repo_id):
+        return repo_id
+
+    cache_path = get_cache_path(repo_id, repo_type='models')
+    if cache_path is None:
+        cache_path = snapshot_download(repo_id=repo_id)
+    assert osp.exists(cache_path) and osp.isdir(cache_path)
+    return cache_path
 
 
 class Emu(BaseModel):
@@ -87,3 +100,175 @@ def generate_inner(self, message, dataset=None):
 
         output_text = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
         return output_text[0]
+
+
+class Emu3_chat(BaseModel):
+    INSTALL_REQ = True
+    INTERLEAVE = False
+
+    def __init__(self, model_path='BAAI/Emu3-Chat', tokenizer_path='BAAI/Emu3-VisionTokenizer', **kwargs):
+        assert model_path is not None
+        assert tokenizer_path is not None
+        try:
+            from transformers import AutoTokenizer, AutoModel, AutoImageProcessor, AutoModelForCausalLM
+            local_root = get_local_root(model_path)
+            sys.path.append(local_root)
+            from processing_emu3 import Emu3Processor
+        except Exception as err:
+            raise err
+
+        # load model wights
+        model = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            device_map='cuda',
+            torch_dtype=torch.bfloat16,
+            attn_implementation="flash_attention_2",
+            trust_remote_code=True)
+        model.eval()
+        self.model = model
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, padding_side="left")
+        self.image_processor = AutoImageProcessor.from_pretrained(tokenizer_path, trust_remote_code=True)
+        self.image_tokenizer = AutoModel.from_pretrained(
+            tokenizer_path, device_map='cuda', trust_remote_code=True).eval()
+        self.processor = Emu3Processor(self.image_processor, self.image_tokenizer, self.tokenizer)
+        self.kwargs = kwargs
+        self.cuda = cuda
+
+    def generate_inner(self, message, dataset=None):
+        query, images = '', []
+        for item in message:
+            if item['type'] == 'image':
+                images.append(Image.open(item['value']).convert('RGB'))
+            elif item['type'] == 'text':
+                query += item['value']
+
+        inputs = self.processor(
+            text=[query],
+            image=images,
+            mode='U',
+            return_tensors="pt",
+            padding="longest",
+        )
+        from transformers.generation.configuration_utils import GenerationConfig
+        # prepare hyper parameters
+        GENERATION_CONFIG = GenerationConfig(
+            pad_token_id=self.tokenizer.pad_token_id,
+            bos_token_id=self.tokenizer.bos_token_id,
+            eos_token_id=self.tokenizer.eos_token_id,
+            max_new_tokens=1024,
+        )
+        # generate
+        outputs = self.model.generate(
+            inputs.input_ids.to(self.cuda),
+            GENERATION_CONFIG,
+            attention_mask=inputs.attention_mask.to(self.cuda),
+        )
+
+        outputs = outputs[:, inputs.input_ids.shape[-1]:]
+        response = self.processor.batch_decode(outputs, skip_special_tokens=True)[0]
+        return response
+
+
+class Emu3_gen(BaseModel):
+    INSTALL_REQ = True
+    INTERLEAVE = False
+
+    def __init__(self,
+                 model_path='BAAI/Emu3-Gen',
+                 tokenizer_path='BAAI/Emu3-VisionTokenizer',
+                 output_path='',
+                 **kwargs):
+
+        assert model_path is not None
+        assert tokenizer_path is not None
+        try:
+            from transformers import AutoTokenizer, AutoModel, AutoImageProcessor, AutoModelForCausalLM
+            local_root = get_local_root(model_path)
+            sys.path.append(local_root)
+            from processing_emu3 import Emu3Processor
+        except Exception as err:
+            raise err
+
+        # load model wights
+        model = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            device_map='cuda',
+            torch_dtype=torch.bfloat16,
+            attn_implementation="flash_attention_2",
+            trust_remote_code=True)
+        model.eval()
+        self.model = model
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, padding_side="left")
+        self.image_processor = AutoImageProcessor.from_pretrained(tokenizer_path, trust_remote_code=True)
+        self.image_tokenizer = AutoModel.from_pretrained(
+            tokenizer_path,
+            device_map='cuda',
+            trust_remote_code=True).eval()
+        self.processor = Emu3Processor(self.image_processor, self.image_tokenizer, self.tokenizer)
+        self.kwargs = kwargs
+        self.cuda = cuda
+        self.output_path = output_path
+
+    def generate_inner(self, message, dataset=None):
+        query = ''
+        for item in message:
+            if item['type'] == 'text':
+                query += item['value']
+            else:
+                raise ValueError('Please input the text in generation stage.')
+
+        # prepare input
+        POSITIVE_PROMPT = " masterpiece, film grained, best quality."
+        NEGATIVE_PROMPT = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry."  # noqa: E501
+
+        classifier_free_guidance = 3.0
+        prompt = "a portrait of young girl."
+        prompt += POSITIVE_PROMPT
+
+        kwargs = dict(
+            mode='G',
+            ratio="1:1",
+            image_area=self.model.config.image_area,
+            return_tensors="pt",
+            padding="longest")  # noqa: E501
+
+        pos_inputs = self.processor(text=prompt, **kwargs)
+        neg_inputs = self.processor(text=NEGATIVE_PROMPT, **kwargs)
+        from transformers.generation.configuration_utils import GenerationConfig
+        # prepare hyper parameters
+        GENERATION_CONFIG = GenerationConfig(
+            use_cache=True,
+            eos_token_id=self.model.config.eos_token_id,
+            pad_token_id=self.model.config.pad_token_id,
+            max_new_tokens=40960,
+            do_sample=True,
+            top_k=2048,
+        )
+
+        h = pos_inputs.image_size[:, 0]
+        w = pos_inputs.image_size[:, 1]
+        constrained_fn = self.processor.build_prefix_constrained_fn(h, w)
+        from transformers.generation import LogitsProcessorList, PrefixConstrainedLogitsProcessor, UnbatchedClassifierFreeGuidanceLogitsProcessor  # noqa: E501
+        logits_processor = LogitsProcessorList([
+            UnbatchedClassifierFreeGuidanceLogitsProcessor(
+                classifier_free_guidance,
+                self.model,
+                unconditional_ids=neg_inputs.input_ids.to("cuda:0"),
+            ),
+            PrefixConstrainedLogitsProcessor(constrained_fn, num_beams=1),
+        ])
+
+        # generate
+        outputs = self.model.generate(
+            pos_inputs.input_ids.to("cuda:0"),
+            GENERATION_CONFIG,
+            logits_processor=logits_processor,
+            attention_mask=pos_inputs.attention_mask.to("cuda:0"),
+        )
+
+        mm_list = self.processor.decode(outputs[0])
+        for idx, im in enumerate(mm_list):
+            if not isinstance(im, Image.Image):
+                continue
+            im.save(os.path.join(self.output_path, f"result_{idx}.png"))