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import torch
from torch import nn
from transformers import (AutoModel, GenerationConfig, Qwen2_5_VLForConditionalGeneration,
Qwen2ForCausalLM)
from transformers.modeling_utils import PreTrainedModel
from .configuration_sa2va_chat import Sa2VAChatConfigQwen
from .sam2 import SAM2
import numpy as np
from torchvision.transforms.functional import to_pil_image
import torch.nn.functional as F
from qwen_vl_utils import process_vision_info
class DirectResize:
def __init__(self, target_length: int) -> None:
self.target_length = target_length
def apply_image(self, image: np.ndarray) -> np.ndarray:
"""
Expects a numpy array with shape HxWxC in uint8 format.
"""
img = to_pil_image(image, mode='RGB')
return np.array(img.resize((self.target_length, self.target_length)))
class Sa2VAChatModelQwen(PreTrainedModel):
config_class = Sa2VAChatConfigQwen
main_input_name = 'pixel_values'
base_model_prefix = 'language_model'
_no_split_modules = ['Qwen2_5_VisionTransformerPretrainedModel', 'Qwen2_5_VLDecoderLayer', 'SAM2']
_supports_flash_attn_2 = True
supports_gradient_checkpointing = True
def __init__(self, config: Sa2VAChatConfigQwen, model=None, use_flash_attn=True):
super().__init__(config)
self.extra_image_processor = DirectResize(target_length=1024, )
self.min_pixels = 512 * 28 * 28
self.max_pixels = 2048 * 28 * 28
self.torch_dtype = torch.bfloat16
if model is not None:
self.model=model
else:
self.model = Qwen2_5_VLForConditionalGeneration(config)
self.model._tied_weights_keys = None
llm_hidden_size = config.text_config.hidden_size
self.grounding_encoder = SAM2()
out_dim = self.grounding_encoder.hidden_dim
in_dim = llm_hidden_size
self.text_hidden_fcs = nn.Sequential(
nn.Linear(in_dim, in_dim), nn.ReLU(inplace=True),
nn.Linear(in_dim, out_dim), nn.Dropout(0.0)
)
@property
def lm_head(self):
return self.model.lm_head
def get_input_embeddings(self):
return self.model.get_input_embeddings()
def get_output_embeddings(self):
return self.model.get_output_embeddings()
def predict_forward(
self,
image=None,
video=None,
text=None,
past_text='',
mask_prompts=None,
tokenizer=None,
processor=None,
):
assert processor is not None
self.processor = processor
self.seg_token_idx = self.processor.tokenizer.convert_tokens_to_ids('[SEG]')
text = text.replace('<image>', "")
if image is None and video is None and '<image>' not in past_text:
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": past_text + text},
],
}
]
# Preparation for inference
processsed_text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
mm_inputs = self.processor(
text=[processsed_text],
images=None,
videos=None,
padding=True,
return_tensors="pt",
)
mm_inputs = mm_inputs.to(self.device)
ret_masks = []
else:
input_dict = {}
if video is not None:
pixel_values = []
extra_pixel_values = []
images = []
content = []
ori_image_size = video[0].size
for frame_idx, frame_image in enumerate(video):
# assert ori_image_size == frame_image.size
g_image = np.array(frame_image) # for grounding
g_image = self.extra_image_processor.apply_image(g_image)
g_image = torch.from_numpy(g_image).permute(2, 0, 1).contiguous()
extra_pixel_values.append(g_image)
if frame_idx < 5:
content.append({"type": "image", "image": frame_image},)
content.append({"type": "text", "text": text})
messages = [
{
"role": "user",
"content": content,
}
]
# Preparation for inference
processsed_text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
mm_inputs = self.processor(
text=[processsed_text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
min_pixels=self.min_pixels,
max_pixels=self.max_pixels
)
mm_inputs = mm_inputs.to(self.device)
g_pixel_values = torch.stack([
self.grounding_encoder.preprocess_image(pixel) for pixel in extra_pixel_values
]).to(self.torch_dtype)
num_frames = min(5, len(video))
else:
ori_image_size = image.size
# prepare grounding images
g_image = np.array(image) # for grounding
g_image = self.extra_image_processor.apply_image(g_image)
g_pixel_values = torch.from_numpy(g_image).permute(2, 0, 1).contiguous().to(self.torch_dtype)
extra_pixel_values = [g_pixel_values]
g_pixel_values = torch.stack([
self.grounding_encoder.preprocess_image(pixel) for pixel in extra_pixel_values
]).to(self.torch_dtype)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image,
},
{"type": "text", "text": text},
],
}
]
# Preparation for inference
processsed_text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
mm_inputs = self.processor(
text=[processsed_text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
min_pixels=self.min_pixels,
max_pixels=self.max_pixels
)
mm_inputs = mm_inputs.to(self.device)
num_frames = 1
input_dict['g_pixel_values'] = g_pixel_values
ret_masks = []
generate_output = self.model.generate(
**mm_inputs,
max_new_tokens=2048,
do_sample=False,
output_hidden_states=True,
return_dict_in_generate=True
)
generate_output_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(mm_inputs.input_ids, generate_output.sequences)
]
predict = self.processor.batch_decode(generate_output_trimmed, skip_special_tokens=False)[0].strip()
if image is None and video is None and '<image>' not in past_text:
return {'prediction': predict, 'prediction_masks': ret_masks, }
# if have seg result, find the seg hidden states
hidden_states = generate_output.hidden_states
last_hidden_states = [item[-1][0] for item in hidden_states]
last_hidden_states = torch.cat(last_hidden_states, dim=0)
seg_hidden_states = get_seg_hidden_states(
last_hidden_states, generate_output.sequences[0][:-1],
seg_id=self.seg_token_idx
)
all_seg_hidden_states = self.text_hidden_fcs(seg_hidden_states)
for seg_hidden_states in all_seg_hidden_states:
seg_hidden_states = seg_hidden_states.unsqueeze(0)
g_pixel_values = input_dict['g_pixel_values']
sam_states = self.grounding_encoder.get_sam2_embeddings(g_pixel_values)
pred_masks = self.grounding_encoder.language_embd_inference(sam_states, [seg_hidden_states] * num_frames)
w, h = ori_image_size
masks = F.interpolate(pred_masks, size=(h, w), mode='bilinear', align_corners=False)
masks = masks[:, 0]
masks = masks.sigmoid() > 0.5
masks = masks.cpu().numpy()
ret_masks.append(masks)
return {'prediction': predict, 'prediction_masks': ret_masks,}
def get_seg_hidden_states(hidden_states, output_ids, seg_id):
seg_mask = output_ids == seg_id
n_out = len(seg_mask)
if n_out == 0:
return hidden_states[0:0]
return hidden_states[-n_out:][seg_mask] |