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try:
import spaces
gpu_decorator = spaces.GPU
from .load_model import load_xclip
except ImportError:
# Define a no-operation decorator as fallback
def gpu_decorator(func):
return func
import PIL
import torch
from .prompts import GetPromptList
ORG_PART_ORDER = ['back', 'beak', 'belly', 'breast', 'crown', 'forehead', 'eyes', 'legs', 'wings', 'nape', 'tail', 'throat']
ORDERED_PARTS = ['crown', 'forehead', 'nape', 'eyes', 'beak', 'throat', 'breast', 'belly', 'back', 'wings', 'legs', 'tail']
def encode_descs_xclip(owlvit_det_processor: callable, model: callable, descs: list[str], device: str, max_batch_size: int = 512):
total_num_batches = len(descs) // max_batch_size + 1
with torch.no_grad():
text_embeds = []
for batch_idx in range(total_num_batches):
query_descs = descs[batch_idx*max_batch_size:(batch_idx+1)*max_batch_size]
query_tokens = owlvit_det_processor(text=query_descs, padding="max_length", truncation=True, return_tensors="pt").to(device)
query_embeds = model.owlvit.get_text_features(**query_tokens)
text_embeds.append(query_embeds.cpu().float())
text_embeds = torch.cat(text_embeds, dim=0)
return text_embeds.to(device)
# def encode_descs_clip(model: callable, descs: list[str], device: str, max_batch_size: int = 512):
# total_num_batches = len(descs) // max_batch_size + 1
# with torch.no_grad():
# text_embeds = []
# for batch_idx in range(total_num_batches):
# desc = descs[batch_idx*max_batch_size:(batch_idx+1)*max_batch_size]
# query_tokens = clip.tokenize(desc).to(device)
# text_embeds.append(model.encode_text(query_tokens).cpu().float())
# text_embeds = torch.cat(text_embeds, dim=0)
# text_embeds = torch.nn.functional.normalize(text_embeds, dim=-1)
# return text_embeds.to(device)
@gpu_decorator
def xclip_pred(new_desc: dict,
new_part_mask: dict,
new_class: str,
org_desc: str,
image: PIL.Image,
model: callable,
owlvit_processor: callable,
device: str,
return_img_embeds: bool = False,
use_precompute_embeddings = True,
image_name: str = None,
cub_embeds: torch.Tensor = None,
cub_idx2name: dict = None,
descriptors: dict = None):
# check if in huggingface space
try:
model.to('cuda')
device = 'cuda'
except:
device = torch.cuda.current_device() if torch.cuda.is_available() else 'cpu'
# reorder the new description and the mask
if new_class is not None:
new_desc_ = {k: new_desc[k] for k in ORG_PART_ORDER}
new_part_mask_ = {k: new_part_mask[k] for k in ORG_PART_ORDER}
desc_mask = list(new_part_mask_.values())
else:
desc_mask = [1] * 12
if cub_embeds is None:
# replace the description if the new class is in the description, otherwise add a new class
getprompt = GetPromptList(org_desc)
if new_class not in getprompt.desc and new_class is not None:
getprompt.name2idx[new_class] = len(getprompt.name2idx)
if new_class is not None:
getprompt.desc[new_class] = list(new_desc_.values())
idx2name = dict(zip(getprompt.name2idx.values(), getprompt.name2idx.keys()))
modified_class_idx = getprompt.name2idx[new_class] if new_class is not None else None
n_classes = len(getprompt.name2idx)
descs, class_idxs, class_mapping, org_desc_mapper, class_list = getprompt('chatgpt-no-template', max_len=12, pad=True)
query_embeds = encode_descs_xclip(owlvit_processor, model, descs, device)
else:
cub_embeds = cub_embeds.to(device)
if new_class is not None:
if new_class in list(cub_idx2name.values()):
new_class = f"{new_class}_custom"
idx2name = cub_idx2name | {200: new_class}
descriptors |= {new_class: list(new_desc_.values())}
n_classes = 201
query_tokens = owlvit_processor(text=list(new_desc_.values()), padding="max_length", truncation=True, return_tensors="pt").to(device)
new_class_embed = model.owlvit.get_text_features(**query_tokens)
query_embeds = torch.cat([cub_embeds, new_class_embed], dim=0)
modified_class_idx = 200
else:
n_classes = 200
query_embeds = cub_embeds
idx2name = cub_idx2name
modified_class_idx = None
model.cls_head.num_classes = n_classes
with torch.no_grad():
part_embeds = owlvit_processor(text=[ORG_PART_ORDER], return_tensors="pt").to(device)
if use_precompute_embeddings:
image_embeds = torch.load(f'data/image_embeddings/{image_name}.pt').to(device)
else:
image_input = owlvit_processor(images=image, return_tensors='pt').to(device)
image_embeds, _ = model.image_embedder(pixel_values = image_input['pixel_values'])
pred_logits, part_logits, output_dict = model(image_embeds, part_embeds, query_embeds, None)
b, c, n = part_logits.shape
mask = torch.tensor(desc_mask, dtype=float).unsqueeze(0).unsqueeze(0).repeat(b, c, 1).to(device)
# overwrite the pred_logits
part_logits = part_logits * mask
pred_logits = torch.sum(part_logits, dim=-1)
pred_class_idx = torch.argmax(pred_logits, dim=-1).cpu()
pred_class_name = idx2name[pred_class_idx.item()]
softmax_scores = torch.softmax(pred_logits, dim=-1).cpu()
softmax_score_top1 = torch.topk(softmax_scores, k=1, dim=-1)[0].squeeze(-1).item()
part_scores = part_logits[0, pred_class_idx].cpu().squeeze(0)
part_scores_dict = dict(zip(ORG_PART_ORDER, part_scores.tolist()))
if modified_class_idx is not None:
modified_score = softmax_scores[0, modified_class_idx].item()
modified_part_scores = part_logits[0, modified_class_idx].cpu().squeeze(0)
modified_part_scores_dict = dict(zip(ORG_PART_ORDER, modified_part_scores.tolist()))
else:
modified_score = None
modified_part_scores_dict = None
output_dict = {"pred_class": pred_class_name,
"pred_score": softmax_score_top1,
"pred_desc_scores": part_scores_dict,
"descriptions": descriptors[pred_class_name],
"modified_class": new_class,
"modified_score": modified_score,
"modified_desc_scores": modified_part_scores_dict,
"modified_descriptions": descriptors.get(new_class),
}
return (output_dict, image_embeds) if return_img_embeds else output_dict
# def sachit_pred(new_desc: list,
# new_class: str,
# org_desc: str,
# image: PIL.Image,
# model: callable,
# preprocess: callable,
# device: str,
# ):
# # replace the description if the new class is in the description, otherwise add a new class
# getprompt = GetPromptList(org_desc)
# if new_class not in getprompt.desc:
# getprompt.name2idx[new_class] = len(getprompt.name2idx)
# getprompt.desc[new_class] = new_desc
# idx2name = dict(zip(getprompt.name2idx.values(), getprompt.name2idx.keys()))
# modified_class_idx = getprompt.name2idx[new_class]
# descs, class_idxs, class_mapping, org_desc_mapper, class_list = getprompt('Sachit-descriptors', max_len=12, pad=True)
# text_embeds = encode_descs_clip(model, descs, device)
# with torch.no_grad():
# image_embed = model.encode_image(preprocess(image).unsqueeze(0).to(device))
# desc_mask = torch.tensor(class_idxs)
# desc_mask = torch.where(desc_mask == -1, 0, 1).unsqueeze(0).to(device)
# sim = torch.matmul(image_embed.float(), text_embeds.T)
# sim = (sim * desc_mask).view(1, -1, 12)
# pred_scores = torch.sum(sim, dim=-1)
# pred_class_idx = torch.argmax(pred_scores, dim=-1).cpu()
# pred_class = idx2name[pred_class_idx.item()]
# softmax_scores = torch.nn.functional.softmax(pred_scores, dim=-1).cpu()
# top1_score = torch.topk(softmax_scores, k=1, dim=-1)[0].squeeze(-1).item()
# modified_score = softmax_scores[0, modified_class_idx].item()
# pred_desc_scores = sim[0, pred_class_idx].cpu().squeeze(0)
# modified_class_scores = sim[0, modified_class_idx].cpu().squeeze(0)
# output_dict = {"pred_class": pred_class,
# "pred_score": top1_score,
# "pred_desc_scores": pred_desc_scores.tolist(),
# "descriptions": getprompt.desc[pred_class],
# "modified_class": new_class,
# "modified_score": modified_score,
# "modified_desc_scores": modified_class_scores.tolist(),
# "modified_descriptions": getprompt.desc[new_class],
# }
# return output_dict |