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grounding_eval_singlegpu_refclef.py
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grounding_eval_singlegpu_refclef.py
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import argparse
import os
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import glob
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch.utils.data import DataLoader
from models.model_eval import ALBEF
from models.vit import interpolate_pos_embed
from models.tokenization_bert import BertTokenizer
import utils
from dataset import create_dataset, create_sampler, create_loader
from dataset.utils import collect_result, grounding_eval
from scheduler import create_scheduler
from optim import create_optimizer
from refTools.refer_python3 import REFER
from pdb import set_trace as breakpoint
def val(model, data_loader, tokenizer, device, gradcam_mode, block_num):
# test
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Evaluation:'
print_freq = 150
if gradcam_mode=='itm':
model.text_encoder.base_model.base_model.encoder.layer[block_num].crossattention.self.save_attention = True
result = []
for image, text, ref_ids, image_path, splits in metric_logger.log_every(data_loader, print_freq, header):
image = image.to(device)
text_input = tokenizer(text, padding='longest', return_tensors="pt").to(device)
if gradcam_mode=='itm':
image_embeds = model.visual_encoder(image)
image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
output = model.text_encoder(text_input.input_ids,
attention_mask = text_input.attention_mask,
encoder_hidden_states = image_embeds,
encoder_attention_mask = image_atts,
return_dict = True,
)
vl_embeddings = output.last_hidden_state[:,0,:]
vl_output = model.itm_head(vl_embeddings)
loss = vl_output[:,1].sum()
model.zero_grad()
loss.backward()
with torch.no_grad():
mask = text_input.attention_mask.view(text_input.attention_mask.size(0),1,-1,1,1)
grads = model.text_encoder.base_model.base_model.encoder.layer[block_num].crossattention.self.get_attn_gradients().detach()
cams = model.text_encoder.base_model.base_model.encoder.layer[block_num].crossattention.self.get_attention_map().detach()
cams = cams[:, :, :, 1:].reshape(image.size(0), 12, -1, 24, 24) * mask
grads = grads[:, :, :, 1:].clamp(min=0).reshape(image.size(0), 12, -1, 24, 24) * mask
gradcam = cams * grads
gradcam = gradcam.mean(1).mean(1)
elif gradcam_mode=='itc':
image_embeds = model.visual_encoder(image, register_blk=block_num)
image_feat = F.normalize(model.vision_proj(image_embeds[:,0,:]),dim=-1)
text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask,
return_dict = True, mode = 'text')
text_embeds = text_output.last_hidden_state
text_feat = F.normalize(model.text_proj(text_embeds[:,0,:]),dim=-1)
sim = image_feat@text_feat.t()/model.temp
loss = sim.diag().sum()
model.zero_grad()
loss.backward()
with torch.no_grad():
grad = model.visual_encoder.blocks[block_num].attn.get_attn_gradients().detach()
cam = model.visual_encoder.blocks[block_num].attn.get_attention_map().detach()
cam = cam[:, :, 0, 1:].reshape(image.size(0), -1, 24, 24)
grad = grad[:, :, 0, 1:].reshape(image.size(0), -1, 24, 24).clamp(0)
gradcam = (cam * grad).mean(1)
for r_id, cam, path, split in zip(ref_ids, gradcam, image_path, splits):
result.append({'ref_id':r_id.item(), 'pred':cam, 'image_path': path, 'split':split})
if gradcam_mode=='itm':
model.text_encoder.base_model.base_model.encoder.layer[block_num].crossattention.self.save_attention = False
return result
def main(args, config):
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
cudnn.benchmark = True
#### Dataset ####
print("Creating dataset")
print(config['test_file'])
grd_test_dataset = create_dataset('grounding', config)
datasets = [grd_test_dataset]
samplers = [None, None]
test_loader = create_loader(datasets,samplers,batch_size=[config['batch_size']], \
num_workers=[4], is_trains=[False], collate_fns=[None])[0]
tokenizer = BertTokenizer.from_pretrained(args.text_encoder)
## refcoco evaluation tools
refer = REFER(config['refcoco_data'], 'refclef', 'unc')
dets = None
#### Model ####
print("Creating model")
model = ALBEF(config = config, text_encoder=args.text_encoder, tokenizer=tokenizer)
model = model.to(device)
if os.path.isdir(args.checkpoint):
all_ = glob.glob('{}/*.pth'.format(args.checkpoint))
all_.sort()
for checkpoint in all_:
filename = 'epoch'+checkpoint[-6:-4]
final_result_file = os.path.join(args.result_dir, '%s.pth'%filename)
if os.path.isfile(final_result_file):
continue
# load pre-trained model
ckpt = torch.load(checkpoint, map_location='cpu')
state_dict = ckpt['model']
pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped
m_pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],model.visual_encoder_m)
state_dict['visual_encoder_m.pos_embed'] = m_pos_embed_reshaped
for key in list(state_dict.keys()):
if 'bert' in key:
encoder_key = key.replace('bert.','')
state_dict[encoder_key] = state_dict[key]
del state_dict[key]
msg = model.load_state_dict(state_dict,strict=False)
print('load checkpoint from %s'%checkpoint)
print(msg)
del ckpt
result = val(model, test_loader, tokenizer, device, args.gradcam_mode, args.block_num)
torch.save(result,final_result_file)
print('result file saved to %s'%final_result_file)
grounding_acc, mean = grounding_eval(result, dets, refer, alpha=0.5, mask_size=24, on_bbox = False, subset = False)
log_stats = {**{f'{k}': v for k, v in grounding_acc.items()},
'epoch': checkpoint[-6:-4],
}
with open(os.path.join(args.result_dir, "log.txt"),"a") as f:
f.write(json.dumps(log_stats) + "\n")
else:
filename = 'epoch00'
final_result_file = os.path.join(args.result_dir, '%s.pth'%filename)
if os.path.isfile(final_result_file):
return
# load pre-trained model
ckpt = torch.load(args.checkpoint, map_location='cpu')
state_dict = ckpt['model']
pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped
m_pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],model.visual_encoder_m)
state_dict['visual_encoder_m.pos_embed'] = m_pos_embed_reshaped
for key in list(state_dict.keys()):
if 'bert' in key:
encoder_key = key.replace('bert.','')
state_dict[encoder_key] = state_dict[key]
del state_dict[key]
msg = model.load_state_dict(state_dict,strict=False)
print('load checkpoint from %s'%args.checkpoint)
print(msg)
del ckpt
result = val(model, test_loader, tokenizer, device, args.gradcam_mode, args.block_num)
torch.save(result,final_result_file)
print('result file saved to %s'%final_result_file)
grounding_acc, mean = grounding_eval(result, dets, refer, alpha=0.5, mask_size=24, on_bbox = False, subset = False)
log_stats = {**{f'{k}': v for k, v in grounding_acc.items()},
'epoch': '00',
}
with open(os.path.join(args.result_dir, "log.txt"),"a") as f:
f.write(json.dumps(log_stats) + "\n")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='./configs/Grounding.yaml')
parser.add_argument('--checkpoint', default='')
parser.add_argument('--output_dir', default='output/RefCOCO')
parser.add_argument('--gradcam_mode', default='itm', choices=['itm','itc'])
parser.add_argument('--block_num', default=8, type=int)
parser.add_argument('--text_encoder', default='bert-base-uncased')
parser.add_argument('--device', default='cuda')
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
parser.add_argument('--distributed', default=True, type=bool)
args = parser.parse_args()
config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
args.result_dir = os.path.join(args.output_dir, 'result')
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
Path(args.result_dir).mkdir(parents=True, exist_ok=True)
yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))
main(args, config)