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product_evaluation.py
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product_evaluation.py
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import torch
import utils
import torch.nn.functional as F
import time, datetime, os
import torch.distributed as dist
import numpy as np
import heapq
import json
from data import create_dataset, create_loader
def read_json(file):
f=open(file,"r",encoding="utf-8").read()
return json.loads(f)
@torch.no_grad()
def evaluation(model, data_loader, device, args, config):
# test
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Evaluation:'
print('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=config['max_words'], return_tensors="pt").to(device)
text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text')
text_embed = F.normalize(model.text_proj(text_output.last_hidden_state[:,0,:]))
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)
image_feats = []
image_embeds = []
for image, img_id in data_loader:
image = image.to(device)
image_feat = model.visual_encoder(image)
image_embed = model.vision_proj(image_feat[:,0,:])
image_embed = F.normalize(image_embed,dim=-1)
image_feats.append(image_feat.cpu())
image_embeds.append(image_embed)
image_feats = torch.cat(image_feats,dim=0)
image_embeds = torch.cat(image_embeds,dim=0)
#i2t
sims_matrix = image_embeds @ text_embeds.t()
score_matrix_i2t = torch.full((len(data_loader.dataset.image),len(texts)),-100.0).to(device)
num_tasks = utils.get_world_size()
rank = 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], 10000, header)):
topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
score_matrix_i2t[start+i,topk_idx] = topk_sim
#t2i
sims_matrix = sims_matrix.t()
score_matrix_t2i = torch.full((len(texts),len(data_loader.dataset.image)),-100.0).to(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], 10000, header)):
topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
score_matrix_t2i[start+i,topk_idx] = topk_sim
if args.distributed:
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)))
print('Evaluation time {}'.format(total_time_str))
return score_matrix_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy()
@torch.no_grad()
def itm_eval(scores_i2t, scores_t2i, txt2img, img2txt):
#Images->Text
ranks = np.zeros(scores_i2t.shape[0])
for index,score in enumerate(scores_i2t):
inds = np.argsort(score)[::-1]
# Score
rank = 1e20
for i in img2txt[index]:#list
tmp = np.where(inds == i)[0][0]
if tmp < rank:
rank = tmp
ranks[index] = rank
# Compute metrics
tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
test_list = [576,690,120,110,141,252,263,309,385]
test_num = 0
for i in range(len(test_list)):
test_num+=test_list[i]
if test_num>=len(ranks):
tag = test_num - test_list[i]
if tag==0:
tag=1
break
tr_task0 = 100.0 * len(np.where(ranks[:tag] < 1)[0]) / len(ranks[:tag])
tr_task1 = 100.0 * len(np.where(ranks[tag:] < 1)[0]) / len(ranks[tag:])
#Text->Images
ranks = np.zeros(scores_t2i.shape[0])
for index,score in enumerate(scores_t2i):
inds = np.argsort(score)[::-1]
ranks[index] = np.where(inds == txt2img[index])[0][0]
# Compute metrics
ir1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
ir5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
ir10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
ir_task0 = 100.0 * len(np.where(ranks[:tag] < 1)[0]) / len(ranks[:tag])
ir_task1 = 100.0 * len(np.where(ranks[tag:] < 1)[0]) / len(ranks[tag:])
print(f'task0 tr/ir{tr_task0:.2f}/{ir_task0:.2f}, task1 tr/ir{tr_task1:.2f}/{ir_task1:.2f}')
tr_mean = (tr1 + tr5 + tr10) / 3
ir_mean = (ir1 + ir5 + ir10) / 3
r_mean = (tr_mean + ir_mean) / 2
eval_result = {'txt_r1': tr1,
'txt_r5': tr5,
'txt_r10': tr10,
'txt_r_mean': tr_mean,
'img_r1': ir1,
'img_r5': ir5,
'img_r10': ir10,
'img_r_mean': ir_mean,
'r_mean': r_mean}
return eval_result
def compute_ap(rank_list,pos_set,topk):
'''
rank_list:
pos_list:
rank_list=["a","d","b","c"]
pos_set=["b","c"]
ap=compute_ap(rank_list,pos_set)
print("ap: ",ap)
'''
intersect_size=0
ap=0
for i in range(topk):
if rank_list[i] in pos_set:
intersect_size += 1
precision = intersect_size / (i+1)
ap+=precision
if intersect_size==0:
return 0
ap/=intersect_size
return ap
@torch.no_grad()
def compute_gallery(model,data_loader,device):
model.eval()
item_ids = []
vl_embeds = []
for item_id, image, caption in data_loader:
image = image.to(device)
vl_embed = model.get_VL_feature(image,caption)
vl_embed = F.normalize(vl_embed,dim=-1)
vl_embeds.append(vl_embed)
item_ids+=item_id
vl_embeds = torch.vstack(vl_embeds)
item_ids = np.hstack(item_ids)
return vl_embeds, item_ids
@torch.no_grad()
def eval_gallery(score_matrix, query_ids, gallery_ids, query_id_label, gallery_id_label, gallery_label_id):
max_topk = 10
retrieval_results = []
for q,each_score in zip(query_ids,score_matrix):
max_index = heapq.nlargest(max_topk, range(len(each_score)), each_score.take)
topk_item_id = gallery_ids[max_index]
topk_item_id=[each_item_id for each_item_id in topk_item_id if each_item_id!=q]
retrieval_results.append([q]+topk_item_id)
topk_list=[1,5,10]
results={}
for topk in topk_list:
topk_temp=topk
mAP, cnt = 0,0
for index, rank_list in enumerate(retrieval_results):
query_id=rank_list[0]
rank_id_list=rank_list[1:]
pos_set=[]
cnt+=1
query_labels=query_id_label[query_id]["label"]
pos_set = gallery_label_id[query_labels]
topk = min(topk_temp, len(pos_set),len(rank_id_list))
ap=compute_ap(rank_id_list,pos_set,topk)
mAP+=ap
mAP=mAP/cnt*100
results["top{}".format(topk_temp)]={
"mAP": mAP,
}
return results
@torch.no_grad()
def evaluation_multi_modal(config, model, query_loader, gallery_loader, device):
query_id_label, gallery_id_label, gallery_label_id = {},{},{}
query_json, gallery_json = read_json(config['query_file']), read_json(config['gallery_file'])
for item_id,info in gallery_json.items():
label = info["cate_name"]
gallery_id_label[item_id]={"label":label}
if label not in gallery_label_id:
gallery_label_id[label]=[item_id]
else:
gallery_label_id[label]+=[item_id]
for item_id,info in query_json.items():
label = info["cate_name"]
query_id_label[item_id]={"label":label}
start_time = time.time()
query_vt_embed, query_item_id = compute_gallery(model,query_loader,device)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('compute query time {}'.format(total_time_str))
start_time = time.time()
gallery_vt_embed, gallery_item_id = compute_gallery(model,gallery_loader,device)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('compute gallery time {}'.format(total_time_str))
print(f'query shape: {query_vt_embed.shape}, gallery shape: {gallery_vt_embed.shape}')
sims_matrix_it2it = query_vt_embed @ gallery_vt_embed.t()
start_time = time.time()
result_vt = eval_gallery(sims_matrix_it2it.cpu().numpy(), query_item_id, gallery_item_id, query_id_label, gallery_id_label, gallery_label_id)
print(f'reslut it2it: {result_vt}')
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Evaluation time {}'.format(total_time_str))
print('{:.2f}/{:.2f}/{:.2f}'.format(result_vt['top1']['mAP'],result_vt['top5']['mAP'],result_vt['top10']['mAP']))
return {'map1_vt':result_vt['top1']['mAP'],'map5_vt':result_vt['top5']['mAP'], 'map10_vt':result_vt['top10']['mAP']}
def eval_all(args, config, device, model_without_ddp):
results = {}
results_map={}
task_list=[]
for iteration, task_i in enumerate(config['task']):
task_list.append(task_i)
print(task_i)
test_dataset = create_dataset('product_test', config, task_i_list=task_list, min_scale=0.2)
test_loader = create_loader([test_dataset],samplers=[None],batch_size=[config['batch_size_test']], num_workers=[4], is_trains=[False], collate_fns=[None])[0]
checkpoint = torch.load(os.path.join(args.output_dir, 'task_%02d.pth'%iteration), map_location='cpu')
state_dict = checkpoint['model']
model_without_ddp.load_state_dict(state_dict,strict=False)
score_test_i2t, score_test_t2i = evaluation(model_without_ddp, test_loader, device, args, config)
if utils.is_main_process():
results[iteration] = itm_eval(score_test_i2t, score_test_t2i, test_loader.dataset.txt2img, test_loader.dataset.img2txt)
print(results[iteration])
query_dataset, galley_dataset = create_dataset('product_query', config, task_i_list=task_list), create_dataset('product_gallery',config, task_i_list=task_list)
query_loader, gallery_loader = create_loader([query_dataset,galley_dataset],[None,None], batch_size=[512,512],num_workers=[4,4], is_trains=[False,False],collate_fns=[None,None])
results_map[iteration] = evaluation_multi_modal(config, model_without_ddp, query_loader=query_loader,gallery_loader=gallery_loader,device=device)
for iteration, task_i in enumerate(config['task']):
task_i_result = results[iteration]
print(f'{iteration} {task_i}:{task_i_result}')
for iteration, task_i in enumerate(config['task']):
task_i_result = results[iteration]
txt_r1,img_r1,mean_r1,r_mean = task_i_result['txt_r1'],task_i_result['img_r1'],(task_i_result['txt_r1']+task_i_result['img_r1'])/2,task_i_result['r_mean']
print('{:.2f}/{:.2f}/{:.2f}/{:.2f}'.format(txt_r1,img_r1,mean_r1,r_mean))
print('VT@map')
for iteration, task_i in enumerate(config['task']):
print('{:.2f}/{:.2f}/{:.2f}'.format(results_map[iteration]['map1_vt'],results_map[iteration]['map5_vt'],results_map[iteration]['map10_vt']))