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import datetime
import time
import einops
import numpy as np
import torch
import torch.nn.functional as F
@torch.no_grad()
def evaluate(model, data_loader, fabric):
model.eval()
fabric.print("Computing features for evaluation...")
start_time = time.time()
tar_img_feats = []
query_feats = []
captions = []
pair_ids = []
for ref_img, tar_feat, caption, pair_id, *_ in data_loader:
pair_ids.extend(pair_id.cpu().numpy().tolist())
captions.extend(caption)
device = ref_img.device
ref_img_embs = model.visual_encoder(ref_img)
ref_img_atts = torch.ones(ref_img_embs.size()[:-1], dtype=torch.long).to(device)
text = model.tokenizer(
caption,
padding="longest",
truncation=True,
max_length=64,
return_tensors="pt",
).to(device)
# Shift encoder
encoder_input_ids = text.input_ids.clone()
encoder_input_ids[:, 0] = model.tokenizer.enc_token_id
query_embs = model.text_encoder(
encoder_input_ids,
attention_mask=text.attention_mask,
encoder_hidden_states=ref_img_embs,
encoder_attention_mask=ref_img_atts,
return_dict=True,
)
query_feat = query_embs.last_hidden_state[:, 0, :]
query_feat = F.normalize(model.text_proj(query_feat), dim=-1)
query_feats.append(query_feat.cpu())
# Encode the target image
tar_img_feats.append(tar_feat.cpu())
query_feats = torch.cat(query_feats, dim=0)
tar_img_feats = torch.cat(tar_img_feats, dim=0)
query_feats = F.normalize(query_feats, dim=-1)
tar_img_feats = F.normalize(tar_img_feats, dim=-1)
ref_img_ids = [data_loader.dataset.pairid2ref[pair_id] for pair_id in pair_ids]
tar_img_ids = [data_loader.dataset.pairid2tar[pair_id] for pair_id in pair_ids]
ref_img_ids = torch.tensor(ref_img_ids, dtype=torch.long)
tar_img_ids = torch.tensor(tar_img_ids, dtype=torch.long)
if fabric.world_size > 1:
# Gather tensors from every process
query_feats = fabric.all_gather(query_feats)
tar_img_feats = fabric.all_gather(tar_img_feats)
ref_img_ids = fabric.all_gather(ref_img_ids)
tar_img_ids = fabric.all_gather(tar_img_ids)
query_feats = einops.rearrange(query_feats, "d b e -> (d b) e")
tar_img_feats = einops.rearrange(tar_img_feats, "d b e -> (d b) e")
ref_img_ids = einops.rearrange(ref_img_ids, "d b -> (d b)")
tar_img_ids = einops.rearrange(tar_img_ids, "d b -> (d b)")
if fabric.global_rank == 0:
sim_q2t = (query_feats @ tar_img_feats.t()).cpu().numpy()
# Add zeros where ref_img_id == tar_img_id
for i in range(len(ref_img_ids)):
for j in range(len(tar_img_ids)):
if ref_img_ids[i] == tar_img_ids[j]:
sim_q2t[i][j] = -10
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print("Evaluation time {}".format(total_time_str))
eval_result = eval_recall(sim_q2t)
fabric.print(eval_result)
fabric.log_dict(
{
"val/R1": eval_result["R1"],
"val/R5": eval_result["R5"],
"val/R10": eval_result["R10"],
"val/R_mean": eval_result["R_mean"],
}
)
fabric.barrier()
@torch.no_grad()
def eval_recall(scores_q2t):
# Query->Target
ranks = np.zeros(scores_q2t.shape[0])
for index, score in enumerate(scores_q2t):
inds = np.argsort(score)[::-1]
ranks[index] = np.where(inds == index)[0][0]
# Compute metrics
tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) # type: ignore
tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
tr50 = 100.0 * len(np.where(ranks < 50)[0]) / len(ranks)
tr_mean = (tr1 + tr5 + tr10) / 3
eval_result = {
"R1": round(tr1, 4),
"R5": round(tr5, 4),
"R10": round(tr10, 4),
"R50": round(tr50, 4),
"R_mean": round(tr_mean, 4),
}
return eval_result
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