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import datetime
import time
from collections import OrderedDict
from pathlib import Path
import einops
import numpy as np
import torch
import torch.nn.functional as F
from src.tools.files import json_dump
class TestCirr:
def __init__(self):
pass
@staticmethod
@torch.no_grad()
def __call__(model, data_loader, fabric):
model.eval()
fabric.print("Computing features for test...")
start_time = time.time()
tar_img_feats = []
query_feats = []
pair_ids = []
for ref_img, tar_feat, caption, pair_id, *_ in data_loader:
pair_ids.extend(pair_id.cpu().numpy().tolist())
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())
pair_ids = torch.tensor(pair_ids, dtype=torch.long)
query_feats = torch.cat(query_feats, dim=0)
tar_img_feats = torch.cat(tar_img_feats, dim=0)
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)
pair_ids = fabric.all_gather(pair_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")
pair_ids = einops.rearrange(pair_ids, "d b -> (d b)")
if fabric.global_rank == 0:
pair_ids = pair_ids.cpu().numpy().tolist()
assert len(query_feats) == len(pair_ids)
img_ids = [data_loader.dataset.pairid2ref[pair_id] for pair_id in pair_ids]
assert len(img_ids) == len(pair_ids)
id2emb = OrderedDict()
for img_id, tar_img_feat in zip(img_ids, tar_img_feats):
if img_id not in id2emb:
id2emb[img_id] = tar_img_feat
tar_feats = torch.stack(list(id2emb.values()), dim=0)
sims_q2t = query_feats @ tar_feats.T
# Create a mapping from pair_id to row index for faster lookup
pairid2index = {pair_id: i for i, pair_id in enumerate(pair_ids)}
# Create a mapping from target_id to column index for faster lookup
tarid2index = {tar_id: j for j, tar_id in enumerate(id2emb.keys())}
# Update the similarity matrix based on the condition
for pair_id, query_feat in zip(pair_ids, query_feats):
que_id = data_loader.dataset.pairid2ref[pair_id]
if que_id in tarid2index:
sims_q2t[pairid2index[pair_id], tarid2index[que_id]] = -100
sims_q2t = sims_q2t.cpu().numpy()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print("Evaluation time {}".format(total_time_str))
recalls = {}
recalls["version"] = "rc2"
recalls["metric"] = "recall"
recalls_subset = {}
recalls_subset["version"] = "rc2"
recalls_subset["metric"] = "recall_subset"
target_imgs = np.array(list(id2emb.keys()))
assert len(sims_q2t) == len(pair_ids)
for pair_id, query_sims in zip(pair_ids, sims_q2t):
sorted_indices = np.argsort(query_sims)[::-1]
query_id_recalls = list(target_imgs[sorted_indices][:50])
query_id_recalls = [
str(data_loader.dataset.int2id[x]) for x in query_id_recalls
]
recalls[str(pair_id)] = query_id_recalls
members = data_loader.dataset.pairid2members[pair_id]
query_id_recalls_subset = [
target
for target in target_imgs[sorted_indices]
if target in members
]
query_id_recalls_subset = [
data_loader.dataset.int2id[x] for x in query_id_recalls_subset
][:3]
recalls_subset[str(pair_id)] = query_id_recalls_subset
json_dump(recalls, "recalls_cirr.json")
json_dump(recalls_subset, "recalls_cirr_subset.json")
print(f"Recalls saved in {Path.cwd()} as recalls_cirr.json")
fabric.barrier()
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