Spaces:
Sleeping
Sleeping
File size: 4,908 Bytes
ed00004 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
import datetime
import time
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 TestWebVidCoVR:
def __init__(self, remove_self_similarity=True):
self.remove_self_similarity = remove_self_similarity
@torch.no_grad()
def __call__(self, 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()
if self.remove_self_similarity:
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))
recalls = eval_recall(sim_q2t)
recalls["annotation"] = Path(data_loader.dataset.annotation_pth).name
fabric.print(recalls)
# Save results
self_sim = "" if self.remove_self_similarity else "_ss"
json_dump(recalls, f"recalls_covr{self_sim}.json")
print(f"Recalls saved in {Path.cwd()} as recalls_covr{self_sim}.json")
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_mean3 = (tr1 + tr5 + tr10) / 3
tr_mean4 = (tr1 + tr5 + tr10 + tr50) / 4
eval_result = {
"R1": round(tr1, 2),
"R5": round(tr5, 2),
"R10": round(tr10, 2),
"R50": round(tr50, 2),
"meanR3": round(tr_mean3, 2),
"meanR4": round(tr_mean4, 2),
}
return eval_result
|