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import datetime
import time
from pathlib import Path
from typing import Dict, List
import einops
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from tabulate import tabulate
from src.tools.files import json_dump, json_load
class TestFashionIQ:
def __init__(self, category: str):
self.category = category
pass
@torch.no_grad()
def __call__(self, model, data_loader, fabric):
model.eval()
fabric.print("Computing features for evaluation...")
start_time = time.time()
query_feats = []
captions = []
idxs = []
for ref_img, _, caption, idx in data_loader:
idxs.extend(idx.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())
query_feats = torch.cat(query_feats, dim=0)
query_feats = F.normalize(query_feats, dim=-1)
idxs = torch.tensor(idxs, dtype=torch.long)
if fabric.world_size > 1:
# Gather tensors from every process
query_feats = fabric.all_gather(query_feats)
idxs = fabric.all_gather(idxs)
query_feats = einops.rearrange(query_feats, "d b e -> (d b) e")
idxs = einops.rearrange(idxs, "d b -> (d b)")
if fabric.global_rank == 0:
idxs = idxs.cpu().numpy()
ref_img_ids = [data_loader.dataset.pairid2ref[idx] for idx in idxs]
ref_img_ids = [data_loader.dataset.int2id[id] for id in ref_img_ids]
tar_img_feats = []
tar_img_ids = []
for target_id in data_loader.dataset.target_ids:
tar_img_ids.append(target_id)
target_emb_pth = data_loader.dataset.id2embpth[target_id]
target_feat = torch.load(target_emb_pth).cpu()
tar_img_feats.append(target_feat.cpu())
tar_img_feats = torch.stack(tar_img_feats)
tar_img_feats = F.normalize(tar_img_feats, dim=-1)
tar_img_feats = tar_img_feats.to(query_feats.device)
sim_q2t = (query_feats @ tar_img_feats.t()).cpu()
# 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))
ref_img_ids = np.array(ref_img_ids)
tar_img_ids = np.array(tar_img_ids)
cor_img_ids = [data_loader.dataset.pairid2tar[idx] for idx in idxs]
cor_img_ids = [data_loader.dataset.int2id[id] for id in cor_img_ids]
recalls = get_recalls_labels(sim_q2t, cor_img_ids, tar_img_ids)
fabric.print(recalls)
# Save results
json_dump(recalls, f"recalls_fiq-{self.category}.json")
print(f"Recalls saved in {Path.cwd()} as recalls_fiq-{self.category}.json")
mean_results(fabric=fabric)
fabric.barrier()
# From google-research/composed_image_retrieval
def recall_at_k_labels(sim, query_lbls, target_lbls, k=10):
distances = 1 - sim
sorted_indices = torch.argsort(distances, dim=-1).cpu()
sorted_index_names = np.array(target_lbls)[sorted_indices]
labels = torch.tensor(
sorted_index_names
== np.repeat(np.array(query_lbls), len(target_lbls)).reshape(
len(query_lbls), -1
)
)
assert torch.equal(
torch.sum(labels, dim=-1).int(), torch.ones(len(query_lbls)).int()
)
return round((torch.sum(labels[:, :k]) / len(labels)).item() * 100, 2)
def get_recalls_labels(
sims, query_lbls, target_lbls, ks: List[int] = [1, 5, 10, 50]
) -> Dict[str, float]:
return {f"R{k}": recall_at_k_labels(sims, query_lbls, target_lbls, k) for k in ks}
def mean_results(dir=".", fabric=None, save=True):
dir = Path(dir)
recall_pths = list(dir.glob("recalls_fiq-*.json"))
recall_pths.sort()
if len(recall_pths) != 3:
return
df = {}
for pth in recall_pths:
name = pth.name.split("_")[1].split(".")[0]
data = json_load(pth)
df[name] = data
df = pd.DataFrame(df)
# FASHION-IQ
df_fiq = df[df.columns[df.columns.str.contains("fiq")]]
assert len(df_fiq.columns) == 3
df_fiq["Average"] = df_fiq.mean(axis=1)
df_fiq["Average"] = df_fiq["Average"].apply(lambda x: round(x, 2))
headers = [
"dress\nR10",
"dress\nR50",
"shirt\nR10",
"shirt\nR50",
"toptee\nR10",
"toptee\nR50",
"Average\nR10",
"Average\nR50",
]
fiq = []
for category in ["fiq-dress", "fiq-shirt", "fiq-toptee", "Average"]:
for recall in ["R10", "R50"]:
value = df_fiq.loc[recall, category]
value = str(value).zfill(2)
fiq.extend([value])
if fabric is None:
print(tabulate([fiq], headers=headers, tablefmt="latex_raw"))
print(" & ".join(fiq))
else:
fabric.print(tabulate([fiq], headers=headers))
fabric.print(" & ".join(fiq))
if save:
df_mean = df_fiq["Average"].to_dict()
df_mean = {k + "_mean": round(v, 2) for k, v in df_mean.items()}
json_dump(df_mean, "recalls_fiq-mean.json")
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