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helpers.py
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helpers.py
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import torch
import torchvision.transforms as T
from torch.utils.data import DataLoader
from proxy_anchor.utils import calc_recall_at_k
from hyptorch.pmath import dist_matrix
import PIL
import multiprocessing
class MultiSample:
def __init__(self, transform, n=2):
self.transform = transform
self.num = n
def __call__(self, x):
return tuple(self.transform(x) for _ in range(self.num))
def evaluate(get_emb_f, ds_name, hyp_c):
if ds_name != "Inshop":
emb_head = get_emb_f(ds_type="eval")
emb_body = get_emb_f(ds_type="eval", skip_head=True)
recall_head = get_recall(*emb_head, ds_name, hyp_c)
recall_body = get_recall(*emb_body, ds_name, 0)
else:
emb_head_query = get_emb_f(ds_type="query")
emb_head_gal = get_emb_f(ds_type="gallery")
emb_body_query = get_emb_f(ds_type="query", skip_head=True)
emb_body_gal = get_emb_f(ds_type="gallery", skip_head=True)
recall_head = get_recall_inshop(*emb_head_query, *emb_head_gal, hyp_c)
recall_body = get_recall_inshop(*emb_body_query, *emb_body_gal, 0)
return recall_head, recall_body
def get_recall(x, y, ds_name, hyp_c):
if ds_name == "CUB" or ds_name == "Cars":
k_list = [1, 2, 4, 8, 16, 32]
elif ds_name == "SOP":
k_list = [1, 10, 100, 1000]
if hyp_c > 0:
dist_m = torch.empty(len(x), len(x), device="cuda")
for i in range(len(x)):
dist_m[i : i + 1] = -dist_matrix(x[i : i + 1], x, hyp_c)
else:
dist_m = x @ x.T
y_cur = y[dist_m.topk(1 + max(k_list), largest=True)[1][:, 1:]]
y = y.cpu()
y_cur = y_cur.float().cpu()
recall = [calc_recall_at_k(y, y_cur, k) for k in k_list]
print(recall)
return recall[0]
def get_recall_inshop(xq, yq, xg, yg, hyp_c):
if hyp_c > 0:
dist_m = torch.empty(len(xq), len(xg), device="cuda")
for i in range(len(xq)):
dist_m[i : i + 1] = -dist_matrix(xq[i : i + 1], xg, hyp_c)
else:
dist_m = xq @ xg.T
def recall_k(cos_sim, query_T, gallery_T, k):
m = len(cos_sim)
match_counter = 0
for i in range(m):
pos_sim = cos_sim[i][gallery_T == query_T[i]]
neg_sim = cos_sim[i][gallery_T != query_T[i]]
thresh = torch.max(pos_sim).item()
if torch.sum(neg_sim > thresh) < k:
match_counter += 1
return match_counter / m
recall = [recall_k(dist_m, yq, yg, k) for k in [1, 10, 20, 30, 40, 50]]
print(recall)
return recall[0]
def get_emb(
model,
ds,
path,
mean_std,
resize=224,
crop=224,
ds_type="eval",
world_size=1,
skip_head=False,
):
eval_tr = T.Compose(
[
T.Resize(resize, interpolation=PIL.Image.BICUBIC),
T.CenterCrop(crop),
T.ToTensor(),
T.Normalize(*mean_std),
]
)
ds_eval = ds(path, ds_type, eval_tr)
if world_size == 1:
sampler = None
else:
sampler = torch.utils.data.distributed.DistributedSampler(ds_eval)
dl_eval = DataLoader(
dataset=ds_eval,
batch_size=100,
shuffle=False,
num_workers=multiprocessing.cpu_count() // world_size,
pin_memory=True,
drop_last=False,
sampler=sampler,
)
model.eval()
x, y = eval_dataset(model, dl_eval, skip_head)
y = y.cuda()
if world_size > 1:
all_x = [torch.zeros_like(x) for _ in range(world_size)]
all_y = [torch.zeros_like(y) for _ in range(world_size)]
torch.distributed.all_gather(all_x, x)
torch.distributed.all_gather(all_y, y)
x, y = torch.cat(all_x), torch.cat(all_y)
model.train()
return x, y
def eval_dataset(model, dl, skip_head):
all_x, all_y = [], []
for x, y in dl:
with torch.no_grad():
x = x.cuda(non_blocking=True)
all_x.append(model(x, skip_head=skip_head))
all_y.append(y)
return torch.cat(all_x), torch.cat(all_y)