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test.py
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import faiss
import torch
import logging
import numpy as np
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Subset
def DTWdist(x,y):
l1 = len(x)
l2 = len(y)
M=np.zeros([l1, l2])
D = np.zeros([l1, l2, 4])
x = x.expand(l2,-1,-1).permute(1,0,2)
y = y.expand(l1,-1,-1)
Mgpu = torch.sqrt(torch.sum((x - y).pow(2), dim=2))+1e-8
M = Mgpu.cpu().numpy()
D[0][0] = [M[0][0], -1, -1, 1]
for i in range(1, l1):
D[i][0][0] = M[i][0] + D[i - 1][0][0]
D[i][0][1] = i - 1
D[i][0][2] = 0
D[i][0][3] = 1 + D[i - 1][0][3]
for j in range(1, l2):
D[0][j][0] = M[0][j] + D[0][j - 1][0]
D[0][j][1] = 0
D[0][j][2] = j - 1
D[0][j][3] = 1 + D[0][j - 1][3]
for i in range(1, l1):
for j in range(1, l2):
cand1 = D[i - 1][j][0] / D[i - 1][j][3]
cand2 = D[i][j - 1][0] / D[i][j - 1][3]
cand3 = D[i - 1][j - 1][0] / D[i - 1][j - 1][3]
minValue = min(cand1, cand2, cand3)
if minValue == cand1:
D[i][j][0] = M[i][j] + D[i - 1][j][0]
D[i][j][1] = i - 1
D[i][j][2] = j
D[i][j][3] = 1 + D[i - 1][j][3]
elif minValue == cand2:
D[i][j][0] = M[i][j] + D[i][j - 1][0]
D[i][j][1] = i
D[i][j][2] = j - 1
D[i][j][3] = 1 + D[i][j - 1][3]
elif minValue == cand3:
D[i][j][0] = M[i][j] + D[i - 1][j - 1][0]
D[i][j][1] = i - 1
D[i][j][2] = j - 1
D[i][j][3] = 1 + D[i - 1][j - 1][3]
align={}
ii=l1-1
jj=l2-1
while(ii!=-1):
align.setdefault(ii, []).append(jj) # if Key "ii" in dictionary, append "jj" to this list; or else new "ii: []" in dict and append "jj" to list
ii,jj=(int(D[ii][jj][1]),int(D[ii][jj][2]))
return align
def writeData(data,fileName):
fp1 = open(fileName, 'w')
for i in range(len(data)):
for j in range(len(data[i])):
fp1.write(str(data[i][j])+'\t')
fp1.write('\n')
fp1.close()
def rerank(predictions,queries_features_a,database_features_a):
pred2 = []
dist2 = []
for query_index, pred in enumerate(predictions):
query_features_a = queries_features_a[query_index]
positives_features_a = database_features_a[pred]
query_features_a = torch.Tensor(query_features_a).cuda()
positives_features_a = torch.Tensor(positives_features_a).cuda()
Dist = []
n = len(positives_features_a)
for ii in range(n):
img1, img2 = positives_features_a[ii], query_features_a
xlen, ylen = img1.shape[0], img1.shape[1]
img1x = img1.flatten(start_dim=1)
img2x = img2.flatten(start_dim=1)
img1y = img1.permute(1,0,2).flatten(start_dim=1)
img2y = img2.permute(1,0,2).flatten(start_dim=1)
xalign = DTWdist(img1x, img2x)
yalign = DTWdist(img1y, img2y)
img1 = img1.expand(xlen, ylen,-1,-1,-1).permute(2,3,0,1,4)
img2 = img2.expand(xlen, ylen,-1,-1,-1)
distm = torch.sqrt(torch.sum((img1 - img2).pow(2),dim=-1)).cpu().numpy()
dn = 0.
n = 0
for i in range(xlen):
for j in range(ylen):
for i2 in xalign[i]:
for j2 in yalign[j]:
dn += distm[i][j][i2][j2]
n += 1
Dist.append([pred[ii],dn/n])
Dist = np.array(sorted(Dist,key=(lambda x:x[1])))
pred2.append(list(map(int,Dist[:,0])))
dist2.append(list(Dist[:,1]))
return pred2,dist2
def test(args, eval_ds, model, test_method="hard_resize", pca=None):
"""Compute features of the given dataset and compute the recalls."""
assert test_method in ["hard_resize", "single_query", "central_crop", "five_crops",
"nearest_crop", "maj_voting"], f"test_method can't be {test_method}"
model = model.eval()
with torch.no_grad():
logging.debug("Extracting database features for evaluation/testing")
# For database use "hard_resize", although it usually has no effect because database images have same resolution
eval_ds.test_method = "hard_resize"
database_subset_ds = Subset(eval_ds, list(range(eval_ds.database_num)))
database_dataloader = DataLoader(dataset=database_subset_ds, num_workers=args.num_workers,
batch_size=args.infer_batch_size, pin_memory=(args.device=="cuda"))
if test_method == "nearest_crop" or test_method == 'maj_voting':
all_features = np.empty((5 * eval_ds.queries_num + eval_ds.database_num, args.features_dim), dtype="float32")
else:
all_features = np.empty((len(eval_ds), args.features_dim), dtype="float32")
all_features_a = np.empty((len(eval_ds), 8,8,384), dtype="float32")
for inputs, indices in tqdm(database_dataloader, ncols=100):
features_a, features = model(inputs.to(args.device))
features = features.cpu().numpy()
features_a = features_a.cpu().numpy()
if pca != None:
features = pca.transform(features)
all_features[indices.numpy(), :] = features
all_features_a[indices.numpy(), :] = features_a
logging.debug("Extracting queries features for evaluation/testing")
queries_infer_batch_size = 1 if test_method == "single_query" else args.infer_batch_size
eval_ds.test_method = test_method
queries_subset_ds = Subset(eval_ds, list(range(eval_ds.database_num, eval_ds.database_num+eval_ds.queries_num)))
queries_dataloader = DataLoader(dataset=queries_subset_ds, num_workers=args.num_workers,
batch_size=queries_infer_batch_size, pin_memory=(args.device=="cuda"))
for inputs, indices in tqdm(queries_dataloader, ncols=100):
if test_method == "five_crops" or test_method == "nearest_crop" or test_method == 'maj_voting':
inputs = torch.cat(tuple(inputs)) # shape = 5*bs x 3 x 480 x 480
features_a, features = model(inputs.to(args.device))
if test_method == "five_crops": # Compute mean along the 5 crops
features = torch.stack(torch.split(features, 5)).mean(1)
features = features.cpu().numpy()
features_a = features_a.cpu().numpy()
if pca != None:
features = pca.transform(features)
if test_method == "nearest_crop" or test_method == 'maj_voting': # store the features of all 5 crops
start_idx = eval_ds.database_num + (indices[0] - eval_ds.database_num) * 5
end_idx = start_idx + indices.shape[0] * 5
indices = np.arange(start_idx, end_idx)
all_features[indices, :] = features
else:
all_features[indices.numpy(), :] = features
all_features_a[indices.numpy(), :] = features_a
queries_features = all_features[eval_ds.database_num:]
database_features = all_features[:eval_ds.database_num]
queries_features_a = all_features_a[eval_ds.database_num:]
database_features_a = all_features_a[:eval_ds.database_num]
faiss_index = faiss.IndexFlatL2(args.features_dim)
faiss_index.add(database_features)
del database_features, all_features
logging.debug("Calculating recalls")
distances, predictions = faiss_index.search(queries_features, max(args.recall_values))
#### For each query, check if the predictions are correct
positives_per_query = eval_ds.get_positives()
# args.recall_values by default is [1, 5, 10, 20]
recalls = np.zeros(len(args.recall_values))
for query_index, pred in enumerate(predictions):
for i, n in enumerate(args.recall_values):
if np.any(np.in1d(pred[:n], positives_per_query[query_index])):
recalls[i:] += 1
#print(query_index,i)
break
# Divide by the number of queries*100, so the recalls are in percentages
recalls = recalls / eval_ds.queries_num * 100
recalls_str =", ".join([f"R@{val}: {rec:.1f}" for val, rec in zip(args.recall_values, recalls)])
logging.info(f"Global retrieval recalls: {recalls_str}")
predictions,distances = rerank(predictions,queries_features_a,database_features_a)
#### For each query, check if the predictions are correct
positives_per_query = eval_ds.get_positives()
# args.recall_values by default is [1, 5, 10, 20]
recalls = np.zeros(len(args.recall_values))
for query_index, pred in enumerate(predictions):
for i, n in enumerate(args.recall_values):
if np.any(np.in1d(pred[:n], positives_per_query[query_index])):
recalls[i:] += 1
#print(query_index,i)
break
# Divide by the number of queries*100, so the recalls are in percentages
recalls = recalls / eval_ds.queries_num * 100
recalls_str = ", ".join([f"R@{val}: {rec:.1f}" for val, rec in zip(args.recall_values, recalls)])
return recalls, recalls_str