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evaluate.py
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evaluate.py
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import torch
from models import *
from dataloader import *
def mean_average_precision(database_hash, database_labels, query_hash, query_labels, R=None):
if R == None:
R = database_hash.shape[0]
query_num = query_hash.shape[0]
sim = np.dot(database_hash, query_hash.T)
ids = np.argsort(-sim, axis=0)
APx = []
for i in range(query_num):
label = query_labels[i, :]
label[label == 0] = -1
idx = ids[:, i]
imatch = np.sum(database_labels[idx[0:R], :] == label, axis=1) > 0
relevant_num = np.sum(imatch)
Lx = np.cumsum(imatch)
Px = Lx.astype(float) / np.arange(1, R + 1, 1)
if relevant_num != 0:
APx.append(np.sum(Px * imatch) / relevant_num)
return np.mean(np.array(APx))
def code_predict(model, loader):
start_test = True
for i, (input, target) in enumerate(loader):
input_var = torch.autograd.Variable(input).cuda()
outputs = model(input_var)
if start_test:
all_output = outputs.data.cpu().float()
all_label = target.float()
start_test = False
else:
all_output = torch.cat((all_output, outputs.data.cpu().float()), 0)
all_label = torch.cat((all_label, target.float()), 0)
return torch.sign(all_output).cpu().numpy(), all_label.numpy()
def evaluate(model, query_loader, database_loader, R=None):
query_hash, query_labels = code_predict(model, query_loader)
database_hash, database_labels = code_predict(model, database_loader)
mAP = mean_average_precision(database_hash, database_labels, query_hash, query_labels, R)
return mAP
if __name__ == "__main__":
model = VGGHash("vgg11", 32, "imagenet").cuda()