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test code
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CaoGang2018
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95
main.py
95
main.py
@ -2,11 +2,20 @@ from torch.utils.data import DataLoader
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from data.CUB_200 import get_dataset, input_transform, input_transform2
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import SCDA
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from torchvision import models
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import pandas as pd
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import torch
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import torch.nn.functional as F
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import csv
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import numpy as np
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from util.model import pool_model
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import random
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def retrieve(q, data, num=5):
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distances = np.sum(np.square((data - q)), axis=-1)
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# distances = np.sum(cosine_similarity(q, data)), axis=-1)
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indices = distances.argsort(axis=0)[:num]
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return indices
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def write_csv(results,file_name):
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@ -26,54 +35,116 @@ data_or = DataLoader(dataset_or, batch_size=10, shuffle=True,num_workers=0)
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net1.eval()
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net2.eval()
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result = []
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label_re = []
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max_ave_pool = pool_model()
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out = open('feat.csv', 'a', newline='')
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csv_write = csv.writer(out, dialect='excel')
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csv_write.writerow(['features', 'label'])
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# out = open('feat.csv', 'a', newline='')
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# csv_write = csv.writer(out, dialect='excel')
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# csv_write.writerow(['features', 'label'])
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for ii, (img1, img2, label) in enumerate(data_or):
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# if ii == 2:
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# exit()
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input1 = img1
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feat_re = net1(input1)
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feat_po = net2(input1)
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input2 = img2
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feat_flip_re = net1(input2)
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feat_flip_po = net2(input2)
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m, _, _, _ = feat_re.shape
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m, _, h1, w1 = feat_po.shape
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m, _, h2, w2 = feat_re.shape
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label = label.detach().numpy()
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f_po = torch.zeros(feat_po.shape)
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f_re = torch.zeros(feat_re.shape)
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filp_po = torch.zeros(feat_flip_po.shape)
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filp_re = torch.zeros(feat_flip_re.shape)
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cc8 = np.zeros((m, h1, w1)) #31
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cc7 = np.zeros((m, h1, w1)) # 31
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cc6 = np.zeros((m, h2, w2))# 28
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cc5 = np.zeros((m, h2, w2))# 28
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for i in range(m):
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# f_re[i] = SCDA.select_aggregate(feat_re[i].detach().numpy())
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f_po[i] = SCDA.select_aggregate(feat_po[i])[0] # 31a
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cc2 = SCDA.select_aggregate(feat_po[i])[1]
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f_re[i] = SCDA.select_aggregate_and(feat_re[i], cc2) # 28a
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cc8[i] = cc2
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f_re[i] = SCDA.select_aggregate_and(feat_re[i], cc2)[0] # 28a
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cc3 = SCDA.select_aggregate_and(feat_re[i], cc2)[1]
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cc6[i] = cc3
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filp_po[i] = SCDA.select_aggregate(feat_flip_po[i])[0] # 31b
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cc2_f = SCDA.select_aggregate(feat_flip_po[i])[1]
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filp_re[i] = SCDA.select_aggregate_and(feat_flip_re[i], cc2_f) # 28b
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cc7[i] = cc2_f
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filp_re[i] = SCDA.select_aggregate_and(feat_flip_re[i], cc2_f)[0]
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cc3_f = SCDA.select_aggregate_and(feat_flip_re[i], cc2_f)[1] # 28b
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cc5[i] = cc3_f
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# print(f.shape)
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# l = int(lable[i])
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# csv_write.writerow([f, l])
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# del f, l
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# result.append((f, l))
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# print(f_po.shape)
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l31a = max_ave_pool(f_po)
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l28a = max_ave_pool(f_re)
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l31a = max_ave_pool(f_po, cc8)
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# exit()
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l28a = max_ave_pool(f_re, cc6)
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l31b = max_ave_pool(filp_po)
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l28b = max_ave_pool(filp_re)
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l31b = max_ave_pool(filp_po, cc7)
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l28b = max_ave_pool(filp_re, cc5)
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# print(f_po.reshape(1, -1))
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# print(f_re)
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# print(filp_po)
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# print(filp_re)
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feat = torch.cat((l31a, 0.5*l28a, l31b, 0.5*l28b), dim=1).reshape(m, -1).detach().numpy()
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# print(feat.shape)
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for i in range(m):
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csv_write.writerow([feat[i], int(label[i])])
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# print(feat[i])
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# print(feat[i].reshape(1,-1).shape)
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result.append(feat[i].tolist())
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label_re.append(int(label[i]))
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# exit()
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# az = feat[i].tolist()
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# print(az)
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# csv_write.writerow([az, int(label[i])])
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print('batch {}/{} complete'.format(ii, len(data_or)))
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print('batch {}/{} complete'.format(ii+1, len(data_or)))
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print("test...")
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feats = np.vstack(result)
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# print(feats.shape)
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label = np.vstack(label_re)
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x = [int(i) for i in range(feats.shape[0])]
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random.shuffle(x)
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feats = feats[x]
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labels = label[x]
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# labels = [i[0] for i in label]
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test_data = feats[:int(0.5*len(labels))]
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train_data = feats[int(0.5*len(labels)):]
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test_label = labels[:int(0.5*len(labels))]
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train_label = labels[int(0.5*len(labels)):]
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top1 = 0
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top5 = 0
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# ap = 0.0
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for i in range(len(test_label)):
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# print(test_data[i].shape)
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# exit()
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inds = retrieve(test_data[i].reshape(1,-1), train_data, num=len(train_data))
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# print(inds.tolist())
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# print(inds)
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labels = train_label[inds]
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if labels[0] == test_label[i]:
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top1 +=1
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if test_label[i] in labels[:5]:
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top5 +=1
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# ap += compute_ap(inds.tolist(), get_gt(test_label[i], train_labels))
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print("Query[%d / %d] complete: top1: %.4f, top5: %.4f"%(i, len(test_label), top1/(i+1), top5/(i+1)))
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print("top1 acc: %.4f, top5 acc %.4f"%(top1/len(test_label), top5/len(test_label)))
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# avg.train_data_L31a
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# maxi.train_data_L31a
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