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train_with_inpaint_read_from_data_fixed.py
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from __future__ import print_function
from __future__ import division
import os
import sys
import time
from PIL import Image
import datetime
import argparse
import os.path as osp
import numpy as np
import random
import cv2
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.optim import lr_scheduler
import torch.nn.functional as F
import torchvision.transforms.functional as Funljj
sys.path.append('./torchFewShot')
from args_mini import argument_parser
#from torchFewShot.models.net import Model
import losses
#from torchFewShot.models.models_gnn import create_models
#from torchFewShot.data_manager_imageori import DataManager
from torchFewShot.data_manager_image_inpainting_data import DataManager
#from torchFewShot.data_manager import DataManager
from torchFewShot.losses import CrossEntropyLoss
from torchFewShot.optimizers import init_optimizer
import transforms as T
from torchFewShot.utils.iotools import save_checkpoint, check_isfile
from torchFewShot.utils.avgmeter import AverageMeter
from torchFewShot.utils.logger import Logger
from torchFewShot.utils.torchtools import one_hot, adjust_learning_rate
parser = argument_parser()
args = parser.parse_args()
#print(args.use_similarity)
#exit(0)
if args.use_similarity:
print('similarity net removed')
exit(0)
else:
from torchFewShot.models.net_related import Model
only_test=False
save_best=True
only_CSEI=False
def main():
os.system('cp ./train_with_inpaint_read_from_data_fixed.py ' +args.save_dir + 'train_with_inpaint_read_from_data_fixed.py')
#exit()
loss_fn = losses.GenericLoss('batchsgm', 0.02, 64)
torch.manual_seed(args.seed)
#os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
#print(use_gpu)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
#torch.cuda.manual_seed(args.seed)
#torch.manual_seed(config.SEED)
#torch.cuda.manual_seed_all(config.SEED)
np.random.seed(args.seed)
random.seed(args.seed)
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = False
torch.cuda.manual_seed_all(args.seed)
torch.cuda.manual_seed(args.seed)
#exit(0)
else:
print("Currently using CPU (GPU is highly recommended)")
print('Initializing image data manager')
dm = DataManager(args, use_gpu)
trainloader, testloader = dm.return_dataloaders()
#model_edge = EdgeConnect(config)
#model_edge.load()
print('\nstart testing...\n')
#model_edge.test()
#print(args.scale_cls,args.num_classes)
#exit(0)
#GNN_model=create_models(args,512)
#print(args.use_similarity)
#exit(0)
if args.use_similarity:
#GNN_model=create_models(args,512)
#model = Model(args,GNN_model,scale_cls=args.scale_cls, num_classes=args.num_classes)
print('similarity remove')
exit()
else:
model = Model(scale_cls=args.scale_cls,only_CSEI=only_CSEI, num_classes=args.num_classes)
#model_tradclass = Model_tradi(scale_cls=args.scale_cls, num_classes=args.num_classes)
#params_tradclass = torch.load('result/%s/CAM/1-shot-seed112_classic_classifier_avg_nouse_CAN/%s' % (args.dataset, 'best_model.pth.tar'))
#model_tradclass.load_state_dict(params_tradclass['state_dict'])
#params = torch.load('result/%s/CAM/5-shot-seed112_inpaint_batchsgmregular_begin_70epoch/%s' % (args.dataset, 'best_model.pth.tar'))
#model.load_state_dict(params['state_dict'])
#print('enter model_tradclass')
#exit(0)
if not only_CSEI:
#params = torch.load('result/%s/CAM/5-shot-seed112_inpaint_batchsgmregular_begin_70epoch/%s' % (args.dataset, 'best_model.pth.tar'))
#params = torch.load('../fewshot-CAN-master/result/%s/CAM/1-shot-seed112_inpaint_use_CAM_argumenttest_nouse_similarity_read_from_data1/%s' % (args.dataset, 'best_model.pth.tar'))
params = torch.load('/home/yfchen/ljj_code/trained_model/mini/only_inpaint/1-shot/%s' % ('best_model.pth.tar'))
#params = torch.load("/home/yfchen/ljj_code/spatial_test/result/miniImageNet/CAM/1-shot_only_augment_test_fixed_GPU0_2333/best_model.pth.tar")
#params = torch.load('./result/%s/CAM/1-shot-seed112_inpaint_support_fuse_Cam_surport_from_65.3_spatial_tanh_8argument_traingloballabelargurement_contrast_normal/%s' % (args.dataset, 'best_model.pth.tar'))
#params = torch.load('result/%s/CAM/1-shot-seed112_inpaint_support_fuse_Cam_surport_from_65.3_debug/%s' % (args.dataset, 'best_model.pth.tar'))
#params = torch.load('result/%s/CAM/1-shot-seed112_inpaint_support_fuse_Cam_surport/%s' % (args.dataset, 'best_model.pth.tar'))
#params_tradclass = torch.load('result/%s/CAM/1-shot-seed112_classic_classifier_global_avg/%s' % (args.dataset, 'checkpoint_inpaint67.pth.tar'))
print(type(params))
model.load_state_dict(params['state_dict'], strict=False)
#model_tradclass.load_state_dict(params_tradclass['state_dict'])
criterion = CrossEntropyLoss()
optimizer = init_optimizer(args.optim, model.parameters(), args.lr, args.weight_decay)
if use_gpu:
model = model.cuda()
#model_tradclass = model_tradclass.cuda()
start_time = time.time()
train_time = 0
best_acc = -np.inf
best_epoch = 0
print("==> Start training")
for i in range(1):
#print(i+14)
acc = test_ori(model, testloader, use_gpu,28)
#acc_5 = test_ori_5(model, testloader, use_gpu)
#print("==> Test 5-way Best accuracy {:.2%}, achieved at epoch {}".format( acc, 0))
#exit()
#print(args.save_dir)
#exit()
best_path=args.save_dir+'best_model.pth.tar'
if only_CSEI:
args.max_epoch=70
for epoch in range(args.max_epoch):
if not args.Classic:
learning_rate = adjust_learning_rate(optimizer, epoch, args.LUT_lr)
else:
optimizer_tradclass = init_optimizer(args.optim, model_tradclass.parameters(), args.lr, args.weight_decay)
learning_rate = adjust_learning_rate(optimizer_tradclass, epoch, args.LUT_lr)
start_train_time = time.time()
#model.base.eval()
#exit(0)
#print(not True)
#exit(0)
if not only_test:
#print(';;;;;;;;;;;')
#exit(0)
if not args.Classic:
print('enter train code')
train(epoch, model, criterion,loss_fn, optimizer, trainloader, learning_rate, use_gpu)
#print('oooo')
else:
acc=train(epoch,model_edge, model_tradclass, criterion, optimizer_tradclass, trainloader, learning_rate, use_gpu)
train_time += round(time.time() - start_train_time)
if epoch == 0 or epoch > (args.stepsize[0]-1) or (epoch + 1) % 10 == 0:
print('enter test code')
#exit(0)
if not args.Classic:
#acc = test(model_edge, model, model_tradclass,weight_softmax, testloader, use_gpu)
acc = test_ori(model, testloader, use_gpu)
#acc_5 = test_ori_5(model, testloader, use_gpu)
is_best = acc > best_acc
#else:
#print(acc)
#exit(0)
if is_best:
best_acc = acc
best_epoch = epoch + 1
if not only_test:
if not args.Classic:
if save_best:
save_checkpoint({
'state_dict': model.state_dict(),
'acc': acc,
'epoch': epoch,
}, is_best, osp.join(args.save_dir, 'checkpoint_inpaint' + str(epoch + 1) + '.pth.tar'))
else:
print('not save')
if args.Classic:
save_checkpoint({
'state_dict': model_tradclass.state_dict(),
'acc': acc,
'epoch': epoch,
}, is_best, osp.join(args.save_dir, 'checkpoint_classic' + str(epoch + 1) + '.pth.tar'))
print("==> Test 5-way Best accuracy {:.2%}, achieved at epoch {}".format(best_acc, best_epoch))
acc_5 = test_ori_5(model,best_path, testloader, use_gpu)
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
print("==========\nArgs:{}\n==========".format(args))
def train(epoch, model, criterion,loss_fn, optimizer, trainloader, learning_rate, use_gpu):
losses = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
std=np.expand_dims(np.array([0.229, 0.224, 0.225]),axis=1)
std=np.expand_dims(std,axis=2)
mean=np.expand_dims(np.array([0.485, 0.456, 0.406]),axis=1)
mean=np.expand_dims(mean,axis=2)
model.train()
#model_edge.eval()
#model_tradclass.eval()
end = time.time()
#print('llllllllllllll','located in train_with_inpaint_final.py at 264')
#exit(0)
#for batch_idx, (images_train, labels_train,tpids,Xt_img_ori,Xt_img_gray,images_test, labels_test, pids) in enumerate(trainloader):
for batch_idx, (images_train,images_train1,images_train2,images_train3,images_train4,images_train5,images_train6,images_train7,images_train8, labels_train,tpids, images_test,images_test1,images_test2,images_test3,images_test4, labels_test, pids) in enumerate(trainloader):
data_time.update(time.time() - end)
#print(Xt_img_ori.shape,Xt_img_gray.shape,images_train.shape,'lll')
edges=[]
if only_CSEI:
augment_k=4
else:
augment_k=8
tpids_4 = tpids.reshape(4,-1,1)#[:,:,0]
tpids_4 = tpids_4.repeat(1,1,augment_k).reshape(4,-1)
K_shot=images_train.shape[1]/5
images_train1=images_train1.reshape(4,-1,1,3,84,84)
images_train2=images_train2.reshape(4,-1,1,3,84,84)
images_train3=images_train3.reshape(4,-1,1,3,84,84)
images_train4=images_train4.reshape(4,-1,1,3,84,84)
images_train5=images_train5.reshape(4,-1,1,3,84,84)
images_train6=images_train6.reshape(4,-1,1,3,84,84)
images_train7=images_train7.reshape(4,-1,1,3,84,84)
images_train8=images_train8.reshape(4,-1,1,3,84,84)
#print(images_test.shape)
#exit(0)
#images_test1=images_test1.reshape(4,30,1,3,84,84)
#images_test2=images_test2.reshape(4,30,1,3,84,84)
#images_test3=images_test3.reshape(4,30,1,3,84,84)
#images_test4=images_test4.reshape(4,30,1,3,84,84)
#if cuda memory enough use follow code
if only_CSEI:
images_train_4=torch.cat((images_train1, images_train2,images_train3,images_train4), 2)
else:
images_train_4=torch.cat((images_train1, images_train2,images_train3,images_train4,images_train5,images_train6,images_train7,images_train8), 2)
#if cuda memory not enough use follow this code
#images_train_4=torch.cat((images_train1, images_train2,images_train3), 2)
#images_train_fuse= torch.cat((images_train.reshape(4,-1,1,3,84,84), images_train1, images_train2,images_train3), 2)
#images_test=images_test.reshape(4,30,1,3,84,84)
#images_test_4=torch.cat((images_test,images_test1, images_test2,images_test3, images_test4), 2)
#images_test_4=torch.cat((images_test,images_test3, images_test4), 2)
#images_test=images_test_4.reshape(4,-1,3,84,84)
labels_train_4 = labels_train.reshape(4,-1,1)#[:,:,0]
labels_train_4 = labels_train_4.repeat(1,1,augment_k)
labels_test_4=labels_train_4[:,:,:augment_k]
labels_train_4 = labels_train_4.reshape(4,-1)
labels_test_4=labels_test_4.reshape(4,-1)
if use_gpu:
images_train, labels_train,images_train_4 = images_train.cuda(), labels_train.cuda(),images_train_4.cuda()
#images_train_fuse=images_train_fuse.cuda()
images_test, labels_test = images_test.cuda(), labels_test.cuda()
pids = pids.cuda()
labels_train_4=labels_train_4.cuda()
labels_test_4=labels_test_4.cuda()
tpids_4 = tpids_4.cuda()
tpids=tpids.cuda()
pids_con=torch.cat((pids, tpids_4), 1)
labels_test_4=torch.cat((labels_test, labels_test_4), 1)
#tpids
batch_size, num_train_examples, channels, height, width = images_train.size()
num_test_examples = images_test.size(1)
labels_train_1hot = one_hot(labels_train).cuda()
train_pid=torch.matmul(labels_train_1hot.transpose(1, 2),tpids.unsqueeze(2).float()).squeeze()
train_pid=(train_pid/K_shot).long()
#exit()
labels_train_1hot_4 = one_hot(labels_train_4).cuda()
#labels_train = labels_train.view(batch_size * num_train_examples)
#print( labels_train)
#exit(0)
labels_test_1hot = one_hot(labels_test).cuda()
labels_test_1hot_4 = one_hot(labels_test_4).cuda()
#support set
switch=np.random.uniform(0,1)
if switch>-1:
images_train=images_train.reshape(4,-1,3,84,84)
else:
images_train=images_train1.cuda().reshape(4,-1,3,84,84)
#images_train1
images_train_4=images_train_4.reshape(4,-1,3,84,84)
#inpaint_tensor=torch.from_numpy(inpaint_img_np).cuda().reshape(4,20,3,84,84).float()
images_test=torch.cat((images_test, images_train_4), 1).reshape(4,-1,3,84,84)#images_train
ytest, cls_scores,features,params_classifier,spatial = model(images_train, images_test, labels_train_1hot, labels_test_1hot_4)#ytest is all class classification
#cls_scores is N-way classifation
loss1 = criterion(ytest, pids_con.view(-1))
loss2 = criterion(cls_scores, labels_test_4.view(-1))
if epoch>900:
loss3 = loss_fn(params_classifier,ytest, features,pids_con)
loss = loss1 + 0.5 * loss2+loss3
else:
loss= loss1 + 0.5 * loss2#+0.5*loss_contrast
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.update(loss.item(), pids.size(0))
batch_time.update(time.time() - end)
end = time.time()
print('Epoch{0} '
'lr: {1} '
'Time:{batch_time.sum:.1f}s '
'Data:{data_time.sum:.1f}s '
'Loss:{loss.avg:.4f} '.format(
epoch+1, learning_rate, batch_time=batch_time,
data_time=data_time, loss=losses))
def test_ori(model, testloader, use_gpu,topK=28):
accs = AverageMeter()
test_accuracies = []
final_accs = AverageMeter()
final_test_accuracies = []
model.eval()
with torch.no_grad():
#for batch_idx , (images_train, labels_train,Xt_img_ori,Xt_img_gray, images_test, labels_test) in enumerate(testloader):
for batch_idx , (images_train, images_train2,images_train3,images_train4,images_train5,labels_train, images_test, labels_test) in enumerate(testloader):
if use_gpu:
images_train = images_train.cuda()
images_test = images_test.cuda()
end = time.time()
batch_size, num_train_examples, channels, height, width = images_train.size()
num_test_examples = images_test.size(1)
labels_train_1hot = one_hot(labels_train).cuda()
labels_test_1hot = one_hot(labels_test).cuda()
cls_scores ,cls_scores_final= model(images_train, images_test, labels_train_1hot, labels_test_1hot,topK)
#print(cls_scores.shape,cls_scores_final.shape)
#exit(0)
cls_scores = cls_scores.view(batch_size * num_test_examples, -1)
cls_scores_final = cls_scores_final.view(batch_size * num_test_examples, -1)
labels_test = labels_test.view(batch_size * num_test_examples)
_, preds = torch.max(cls_scores.detach().cpu(), 1)
_, preds_final = torch.max(cls_scores_final.detach().cpu(), 1)
acc = (torch.sum(preds == labels_test.detach().cpu()).float()) / labels_test.size(0)
accs.update(acc.item(), labels_test.size(0))
acc_final = (torch.sum(preds_final == labels_test.detach().cpu()).float()) / labels_test.size(0)
final_accs.update(acc_final.item(), labels_test.size(0))
gt = (preds == labels_test.detach().cpu()).float()
gt = gt.view(batch_size, num_test_examples).numpy() #[b, n]
gt_final = (preds_final == labels_test.detach().cpu()).float()
gt_final = gt_final.view(batch_size, num_test_examples).numpy() #[b, n]
acc = np.sum(gt, 1) / num_test_examples
acc = np.reshape(acc, (batch_size))
test_accuracies.append(acc)
acc_final = np.sum(gt_final, 1) / num_test_examples
acc_final = np.reshape(acc_final, (batch_size))
final_test_accuracies.append(acc_final)
accuracy = accs.avg
test_accuracies = np.array(test_accuracies)
test_accuracies = np.reshape(test_accuracies, -1)
stds = np.std(test_accuracies, 0)
ci95 = 1.96 * stds / np.sqrt(args.epoch_size)
accuracy_final = final_accs.avg
test_accuracies_final = np.array(final_test_accuracies)
test_accuracies_final = np.reshape(test_accuracies_final, -1)
stds_final = np.std(test_accuracies_final, 0)
ci95_final = 1.96 * stds_final / np.sqrt(args.epoch_size)
print('Accuracy: {:.2%}, std: :{:.2%}'.format(accuracy, ci95))
print('Accuracy: {:.2%}, std: :{:.2%}'.format(accuracy_final, ci95_final))
return accuracy
def test_ori_5(model,best_path, testloader, use_gpu,topK=28):
accs = AverageMeter()
test_accuracies = []
final_accs = AverageMeter()
final_test_accuracies = []
params = torch.load(best_path)
model.load_state_dict(params['state_dict'], strict=True)
model.eval()
with torch.no_grad():
#for batch_idx , (images_train, labels_train,Xt_img_ori,Xt_img_gray, images_test, labels_test) in enumerate(testloader):
for batch_idx , (images_train, images_train2,images_train3,images_train4,images_train5,labels_train, images_test, labels_test) in enumerate(testloader):
shape_test=images_train.shape[0]
images_train1=images_train.reshape(shape_test,-1,1,3,84,84)
images_train2=images_train2.reshape(shape_test,-1,1,3,84,84)
images_train3=images_train3.reshape(shape_test,-1,1,3,84,84)
images_train4=images_train4.reshape(shape_test,-1,1,3,84,84)
images_train5=images_train5.reshape(shape_test,-1,1,3,84,84)
labels_train_5 = labels_train.reshape(shape_test,-1,1)#[:,:,0]
labels_train_5 = labels_train_5.repeat(1,1,5)
labels_train = labels_train_5.reshape(shape_test,-1)
images_train_5=torch.cat((images_train1, images_train2,images_train3,images_train4,images_train5), 2)
images_train=images_train_5.reshape(shape_test,-1,3,84,84)
if use_gpu:
images_train = images_train.cuda()
#images_train_5 = images_train_5.cuda()
images_test = images_test.cuda()
end = time.time()
#print(images_train.shape,labels_train.shape)
#exit()
batch_size, num_train_examples, channels, height, width = images_train.size()
num_test_examples = images_test.size(1)
labels_train_1hot = one_hot(labels_train).cuda()
labels_test_1hot = one_hot(labels_test).cuda()
#print(images_train.shape,images_test.shape)
cls_scores ,cls_scores_final= model(images_train, images_test, labels_train_1hot, labels_test_1hot,topK)
#print(cls_scores.shape,cls_scores_final.shape)
#exit(0)
cls_scores = cls_scores.view(batch_size * num_test_examples, -1)
cls_scores_final = cls_scores_final.view(batch_size * num_test_examples, -1)
labels_test = labels_test.view(batch_size * num_test_examples)
_, preds = torch.max(cls_scores.detach().cpu(), 1)
_, preds_final = torch.max(cls_scores_final.detach().cpu(), 1)
acc = (torch.sum(preds == labels_test.detach().cpu()).float()) / labels_test.size(0)
accs.update(acc.item(), labels_test.size(0))
acc_final = (torch.sum(preds_final == labels_test.detach().cpu()).float()) / labels_test.size(0)
final_accs.update(acc_final.item(), labels_test.size(0))
gt = (preds == labels_test.detach().cpu()).float()
gt = gt.view(batch_size, num_test_examples).numpy() #[b, n]
gt_final = (preds_final == labels_test.detach().cpu()).float()
gt_final = gt_final.view(batch_size, num_test_examples).numpy() #[b, n]
acc = np.sum(gt, 1) / num_test_examples
acc = np.reshape(acc, (batch_size))
test_accuracies.append(acc)
acc_final = np.sum(gt_final, 1) / num_test_examples
acc_final = np.reshape(acc_final, (batch_size))
final_test_accuracies.append(acc_final)
accuracy = accs.avg
test_accuracies = np.array(test_accuracies)
test_accuracies = np.reshape(test_accuracies, -1)
stds = np.std(test_accuracies, 0)
ci95 = 1.96 * stds / np.sqrt(args.epoch_size)
accuracy_final = final_accs.avg
test_accuracies_final = np.array(final_test_accuracies)
test_accuracies_final = np.reshape(test_accuracies_final, -1)
stds_final = np.std(test_accuracies_final, 0)
ci95_final = 1.96 * stds_final / np.sqrt(args.epoch_size)
print('Accuracy: {:.2%}, std: :{:.2%}'.format(accuracy, ci95))
print('Accuracy: {:.2%}, std: :{:.2%}'.format(accuracy_final, ci95_final))
return accuracy
def test_vis(model, testloader, use_gpu):
accs = AverageMeter()
test_accuracies = []
model.eval()
with torch.no_grad():
for batch_idx , (images_train, labels_train, images_test, labels_test) in enumerate(testloader):
if use_gpu:
images_train = images_train.cuda()
images_test = images_test.cuda()
#print(images_test.shape, 'located in train.py at 177' )
#print(images_test.shape[0],'located in train.py at 177')
#exit(0)
std=np.expand_dims(np.array([0.229, 0.224, 0.225]),axis=1)
std=np.expand_dims(std,axis=2)
mean=np.expand_dims(np.array([0.485, 0.456, 0.406]),axis=1)
mean=np.expand_dims(mean,axis=2)
#print(std.shape,mean.shape)
#exit(0)
#for i in range(images_test.shape[0]):
#for j in range(images_test.shape[1]):
#images_temp=images_test[i,j,:,:].cpu().numpy()
#print(images_temp.shape)
#images_temp=images_temp*std+mean
#images_ori=images_temp.transpose((1,2,0))
#print(images_ori.shape)
#print(images_ori.max(0).max(0).max(0),images_ori.min(0).min(0).min(0))
#exit(0)
#images_ori=np.uint8(images_ori*255)
#cv2.imwrite('./result/vis_images/images_ori.jpg',images_ori)
#exit(0)
end = time.time()
batch_size, num_train_examples, channels, height, width = images_train.size()
num_test_examples = images_test.size(1)
labels_train_1hot = one_hot(labels_train).cuda()
labels_test_1hot = one_hot(labels_test).cuda()
cls_scores,a1,a2 = model(images_train, images_test, labels_train_1hot, labels_test_1hot,True)
#a1.
#print(a1.shape,a2.shape,'located in train.py at 209',(a1-1).max(),(a1-1).min())#[4,5,75,6,6]
#print(type(a1.max(3)))
#exit(0)
max_a1=a1.max(3)[0].max(3)[0].unsqueeze(3).unsqueeze(3)
min_a1=a1.min(3)[0].min(3)[0].unsqueeze(3).unsqueeze(3)
max_a2=a2.max(3)[0].max(3)[0].unsqueeze(3).unsqueeze(3)
min_a2=a2.min(3)[0].min(3)[0].unsqueeze(3).unsqueeze(3)
#print(min_a1.shape,min_a1[0,0,0],max_a1[0,0,0])
#exit(0)
#print(std.shape,mean.shape)
#exit(0)
scale_a1=torch.div((a1-min_a1),(max_a1-min_a1))
scale_a2=torch.div((a2-min_a2),(max_a2-min_a2))
#print(images_train.shape[1],images_test.shape[1],'located in train.py at 224')
#exit(0)
#print(scale_a1[0,0,1],scale_a2[0,0,1])
#exit(0)
result_surpport_imgs=np.zeros((84*5+8*4,84*4+8*3,3)).astype(dtype=np.uint8)
#print(labels_test[0])
#exit(0)
#result_test_imgs=np.zeros((84+3)*20,(84+3)*75,3)
cls_scores = cls_scores.view(batch_size * num_test_examples, -1)
labels_test = labels_test.view(batch_size * num_test_examples)
_, preds = torch.max(cls_scores.detach().cpu(), 1)
#print(labels_test.numpy()[:75])
#print(preds.numpy()[:75])
#exit(0)
acc = (torch.sum(preds == labels_test.detach().cpu()).float()) / labels_test.size(0)
accs.update(acc.item(), labels_test.size(0))
#print(images_train.shape,images_test.shape)
#print(scale_a1.shape)
#exit(0)
if only_test:
for i in range(images_test.shape[0]):
for k in range(images_test.shape[1]):
for j in range(images_train.shape[1]):
images_temp_test=images_test[i,k,:,:].cpu().numpy()
images_temp_train=images_train[i,j,:,:].cpu().numpy()
#print(images_temp.shape)
index_support=labels_train[i,j]
index_test= labels_test[i*num_test_examples+k]
#print(label_gt,label_pred)
#exit(0)
images_temp_test=images_temp_test*std+mean
images_ori_test=images_temp_test.transpose((1,2,0))[:,:,::-1]
images_temp_train=images_temp_train*std+mean
images_ori_train=images_temp_train.transpose((1,2,0))[:,:,::-1]
#print(images_ori.shape)
#print(images_ori.max(0).max(0).max(0),images_ori.min(0).min(0).min(0))
#exit(0)
hot_a1=cv2.resize(np.uint8(scale_a1[i,index_support,k].cpu().numpy()*255),(84,84))
hot_a2=cv2.resize(np.uint8(scale_a2[i,index_support,k].cpu().numpy()*255),(84,84))
heatmap_a1 = cv2.applyColorMap(hot_a1, cv2.COLORMAP_JET)
heatmap_a2 = cv2.applyColorMap(hot_a2, cv2.COLORMAP_JET)
#print(heatmap_a1.shape)
#exit(0)
images_ori_test=np.uint8(images_ori_test*255)
images_ori_train=np.uint8(images_ori_train*255)
vis_test=images_ori_test*0.7+heatmap_a2*0.3
#hot_a1=scale_a1[i,k,j]
#hot_a2=scale_a2[i,k,j]
vis_train=images_ori_train*0.7+heatmap_a1*0.3
#cv2.imwrite('./result/vis_images/images_ori_test.jpg',images_ori_test)
#cv2.imwrite('./result/vis_images/images_test.jpg',vis_test)
#cv2.imwrite('./result/vis_images/images_ori_train.jpg',images_ori_train)
#cv2.imwrite('./result/vis_images/images_train.jpg',vis_train)
result_surpport_imgs[84*index_support+8*index_support:84*(index_support+1)+8*index_support,:84,:]=images_ori_test
result_surpport_imgs[84*index_support+8*index_support:84*(index_support+1)+8*index_support,84+8:84+84+8,:]=images_ori_train
result_surpport_imgs[84*index_support+8*index_support:84*(index_support+1)+8*index_support,84*2+8*2:84*3+8*2,:]=vis_test
result_surpport_imgs[84*index_support+8*index_support:84*(index_support+1)+8*index_support,84*3+8*3:84*4+8*3,:]=vis_train
label_gt=int(labels_test.numpy()[k])
label_pred=int(preds.numpy()[k])
cv2.imwrite('./result/vis_images/vis'+'_'+str(batch_idx)+'_'+str(i)+'_'+str(k)+'_'+str(label_gt)+'_'+str(label_pred)+'.jpg',result_surpport_imgs)
#exit(0)
if not True:
if batch_idx>12:
break
gt = (preds == labels_test.detach().cpu()).float()
gt = gt.view(batch_size, num_test_examples).numpy() #[b, n]
acc = np.sum(gt, 1) / num_test_examples
acc = np.reshape(acc, (batch_size))
test_accuracies.append(acc)
#exit(0)
accuracy = accs.avg
test_accuracies = np.array(test_accuracies)
test_accuracies = np.reshape(test_accuracies, -1)
stds = np.std(test_accuracies, 0)
ci95 = 1.96 * stds / np.sqrt(args.epoch_size)
print('Accuracy: {:.2%}, std: :{:.2%}'.format(accuracy, ci95))
#exit(0)
return accuracy
if __name__ == '__main__':
main()