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kan_train_one_shot.py
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# -------------------------------------
# Project: Learning to Compare: Relation Network for Few-Shot Learning
# Date: 2017.9.21
# Author: Flood Sung
# All Rights Reserved
# -------------------------------------
import gc
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.optim.lr_scheduler import StepLR
import numpy as np
import task_generator as tg
import os
import math
import argparse
import random
from src.efficient_kan import KAN
import CNNEncoder1
import vit
parser = argparse.ArgumentParser(description="One Shot Visual Recognition")
parser.add_argument("-f", "--feature_dim", type=int, default=128)
parser.add_argument("-r", "--relation_dim", type=int, default=8)
parser.add_argument("-w", "--class_num", type=int, default=2)
parser.add_argument("-s", "--sample_num_per_class", type=int, default=1)
parser.add_argument("-b", "--batch_num_per_class", type=int, default=4) #此处原来是19但我显存不够了
parser.add_argument("-e", "--episode", type=int, default=1000)
parser.add_argument("-t", "--test_episode", type=int, default=100)
parser.add_argument("-l", "--learning_rate", type=float, default=0.001)
parser.add_argument("-g", "--gpu", type=int, default=0)
parser.add_argument("-u", "--hidden_unit", type=int, default=10)
parser.add_argument("-c", "--cpu", type=int, default=0)
args = parser.parse_args()
# Hyper Parameters
FEATURE_DIM = args.feature_dim
RELATION_DIM = args.relation_dim
CLASS_NUM = args.class_num
SAMPLE_NUM_PER_CLASS = args.sample_num_per_class
BATCH_NUM_PER_CLASS = args.batch_num_per_class
EPISODE = args.episode
TEST_EPISODE = args.test_episode
LEARNING_RATE = args.learning_rate
GPU = args.gpu
HIDDEN_UNIT = args.hidden_unit
train_result = './train_result/'
label_list = ['Health', 'anomaly']
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif classname.find('BatchNorm') != -1:
m.weight.data.fill_(1)
m.bias.data.zero_()
elif classname.find('Linear') != -1:
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data = torch.ones(m.bias.data.size())
def main():
loos_result = []
loos_result_1 = []
loos_result_2 = []
loos_result_3 = []
accuray_result_1 = []
accuray_result_2 = []
accuray_result_3 = []
# Step 1: init data folders
print("init data folders")
# init character folders for dataset construction
# metatrain_character_folders,metatest_character_folders = tg.omniglot_character_folders(train,test,label_list)
# Step 2: init neural networks
print("init neural networks")
# 定义化网络
feature_encoder = CNNEncoder1.rsnet() # 特征提取
# relation_network = vit.ViT(image_size=28, patch_size=7, num_classes=2, dim=1024, depth=4, heads=8, mlp_dim=2048,
# dropout=0.1, emb_dropout=0.1) # 定义关系网络
relation_network = KAN([28 * 28, 64, 8])
# relation_network = KAN([28 * 28 * 512, 2])
kan = KAN([8 * 512, 512, 32, 2])
# 关系网络和特征提取模块的加载话
# feature_encoder.apply(weights_init)
# relation_network.apply(weights_init)
feature_encoder.cuda(GPU)
relation_network.cuda(GPU)
kan.cuda(GPU)
# 优化器的定义
feature_encoder_optim = torch.optim.Adam(feature_encoder.parameters(), lr=LEARNING_RATE)
feature_encoder_scheduler = StepLR(feature_encoder_optim, step_size=100000, gamma=0.5)
relation_network_optim = torch.optim.Adam(relation_network.parameters(), lr=LEARNING_RATE)
relation_network_scheduler = StepLR(relation_network_optim, step_size=100000, gamma=0.5)
#
# if os.path.exists(
# str("./models/feature_encoder_" + str(CLASS_NUM) + "way_" + str(SAMPLE_NUM_PER_CLASS) + "shot.pkl")):
# feature_encoder.load_state_dict(torch.load(
# str("./models/feature_encoder_" + str(CLASS_NUM) + "way_" + str(SAMPLE_NUM_PER_CLASS) + "shot.pkl")))
# print("load feature encoder success")
# if os.path.exists(str("./models/relation_network_"+ str(CLASS_NUM) +"way_" + str(SAMPLE_NUM_PER_CLASS) +"shot.pkl")):
# relation_network.load_state_dict(torch.load(str("./models/relation_network_"+ str(CLASS_NUM) +"way_" + str(SAMPLE_NUM_PER_CLASS) +"shot.pkl")))
# print("load relation network success")
# Step 3: build graph
print("Training...")
last_accuracy = 0
for episode in range(EPISODE):
# print('episode', episode)
feature_encoder_scheduler.step(episode)
relation_network_scheduler.step(episode)
# init dataset
# sample_dataloader is to obtain previous samples for compare
# batch_dataloader is to batch samples for training
degrees = random.choice([0, 90, 180, 270])
##第一个监测点 轴箱leftaxlebox
metatrain_character_folders_1 = ['../CWT-1000/leftaxlebox/train/health',
'../CWT-1000/leftaxlebox/train/anomaly']
task_1 = tg.OmniglotTask(metatrain_character_folders_1, CLASS_NUM, SAMPLE_NUM_PER_CLASS, BATCH_NUM_PER_CLASS)
sample_dataloader_1 = tg.get_data_loader(task_1, num_per_class=SAMPLE_NUM_PER_CLASS, split="train",
shuffle=False, rotation=degrees)
batch_dataloader_1 = tg.get_data_loader(task_1, num_per_class=BATCH_NUM_PER_CLASS, split="test", shuffle=True,
rotation=degrees)
# sample datas
sample_dataloader_1 = iter(sample_dataloader_1)
samples_1, sample_labels_1 = next(sample_dataloader_1)
# print('samples',samples.shape)
batch_dataloader_1 = iter(batch_dataloader_1)
batches_1, batch_labels_1 = next(batch_dataloader_1)
# ##第二个监测点 齿轮箱gearbox
metatrain_character_folders_2 = ['../CWT-1000/gearbox/train/health',
'../CWT-1000/gearbox/train/anomaly']
task_2 = tg.OmniglotTask(metatrain_character_folders_2, CLASS_NUM, SAMPLE_NUM_PER_CLASS, BATCH_NUM_PER_CLASS)
sample_dataloader_2 = tg.get_data_loader(task_2, num_per_class=SAMPLE_NUM_PER_CLASS, split="train",
shuffle=False, rotation=degrees)
batch_dataloader_2 = tg.get_data_loader(task_2, num_per_class=BATCH_NUM_PER_CLASS, split="test", shuffle=True,
rotation=degrees)
# sample datas
sample_dataloader_2 = iter(sample_dataloader_2)
samples_2, sample_labels_2 = next(sample_dataloader_2)
# print('samples',samples.shape)
batch_dataloader_2 = iter(batch_dataloader_2)
batches_2, batch_labels_2 = next(batch_dataloader_2)
# ##第三个监测点 电机motor
metatrain_character_folders_3 = ['../CWT-1000/motor/train/health',
'../CWT-1000/motor/train/anomaly']
task_3 = tg.OmniglotTask(metatrain_character_folders_3, CLASS_NUM, SAMPLE_NUM_PER_CLASS, BATCH_NUM_PER_CLASS)
sample_dataloader_3 = tg.get_data_loader(task_3, num_per_class=SAMPLE_NUM_PER_CLASS, split="train",
shuffle=False, rotation=degrees)
batch_dataloader_3 = tg.get_data_loader(task_3, num_per_class=BATCH_NUM_PER_CLASS, split="test", shuffle=True,
rotation=degrees)
# sample datas
sample_dataloader_3 = iter(sample_dataloader_3)
samples_3, sample_labels_3 = next(sample_dataloader_3)
# print('samples',samples.shape)
batch_dataloader_3 = iter(batch_dataloader_3)
batches_3, batch_labels_3 = next(batch_dataloader_3)
## 特征提取
sample_features_1 = feature_encoder(Variable(samples_1).cuda(GPU)) # 5x64*5*5
batch_features_1 = feature_encoder(Variable(batches_1).cuda(GPU)) # 20x64*5*5
#######################################################################################
sample_features_2 = feature_encoder(Variable(samples_2).cuda(GPU)) # 5x64*5*5
batch_features_2 = feature_encoder(Variable(batches_2).cuda(GPU)) # 20x64*5*5
########################################################################################
sample_features_3 = feature_encoder(Variable(samples_3).cuda(GPU)) # 5x64*5*5
batch_features_3 = feature_encoder(Variable(batches_3).cuda(GPU)) # 20x64*5*5
## 特征拼接
# print(sample_features_1.shape) # 2,128,28,28
# print(sample_labels_1.shape) # 2
sample_features_ext_1 = sample_features_1.unsqueeze(0).repeat(BATCH_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
sample_labels_1 = sample_labels_1.repeat(BATCH_NUM_PER_CLASS)
sample_labels_1 = sample_labels_1.long()
batch_features_ext_1 = batch_features_1.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
batch_features_ext_1 = torch.transpose(batch_features_ext_1, 0, 1)
# print(batch_features_ext_1.shape) # 38,2,128,28,28
relation_pairs_1 = torch.cat((sample_features_ext_1, batch_features_ext_1), 2)
# print(relation_pairs_1.shape) #38,2,256,28,28
# relation_pairs_1 = relation_pairs_1.view(-1, FEATURE_DIM * 2, 28, 28)
relation_pairs_1 = relation_pairs_1.view(-1, FEATURE_DIM * 4, 28 * 28)
# relation_pairs_1 = relation_pairs_1.view(-1, FEATURE_DIM * 4* 28 * 28)
########################################################################################
sample_features_ext_2 = sample_features_2.unsqueeze(0).repeat(BATCH_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
sample_labels_2 = sample_labels_2.repeat(BATCH_NUM_PER_CLASS)
sample_labels_2 = sample_labels_2.long()
batch_features_ext_2 = batch_features_2.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
batch_features_ext_2 = torch.transpose(batch_features_ext_2, 0, 1)
relation_pairs_2 = torch.cat((sample_features_ext_2, batch_features_ext_2), 2)
# relation_pairs_2 =relation_pairs_2.view(-1,FEATURE_DIM*2,28,28)
relation_pairs_2 = relation_pairs_2.view(-1, FEATURE_DIM * 4, 28 * 28)
# relation_pairs_2 = relation_pairs_2.view(-1, FEATURE_DIM * 4*28 * 28)
########################################################################################
sample_features_ext_3 = sample_features_3.unsqueeze(0).repeat(BATCH_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
sample_labels_3 = sample_labels_3.repeat(BATCH_NUM_PER_CLASS)
sample_labels_3 = sample_labels_3.long()
batch_features_ext_3 = batch_features_3.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
batch_features_ext_3 = torch.transpose(batch_features_ext_3, 0, 1)
relation_pairs_3 = torch.cat((sample_features_ext_3, batch_features_ext_3), 2)
# relation_pairs_3 =relation_pairs_3.view(-1,FEATURE_DIM*2,28,28)
relation_pairs_3 = relation_pairs_3.view(-1, FEATURE_DIM * 4, 28 * 28)
#
# relation_pairs_3 = relation_pairs_3.view(-1, FEATURE_DIM * 4* 28 * 28)
##计算关系分数 transformer
# # print(relation_pairs_1.shape)
# relations_1 = relation_network(relation_pairs_1)
# # # print(relations_1.shape)
# # relations_1 = relations_1.view(38, 8 * 512)
# # relations_1 = kan(relations_1)
# relations_1 = relations_1.view(-1, CLASS_NUM)
# # print(relations_1.shape)
# # print(relations_1.shape)
# ########################################################################################
# relations_2 = relation_network(relation_pairs_2)
# relations_2 = relations_2.view(-1, CLASS_NUM)
# ########################################################################################
# relations_3 = relation_network(relation_pairs_3)
# relations_3 = relations_3.view(-1, CLASS_NUM)
##计算关系分数 kan
# print(relation_pairs_1.shape)
relations_1 = relation_network(relation_pairs_1)
relations_1 = relations_1.view(-1, 8 * 512)
relations_1 = kan(relations_1)
relations_1 = relations_1.view(-1, CLASS_NUM)
# print(relations_1.shape)
# print(relations_1.shape)
########################################################################################
relations_2 = relation_network(relation_pairs_2)
relations_2 = relations_2.view(-1, 8 * 512)
relations_2 = kan(relations_2)
relations_2 = relations_2.view(-1, CLASS_NUM)
########################################################################################
relations_3 = relation_network(relation_pairs_3)
relations_3 = relations_3.view(-1, 8 * 512)
relations_3 = kan(relations_3)
relations_3 = relations_3.view(-1, CLASS_NUM)
mse = nn.MSELoss().cuda(GPU)
##计算LOSS
batch_labels_1 = batch_labels_1.long()
# print(batches_1.shape)
# print(batch_labels_1.view(-1,1))
one_hot_labels_1 = torch.zeros(BATCH_NUM_PER_CLASS * CLASS_NUM, CLASS_NUM).scatter_(1,
batch_labels_1.view(-1, 1),
1).cuda(GPU)
# print(one_hot_labels_1.shape)
# print(one_hot_labels_1.shape)
# print(relations_1.shape)
loss_1 = mse(relations_1, one_hot_labels_1)
########################################################################################
batch_labels_2 = batch_labels_2.long()
one_hot_labels_2 = Variable(
torch.zeros(BATCH_NUM_PER_CLASS * CLASS_NUM, CLASS_NUM).scatter_(1, batch_labels_2.view(-1, 1), 1)).cuda(
GPU)
loss_2 = mse(relations_2, one_hot_labels_2)
########################################################################################
batch_labels_3 = batch_labels_3.long()
one_hot_labels_3 = Variable(
torch.zeros(BATCH_NUM_PER_CLASS * CLASS_NUM, CLASS_NUM).scatter_(1, batch_labels_3.view(-1, 1), 1)).cuda(
GPU)
loss_3 = mse(relations_3, one_hot_labels_3)
# training
feature_encoder.zero_grad()
relation_network.zero_grad()
loss = loss_1 + loss_2 + loss_3
loss.backward()
torch.nn.utils.clip_grad_norm(feature_encoder.parameters(), 0.5)
torch.nn.utils.clip_grad_norm(relation_network.parameters(), 0.5)
feature_encoder_optim.step()
relation_network_optim.step()
loos_result_1.append(loss_1)
loos_result_2.append(loss_2)
loos_result_3.append(loss_3)
loos_result.append(loss)
if (episode + 1) % 50 == 0:
print("episode:", episode + 1, "loss", loss.item())
loos_result.append(loss)
if (episode + 1) % 100 == 0:
# test
print("Testing...1,lefaxlebox")
total_rewards_1 = 0
for i in range(TEST_EPISODE):
degrees = random.choice([0, 90, 180, 270])
metatest_character_folders1 = ['../CWT-1000/leftaxlebox/test/health',
'../CWT-1000/leftaxlebox/test/anomaly/anomalyTYPE13']
metatrain_character_folders1 = ['../CWT-1000/leftaxlebox/train/health',
'../CWT-1000/leftaxlebox/train/anomaly']
task = tg.OmniglotTask(metatest_character_folders1, CLASS_NUM, SAMPLE_NUM_PER_CLASS,
SAMPLE_NUM_PER_CLASS, )
task1 = tg.OmniglotTask(metatrain_character_folders1, CLASS_NUM, SAMPLE_NUM_PER_CLASS,
BATCH_NUM_PER_CLASS)
sample_dataloader = tg.get_data_loader(task1, num_per_class=SAMPLE_NUM_PER_CLASS, split="train",
shuffle=False, rotation=degrees)
test_dataloader = tg.get_data_loader(task, num_per_class=SAMPLE_NUM_PER_CLASS, split="test",
shuffle=True, rotation=degrees)
sample_dataloader = iter(sample_dataloader)
sample_images, sample_labels = next(sample_dataloader)
test_dataloader = iter(test_dataloader)
test_images, test_labels = next(test_dataloader)
sample_features = feature_encoder(Variable(sample_images).cuda(GPU)) # 5x64
test_features = feature_encoder(Variable(test_images).cuda(GPU)) # 20x64
sample_features_ext = sample_features.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
test_features_ext = test_features.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
test_features_ext = torch.transpose(test_features_ext, 0, 1)
# print(torch.cat((sample_features_ext, test_features_ext), 2).shape) #2,2,256,28,28
# relation_pairs = torch.cat((sample_features_ext, test_features_ext), 2).view(-1, FEATURE_DIM * 2, 28,
# 28)
relation_pairs = torch.cat((sample_features_ext, test_features_ext), 2).view(-1,
FEATURE_DIM * 4, 28 * 28)
# relation_pairs = torch.cat((sample_features_ext, test_features_ext), 2).view(-1,
# FEATURE_DIM * 4* 28 * 28)
# kan
relations1 = relation_network(relation_pairs)
relations1 = relations1.view(2, 8 * 512)
relations1 = kan(relations1)
relations = relations1.view(-1, CLASS_NUM)
bb = Variable(torch.zeros(CLASS_NUM)).cuda(GPU)
# # transformer
# relations1 = relation_network(relation_pairs)
# relations = relations1.view(-1, CLASS_NUM)
# bb = Variable(torch.zeros(CLASS_NUM)).cuda(GPU)
# print(relations)
for j in range(len(relations)):
if relations[j][0] > 0.9:
bb[j] = 0
else:
bb[j] = 1
# _,predict_labels = torch.max(relations.data,1)
predict_labels = bb.cpu()
rewards = [1 if predict_labels[j] == test_labels[j] else 0 for j in range(CLASS_NUM)]
total_rewards_1 += np.sum(rewards)
test_accuracy_1 = total_rewards_1 / 1.0 / CLASS_NUM / TEST_EPISODE
accuray_result_1.append(test_accuracy_1)
print("test accuracy 1:", test_accuracy_1)
#############################################################################################################
print("Testing...2,gearbox")
total_rewards_2 = 0
for i in range(TEST_EPISODE):
degrees = random.choice([0, 90, 180, 270])
metatest_character_folders1 = ['../CWT-1000/gearbox/test/health',
'../CWT-1000/gearbox/test/anomaly/anomalyTYPE9']
metatrain_character_folders1 = ['../CWT-1000/gearbox/train/health',
'../CWT-1000/gearbox/train/anomaly']
task = tg.OmniglotTask(metatest_character_folders1, CLASS_NUM, SAMPLE_NUM_PER_CLASS,
SAMPLE_NUM_PER_CLASS, )
task1 = tg.OmniglotTask(metatrain_character_folders1, CLASS_NUM, SAMPLE_NUM_PER_CLASS,
BATCH_NUM_PER_CLASS)
sample_dataloader = tg.get_data_loader(task1, num_per_class=SAMPLE_NUM_PER_CLASS, split="train",
shuffle=False, rotation=degrees)
test_dataloader = tg.get_data_loader(task, num_per_class=SAMPLE_NUM_PER_CLASS, split="test",
shuffle=True, rotation=degrees)
sample_dataloader = iter(sample_dataloader)
sample_images, sample_labels = next(sample_dataloader)
test_dataloader = iter(test_dataloader)
test_images, test_labels = next(test_dataloader)
sample_features = feature_encoder(Variable(sample_images).cuda(GPU)) # 5x64
test_features = feature_encoder(Variable(test_images).cuda(GPU)) # 20x64
sample_features_ext = sample_features.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
test_features_ext = test_features.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
test_features_ext = torch.transpose(test_features_ext, 0, 1)
# relation_pairs = torch.cat((sample_features_ext, test_features_ext), 2).view(-1, FEATURE_DIM * 2, 28,
# 28)
relation_pairs = torch.cat((sample_features_ext, test_features_ext), 2).view(-1, FEATURE_DIM * 2 *
2 ,28 * 28)
relations2 = relation_network(relation_pairs)
relations2 = relations2.view(2, 8 * 512)
relations2 = kan(relations2)
relations = relations2.view(-1, CLASS_NUM)
bb = Variable(torch.zeros(CLASS_NUM)).cuda(GPU)
# print(relations)
for j in range(len(relations)):
if relations[j][0] > 0.9:
bb[j] = 0
else:
bb[j] = 1
# _,predict_labels = torch.max(relations.data,1)
predict_labels = bb.cpu()
rewards = [1 if predict_labels[j] == test_labels[j] else 0 for j in range(CLASS_NUM)]
total_rewards_2 += np.sum(rewards)
test_accuracy_2 = total_rewards_2 / 1.0 / CLASS_NUM / TEST_EPISODE
accuray_result_2.append(test_accuracy_2)
print("test accuracy 2:", test_accuracy_2)
#############################################################################################################
print("Testing...3,motor")
total_rewards_3 = 0
for i in range(TEST_EPISODE):
degrees = random.choice([0, 90, 180, 270])
metatest_character_folders1 = ['../CWT-1000/motor/test/health',
'../CWT-1000/motor/test/anomaly/anomalyTYPE1']
metatrain_character_folders1 = ['../CWT-1000/motor/train/health',
'../CWT-1000/motor/train/anomaly']
task = tg.OmniglotTask(metatest_character_folders1, CLASS_NUM, SAMPLE_NUM_PER_CLASS,
SAMPLE_NUM_PER_CLASS, )
task1 = tg.OmniglotTask(metatrain_character_folders1, CLASS_NUM, SAMPLE_NUM_PER_CLASS,
BATCH_NUM_PER_CLASS)
sample_dataloader = tg.get_data_loader(task1, num_per_class=SAMPLE_NUM_PER_CLASS, split="train",
shuffle=False, rotation=degrees)
test_dataloader = tg.get_data_loader(task, num_per_class=SAMPLE_NUM_PER_CLASS, split="test",
shuffle=True, rotation=degrees)
sample_dataloader = iter(sample_dataloader)
sample_images, sample_labels = next(sample_dataloader)
test_dataloader = iter(test_dataloader)
test_images, test_labels = next(test_dataloader)
sample_features = feature_encoder(Variable(sample_images).cuda(GPU)) # 5x64
test_features = feature_encoder(Variable(test_images).cuda(GPU)) # 20x64
sample_features_ext = sample_features.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
test_features_ext = test_features.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
test_features_ext = torch.transpose(test_features_ext, 0, 1)
# relation_pairs = torch.cat((sample_features_ext, test_features_ext), 2).view(-1, FEATURE_DIM * 2, 28,
# 28)
relation_pairs = torch.cat((sample_features_ext, test_features_ext), 2).view(-1, FEATURE_DIM * 4 ,
28 * 28)
relations1 = relation_network(relation_pairs)
relations1 = relations1.view(2, 8 * 512)
relations1 = kan(relations1)
relations = relations1.view(-1, CLASS_NUM)
bb = Variable(torch.zeros(CLASS_NUM)).cuda(GPU)
# print(relations)
for j in range(len(relations)):
if relations[j][0] > 0.9:
bb[j] = 0
else:
bb[j] = 1
# _,predict_labels = torch.max(relations.data,1)
predict_labels = bb.cpu()
rewards = [1 if predict_labels[j] == test_labels[j] else 0 for j in range(CLASS_NUM)]
total_rewards_3 += np.sum(rewards)
test_accuracy_3 = total_rewards_3 / 1.0 / CLASS_NUM / TEST_EPISODE
accuray_result_3.append(test_accuracy_3)
print("test accuracy 3:", test_accuracy_3)
###总精度
test_accuracy = test_accuracy_1 + test_accuracy_2 + test_accuracy_3
test_accuracy = test_accuracy / 3.0
print("TOtal_ accuracy:", test_accuracy)
if test_accuracy >= last_accuracy:
# save networks
torch.save(feature_encoder.state_dict(),
str("./models/feature_encoder_" + str(CLASS_NUM) + "way_" + str(
SAMPLE_NUM_PER_CLASS) + "shot.pkl"))
torch.save(relation_network.state_dict(),
str("./models/relation_network_" + str(CLASS_NUM) + "way_" + str(
SAMPLE_NUM_PER_CLASS) + "shot.pkl"))
print("save networks for episode:", episode)
last_accuracy = test_accuracy
return loos_result_1, loos_result_2, loos_result_3, loos_result
if __name__ == '__main__':
loos_result_1, loos_result_2, loos_result_3, loos_result = main()
loos_result_1_cpu = [x_1.cpu().detach().numpy() for x_1 in loos_result_1]
loos_result_2_cpu = [x_2.cpu().detach().numpy() for x_2 in loos_result_2]
loos_result_3_cpu = [x_3.cpu().detach().numpy() for x_3 in loos_result_3]
loos_result_cpu = [x.cpu().detach().numpy() for x in loos_result]
np.savetxt(train_result + 'train_loss_1.csv', loos_result_1_cpu, fmt='%.8f', delimiter=',')
np.savetxt(train_result + 'train_loss_2.csv', loos_result_2_cpu, fmt='%.8f', delimiter=',')
np.savetxt(train_result + 'train_loss_3.csv', loos_result_3_cpu, fmt='%.8f', delimiter=',')
np.savetxt(train_result + 'train_loss_Toal.csv', loos_result_cpu, fmt='%.8f', delimiter=',')