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resnet_train.py
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resnet_train.py
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import torch
import torch.nn as nn
from utils import weight_init
from RNet_resnet import RelationNetWork
#from embed import CNNEncoder
from MiniImagenet2 import MiniImagenet
from torch.utils.data import DataLoader
from torch.autograd import Variable
import os
import numpy as np
from logger import Logger
from ResNet_feature import resnet18
from torch.optim.lr_scheduler import StepLR
LOG_DIR = './log'
logger = Logger(LOG_DIR)
# os.environ['CUDA_VISIBLE_DEVICES'] = '4'
def main():
n_way = 5
k_shot = 1
k_query = 5 # 5-way-5shot
batchsz = 3
best_acc = 0
mdfile1 = './ckpy/res_feature-%d-way-%d-shot.pkl' %(n_way,k_shot)
mdfile2 = './ckpy/res_relation-%d-way-%d-shot.pkl' %(n_way,k_shot)
# feature_embed = CNNEncoder().cuda()
# print(torch.cuda.is_available())
feature_embed = resnet18().cuda()
Relation_score = RelationNetWork(64, 8).cuda() # relation_dim == 8 ??
Relation_score.apply(weight_init)
feature_optim = torch.optim.Adam(feature_embed.parameters(), lr=0.001)
relation_opim = torch.optim.Adam(Relation_score.parameters(), lr=0.001)
feature_optim_scheduler = StepLR(feature_optim,step_size=10,gamma=0.5) # 1-shot 1w , 5-shot 5k
relation_opim_scheduler = StepLR(relation_opim,step_size=10,gamma=0.5)
loss_fn = torch.nn.MSELoss().cuda()
if os.path.exists(mdfile1):
print("load mdfile1...")
feature_embed.load_state_dict(torch.load(mdfile1))
if os.path.exists(mdfile2):
print("load mdfile2...")
Relation_score.load_state_dict(torch.load(mdfile2))
for epoch in range(100):
feature_optim_scheduler.step(epoch) # 降低学习率
relation_opim_scheduler.step(epoch)
mini = MiniImagenet('./mini-imagenet/', mode='train', n_way=n_way, k_shot=k_shot, k_query=k_query, batchsz=6000, resize=224) #38400
db = DataLoader(mini,batch_size=batchsz,shuffle=True,num_workers=0,pin_memory=False) # 64 , 5*(1+15) , c, h, w
mini_val = MiniImagenet('./mini-imagenet/', mode='val', n_way=n_way, k_shot=k_shot, k_query=k_query, batchsz=200, resize=224) #9600
db_val = DataLoader(mini_val,batch_size=batchsz,shuffle=True,num_workers=0,pin_memory=False)
for step,batch in enumerate(db):
support_x = Variable(batch[0]).cuda() # [batch_size, n_way*(k_shot+k_query), c , h , w]
support_y = Variable(batch[1]).cuda()
query_x = Variable(batch[2]).cuda()
query_y = Variable(batch[3]).cuda()
bh,set1,c,h,w = support_x.size()
set2 = query_x.size(1)
feature_embed.train()
Relation_score.train()
# support_xf = feature_embed(support_x.view(bh*set1,c,h,w)).view(bh,set1,64,19,19) # 在 test 的 时候 重复
support_xf = feature_embed(support_x.view(bh * set1, c, h, w)).view(bh, set1, 256, 14, 14)
# query_xf = feature_embed(query_x.view(bh*set2,c,h,w)).view(bh,set2,64,19,19)
query_xf = feature_embed(query_x.view(bh*set2,c,h,w)).view(bh,set2,256,14,14)
# print("query_f:", query_xf.size())
# support_xf = support_xf.unsqueeze(1).expand(bh,set2,set1,64,19,19)
support_xf = support_xf.unsqueeze(1).expand(bh,set2,set1,256,14,14)
query_xf = query_xf.unsqueeze(2).expand(bh,set2,set1,256,14,14)
comb = torch.cat((support_xf,query_xf),dim=3) # bh,set2,set1,2c,h,w
# print(comb.is_cuda)
# print(comb.view(bh*set2*set1,2*64,19,19).is_cuda)
# print(comb.size())
score = Relation_score(comb.view(bh*set2*set1,2*256,14,14)).view(bh,set2,set1,1).squeeze(3)
support_yf = support_y.unsqueeze(1).expand(bh,set2,set1)
query_yf = query_y.unsqueeze(2).expand(bh,set2,set1)
label = torch.eq(support_yf,query_yf).float()
feature_optim.zero_grad()
relation_opim.zero_grad()
loss = loss_fn(score,label)
loss.backward()
torch.nn.utils.clip_grad_norm(feature_embed.parameters(),0.5) # 梯度裁剪？ 降低学习率？
torch.nn.utils.clip_grad_norm(Relation_score.parameters(),0.5)
feature_optim.step()
relation_opim.step()
# if step%100==0:
# print("step:",epoch+1,"train_loss: ",loss.data[0])
logger.log_value('resnet_{}-way-{}-shot loss：'.format(n_way, k_shot),loss.data[0])
if step%200==0:
print("---------test--------")
total_correct = 0
total_num = 0
accuracy = 0
for j,batch_test in enumerate(db_val):
# if (j%100==0):
# print(j,'-------------')
support_x = Variable(batch_test[0]).cuda()
support_y = Variable(batch_test[1]).cuda()
query_x = Variable(batch_test[2]).cuda()
query_y = Variable(batch_test[3]).cuda()
bh,set1,c,h,w = support_x.size()
set2 = query_x.size(1)
feature_embed.eval()
Relation_score.eval()
support_xf = feature_embed(support_x.view(bh*set1,c,h,w)).view(bh,set1,256,14,14) # 在 test 的 时候 重复
query_xf = feature_embed(query_x.view(bh*set2,c,h,w)).view(bh,set2,256,14,14)
support_xf = support_xf.unsqueeze(1).expand(bh,set2,set1,256,14,14)
query_xf = query_xf.unsqueeze(2).expand(bh,set2,set1,256,14,14)
comb = torch.cat((support_xf,query_xf),dim=3) # bh,set2,set1,2c,h,w
score = Relation_score(comb.view(bh*set2*set1,2*256,14,14)).view(bh,set2,set1,1).squeeze(3)
rn_score_np = score.cpu().data.numpy() # 转numpy cpu
pred = []
support_y_np = support_y.cpu().data.numpy()
for ii,tb in enumerate(rn_score_np):
for jj,tset in enumerate(tb):
sim = []
for way in range(n_way):
sim.append(np.sum(tset[way*k_shot:(way+1)*k_shot]))
idx = np.array(sim).argmax()
pred.append(support_y_np[ii,idx*k_shot]) # 同一个类标签相同 ，注意还有batch维度
# ×k_shot是因为，上一个步用sum将k_shot压缩了
#此时的pred.size = [b.set2]
#print("pred.size=", np.array(pred).shape)
pred = Variable(torch.from_numpy(np.array(pred).reshape(bh,set2))).cuda()
correct = torch.eq(pred,query_y).sum()
total_correct += correct.data[0]
total_num += query_y.size(0)*query_y.size(1)
accuracy = total_correct/total_num
logger.log_value('acc : ', accuracy)
print("epoch:",epoch,"acc:",accuracy)
if accuracy>best_acc:
print("-------------------epoch",epoch,"step:",step,"acc:",accuracy,"---------------------------------------")
best_acc = accuracy
torch.save(feature_embed.state_dict(),mdfile1)
torch.save(Relation_score.state_dict(),mdfile2)
logger.step()
#if step% == 0 and step != 0:
# print("%d-way %d-shot %d batch | epoch:%d step:%d, loss:%f" %(n_way,k_shot,batchsz,epoch,step,loss.cpu().data[0]))
#logger.step()
if __name__=='__main__':
main()