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main_meta_transfer333.py
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main_meta_transfer333.py
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import torch
import torch.nn as nn
from utlis.models3 import CNNEncoder1d, CNNEncoder2d, RelationNetwork1d, RelationNetwork2d
from torch.autograd import Variable
from torch.optim.lr_scheduler import StepLR
import numpy as np
import os
import utlis.transfer_generator as tg
import argparse
import pickle
from utlis.logger import setlogger
import logging
import matplotlib
#matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import torch.nn.functional as F
parser = argparse.ArgumentParser(description="meta transfer learning")
parser.add_argument("-f", "--feature_dim", type=int, default=64)
parser.add_argument("-r", "--relation_dim", type=int, default=8)
parser.add_argument("-w", "--class_num", type=int, default=5)
parser.add_argument("-s", "--sample_num_per_class", type=int, default=1)
parser.add_argument("-b", "--batch_num_per_class", type=int, default=15)
parser.add_argument("-e", "--episode", type=int, default=100)
parser.add_argument("-t", "--test_episode", type=int, default=1000)
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("-d", "--datatype", type=str, default='fft')
parser.add_argument("-m", "--modeltype", type=str, default='1d')
parser.add_argument("-n", "--snr", type=int, default=-100)
parser.add_argument("-ra", "--reductionration", type=int, default=8)
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
DATATYPE = args.datatype
MODELTYPE = args.modeltype
testgap = 5
SNR = args.snr
RATIO = args.reductionration
torch.set_default_tensor_type('torch.cuda.FloatTensor')
tg.set_seed(3)
# -----------------------------path---------------------------
root = '.\\result_transfer_mean'
path1 = DATATYPE + '_' + MODELTYPE + '_' + str(CLASS_NUM) + 'way' + str(SAMPLE_NUM_PER_CLASS) + 'shot'
path = os.path.join(root, path1)
if not os.path.exists("%s" % path):
os.makedirs("%s" % path)
# ----------------------------logging-----------------------
setlogger(os.path.join("%s" % path, 'train.log'))
f = open("%s\\train.log" % path, 'w')
f.truncate()
f.close()
def get_entropy(p_softmax):
# exploit ENTropy minimization (ENT) to help DA,
mask = p_softmax.ge(0.000001)
mask_out = torch.masked_select(p_softmax, mask)
entropy = -(torch.sum(mask_out * torch.log(mask_out)))
return (entropy / float(p_softmax.size(0)))
def get_causality_loss(x_IN_entropy, x_useful_entropy, x_useless_entropy):
ranking_loss = torch.nn.SoftMarginLoss()
y = torch.ones_like(x_IN_entropy)
return ranking_loss(x_IN_entropy - x_useful_entropy, y) + ranking_loss(x_useless_entropy - x_IN_entropy,
y)
def main():
# Step 1: init data folders
logging.info("init data folders")
# init character folders for dataset construction
datapath = '.\\tempdata\\' + str(CLASS_NUM) + 'way' + '.pkl'
datapath2 = '.\\tempdata_anchor\\' + str(CLASS_NUM) + 'way' + '.pkl'
if not os.path.exists("%s" % datapath):
metatrain_character_folders, metatest_character_folders = tg.pu_folders(CLASS_NUM)
os.makedirs('.\\tempdata')
output = open(datapath, 'wb')
pickle.dump((metatrain_character_folders, metatest_character_folders), output)
output.close()
else:
with open(datapath, 'rb') as pkl:
metatrain_character_folders, metatest_character_folders = pickle.load(pkl)
if not os.path.exists("%s" % datapath2):
metatrain_character_folders_anchor, metatest_character_folders_anchor = tg.pu_folders_anchor()
os.makedirs('.\\tempdata_anchor')
output = open(datapath2, 'wb')
pickle.dump((metatrain_character_folders_anchor, metatest_character_folders_anchor), output)
output.close()
else:
with open(datapath2, 'rb') as pkl:
metatrain_character_folders_anchor, metatest_character_folders_anchor = pickle.load(pkl)
# Step 2: init neural networks
logging.info("init neural networks")
if MODELTYPE == '1d':
feature_encoder = CNNEncoder1d(FEATURE_DIM,RATIO,False,CLASS_NUM)
feature_encoder_anchor = CNNEncoder1d(FEATURE_DIM, RATIO, True, CLASS_NUM)
relation_network = RelationNetwork1d(FEATURE_DIM, RELATION_DIM)
elif MODELTYPE == '2d':
feature_encoder = CNNEncoder2d(FEATURE_DIM)
relation_network = RelationNetwork2d(FEATURE_DIM, RELATION_DIM)
feature_encoder.cuda(GPU)
feature_encoder_anchor.cuda(GPU)
relation_network.cuda(GPU)
feature_encoder_optim = torch.optim.Adam(feature_encoder.parameters(), lr=LEARNING_RATE)
feature_encoder_scheduler = StepLR(feature_encoder_optim, step_size=1000, gamma=0.5)
feature_encoder_optim_anchor = torch.optim.Adam(feature_encoder_anchor.parameters(), lr=LEARNING_RATE)
feature_encoder_scheduler_anchor = StepLR(feature_encoder_optim_anchor, step_size=1000, gamma=0.5)
relation_network_optim = torch.optim.Adam(relation_network.parameters(), lr=LEARNING_RATE)
relation_network_scheduler = StepLR(relation_network_optim, step_size=1000, gamma=0.5)
# Step 3: build graph
logging.info("Training...")
last_accuracy = 0.0
accuracys = []
aepochs = []
losses = []
lepochs = []
finalsum = 0
for episode in range(EPISODE):
feature_encoder_scheduler.step(episode)
feature_encoder_scheduler_anchor.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
task = tg.puTask(metatrain_character_folders, CLASS_NUM, SAMPLE_NUM_PER_CLASS, BATCH_NUM_PER_CLASS)
task_anchor = tg.puTask(metatrain_character_folders_anchor, 1, SAMPLE_NUM_PER_CLASS, BATCH_NUM_PER_CLASS)
task_positive = tg.puTask(metatrain_character_folders_anchor, 1, SAMPLE_NUM_PER_CLASS, BATCH_NUM_PER_CLASS)
task_negative = tg.puTask(metatest_character_folders_anchor, 1, SAMPLE_NUM_PER_CLASS, BATCH_NUM_PER_CLASS)
sample_dataloader = tg.get_data_loader(task, num_per_class=SAMPLE_NUM_PER_CLASS, split="train", shuffle=False,
dt=DATATYPE, mt=MODELTYPE)
sample_dataloader_anchor = tg.get_data_loader(task_anchor, num_per_class=SAMPLE_NUM_PER_CLASS, split="train", shuffle=False,
dt=DATATYPE, mt=MODELTYPE)
sample_dataloader_positive = tg.get_data_loader(task_positive, num_per_class=SAMPLE_NUM_PER_CLASS, split="train",shuffle=False,
dt=DATATYPE, mt=MODELTYPE)
sample_dataloader_negative = tg.get_data_loader(task_negative, num_per_class=SAMPLE_NUM_PER_CLASS, split="train",shuffle=False,
dt=DATATYPE, mt=MODELTYPE)
batch_dataloader = tg.get_data_loader(task, num_per_class=BATCH_NUM_PER_CLASS, split="test", shuffle=False,
dt=DATATYPE, mt=MODELTYPE,snr = SNR)
# sample datas
samples, sample_labels = sample_dataloader.__iter__().next()
samples_anchor, sample_labels_anchor = sample_dataloader_anchor.__iter__().next()
samples_positive, sample_labels_positive = sample_dataloader_positive.__iter__().next()
samples_negative, sample_labels_negative = sample_dataloader_negative.__iter__().next()
batches, batch_labels = batch_dataloader.__iter__().next()
# calculate features
sample_features_o = feature_encoder(Variable(samples).cuda(GPU).float())[0] # 5x64*5*5
sample_features_o_anchor = feature_encoder_anchor(Variable(samples_anchor).cuda(GPU).float())[1]
sample_features_o_positive = feature_encoder_anchor(Variable(samples_anchor).cuda(GPU).float())[2]
sample_features_o_negative = feature_encoder_anchor(Variable(samples_negative).cuda(GPU).float())[3]
if MODELTYPE == '1d':
sample_features = sample_features_o.view(CLASS_NUM, SAMPLE_NUM_PER_CLASS, FEATURE_DIM, 5 * 5)
# sample_features_anchor = sample_features_o_anchor.view(1, SAMPLE_NUM_PER_CLASS, FEATURE_DIM, 5 * 5)
# sample_features_positive = sample_features_o_positive.view(1, SAMPLE_NUM_PER_CLASS, FEATURE_DIM, 5 * 5)
# sample_features_negative = sample_features_o_negative.view(1, SAMPLE_NUM_PER_CLASS, FEATURE_DIM, 5 * 5)
else:
sample_features = sample_features_o.view(CLASS_NUM, SAMPLE_NUM_PER_CLASS, FEATURE_DIM, 5, 5)
sample_features = torch.mean(sample_features, 1).squeeze(1)
# sample_features_anchor = torch.mean(sample_features_anchor, 1).squeeze(1)
# sample_features_postive = torch.mean(sample_features_positive, 1).squeeze(1)
# sample_features_negiative = torch.mean(sample_features_negative, 1).squeeze(1)
batch_features = feature_encoder(Variable(batches.float()).cuda(GPU))[0] # 20x64*5*5
# calculate relations
# each batch sample link to every samples to calculate relations
# to form a 100x128 matrix for relation network
if MODELTYPE == '1d':
sample_features_ext = sample_features.unsqueeze(0).repeat(BATCH_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1)
batch_features_ext = batch_features.unsqueeze(0).repeat(CLASS_NUM, 1, 1, 1)
batch_features_ext = torch.transpose(batch_features_ext, 0, 1)
relation_pairs = torch.cat((sample_features_ext, batch_features_ext), 2).view(-1, FEATURE_DIM * 2, 5 * 5)
else:
sample_features_ext = sample_features.unsqueeze(0).repeat(BATCH_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1, 1)
batch_features_ext = batch_features.unsqueeze(0).repeat(CLASS_NUM, 1, 1, 1, 1)
batch_features_ext = torch.transpose(batch_features_ext, 0, 1)
relation_pairs = torch.cat((sample_features_ext, batch_features_ext), 2).view(-1, FEATURE_DIM * 2, 5, 5)
relations = relation_network(relation_pairs).view(-1, CLASS_NUM)
#loss caculate
# loss_causality = 0.01 * get_causality_loss(get_entropy(sample_features_o_anchor),
# get_entropy(sample_features_o_positive),
# get_entropy(sample_features_o_negative))
mse = nn.MSELoss().cuda(GPU)
one_hot_labels = Variable(
torch.zeros(BATCH_NUM_PER_CLASS * CLASS_NUM, CLASS_NUM).scatter_(1, batch_labels.cuda().long().view(-1, 1),
1)).cuda(GPU)
loss = mse(relations, one_hot_labels)
# training
feature_encoder.zero_grad()
feature_encoder_anchor.zero_grad()
relation_network.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(feature_encoder.parameters(), 0.5)
torch.nn.utils.clip_grad_norm_(feature_encoder_anchor.parameters(), 0.5)
torch.nn.utils.clip_grad_norm_(relation_network.parameters(), 0.5)
feature_encoder_optim.step()
feature_encoder_optim_anchor.step()
relation_network_optim.step()
logging.info("episode:" + str(episode + 1) + " loss: " + str(loss.item()))
losses.append(loss.item())
lepochs.append(episode)
if (episode + 1) % testgap == 0:
# test
logging.info("Testing...")
total_rewards = 0
for i in range(TEST_EPISODE):
task = tg.puTask(metatest_character_folders, CLASS_NUM, SAMPLE_NUM_PER_CLASS, SAMPLE_NUM_PER_CLASS, )
sample_dataloader = tg.get_data_loader(task, num_per_class=SAMPLE_NUM_PER_CLASS, split="train",
shuffle=False, dt=DATATYPE, mt=MODELTYPE)
test_dataloader = tg.get_data_loader(task, num_per_class=SAMPLE_NUM_PER_CLASS, split="test",
shuffle=True, dt=DATATYPE, mt=MODELTYPE,snr = SNR)
sample_images, sample_labels = sample_dataloader.__iter__().next()
test_images, test_labels = test_dataloader.__iter__().next()
# calculate features
sample_features = feature_encoder(Variable(sample_images).cuda(GPU).float())[0] # 5x64
if MODELTYPE == '1d':
sample_features = sample_features.view(CLASS_NUM, SAMPLE_NUM_PER_CLASS, FEATURE_DIM, 5 * 5)
else:
sample_features = sample_features.view(CLASS_NUM, SAMPLE_NUM_PER_CLASS, FEATURE_DIM, 5, 5)
sample_features = torch.mean(sample_features, 1).squeeze(1)
test_features = feature_encoder(Variable(test_images).cuda(GPU).float())[0] # 20x64
# calculate relations
# each batch sample link to every samples to calculate relations
if MODELTYPE == '1d':
sample_features_ext = sample_features.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS * CLASS_NUM, 1, 1, 1)
test_features_ext = test_features.unsqueeze(0).repeat(CLASS_NUM, 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,
5 * 5)
else:
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(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, 5,
5)
relations = relation_network(relation_pairs).view(-1, CLASS_NUM)
_, predict_labels = torch.max(relations.data, 1)
predict_labels = predict_labels.int()
rewards = [1 if predict_labels[j] == test_labels[j] else 0 for j in
range(CLASS_NUM * SAMPLE_NUM_PER_CLASS)]
total_rewards += np.sum(rewards)
test_accuracy = total_rewards / 1.0 / CLASS_NUM / SAMPLE_NUM_PER_CLASS / TEST_EPISODE
logging.info("test accuracy:" + str(test_accuracy))
if episode+1 > 80:
finalsum += test_accuracy
accuracys.append(test_accuracy)
aepochs.append(episode)
if test_accuracy > last_accuracy:
# save networks
torch.save(feature_encoder.state_dict(), os.path.join(path, "feature_encoder_%f.pkl" % test_accuracy))
torch.save(relation_network.state_dict(), os.path.join(path, "relation_network_%f.pkl" % test_accuracy))
logging.info("save networks for episode:" + str(episode))
last_accuracy = test_accuracy
torch.save(feature_encoder.state_dict(), os.path.join(path, "feature_encoder_final.pkl"))
torch.save(relation_network.state_dict(), os.path.join(path, "relation_network_final.pkl"))
logging.info("final accuracy :" + str(test_accuracy))
logging.info("final finalsum :" + str(finalsum/4))
plt.figure()
plt.suptitle(path1)
plt.subplot(2, 1, 1)
plt.plot(aepochs, accuracys)
plt.title('test accuracy')
plt.subplot(2, 1, 2)
plt.plot(lepochs, losses)
plt.title('train loss')
plt.savefig('%s\\accuracy.png' % path)
output = open('%s\\accuracy.pkl' % path, 'wb')
pickle.dump((aepochs, accuracys, lepochs, losses), output)
output.close()
if __name__ == '__main__':
main()