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trainer.py
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import torch
from torch import nn
from time import time
from torch.autograd import Variable
import numpy as np
from Util import few_data, device
from task_generator import OmniglotTask, get_data_loader
import random
import Constants
def train(feature_encoder, relation_network, train_data, config):
task = OmniglotTask(train_data, config["CLASS_NUM"], config["SAMPLE_NUM_PER_CLASS"], config["BATCH_NUM_PER_CLASS"],
"train")
sample_dataloader = get_data_loader(task, config, num_per_class=config["SAMPLE_NUM_PER_CLASS"], split="train",
shuffle=False)
batch_dataloader = get_data_loader(task, config, num_per_class=config["BATCH_NUM_PER_CLASS"], split="test",
shuffle=True)
# sample datas
samples, sample_labels, class_folders = sample_dataloader.__iter__().next()
batches, batch_labels, class_folders = batch_dataloader.__iter__().next()
# calculate features
sample_features0 = feature_encoder(Variable(samples)).to(device) # 25*128
sample_features1 = sample_features0.view(config["CLASS_NUM"], config["SAMPLE_NUM_PER_CLASS"], -1)
sample_features = torch.sum(sample_features1, 1).squeeze(1)
batch_features = feature_encoder(Variable(batches)).to(device) # 75*300
# calculate relations
sample_features_ext = sample_features.unsqueeze(0).repeat(config["BATCH_NUM_PER_CLASS"] * config["CLASS_NUM"], 1, 1)
batch_features_ext = batch_features.unsqueeze(0).repeat(config["CLASS_NUM"], 1, 1)
batch_features_ext = torch.transpose(batch_features_ext, 0, 1)
relations = relation_network(sample_features_ext, batch_features_ext).view(-1, config["CLASS_NUM"])
mse = nn.MSELoss().to(device)
batch_labels = batch_labels.long()
one_hot_labels = torch.zeros(config["BATCH_NUM_PER_CLASS"] * config["CLASS_NUM"], config["CLASS_NUM"])
one_hot_labels = one_hot_labels.scatter_(1, batch_labels.view(-1, 1), 1)
one_hot_labels = Variable(one_hot_labels).to(device)
loss = mse(relations, one_hot_labels)
feature_encoder.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_(relation_network.parameters(), 0.5)
return loss.item()
def valid(feature_encoder, relation_network, test_data, config, word2index):
# test
feature_encoder.eval()
relation_network.eval()
total_rewards = 0
for i in range(config["TEST_EPISODE"]): # 训练测试 集合数量不同
task = OmniglotTask(test_data, config["CLASS_NUM"], config["SAMPLE_NUM_PER_CLASS"],
config["BATCH_NUM_PER_CLASS"], "test")
sample_dataloader = get_data_loader(task, config, num_per_class=config["SAMPLE_NUM_PER_CLASS"], split="train",
shuffle=False)
test_dataloader = get_data_loader(task, config, num_per_class=config["SAMPLE_NUM_PER_CLASS"], split="test",
shuffle=True)
sample_images, sample_labels, class_folders = sample_dataloader.__iter__().next()
test_images, test_labels, class_folders = test_dataloader.__iter__().next()
# calculate features
sample_features = feature_encoder(Variable(sample_images).to(device)) # 5x28-> #5*50
sample_features = sample_features.view(config["CLASS_NUM"], config["SAMPLE_NUM_PER_CLASS"], -1)
sample_features = torch.sum(sample_features, 1).squeeze(1)
test_features = feature_encoder(Variable(test_images).to(device)) # 20x64
sample_features_ext = sample_features.unsqueeze(0).repeat(config["SAMPLE_NUM_PER_CLASS"] * config["CLASS_NUM"],
1, 1)
test_features_ext = test_features.unsqueeze(0).repeat(config["CLASS_NUM"], 1, 1)
test_features_ext = torch.transpose(test_features_ext, 0, 1)
relations = relation_network(sample_features_ext, test_features_ext) # 25
relations = relations.view(-1, config["CLASS_NUM"]) # 5*5
_, predict_labels = torch.max(relations.data, 1)
predict_labels = predict_labels.cpu()
rewards = [1 if int(predict_labels[j]) == int(test_labels[j]) else 0 for j in
range(config["CLASS_NUM"] * config["SAMPLE_NUM_PER_CLASS"])]
if i % 10 == 0:
pass
# print("测试集，目标值：{}，预测结果：{}".format(test_labels,predict_labels))
if i % 100 == 0:
sentence = "目的地改为哈尔滨"
# task = OmniglotTask(test_data, config["CLASS_NUM"], config["SAMPLE_NUM_PER_CLASS"],
# config["BATCH_NUM_PER_CLASS"], "test")
keys = test_data.keys()
support_inputs = []
choice_num=config["CLASS_NUM"]*config["SAMPLE_NUM_PER_CLASS"]//len(keys)
for categeory in keys:
class_folders = random.sample(test_data[categeory], choice_num)
for sentence_i in class_folders:
sentence_index = sentence2indices(sentence_i, word2index, config["max_len"], Constants.PAD)
support_inputs.append(sentence_index)
# support_inputs.extend([support_inputs[-1]*(config["CLASS_NUM"]*config["SAMPLE_NUM_PER_CLASS"]-len(support_inputs))])
# sample_dataloader = get_data_loader(task, config, num_per_class=config["SAMPLE_NUM_PER_CLASS"],
# split="train",
# shuffle=False)
# sample_images, sample_labels,class_folders = sample_dataloader.__iter__().next()
support_inputs = torch.tensor(support_inputs)
""""dfalsfd"""
sentence_id = [config["word2index"].get(word, Constants.PAD) for word in sentence]
sentence_id += [0] * (12 - len(sentence_id))
sentence_images = torch.tensor([sentence_id])
sentence_images = sentence_images.repeat(config["CLASS_NUM"] * config["SAMPLE_NUM_PER_CLASS"], 1)
test_images = sentence_images
sample_features = feature_encoder(Variable(support_inputs).to(device)) # 5x28-> #5*50
sample_features = sample_features.view(config["CLASS_NUM"], config["SAMPLE_NUM_PER_CLASS"], -1)
sample_features = torch.sum(sample_features, 1).squeeze(1)
test_features = feature_encoder(Variable(test_images).to(device)) # 20x64
sample_features_ext = sample_features.unsqueeze(0).repeat(
config["SAMPLE_NUM_PER_CLASS"] * config["CLASS_NUM"],
1, 1)
test_features_ext = test_features.unsqueeze(0).repeat(config["CLASS_NUM"], 1, 1)
test_features_ext = torch.transpose(test_features_ext, 0, 1)
relations = relation_network(sample_features_ext, test_features_ext) # 25
relations = relations.view(-1, config["CLASS_NUM"]) # 5*5
_, predict_labels = torch.max(relations.data, 1)
predict_labels = predict_labels.cpu()
print("预测概率为：", predict_labels)
print("预测值为", predict_labels, keys)
total_rewards += np.sum(rewards)
test_accuracy = total_rewards / 1.0 / config["CLASS_NUM"] / config["SAMPLE_NUM_PER_CLASS"] / config["TEST_EPISODE"]
print("test accuracy:", test_accuracy)
return test_accuracy
def sentence2indices(line, word2index, max_len=None, padding_index=None, unk=None, began=None, end=None):
result = [word2index.get(word, unk) for word in line if word in word2index]
if max_len is not None:
result = result[:max_len]
if began is not None:
result.insert(0, began)
if end is not None:
result.append(end)
if padding_index is not None and len(result) < max_len:
result += [padding_index] * (max_len - len(result))
if not result:
a = 0
# assert len(result) == max_len
return result
def predict(feature_encoder, relation_network, config, word2index):
'''
预测函数
:param feature_encoder:
:param relation_network:
:param config:
:return:
'''
sentence = "这个月金蝶中国的收款"
sentence_id = [word2index[word] for word in sentence]
sentence_id += [0] * (12 - len(sentence_id))
sample_sentence = torch.tensor(sentence_id)
# calculate features
sample_features = feature_encoder(Variable(sample_sentence).to(device)) # 5x28-> #5*50
sample_features = sample_features.view(config["CLASS_NUM"], config["SAMPLE_NUM_PER_CLASS"], -1)
sample_features = torch.sum(sample_features, 1).squeeze(1)
sample_features_ext = sample_features.unsqueeze(0).repeat(config["SAMPLE_NUM_PER_CLASS"] * config["CLASS_NUM"],
1, 1)
test_features_ext = test_features.unsqueeze(0).repeat(config["CLASS_NUM"], 1, 1)
test_features_ext = torch.transpose(test_features_ext, 0, 1)
relations = relation_network(sample_features_ext, test_features_ext) # 25
relations = relations.view(-1, config["CLASS_NUM"]) # 5*5
_, predict_labels = torch.max(relations.data, 1)
if __name__ == "__main__":
t0 = time()
print("耗时", time() - t0)