def main(params):
net = getattr(resnet, params['model'])()
net.load_state_dict(
torch.load(os.path.join(params['model_root'],
params['model'] + '.pth')))
my_resnet = myResnet(net)
my_resnet.cuda()
my_resnet.eval()
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
N = len([i for i in imgs if i['filepath'] == 'val2014'])
import pdb
pdb.set_trace()
seed(123) # make reproducible
dir_fc = params['output_dir'] + '_fc'
dir_att = params['output_dir'] + '_att'
if not os.path.isdir(dir_fc):
os.mkdir(dir_fc)
if not os.path.isdir(dir_att):
os.mkdir(dir_att)
with h5py.File(os.path.join(dir_fc, 'feats_fc.h5')) as file_fc,\
h5py.File(os.path.join(dir_att, 'feats_att.h5')) as file_att:
for i, img in enumerate(imgs):
if img['filepath'] == 'val2014':
# load the image
I = skimage.io.imread(
os.path.join(params['images_root'], img['filepath'],
img['filename']))
# handle grayscale input images
if len(I.shape) == 2:
I = I[:, :, np.newaxis]
I = np.concatenate((I, I, I), axis=2)
I = I.astype('float32') / 255.0
I = torch.from_numpy(I.transpose([2, 0, 1])).cuda()
with torch.no_grad():
I = Variable(preprocess(I))
tmp_fc, tmp_att = my_resnet(I, params['att_size'])
# write to hdf5
d_set_fc = file_fc.create_dataset(str(img['cocoid']), (2048, ),
dtype="float")
d_set_att = file_att.create_dataset(
str(img['cocoid']),
(params['att_size'], params['att_size'], 2048),
dtype="float")
d_set_fc[...] = tmp_fc.cpu().float().numpy()
d_set_att[...] = tmp_att.cpu().float().numpy()
if i % 1000 == 0:
print('processing %d/%d (%.2f%% done)' %
(i, N, i * 100.0 / N))
file_fc.close()
file_att.close()
def main(params):
net = getattr(resnet, params['model'])()
net.load_state_dict(
torch.load(os.path.join(params['model_root'],
params['model'] + '.pth')))
torch.cuda.empty_cache()
gc.collect()
my_resnet = myResnet(net)
my_resnet.cuda()
my_resnet.eval()
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
N = len(imgs)
seed(123) # make reproducible
dir_fc = params['output_dir'] + '_fc'
dir_att = params['output_dir'] + '_att'
if not os.path.isdir(dir_fc):
os.mkdir(dir_fc)
if not os.path.isdir(dir_att):
os.mkdir(dir_att)
for i, img in enumerate(imgs):
gc.collect()
torch.cuda.empty_cache()
# load the image
I = skimage.io.imread(
os.path.join(params['images_root'], img['filepath'],
img['filename']))
# handle grayscale input images
if len(I.shape) == 2:
I = I[:, :, np.newaxis]
I = np.concatenate((I, I, I), axis=2)
I = I.astype('float32') / 255.0
I = torch.from_numpy(I.transpose([2, 0, 1])).cuda()
I = preprocess(I)
with torch.no_grad():
tmp_fc, tmp_att = my_resnet(I, params['att_size'])
# write to pkl
np.save(os.path.join(dir_fc, str(img['imgid'])),
tmp_fc.data.cpu().float().numpy())
np.savez_compressed(os.path.join(dir_att, str(img['imgid'])),
feat=tmp_att.data.cpu().float().numpy())
del tmp_fc, tmp_att, I
if i % 100 == 0:
torch.cuda.empty_cache()
if i % 1000 == 0:
print('processing %d/%d (%.2f%% done)' % (i, N, i * 100.0 / N))
print('wrote ', params['output_dir'])
from torch.autograd import Variable
import skimage.io
from torchvision import transforms as trn
preprocess = trn.Compose([
#trn.ToPILImage(),
#trn.Scale(256),
#trn.ToTensor(),
trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
from resnet_utils import myResnet
resnet = models.resnet101()
resnet.load_state_dict(torch.load('/home-nfs/rluo/rluo/model/resnet101-5d3b4d8f.pth'))
my_resnet = myResnet(resnet)
my_resnet.cuda()
my_resnet.eval()
def build_vocab(imgs, params):
count_thr = params['word_count_threshold']
# count up the number of words
counts = {}
for img in imgs:
for sent in img['sentences']:
for w in sent['tokens']:
counts[w] = counts.get(w, 0) + 1
cw = sorted([(count,w) for w,count in counts.iteritems()], reverse=True)
print 'top words and their counts:'
print '\n'.join(map(str,cw[:20]))
th.start() #启动线程
if os.path.isfile(词数词典路径) and os.path.isfile(数_词表路径):
词_数表, 数_词表 = 读出引索(词数词典路径, 数_词表路径)
with open(词数词典路径, encoding='utf8') as f:
词数词典 = json.load(f)
with open(操作查询路径, encoding='utf8') as f:
操作查询词典 = json.load(f)
方向表 = ['上移', '下移', '左移', '右移', '左上移', '左下移', '右上移', '右下移']
设备 = MyMNTDevice(_DEVICE_ID)
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
mod = torchvision.models.resnet101(
pretrained=True).eval().cuda(device).requires_grad_(False)
resnet101 = myResnet(mod)
config = TransformerConfig()
model = get_model(config, 130, 模型名称)
model = model.cuda(device).requires_grad_(False)
while True:
if AI打开:
图片路径 = 训练数据保存目录 + '/{}/'.format(str(int(time.time())))
os.mkdir(图片路径)
记录文件 = open(图片路径 + '_操作数据.json', 'w+')
图片张量 = torch.Tensor(0)
def main(params):
net = models.resnet101(pretrained=True)
#net = getattr(resnet, params['model'])()
#net.load_state_dict(torch.load(os.path.join(params['model_root'],params['model']+'.pth')))
my_resnet = myResnet(net)
#my_resnet.cuda()
my_resnet.eval()
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
N = len(imgs)
#get rid of images not exist
if params['iscmplt'] == 0:
new_img = []
for i, img in enumerate(imgs):
if len(new_img) == 100:
break
k = os.path.join('images', img['filename'])
try:
I = skimage.io.imread(k)
new_img.append(imgs[i])
except:
print("none")
imgs = new_img
print("end selecting")
seed(123) # make reproducible
dir_fc = params['output_dir'] + '_fc'
dir_att = params['output_dir'] + '_att'
if not os.path.isdir(dir_fc):
os.mkdir(dir_fc)
if not os.path.isdir(dir_att):
os.mkdir(dir_att)
for i, img in enumerate(imgs):
print(i)
# load the image
#I = skimage.io.imread(os.path.join(params['images_root'], img['filepath'], img['filename']))
I = skimage.io.imread(
os.path.join(params['images_root'], img['filename']))
# handle grayscale input images
if len(I.shape) == 2:
I = I[:, :, np.newaxis]
I = np.concatenate((I, I, I), axis=2)
I = I.astype('float32') / 255.0
I = torch.from_numpy(I.transpose([2, 0, 1]))
#I = torch.from_numpy(I.transpose([2,0,1])).cuda()
I = Variable(preprocess(I), volatile=True)
tmp_fc, tmp_att = my_resnet(I, params['att_size'])
# write to pkl
np.save(os.path.join(dir_fc, str(img['cocoid'])),
tmp_fc.data.cpu().float().numpy())
np.savez_compressed(os.path.join(dir_att, str(img['cocoid'])),
feat=tmp_att.data.cpu().float().numpy())
if i % 1000 == 0:
print('processing %d/%d (%.2f%% done)' % (i, N, i * 100.0 / N))
# if i==4:
# break
print('wrote ', params['output_dir'])
import torch
import torchvision
import numpy as np
import os
import json
from PIL import Image
from resnet_utils import myResnet
操作记录='../训练数据样本'
if not os.path.exists(操作记录):
os.makedirs(操作记录)
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
resnet101=torchvision.models.resnet101(pretrained=True).eval()
resnet101=myResnet(resnet101).cuda(device).requires_grad_(False)
词数词典路径="./json/词_数表.json"
with open(词数词典路径, encoding='utf8') as f:
词数词典=json.load(f)
for root, dirs, files in os.walk(操作记录):
if len(dirs)>0:
break
for 号 in dirs:
路径json = 操作记录+'/' + 号 + '/_操作数据.json'
numpy数组路径= 操作记录+'/' + 号 + '/图片_操作预处理数据2.npz'
if os.path.isfile(numpy数组路径):
continue
图片张量 = torch.Tensor(0)
def main():
global AI打开
global 操作列
加三技能 = '6'
加二技能 = '5'
加一技能 = '4'
购买 = 'f1'
词数词典路径 = "./json/词_数表.json"
数_词表路径 = "./json/数_词表.json"
操作查询路径 = "./json/名称_操作.json"
操作词典 = {"图片号": "0", "移动操作": "无移动", "动作操作": "无动作"}
th = threading.Thread(target=start_listen, )
th.start() # 启动线程
if os.path.isfile(词数词典路径) and os.path.isfile(数_词表路径):
词_数表, 数_词表 = 读出引索(词数词典路径, 数_词表路径)
with open(词数词典路径, encoding='utf8') as f:
词数词典 = json.load(f)
with open(操作查询路径, encoding='utf8') as f:
操作查询词典 = json.load(f)
方向表 = ['上移', '下移', '左移', '右移', '左上移', '左下移', '右上移', '右下移']
设备 = MyMNTDevice(_DEVICE_ID)
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
# mod = torchvision.models.resnet101(pretrained=True).eval().cuda(device).requires_grad_(False)
# mod = torchvision.models.resnet101(pretrained=True).eval().cpu().requires_grad_(False)
# mod = torchvision.models.resnet50(pretrained=True).eval().cpu().requires_grad_(False)
# mod = torchvision.models.resnet34(pretrained=True).eval().cpu().requires_grad_(False)
mod = torchvision.models.resnet18(
pretrained=True).eval().cpu().requires_grad_(False)
resnet101 = myResnet(mod)
config = TransformerConfig()
model = get_model(config, 130, 模型名称)
# model = model.cuda(device).requires_grad_(False)
model = model.cpu().requires_grad_(False)
抽样np = 0
if AI打开:
图片张量 = torch.Tensor(0)
操作张量 = torch.Tensor(0)
# 伪词序列 = torch.from_numpy(np.ones((1, 60)).astype(np.int64)).cuda(device).unsqueeze(0)
伪词序列 = torch.from_numpy(np.ones(
(1, 60)).astype(np.int64)).cpu().unsqueeze(0)
操作序列 = np.ones((1, ))
操作序列[0] = 128
计数 = 0
time_start = time.time()
旧指令 = '移动停'
for i in range(1000000):
# logger.info("++++001+++++")
if AI打开 == False:
break
try:
imgA = 取图(窗口名称)
except:
AI打开 = False
print('取图失败')
break
# logger.info("+++++002++++")
计时开始 = time.time()
# preprocess
图片张量, 操作序列 = preprocess(图片张量, imgA, resnet101, 操作序列, 抽样np)
pre_process = time.time()
logger.info("pre_process : {} ms ".format(pre_process - 计时开始))
# transform model
# 操作张量 = torch.from_numpy(操作序列.astype(np.int64)).cuda(device)
操作张量 = torch.from_numpy(操作序列.astype(np.int64)).cpu()
src_mask, trg_mask = create_masks(操作张量.unsqueeze(0),
操作张量.unsqueeze(0), device)
输出_实际_A = model(图片张量.unsqueeze(0), 操作张量.unsqueeze(0), trg_mask)
# logger.info("+++++003++++")
LI = 操作张量.contiguous().view(-1)
# LA=输出_实际_A.view(-1, 输出_实际_A.size(-1))
if 计数 % 20 == 0 and 计数 != 0:
print("jineng + zhuangbei ")
设备.发送(购买)
设备.发送(加三技能)
设备.发送(加二技能)
设备.发送(加一技能)
设备.发送('移动停')
logger.warning("{} {}".format(旧指令, '周期'))
# print(旧指令, '周期')
# time.sleep(0.02)
设备.发送(旧指令)
# logger.info("++++004+++++")
if 计数 % 1 == 0:
time_end = time.time()
输出_实际_A = F.softmax(输出_实际_A, dim=-1)
输出_实际_A = 输出_实际_A[:, -1, :]
抽样 = torch.multinomial(输出_实际_A, num_samples=1)
抽样np = 抽样.cpu().numpy()
指令 = 数_词表[str(抽样np[0, -1])]
指令集 = 指令.split('_')
# 操作词典 = {"图片号": "0", "移动操作": "无移动", "动作操作": "无动作"}
操作词典['图片号'] = str(i)
方向结果 = 处理方向()
# logger.info("++++005+++++")
logger.info("方向结果:{} 操作列:{} 攻击态:{}".format(
方向结果, len(操作列), 攻击态))
# deal with output
操作列, output_suc = output(方向结果, 操作列, 操作词典, 指令集, 旧指令, 设备, imgA,
i)
if output_suc == 0:
AI打开 = False
break
# logging
# logger.info("++++008+++++")
用时1 = 0.22 - (time.time() - 计时开始)
if 用时1 > 0:
logger.info("++++sleep+++++")
time.sleep(用时1)
logger.info("+++++++++")
用时 = time_end - time_start
print("用时{} 第{}张 延时{}".format(用时, i, 用时1), 'A键按下', A键按下,
'W键按下', W键按下, 'S键按下', S键按下, 'D键按下', D键按下, '旧指令', 旧指令,
'AI打开', AI打开, '操作列', 操作列)
计数 = 计数 + 1
# logger.info("++++009+++++")
记录文件.close()
time.sleep(1)
print('AI打开', AI打开)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", default='../data', type=str)
parser.add_argument("--image_dir", default='../image', type=str)
parser.add_argument(
"--bert_model",
default="bert-base-uncased",
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--max_seq_length",
default=77,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument(
"--max_hashtag_length",
default=12,
type=int,
help=
"The maximum total hashtag input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_test",
action='store_true',
help="Whether to run on the test set.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=16,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=8.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--model_select', default='MsdBERT', help='model select'
) # select from MsdBERT, ResNetOnly, BertOnly, Res_BERT
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
logger.info("device: {} n_gpu: {}".format(device, n_gpu))
logger.info("************** Using: " + torch.cuda.get_device_name(0) +
" ******************")
# set seed val
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
random.seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_test:
raise ValueError(
"At least one of 'do_train' or 'do_test' must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
processor = Processer(args.data_dir, args.image_dir, args.model_select,
args.max_seq_length, args.max_hashtag_length)
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=True)
train_examples = None
num_train_steps = None
eval_examples = None
if args.do_train:
train_examples = processor.get_train_examples()
eval_examples = processor.get_eval_examples()
num_train_steps = int((len(train_examples) * args.num_train_epochs) /
args.train_batch_size)
if args.model_select == 'BertOnly':
model = BertOnly()
elif args.model_select == 'ResNetOnly':
model = ResNetOnly()
elif args.model_select == 'Res_BERT':
model = Res_BERT()
elif args.model_select == 'MsdBERT':
model = MsdBERT()
else:
raise ValueError("A model must be given.")
model.to(device)
net = torchvision.models.resnet152(pretrained=True)
encoder = myResnet(net).to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
encoder = torch.nn.DataParallel(encoder)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}, {
'params': encoder.parameters()
}]
t_total = num_train_steps
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
output_encoder_file = os.path.join(args.output_dir, "pytorch_encoder.bin")
train_loss = 0
if args.do_train:
train_features = processor.convert_mm_examples_to_features(
train_examples, label_list, tokenizer)
eval_features = processor.convert_mm_examples_to_features(
eval_examples, label_list, tokenizer)
train_input_ids, train_input_mask, train_added_input_mask, train_img_feats, \
train_hashtag_input_ids, train_hashtag_input_mask, train_label_ids = train_features
train_data = TensorDataset(train_input_ids, train_input_mask, train_added_input_mask, train_img_feats, \
train_hashtag_input_ids, train_hashtag_input_mask, train_label_ids)
train_dataloader = DataLoader(train_data,
sampler=RandomSampler(train_data),
batch_size=args.train_batch_size)
eval_input_ids, eval_input_mask, eval_added_input_mask, eval_img_feats, \
eval_hashtag_input_ids, eval_hashtag_input_mask, eval_label_ids = eval_features
eval_data = TensorDataset(eval_input_ids, eval_input_mask, eval_added_input_mask, eval_img_feats, \
eval_hashtag_input_ids, eval_hashtag_input_mask, eval_label_ids)
eval_dataloader = DataLoader(eval_data,
sampler=SequentialSampler(eval_data),
batch_size=args.eval_batch_size)
max_acc = 0.0
logger.info("*************** Running training ***************")
for train_idx in trange(int(args.num_train_epochs), desc="Epoch"):
logger.info("********** Epoch: " + str(train_idx + 1) +
" **********")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
model.train()
encoder.train()
tr_loss = 0
nb_tr_steps = 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
train_input_ids, train_input_mask, train_added_input_mask, train_img_feats, \
train_hashtag_input_ids, train_hashtag_input_mask, train_label_ids = batch
imgs_f, img_mean, train_img_att = encoder(train_img_feats)
loss = model(train_input_ids, train_img_att, train_input_mask,
train_added_input_mask, train_hashtag_input_ids,
train_hashtag_input_mask, train_label_ids)
if n_gpu > 1:
loss = loss.mean()
loss.backward()
tr_loss += loss.item()
nb_tr_steps += 1
optimizer.step()
optimizer.zero_grad()
logger.info("***** Running evaluation on Dev Set*****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
encoder.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
true_label_list = []
pred_label_list = []
for batch in eval_dataloader:
batch = tuple(t.to(device) for t in batch)
eval_input_ids, eval_input_mask, eval_added_input_mask, eval_img_feats, \
eval_hashtag_input_ids, eval_hashtag_input_mask, eval_label_ids = batch
imgs_f, img_mean, eval_img_att = encoder(eval_img_feats)
with torch.no_grad():
tmp_eval_loss = model(eval_input_ids, eval_img_att,
eval_input_mask,
eval_added_input_mask,
eval_hashtag_input_ids,
eval_hashtag_input_mask,
eval_label_ids)
logits = model(eval_input_ids, eval_img_att,
eval_input_mask, eval_added_input_mask,
eval_hashtag_input_ids,
eval_hashtag_input_mask)
logits = logits.detach().cpu().numpy()
label_ids = eval_label_ids.to('cpu').numpy()
true_label_list.append(label_ids)
pred_label_list.append(logits)
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += eval_input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
train_loss = tr_loss / nb_tr_steps if args.do_train else None
true_label = np.concatenate(true_label_list)
pred_outputs = np.concatenate(pred_label_list)
precision, recall, F_score = macro_f1(true_label, pred_outputs)
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'precision': precision,
'recall': recall,
'f_score': F_score,
'train_loss': train_loss
}
logger.info("***** Dev Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
if eval_accuracy > max_acc:
torch.save(model.state_dict(), output_model_file)
torch.save(encoder.state_dict(), output_encoder_file)
logger.info("better model")
max_acc = eval_accuracy
if args.do_test:
model.load_state_dict(torch.load(output_model_file))
encoder.load_state_dict(torch.load(output_encoder_file))
model.to(device)
encoder.to(device)
model.eval()
encoder.eval()
test_examples = processor.get_test_examples()
logger.info("***** Running evaluation on Test Set*****")
logger.info(" Num examples = %d", len(test_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
test_features = processor.convert_mm_examples_to_features(
test_examples, label_list, tokenizer)
test_input_ids, test_input_mask, test_added_input_mask, test_img_feats, \
test_hashtag_input_ids, test_hashtag_input_mask, test_label_ids = test_features
test_data = TensorDataset(test_input_ids, test_input_mask, test_added_input_mask, test_img_feats, \
test_hashtag_input_ids, test_hashtag_input_mask, test_label_ids)
test_dataloader = DataLoader(test_data,
sampler=SequentialSampler(test_data),
batch_size=args.eval_batch_size)
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
true_label_list = []
pred_label_list = []
for batch in test_dataloader:
batch = tuple(t.to(device) for t in batch)
test_input_ids, test_input_mask, test_added_input_mask, test_img_feats, \
test_hashtag_input_ids, test_hashtag_input_mask, test_label_ids = batch
imgs_f, img_mean, test_img_att = encoder(test_img_feats)
with torch.no_grad():
tmp_eval_loss = model(test_input_ids,test_img_att, test_input_mask, test_added_input_mask, \
test_hashtag_input_ids, test_hashtag_input_mask, test_label_ids)
logits = model(test_input_ids, test_img_att, test_input_mask, test_added_input_mask, \
test_hashtag_input_ids, test_hashtag_input_mask)
logits = logits.detach().cpu().numpy()
label_ids = test_label_ids.to('cpu').numpy()
true_label_list.append(label_ids)
pred_label_list.append(logits)
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += test_input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
loss = train_loss if args.do_train else None
true_label = np.concatenate(true_label_list)
pred_outputs = np.concatenate(pred_label_list)
precision, recall, F_score = macro_f1(true_label, pred_outputs)
result = {
'test_loss': eval_loss,
'test_accuracy': eval_accuracy,
'precision': precision,
'recall': recall,
'f_score': F_score,
'train_loss': loss
}
pred_label = np.argmax(pred_outputs, axis=-1)
fout_p = open(os.path.join(args.output_dir, "pred.txt"), 'w')
fout_t = open(os.path.join(args.output_dir, "true.txt"), 'w')
for i in range(len(pred_label)):
attstr = str(pred_label[i])
fout_p.write(attstr + '\n')
for i in range(len(true_label)):
attstr = str(true_label[i])
fout_t.write(attstr + '\n')
fout_p.close()
fout_t.close()
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Test Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
import numpy as np
import os
import json
from PIL import Image
from resnet_utils import myResnet
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
操作记录 = '../训练数据样本'
if not os.path.exists(操作记录):
os.makedirs(操作记录)
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
resnet101 = torchvision.models.resnet101(pretrained=True).eval()
# resnet101=myResnet(resnet101).cuda(device).requires_grad_(False)
resnet101 = myResnet(resnet101).cpu().requires_grad_(False)
词数词典路径 = "./json/词_数表.json"
with open(词数词典路径, encoding='utf8') as f:
词数词典 = json.load(f)
for root, dirs, files in os.walk(操作记录):
if len(dirs) > 0:
break
for 号 in dirs:
路径json = 操作记录 + '/' + 号 + '/_操作数据.json'
numpy数组路径 = 操作记录 + '/' + 号 + '/图片_操作预处理数据.npz'
if os.path.isfile(numpy数组路径):
continue
图片张量 = torch.Tensor(0)