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train_test_2.py
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import sys
import os
sys.path.append(os.getcwd())
import time
from torch import save, load
from torch.optim import SGD, Adam, Adadelta
from torch.utils.data import Dataset, DataLoader
# from backbone.RPN_10s import RPN
# from backbone.RPN_10s import tool as T
from data_loader import loader
from config import cfg
from detection_collate_mit import call_back
# from train.eval_util import *
from rcn_tool_c import rcn_tool_c
from base_process import base_process
# from backbone.backbone_ import weights_init, get_paprams, adjust_learning_rate, adjust_learning_rate2
from backbone_ import *
import torch
from RCN import ROI as roi
import sklearn.metrics as metrics
from torch.nn import DataParallel
from imblearn.metrics import specificity_score
class model(base_process):
def train_stage_2(self):
batch = 240
lr1 = 0.15
data_set = loader(os.path.join(os.getcwd(), 'data_2'), {"mode": "training"})
data_set_test = loader(os.path.join(os.getcwd(), 'data_2'),{"mode": "test"}, data_set.index)
data_set_eval = loader(os.path.join(os.getcwd(), 'data_2'),{"mode": "eval"}, data_set.index)
data_loader = DataLoader(data_set, batch, True, collate_fn=call_back.detection_collate_RPN)
data_loader_test = DataLoader(data_set_test, batch, False, collate_fn=call_back.detection_collate_RPN)
data_loader_eval = DataLoader(data_set_eval, batch, False, collate_fn=call_back.detection_collate_RPN)
# optim = Adadelta(self.ROI.parameters(), lr=lr1, weight_decay=1e-5)
start_time = time.time()
optim_a = Adadelta([{'params': self.pre.parameters()},
{'params': self.ROI.parameters()}], lr=0.15, weight_decay=1e-5)
cfg.test = False
count = 0
for epoch in range(200):
runing_losss = 0.0
cls_loss = 0
coor_loss = 0
cls_loss2 = 0
coor_loss2 = 0
count += 1
# base_time = RPN_time = ROI_time = nms_time = pre_gt = loss_time = linear_time = 0
for data in data_loader:
y = data[1]
x = data[0].cuda()
peak = data[2]
num = data[3]
optim_a.zero_grad()
with torch.no_grad():
if self.flag >= 2:
result = self.base_process(x, y, peak)
feat1 = result['feat_8']
feat2 = result['feat_16']
feat3 = result['feat_32']
feat4 = result['feat_64']
label = result['label']
loss_box = result['loss_box']
cross_entropy = result['cross_entropy']
cls_score = self.pre(feat1, feat2, feat3, feat4)
cls_score = self.ROI(cls_score)
cross_entropy2 = self.tool2.cal_loss2(cls_score, label)
loss_total = cross_entropy2
loss_total.backward()
optim_a.step()
runing_losss += loss_total.item()
cls_loss2 += cross_entropy2.item()
cls_loss += cross_entropy.item()
coor_loss += loss_box.item()
end_time = time.time()
torch.cuda.empty_cache()
print(
"epoch:{a} time:{ff}: loss:{b:.4f} cls:{d:.4f} cor{e:.4f} cls2:{f:.4f} cor2:{g:.4f} date:{fff}".format(
a=epoch,
b=runing_losss,
d=cls_loss,
e=coor_loss,
f=cls_loss2,
g=coor_loss2, ff=int(end_time - start_time),
fff=time.asctime()))
# if epoch % 10 == 0:
# adjust_learning_rate(optim, 0.9, epoch, 50, lr1)
p = None
# if epoch % 2 == 0:
# print("test result")
# save(self.RPN.module.state_dict(),
# os.path.join(os.getcwd(), str(epoch) + 'rpn_a2.p'))
# save(self.RPN.module.state_dict(),
# os.path.join(os.getcwd(), str(epoch) + 'base_a2.p'))
start_time = end_time
all_data = []
all_label = []
for data in data_loader:
y = data[1]
x = data[0].cuda()
num = data[3]
peak = data[2]
with torch.no_grad():
if self.flag >= 2:
result = self.base_process_2(x, y, peak)
data_ = result['x']
label = result['label']
loss_box = result['loss_box']
cross_entropy = result['cross_entropy']
all_data.extend(data_.cpu())
all_label.extend(label.cpu())
for data in data_loader_eval:
y = data[1]
x = data[0].cuda()
num = data[3]
peak = data[2]
with torch.no_grad():
if self.flag >= 2:
result = self.base_process_2(x, y, peak)
data_ = result['x']
label = result['label']
loss_box = result['loss_box']
cross_entropy = result['cross_entropy']
all_data.extend(data_.cpu())
all_label.extend(label.cpu())
for data in data_loader_test:
y = data[1]
x = data[0].cuda()
num = data[3]
peak = data[2]
with torch.no_grad():
if self.flag >= 2:
result = self.base_process_2(x, y, peak)
data_ = result['x']
label = result['label']
loss_box = result['loss_box']
cross_entropy = result['cross_entropy']
all_data.extend(data_.cpu())
all_label.extend(label.cpu())
all_data = torch.stack(all_data, 0).numpy()
all_label = torch.LongTensor(all_label).numpy()
from imblearn.over_sampling import SMOTE
fun = SMOTE()
all_data, all_label = fun.fit_resample(all_data, all_label)
total = len(all_label)
training_label = all_label[:int(0.7 * total)]
training_data = all_data[:int(0.7 * total)]
test_label = all_label[-int(0.2 * total):]
test_data = all_data[-int(0.2 * total):]
count = 0
self.ROI = roi().cuda()
self.ROI = DataParallel(self.ROI, device_ids=[0])
self.ROI.apply(weights_init)
optim_b = Adadelta(self.ROI.parameters(), lr=0.15, weight_decay=1e-5)
for epoch in range(1200):
runing_losss = 0.0
cls_loss = 0
coor_loss = 0
cls_loss2 = 0
coor_loss2 = 0
count += 1
optim_b.zero_grad()
optim_a.zero_grad()
# base_time = RPN_time = ROI_time = nms_time = pre_gt = loss_time = linear_time = 0
for j in range(int(len(training_label) / 240)):
data_ = torch.Tensor(training_data[j * 240:j * 240 + 240]).view(240, 1024, 15).cuda()
label_ = torch.LongTensor(training_label[j * 240:j * 240 + 240]).cuda()
optim_b.zero_grad()
cls_score = self.ROI(data_)
cross_entropy2 = self.tool2.cal_loss2(cls_score, label_)
loss_total = cross_entropy2
loss_total.backward()
optim_b.step()
runing_losss += loss_total.item()
cls_loss2 += cross_entropy2.item()
cls_loss += cross_entropy.item()
coor_loss += loss_box.item()
end_time = time.time()
torch.cuda.empty_cache()
print(
"epoch:{a} time:{ff}: loss:{b:.4f} cls:{d:.4f} cor{e:.4f} cls2:{f:.4f} cor2:{g:.4f} date:{fff}".format(
a=epoch,
b=runing_losss,
d=cls_loss,
e=coor_loss,
f=cls_loss2,
g=coor_loss2, ff=int(end_time - start_time),
fff=time.asctime()))
if epoch % 10 == 0 and epoch > 0:
adjust_learning_rate(optim_b, 0.9, epoch, 50, 0.3)
p = None
self.eval_(test_data, test_label)
# self.ROI_eval(data_loader_eval, {"epoch": epoch})
start_time = end_time
print('finish')
def eval_(self, data, label):
self.ROI = self.ROI.eval()
gt = []
pre = []
total = int(len(label) / 240)
with torch.no_grad():
for i in range(total):
a = i * 240
b = a + 240
sin_x = torch.Tensor(data[a:b]).cuda()
sin_x = sin_x.view(240, 1024, 15)
sin_y = label[a:b]
predict = self.ROI(sin_x)
predict, index = torch.max(predict, 1)
pre.extend(index.cpu().tolist())
gt.extend(sin_y)
print("ppv:{}".format(metrics.precision_score(gt, pre, average='micro')))
print("spe:{}".format(specificity_score(gt, pre, average='micro')))
print("sen:{}".format(metrics.recall_score(gt, pre, average='micro')))
def base_process_2(self, x, y, peak):
cross_entropy, loss_box = torch.ones(1), torch.ones(1)
with torch.no_grad():
x1, x2, x3, x4 = self.features(x)
if self.flag == 3:
predict_confidence, box_predict = self.RPN(x1, x2, x3, x4)
proposal, batch_offset, batch_conf = self.tool.get_proposal(predict_confidence, box_predict,
y, test=True)
# save_proposal = [i.cpu().numpy() for i in proposal]
# save_data = x.cpu().numpy()
# save_y = [i.numpy() for i in y]
# self.save_dict['data'].append(save_data)
# self.save_dict['label'].append(save_y)
# self.save_dict['predict'].append(save_proposal)
proposal, label = self.tool2.pre_gt_match_uniform(proposal, y, training=True, params={'peak': peak})
if 1:
for i in range(len(proposal)):
tmp = torch.zeros(proposal[i].size()[0], 1).fill_(
i).cuda()
proposal[i] = torch.cat([tmp, proposal[i]], 1)
proposal = torch.cat(proposal, 0)
feat4, label, class_num = self.tool2.roi_pooling_cuda(x4, proposal, label=label, stride=64,
pool=self.pool4,
batch=True)
feat3 = \
self.tool2.roi_pooling_cuda(x3, proposal, stride=64, pool=self.pool3,
batch=True, label=None)[
0]
feat2 = \
self.tool2.roi_pooling_cuda(x2, proposal, stride=32,
pool=self.pool2,
batch=True, label=None)[0]
feat1 = \
self.tool2.roi_pooling_cuda(x1, proposal, stride=16,
pool=self.pool1,
batch=True, label=None, )[0]
x = self.pre(feat1, feat2, feat3, feat4)
x = x.view(-1, 1024 * 15)
if self.flag == 2:
result = {}
result['x'] = x
result['label'] = label
result['predict_offset'] = 0
result['class_num'] = class_num
result['batch_cor_weight'] = 0
result['cross_entropy'] = cross_entropy
result['loss_box'] = loss_box
return result
elif self.flag == 3:
result = {}
result['x'] = x
result['label'] = label
result['class_num'] = class_num
result['cross_entropy'] = cross_entropy
result['loss_box'] = loss_box
return result
if __name__ == '__main__':
a = model()
a.train_stage_2()
# a.test()
# a.save_data()
# a.smote_train()