def main():
# # FOR DATA PARSING
# parse_data("./data/VOC2007/","./data/VOC2012/", "./output/")
# # FOR TRAINING
# train(resume=True, seed=94743, opt_level='O1', resize_dims=(500,500))
# # FOR EVALUATION AND TESTING
model = SSD(21)
state_dict = torch.load("./output/checkpoint.pt", map_location=device)
model.load_state_dict(state_dict)
model = model.to(device)
# # FOR EVALUATION ON THE TESTING SET
# test(model, "ALLPT", resize_dims=(500,500))
# test(model, "11PT", resize_dims=(500,500))
# # FOR DETECTION ON A SINGLE IMAGE
# image = "/home/numan947/MyHome/AIMLDL/Re:SSD - An Implementation of Single Shot MultiBox Detector/data/VOC2012/JPEGImages/2011_001254.jpg"
# original_image = Image.open(image, mode='r')
# original_image = original_image.convert('RGB')
# detect_single_image(model, original_image, min_score=0.25, max_overlap=0.4, top_k=200, resize_dims=(500,500)).show()
# # FOR DEMO USING CAMERA
# camera(model, min_score=0.3, max_overlap=0.25, top_k=20, resize_dims=(500,500))
# # FOR GENERATING SHOW CASE
ds = PascalGeneratorDataset("./output/", split="test")
generate(model, ds, n=50)
def train(seed, resume=False, opt_level='O1', resize_dims=(300, 300)):
clear_cuda()
torch.manual_seed(seed)
data_folder = "./output/"
keep_difficult = True
n_classes = len(label_map)
other_checkpoint = data_folder+"oth_checkpoint.pt"
model_checkpoint = data_folder+"checkpoint.pt"
early_stopping = EarlyStopping(save_model_name=model_checkpoint, patience=3)
pin_memory = True
batch_size = 8
accumulation_factor = 6
iterations = 100000
workers = 4*torch.cuda.device_count()
lr = 1e-3
decay_lr_at = [80000]
decay_lr_to = 0.2
early_stopper_lr_decrease = 0.5
early_stopper_lr_decrease_count = 7
momentum = 0.9
weight_decay = 5e-3
grad_clip = None
torch.backends.cudnn.benchmark = True
optimizer = None
start_epoch = None
history = pd.DataFrame()
history.index.name="Epoch"
history_path = "./output/HISTORY"+"_SEED_{}".format(seed)+".csv"
model = SSD(n_classes)
biases = list()
not_biases = list()
for param_name, param in model.named_parameters():
if param.requires_grad:
if param_name.endswith('.bias'):
biases.append(param)
else:
not_biases.append(param)
# optimizer = RAdam(params=[{'params':biases,'lr':2*lr},{'params':not_biases}], lr=lr, weight_decay=weight_decay)
# optimizer = torch.optim.RMSprop(params=[{'params':biases,'lr':2*lr},{'params':not_biases}], lr=lr, weight_decay=weight_decay, momentum=momentum)
# optimizer = torch.optim.SGD(params=[{'params':biases,'lr':2*lr},{'params':not_biases}], lr=lr, weight_decay=weight_decay, momentum=momentum)
optimizer = apex.optimizers.FusedLAMB(params=[{'params':biases,'lr':2*lr},{'params':not_biases}], lr=lr, weight_decay=weight_decay)#, momentum=momentum)
model = model.to(device)
criterion = MultiBoxLoss(model.priors_cxcy).to(device)
model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level, loss_scale=1.0)
start_epoch = 0
if resume and os.path.exists(other_checkpoint):
ocheckpt = torch.load(other_checkpoint, map_location=device)
optimizer.load_state_dict(ocheckpt['optimizer_state'])
start_epoch = ocheckpt['epoch'] + 1
lr = get_lr(optimizer)
history = pd.read_csv(history_path, index_col="Epoch")
model.load_state_dict(torch.load(model_checkpoint, map_location=device))
# adjust_learning_rate(optimizer, 0.1)
lr = get_lr(optimizer)
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
amp.load_state_dict(ocheckpt['amp_state'])
train_dataset = PascalDataset(data_folder, split="train", keep_difficult=keep_difficult, resize_dims=resize_dims)
train_len = int(0.85*len(train_dataset))
valid_len = len(train_dataset) - train_len
train_data, valid_data = torch.utils.data.dataset.random_split(train_dataset, [train_len, valid_len])
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, collate_fn=train_dataset.collate_fn,
num_workers=workers, pin_memory=pin_memory)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size, shuffle=False, collate_fn=train_dataset.collate_fn,
num_workers=workers, pin_memory=pin_memory)
total_epochs = iterations//(len(train_dataset)//32)
decay_lr_at = [it//(len(train_dataset)//32) for it in decay_lr_at]
total_epochs = 125
decay_lr_at = [100]
print("Training For: {}".format(total_epochs))
print("Decay LR at:", decay_lr_at)
for epoch in range(start_epoch, total_epochs):
if epoch in decay_lr_at:
adjust_learning_rate(optimizer, decay_lr_to)
lr*=decay_lr_to
print("Learning Rate Adjusted")
st = time.time()
print("EPOCH: {}/{} -- Current LR: {}".format(epoch+1, total_epochs, lr))
clear_cuda()
tl,ta = train_single_epoch(epoch, model, train_loader, optimizer, criterion, plot=False, clip_grad=grad_clip, accumulation_factor=accumulation_factor)
clear_cuda()
vl, va = evaluate(model, valid_loader, criterion)
print_epoch_stat(epoch, time.time()-st, history=history, train_loss=tl, valid_loss=vl)
early_stopping(tl, model)
other_state = {
'epoch': epoch,
'optimizer_state': optimizer.state_dict(),
'amp_state': amp.state_dict()
}
torch.save(other_state, other_checkpoint)
history.loc[epoch, "LR"] = lr
history.to_csv(history_path)
if early_stopping.early_stop:
if early_stopper_lr_decrease_count>0:
early_stopper_lr_decrease_count = early_stopper_lr_decrease_count-1
adjust_learning_rate(optimizer, early_stopper_lr_decrease)
early_stopping.early_stop = False
early_stopping.counter = 0
lr*=early_stopper_lr_decrease
print("Learning Rate Adjusted")
accumulation_factor*=2
else:
break
def train(self, vis=False):
print("begin training....")
if not os.path.exists('weights'):
os.mkdir('weights')
# Device settings
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device1 = torch.device(
"cuda:1" if torch.cuda.is_available() else "cpu")
if self.multigpu:
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
self.model = nn.DataParallel(self.model)
self.model = self.model.to(device)
eval_model = SSD(num_classes=cfg.num_classes,
num_blocks=cfg.mbox,
top_k=cfg.top_k,
conf_thresh=cfg.conf_thresh,
nms_thresh=cfg.nms_thresh,
variance=cfg.variance)
eval_model = eval_model.to(device1)
for item in self.model.parameters():
print(item.requires_grad)
total_epoch = cfg.epoch
criterion = DEC_loss(num_classes=cfg.num_classes,
variances=cfg.variance,
device=device)
optimizer = optim.SGD(params=filter(lambda p: p.requires_grad,
self.model.parameters()),
lr=cfg.init_lr,
momentum=0.9,
weight_decay=cfg.weight_decay)
# scheduler = lr_scheduler.StepLR(optimizer, step_size=cfg.lr_decay_epoch, gamma=0.1)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=cfg.milestones,
gamma=0.1)
print('Loading Datasets...')
dsets = PASCALVOC(root=cfg.root,
image_sets=cfg.train_sets,
transform=transforms.DEC_transforms(
phase='train',
size=cfg.img_size,
mean=cfg.means,
std=cfg.std))
dsets_val = PASCALVOC(root=cfg.root,
image_sets=cfg.test_sets,
transform=transforms.DEC_transforms(
phase='val',
size=cfg.img_size,
mean=cfg.means,
std=cfg.std))
dset_loaders = torch.utils.data.DataLoader(
dsets,
cfg.batch_size,
num_workers=4,
shuffle=True,
collate_fn=detection_collate,
pin_memory=True)
if vis:
viewDatasets_DEC(dset_loaders)
train_loss_dict = []
mAP_dict = []
for epoch in range(total_epoch):
print('Epoch {}/{}'.format(epoch, total_epoch - 1))
print('-' * 10)
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
self.model.train()
running_loss = 0.0
for data in dset_loaders:
inputs, target = data
inputs = inputs.to(device)
target = [item.to(device) for item in target]
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = self.model(inputs, phase)
# backprop
loss_l, loss_c = criterion(outputs, target)
loss = loss_l + loss_c
loss.backward()
optimizer.step()
running_loss += loss.item()
epoch_loss = running_loss / len(dsets)
print('{} Loss: {:.6}'.format(epoch, epoch_loss))
train_loss_dict.append(epoch_loss)
np.savetxt('train_loss.txt', train_loss_dict, fmt='%.6f')
if epoch % 5 == 0:
torch.save(
self.model.state_dict(),
os.path.join(
'weights', '{:d}_{:.4f}_model.pth'.format(
epoch, epoch_loss)))
torch.save(self.model.state_dict(),
os.path.join('weights', 'end_model.pth'))
else:
if epoch % 5 == 0:
model_dict = self.model.state_dict()
val_dict = {k[7:]: v for k, v in model_dict.items()}
eval_model.load_state_dict(val_dict)
maps = self.eval(device1, eval_model, dsets_val)
mAP_dict.append(maps)
np.savetxt('mAP.txt', mAP_dict, fmt='%.6f')
class DEC_Module(object):
def __init__(self, multigpu, resume):
self.model = SSD(num_classes=cfg.num_classes,
num_blocks=cfg.mbox,
top_k=cfg.top_k,
conf_thresh=cfg.conf_thresh,
nms_thresh=cfg.nms_thresh,
variance=cfg.variance)
if resume is not None:
print('Resuming training weights from {} ...'.format(resume))
resume_dict = torch.load(resume)
resume_dict_update = {}
for k in resume_dict:
if k.startswith('module') and not k.startswith('module_list'):
resume_dict_update[k[7:]] = resume_dict[k]
else:
resume_dict_update[k] = resume_dict[k]
self.model.load_state_dict(resume_dict_update)
else:
resnet = "resnet101"
print('Resuming weights from {} ...'.format(resnet))
pre_trained_dict = model_zoo.load_url(model_urls[resnet])
model_dict = self.model.state_dict()
updated_dict = {
k: v
for k, v in pre_trained_dict.items() if k in model_dict
}
model_dict.update(updated_dict)
self.model.load_state_dict(model_dict)
self.multigpu = multigpu
def train(self, vis=False):
print("begin training....")
if not os.path.exists('weights'):
os.mkdir('weights')
# Device settings
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device1 = torch.device(
"cuda:1" if torch.cuda.is_available() else "cpu")
if self.multigpu:
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
self.model = nn.DataParallel(self.model)
self.model = self.model.to(device)
eval_model = SSD(num_classes=cfg.num_classes,
num_blocks=cfg.mbox,
top_k=cfg.top_k,
conf_thresh=cfg.conf_thresh,
nms_thresh=cfg.nms_thresh,
variance=cfg.variance)
eval_model = eval_model.to(device1)
for item in self.model.parameters():
print(item.requires_grad)
total_epoch = cfg.epoch
criterion = DEC_loss(num_classes=cfg.num_classes,
variances=cfg.variance,
device=device)
optimizer = optim.SGD(params=filter(lambda p: p.requires_grad,
self.model.parameters()),
lr=cfg.init_lr,
momentum=0.9,
weight_decay=cfg.weight_decay)
# scheduler = lr_scheduler.StepLR(optimizer, step_size=cfg.lr_decay_epoch, gamma=0.1)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=cfg.milestones,
gamma=0.1)
print('Loading Datasets...')
dsets = PASCALVOC(root=cfg.root,
image_sets=cfg.train_sets,
transform=transforms.DEC_transforms(
phase='train',
size=cfg.img_size,
mean=cfg.means,
std=cfg.std))
dsets_val = PASCALVOC(root=cfg.root,
image_sets=cfg.test_sets,
transform=transforms.DEC_transforms(
phase='val',
size=cfg.img_size,
mean=cfg.means,
std=cfg.std))
dset_loaders = torch.utils.data.DataLoader(
dsets,
cfg.batch_size,
num_workers=4,
shuffle=True,
collate_fn=detection_collate,
pin_memory=True)
if vis:
viewDatasets_DEC(dset_loaders)
train_loss_dict = []
mAP_dict = []
for epoch in range(total_epoch):
print('Epoch {}/{}'.format(epoch, total_epoch - 1))
print('-' * 10)
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
self.model.train()
running_loss = 0.0
for data in dset_loaders:
inputs, target = data
inputs = inputs.to(device)
target = [item.to(device) for item in target]
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = self.model(inputs, phase)
# backprop
loss_l, loss_c = criterion(outputs, target)
loss = loss_l + loss_c
loss.backward()
optimizer.step()
running_loss += loss.item()
epoch_loss = running_loss / len(dsets)
print('{} Loss: {:.6}'.format(epoch, epoch_loss))
train_loss_dict.append(epoch_loss)
np.savetxt('train_loss.txt', train_loss_dict, fmt='%.6f')
if epoch % 5 == 0:
torch.save(
self.model.state_dict(),
os.path.join(
'weights', '{:d}_{:.4f}_model.pth'.format(
epoch, epoch_loss)))
torch.save(self.model.state_dict(),
os.path.join('weights', 'end_model.pth'))
else:
if epoch % 5 == 0:
model_dict = self.model.state_dict()
val_dict = {k[7:]: v for k, v in model_dict.items()}
eval_model.load_state_dict(val_dict)
maps = self.eval(device1, eval_model, dsets_val)
mAP_dict.append(maps)
np.savetxt('mAP.txt', mAP_dict, fmt='%.6f')
def test(self):
print('testing, evaluation mode...')
self.model.eval()
print('loading data...')
dsets = PASCALVOC(root=cfg.root,
image_sets=cfg.test_sets,
transform=transforms.DEC_transforms(
phase='val',
size=cfg.img_size,
mean=cfg.means,
std=cfg.std))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.model = self.model.to(device)
num_imgs = len(dsets)
test_timer = Timer()
cv2.namedWindow('img')
for i in range(num_imgs):
print('testing {}...'.format(dsets.img_ids[i]))
img, target = dsets.__getitem__(i)
ori_img = cv2.imread(dsets._imgpath % dsets.img_ids[i])
h, w, c = ori_img.shape
x = img.unsqueeze(0)
x = x.to(device)
test_timer.tic()
detections = self.model(x, 'test')
detect_time = test_timer.toc(average=False)
print('test time: {}'.format(detect_time))
for j in range(1, detections.size(1)):
dets = detections[0, j, :]
mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.shape[0] == 0:
continue
if j:
boxes = dets[:, 1:]
boxes[:, 0] *= h
boxes[:, 1] *= w
boxes[:, 2] *= h
boxes[:, 3] *= w
scores = dets[:, 0].cpu().numpy()
for box, score in zip(boxes, scores):
y1, x1, y2, x2 = box
y1 = int(y1)
x1 = int(x1)
y2 = int(y2)
x2 = int(x2)
cv2.rectangle(ori_img, (x1, y1), (x2, y2), (0, 255, 0),
2, 2)
cv2.putText(ori_img,
cfg.VOC_CLASSES[int(j)] + "%.2f" % score,
(x1, y1 + 20), cv2.FONT_HERSHEY_SIMPLEX,
0.6, (255, 0, 255))
cv2.imshow('img', ori_img)
k = cv2.waitKey(0)
if k & 0xFF == ord('q'):
cv2.destroyAllWindows()
exit()
cv2.destroyAllWindows()
exit()
def eval(self, device, eval_model, dsets):
# print('evaluation mode...')
# self.model.eval()
eval_model.eval()
output_dir = cfg.output_dir
if not os.path.exists(output_dir):
os.mkdir(output_dir)
else:
shutil.rmtree(output_dir)
os.mkdir(output_dir)
num_imgs = len(dsets)
total_time = 0
det_file = os.path.join(output_dir, 'detections.pkl')
# print('Detecting bounding boxes...')
all_boxes = [[[] for _ in range(num_imgs)]
for _ in range(cfg.num_classes)]
_t = {'im_detect': Timer(), 'misc': Timer()}
for i in range(num_imgs):
img, target = dsets.__getitem__(i)
ori_img = cv2.imread(dsets._imgpath % dsets.img_ids[i])
h, w, c = ori_img.shape
x = img.unsqueeze(0)
x = x.to(device)
_t['im_detect'].tic()
detections = eval_model(x, 'test')
detect_time = _t['im_detect'].toc(average=False)
# ignore the background boxes
for j in range(1, detections.size(1)):
dets = detections[0, j, :]
mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.shape[0] == 0:
continue
boxes = dets[:, 1:]
boxes[:, 0] *= h
boxes[:, 1] *= w
boxes[:, 2] *= h
boxes[:, 3] *= w
scores = dets[:, 0].cpu().numpy()
cls_dets = np.hstack((boxes.cpu().numpy(), scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
all_boxes[j][i] = cls_dets
# print('img-detect: {:d}/{:d} {:.3f}s'.format(i + 1, num_imgs, detect_time))
total_time += detect_time
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
f.close()
print('Saving the results...')
for cls_ind, cls in enumerate(cfg.labelmap):
# print('Writing {:s} VOC results file'.format(cls))
filename = dec_eval.get_voc_results_file_template('test', cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(dsets.img_ids):
dets = all_boxes[cls_ind + 1][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write(
'{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(
index[1], dets[k, -1], dets[k, 0] + 1,
dets[k, 1] + 1, dets[k, 2] + 1,
dets[k, 3] + 1))
# print('Evaluating detections....')
print('average time is {}'.format(float(total_time) / num_imgs))
maps = dec_eval.do_python_eval(output_dir=output_dir, use_07=True)
return maps
def eval_single(self):
print('evaluation mode...')
self.model.eval()
print('loading data...')
dsets = PASCALVOC(root=cfg.root,
image_sets=cfg.test_sets,
transform=transforms.DEC_transforms(
phase='val',
size=cfg.img_size,
mean=cfg.means,
std=cfg.std))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.model = self.model.to(device)
output_dir = cfg.output_dir
if not os.path.exists(output_dir):
os.mkdir(output_dir)
else:
shutil.rmtree(output_dir)
os.mkdir(output_dir)
num_imgs = len(dsets)
det_file = os.path.join(output_dir, 'detections.pkl')
print('Detecting bounding boxes...')
all_boxes = [[[] for _ in range(num_imgs)]
for _ in range(cfg.num_classes)]
_t = {'im_detect': Timer(), 'misc': Timer()}
total_time = 0
for i in range(num_imgs):
img, target = dsets.__getitem__(i)
ori_img = cv2.imread(dsets._imgpath % dsets.img_ids[i])
h, w, c = ori_img.shape
x = img.unsqueeze(0)
x = x.to(device)
_t['im_detect'].tic()
detections = self.model(x, 'test')
detect_time = _t['im_detect'].toc(average=False)
total_time += detect_time
# ignore the background boxes
for j in range(1, detections.size(1)):
dets = detections[0, j, :]
mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.shape[0] == 0:
continue
boxes = dets[:, 1:]
boxes[:, 0] *= h
boxes[:, 1] *= w
boxes[:, 2] *= h
boxes[:, 3] *= w
boxes[:, 0] = np.maximum(0., boxes[:, 0])
boxes[:, 1] = np.maximum(0., boxes[:, 1])
boxes[:, 2] = np.minimum(h, boxes[:, 2])
boxes[:, 3] = np.minimum(w, boxes[:, 3])
scores = dets[:, 0].cpu().numpy()
cls_dets = np.hstack((boxes.cpu().numpy(), scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
all_boxes[j][i] = cls_dets
print('img-detect: {:d}/{:d} {:.3f}s'.format(
i + 1, num_imgs, detect_time))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
f.close()
print('average time is {}'.format(float(total_time) / num_imgs))
print('Saving the results...')
for cls_ind, cls in enumerate(cfg.labelmap):
print('Writing {:s} VOC results file'.format(cls))
filename = dec_eval.get_voc_results_file_template('test', cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(dsets.img_ids):
dets = all_boxes[cls_ind + 1][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write(
'{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(
index[1], dets[k, -1], dets[k, 0] + 1,
dets[k, 1] + 1, dets[k, 2] + 1,
dets[k, 3] + 1))
print('Evaluating detections....')
dec_eval.do_python_eval(output_dir=output_dir, use_07=True)
def train():
ssd_cfg = {
'num_classes': 21, # 背景クラスを含めた合計クラス数
'input_size': 300, # 画像の入力サイズ
'bbox_aspect_num': [4, 6, 6, 6, 4, 4], # 出力するDBoxのアスペクト比の種類
'feature_maps': [38, 19, 10, 5, 3, 1], # 各sourceの画像サイズ
'pix_sizes': [8, 16, 32, 64, 100, 300], # DBOXの大きさを決める
'min_sizes': [30, 60, 111, 162, 213, 264], # DBOXの大きさを決める
'max_sizes': [60, 111, 162, 213, 264, 315], # DBOXの大きさを決める
'aspect_ratios': [[2], [2, 3], [2, 3], [2, 3], [2], [2]],
}
# SSDネットワークモデル
net = SSD(phase="train", cfg=ssd_cfg)
root_dir = "./data/VOCdevkit/VOC2012/"
train_dataset, val_dataset = load_data(root_dir)
# SSDの初期の重みを設定
# ssdのvgg部分に重みをロードする
#vgg_weights = torch.load('./weights/vgg16_reducedfc.pth')
#net.vgg.load_state_dict(vgg_weights)
# ssdのその他のネットワークの重みはHeの初期値で初期化
def weights_init(m):
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight.data)
if m.bias is not None: # バイアス項がある場合
nn.init.constant_(m.bias, 0.0)
# Heの初期値を適用
net.extras.apply(weights_init)
net.loc.apply(weights_init)
net.conf.apply(weights_init)
# 損失関数の設定
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
criterion = MultiBoxLoss(jaccard_thresh=0.5, neg_pos=3, device=device)
# 最適化手法の設定
optimizer = optim.SGD(net.parameters(), lr=1e-3,
momentum=0.9, weight_decay=5e-4)
num_epochs = 10
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32,
shuffle=False, num_workers=1, collate_fn=od_collate_fn)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32,
shuffle=False, num_workers=1, collate_fn=od_collate_fn)
print("使用デバイス：", device)
# ネットワークをGPUへ
net.to(device)
# ネットワークがある程度固定であれば、高速化させる
torch.backends.cudnn.benchmark = True
# イテレーションカウンタをセット
iteration = 1
epoch_train_loss = 0.0 # epochの損失和
epoch_val_loss = 0.0 # epochの損失和
logs = []
# epochのループ
for epoch in range(num_epochs+1):
# 開始時刻を保存
t_epoch_start = time.time()
t_iter_start = time.time()
print('-------------')
print('Epoch {}/{}'.format(epoch+1, num_epochs))
print('-------------')
# epochごとの訓練と検証のループ
for phase in ['train', 'val']:
if phase == 'train':
net.train() # モデルを訓練モードに
print('（train）')
else:
if((epoch+1) % 10 == 0):
net.eval() # モデルを検証モードに
print('-------------')
print('（val）')
else:
# 検証は10回に1回だけ行う
continue
# データローダーからminibatchずつ取り出すループ
for images, targets in train_loader:
# GPUが使えるならGPUにデータを送る
images = images.to(device)
targets = [ann.to(device)
for ann in targets] # リストの各要素のテンソルをGPUへ
# optimizerを初期化
optimizer.zero_grad()
iteration += 1
# 順伝搬（forward）計算
with torch.set_grad_enabled(phase == 'train'):
# 順伝搬（forward）計算
outputs = net(images)
# 損失の計算
loss_l, loss_c = criterion(outputs, targets)
loss = loss_l + loss_c
if phase == 'train':
loss.backward() # 勾配の計算
# 勾配が大きくなりすぎると計算が不安定になるので、clipで最大でも勾配2.0に留める
nn.utils.clip_grad_value_(
net.parameters(), clip_value=2.0)
optimizer.step() # パラメータ更新
if (iteration % 100 == 0): # 100iterに1度、lossを表示
t_iter_finish = time.time()
duration = t_iter_finish - t_iter_start
print('イテレーション {} || Loss: {:.4f} || 10iter: {:.4f} sec.'.format(
iteration, loss.item(), duration))
t_iter_start = time.time()
epoch_train_loss += loss.item()
for images, targets in val_loader:
with torch.set_grad_enabled(phase == 'valid'):
# 順伝搬（forward）計算
outputs = net(images)
# GPUが使えるならGPUにデータを送る
images = images.to(device)
targets = [ann.to(device)
for ann in targets] # リストの各要素のテンソルをGPUへ