def __init__(self, config, vocab):
super().__init__()
self.config = config
self.vocab = vocab
self.model = PointerGenerator(config, vocab)
self.criterion = Loss(args=config.loss.args)
self.num_step = 0
self.cov_weight = config.loss.args.cov_weight
self.rouge = Rouge()
def __init__(self, embedding_matrix, config, vocab):
super().__init__()
self.config = config
self.vocab = vocab
self.model = PointerGenerator(embedding_matrix, config, vocab)
self.criterion = Loss(args=config)
self.num_step = 0
self.cov_weight = config.cov_loss_wt
self.rouge = Rouge()
def active_losses():
losses = []
sql = "SELECT * FROM losses where recovered = False"
results = run_sql(sql)
for row in results:
wizard = wiz_repo.select(row['wizard_id'])
item = item_repo.select(row['item_id'])
loss = Loss(row['day'], row['month'], row['year'], row['details'],
wizard, item, row['recovered'], row['id'])
losses.append(loss)
return losses
def select(id):
loss = None
sql = "SELECT * FROM losses WHERE id = %s"
values = [id]
result = run_sql(sql, values)[0]
if result is not None:
wizard = wiz_repo.select(result['wizard_id'])
item = item_repo.select(result['spell_id'])
loss = Loss(result['day'], result['month'], result['year'],
result['details'], wizard, item, result['recovered'],
result['id'])
return loss
def loss_history(wizard):
losses = []
sql = "SELECT * FROM losses where wizard_id = %s AND recovered = False"
values = [wizard.id]
results = run_sql(sql, values)
for row in results:
wizard = wiz_repo.select(row['wizard_id'])
item = item_repo.select(row['item_id'])
loss = Loss(row['day'], row['month'], row['year'], row['details'],
wizard, item, row['recovered'], row['id'])
losses.append(loss)
return losses
def main(config, resume):
train_logger = Logger()
# load data
train_dataloader = ICDARDataLoader(config).train()
# initial model
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(i) for i in config['gpus']])
model = Model(config)
model.summary()
loss = Loss()
trainer = Trainer(model, loss, resume, config, train_dataloader, train_logger)
trainer.train()
def __init__(self, args):
super(NNUnet, self).__init__()
self.args = args
self.save_hyperparameters()
self.build_nnunet()
self.loss = Loss(self.args.focal)
self.dice = Dice(self.n_class)
self.best_sum = 0
self.best_sum_epoch = 0
self.best_dice = self.n_class * [0]
self.best_epoch = self.n_class * [0]
self.best_sum_dice = self.n_class * [0]
self.learning_rate = args.learning_rate
self.tta_flips = get_tta_flips(args.dim)
self.test_idx = 0
self.test_imgs = []
if self.args.exec_mode in ["train", "evaluate"]:
self.dllogger = get_dllogger(args.results)
def setUp(self):
wizard = Wizard("Norman", "Stargazer", 1000)
item = Item("Hat", "Pink", "Glittery", wizard)
self.loss = Loss("02", "02", "2000",
"Lost at event at Gimli's Nightclub", wizard, item)
def loss(self, predictions, labels):
user_feat, pos_item_feat, neg_item_feat = predictions
loss_func = Loss.factory(self.hparam)
loss = loss_func(user_feat, pos_item_feat, neg_item_feat)
return loss
def test_test():
img_path_list, target_list = \
make_dataset(r"D:\datasets\VOCdevkit\VOC0712\JPEGImages",
r"D:\datasets\VOCdevkit\VOC0712\Annotations",
r"D:\datasets\VOCdevkit\VOC0712\pkl\voc_0712_test.pkl",
r"D:\datasets\VOCdevkit\VOC0712\ImageSets\Main\test.txt", "voc", False)
dataset = LoadImagesAndLabels(img_path_list, target_list, 640, 32, 0.5,
False, {"batch_size": 1})
# x: Tensor[C, H, W], target_out: Tensor[X, 6], path: str. [idx, cls, *xywh]
# test show
def test1():
for x, target, img_path in dataset:
print(x.shape, target, img_path)
x = x.numpy()
x = x.transpose(1, 2, 0)[:, :, ::-1] # to (H, W, C), RGB to BGR,
x = np.ascontiguousarray(x)
h, w = x.shape[:2]
boxes = target[:, 2:].numpy()
labels = target[:, 1].numpy()
boxes = cxcywh2ltrb(boxes)
boxes[:, 0::2] *= w # lr
boxes[:, 1::2] *= h # tb
draw_target_in_image(x, boxes, labels, None, "voc")
cv.imshow("1", x)
cv.waitKey(0)
# test1()
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = YOLOv5(20).to(device)
print(load_params(model, "weights/yolov5s_voc.pth", strict=False))
model.float().fuse().eval().requires_grad_(False)
half = (device.type != 'cpu')
model.half() if half else None
hyp = {
"obj_pw": 0.911,
"cls_pw": 0.631,
"anchor_t": 2.91,
"box_lw": 0.0296,
"obj_lw": 0.301,
"cls_lw": 0.06075
}
loss_func = Loss(model, hyp)
loss = torch.zeros((4, ), device=device)
for x, target0, img_path in reversed(dataset):
img0 = x.numpy()
img0 = img0.transpose(1, 2, 0)[:, :, ::-1] # to (H, W, C), RGB to BGR,
img0 = np.ascontiguousarray(img0)
img = img0.copy()
# 预处理
x, target0 = x.to(device), target0.to(device)
x = x.half() if half else x.float()
x /= 255
if x.dim() == 3:
x = x[None]
# 预测
target, loss_target = model(x)
loss += loss_func([loss_t.float() for loss_t in loss_target],
target0)[1]
target = nms(target, 0.001, 0.6)
# 后处理
# 1
target = target[0]
boxes = target[:, :4].cpu().numpy()
scores = target[:, 4].cpu().numpy()
labels = target[:, 5].cpu().numpy()
draw_target_in_image(img, boxes, labels, scores, "voc")
cv.imshow("pred", img)
# 2
img2 = img0.copy()
h, w = img2.shape[:2]
boxes = target0[:, 2:].cpu().numpy()
labels = target0[:, 1].cpu().numpy()
boxes = cxcywh2ltrb(boxes)
boxes[:, 0::2] *= w # lr
boxes[:, 1::2] *= h # tb
draw_target_in_image(img2, boxes, labels, None, "voc")
cv.imshow("target", img2)
cv.waitKey(0)
item_repo.save(hat3)
shoes1 = Item("Shoes", "Red and Black", "Jordan Air's", wizard1)
item_repo.save(shoes1)
shoes2 = Item("Shoes", "Blue", "Curly Pointed Slippers", wizard2)
item_repo.save(shoes1)
shoes3 = Item("Shoes", "Black", "90s Platform Trainers", wizard3)
item_repo.save(shoes1)
wand1 = Item("Wand", "Light brown", "Knobbly", wizard1)
item_repo.save(wand1)
wand2 = Item("Wand", "Black", "Straight, smooth", wizard2)
item_repo.save(wand2)
wand3 = Item("Wand", "Brown", "Zig-zagged", wizard3)
item_repo.save(wand3)
# day, month, year, details, wizard_id, item_id, recovered
loss1 = Loss("01","01","2020", "Lost at event at Gimli's Nightclub", wizard1, hat1)
loss_repo.save(loss1)
loss2 = Loss("04","03","2019", "Lost in forest (forest name unknown)", wizard2, hat2)
loss_repo.save(loss2)
loss3 = Loss("07","07","2019", "Woke up without it on the bus this morning", wizard3, wand3)
loss_repo.save(loss3)
loss4 = Loss("03", "07", "2019", "No memory of loss", wizard1, shoes1)
loss_repo.save(loss4)
# wizards = wiz_repo.select_all()
# items = item_repo.select_all()
# losses = loss_repo.select_all()
# Test Update Wizard Here (Change Age)
wizard1.age = 50000
wiz_repo.update(wizard1)