def init_testloader(self, **kwargs):
super(SarRecognitionTemplate, self).init_testloader(**kwargs)
test_transforms = get('test_transforms', kwargs, None)
if test_transforms is None:
test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
kwargs['transforms'] = test_transforms
test_index = get_valid('test_index', kwargs)
test_folder = get_valid('test_folder', kwargs)
test_dataset = SarDataset(index_path=test_index,
folder=test_folder,
**kwargs)
test_batch = get('test_batch', kwargs, 64)
test_worker = get('test_worker', kwargs, 8)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=self.get_batch_size(test_batch),
num_workers=test_worker)
test_data_num = len(test_dataset)
print('Analyse test index: {}, test data num: {}!'.format(
test_index, test_data_num))
return test_loader
def retrieval(self, **kwargs):
target_folder = get_valid('target_folder', kwargs)
lsh = get_valid('lsh', kwargs)
query_num = get('query_num', kwargs, 1)
query_num = int(query_num)
images = ImageProcess(target_folder).process()
print('extract target feature')
vecs, img_paths = extract_vectors(self.model,
images,
1024,
self.transform,
ms=self.ms)
target_feature = dict(
zip(img_paths, list(vecs.detach().cpu().numpy().T)))
for q_path, q_vec in target_feature.items():
try:
response = lsh.query(q_vec.flatten(),
num_results=query_num,
distance_func="cosine")
print('target img: {}'.format(q_path))
for idx in range(query_num):
query_img_path = response[idx][0][1]
print('{}th query img: {}'.format(idx, query_img_path))
print('*' * 20)
except:
print('error occur on: {}'.format(q_path))
continue
def init_trainloader(self, **kwargs):
super(SarRecognitionTemplate, self).init_trainloader(**kwargs)
train_transforms = get('train_transforms', kwargs, None)
if train_transforms is None:
distort_num = get('distort_num', kwargs, 10)
distort_ratio = get('distort_ratio', kwargs, 0.1)
train_transforms = transforms.Compose([
RandomDistort(distort_num, distort_ratio),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
kwargs['transforms'] = train_transforms
train_index = get_valid('train_index', kwargs)
train_folder = get_valid('train_folder', kwargs)
train_dataset = SarDataset(index_path=train_index,
folder=train_folder,
**kwargs)
train_batch = get('train_batch', kwargs, 64)
train_worker = get('train_worker', kwargs, 8)
drop_last = get('drop_last', kwargs, True)
shuffle = get('shuffle', kwargs, True)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=self.get_batch_size(train_batch),
num_workers=train_worker,
drop_last=drop_last,
shuffle=shuffle)
train_data_num = len(train_dataset)
print('Analyse train index: {}, train data num: {}!'.format(
train_index, train_data_num))
return train_loader
def init_testloader(self, **kwargs):
test_transforms = get('test_transforms', kwargs, None)
if test_transforms is None:
test_transforms = transforms.Compose([
# Denoise(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
kwargs['transforms'] = test_transforms
test_folder = get_valid('test_folder', kwargs)
test_batch = get('test_batch', kwargs, 64)
test_worker = get('test_worker', kwargs, 8)
class_dict = get_valid('class_dict', kwargs)
test_loader_dict = dict()
for cls in class_dict:
cls_folder = os.path.join(test_folder, cls)
cls_index_path = os.path.join(cls_folder, 'label.txt')
test_dataset = SarDataset(index_path=cls_index_path,
folder=cls_folder,
**kwargs)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=self.get_batch_size(test_batch),
num_workers=test_worker)
cls_int = class_dict[cls]
test_loader_dict[cls_int] = test_loader
print('test {} data num: {}!'.format(cls, len(test_dataset)))
return test_loader_dict
def save_model(self, **kwargs):
model = get_valid('model', kwargs)
ckpt_folder = get_valid('ckpt_folder', kwargs)
epoch = get_valid('epoch', kwargs)
loss = get_valid('loss', kwargs)
eval_type = get('eval_type', kwargs, 'acc')
acc = get('acc', kwargs)
acc = round(acc, 4)
model_name = get('model_name', kwargs, '')
state = {
'epoch': epoch,
'state_dict': model.state_dict(),
'eval_type': eval_type,
'acc': acc,
'loss': loss
}
loss = round(loss, 4)
ckpt_name = '{}epoch{}_{}{}_loss{}.pth'.format(model_name, epoch,
eval_type, acc, loss)
ckpt_path = os.path.join(ckpt_folder, ckpt_name)
torch.save(state, ckpt_path)
print('Save checkpoint: {}'.format(ckpt_path))
return ckpt_path
def save_model(self, **kwargs):
model = get_valid('model', kwargs)
ckpt_folder = get_valid('ckpt_folder', kwargs)
epoch = get_valid('epoch', kwargs)
loss = get_valid('loss', kwargs)
eval_type = get('eval_type', kwargs, 'score')
eval_score = get('eval_score', kwargs, 0)
model_name = get('model_name', kwargs, '')
state = {
'epoch': epoch,
'state_dict': model.state_dict(),
'eval_type': eval_type,
'eval_score': eval_score,
'loss': loss
}
if eval_type is None:
eval_type = ''
if eval_score is None:
eval_score = ''
else:
eval_score = round(eval_score, 4)
eval_score = str(eval_score)
loss = round(loss, 4)
loss = str(loss)
ckpt_name = '{}epoch{}{}{}loss{}.pth'.format(model_name, epoch,
eval_type, eval_score,
loss)
ckpt_path = os.path.join(ckpt_folder, ckpt_name)
torch.save(state, ckpt_path)
print('Save checkpoint: {}'.format(ckpt_path))
return ckpt_path
def forward(self, **kwargs):
input = get_valid('input', kwargs)
hidden = get_valid('hidden', kwargs)
feature = get_valid('feature', kwargs)
mask = get_valid('mask', kwargs)
input = input.long()
input = self.embedding(input)
input = self.dropout(input)
input = input.unsqueeze(0)
_, hidden = self.rnn(input, hidden)
hidden_tmp = hidden[1:].permute(1, 2, 0).unsqueeze(3)
hidden_tmp = self.conv1(hidden_tmp)
hidden_tmp = hidden_tmp.expand_as(feature)
encode_conv = self.conv2(feature)
encode_conv = self.conv3(torch.tanh(encode_conv + hidden_tmp)).view(
encode_conv.shape[0], 1, -1)
mask = mask.view(mask.shape[0], mask.shape[1], -1)
w = self.mask_softmax(encode_conv, dim=2, mask=mask)
# feature = self.feature_conv(feature)
# feature = self.bn(feature)
# feature = self.relu(feature)
feature = feature.view(feature.shape[0], feature.shape[1], -1)
c = torch.sum(feature * w, 2)
ouput = torch.cat([hidden[1], c], 1)
ouput = self.out(ouput)
return ouput, hidden
def train_model(self, **kwargs):
model = get_valid('model', kwargs)
model.train()
optimizer = get_valid('optimizer', kwargs)
epoch = get_valid('epoch', kwargs)
criterion = get_valid('criterion', kwargs)
train_loader = get_valid('train_loader', kwargs)
total_loss = 0
iter_num = len(train_loader)
log_step = iter_num // 100
log_step = max(1, log_step)
for idx, data in enumerate(train_loader):
img, label = data[0], data[1]
if self.gpu is not None:
img = img.cuda()
label = label.cuda()
model = model.cuda()
optimizer.zero_grad()
pred = model(image=img, type='classify')
loss = criterion['cls'](pred, label)
loss.backward()
optimizer.step()
if idx % log_step == 0 and idx != 0:
finish_percent = int(idx / iter_num * 100)
print('Train: finish {}%, loss: {}'.format(
finish_percent, loss.data.item()))
total_loss += loss.data.item()
avg_loss = total_loss / iter_num
log = generate_log(epoch=epoch, name='Train', avg_loss=avg_loss)
return log, avg_loss
def forward(self, **kwargs):
super(SarRecognitionModel, self).forward(**kwargs)
image = get_valid('image', kwargs)
target_variable = get_valid('target', kwargs)
mask = get_valid('mask', kwargs)
teacher_forcing_ratio = get('teacher_forcing_ratio', kwargs, 1)
hidden, feature = self.encoder(image=image)
hidden = hidden.permute(2, 0, 1)
decoder_input = target_variable[:, 0]
output_list = list()
for di in range(1, target_variable.shape[1]):
output, hidden = self.decoder(input=decoder_input,
hidden=hidden,
feature=feature,
mask=mask)
output_list.append(output.unsqueeze(1))
teacher_forcing = random.random() < teacher_forcing_ratio
if teacher_forcing:
decoder_input = target_variable[:, di]
else:
_, topi = output.data.topk(1)
decoder_input = topi.squeeze(1)
output_list = torch.cat(output_list, 1)
return output_list
def __init__(self, **kwargs):
self.folder = get_valid('folder', kwargs)
self.is_training = get_valid('is_training', kwargs)
self.class_dict = get_valid('class_dict', kwargs)
self.transforms = get_valid('transforms', kwargs)
self.split = get('split', kwargs, ',')
self.index_list = self.__generate_index()
def __init__(self, **kwargs):
self.index_path = get_valid('index_path', kwargs)
self.folder = get_valid('folder', kwargs)
self.transforms = get('transforms', kwargs, None)
self.encoding = get('encoding', kwargs, 'UTF-8-sig')
self.size = get('size', kwargs, (32, 256, 3))
self.label_split = get('label_split', kwargs, ',')
self.data_list = self.get_data_list()
self.max_len = get('max_len', kwargs, 64)
self.mask_ratio = get('mask_ratio', kwargs, (8, 4))
def test_model(self, **kwargs):
model = get_valid('model', kwargs)
model.eval()
epoch = get_valid('epoch', kwargs)
criterion = get_valid('criterion', kwargs)
test_loader = get_valid('test_loader', kwargs)
total_loss = 0
iter_num = len(test_loader)
log_step = iter_num // 100
log_step = max(1, log_step)
total_correct = 0
total_pred = 0
with torch.no_grad():
for idx, (image, label, mask) in enumerate(test_loader):
target = self.encode(label, False)
target = torch.from_numpy(target)
target = target.long()
target_cp = self.encode(label, True)
target_cp = torch.from_numpy(target_cp)
target_cp = target_cp.long()
if self.use_gpu:
image = image.cuda()
target = target.cuda()
mask = mask.cuda()
target_cp = target_cp.cuda()
output = model(image=image, target=target, mask=mask)
pred_label = self.decode(output)
acc, correct, pred_num = self.compute_acc(pred_label, label)
total_correct += correct
total_pred += pred_num
output = output.contiguous().view(-1, self.class_num)
target_cp = target_cp[:, 1:].contiguous().view(-1)
loss = criterion(output, target_cp, ignore_index=-1)
total_loss += loss.data.item()
if idx % log_step == 0 and idx != 0:
finish_percent = int(idx / iter_num * 100)
print('Test: finish {}%, loss: {}'.format(
finish_percent, loss.data.item()))
acc = total_correct / total_pred
avg_loss = total_loss / iter_num
log = generate_log(epoch=epoch, name='Test', avg_loss=avg_loss)
return log, avg_loss, acc
def init_trainloader(self, **kwargs):
train_transforms = get('train_transforms', kwargs, None)
if train_transforms is None:
distort_num = get('distort_num', kwargs, 10)
distort_ratio = get('distort_ratio', kwargs, 0.1)
train_transforms = transforms.Compose([
RandomDistort(distort_num, distort_ratio),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
kwargs['transforms'] = train_transforms
train_folder = get_valid('train_folder', kwargs)
class_dict = get_valid('class_dict', kwargs)
train_batch = get('train_batch', kwargs, 64)
train_worker = get('train_worker', kwargs, 8)
drop_last = get('drop_last', kwargs, True)
shuffle = get('shuffle', kwargs, True)
train_loader_dict = dict()
max_data_num = 0
for cls in class_dict:
cls_folder = os.path.join(train_folder, cls)
cls_index_path = os.path.join(cls_folder, 'label.txt')
train_dataset = SarDataset(index_path=cls_index_path,
folder=cls_folder,
**kwargs)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=self.get_batch_size(train_batch),
num_workers=train_worker,
drop_last=drop_last,
shuffle=shuffle)
cls_int = class_dict[cls]
train_loader_dict[cls_int] = dict()
train_loader_dict[cls_int]['data_loader'] = train_loader
data_num = len(train_dataset)
train_loader_dict[cls_int]['data_num'] = data_num
if data_num > max_data_num:
max_data_num = data_num
print('train {} data num: {}!'.format(cls, data_num))
for cls in class_dict:
cls_int = class_dict[cls]
data_num = train_loader_dict[cls_int]['data_num']
train_loader_dict[cls_int]['repeat_num'] = int(max_data_num /
data_num)
return train_loader_dict
def eval_model(self, **kwargs):
model = get_valid('model', kwargs)
model.eval()
eval_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
test_folder = get_valid('test_folder', kwargs)
pred_folder = get_valid('pred_folder', kwargs)
scale = get('scale', kwargs, 4)
img_list = get_img_list(test_folder)
for img in img_list:
img_path = os.path.join(test_folder, img)
image = cv2.imread(img_path)
image = image.astype(np.float32)
image = eval_transforms(image)
image = image.unsqueeze(0)
if self.gpu is not None:
image = image.cuda()
with torch.no_grad():
output = model(image=image)
preds, boxes_list = pse_decode(output[0], scale)
pred_list = list()
if len(boxes_list)>0:
for box in boxes_list:
box = sort_poly(box.astype(np.int32))
pred = '{},{},{},{},{},{},{},{}'.format(box[0, 0], box[0, 1], box[1, 0], box[1, 1],
box[2, 0], box[2, 1], box[3, 0], box[3, 1])
pred_list.append(pred)
txt_name = img.split('.')[0] + '.txt'
txt_path = os.path.join(pred_folder, txt_name)
list2txt(boxes_list, txt_path)
iou_threshold = get('iou_threshold', kwargs, 0.5)
score, total_label_num, total_pred_num, total_correct_num = f1score(test_folder, pred_folder, iou_threshold)
if total_label_num == 0 or total_correct_num == 0 or total_pred_num == 0:
precision = 0
recall = 0
else:
precision = total_correct_num / total_label_num
recall = total_correct_num / total_pred_num
print('f1score: {}, precision={}/{}={}, recall={}/{}={}, iou threshold: {};'.format(score, total_correct_num,
total_label_num, precision,
total_correct_num,
total_pred_num, recall,
iou_threshold))
return score
def train_model(self, **kwargs):
super(EastDetectionTemplate, self).train_model(**kwargs)
model = get_valid('model', kwargs)
model = model.train()
optimizer = get_valid('optimizer', kwargs)
epoch = get_valid('epoch', kwargs)
criterion = get_valid('criterion', kwargs)
train_loader = get_valid('train_loader', kwargs)
total_loss = 0
aabb_loss = 0
theta_loss = 0
cls_loss = 0
iter_num = len(train_loader)
if iter_num == 0:
raise RuntimeError('training data num < batch num!')
for data in train_loader:
img = data['img']
score_map = data['score_map']
geo_map = data['geo_map']
training_mask = data['training_mask']
if self.gpu is not None:
img = img.cuda()
score_map = score_map.cuda()
geo_map = geo_map.cuda()
training_mask = training_mask.cuda()
f_score, f_geometry = model(image=img)
geo_map = geo_map.permute(0, 3, 1, 2).contiguous()
loss, l_aabb, l_theta, l_cls = criterion(score_map, f_score, geo_map, f_geometry, training_mask)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.data.item()
aabb_loss += l_aabb.data.item()
theta_loss += l_theta.data.item()
cls_loss += l_cls.data.item()
avg_loss = total_loss / iter_num
avg_aabb_loss = aabb_loss / iter_num
avg_theta_loss = theta_loss / iter_num
avg_cls_loss = cls_loss / iter_num
log = generate_log(epoch=epoch,
name='Train',
avg_loss=avg_loss,
avg_aabb_loss=avg_aabb_loss,
avg_theta_loss=avg_theta_loss,
avg_cls_loss=avg_cls_loss)
return log, avg_loss
def __init__(self, **kwargs):
super(AbstractEncoderDecoderModel, self).__init__()
encoder = get_valid('encoder', kwargs)
decoder = get_valid('decoder', kwargs)
model_name = get_valid('model_name', kwargs)
encoder_factory = EncoderModelFactory()
kwargs['model_name'] = encoder
self.encoder = encoder_factory.get_model(**kwargs)
decoder_factory = DecoderModelFactory()
kwargs['model_name'] = decoder
self.decoder = decoder_factory.get_model(**kwargs)
kwargs['model_name'] = model_name
def load_model(self, **kwargs):
model = get_valid('model', kwargs)
checkpoint = get_valid('checkpoint', kwargs)
assert os.path.exists(checkpoint)
if self.use_gpu is False:
ckpt = torch.load(checkpoint, map_location='cpu')
else:
ckpt = torch.load(checkpoint)
new_state_dict = OrderedDict()
for k, v in ckpt['state_dict'].items():
name = k[7:]
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
return model
def test_model(self, **kwargs):
super(EastDetectionTemplate, self).test_model(**kwargs)
model = get_valid('model', kwargs)
model = model.eval()
epoch = get_valid('epoch', kwargs)
criterion = get_valid('criterion', kwargs)
test_loader = get_valid('test_loader', kwargs)
total_loss = 0
aabb_loss = 0
theta_loss = 0
cls_loss = 0
iter_num = len(test_loader)
with torch.no_grad():
for data in test_loader:
img = data['img']
score_map = data['score_map']
geo_map = data['geo_map']
training_mask = data['training_mask']
# img_path = data['img_path']
if self.gpu is not None:
img = img.cuda()
score_map = score_map.cuda()
geo_map = geo_map.cuda()
training_mask = training_mask.cuda()
f_score, f_geometry = model(image=img)
geo_map = geo_map.permute(0, 3, 1, 2).contiguous()
loss, l_aabb, l_theta, l_cls = criterion(score_map, f_score, geo_map, f_geometry, training_mask)
total_loss += loss.data.item()
aabb_loss += l_aabb.data.item()
theta_loss += l_theta.data.item()
cls_loss += l_cls.data.item()
avg_loss = total_loss / iter_num
avg_aabb_loss = aabb_loss / iter_num
avg_theta_loss = theta_loss / iter_num
avg_cls_loss = cls_loss / iter_num
log = generate_log(epoch=epoch,
name='Test',
avg_loss=avg_loss,
avg_aabb_loss=avg_aabb_loss,
avg_theta_loss=avg_theta_loss,
avg_cls_loss=avg_cls_loss)
return log, avg_loss
def train_model(self, **kwargs):
model = get_valid('model', kwargs)
model.train()
optimizer = get_valid('optimizer', kwargs)
epoch = get_valid('epoch', kwargs)
criterion = get_valid('criterion', kwargs)
train_loader = get_valid('train_loader', kwargs)
total_loss = 0
iter_num = len(train_loader)
log_step = iter_num // 100
log_step = max(1, log_step)
if iter_num == 0:
raise RuntimeError('training data num < batch num!')
for idx, (image, label, mask) in enumerate(train_loader):
target = self.encode(label, False)
target = torch.from_numpy(target)
target = target.long()
target_cp = self.encode(label, True)
target_cp = torch.from_numpy(target_cp)
target_cp = target_cp.long()
if self.use_gpu:
image = image.cuda()
mask = mask.cuda()
target = target.cuda()
target_cp = target_cp.cuda()
output = model(image=image, target=target, mask=mask)
output = output.contiguous().view(-1, self.class_num)
target_cp = target_cp[:, 1:].contiguous().view(-1)
loss = criterion(output, target_cp, ignore_index=-1)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.data.item()
if idx % log_step == 0 and idx != 0:
finish_percent = int(idx / iter_num * 100)
print('Train: finish {}%, loss: {}'.format(
finish_percent, loss.data.item()))
avg_loss = total_loss / iter_num
log = generate_log(epoch=epoch, name='Train', avg_loss=avg_loss)
return log, avg_loss
def __init__(self, **kwargs):
super(PseDetectionModel, self).__init__(**kwargs)
n = get('n', kwargs, 6)
scale = get('scale', kwargs, 1)
channels = get_valid('channels', kwargs)
self.scale = scale
conv_out = 256
# Top layer
self.toplayer = nn.Conv2d(channels[0], conv_out, kernel_size=1, stride=1, padding=0) # Reduce channels
# Lateral layers
self.latlayer1 = nn.Conv2d(channels[1], conv_out, kernel_size=1, stride=1, padding=0)
self.latlayer2 = nn.Conv2d(channels[2], conv_out, kernel_size=1, stride=1, padding=0)
self.latlayer3 = nn.Conv2d(channels[3], conv_out, kernel_size=1, stride=1, padding=0)
# Smooth layers
self.smooth1 = nn.Conv2d(conv_out, conv_out, kernel_size=3, stride=1, padding=1)
self.smooth2 = nn.Conv2d(conv_out, conv_out, kernel_size=3, stride=1, padding=1)
self.smooth3 = nn.Conv2d(conv_out, conv_out, kernel_size=3, stride=1, padding=1)
self.conv = nn.Sequential(
nn.Conv2d(conv_out * 4, conv_out, kernel_size=3, padding=1, stride=1),
nn.BatchNorm2d(conv_out),
nn.ReLU(inplace=True)
)
self.out_conv = nn.Conv2d(conv_out, n, kernel_size=1, stride=1)
def init_testloader(self, **kwargs):
super(EastDetectionTemplate, self).init_testloader(**kwargs)
test_transforms = get('test_transforms', kwargs, None)
if test_transforms is None:
test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
kwargs['transforms'] = test_transforms
test_folder = get_valid('test_folder', kwargs)
assert os.path.exists(test_folder)
test_dataset = EastDataset(folder=test_folder,
**kwargs)
test_data_num = len(test_dataset)
test_batch = get('test_batch', kwargs, 4)
test_worker = get('test_worker', kwargs, 8)
if self.gpu is None:
batch_size = test_batch
else:
batch_size = test_batch * len(self.gpu)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
num_workers=test_worker)
print('Generate test data loader, test data folder: {}, train data num: {}'.format(test_folder,
test_data_num))
return test_loader
def init_testloader(self, **kwargs):
test_transforms = get('test_transforms', kwargs, None)
if test_transforms is None:
test_transforms = transforms.Compose([
Denoise(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
test_folder = get_valid('test_folder', kwargs)
test_dataset = MultiDecoderClassifyDataset(folder=test_folder,
is_training=False,
transforms=test_transforms,
**kwargs)
test_batch = get('test_batch', kwargs, 16)
test_worker = get('test_worker', kwargs, 8)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=test_batch,
num_workers=test_worker)
test_data_num = len(test_dataset)
self.test_data_num = test_data_num
print(
'generate test data loader, test data folder: {}, test data num:{}'
.format(test_folder, test_data_num))
return test_loader
def init_trainloader(self, **kwargs):
train_transforms = get('train_transforms', kwargs, None)
if train_transforms is None:
distort_num = get('distort_num', kwargs, 10)
distort_ratio = get('distort_ratio', kwargs, 0.1)
train_transforms = transforms.Compose([
RandomDistort(distort_num, distort_ratio),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
train_folder = get_valid('train_folder', kwargs)
train_dataset = MultiDecoderClassifyDataset(
folder=train_folder,
is_training=True,
transforms=train_transforms,
**kwargs)
train_batch = get('train_batch', kwargs, 16)
train_worker = get('train_worker', kwargs, 8)
drop_last = get('drop_last', kwargs, True)
shuffle = get('shuffle', kwargs, True)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=train_batch,
num_workers=train_worker,
drop_last=drop_last,
shuffle=shuffle)
train_data_num = len(train_dataset)
self.train_data_num = train_data_num
print(
'Generate train data loader, train data folder: {}, train data num: {}'
.format(train_folder, train_data_num))
return train_loader
def __init__(self, **kwargs):
super(SarRecognitionTemplate, self).__init__(**kwargs)
word_index_path = get_valid('word_index_path', kwargs)
self.word2num, self.num2word = self.generate_dict(word_index_path)
assert len(self.word2num) == len(self.num2word)
self.class_num = len(self.word2num)
self.max_len = get('max_len', kwargs, 64)
def forward(self, **kwargs):
super(NTSClassifyModel, self).forward(**kwargs)
image = get_valid('image', kwargs)
feature_list = self.backbone(image)
resnet_out = feature_list[2]
rpn_feature = feature_list[0]
feature = feature_list[1]
x_pad = F.pad(
image,
(self.pad_side, self.pad_side, self.pad_side, self.pad_side),
mode='constant',
value=0)
batch = image.size(0)
# we will reshape rpn to shape: batch * nb_anchor
rpn_score = self.proposal_net(rpn_feature.detach())
all_cdds = [
np.concatenate((x.reshape(-1, 1), self.edge_anchors.copy(),
np.arange(0, len(x)).reshape(-1, 1)),
axis=1) for x in rpn_score.data.cpu().numpy()
]
top_n_cdds = [
self.hard_nms(x, topn=self.top_n, iou_thresh=0.25)
for x in all_cdds
]
top_n_cdds = np.array(top_n_cdds)
top_n_index = top_n_cdds[:, :, -1].astype(np.int)
if self.use_gpu:
top_n_index = torch.from_numpy(top_n_index).cuda()
else:
top_n_index = torch.from_numpy(top_n_index)
top_n_prob = torch.gather(rpn_score, dim=1, index=top_n_index.long())
if self.use_gpu:
part_imgs = torch.zeros([batch, self.top_n, 3, 224, 224]).cuda()
else:
part_imgs = torch.zeros([batch, self.top_n, 3, 224, 224])
for i in range(batch):
for j in range(self.top_n):
[y0, x0, y1, x1] = top_n_cdds[i][j, 1:5].astype(np.int)
part_imgs[i:i + 1, j] = F.interpolate(x_pad[i:i + 1, :, y0:y1,
x0:x1],
size=(224, 224),
mode='bilinear',
align_corners=True)
part_imgs = part_imgs.view(batch * self.top_n, 3, 224, 224)
feature_list2 = self.backbone(part_imgs.detach())
part_features = feature_list2[1]
part_feature = part_features.view(batch, self.top_n, -1)
part_feature = part_feature[:, :self.cat_num, ...].contiguous()
part_feature = part_feature.view(batch, -1)
concat_out = torch.cat([part_feature, feature], dim=1)
concat_logits = self.concat_net(concat_out)
raw_logits = resnet_out
part_logits = self.partcls_net(part_features).view(
batch, self.top_n, -1)
return [
raw_logits, concat_logits, part_logits, top_n_index, top_n_prob
]
def __init__(self, **kwargs):
self.folder = get_valid('folder', kwargs)
self.transforms = get('transforms', kwargs, None)
self.input_size = get('input_size', kwargs, (768, 768))
self.detection_transforms = get('detection_transforms', kwargs, None)
self.data_list = self.__get_data_list()
self.n = get('n', kwargs, 6)
self.m = get('m', kwargs, 0.5)
def __init__(self, **kwargs):
super(PredictClassification, self).__init__(**kwargs)
self.class_map = get_valid('class_map', kwargs)
model = self.init_model(**kwargs)
model = self.load_model(model=model, **kwargs)
if self.use_gpu:
model = model.cuda()
model = DataParallel(model)
self.model = model.eval()
def __init__(self, **kwargs):
super(ProposalNet, self).__init__()
self.nts_fc_ratio = get_valid("nts_fc_ratio", kwargs)
self.down1 = nn.Conv2d(512 * self.nts_fc_ratio, 128, 3, 1, 1)
self.down2 = nn.Conv2d(128, 128, 3, 2, 1)
self.down3 = nn.Conv2d(128, 128, 3, 2, 1)
self.ReLU = nn.ReLU()
self.tidy1 = nn.Conv2d(128, 6, 1, 1, 0)
self.tidy2 = nn.Conv2d(128, 6, 1, 1, 0)
self.tidy3 = nn.Conv2d(128, 9, 1, 1, 0)
def train_model(self, **kwargs):
super(PseDetectionTemplate, self).train_model(**kwargs)
model = get_valid('model', kwargs)
model.train()
optimizer = get_valid('optimizer', kwargs)
epoch = get_valid('epoch', kwargs)
criterion = get_valid('criterion', kwargs)
train_loader = get_valid('train_loader', kwargs)
total_loss = 0
total_text_loss = 0
total_kernels_loss = 0
iter_num = len(train_loader)
if iter_num == 0:
raise RuntimeError('training data num < batch num!')
for data in train_loader:
img = data['img']
score_maps = data['score_maps']
training_mask = data['training_mask']
if self.gpu is not None:
img = img.cuda()
score_maps = score_maps.cuda()
training_mask = training_mask.cuda()
output = model(image=img)
loss_text, loss_kernels, loss = criterion(output, score_maps, training_mask)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.data.item()
total_text_loss += loss_text.data.item()
total_kernels_loss += loss_kernels.data.item()
avg_loss = total_loss / iter_num
avg_text_loss = total_text_loss / iter_num
avg_kernels_loss = total_kernels_loss / iter_num
log = generate_log(epoch=epoch,
name='Train',
avg_loss=avg_loss,
avg_text_loss=avg_text_loss,
avg_kernels_loss=avg_kernels_loss)
return log, avg_loss
def init_optimizer(self, **kwargs):
super(ClassifyTemplate, self).init_optimizer(**kwargs)
model = get_valid('model', kwargs)
lr = get('lr', kwargs, 0.001)
weight_decay = get('weight_decay', kwargs, 1e-4)
raw_optimizer = torch.optim.SGD(model.parameters(),
lr=lr,
momentum=0.9,
weight_decay=weight_decay)
return raw_optimizer
