def classify_train(name, learning_rate, init_weight=None):
print('start classify_train')
net = Resnet()
#net = SimpleVgg()
model = net.get_model(learning_rate)
if not init_weight == None:
model.load_weights(init_weight)
model.summary()
generator = DataGenerator(name=name)
run = '{}-{}-{}'.format(name,
time.localtime().tm_hour,
time.localtime().tm_min)
log_dir = CLASSIFY_LOG_DIR.format(run)
check_point = log_dir + '/' + name + '_checkpoint-{epoch:02d}-{val_loss:.4f}.hdf5'
print("classify train round {}".format(run))
tensorboard = TensorBoard(log_dir=log_dir, write_graph=False)
checkpoint = ModelCheckpoint(filepath=check_point,
monitor='val_loss',
verbose=1,
save_best_only=True)
early_stopping = EarlyStopping(monitor='val_loss',
patience=TRAIN_EARLY_STOPPING,
verbose=1)
model.fit_generator(
generator.flow_classfication(mode='train'),
steps_per_epoch=TRAIN_STEPS_PER_EPOCH,
validation_data=generator.flow_classfication(mode='val'),
validation_steps=TRAIN_VALID_STEPS,
epochs=TRAIN_EPOCHS,
verbose=1,
callbacks=[tensorboard, checkpoint, early_stopping])
def predict_test_only_classification():
net = Resnet()
name = 'Resnet'
model = net.get_model()
model.load_weights(CLASS_MODEL_PATH)
columns = [
'seriesuid', 'coordX', 'coordY', 'coordZ', 'class', 'probability'
]
df = pd.DataFrame(columns=columns)
#Use two round to detect. same step,different start point
print('Round 1')
df = predict_box(np.array([16, 16, 16]), model, columns, df)
print('Round 2')
df = predict_box(np.array([0, 0, 0]), model, columns, df)
df.to_csv('./output/result_only_class.csv', index=False)
def predict_test(name='lung',
mode='test',
seg_model_path=SEG_LUNG_TRAIN_WEIGHT,
class_model_path=CLASS_LUNG_TRAIN_WEIGHT,
seg_thresh_hold=0.8,
limit=[0, 0]):
detect_net = UNet()
class_net = Resnet()
detect_model = detect_net.get_model(0.1)
detect_model.load_weights(seg_model_path)
class_model = class_net.get_model(0.1)
class_model.load_weights(class_model_path)
columns = [
'seriesuid', 'coordX', 'coordY', 'coordZ', 'class', 'probability'
]
df = pd.DataFrame(columns=columns)
for img, meta in get_files(name, mode):
count = 0
cubs = []
cub_sizes = []
for w in range(limit[0], img.shape[0] - limit[0], 32):
for h in range(limit[1], img.shape[1] - limit[1], 32):
for d in range(0, img.shape[2], 32):
if d + INPUT_DEPTH > img.shape[2]:
d = img.shape[2] - INPUT_DEPTH
if h + INPUT_HEIGHT > img.shape[1]:
h = img.shape[1] - INPUT_HEIGHT
if w + INPUT_WIDTH > img.shape[0]:
w = img.shape[0] - INPUT_WIDTH
cub = img[w:w + INPUT_WIDTH, h:h + INPUT_HEIGHT,
d:d + INPUT_DEPTH]
if np.all(cub == ZERO_CENTER):
continue
#batch_cub = cub[np.newaxis, ..., np.newaxis]
cubs.append(cub)
cub_sizes.append([w, h, d])
for k in range(0, len(cub_sizes), 16):
t = 16
if k + 16 >= len(cub_sizes):
t = len(cub_sizes) - k
batch_cub = np.array(cubs[k:t + k])
batch_cub_sizes = cub_sizes[k:t + k]
batch_cub = batch_cub[..., np.newaxis]
pre_y_batch = detect_model.predict(batch_cub)
for k in range(pre_y_batch.shape[0]):
pre_y = pre_y_batch[k, :, :, :, 0] > seg_thresh_hold
#print('predicted pix:'+ str(np.sum(pre_y)))
if np.sum(pre_y) > 0:
crops, crop_centers, diameter, bboxes = crop_for_class(
img, pre_y, np.array(batch_cub_sizes[k]))
#print('find:'+str(len(crop_centers)))
for i, center in enumerate(crop_centers):
crop = crops[i]
crop_cub = crop[np.newaxis, ..., np.newaxis]
class_type = class_model.predict(crop_cub)
class_type = class_type[0]
index = np.argmax(class_type)
if index > 0:
#print('Add one')
location = meta['origin'] + center
new_row = pd.DataFrame([[
meta['seriesuid'], location[0], location[1],
location[2], label_softmax_reverse[index],
class_type[index]
]],
columns=columns)
df = df.append(new_row, ignore_index=True)
df.to_csv('./output/predict_' + name + '_' + mode + '.csv',
index=False)
print('finished')
