def run_test():
BS = 128
print('Loading and preprocessing test data...')
mean, std = Learner.load_meanstd()
imgs_test = load_test_data()
imgs_test = preprocess(imgs_test)
imgs_test = imgs_test.astype('float32')
imgs_test -= mean
imgs_test /= std
print('Loading saved weights...')
model = get_unet(Adam(0.001))
print ('Loading weights from %s' % Learner.best_weight_path)
model.load_weights(Learner.best_weight_path)
print ('Augment')
alen, dlen = len(transforms), len(imgs_test)
test_x = np.ndarray((alen, dlen, 1, img_rows, img_cols), dtype=np.float32)
for i in range(dlen):
for j, transform in enumerate(transforms):
test_x[j,i] = transform['do'](imgs_test[i].copy())
#
print('Predicting masks on test data...')
outputs = []
asis_res = model.predict(imgs_test, batch_size=BS, verbose=1)
outputs.append(asis_res)
for j, transform in enumerate(transforms):
t_y = model.predict(test_x[j], batch_size=BS, verbose=1)
outputs.append(t_y)
#
print('Analyzing')
test_masks = np.ndarray((dlen, 1, img_rows, img_cols), dtype=np.float32)
test_probs = np.ndarray((dlen, ), dtype=np.float32)
for i in range(dlen):
masks = np.ndarray((alen+1, 1, img_rows, img_cols), dtype=np.float32)
probs = np.ndarray((alen+1, ), dtype=np.float32)
for j, t_y in enumerate(outputs):
mask, prob = t_y[0][i], t_y[1][i]
if j:
mask = transforms[j-1]['undo'](mask)
masks[j] = mask
probs[j] = prob
#
test_masks[i] = np.mean(masks, 0)
test_probs[i] = np.mean(probs)
print('Saving ')
np.save(Learner.test_mask_res, test_masks)
np.save(Learner.test_mask_exist_res, test_probs)
def run_test():
BS = 256
print('Loading and preprocessing test data...')
mean, std = Learner.load_meanstd()
imgs_test = load_test_data()
# imgs_test = imgs_test[:100]
# print ('test')
imgs_test = preprocess(imgs_test)
imgs_test = imgs_test.astype('float32')
imgs_test -= mean
imgs_test /= std
print('Augment')
alen, dlen = len(transforms), len(imgs_test)
test_x = np.ndarray((alen, dlen, 1, img_rows, img_cols), dtype=np.float32)
for i in xrange(dlen):
for j, transform in enumerate(transforms):
test_x[j, i] = transform['do'](imgs_test[i].copy())
#
kfold = 6
kfold_masks, kfold_prob = [], []
for _iter in xrange(kfold):
print('Iter=%d, Loading saved weights...' % _iter)
model = get_unet(Adam(0.001))
filepath = Learner.best_weight_path + '_%d.fold' % _iter
print('Loading weights from %s' % filepath)
model.load_weights(filepath)
#
print('Predicting masks on test data...')
outputs = []
asis_res = model.predict(imgs_test, batch_size=BS, verbose=1)
outputs.append(asis_res)
for j, transform in enumerate(transforms):
t_y = model.predict(test_x[j], batch_size=BS, verbose=1)
outputs.append(t_y)
#
print('Analyzing')
test_masks = np.ndarray((dlen, 1, img_rows, img_cols),
dtype=np.float32)
test_probs = np.ndarray((dlen, ), dtype=np.float32)
for i in xrange(dlen):
masks = np.ndarray((alen + 1, 1, img_rows, img_cols),
dtype=np.float32)
probs = np.ndarray((alen + 1, ), dtype=np.float32)
for j, t_y in enumerate(outputs):
mask, prob = t_y[0][i], t_y[1][i]
if j:
mask = transforms[j - 1]['undo'](mask.copy())
masks[j] = mask
probs[j] = prob
#
test_masks[i] = np.mean(masks, 0)
test_probs[i] = np.mean(probs)
kfold_masks.append(test_masks)
kfold_prob.append(test_probs)
print 'Summing results of ensemble'
#
res_masks = np.ndarray((dlen, 1, img_rows, img_cols), dtype=np.float32)
res_probs = np.ndarray((dlen, ), dtype=np.float32)
for i in xrange(dlen):
masks = np.ndarray((kfold, 1, img_rows, img_cols), dtype=np.float32)
probs = np.ndarray((kfold, ), dtype=np.float32)
for k in xrange(kfold):
masks[k] = kfold_masks[k][i]
probs[k] = kfold_prob[k][i]
res_masks[i] = np.mean(masks, 0)
res_probs[i] = np.mean(probs)
print('Saving ')
np.save(Learner.test_mask_res, res_masks)
np.save(Learner.test_mask_exist_res, res_probs)
def run_test():
BS = 128
print('Loading and preprocessing test data...')
mean, std = Learner.load_meanstd()
imgs_test, img_test_mask_gt = load_test_data()
test_img_id = load_test_ids()
imgs_test = preprocess(imgs_test)
img_test_mask_gt = preprocess(img_test_mask_gt)
imgs_test = imgs_test.astype('float32')
imgs_test -= mean
imgs_test /= std
img_test_mask_gt = img_test_mask_gt.astype('float32')
# mask_array = np.array(mask_array, dtype=np.float32)
img_test_mask_gt /= 255.0
print('Loading saved weights...')
model = get_unet(Adam(0.001))
print ('Loading weights from %s' % Learner.best_weight_path)
model.load_weights(Learner.best_weight_path)
print ('Augment')
alen, dlen = len(transforms), len(imgs_test)
test_x = np.ndarray((alen, dlen, 1, img_rows, img_cols), dtype=np.float32)
for i in range(dlen):
for j, transform in enumerate(transforms):
test_x[j,i] = transform['do'](imgs_test[i].copy())
#
print('Predicting masks on test data...')
outputs = []
asis_res = model.predict(imgs_test, batch_size=BS, verbose=1)
outputs.append(asis_res)
for j, transform in enumerate(transforms):
t_y = model.predict(test_x[j], batch_size=BS, verbose=1)
outputs.append(t_y)
#
print('Analyzing')
test_masks = np.ndarray((dlen, 1, img_rows, img_cols), dtype=np.float32)
test_probs = np.ndarray((dlen, ), dtype=np.float32)
for i in range(dlen):
masks = np.ndarray((alen+1, 1, img_rows, img_cols), dtype=np.float32)
probs = np.ndarray((alen+1, ), dtype=np.float32)
for j, t_y in enumerate(outputs):
mask, prob = t_y[0][i], t_y[1][i]
if j:
mask = transforms[j-1]['undo'](mask)
masks[j] = mask
probs[j] = prob
#
test_masks[i] = np.mean(masks, 0)
test_probs[i] = np.mean(probs)
# test_eval = model.evaluate(imgs_test, img_test_mask_gt, batch_size=BS)
print(img_test_mask_gt.shape)
print(test_masks.shape)
# pred_dir = 'preds'
# if not os.path.exists(pred_dir):
# os.mkdir(pred_dir)
# for image, image_id in zip(test_masks, test_img_id):
# # image = (image[:, :, 0] * 255.).astype(np.uint8)
# image = (image[0, :, :] * 255.)
# print(image)
# print(image.shape)
# imsave(os.path.join(pred_dir, str(image_id) + '_pred.png'), image)
print('Saving ')
np.save(Learner.test_mask_res, test_masks)
np.save(Learner.test_mask_exist_res, test_probs)
np.save(Learner.test_mask_gt, img_test_mask_gt)
def train_and_predict():
print('-' * 30)
print('Loading and preprocessing train data...')
print('-' * 30)
imgs_train, imgs_mask_train = load_train_data()
imgs_present = load_nerve_presence()
imgs_train = preprocess(imgs_train)
imgs_mask_train = preprocess(imgs_mask_train)
# centering and standardising the images
imgs_train = imgs_train.astype('float32')
mean = np.mean(imgs_train)
std = np.std(imgs_train)
imgs_train -= mean
imgs_train /= std
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_mask_train /= 255. # scale masks to be in {0, 1} instead of {0, 255}
print('-' * 30)
print('Creating and compiling model...')
print('-' * 30)
# load model - the Learning rate scheduler choice is most important here
model = get_unet(optimizer=OPTIMIZER, pars=PARS)
model_checkpoint = ModelCheckpoint('weights.h5', monitor='val_loss', save_best_only=True)
early_stopping = EarlyStopping(patience=5, verbose=1)
print('-' * 30)
print('Fitting model...')
print('-' * 30)
if PARS['outputs'] == 1:
imgs_labels = imgs_mask_train
else:
imgs_labels = [imgs_mask_train, imgs_present]
model.fit(imgs_train, imgs_labels,
batch_size=128, epochs=50,
verbose=1, shuffle=True,
validation_split=0.2,
callbacks=[model_checkpoint, early_stopping])
print('-' * 30)
print('Loading and preprocessing test data...')
print('-' * 30)
imgs_test = load_test_data()
imgs_test = preprocess(imgs_test)
imgs_test = imgs_test.astype('float32')
imgs_test -= mean
imgs_test /= std
print('-' * 30)
print('Loading saved weights...')
print('-' * 30)
model.load_weights('weights.h5')
print('-' * 30)
print('Predicting masks on test data...')
print('-' * 30)
imgs_mask_test = model.predict(imgs_test, verbose=1)
if PARS['outputs'] == 1:
np.save('imgs_mask_test.npy', imgs_mask_test)
else:
np.save('imgs_mask_test.npy', imgs_mask_test[0])
np.save('imgs_mask_test_present.npy', imgs_mask_test[1])