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- fixes, more tests
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@dsmic
dsmic committed Aug 19, 2019
1 parent 9040ae4 commit bfab011
Showing 1 changed file with 55 additions and 28 deletions.
83 changes: 55 additions & 28 deletions mini_imagenet_dataloader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,9 @@
parser.add_argument('--dataset', dest='dataset', type=str, default='train')
parser.add_argument('--lr', dest='lr', type=float, default=1e-3)
parser.add_argument('--epochs', dest='epochs', type=int, default=10)

parser.add_argument('--final_name', dest='final_name', type=str, default='final_model')
parser.add_argument('--shuffle_images', dest='shuffle_images', action='store_true')
parser.add_argument('--enable_idx_increase', dest='enable_idx_increase', action='store_true')
args = parser.parse_args()

class MiniImageNetDataLoader(object):
Expand All @@ -44,7 +46,7 @@ def __init__(self, shot_num, way_num, episode_test_sample_num):
if not os.path.exists(npy_dir):
os.mkdir(npy_dir)

self.npy_base_dir = npy_dir + str(self.shot_num) + 'shot_' + str(self.way_num) + 'way_' + str(episode_test_sample_num) + '/'
self.npy_base_dir = npy_dir + str(self.shot_num) + 'shot_' + str(self.way_num) + 'way_' + str(episode_test_sample_num) + 'shuffled_' + str(args.shuffle_images) + '/'
if not os.path.exists(self.npy_base_dir):
os.mkdir(self.npy_base_dir)

Expand Down Expand Up @@ -84,7 +86,7 @@ def generate_data_list(self, phase='train', episode_num=None):
for _ in trange(episode_num):
sampled_character_folders = random.sample(folders, self.way_num)
random.shuffle(sampled_character_folders)
labels_and_images = self.get_images(sampled_character_folders, range(self.way_num), nb_samples=self.num_samples_per_class, shuffle=False)
labels_and_images = self.get_images(sampled_character_folders, range(self.way_num), nb_samples=self.num_samples_per_class, shuffle=args.shuffle_images)
labels = [li[0] for li in labels_and_images]
filenames = [li[1] for li in labels_and_images]
all_filenames.extend(filenames)
Expand All @@ -102,7 +104,7 @@ def generate_data_list(self, phase='train', episode_num=None):
for _ in trange(episode_num):
sampled_character_folders = random.sample(folders, self.way_num)
random.shuffle(sampled_character_folders)
labels_and_images = self.get_images(sampled_character_folders, range(self.way_num), nb_samples=self.num_samples_per_class, shuffle=False)
labels_and_images = self.get_images(sampled_character_folders, range(self.way_num), nb_samples=self.num_samples_per_class, shuffle=args.shuffle_images)
labels = [li[0] for li in labels_and_images]
filenames = [li[1] for li in labels_and_images]
all_filenames.extend(filenames)
Expand All @@ -120,7 +122,7 @@ def generate_data_list(self, phase='train', episode_num=None):
for _ in trange(episode_num):
sampled_character_folders = random.sample(folders, self.way_num)
random.shuffle(sampled_character_folders)
labels_and_images = self.get_images(sampled_character_folders, range(self.way_num), nb_samples=self.num_samples_per_class, shuffle=False)
labels_and_images = self.get_images(sampled_character_folders, range(self.way_num), nb_samples=self.num_samples_per_class, shuffle=args.shuffle_images)
labels = [li[0] for li in labels_and_images]
filenames = [li[1] for li in labels_and_images]
all_filenames.extend(filenames)
Expand Down Expand Up @@ -159,6 +161,8 @@ def load_list(self, phase='train'):
def process_batch(self, input_filename_list, input_label_list, batch_sample_num, reshape_with_one=True):
new_path_list = []
new_label_list = []
#assert(input_filename_list[0].startswith('./processed_images/test'))
#print('process batch', input_filename_list[0])
for k in range(batch_sample_num):
class_idxs = list(range(0, self.way_num))
random.shuffle(class_idxs)
Expand Down Expand Up @@ -219,6 +223,7 @@ def get_batch(self, phase='train', idx=0):
this_task_te_filenames += this_class_filenames[epitr_sample_num:]
this_task_te_labels += this_class_label[epitr_sample_num:]

#print(this_task_tr_filenames[0][23:33],this_task_tr_labels[0],this_task_te_filenames[0][23:33],this_task_te_labels[0])
this_inputa, this_labela = self.process_batch(this_task_tr_filenames, this_task_tr_labels, epitr_sample_num, reshape_with_one=False)
this_inputb, this_labelb = self.process_batch(this_task_te_filenames, this_task_te_labels, epite_sample_num, reshape_with_one=False)

Expand All @@ -227,19 +232,25 @@ def get_batch(self, phase='train', idx=0):
cathegories = 5
dataloader = MiniImageNetDataLoader(shot_num=5, way_num=cathegories, episode_test_sample_num=15)

dataloader.generate_data_list(phase='train', episode_num = 50000)
dataloader.generate_data_list(phase='train')
dataloader.generate_data_list(phase='val')
dataloader.generate_data_list(phase='test')

dataloader.load_list(phase=args.dataset)
print('mode is',args.dataset)
dataloader.load_list('all')

#print('train',dataloader.train_filenames)
#print('val',dataloader.val_filenames)
#print('test',dataloader.test_filenames)


episode_train_img, episode_train_label, episode_test_img, episode_test_label = \
dataloader.get_batch(phase=args.dataset, idx=0)
base_train_img, base_train_label, base_test_img, base_test_label = \
dataloader.get_batch(phase='train', idx=0)

train_epoch_size = episode_train_img.shape[0]
test_epoch_size = episode_test_img.shape[0]
train_epoch_size = base_train_img.shape[0]
test_epoch_size = base_test_img.shape[0]

print("epoch training size:", train_epoch_size, episode_train_label.shape[0], "epoch testing size", test_epoch_size)
print("epoch training size:", train_epoch_size, base_train_label.shape[0], "epoch testing size", test_epoch_size)

class KerasBatchGenerator(object):

Expand All @@ -253,32 +264,45 @@ def generate(self, phase='train'):
# dataloader.get_batch(phase='train', idx=idx)
if phase == 'train':
#print(episode_train_img.shape[0])
for i in range(episode_train_img.shape[0]):
yield episode_train_img[i:i+1], episode_train_label[i:i+1]
for i in range(base_train_img.shape[0]):
yield base_train_img[i:i+1], base_train_label[i:i+1]
else:
#print(episode_test_img.shape[0])
for i in range(episode_test_img.shape[0]):
yield episode_test_img[i:i+1], episode_test_label[i:i+1]
for i in range(base_test_img.shape[0]):
yield base_test_img[i:i+1], base_test_label[i:i+1]

def generate_add_samples(self, phase = 'train'):
self.idx = 0
while True:
episode_train_img, episode_train_label, episode_test_img, episode_test_label = \
dataloader.get_batch(phase=args.dataset, idx=self.idx)

# this depends on what we are trying to train.
# care must be taken, that with a different dataset the labels have a different meaning. Thus if we use a new dataset, we must
# use network_base which fits to the database. Therefore there must be taken images with label from the same dataset.
network_base_img = episode_train_img
network_base_label = episode_train_label

if phase == 'train':
self.idx += 1 # only train phase allowed to change
if args.enable_idx_increase:
self.idx += 1 # only train phase allowed to change
#print(episode_train_img.shape[0])
assert(episode_train_img.shape[0] == 25)
#assert(episode_train_img.shape[0] == 25)
for i in range(episode_train_img.shape[0]):
yield [[episode_train_img[i:i+1]], [episode_train_img], [episode_train_label]], episode_train_label[i:i+1]
yield [[episode_train_img[i:i+1]], [network_base_img], [network_base_label]], episode_train_label[i:i+1]
else:
#print(episode_test_img.shape[0])
#assert(0)
assert(episode_test_img.shape[0] == 75)
#assert(episode_test_img.shape[0] == 75)
#assert(self.idx < 50)
for i in range(episode_test_img.shape[0]):
yield [[episode_test_img[i:i+1]], [episode_test_img], [episode_test_label]], episode_test_label[i:i+1]
# yield [img, K.variable(episode_train_img), K.variable(episode_train_label)], label
#print('i',i)
yield [[episode_test_img[i:i+1]], [network_base_img], [network_base_label]], episode_test_label[i:i+1]






keras_gen_train = KerasBatchGenerator()
gen_train = keras_gen_train.generate()
Expand Down Expand Up @@ -323,11 +347,13 @@ def generate_add_samples(self, phase = 'train'):

inputs = Input(shape=(None,84,84,3))
print('the shape', inputs.shape)
conv1 = TimeDistributed(Conv2D(100, 7, 1 , activation = 'relu'))(inputs)
conv2 = TimeDistributed(MaxPooling2D(pool_size = (9,9)))(conv1)
conv1 = TimeDistributed(Conv2D(50, 7, 1 , activation = 'relu'))(inputs)
conv2 = TimeDistributed(MaxPooling2D(pool_size = (3,3)))(conv1)
conv3 = TimeDistributed(Conv2D(200, 7, 1 , activation = 'relu'))(conv2)
conv4 = TimeDistributed(MaxPooling2D(pool_size = (3,3)))(conv3)
#conv3 = TimeDistributed(Conv2D(5, 5, (3,3) , padding='same', activation = 'relu'))(conv2)
flat = TimeDistributed(Flatten())(conv2)
x = Dense(100, activation = 'relu')(flat)
flat = TimeDistributed(Flatten())(conv4)
x = TimeDistributed(Dense(100, activation = 'relu'))(flat)
predictions = Activation('softmax')(x)

model_img = Model(inputs=inputs, outputs=predictions)
Expand Down Expand Up @@ -393,7 +419,7 @@ def call(x):

# testing with additional batch axis ?!
i=1
test_lambda = lambda_model([K.expand_dims(K.variable(episode_train_img[0:0+1]),axis=0),K.expand_dims(K.variable(episode_train_img), axis=0), K.expand_dims(K.variable(episode_train_label), axis=0)])
test_lambda = lambda_model([K.expand_dims(K.variable(base_train_img[0:0+1]),axis=0),K.expand_dims(K.variable(base_train_img), axis=0), K.expand_dims(K.variable(base_train_label), axis=0)])
#
print('test lambda', K.eval(test_lambda))

Expand All @@ -402,7 +428,8 @@ def call(x):
checkpointer = ModelCheckpoint(filepath='checkpoints/model-{epoch:02d}.hdf5', verbose=1)
tensorboard = TensorBoard()
lambda_model.fit_generator(keras_gen_train.generate_add_samples(), train_epoch_size, args.epochs,
validation_data=keras_gen_train.generate_add_samples('test'), validation_steps=test_epoch_size, callbacks = [checkpointer, tensorboard])
validation_data=keras_gen_train.generate_add_samples('test'), validation_steps=test_epoch_size, callbacks = [tensorboard])
lambda_model.save(args.final_name+'.hdf5')


def get_weight_grad(model, inputs, outputs):
Expand Down

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