Skip to content

Commit

Permalink
- first tests to load all but the biaslayer2 to allow pretrained models
Browse files Browse the repository at this point in the history 
- therefore the weights of the siamese submodel are loaded seperatly
  • Loading branch information
@dsmic
dsmic committed Sep 6, 2019
1 parent c22e566 commit 986e70d
Show file tree
Hide file tree
Showing 2 changed files with 41 additions and 17 deletions.
35 changes: 26 additions & 9 deletions few_shot_tests.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -40,12 +40,16 @@
parser.add_argument('--debug', dest='debug', action='store_true')
parser.add_argument('--set_model_img_to_weights', dest='set_model_img_to_weights', action='store_true')
parser.add_argument('--load_weights_name', dest='load_weights_name', type=str, default=None)

parser.add_argument('--scale_gradient_layer', dest='scale_gradient_layer', type=float, default=1.0)

parser.add_argument('--increase_idx_every', dest='increase_idx_every', type=int, default=1)
parser.add_argument('--dont_shuffle_batch', dest='dont_shuffle_batch', action='store_true')
parser.add_argument('--cathegories', dest='cathegories', type=int, default=5)
parser.add_argument('--only_one_samplefolder', dest='only_one_samplefolder', action='store_true')
parser.add_argument('--load_subnet', dest='load_subnet', action='store_true')
args = parser.parse_args()

# uncomment the following to disable CuDNN support
#import os
#os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
###########################################

Expand Down Expand Up @@ -134,9 +138,9 @@ def get_batch(self, phase='train', idx=0, dont_shuffle_batch = False):
return this_inputa, this_labela, this_inputb, this_labelb


cathegories = 5
cathegories = args.cathegories
shots = args.shots
dataloader = OurMiniImageNetDataLoader(shot_num=shots * 2, way_num=cathegories, episode_test_sample_num=args.episode_test_sample_num, shuffle_images = args.shuffle_images) #twice shot_num is because one might be uses as the base for the samples
dataloader = OurMiniImageNetDataLoader(shot_num=shots * 2, way_num=cathegories, episode_test_sample_num=args.episode_test_sample_num, shuffle_images = args.shuffle_images, only_one_samplefolder = args.only_one_samplefolder) #twice shot_num is because one might be uses as the base for the samples

dataloader.generate_data_list(phase=args.dataset)

Expand Down Expand Up @@ -164,10 +168,11 @@ class KerasBatchGenerator(object):
# yield base_test_img[i:i+1], base_test_label[i:i+1]

def generate_add_samples(self, phase = 'train'):
self.increase_every_counter = 1
self.idx = 0
while True:
batch_train_img, batch_train_label, episode_test_img, episode_test_label = \
dataloader.get_batch(phase=args.dataset, idx=self.idx, dont_shuffle_batch = (self.idx==0))
dataloader.get_batch(phase=args.dataset, idx=self.idx, dont_shuffle_batch = args.dont_shuffle_batch)

# 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
Expand All @@ -189,7 +194,12 @@ def generate_add_samples(self, phase = 'train'):
episode_train_label = batch_train_label
if phase == 'train':
if args.enable_idx_increase:
self.idx += 1 # only train phase allowed to change
if self.increase_every_counter >= args.increase_idx_every:
self.increase_every_counter = 1
self.idx += 1 # only train phase allowed to change
#print('newidx',self.idx)
else:
self.increase_every_counter += 1
if dataloader.idx_to_big(args.dataset, self.idx):
self.idx=0
print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
Expand Down Expand Up @@ -353,11 +363,11 @@ def get_config(self):
input1 = Input(shape=(None,84,84,3))
input2 = Input(shape=(None,84,84,3)) #, tensor = K.variable(episode_train_img[0:0]))

input2b = BiasLayer(shots * cathegories, args.biaslayer1, bias_num = 1, name = 'bias1')(input2)
input2b = BiasLayer(shots * cathegories, args.biaslayer1, bias_num = 1, name = 'bias1'+str(cathegories)+'_'+str(args.shots)+'t')(input2)
encoded_l = model_img(input1)
encoded_r = model_img(input2b)

encoded_rb = BiasLayer(shots * cathegories, args.biaslayer2, bias_num = 2, name = 'bias2')(encoded_r)
encoded_rb = BiasLayer(shots * cathegories, args.biaslayer2, bias_num = 2, name = 'bias2_'+str(cathegories)+'_'+str(args.shots)+'t')(encoded_r)
if args.scale_gradient_layer != 1.0:
encoded_rb_scale = ScaleGradientLayer()(encoded_rb)
else:
Expand All @@ -370,7 +380,11 @@ def get_config(self):
prediction = Dense(1, name = 'dense_siamese')(L1_distance)

# Connect the inputs with the outputs
siamese_net = Model(inputs=[input1,input2],outputs=prediction)
if args.load_subnet:
submodel_name = "model_changed"
else:
submodel_name ="model"
siamese_net = Model(inputs=[input1,input2],outputs=prediction, name=submodel_name)

#siamese_net.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['categorical_accuracy'])
print(siamese_net.summary(line_length=180, positions = [.33, .55, .67, 1.]))
Expand Down Expand Up @@ -401,6 +415,8 @@ def call(x):

if args.load_weights_name:
lambda_model.load_weights(args.load_weights_name, by_name=True)
if args.load_subnet:
lambda_model.layers[2].load_weights(args.load_weights_name+'_subnet.hdf5', by_name=True)
print('weights loaded')

# models in models forget the layer name, therefore one must use the automatically given layer name and iterate throught the models by hand
Expand Down Expand Up @@ -537,6 +553,7 @@ def print_FindModels(model):

lambda_model.save(args.final_name+'.hdf5')
lambda_model.save_weights(args.final_name+'-weights.hdf5')
lambda_model.layers[2].save_weights(args.final_name + '-weights.hdf5' + '_subnet.hdf5')

# tools for debugging
def get_weight_grad(model, inputs, outputs):
Expand Down
23 changes: 15 additions & 8 deletions mini_imagenet_dataloader.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -15,12 +15,13 @@
import imageio

class MiniImageNetDataLoader(object):
def __init__(self, shot_num, way_num, episode_test_sample_num, shuffle_images = False):
def __init__(self, shot_num, way_num, episode_test_sample_num, shuffle_images = False, only_one_samplefolder = False):
self.shot_num = shot_num
self.way_num = way_num
self.episode_test_sample_num = episode_test_sample_num
self.num_samples_per_class = episode_test_sample_num + shot_num
self.shuffle_images = shuffle_images
self.only_one_samplefolder = only_one_samplefolder
metatrain_folder = './processed_images/train'
metaval_folder = './processed_images/val'
metatest_folder = './processed_images/test'
Expand All @@ -29,7 +30,7 @@ def __init__(self, shot_num, way_num, episode_test_sample_num, shuffle_images =
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) + 'shuffled_' + str(self.shuffle_images) + '/'
self.npy_base_dir = npy_dir + str(self.shot_num) + 'shot_' + str(self.way_num) + 'way_' + str(episode_test_sample_num) + 'shuffled_' + str(self.shuffle_images) + 'onesamplefolder_' + str(self.only_one_samplefolder) +'/'
if not os.path.exists(self.npy_base_dir):
os.mkdir(self.npy_base_dir)

Expand Down Expand Up @@ -66,9 +67,11 @@ def generate_data_list(self, phase='train', episode_num=None):
if not os.path.exists(self.npy_base_dir+'/train_filenames.npy'):
print('Generating train filenames')
all_filenames = []
sampled_character_folders = random.sample(folders, self.way_num)
for _ in trange(episode_num):
sampled_character_folders = random.sample(folders, self.way_num)
random.shuffle(sampled_character_folders)
if not self.only_one_samplefolder:
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=self.shuffle_images)
labels = [li[0] for li in labels_and_images]
filenames = [li[1] for li in labels_and_images]
Expand All @@ -84,9 +87,11 @@ def generate_data_list(self, phase='train', episode_num=None):
if not os.path.exists(self.npy_base_dir+'/val_filenames.npy'):
print('Generating val filenames')
all_filenames = []
sampled_character_folders = random.sample(folders, self.way_num)
for _ in trange(episode_num):
sampled_character_folders = random.sample(folders, self.way_num)
random.shuffle(sampled_character_folders)
if not self.only_one_samplefolder:
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=self.shuffle_images)
labels = [li[0] for li in labels_and_images]
filenames = [li[1] for li in labels_and_images]
Expand All @@ -102,9 +107,11 @@ def generate_data_list(self, phase='train', episode_num=None):
if not os.path.exists(self.npy_base_dir+'/test_filenames.npy'):
print('Generating test filenames')
all_filenames = []
sampled_character_folders = random.sample(folders, self.way_num)
for _ in trange(episode_num):
sampled_character_folders = random.sample(folders, self.way_num)
random.shuffle(sampled_character_folders)
if not self.only_one_samplefolder:
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=self.shuffle_images)
labels = [li[0] for li in labels_and_images]
filenames = [li[1] for li in labels_and_images]
Expand Down

0 comments on commit 986e70d

Please sign in to comment.