else:
print('Build model...')
model = Sequential()
model.add(
WordContextProduct(max_features, proj_dim=dim_proj,
init="uniform"))
model.compile(loss='mse', optimizer='rmsprop')
sampling_table = sequence.make_sampling_table(max_features)
for e in range(nb_epoch):
print('-' * 40)
print('Epoch', e)
print('-' * 40)
progbar = generic_utils.Progbar(tokenizer.document_count)
samples_seen = 0
losses = []
for i, seq in enumerate(
tokenizer.texts_to_sequences_generator(text_generator())):
# get skipgram couples for one text in the dataset
couples, labels = sequence.skipgrams(seq,
max_features,
window_size=4,
negative_samples=1.,
sampling_table=sampling_table)
if couples:
# one gradient update per sentence (one sentence = a few 1000s of word couples)
X = np.array(couples, dtype="int32")
loss = model.train(X, labels)
model.add(Activation('relu'))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
XX = model.get_input()
YY = model.layers[0].get_output()
F = theano.function([XX], YY)
nb_epochs = 10 # you probably want to go longer than this
batch_size = 256
#fig = plt.figure()
try:
for e in range(nb_epochs):
print('-' * 40)
progbar = generic_utils.Progbar(X_train.shape[0])
for b in range(X_train.shape[0] / batch_size):
f = b * batch_size
l = (b + 1) * batch_size
X_batch = X_train[f:l].astype('float32')
y_batch = y_train[f:l].astype('float32')
loss = model.train_on_batch(X_batch, y_batch)
progbar.add(X_batch.shape[0])
scorev = model.evaluate(X_valid,
y_valid,
show_accuracy=True,
verbose=0)[1]
scoret = model.evaluate(X_test, y_test, show_accuracy=True,
verbose=0)[1]
print('Epoch: {0} | Valid: {1} | Test: {2}'.format(e, scorev, scoret))
iter_num = 0
losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor, rpn_accuracy_for_epoch = [], []
t0 = start_time = time.time()
best_loss = np.Inf
with open('out.csv', 'w') as f:
f.write(
'Accuracy,RPN classifier,RPN regression,Detector classifier,Detector regression,Total'
)
f.write('\t')
try:
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
while True:
try:
if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(
rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
)
num_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder, '../models/labelencoder.pkl')
#model = BOW_QI(joint_method = "concat")
#model = BOW_QI(joint_method = "mcb")
#model = BOW_QI(joint_method = "mul")
#model = LSTM_QI(joint_method = "concat")
#model = LSTM_QI(joint_method = "mcb")
model = LSTM_QI(joint_method="mul")
model.build(num_classes)
print 'Training...'
for epoch in xrange(args.num_epochs):
print "epoch", epoch
index_shuf = range(len(questions_train))
shuffle(index_shuf)
progbar = generic_utils.Progbar(len(questions_train))
for batch in range(int(np.ceil(len(questions_train) /
args.batch_size))):
V, Q, A = [], [], []
for i in range(args.batch_size * batch,
args.batch_size * (batch + 1)):
if i >= len(questions_train):
break
question = questions_train[index_shuf[i]]
V.append(images_train[question["image_index"]])
Q.append(
model.extract_question_feature(nlp, question["question"]))
A.append(
np_utils.to_categorical(
labelencoder.transform([question["answer"]]),
num_classes))
#cifar-10 20%数据 + Data Augmentation.训练结果
# 设置生成参数
img_generator = ImageDataGenerator(rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
zoom_range=0.2)
model_2 = build_model()
model_2.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# Data Augmentation
for e in range(epochs):
print('Epoch', e)
print('Training...')
progbar = generic_utils.Progbar(x_train_part.shape[0])
batches = 0
for x_batch, y_batch in img_generator.flow(x_train_part,
y_train_part,
batch_size=batch_size,
shuffle=True):
loss, train_acc = model_2.train_on_batch(x_batch, y_batch)
batches += x_batch.shape[0]
if batches > x_train_part.shape[0]:
break
progbar.add(x_batch.shape[0],
values=[('train loss', loss), ('train acc', train_acc)])
loss, acc = model_2.evaluate(x_test, y_test, batch_size=32)
print('Loss: ', loss)
print('Accuracy: ', acc)
# inpu/oputputt channels in image
input_channels = 1
output_channels = 1
# image dims
input_img_dim = (input_channels, im_width, im_height)
output_img_dim = (output_channels, im_width, im_height)
generator = pix2pix_generator(input_img_dim)
discriminator = super_lightweight_discriminator()
opt_discriminator = Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
discriminator.compile(loss='binary_crossentropy', optimizer=opt_discriminator)
batch_size = 1
nb_epoch = 100
n_images_per_epoch = 10
print('Training starting...')
for epoch in range(0, nb_epoch):
print('Epoch {}'.format(epoch))
batch_counter = 1
start = time.time()
progbar = keras_generic_utils.Progbar(n_images_per_epoch)
# go through 1... n_images_per_epoch (which will go through all buckets as well
for mini_batch_i in range(0, n_images_per_epoch, batch_size)
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--name',
help='Name of the created file HDF5 file.',
default='sofia.h5')
parser.add_argument('--meta', help='Path to the meta HDF5 file')
parser.add_argument('--source', help='Where bulgaria.h5 resides')
parser.add_argument('--chunksize',
help='Chunk size to iterate over HDF5 file',
type=int,
default=50000)
args = parser.parse_args()
with pd.get_store(args.meta) as meta_store, \
pd.get_store(args.source) as data_store, \
pd.HDFStore(args.name, 'w', complevel=9, complib='blosc') as sofia_store:
site_info = meta_store['site_info']
sofia = site_info.query("Region == 'SOFIA_CITY'")
progbar = generic_utils.Progbar(data_store.get_storer('data').nrows)
for chunk in data_store.select('data', chunksize=args.chunksize):
chunk['in_sofia'] = chunk.site_ID.apply(
lambda x: int(x) in sofia.index)
chunk = chunk.query('in_sofia == True')
del chunk['in_sofia']
sofia_store.append('data', chunk, data_columns=True)
progbar.add(args.chunksize)
print 'Sofia users stored in {}'.format(args.name)
def run_train(train_path,
output_weight_path,
config_filename,
parser='simple',
input_weight_path=None,
network='resnet50',
num_rois=32,
lr=1e-5,
iters=100,
num_epochs=100,
overlap_th=0.7):
C = config.Config()
C.model_path = output_weight_path
C.num_rois = int(num_rois)
if network == 'vgg':
C.network = 'vgg'
from keras_frcnn import vgg as nn
elif network == 'resnet50':
from keras_frcnn import resnet as nn
C.network = 'resnet50'
else:
print('Not a valid model')
raise ValueError
if parser == 'pascal_voc':
from keras_frcnn.pascal_voc_parser import get_data
elif parser == 'simple':
from keras_frcnn.simple_parser import get_data
else:
print('Wrong parser method')
raise ValueError
if input_weight_path is not None:
C.base_net_weights = input_weight_path
else:
C.base_net_weights = nn.get_weight_path()
all_imgs, classes_count, class_mapping = get_data(train_path)
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
inv_map = {v: k for k, v in class_mapping.items()}
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
config_output_filename = config_filename
with open(config_output_filename, 'wb') as config_f:
pickle.dump(C, config_f)
print(
'Config has been written to {}, and can be loaded when testing to ensure correct results'
.format(config_output_filename))
random.shuffle(all_imgs)
num_imgs = len(all_imgs)
train_imgs = [s for s in all_imgs if s['imageset'] == 'trainval']
val_imgs = [s for s in all_imgs if s['imageset'] == 'test']
print('Num train samples {}'.format(len(train_imgs)))
print('Num val samples {}'.format(len(val_imgs)))
data_gen_train = data_generators.get_anchor_gt(train_imgs,
classes_count,
C,
nn.get_img_output_length,
K.image_dim_ordering(),
mode='train')
data_gen_val = data_generators.get_anchor_gt(val_imgs,
classes_count,
C,
nn.get_img_output_length,
K.image_dim_ordering(),
mode='val')
if K.image_dim_ordering() == 'th':
input_shape_img = (3, None, None)
else:
input_shape_img = (None, None, 3)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(None, 4))
# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = nn.nn_base(img_input, trainable=True)
# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn = nn.rpn(shared_layers, num_anchors)
classifier = nn.classifier(shared_layers,
roi_input,
C.num_rois,
nb_classes=len(classes_count),
trainable=True)
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
# this is a model that holds both the RPN and the classifier, used to load/save weights for the models
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
try:
print('loading weights from {}'.format(C.base_net_weights))
model_rpn.load_weights(C.base_net_weights, by_name=True)
model_classifier.load_weights(C.base_net_weights, by_name=True)
except:
print('Could not load pretrained model weights...')
optimizer = Adam(lr=lr)
optimizer_classifier = Adam(lr=lr)
from keras_frcnn import losses as losses
model_rpn.compile(optimizer=optimizer,
loss=[
losses.rpn_loss_cls(num_anchors),
losses.rpn_loss_regr(num_anchors)
])
model_classifier.compile(
optimizer=optimizer_classifier,
loss=[
losses.class_loss_cls,
losses.class_loss_regr(len(classes_count) - 1)
],
metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer='sgd', loss='mae')
epoch_length = int(iters)
num_epochs = int(num_epochs)
iter_num = 0
losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
start_time = time.time()
best_loss = np.Inf
class_mapping_inv = {v: k for k, v in class_mapping.items()}
print('Starting training')
vis = True
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
while True:
try:
if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(
rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
)
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(P_rpn[0],
P_rpn[1],
C,
K.image_dim_ordering(),
use_regr=True,
overlap_thresh=overlap_th,
max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(
R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, C.num_rois // 2,
replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.train_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
losses[iter_num, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3]
iter_num += 1
progbar.update(
iter_num,
[('rpn_cls', np.mean(losses[:iter_num, 0])),
('rpn_regr', np.mean(losses[:iter_num, 1])),
('detector_cls', np.mean(losses[:iter_num, 2])),
('detector_regr', np.mean(losses[:iter_num, 3]))])
if iter_num == epoch_length:
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(sum(
rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if C.verbose:
print(
'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'
.format(mean_overlapping_bboxes))
print(
'Classifier accuracy for bounding boxes from RPN: {}'
.format(class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(
loss_class_cls))
print('Loss Detector regression: {}'.format(
loss_class_regr))
print('Elapsed time: {}'.format(time.time() -
start_time))
curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
iter_num = 0
start_time = time.time()
if curr_loss < best_loss:
if C.verbose:
print(
'Total loss decreased from {} to {}, saving weights'
.format(best_loss, curr_loss))
best_loss = curr_loss
model_all.save_weights(C.model_path)
break
except Exception as e:
print('Exception: {}'.format(e))
continue
print('Training complete, exiting.')
# Prefer mean_absolute_error, since that is the one used for the challenge
# Saving model
jsonString = model.to_json()
open(model_file_name + '.json', 'w').write(jsonString)
minScore = 1.0
# raw_input('WAIT')
print 'Training started...'
for k in xrange(num_epochs):
#shuffle the data points before going through them
random.shuffle(vidNames)
progbar = generic_utils.Progbar(len(vidNames))
for i in xrange(numBatch):
# Read numPerBatch files, get the images and answers
# print 'Starting reading the batch'
X_batch, Y_batch = readData(vidNames[(i*numPerBatch):((i+1)*numPerBatch)], trueVal, 'C')
# print X_batch.shape
X_batch = X_batch.reshape(X_batch.shape[0], 1, row, col)
# print X_batch.shape
# print 'Finished reading the batch'
X_batch = X_batch.astype('float32')
X_batch /= 255
def my_train(args):
# create output finder
if not os.path.exists(os.path.expanduser(args.datasetpath)):
os.mkdir(args.datasetpath)
# create figures
if not os.path.exists('./figures'):
os.mkdir('./figures')
# load data
procImage, rawImage, procImage_val, rawImage_val, rawImage_test = my_load_data(
args.datasetpath)
print('procImage.shape : ', procImage.shape)
print('rawImage.shape : ', rawImage.shape)
print('procImage_val : ', procImage_val.shape)
print('rawImage_val : ', rawImage_val.shape)
# パッチサイズと画像サイズを指定
img_shape = rawImage.shape[-3:]
print('img_shape : ', img_shape)
patch_num = (img_shape[0] // args.patch_size) * (img_shape[1] //
args.patch_size)
disc_img_shape = (args.patch_size, args.patch_size, procImage.shape[-1])
print('disc_img_shape : ', disc_img_shape)
# train
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
opt_discriminator = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# load generator model
generator_model = models.my_load_generator(img_shape, disc_img_shape)
# load discriminator model
discriminator_model = models.my_load_DCGAN_discriminator(
img_shape, disc_img_shape, patch_num)
generator_model.compile(loss='mae', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.my_load_DCGAN(generator_model, discriminator_model,
img_shape, args.patch_size)
loss = [l1_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss,
loss_weights=loss_weights,
optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy',
optimizer=opt_discriminator)
# start training
print('start training')
for e in range(args.epoch):
# 時間計測
starttime = time.time()
# シャッフル iterを作る
perm = np.random.permutation(rawImage.shape[0])
X_procImage = procImage[perm]
X_rawImage = rawImage[perm]
X_procImageIter = [
X_procImage[i:i + args.batch_size]
for i in range(0, rawImage.shape[0], args.batch_size)
]
X_rawImageIter = [
X_rawImage[i:i + args.batch_size]
for i in range(0, rawImage.shape[0], args.batch_size)
]
b_it = 0
# 経過確認用
progbar = generic_utils.Progbar(len(X_procImageIter) * args.batch_size)
for (X_proc_batch, X_raw_batch) in zip(X_procImageIter,
X_rawImageIter):
b_it += 1
X_disc, y_disc = get_disc_batch(X_proc_batch, X_raw_batch,
generator_model, b_it,
args.patch_size)
raw_disc, _ = get_disc_batch(X_raw_batch, X_raw_batch,
generator_model, 1, args.patch_size)
x_disc = X_disc + raw_disc
# update the discriminator
disc_loss = discriminator_model.train_on_batch(x_disc, y_disc)
# create a batch to feed the generator model 順番入れ替え
idx = np.random.choice(procImage.shape[0], args.batch_size)
X_gen_target, X_gen = procImage[idx], rawImage[idx]
y_gen = np.zeros((X_gen.shape[0], 2), dtype=np.uint8)
y_gen[:, 1] = 1
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen, [X_gen_target, y_gen])
# Unfreeze the discriminator
discriminator_model.trainable = True
progbar.add(args.batch_size,
values=[("D logloss", disc_loss),
("G tot", gen_loss[0]), ("G L1", gen_loss[1]),
("G logloss", gen_loss[2])])
# save images for visualization
if b_it % (procImage.shape[0] // args.batch_size // 2) == 0:
plot_generated_batch(X_proc_batch, X_raw_batch,
generator_model, args.batch_size, b_it,
"training")
idx = np.random.choice(procImage_val.shape[0],
args.batch_size,
replace=False)
X_gen_target, X_gen = procImage_val[idx], rawImage_val[idx]
plot_generated_batch(X_gen_target, X_gen, generator_model,
args.batch_size, b_it, "validation")
idx = np.random.choice(rawImage_test.shape[0],
rawImage_test.shape[0],
replace=False)
X_gen = rawImage_test[idx]
plot_generated_batch_test(X_gen, generator_model,
args.batch_size, b_it)
print("")
print('Epoch %s/%s, Time: %s' %
(e + 1, args.epoch, time.time() - starttime))
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
model_name = kwargs["model_name"]
generator = kwargs["generator"]
image_data_format = kwargs["image_data_format"]
img_dim = kwargs["img_dim"]
patch_size = kwargs["patch_size"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["use_label_smoothing"]
label_flipping = kwargs["label_flipping"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
#general_utils.setup_logging(model_name)
# Load and rescale data
#X_full_train, X_sketch_train, X_full_val, X_sketch_val = data_utils.load_data(dset, image_data_format)
img_dim = (256, 256, 3) # Manual entry
# Get the number of non overlapping patch and the size of input image to the discriminator
nb_patch, img_dim_disc = data_utils.get_nb_patch(img_dim, patch_size,
image_data_format)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
opt_discriminator = Adam(lr=1E-3,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08)
# Load generator model
generator_model = models.load("generator_unet_%s" % generator, img_dim,
nb_patch, bn_mode, use_mbd, batch_size)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator", img_dim_disc,
nb_patch, bn_mode, use_mbd,
batch_size)
generator_model.compile(loss="mae", optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model, discriminator_model,
img_dim, patch_size, image_data_format)
loss = [l1_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss,
loss_weights=loss_weights,
optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy',
optimizer=opt_discriminator)
gen_loss = 100
disc_loss = 100
best_loss = [100] * 3
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_full_batch, X_sketch_batch in data_utils.facades_generator(
img_dim, batch_size=batch_size):
X_gen, X_gen_target = next(
data_utils.facades_generator(img_dim,
batch_size=batch_size))
generator_model.train_on_batch(X_gen, X_gen_target)
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(
X_full_batch,
X_sketch_batch,
generator_model,
batch_counter,
patch_size,
image_data_format,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(
X_disc, y_disc) # X_disc, y_disc
# Create a batch to feed the generator model
X_gen, X_gen_target = next(
data_utils.facades_generator(img_dim,
batch_size=batch_size))
y_gen = np.zeros((X_gen.shape[0], 2), dtype=np.uint8)
y_gen[:, 1] = 1
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen,
[X_gen_target, y_gen])
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size,
values=[("D logloss", disc_loss),
("G tot", gen_loss[0]),
("G L1", gen_loss[1]),
("G logloss", gen_loss[2])])
# Save images for visualization
if batch_counter % (n_batch_per_epoch / 2) == 0:
# Get new images from validation
figure_name = "training_" + str(e)
data_utils.plot_generated_batch(
X_full_batch, X_sketch_batch, generator_model,
batch_size, image_data_format, figure_name)
if batch_counter >= n_batch_per_epoch:
break
print("")
print(('Epoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start)))
if e % 5 == 0:
gen_weights_path = os.path.join(
'../../models/%s/gen_weights_epoch%s.h5' % (model_name, e))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join(
'../../models/%s/disc_weights_epoch%s.h5' %
(model_name, e))
discriminator_model.save_weights(disc_weights_path,
overwrite=True)
DCGAN_weights_path = os.path.join(
'../../models/%s/DCGAN_weights_epoch%s.h5' %
(model_name, e))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
Best_gen_L1_weights_path = os.path.join(
'../../models/%s/best_gen_L1_weights_epoch.h5' %
(model_name))
if (gen_loss[1] <= best_loss[1]):
generator_model.save_weights(Best_gen_L1_weights_path,
overwrite=True)
best_loss[1] = gen_loss[1]
Best_gen_Totweights_path = os.path.join(
'../../models/%s/best_gen_Totweights_epoch.h5' %
(model_name))
if (gen_loss[0] <= best_loss[0]):
generator_model.save_weights(Best_gen_Totweights_path,
overwrite=True)
best_loss[0] = gen_loss[0]
except KeyboardInterrupt:
pass
def main():
start_time = time.time()
parser = argparse.ArgumentParser(
prog='trainLSTM_MLP.py',
description='Train LSTM-MLP model for visual question answering')
parser.add_argument('--mlp-hidden-units',
type=int,
default=1024,
metavar='<mlp-hidden-units>')
parser.add_argument('--lstm-hidden-units',
type=int,
default=512,
metavar='<lstm-hidden-units>')
parser.add_argument('--mlp-hidden-layers',
type=int,
default=3,
metavar='<mlp-hidden-layers>')
parser.add_argument('--lstm-hidden-layers',
type=int,
default=1,
metavar='<lstm-hidden-layers>')
parser.add_argument('--dropout',
type=float,
default=0.5,
metavar='<dropout-rate>')
parser.add_argument('--mlp-activation',
type=str,
default='tanh',
metavar='<activation-function>')
parser.add_argument('--num-epochs',
type=int,
default=20,
metavar='<num-epochs>')
parser.add_argument('--batch-size',
type=int,
default=128,
metavar='<batch-size>')
parser.add_argument('--learning-rate',
type=float,
default=0.001,
metavar='<learning-rate>')
parser.add_argument('--dev-accuracy-path',
type=str,
required=True,
metavar='<accuracy-path>')
args = parser.parse_args()
word_vec_dim = 300
img_dim = 4096
max_len = 30
######################
# Load Data #
######################
data_dir = '/home/mlds/data/0.2_val/'
print('Loading data...')
train_id_pairs, train_image_ids = LoadIds('train', data_dir)
dev_id_pairs, dev_image_ids = LoadIds('dev', data_dir)
train_questions = LoadQuestions('train', data_dir)
dev_questions = LoadQuestions('dev', data_dir)
train_choices = LoadChoices('train', data_dir)
dev_choices = LoadChoices('dev', data_dir)
train_answers = LoadAnswers('train', data_dir)
dev_answers = LoadAnswers('dev', data_dir)
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
######################
# Model Descriptions #
######################
print('Generating and compiling model...')
# image model (CNN features)
image_model = Sequential()
image_model.add(Reshape(input_shape=(img_dim, ), dims=(img_dim, )))
# language model (LSTM)
language_model = Sequential()
if args.lstm_hidden_layers == 1:
language_model.add(
LSTM(output_dim=args.lstm_hidden_units,
return_sequences=False,
input_shape=(max_len, word_vec_dim)))
else:
language_model.add(
LSTM(output_dim=args.lstm_hidden_units,
return_sequences=True,
input_shape=(max_len, word_vec_dim)))
for i in range(args.lstm_hidden_layers - 2):
language_model.add(
LSTM(output_dim=args.lstm_hidden_units, return_sequences=True))
language_model.add(
LSTM(output_dim=args.lstm_hidden_units, return_sequences=False))
# feedforward model (MLP)
model = Sequential()
model.add(
Merge([language_model, image_model], mode='concat', concat_axis=1))
for i in range(args.mlp_hidden_layers):
model.add(Dense(args.mlp_hidden_units, init='uniform'))
model.add(Activation(args.mlp_activation))
model.add(Dropout(args.dropout))
model.add(Dense(word_vec_dim))
#model.add(Activation('softmax'))
json_string = model.to_json()
model_filename = 'models/mse_lstm_units_%i_layers_%i_mlp_units_%i_layers_%i_%s_lr%.1e_dropout%.2f' % (
args.lstm_hidden_units, args.lstm_hidden_layers, args.mlp_hidden_units,
args.mlp_hidden_layers, args.mlp_activation, args.learning_rate,
args.dropout)
#model_filename = 'models/vgg_lstm_units_%i_layers_%i_mlp_units_%i_layers_%i_%s_lr%.1e_dropout%.2f_loss_cosine' % (args.lstm_hidden_units, args.lstm_hidden_layers, args.mlp_hidden_units, args.mlp_hidden_layers, args.mlp_activation, args.learning_rate, args.dropout)
open(model_filename + '.json', 'w').write(json_string)
# loss and optimizer
rmsprop = RMSprop(lr=args.learning_rate)
#model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
model.compile(loss="mse", optimizer=rmsprop)
print('Compilation finished.')
print('Time: %f s' % (time.time() - start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time() - start_time))
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# training batches
train_question_batches = [
b for b in MakeBatches(
train_questions, args.batch_size, fillvalue=train_questions[-1])
]
train_answer_batches = [
b for b in MakeBatches(train_answers['toks'],
args.batch_size,
fillvalue=train_answers['toks'][-1])
]
train_image_batches = [
b for b in MakeBatches(
train_image_ids, args.batch_size, fillvalue=train_image_ids[-1])
]
train_indices = list(range(len(train_question_batches)))
# validation batches
dev_question_batches = [
b for b in MakeBatches(
dev_questions, args.batch_size, fillvalue=dev_questions[-1])
]
dev_answer_batches = [
b for b in MakeBatches(dev_answers['labs'],
args.batch_size,
fillvalue=dev_answers['labs'][-1])
]
dev_choice_batches = [
b for b in MakeBatches(
dev_choices, args.batch_size, fillvalue=dev_choices[-1])
]
dev_image_batches = [
b for b in MakeBatches(
dev_image_ids, args.batch_size, fillvalue=dev_image_ids[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Training #
######################
acc_file = open(args.dev_accuracy_path, 'w')
dev_accs = []
max_acc = -1
max_acc_epoch = -1
# define interrupt handler
def PrintDevAcc():
print('Max validation accuracy epoch: %i' % max_acc_epoch)
print(dev_accs)
def InterruptHandler(sig, frame):
print(str(sig))
PrintDevAcc()
sys.exit(-1)
signal.signal(signal.SIGINT, InterruptHandler)
signal.signal(signal.SIGTERM, InterruptHandler)
# print training information
print('-' * 80)
print('Training Information')
print('# of LSTM hidden units: %i' % args.lstm_hidden_units)
print('# of LSTM hidden layers: %i' % args.lstm_hidden_layers)
print('# of MLP hidden units: %i' % args.mlp_hidden_units)
print('# of MLP hidden layers: %i' % args.mlp_hidden_layers)
print('Dropout: %f' % args.dropout)
print('MLP activation function: %s' % args.mlp_activation)
print('# of training epochs: %i' % args.num_epochs)
print('Batch size: %i' % args.batch_size)
print('Learning rate: %f' % args.learning_rate)
print('# of train questions: %i' % len(train_questions))
print('# of dev questions: %i' % len(dev_questions))
print('-' * 80)
acc_file.write('-' * 80 + '\n')
acc_file.write('Training Information\n')
acc_file.write('# of LSTM hidden units: %i\n' % args.lstm_hidden_units)
acc_file.write('# of LSTM hidden layers: %i\n' % args.lstm_hidden_layers)
acc_file.write('# of MLP hidden units: %i\n' % args.mlp_hidden_units)
acc_file.write('# of MLP hidden layers: %i\n' % args.mlp_hidden_layers)
acc_file.write('Dropout: %f\n' % args.dropout)
acc_file.write('MLP activation function: %s\n' % args.mlp_activation)
acc_file.write('# of training epochs: %i\n' % args.num_epochs)
acc_file.write('Batch size: %i\n' % args.batch_size)
acc_file.write('Learning rate: %f\n' % args.learning_rate)
acc_file.write('# of train questions: %i\n' % len(train_questions))
acc_file.write('# of dev questions: %i\n' % len(dev_questions))
acc_file.write('-' * 80 + '\n')
# start training
print('Training started...')
for k in range(args.num_epochs):
print('-' * 80)
print('Epoch %i' % (k + 1))
progbar = generic_utils.Progbar(len(train_indices) * args.batch_size)
# shuffle batch indices
random.shuffle(train_indices)
for i in train_indices:
X_question_batch = GetQuestionsTensor(train_question_batches[i],
word_embedding, word_map)
X_image_batch = GetImagesMatrix(train_image_batches[i], img_map,
VGG_features)
Y_answer_batch = GetAnswersMatrix(train_answer_batches[i],
word_embedding, word_map)
loss = model.train_on_batch([X_question_batch, X_image_batch],
Y_answer_batch)
loss = loss[0].tolist()
progbar.add(args.batch_size, values=[('train loss', loss)])
print('Time: %f s' % (time.time() - start_time))
# evaluate on dev set
pbar = generic_utils.Progbar(
len(dev_question_batches) * args.batch_size)
dev_correct = 0
# feed forward
for i in range(len(dev_question_batches)):
X_question_batch = GetQuestionsTensor(dev_question_batches[i],
word_embedding, word_map)
X_image_batch = GetImagesMatrix(dev_image_batches[i], img_map,
VGG_features)
prob = model.predict_proba([X_question_batch, X_image_batch],
args.batch_size,
verbose=0)
# get word vecs of choices
choice_feats = GetChoicesTensor(dev_choice_batches[i],
word_embedding, word_map)
similarity = np.zeros((5, args.batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(
pairwise_distances(prob,
choice_feats[j],
metric='euclidean'))
# take argmax of cosine distances
pred = np.argmin(similarity, axis=0) + 1
if i != (len(dev_question_batches) - 1):
dev_correct += np.count_nonzero(dev_answer_batches[i] == pred)
else:
num_padding = args.batch_size * len(
dev_question_batches) - len(dev_questions)
last_idx = args.batch_size - num_padding
dev_correct += np.count_nonzero(
dev_answer_batches[:last_idx] == pred[:last_idx])
pbar.add(args.batch_size)
dev_acc = float(dev_correct) / len(dev_questions)
dev_accs.append(dev_acc)
print('Validation Accuracy: %f' % dev_acc)
print('Time: %f s' % (time.time() - start_time))
if dev_acc > max_acc:
max_acc = dev_acc
max_acc_epoch = k
model.save_weights(model_filename + '_best.hdf5', overwrite=True)
#model.save_weights(model_filename + '_epoch_{:03d}.hdf5'.format(k+1))
print(dev_accs)
for acc in dev_accs:
acc_file.write('%f\n' % acc)
print('Best validation accuracy: %f; epoch#%i' % (max_acc,
(max_acc_epoch + 1)))
acc_file.write('Best validation accuracy: %f; epoch#%i\n' %
(max_acc, (max_acc_epoch + 1)))
print('Training finished.')
acc_file.write('Training finished.\n')
print('Time: %f s' % (time.time() - start_time))
acc_file.write('Time: %f s\n' % (time.time() - start_time))
acc_file.close()
def train(train_path,
input_weight_path=None,
num_rois=None,
network=None,
parser=None,
horizontal_flips=None,
vertical_flips=None,
rot_90=None,
num_epochs=None,
num_epochs_len=None,
config_filename=None,
output_weight_path=None):
"""
Keyword arguments:
train_path -- Path to training data. (required)
input_weight_path -- Input path for weights. If not specified, will try to
load default weights provided by keras. (Default false)
num_rois -- Number of RoIs to process at once. (Default 32)
network -- Base network to use. Supports vgg or resnet50. (Default 'resnet50')
parser -- Parser to use. One of simple or pascal_voc. (Default 'pascal_voc')
horizontal_flips -- Augment with horizontal
flips in training. (Default false).
vertical_flips -- Augment with vertical flips in training. (Default false).
rot_90 -- Augment with 90 degree rotations in training. (Default false).
num_epochs -- Number of epochs. (Default 200)
num_epochs_len -- Length of epochs. (Default 1000)
config_filename -- Location to store all the metadata related to the training
(to be used when testing). (Default 'config.pickle')
output_weight_path -- Output path for weights. (Default './model_frcnn.hdf5')
"""
global losses
num_rois = 32 if num_rois is None else num_rois
network = 'resnet50' if network is None else network
parser = 'pascal_voc' if parser is None else parser
horizontal_flips = False if horizontal_flips is None else horizontal_flips
vertical_flips = False if vertical_flips is None else vertical_flips
rot_90 = False if rot_90 is None else rot_90
num_epochs = 200 if num_epochs is None else num_epochs
num_epochs_len = 1000 if num_epochs_len is None else num_epochs_len
config_filename = 'config.pickle' if config_filename is None else config_filename
output_weight_path = './model_frcnn.hdf5' if output_weight_path is None else output_weight_path
if parser == 'pascal_voc':
from ekfrcnn.kfrcnn.pascal_voc_parser import get_data
elif parser == 'simple':
from ekfrcnn.kfrcnn.simple_parser import get_data
else:
raise ValueError(
"Command line option parser must be one of 'pascal_voc' or 'simple'"
)
# persist the settings in the config object
C = config.Config()
C.use_horizontal_flips = bool(horizontal_flips)
C.use_vertical_flips = bool(vertical_flips)
C.rot_90 = bool(rot_90)
C.model_path = output_weight_path
C.num_rois = int(num_rois)
if network == 'vgg':
from ekfrcnn.kfrcnn import vgg as nn
C.network = 'vgg'
elif network == 'resnet50':
from ekfrcnn.kfrcnn import resnet as nn
C.network = 'resnet50'
else:
print('Not a valid model')
raise ValueError
if input_weight_path:
C.base_net_weights = input_weight_path
else:
# set the path to weights based on backend and model
C.base_net_weights = nn.get_weight_path()
all_imgs, classes_count, class_mapping = get_data(train_path)
print('all_imgs {}'.format(all_imgs))
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
inv_map = {v: k for k, v in class_mapping.items()}
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
config_output_filename = config_filename
with open(config_output_filename, 'wb') as config_f:
pickle.dump(C, config_f)
print(
'Config has been written to {}, and can be loaded when testing to ensure correct results'
.format(config_output_filename))
random.shuffle(all_imgs)
num_imgs = len(all_imgs)
train_imgs = [s for s in all_imgs if s['imageset'] == 'trainval']
val_imgs = [s for s in all_imgs if s['imageset'] == 'test']
print('Num train samples {}'.format(len(train_imgs)))
print('Num val samples {}'.format(len(val_imgs)))
data_gen_train = data_generators.get_anchor_gt(train_imgs,
classes_count,
C,
nn.get_img_output_length,
K.image_dim_ordering(),
mode='train')
data_gen_val = data_generators.get_anchor_gt(val_imgs,
classes_count,
C,
nn.get_img_output_length,
K.image_dim_ordering(),
mode='val')
if K.image_dim_ordering() == 'th':
input_shape_img = (3, None, None)
else:
input_shape_img = (None, None, 3)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(None, 4))
# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = nn.nn_base(img_input, trainable=True)
# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn = nn.rpn(shared_layers, num_anchors)
classifier = nn.classifier(shared_layers,
roi_input,
C.num_rois,
nb_classes=len(classes_count),
trainable=True)
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
# this is a model that holds both the RPN and the classifier, used to load/save weights for the models
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
try:
print('loading weights from {}'.format(C.base_net_weights))
model_rpn.load_weights(C.base_net_weights, by_name=True)
model_classifier.load_weights(C.base_net_weights, by_name=True)
except:
print('Could not load pretrained model weights.')
optimizer = Adam(lr=1e-5)
optimizer_classifier = Adam(lr=1e-5)
model_rpn.compile(optimizer=optimizer,
loss=[
losses.rpn_loss_cls(num_anchors),
losses.rpn_loss_regr(num_anchors)
])
model_classifier.compile(
optimizer=optimizer_classifier,
loss=[
losses.class_loss_cls,
losses.class_loss_regr(len(classes_count) - 1)
],
metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer='sgd', loss='mae')
epoch_length = num_epochs_len
num_epochs = int(num_epochs)
iter_num = 0
losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
start_time = time.time()
best_loss = np.Inf
class_mapping_inv = {v: k for k, v in class_mapping.items()}
print('Starting training')
vis = True
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
while True:
try:
if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
print('first print:{}'.format(
len(rpn_accuracy_rpn_monitor)))
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(
rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
)
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(P_rpn[0],
P_rpn[1],
C,
K.image_dim_ordering(),
use_regr=True,
overlap_thresh=0.7,
max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(
R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
print('neg_samples:{}'.format(len(neg_samples)))
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, C.num_rois // 2,
replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.train_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
losses[iter_num, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3]
iter_num += 1
progbar.update(
iter_num,
[('rpn_cls', np.mean(losses[:iter_num, 0])),
('rpn_regr', np.mean(losses[:iter_num, 1])),
('detector_cls', np.mean(losses[:iter_num, 2])),
('detector_regr', np.mean(losses[:iter_num, 3]))])
if iter_num == epoch_length:
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
print('second print:{}'.format(
len(rpn_accuracy_for_epoch)))
mean_overlapping_bboxes = float(sum(
rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if C.verbose:
print(
'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'
.format(mean_overlapping_bboxes))
print(
'Classifier accuracy for bounding boxes from RPN: {}'
.format(class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(
loss_class_cls))
print('Loss Detector regression: {}'.format(
loss_class_regr))
print('Elapsed time: {}'.format(time.time() -
start_time))
curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
iter_num = 0
start_time = time.time()
if curr_loss < best_loss:
if C.verbose:
print(
'Total loss decreased from {} to {}, saving weights'
.format(best_loss, curr_loss))
best_loss = curr_loss
model_all.save_weights(C.model_path)
break
except Exception as e:
print('Exception: {}'.format(e))
continue
print('Training complete, exiting.')
# train the model, output generated text after each iteration
train_num_samples = int(samples_frac * len(train_sequences))
valid_num_samples = int(samples_frac * len(valid_sequences))
train_num_samples = (train_num_samples // batch_size) * batch_size
valid_num_samples = (valid_num_samples // batch_size) * batch_size
best_loss = 10000
iteration = 0
while True:
iteration += 1
print()
print('-' * 50)
print('Iteration', iteration)
print("Training")
progbar = generic_utils.Progbar(train_num_samples)
gen = samples_generator(train_sequences,
batch_size,
num_samples=train_num_samples)
for X, y in gen:
loss, accuracy = model.train_on_batch(X, y, accuracy=True)
progbar.add(batch_size,
values=[("train loss", loss), ("train acc", accuracy)])
print()
print("Validating")
progbar = generic_utils.Progbar(valid_num_samples)
gen = samples_generator(valid_sequences,
batch_size,
num_samples=valid_num_samples)
valid_loss = 0
def train_kitti():
# config for data argument
cfg = config.Config()
cfg.use_horizontal_flips = True
cfg.use_vertical_flips = True
cfg.rot_90 = True
cfg.num_rois = 32
cfg.base_net_weights = os.path.join('./model/', nn.get_weight_path())
# cfg.base_net_weights=r''
# TODO: the only file should to be change for other data to train
cfg.model_path = '/media/private/Ci/log/plane/frcnn/vgg-adam'
now = datetime.datetime.now()
day = now.strftime('%y-%m-%d')
for i in range(10000):
if not os.path.exists('%s-%s-%d' % (cfg.model_path, day, i)):
cfg.model_path = '%s-%s-%d' % (cfg.model_path, day, i)
break
make_dir(cfg.model_path)
make_dir(cfg.model_path + '/loss')
make_dir(cfg.model_path + '/loss_rpn_cls')
make_dir(cfg.model_path + '/loss_rpn_regr')
make_dir(cfg.model_path + '/loss_class_cls')
make_dir(cfg.model_path + '/loss_class_regr')
cfg.simple_label_file = '/media/public/GEOWAY/plane/plane0817.csv'
all_images, classes_count, class_mapping = get_data(cfg.simple_label_file)
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
cfg.class_mapping = class_mapping
cfg.config_save_file = os.path.join(cfg.model_path, 'config.pickle')
with open(cfg.config_save_file, 'wb') as config_f:
pickle.dump(cfg, config_f)
print(
'Config has been written to {}, and can be loaded when testing to ensure correct results'
.format(cfg.config_save_file))
inv_map = {v: k for k, v in class_mapping.items()}
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
random.shuffle(all_images)
num_imgs = len(all_images)
train_imgs = [s for s in all_images if s['imageset'] == 'trainval']
val_imgs = [s for s in all_images if s['imageset'] == 'test']
print('Num train samples {}'.format(len(train_imgs)))
print('Num val samples {}'.format(len(val_imgs)))
data_gen_train = data_generators.get_anchor_gt(train_imgs,
classes_count,
cfg,
nn.get_img_output_length,
K.image_dim_ordering(),
mode='train')
data_gen_val = data_generators.get_anchor_gt(val_imgs,
classes_count,
cfg,
nn.get_img_output_length,
K.image_dim_ordering(),
mode='val')
Q = multiprocessing.Manager().Queue(maxsize=30)
def fill_Q(n):
while True:
if not Q.full():
Q.put(next(data_gen_train))
#print(Q.qsize(),'put',n)
else:
time.sleep(0.00001)
threads = []
for i in range(4):
thread = multiprocessing.Process(target=fill_Q, args=(i, ))
threads.append(thread)
thread.start()
if K.image_dim_ordering() == 'th':
input_shape_img = (3, None, None)
else:
input_shape_img = (None, None, 3)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(None, 4))
# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = nn.nn_base(img_input, trainable=True)
# define the RPN, built on the base layers
num_anchors = len(cfg.anchor_box_scales) * len(cfg.anchor_box_ratios)
rpn = nn.rpn(shared_layers, num_anchors)
classifier = nn.classifier(shared_layers,
roi_input,
cfg.num_rois,
nb_classes=len(classes_count),
trainable=True)
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
# this is a model that holds both the RPN and the classifier, used to load/save weights for the models
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
# model_all.summary()
from keras.utils import plot_model
# os.environ['PATH'] = os.environ['PATH'] + r';C:\Program Files (x86)\Graphviz2.38\bin;'
plot_model(model_all,
'model_all.png',
show_layer_names=True,
show_shapes=True)
plot_model(model_classifier,
'model_classifier.png',
show_layer_names=True,
show_shapes=True)
plot_model(model_rpn,
'model_rpn.png',
show_layer_names=True,
show_shapes=True)
'''
try:
print('loading weights from {}'.format(cfg.base_net_weights))
model_rpn.load_weights(cfg.model_path, by_name=True)
model_classifier.load_weights(cfg.model_path, by_name=True)
except Exception as e:
print(e)
print('Could not load pretrained model weights. Weights can be found in the keras application folder '
'https://github.com/fchollet/keras/tree/master/keras/applications')
'''
optimizer = adadelta()
optimizer_classifier = adadelta()
model_rpn.compile(optimizer=optimizer,
loss=[
losses_fn.rpn_loss_cls(num_anchors),
losses_fn.rpn_loss_regr(num_anchors)
])
model_classifier.compile(
optimizer=optimizer_classifier,
loss=[
losses_fn.class_loss_cls,
losses_fn.class_loss_regr(len(classes_count) - 1)
],
metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer='sgd', loss='mae')
epoch_length = 10
num_epochs = int(cfg.num_epochs)
iter_num = 0
losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
start_time = time.time()
best_loss = np.Inf
best_rpn_cls = np.Inf
best_rpn_regr = np.Inf
best_class_cls = np.Inf
best_class_regr = np.Inf
class_mapping_inv = {v: k for k, v in class_mapping.items()}
print('Starting training')
vis = True
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
while True:
try:
if len(rpn_accuracy_rpn_monitor
) == epoch_length and cfg.verbose:
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(
rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap'
' the ground truth boxes. Check RPN settings or keep training.'
)
# X, Y, img_data = next(data_gen_train)
while True:
if Q.empty():
time.sleep(0.00001)
continue
X, Y, img_data = Q.get()
# print(Q.qsize(),'get')
break
# print(X.shape,Y.shape)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
result = roi_helpers.rpn_to_roi(P_rpn[0],
P_rpn[1],
cfg,
K.image_dim_ordering(),
use_regr=True,
overlap_thresh=0.7,
max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(
result, img_data, cfg, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if cfg.num_rois > 1:
if len(pos_samples) < cfg.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, cfg.num_rois // 2,
replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
cfg.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
cfg.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.train_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
losses[iter_num, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3]
iter_num += 1
progbar.update(
iter_num,
[('rpn_cls', np.mean(losses[:iter_num, 0])),
('rpn_regr', np.mean(losses[:iter_num, 1])),
('detector_cls', np.mean(losses[:iter_num, 2])),
('detector_regr', np.mean(losses[:iter_num, 3]))])
if iter_num == epoch_length:
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(sum(
rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if cfg.verbose:
print(
'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'
.format(mean_overlapping_bboxes))
print(
'Classifier accuracy for bounding boxes from RPN: {}'
.format(class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(
loss_class_cls))
print('Loss Detector regression: {}'.format(
loss_class_regr))
print('Elapsed time: {}'.format(time.time() -
start_time))
curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
iter_num = 0
start_time = time.time()
if curr_loss < best_loss:
if cfg.verbose:
print(
'Total loss decreased from {} to {}, saving weights'
.format(best_loss, curr_loss))
best_loss = curr_loss
model_all.save_weights(
'%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5'
% (cfg.model_path, 'loss', epoch_num, curr_loss,
loss_rpn_cls, loss_rpn_regr, loss_class_cls,
loss_class_regr))
if loss_rpn_cls < best_rpn_cls:
if cfg.verbose:
print(
'loss_rpn_cls decreased from {} to {}, saving weights'
.format(best_rpn_cls, loss_rpn_cls))
best_rpn_cls = loss_rpn_cls
model_all.save_weights(
'%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5'
% (cfg.model_path, 'loss_rpn_cls', epoch_num,
curr_loss, loss_rpn_cls, loss_rpn_regr,
loss_class_cls, loss_class_regr))
if loss_rpn_regr < best_rpn_regr:
if cfg.verbose:
print(
'loss_rpn_regr decreased from {} to {}, saving weights'
.format(best_rpn_regr, loss_rpn_regr))
best_rpn_regr = loss_rpn_regr
model_all.save_weights(
'%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5'
% (cfg.model_path, 'loss_rpn_regr', epoch_num,
curr_loss, loss_rpn_cls, loss_rpn_regr,
loss_class_cls, loss_class_regr))
if loss_class_cls < best_class_cls:
if cfg.verbose:
print(
'loss_class_cls decreased from {} to {}, saving weights'
.format(best_loss, loss_class_cls))
best_class_cls = loss_class_cls
model_all.save_weights(
'%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5'
% (cfg.model_path, 'loss_class_cls', epoch_num,
curr_loss, loss_rpn_cls, loss_rpn_regr,
loss_class_cls, loss_class_regr))
if loss_class_regr < best_class_regr:
if cfg.verbose:
print(
'loss_class_regr decreased from {} to {}, saving weights'
.format(best_loss, loss_class_regr))
best_class_regr = loss_class_regr
model_all.save_weights(
'%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5'
% (cfg.model_path, 'loss_class_regr', epoch_num,
curr_loss, loss_rpn_cls, loss_rpn_regr,
loss_class_cls, loss_class_regr))
break
except Exception as e:
# print('Exception: {}'.format(e))
# save model
# model_all.save_weights(cfg.model_path)
continue
print('Training complete, exiting.')
else:
batchSizeFinal = round((numGPUs - .5) * batchSize)
#get final prediction size in case need to reshape segmentation
predictions_dim = predictions._keras_shape
# use DataLoader class to randomly flip and crop images
loader_train = DataLoader(X1, Y1, IMAGE_SIZE, crop_size)
loader_test = DataLoader(X1_test, Y1_test, IMAGE_SIZE, crop_size)
runningLoss_total = np.zeros(shape=(epochNum, 2))
# train model
for e in range(epochNum):
print('Epoch', e)
start = time.time()
batches = 0
progbar = generic_utils.Progbar(
math.ceil(IMAGE_SIZE[0] / batchSizeFinal) * batchSizeFinal)
runningLoss = 0.0
runningLossTest = 0.0
while (batches <= IMAGE_SIZE[0] / batchSizeFinal):
x_batch, y_batch, temp_images, temp_labels = loader_train.next_batch(
batchSizeFinal)
y_batch_crop = y_batch[:, 0:predictions_dim[1],
0:predictions_dim[2], :]
model_loss = model.train_on_batch(x_batch, y_batch_crop)
batches += 1
runningLoss = ((runningLoss * (batches - 1)) + model_loss) / (batches)
#x_batch_test, y_batch_test, temp_images, temp_labels = loader_test.next_batch(batchSizeFinal)
#y_batch_crop_test = y_batch_test[:, 0:predictions_dim[1], 0:predictions_dim[2], :]
# training uses sequences of length 1 to 20. Test uses series of length 100.
def test_model(model, file_name, min_size=100):
I, V, sw = get_sample(batch_size=500,
n_bits=input_dim,
max_size=min_size + 1,
min_size=min_size)
Y = np.asarray(model.predict(I, batch_size=100) > .5).astype('float64')
acc = (V[:, -min_size:, :] == Y[:, -min_size:, :]).mean() * 100
show_pattern(Y[0], V[0], sw[0], file_name)
return acc
##### TRAIN ######
nb_epoch = 4000
progbar = generic_utils.Progbar(nb_epoch)
for e in range(nb_epoch):
I, V, sw = get_sample(n_bits=input_dim,
max_size=20,
min_size=1,
batch_size=100)
loss1 = model.train_on_batch(I, V, sample_weight=sw)
loss2 = lstm.train_on_batch(I, V, sample_weight=sw)
progbar.add(1, values=[("NTM", loss1), ("LSTM", loss2)])
if e % 500 == 0:
print("")
acc1 = test_model(model, 'ntm.png')
acc2 = test_model(lstm, 'lstm.png')
def main():
# Set seed.
np.random.seed(SEED)
tf.set_random_seed(SEED)
# Read config.
C = config.Config()
class_mapping = C.class_mapping
# Read training data.
data_train, class_count, _ = get_data(TRAIN_ANNOT_PATH, TRAIN_DATA_PATH, C.img_types)
# Load base model.
if C.network == 'vgg16':
from faster_rcnn.base_models import vgg16 as base_model
elif C.network == 'resnet50':
from faster_rcnn.base_models import resnet50 as base_model
else:
print('Not a valid base model!')
sys.exit(1)
# Read validation data.
if USE_VALIDATION:
data_val, _, _ = get_data(VAL_ANNOT_PATH, VAL_DATA_PATH, C.img_types)
# Create paths.
if MODEL_NAME != None:
#model_name = C.model_path + '_' + datetime.today().strftime('%y%m%d') + '_' + MODEL_NAME
model_name = C.model_path + '_' + MODEL_NAME
if os.path.exists(os.path.join(MODELS_PATH, model_name)):
print('Model already exist.')
sys.exit(1)
else:
model_name = C.model_path + '_' + datetime.today().strftime('%y%m%d') + '_' + silly_name_gen()
model_path = os.path.join(MODELS_PATH, model_name)
weights_path = os.path.join(model_path, 'weights.hdf5')
config_path = os.path.join(model_path, 'config.pickle')
config_json_path = os.path.join(model_path, 'config.json')
record_path = os.path.join(model_path, 'record.csv')
C.weights_path = weights_path
# Create model folder.
create_model_folder(model_name)
# Save config.
with open(config_path, 'wb') as f:
pickle.dump(C, f)
with open(config_json_path, 'w') as f:
json.dump(C.__dict__, f, indent=4)
# Create generators.
data_train_gen = get_tile_generator(data_train, C, base_model.get_img_output_length, class_count, base_model.preprocess, train_mode=True, verbose=False)
if USE_VALIDATION:
data_val_gen = get_tile_generator(data_val, C, base_model.get_img_output_length, class_count, base_model.preprocess, train_mode=False, verbose=False)
# Define shapes.
img_input_shape = (None, None, 3)
img_input = Input(shape=img_input_shape)
roi_input = Input(shape=(None, 4))
n_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
# Define base network with shared layers, RPN and classifier.
base_net_output_layer = base_model.nn_base(img_input, trainable=C.base_net_trainable, weights=C.base_net_weights)
rpn = rpn_layer(base_net_output_layer, n_anchors)
classifier = base_model.classifier_layer(
base_net_output_layer,
roi_input,
C.n_rois,
nb_classes=len(class_mapping)
)
# Create models.
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
# Create var for recording training process.
df_record = pd.DataFrame(
columns=[
'elapsed_time',
'mean_overlapping_bboxes',
'val_mean_overlapping_bboxes',
'loss_rpn_cls',
'val_loss_rpn_cls',
'loss_rpn_regr',
'val_loss_rpn_regr',
'loss_detector_cls',
'val_loss_detector_cls',
'loss_detector_regr',
'val_loss_detector_regr',
'total_loss',
'val_total_loss',
'detector_acc',
'val_detector_acc',
'model_improvement'
]
)
# Compile models.
model_rpn.compile(
optimizer=Adam(lr=1e-5 * 5.0), #SGD(lr=1e-3, momentum=0.9, decay=0.0005),
loss=[
rpn_loss_cls(n_anchors),
rpn_loss_regr(n_anchors)
]
)
model_classifier.compile(
optimizer=Adam(lr=1e-5 * 5.0), #SGD(lr=1e-3, momentum=0.9, decay=0.0005),
loss=[
class_loss_cls,
class_loss_regr(len(class_mapping)-1)
],
metrics={
'dense_class_{}'.format(len(class_mapping)): 'accuracy'
}
)
model_all.compile(
optimizer='sgd',
loss='mae'
)
# Setup Tensorboard.
callback = TensorBoard(model_path)
callback.set_model(model_all)
# Training settings.
iter_num = 0
train_step = 0
losses = np.zeros((EPOCH_LENGTH, 5))
rpn_accuracy_for_epoch = []
best_total_loss = np.Inf
# Start training.
start_time = time.time()
print('\n\nStart training.')
for epoch_num in range(N_EPOCHS):
pbar = generic_utils.Progbar(EPOCH_LENGTH)
print('Epoch {}/{}'.format(epoch_num + 1, N_EPOCHS))
while True:
# Get next batch (image).
img, Y, img_data, img_debug, best_anchor_for_bbox, debug_n_pos = next(data_train_gen)
# If no GT boxes.
if len(img_data['bboxes']) == 0:
continue
# Train on batch.
loss_rpn = model_rpn.train_on_batch(img, Y)
# Get predicted RPN from RPN model [rpn_cls, rpn_regr].
P_rpn = model_rpn.predict_on_batch(img)
'''
if iter_num == 0:
colormap = [
((166,206,227), (31,120,180)), # Light Blue, Blue
((178,223,138), (51,160,44)), # Light Green, Green
((251,154,153), (227,26,28)), # Light Red, Red
((253,191,111), (255,127,0)), # Light Orange, Orange
((202,178,214), (106,61,154)), # Light Purple, Purple
]
img_debug = cv2.cvtColor(img_debug, cv2.COLOR_BGR2GRAY)
img_debug = cv2.cvtColor(img_debug, cv2.COLOR_GRAY2RGB)
_cls = Y[0][0]
_regr = Y[1][0]
pos_cls = np.where(_cls==1)
pos_regr = np.where(_regr==1)
for i in range(debug_n_pos):
color = colormap[i%len(colormap)][0]
idx = pos_regr[2][i*4]/4
anchor_size = C.anchor_box_scales[int(idx/len(C.anchor_box_ratios))]
anchor_ratio = C.anchor_box_ratios[int(idx%len(C.anchor_box_ratios))]
center = (pos_regr[1][i*4]*C.rpn_stride, pos_regr[0][i*4]*C.rpn_stride)
anchor_width = anchor_size*anchor_ratio[0]
anchor_height = anchor_size*anchor_ratio[1]
cv2.circle(img_debug, center, 3, color, -1)
cv2.rectangle(
img_debug,
(center[0]-int(anchor_width/2), center[1]-int(anchor_height/2)),
(center[0]+int(anchor_width/2), center[1]+int(anchor_height/2)),
color,
2
)
plt.figure(figsize=(8,8))
plt.imshow(img_debug)
plt.title(img_data['filepath'])
plt.show()
for i in range(9):
fig, ax = plt.subplots()
im = ax.imshow(Y[0][0, :, :, i])
plt.colorbar(im)
plt.title('isValid' + str(i))
plt.show()
fig, ax = plt.subplots()
im = ax.imshow(Y[0][0, :, :, i+9])
plt.colorbar(im)
plt.title('isObject' + str(i))
plt.show()
fig, ax = plt.subplots()
im = ax.imshow(P_rpn[0][0, :, :, i])
plt.colorbar(im)
plt.title('Prediction' + str(i))
plt.show()
'''
# R: bboxes (shape=(300,4))
# Convert RPN layer to ROI bboxes.
R = rpn_to_roi(
P_rpn[0],
P_rpn[1],
C,
use_regr=True,
overlap_thresh=0.7,
max_boxes=300
)
# Note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
# X2: bboxes that iou > C.classifier_min_overlap for all gt bboxes in 300 non_max_suppression bboxes.
# Y1: one hot code for bboxes from above => x_roi (X)
# Y2: corresponding labels and corresponding gt bboxes
X2, Y1, Y2, IouS = calc_iou(R, img_data, C, class_mapping)
# If X2 is None means there are no matching bboxes
if X2 is None:
rpn_accuracy_for_epoch.append(0)
continue
sel_samples, n_pos_samples = get_selected_samples(Y1, C)
rpn_accuracy_for_epoch.append(n_pos_samples)
# training_data: [img, X2[:, sel_samples, :]]
# labels: [Y1[:, sel_samples, :], Y2[:, sel_samples, :]]
# img => img_data resized image
# X2[:, sel_samples, :] => n_rois (4 in here) bboxes which contains selected neg and pos
# Y1[:, sel_samples, :] => one hot encode for n_rois bboxes which contains selected neg and pos
# Y2[:, sel_samples, :] => labels and gt bboxes for n_rois bboxes which contains selected neg and pos
loss_detector = model_classifier.train_on_batch(
[
img,
X2[:, sel_samples, :]
],
[
Y1[:, sel_samples, :],
Y2[:, sel_samples, :]
]
)
# Log losses.
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
write_log(
callback,
['rpn_cls_loss', 'rpn_reg_loss'],
[loss_rpn[1], loss_rpn[2]],
train_step
)
losses[iter_num, 2] = loss_detector[1]
losses[iter_num, 3] = loss_detector[2]
losses[iter_num, 4] = loss_detector[3]
write_log(
callback,
['detector_cls_loss', 'detector_reg_loss', 'detector_acc'],
[loss_detector[1], loss_detector[2], loss_detector[3]],
train_step
)
iter_num += 1
train_step += 1
pbar.update(
iter_num,
[
('rpn_cls', losses[iter_num-1, 0]),
('rpn_regr', losses[iter_num-1, 1]),
('detector_cls', losses[iter_num-1, 2]),
('detector_regr', losses[iter_num-1, 3])
]
)
if iter_num == EPOCH_LENGTH:
# Compute epoch losses.
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_detector_cls = np.mean(losses[:, 2])
loss_detector_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
elapsed_time = (time.time() - start_time)
curr_total_loss = loss_rpn_cls + loss_rpn_regr + loss_detector_cls + loss_detector_regr
iter_num = 0
if C.verbose:
print('')
if mean_overlapping_bboxes == 0:
print('RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
else:
print('(TRAINING) Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
print('(TRAINING) Loss RPN classifier: {}'.format(loss_rpn_cls))
print('(TRAINING) Loss RPN regression: {}'.format(loss_rpn_regr))
print('(TRAINING) Loss Detector classifier: {}'.format(loss_detector_cls))
print('(TRAINING) Loss Detector regression: {}'.format(loss_detector_regr))
print('(TRAINING) Detector accuracy for bounding boxes from RPN: {}'.format(class_acc))
print('(TRAINING) Total Loss: {}'.format(curr_total_loss))
print('Elapsed time: ' + ms_output(elapsed_time))
print('')
# Validation.
record_row = {}
if USE_VALIDATION:
val_start_time = time.time()
print('\nPerforming Validation.')
val_rpn_accuracy = []
val_rpn_cls_loss = []
val_rpn_reg_loss = []
val_detector_cls_loss = []
val_detector_reg_loss = []
val_detector_acc = []
while True:
try:
img_val, Y_val, img_data_val, _, _, _ = next(data_val_gen)
# Validate on batch.
val_loss_rpn = model_rpn.test_on_batch(img_val, Y_val)
P_rpn_val = model_rpn.predict_on_batch(img_val)
R_val = rpn_to_roi(
P_rpn_val[0],
P_rpn_val[1],
C,
use_regr=True,
overlap_thresh=0.7,
max_boxes=300
)
X2_val, Y1_val, Y2_val, _ = calc_iou(R_val, img_data_val, C, class_mapping)
if X2_val is None:
continue
val_sel_samples, val_n_pos_samples = get_selected_samples(Y1_val, C)
val_loss_detector = model_classifier.test_on_batch(
[
img_val,
X2_val[:, val_sel_samples, :]
],
[
Y1_val[:, val_sel_samples, :],
Y2_val[:, val_sel_samples, :]
]
)
val_rpn_accuracy.append(val_n_pos_samples)
val_rpn_cls_loss.append(val_loss_rpn[1])
val_rpn_reg_loss.append(val_loss_rpn[2])
val_detector_cls_loss.append(val_loss_detector[1])
val_detector_reg_loss.append(val_loss_detector[2])
val_detector_acc.append(val_loss_detector[3])
except RuntimeError:
break
except StopIteration:
break
except:
print(traceback.print_exc())
sys.exit(1)
data_val_gen = get_tile_generator(data_val, C, base_model.get_img_output_length, class_count, base_model.preprocess, train_mode=False, verbose=False)
val_mean_overlapping_bboxes = float(sum(val_rpn_accuracy)) / len(val_rpn_accuracy)
val_total_loss = np.mean(val_rpn_cls_loss) + np.mean(val_rpn_reg_loss) + np.mean(val_detector_cls_loss) + np.mean(val_detector_reg_loss)
print('(VALIDATION) Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(val_mean_overlapping_bboxes))
print('(VALIDATION) Mean Loss RPN classifier: {}'.format(np.mean(val_rpn_cls_loss)))
print('(VALIDATION) Mean Loss RPN regression: {}'.format(np.mean(val_rpn_reg_loss)))
print('(VALIDATION) Mean Loss Detector classifier: {}'.format(np.mean(val_detector_cls_loss)))
print('(VALIDATION) Mean Loss Detector regression: {}'.format(np.mean(val_detector_reg_loss)))
print('(VALIDATION) Mean Detector accuracy for bounding boxes from RPN: {}'.format(np.mean(val_detector_acc)))
print('(VALIDATION) Total Loss: {}'.format(val_total_loss))
record_row['val_mean_overlapping_bboxes'] = round(val_mean_overlapping_bboxes, 3)
record_row['val_detector_acc'] = round(np.mean(val_detector_acc), 3)
record_row['val_loss_rpn_cls'] = round(np.mean(val_rpn_cls_loss), 3)
record_row['val_loss_rpn_regr'] = round(np.mean(val_rpn_reg_loss), 3)
record_row['val_loss_detector_cls'] = round(np.mean(val_detector_cls_loss), 3)
record_row['val_loss_detector_regr'] = round(np.mean(val_detector_reg_loss), 3)
record_row['val_total_loss'] = round(val_total_loss, 3)
val_elapsed_time = (time.time() - val_start_time)
print('Validation execution time: ' + ms_output(val_elapsed_time))
print('')
if val_total_loss < best_total_loss:
record_row['model_improvement'] = val_total_loss - best_total_loss
if C.verbose:
print('Total loss decreased from {} to {}, saving weights'.format(best_total_loss, val_total_loss))
print('')
best_total_loss = val_total_loss
model_all.save_weights(weights_path)
else:
record_row['model_improvement'] = None
else:
record_row['val_mean_overlapping_bboxes'] = None
record_row['val_detector_acc'] = None
record_row['val_loss_rpn_cls'] = None
record_row['val_loss_rpn_regr'] = None
record_row['val_loss_detector_cls'] = None
record_row['val_loss_detector_regr'] = None
record_row['val_total_loss'] = None
if curr_total_loss < best_total_loss:
record_row['model_improvement'] = curr_total_loss - best_total_loss
if C.verbose:
print('Total loss decreased from {} to {}, saving weights'.format(best_total_loss, curr_total_loss))
print('')
best_total_loss = curr_total_loss
model_all.save_weights(weights_path)
else:
record_row['model_improvement'] = None
# Log epoch averages.
write_log(
callback,
[
'Elapsed_time',
'mean_overlapping_bboxes',
'mean_rpn_cls_loss',
'mean_rpn_reg_loss',
'mean_detector_cls_loss',
'mean_detector_reg_loss',
'mean_detector_acc',
'total_loss'
],
[
elapsed_time/60,
mean_overlapping_bboxes,
loss_rpn_cls,
loss_rpn_regr,
loss_detector_cls,
loss_detector_regr,
class_acc,
curr_total_loss
],
epoch_num
)
record_row['mean_overlapping_bboxes'] = round(mean_overlapping_bboxes, 3)
record_row['detector_acc'] = round(class_acc, 3)
record_row['loss_rpn_cls'] = round(loss_rpn_cls, 3)
record_row['loss_rpn_regr'] = round(loss_rpn_regr, 3)
record_row['loss_detector_cls'] = round(loss_detector_cls, 3)
record_row['loss_detector_regr'] = round(loss_detector_regr, 3)
record_row['total_loss'] = round(curr_total_loss, 3)
record_row['elapsed_time'] = round(elapsed_time/60, 3)
df_record = df_record.append(record_row, ignore_index=True)
df_record.to_csv(record_path, index=0)
break
print('Training Complete! Exiting.')
fig = plt.figure(figsize=(15,5))
plt.subplot(1,2,1)
plt.plot(np.arange(0, df_record.shape[0]), df_record['mean_overlapping_bboxes'], 'r', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_mean_overlapping_bboxes'], 'b', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['mean_overlapping_bboxes'].rolling(window=20).mean(), 'r', label='Train')
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_mean_overlapping_bboxes'].rolling(window=20).mean(), 'b', label='Val')
plt.title('mean_overlapping_bboxes')
plt.legend()
plt.subplot(1,2,2)
plt.plot(np.arange(0, df_record.shape[0]), df_record['detector_acc'], 'r', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_detector_acc'], 'b', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['detector_acc'].rolling(window=20).mean(), 'r', label='Train')
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_detector_acc'].rolling(window=20).mean(), 'b', label='Val')
plt.title('class_acc')
plt.legend()
fig.savefig(os.path.join(model_path, 'viz/accuracy.png'))
fig = plt.figure(figsize=(15,5))
plt.subplot(1,2,1)
plt.plot(np.arange(0, df_record.shape[0]), df_record['loss_rpn_cls'], 'r', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_loss_rpn_cls'], 'b', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['loss_rpn_cls'].rolling(window=20).mean(), 'r', label='Train')
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_loss_rpn_cls'].rolling(window=20).mean(), 'b', label='Val')
plt.title('loss_rpn_cls')
plt.legend()
plt.subplot(1,2,2)
plt.plot(np.arange(0, df_record.shape[0]), df_record['loss_rpn_regr'], 'r', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_loss_rpn_regr'], 'b', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['loss_rpn_regr'].rolling(window=20).mean(), 'r', label='Train')
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_loss_rpn_regr'].rolling(window=20).mean(), 'b', label='Val')
plt.title('loss_rpn_regr')
plt.legend()
fig.savefig(os.path.join(model_path, 'viz/rpn_loss.png'))
fig = plt.figure(figsize=(15,5))
plt.subplot(1,2,1)
plt.plot(np.arange(0, df_record.shape[0]), df_record['loss_detector_cls'], 'r', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_loss_detector_cls'], 'b', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['loss_detector_cls'].rolling(window=20).mean(), 'r', label='Train')
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_loss_detector_cls'].rolling(window=20).mean(), 'b', label='Val')
plt.title('loss_detector_cls')
plt.legend()
plt.subplot(1,2,2)
plt.plot(np.arange(0, df_record.shape[0]), df_record['loss_detector_regr'], 'r', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_loss_detector_regr'], 'b', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['loss_detector_regr'].rolling(window=20).mean(), 'r', label='Train')
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_loss_detector_regr'].rolling(window=20).mean(), 'b', label='Val')
plt.title('loss_detector_regr')
plt.legend()
fig.savefig(os.path.join(model_path, 'viz/detector_loss.png'))
fig = plt.figure(figsize=(16,8))
plt.plot(np.arange(0, df_record.shape[0]), df_record['total_loss'], 'r', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_total_loss'], 'b', alpha=0.3)
plt.plot(np.arange(0, df_record.shape[0]), df_record['total_loss'].rolling(window=20).mean(), 'r', label='Train')
plt.plot(np.arange(0, df_record.shape[0]), df_record['val_total_loss'].rolling(window=20).mean(), 'b', label='Val')
plt.title('total_loss')
plt.legend()
fig.savefig(os.path.join(model_path, 'viz/total_loss.png'))
def train(path, batch_size=10, epochs=50, steps_per_epoch=None):
# input_shape = (256, 256, 3)
# padding_width = 64
input_shape = (128, 128, 3)
padding_width = 32
is_log = get_counter()
data_generator = DataGenerator(path, input_shape[:2], batch_size,
padding_width)
data_size = len(data_generator)
print('data size: {}'.format(data_size))
if steps_per_epoch is None:
steps_per_epoch = data_size // batch_size
G, D, C = build_model(input_shape, padding_width)
t1_epochs = epochs * 16 // 100
t2_epochs = epochs * 2 // 100
t3_epochs = epochs * 70 // 100
g_history = {'loss': [], 'val_loss': []}
d_history = {'loss': [], 'val_loss': []}
c_history = {'loss': [], 'val_loss': []}
class LogCallback(keras.callbacks.Callback):
def on_batch_end(self, batch, logs=None):
if is_log():
g_history['loss'].append(logs['loss'])
padded_iamges, real_images = next(
data_generator.validation_flow())
val_loss = G.evaluate(padded_iamges, real_images, batch_size)
g_history['val_loss'].append(val_loss)
plot_history(g_history, 'G')
plot_generated_image(G, data_generator)
print('Phase 1')
if os.path.exists('checkpoint/g.h5'):
G.load_weights('checkpoint/g.h5')
g_history = json.load(
open('checkpoint/g_history.json', 'r', encoding='utf-8'))
print('get trained G weight')
else:
g_loss = G.fit_generator(
data_generator.flow(),
epochs=t1_epochs,
steps_per_epoch=steps_per_epoch,
validation_data=data_generator.validation_flow(),
validation_steps=2,
callbacks=[LogCallback()])
G.save_weights('checkpoint/g.h5')
g_history = g_loss.history
json.dump(g_history,
open('checkpoint/g_history.json', 'w', encoding='utf-8'))
json.dump(c_history,
open('checkpoint/c_history.json', 'w', encoding='utf-8'))
json.dump(d_history,
open('checkpoint/d_history.json', 'w', encoding='utf-8'))
json.dump(g_history,
open('checkpoint/g_end_history.json', 'w', encoding='utf-8'))
C.save_weights('checkpoint/c.h5')
D.save_weights('checkpoint/d.h5')
G.save_weights('checkpoint/g_end.h5')
print('Phase 2')
counter = 0
D.summary()
for cur_epoch in range(t2_epochs):
print('Epoch {}/{}'.format(cur_epoch, t2_epochs))
progbar = generic_utils.Progbar(steps_per_epoch)
for d in itertools.islice(data_generator.flow(), None,
steps_per_epoch):
padded_iamges, real_images = d
fake_images = G.predict(padded_iamges)
d_loss_real = D.train_on_batch(
real_images, np.ones(batch_size, dtype='float32'))
d_loss_fake = D.train_on_batch(
fake_images, np.zeros(batch_size, dtype='float32'))
d_loss = (d_loss_real + d_loss_fake) / 2
progbar.add(1, values=[("D loss", d_loss)])
if is_log():
d_history['loss'].append(float(d_loss))
padded_iamges, real_images = next(
data_generator.validation_flow())
combined_images, labels = combine_image_and_label(
G, padded_iamges, real_images, batch_size)
d_val_loss = D.evaluate(combined_images, labels)
d_history['val_loss'].append(float(d_val_loss))
plot_history(d_history, 'D')
print('Phase 3')
for cur_epoch in range(t3_epochs):
print('Epoch {}/{}'.format(cur_epoch, t3_epochs))
progbar = generic_utils.Progbar(steps_per_epoch)
for d in itertools.islice(data_generator.flow(), None,
steps_per_epoch):
padded_iamges, real_images = d
fake_images = G.predict(padded_iamges)
d_loss_real = D.train_on_batch(
real_images, np.ones(batch_size, dtype='float32'))
d_loss_fake = D.train_on_batch(
fake_images, np.zeros(batch_size, dtype='float32'))
d_loss = (d_loss_real + d_loss_fake) / 2
c_loss = C.train_on_batch(
padded_iamges,
[real_images,
np.ones((batch_size, 1), dtype='float32')])
progbar.add(1,
values=[("D loss", d_loss), ('C loss', c_loss[0]),
('G loss', c_loss[1])])
if is_log():
d_history['loss'].append(float(d_loss))
c_history['loss'].append(float(c_loss[0]))
g_history['loss'].append(float(c_loss[1]))
padded_iamges, real_images = next(
data_generator.validation_flow())
c_loss = C.evaluate(
padded_iamges,
[real_images,
np.ones((batch_size, 1), dtype='float32')])
combined_images, labels = combine_image_and_label(
G, padded_iamges, real_images, batch_size)
d_loss = D.evaluate(combined_images, labels)
d_history['val_loss'].append(float(d_loss))
c_history['val_loss'].append(float(c_loss[0]))
g_history['val_loss'].append(float(c_loss[1]))
plot_history(d_history, 'D')
plot_history(c_history, 'C')
plot_history(g_history, 'G')
plot_generated_image(G, data_generator)
G.save_weights('checkpoint/g_end.h5')
json.dump(g_history,
open('checkpoint/g_end_history.json', 'w', encoding='utf-8'))
D.save_weights('checkpoint/d.h5')
json.dump(d_history,
open('checkpoint/d_history.json', 'w', encoding='utf-8'))
if i == 20:
model_rpn.compile(
loss={
'regression': smooth_l1(),
'classification': cls_loss()
},
optimizer=keras.optimizers.Adam(lr=Learning_rate / 10))
model_classifier.compile(
loss=[class_loss_cls,
class_loss_regr(NUM_CLASSES - 1)],
metrics={'dense_class_{}'.format(NUM_CLASSES): 'accuracy'},
optimizer=keras.optimizers.Adam(lr=Learning_rate / 10))
print("Learning rate decrease")
progbar = generic_utils.Progbar(EPOCH_LENGTH)
print('Epoch {}/{}'.format(i + 1, EPOCH))
for iteration, batch in enumerate(rpn_train):
if len(rpn_accuracy_rpn_monitor
) == EPOCH_LENGTH and config.verbose:
mean_overlapping_bboxes = float(sum(
rpn_accuracy_rpn_monitor)) / len(rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, EPOCH_LENGTH))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
)
def train_loop(identifier, model, optimizer, epoch_start, history, la_plotter,
ia_train, ia_val, X_train, y_train, X_val, y_val):
"""Perform the training loop.
Args:
identifier: Identifier of the experiment.
model: The network to train (and validate).
optimizer: The network's optimizer (e.g. SGD).
epoch_start: The epoch to start the training at. Usually 0, but
can be higher if an old training is continued/loaded.
history: The history for this training.
Can be filled already, if an old training is continued/loaded.
la_plotter: The plotter used to plot loss and accuracy values.
Can be filled already, if an old training is continued/loaded.
ia_train: ImageAugmenter to use to augment the training images.
ia_val: ImageAugmenter to use to augment the validation images.
X_train: The training set images.
y_train: The training set labels (same persons, different persons).
X_val: The validation set images.
y_val: The validation set labels.
"""
# Loop over each epoch, i.e. executes 20 times if epochs set to 20
# start_epoch is not 0 if we continue an older model.
for epoch in range(epoch_start, EPOCHS):
print("Epoch", epoch)
# Variables to collect the sums for loss and accuracy (for training and
# validation dataset). We will use them to calculate the loss/acc per
# example (which will be ploted and added to the history).
loss_train_sum = 0
loss_val_sum = 0
acc_train_sum = 0
acc_val_sum = 0
nb_examples_train = X_train.shape[0]
nb_examples_val = X_val.shape[0]
# Training loop
progbar = generic_utils.Progbar(nb_examples_train)
for X_batch, Y_batch in flow_batches(X_train,
y_train,
ia_train,
shuffle=True,
train=True):
loss, acc = model.train_on_batch(X_batch, Y_batch, accuracy=True)
progbar.add(len(X_batch),
values=[("train loss", loss), ("train acc", acc)])
loss_train_sum += (loss * len(X_batch))
acc_train_sum += (acc * len(X_batch))
# Validation loop
progbar = generic_utils.Progbar(nb_examples_val)
# Iterate over each batch in the validation data
# and calculate loss and accuracy for each batch
for X_batch, Y_batch in flow_batches(X_val,
y_val,
ia_val,
shuffle=False,
train=False):
loss, acc = model.test_on_batch(X_batch, Y_batch, accuracy=True)
progbar.add(len(X_batch),
values=[("val loss", loss), ("val acc", acc)])
loss_val_sum += (loss * len(X_batch))
acc_val_sum += (acc * len(X_batch))
# Calculate the loss and accuracy for this epoch
# (averaged over all training data batches)
loss_train = loss_train_sum / nb_examples_train
acc_train = acc_train_sum / nb_examples_train
loss_val = loss_val_sum / nb_examples_val
acc_val = acc_val_sum / nb_examples_val
history.add(epoch,
loss_train=loss_train,
loss_val=loss_val,
acc_train=acc_train,
acc_val=acc_val)
# Update plots with new data from this epoch
# We start plotting _after_ the first epoch as the first one usually contains
# a huge fall in loss (increase in accuracy) making it harder to see the
# minor swings at epoch 1000 and later.
if epoch > 0:
la_plotter.add_values(epoch,
loss_train=loss_train,
loss_val=loss_val,
acc_train=acc_train,
acc_val=acc_val)
# Save the history to a csv file
if SAVE_CSV_FILEPATH is not None:
csv_filepath = SAVE_CSV_FILEPATH.format(identifier=identifier)
history.save_to_filepath(csv_filepath)
# Save the weights and optimizer state to files
swae = SAVE_WEIGHTS_AFTER_EPOCHS
if swae and swae > 0 and (epoch + 1) % swae == 0:
print("Saving model...")
save_model_weights(model,
SAVE_WEIGHTS_DIR,
"{}.last.weights".format(identifier),
overwrite=True)
save_optimizer_state(optimizer,
SAVE_OPTIMIZER_STATE_DIR,
"{}.last.optstate".format(identifier),
overwrite=True)
model.summary()
# Define optimizer
optim = SGD(lr=lr, momentum=0.9)
# Compile model
model.compile(loss='binary_crossentropy', optimizer=optim)
# Archiv model architecture to json
model_json = model.to_json()
with open("/media/zhaojian/6TB/project/ms-celeb-1m/c2/models/ms-siamese.json", "w") as json_file:
json_file.write(model_json)
# Start training
for e in range(nb_epoch):
progbar = generic_utils.Progbar(nb_batch*batch_size)
start = time.time()
for b in range(nb_batch):
# Get a batch of training pairs
tr_X1_batch = np.zeros((batch_size,img_w,img_h,img_c), dtype=np.float32)
tr_X1_file = tr_Pair1_file_list[b * batch_size:(b + 1) * batch_size]
tr_label_batch = tr_Pair_label[b * batch_size:(b + 1) * batch_size]
for i in range(batch_size):
x = image.load_img(tr_X1_file[i], target_size=(224,224))
x = image.img_to_array(x)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
tr_X1_batch[i, ...] = x
tr_X2_batch = np.zeros((batch_size,img_w,img_h,img_c), dtype=np.float32)
def main():
start_time = time.time()
parser = argparse.ArgumentParser(
prog='valLSTM_MLP.py',
description='Test LSTM-MLP model for visual question answering')
parser.add_argument('--model-vgg',
type=str,
required=True,
metavar='<model-path>')
parser.add_argument('--weights-vgg',
type=str,
required=True,
metavar='<weights-path>')
parser.add_argument('--model-inc',
type=str,
required=True,
metavar='<model-path>')
parser.add_argument('--weights-inc',
type=str,
required=True,
metavar='<weights-path>')
parser.add_argument('--output',
type=str,
required=True,
metavar='<prediction-path>')
args = parser.parse_args()
word_vec_dim = 300
batch_size = 128
vgg_weight = 0.25
inc_weight = 1 - vgg_weight
#######################
# Load Models #
#######################
print('Loading models and weights...')
model_vgg = model_from_json(open(args.model_vgg, 'r').read())
model_vgg.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model_vgg.load_weights(args.weights_vgg)
model_inc = model_from_json(open(args.model_inc, 'r').read())
model_inc.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model_inc.load_weights(args.weights_inc)
print('Models and weights loaded.')
print('Time: %f s' % (time.time() - start_time))
######################
# Load Data #
######################
data_dir = '/home/mlds/data/0.05_val/'
print('Loading data...')
#train_id_pairs, train_image_ids = LoadIds('train')
#dev_id_pairs, dev_image_ids = LoadIds('dev')
test_q_ids, test_image_ids = LoadIds('test', data_dir)
#train_questions = LoadQuestions('train')
#dev_questions = LoadQuestions('dev')
test_questions = LoadQuestions('test', data_dir)
#train_choices = LoadChoices('train')
#dev_choices = LoadChoices('dev')
test_choices = LoadChoices('test', data_dir)
#train_answers = LoadAnswers('train')
#dev_answers = LoadAnswers('dev')
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
print('Loading VGG features...')
VGG_features, vgg_img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time() - start_time))
# load Inception features
print('Loading Inception features...')
INC_features, inc_img_map = LoadInceptionFeatures()
print('Inception features loaded')
print('Time: %f s' % (time.time() - start_time))
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# train batches
# train_question_batches = [ b for b in MakeBatches(train_questions, batch_size, fillvalue=train_questions[-1]) ]
# train_answer_batches = [ b for b in MakeBatches(train_answers['labs'], batch_size, fillvalue=train_answers['labs'][-1]) ]
# train_choice_batches = [ b for b in MakeBatches(train_choices, batch_size, fillvalue=train_choices[-1]) ]
# train_image_batches = [ b for b in MakeBatches(train_image_ids, batch_size, fillvalue=train_image_ids[-1]) ]
# validation batches
# dev_question_batches = [ b for b in MakeBatches(dev_questions, batch_size, fillvalue=dev_questions[-1]) ]
# dev_answer_batches = [ b for b in MakeBatches(dev_answers['labs'], batch_size, fillvalue=dev_answers['labs'][-1]) ]
# dev_choice_batches = [ b for b in MakeBatches(dev_choices, batch_size, fillvalue=dev_choices[-1]) ]
# dev_image_batches = [ b for b in MakeBatches(dev_image_ids, batch_size, fillvalue=dev_image_ids[-1]) ]
# testing batches
test_question_batches = [
b for b in MakeBatches(
test_questions, batch_size, fillvalue=test_questions[-1])
]
test_choice_batches = [
b for b in MakeBatches(
test_choices, batch_size, fillvalue=test_choices[-1])
]
test_image_batches = [
b for b in MakeBatches(
test_image_ids, batch_size, fillvalue=test_image_ids[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Testing #
######################
predictions = []
pbar = generic_utils.Progbar(len(test_question_batches) * batch_size)
for i in range(len(test_question_batches)):
# feed forward
X_question_batch = GetQuestionsTensor(test_question_batches[i],
word_embedding, word_map)
X_vgg_image_batch = GetImagesMatrix(test_image_batches[i], vgg_img_map,
VGG_features)
X_inc_image_batch = GetImagesMatrix(test_image_batches[i], inc_img_map,
INC_features)
prob_vgg = model_vgg.predict_proba(
[X_question_batch, X_vgg_image_batch], batch_size, verbose=0)
prob_inc = model_inc.predict_proba(
[X_question_batch, X_inc_image_batch], batch_size, verbose=0)
prob = (vgg_weight * prob_vgg + inc_weight * prob_inc)
# get word vecs of choices
choice_feats = GetChoicesTensor(test_choice_batches[i], word_embedding,
word_map)
similarity = np.zeros((5, batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
predictions.extend(pred.tolist())
pbar.add(batch_size)
SavePredictions(args.output, predictions, test_q_ids)
print('Time: %f s' % (time.time() - start_time))
print('Testing finished.')
def main():
cwd = os.getcwd()
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units', type=int, default=1024)
parser.add_argument('-num_hidden_layers', type=int, default=3)
parser.add_argument('-dropout', type=float, default=0.5)
parser.add_argument('-activation', type=str, default='tanh')
parser.add_argument('-language_only', type=bool, default=False)
parser.add_argument('-num_epochs', type=int, default=2)
parser.add_argument('-model_save_interval', type=int, default=10)
parser.add_argument('-model_weights_path',
type=str,
default=cwd + '/vgg/vgg16_weights.h5')
parser.add_argument('-batch_size', type=int, default=128)
parser.add_argument('-questions_train',
type=str,
default=cwd +
'/data/preprocessed/questions_train2015.txt')
parser.add_argument('-answers_train',
type=str,
default=cwd +
'/data/preprocessed/answers_train2015_modal.txt')
parser.add_argument(
'-im_dir',
type=str,
default=cwd + '/data/preprocessed/scene_img_abstract_v002_train2015/')
#parser.add_argument('-questions_train',type=str, default = cwd+'/data/preprocessed/questions_train2014.txt')
args = parser.parse_args()
questions_train = open(args.questions_train,
'r').read().decode('utf8').splitlines()
answers_train = open(args.answers_train,
'r').read().decode('utf8').splitlines()
images_train = open(cwd + '/data/preprocessed/images_train2015.txt',
'r').read().decode('utf8').splitlines()
#vgg_model_path = cwd+'/features/coco/vgg_feats.mat' #this needs to change
maxAnswers = 100
questions_train, answers_train, images_train = selectFrequentAnswers(
questions_train, answers_train, images_train, maxAnswers)
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder, cwd + '/models/labelencoder.pkl')
#features_struct = scipy.io.loadmat(vgg_model_path)
#VGGfeatures = features_struct['feats']
# print 'loaded vgg features'
# image_ids = open(cwd+'/features/coco_vgg_IDMap.txt').read().splitlines()
# id_map = {}
# for ids in image_ids:
# id_split = ids.split()
# id_map[id_split[0]] = int(id_split[1])
vgg_model = vgg16.VGG_16(args.model_weights_path)
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
vgg_model.compile(optimizer=sgd, loss='categorical_crossentropy')
print 'loaded vgg model...'
nlp = English()
print 'loaded word2vec features...'
img_dim = 4096
word_vec_dim = 300
model = Sequential()
if args.language_only:
model.add(
Dense(args.num_hidden_units,
input_dim=word_vec_dim,
init='uniform'))
else:
model.add(
Dense(args.num_hidden_units,
input_dim=img_dim + word_vec_dim,
init='uniform'))
model.add(Activation(args.activation))
if args.dropout > 0:
model.add(Dropout(args.dropout))
for i in xrange(args.num_hidden_layers - 1):
model.add(Dense(args.num_hidden_units, init='uniform'))
model.add(Activation(args.activation))
if args.dropout > 0:
model.add(Dropout(args.dropout))
model.add(Dense(nb_classes, init='uniform'))
model.add(Activation('softmax'))
json_string = model.to_json()
model_file_name = cwd + '/models/mlp_num_hidden_units_' + str(
args.num_hidden_units) + '_num_hidden_layers_' + str(
args.num_hidden_layers)
open(model_file_name + '.json', 'w').write(json_string)
print 'Training started...'
for k in xrange(args.num_epochs):
#shuffle the data points before going through them
index_shuf = range(len(questions_train))
shuffle(index_shuf)
questions_train = [questions_train[i] for i in index_shuf]
answers_train = [answers_train[i] for i in index_shuf]
images_train = [images_train[i] for i in index_shuf]
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch, an_batch, im_batch in zip(
grouper(questions_train,
args.batch_size,
fillvalue=questions_train[-1]),
grouper(answers_train,
args.batch_size,
fillvalue=answers_train[-1]),
grouper(images_train,
args.batch_size,
fillvalue=images_train[-1])):
X_q_batch = get_questions_matrix_sum(qu_batch, nlp)
im_path = args.im_dir + "abstract_v002_train2015_"
#print 'getting image features...'
#X_i_batch = get_images_matrix_from_model(vgg_model, im_batch, im_path)
#X_batch = np.hstack((X_q_batch, X_i_batch))
Y_batch = get_answers_matrix(an_batch, labelencoder)
print 'running training on batch...'
loss = model.train_on_batch(X_batch, Y_batch)
progbar.add(args.batch_size, values=[("train loss", loss)])
if k % args.model_save_interval == 0:
model.save_weights(model_file_name +
'_epoch_{:02d}.hdf5'.format(k))
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k))
def train(**kwargs):
"""
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
generator = kwargs["generator"]
discriminator = kwargs["discriminator"]
dset = kwargs["dset"]
img_dim = kwargs["img_dim"]
nb_epoch = kwargs["nb_epoch"]
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
bn_mode = kwargs["bn_mode"]
noise_dim = kwargs["noise_dim"]
noise_scale = kwargs["noise_scale"]
lr_D = kwargs["lr_D"]
lr_G = kwargs["lr_G"]
opt_D = kwargs["opt_D"]
opt_G = kwargs["opt_G"]
use_mbd = kwargs["use_mbd"]
image_dim_ordering = kwargs["image_dim_ordering"]
epoch_size = n_batch_per_epoch * batch_size
deterministic = kwargs["deterministic"]
inject_noise = kwargs["inject_noise"]
model = kwargs["model"]
no_supertrain = kwargs["no_supertrain"]
pureGAN = kwargs["pureGAN"]
lsmooth = kwargs["lsmooth"]
simple_disc = kwargs["simple_disc"]
resume = kwargs["resume"]
name = kwargs["name"]
wd = kwargs["wd"]
history_size = kwargs["history_size"]
monsterClass = kwargs["monsterClass"]
print("\nExperiment parameters:")
for key in kwargs.keys():
print key, kwargs[key]
print("\n")
# Setup environment (logging directory etc)
general_utils.setup_logging("DCGAN")
# Load and normalize data
if dset == "mnistM":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='mnist')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='mnistM')
#code.interact(local=locals())
elif dset == "washington_vandal50k":
X_source_train = data_utils.load_image_dataset(img_dim,
image_dim_ordering,
dset='washington')
X_dest_train = data_utils.load_image_dataset(img_dim,
image_dim_ordering,
dset='vandal50k')
elif dset == "washington_vandal12classes":
X_source_train = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='washington12classes')
X_dest_train = data_utils.load_image_dataset(img_dim,
image_dim_ordering,
dset='vandal12classes')
elif dset == "washington_vandal12classesNoBackground":
X_source_train, Y_source_train, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='washington12classes')
X_dest_train, Y_dest_train, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='vandal12classesNoBackground')
elif dset == "Wash_Vand_12class_LMDB":
X_source_train, Y_source_train, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='Wash_12class_LMDB')
elif dset == "OfficeDslrToAmazon":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='OfficeDslr')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='OfficeAmazon')
elif dset == "bedrooms128":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='bedrooms128small')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='bedrooms128')
elif dset == "bedrooms":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='bedrooms_small')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='bedrooms')
elif dset == "Vand_Vand_12class_LMDB":
X_source_train, Y_source_train, X_source_test, Y_source_test, n_classes1 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='Vand_12class_LMDB_Background')
X_dest_train, Y_dest_train, X_dest_test, Y_dest_test, n_classes2 = data_utils.load_image_dataset(
img_dim, image_dim_ordering, dset='Vand_12class_LMDB')
else:
print "dataset not supported"
if n_classes1 != n_classes2: #sanity check
print "number of classes mismatch between source and dest domains"
n_classes = n_classes1 #
img_source_dim = X_source_train.shape[-3:] # is it backend agnostic?
img_dest_dim = X_dest_train.shape[-3:]
# Create optimizers
opt_D = data_utils.get_optimizer(opt_D, lr_D)
opt_G = data_utils.get_optimizer(opt_G, lr_G)
opt_C = data_utils.get_optimizer('SGD', 0.01)
opt_Z = data_utils.get_optimizer('Adam', lr_G)
#######################
# Load models
#######################
noise_dim = (noise_dim, )
generator_model = models.generator_deconv(noise_dim, img_source_dim,
img_dest_dim, bn_mode,
deterministic, pureGAN,
inject_noise, wd)
discriminator_model = models.discriminator_naive1(
img_dest_dim, wd, inject_noise, model_name="discriminator_naive1")
disc_penalty_model = models.disc_penalty(discriminator_model,
noise_dim,
img_source_dim,
opt_D,
model_name="disc_penalty_model")
DCGAN_model = models.DCGAN_naive(generator_model, discriminator_model,
noise_dim, img_source_dim)
#zclass_model = z_coerence(generator_model,img_source_dim, bn_mode,wd,inject_noise,n_classes,noise_dim, model_name="zClass")
############################
# Compile models
############################
generator_model.compile(loss='mse', optimizer=opt_G)
models.make_trainable(discriminator_model, False)
models.make_trainable(disc_penalty_model, False)
if model == 'wgan':
DCGAN_model.compile(loss=models.wasserstein, optimizer=opt_G)
models.make_trainable(discriminator_model, True)
models.make_trainable(disc_penalty_model, True)
discriminator_model.compile(loss=models.wasserstein, optimizer=opt_D)
if model == 'lsgan':
if simple_disc:
DCGAN_model.compile(loss=['mse'], optimizer=opt_G)
models.make_trainable(discriminator_model, True)
models.make_trainable(disc_penalty_model, True)
discriminator_model.compile(loss=['mse'], optimizer=opt_D)
#zclass_model.compile(loss=['mse'],optimizer = opt_Z)
elif monsterClass:
DCGAN_model.compile(loss=['categorical_crossentropy'],
optimizer=opt_G)
models.make_trainable(disc_penalty_model, True)
models.make_trainable(discriminator_model, True)
discriminator_model.compile(loss=['categorical_crossentropy'],
optimizer=opt_D)
else:
DCGAN_model.compile(loss=['mse', 'categorical_crossentropy'],
loss_weights=[1.0, 1.0],
optimizer=opt_G)
models.make_trainable(disc_penalty_model, True)
models.make_trainable(discriminator_model, True)
discriminator_model.compile(
loss=['mse', 'categorical_crossentropy'],
loss_weights=[1.0, 1.0],
optimizer=opt_D)
visualize = True
if resume: ########loading previous saved model weights
data_utils.load_model_weights(generator_model, discriminator_model,
DCGAN_model, name)
###############
####WEIGHTNORM DATA INIT:
###############
#####################
###train zclass
#############
X_gen = data_utils.sample_noise(noise_scale, X_source_train.shape[0],
noise_dim)
#data_based_init(generator_model, [X_gen,X_source_train])
# zclass_loss = zclass_model.fit([X_gen,X_source_train],[X_gen],batch_size=256,nb_epoch=10)
#####################
###classifier
#####################
if not ((dset == 'mnistM') or (dset == 'bedrooms') or
(dset == 'bedrooms128')):
classifier, GenToClassifierModel = classifier_build_test(
img_dest_dim,
n_classes,
generator_model,
noise_dim,
noise_scale,
img_source_dim,
opt_C,
X_source_test,
Y_source_test,
X_dest_test,
Y_dest_test,
wd=0.0001)
gen_iterations = 0
max_history_size = int(history_size * batch_size)
img_buffer = ImageHistoryBuffer((0, ) + img_source_dim, max_history_size,
batch_size, n_classes)
#################
# Start training
################
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
while batch_counter < n_batch_per_epoch:
if no_supertrain is None:
if (gen_iterations < 25) and (not resume):
disc_iterations = 50
if gen_iterations % 500 == 0:
disc_iterations = 50
else:
disc_iterations = kwargs["disc_iterations"]
else:
if (gen_iterations < 25) and (not resume):
disc_iterations = 50
else:
disc_iterations = kwargs["disc_iterations"]
###################################
# 1) Train the critic / discriminator
###################################
list_disc_loss_real = deque(4 * [0], 4)
list_disc_loss_gen = deque(4 * [0], 4)
list_gen_loss = deque(4 * [0], 4)
list_zclass_loss = []
list_gp_loss = deque(4 * [0], 4)
for disc_it in range(disc_iterations):
X_dest_batch, Y_dest_batch, idx_dest_batch = next(
data_utils.gen_batch(X_dest_train, Y_dest_train,
batch_size))
X_source_batch, Y_source_batch, idx_source_batch = next(
data_utils.gen_batch(X_source_train, Y_source_train,
batch_size))
# Create a batch to feed the discriminator model
X_disc_real, X_disc_gen = data_utils.get_disc_batch(
X_dest_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
X_source_batch,
noise_scale=noise_scale)
if model == 'wgan':
# Update the discriminator
current_labels_real = -np.ones(X_disc_real.shape[0])
current_labels_gen = np.ones(X_disc_gen.shape[0])
if model == 'lsgan':
if simple_disc: #for real domain I put [labels 0 0 0...0], for fake domain I put [0 0...0 labels]
current_labels_real = np.ones(X_disc_real.shape[0])
#current_labels_gen = -np.ones(X_disc_gen.shape[0])
current_labels_gen = np.zeros(X_disc_gen.shape[0])
elif monsterClass: #for real domain I put [labels 0 0 0...0], for fake domain I put [0 0...0 labels]
current_labels_real = np.concatenate(
(Y_dest_batch,
np.zeros((X_disc_real.shape[0], n_classes))),
axis=1)
current_labels_gen = np.concatenate((np.zeros(
(X_disc_real.shape[0], n_classes)),
Y_source_batch),
axis=1)
else:
current_labels_real = [
np.ones(X_disc_real.shape[0]), Y_dest_batch
]
Y_fake_batch = (1.0 / n_classes) * np.ones(
[X_disc_gen.shape[0], n_classes])
current_labels_gen = [
np.zeros(X_disc_gen.shape[0]), Y_fake_batch
]
#label smoothing
#current_labels_real = np.multiply(current_labels_real, lsmooth) #usually lsmooth = 0.7
disc_loss_real = discriminator_model.train_on_batch(
X_disc_real, current_labels_real)
img_buffer.add_to_buffer(X_disc_gen, current_labels_gen,
batch_size)
bufferImages, bufferLabels = img_buffer.get_from_buffer(
batch_size)
disc_loss_gen = discriminator_model.train_on_batch(
bufferImages, bufferLabels)
list_disc_loss_real.appendleft(disc_loss_real)
list_disc_loss_gen.appendleft(disc_loss_gen)
#############
####Train the discriminator w.r.t gradient penalty
#############
gp_loss = disc_penalty_model.train_on_batch(
[X_disc_real, X_disc_gen], current_labels_real
) #dummy labels,not used in the loss function
list_gp_loss.appendleft(gp_loss)
#######################
# 2) Train the generator
#######################
X_gen = data_utils.sample_noise(noise_scale, batch_size, noise_dim)
X_source_batch2, Y_source_batch2, idx_source_batch2 = next(
data_utils.gen_batch(X_source_train, Y_source_train,
batch_size))
#zclass_loss = zclass_model.train_on_batch([X_gen,X_source_batch2],[X_gen])
#list_zclass_loss.append(zclass_loss)
# w1 = classifier.get_weights() #FOR DEBUG
if model == 'wgan':
gen_loss = DCGAN_model.train_on_batch([X_gen, X_source_batch2],
-np.ones(X_gen.shape[0]))
if model == 'lsgan':
if simple_disc:
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2],
np.ones(X_gen.shape[0])) #TRYING SAME BATCH OF DISC?
elif monsterClass:
labels_gen = np.concatenate(
(Y_source_batch2,
np.zeros((X_disc_real.shape[0], n_classes))),
axis=1)
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2], labels_gen)
else:
gen_loss = DCGAN_model.train_on_batch(
[X_gen, X_source_batch2],
[np.ones(X_gen.shape[0]), Y_source_batch2])
# gen_loss2 = GenToClassifierModel.train_on_batch([X_gen,X_source_batch2], Y_source_batch2)
# w2 = classifier.get_weights() #FOR DEBUG
# for a,b in zip(w1, w2):
# if np.all(a == b):
# print "no bug in GEN model update"
# else:
# print "BUG IN GEN MODEL UPDATE"
list_gen_loss.appendleft(gen_loss)
gen_iterations += 1
batch_counter += 1
progbar.add(batch_size,
values=[("Loss_D", 0.5 * np.mean(list_disc_loss_real) +
0.5 * np.mean(list_disc_loss_gen)),
("Loss_D_real", np.mean(list_disc_loss_real)),
("Loss_D_gen", np.mean(list_disc_loss_gen)),
("Loss_G", np.mean(list_gen_loss)),
("Loss_gp", np.mean(list_gp_loss))])
# plot images 1 times per epoch
if batch_counter % (n_batch_per_epoch) == 0:
X_source_batch_plot, Y_source_batch_plot, idx_source_plot = next(
data_utils.gen_batch(X_source_test,
Y_source_test,
batch_size=32))
data_utils.plot_generated_batch(X_dest_test,
X_source_test,
generator_model,
noise_dim,
image_dim_ordering,
idx_source_plot,
batch_size=32)
if gen_iterations % (n_batch_per_epoch * 5) == 0:
if visualize:
BIG_ASS_VISUALIZATION_slerp(X_source_train[1],
generator_model, noise_dim)
print("Dest labels:")
print(Y_dest_test[idx_source_plot].argmax(1))
print("Source labels:")
print(Y_source_batch_plot.argmax(1))
print('\nEpoch %s/%s, Time: %s' %
(e + 1, nb_epoch, time.time() - start))
# Save model weights (by default, every 5 epochs)
data_utils.save_model_weights(generator_model, discriminator_model,
DCGAN_model, e, name)
# gen_model.load_weights("models/Gen/model_at_epoch_0.h5")
# disc_model.load_weights("models/Disc/model_at_epoch_0.h5")
mr_data = get_data(mr_path)
ct_data = get_data(ct_path)
Y_true_batch = np.ones((batch_size, 1), dtype="float32")
Y_fake_batch = np.zeros((batch_size, 1), dtype="float32")
y_gen = np.ones((batch_size, 1), dtype="float32")
zero_flow = np.zeros(
(batch_size, data_shape[0], data_shape[1], data_shape[2], 3),
dtype="float32")
for ep in range(epochs):
print("epochs:" + str(ep))
progbar = keras_generic_utils.Progbar(train_num)
for mini_batch in range(0, train_num, batch_size):
# -----------------------------------train discriminator-------------------------------------------
disc_model.trainable = True
idx_mr = np.random.choice(mr_data.shape[0],
batch_size,
replace=False)
mr_batch = mr_data[idx_mr]
ct_batch = ct_data[idx_mr]
src_t, flow, ct_gen = reg_gen_model.predict([mr_batch, ct_batch])
if random.randint(0, 1) == 0:
X_disc_batch = np.concatenate((ct_batch, ct_gen), axis=0)
Y_disc_batch = np.concatenate((Y_true_batch, Y_fake_batch),
axis=0)
else:
losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
start_time = time.time()
best_loss = np.Inf
class_mapping_inv = {v: k for k, v in class_mapping.items()}
print('Starting training')
# vis = True
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length) # keras progress bar 사용
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
while True:
# try:
# mean overlapping bboxes 출력
if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units', type=int, default=512)
parser.add_argument('-num_lstm_layers', type=int, default=2)
parser.add_argument('-dropout', type=float, default=0.2)
parser.add_argument('-activation', type=str, default='tanh')
args = parser.parse_args()
questions_train = open('../data/preprocessed/questions_train2014.txt',
'r').read().decode('utf8').splitlines()
questions_lengths_train = open(
'../data/preprocessed/questions_lengths_train2014.txt',
'r').read().decode('utf8').splitlines()
answers_train = open('../data/preprocessed/answers_train2014_modal.txt',
'r').read().decode('utf8').splitlines()
images_train = open('../data/preprocessed/images_train2014.txt',
'r').read().decode('utf8').splitlines()
max_answers = 1000
questions_train, answers_train, images_train = selectFrequentAnswers(
questions_train, answers_train, images_train, max_answers)
print 'Loaded questions, sorting by length...'
questions_lengths_train, questions_train, answers_train = (
list(t) for t in zip(*sorted(
zip(questions_lengths_train, questions_train, answers_train))))
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder, '../models/labelencoder.pkl')
max_len = 30 #25 is max for training, 27 is max for validation
word_vec_dim = 300
model = Sequential()
model.add(
LSTM(output_dim=args.num_hidden_units,
activation='tanh',
return_sequences=True,
input_shape=(max_len, word_vec_dim)))
model.add(Dropout(args.dropout))
model.add(LSTM(args.num_hidden_units, return_sequences=False))
model.add(Dense(nb_classes, init='uniform'))
model.add(Activation('softmax'))
json_string = model.to_json()
model_file_name = '../models/lstm_language_only_num_hidden_units_' + str(
args.num_hidden_units) + '_num_lstm_layers_' + str(
args.num_lstm_layers) + '_dropout_' + str(args.dropout)
open(model_file_name + '.json', 'w').write(json_string)
print 'Compiling model...'
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print 'Compilation done...'
#set up word vectors
nlp = English()
print 'loaded word2vec features...'
## training
print 'Training started...'
numEpochs = 100
model_save_interval = 5
batchSize = 128
for k in xrange(numEpochs):
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch, an_batch, im_batch in zip(
grouper(questions_train,
batchSize,
fillvalue=questions_train[0]),
grouper(answers_train, batchSize, fillvalue=answers_train[0]),
grouper(images_train, batchSize, fillvalue=images_train[0])):
timesteps = len(nlp(
qu_batch[-1])) #questions sorted in descending order of length
X_q_batch = get_questions_tensor_timeseries(
qu_batch, nlp, timesteps)
Y_batch = get_answers_matrix(an_batch, labelencoder)
loss = model.train_on_batch(X_q_batch, Y_batch)
progbar.add(batchSize, values=[("train loss", loss)])
if k % model_save_interval == 0:
model.save_weights(model_file_name +
'_epoch_{:02d}.hdf5'.format(k))
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k + 1))
img_dim=(256, 256, 3))
# DCGAN_model.load_weights('./models/pix2pix/DCGAN_weights_epoch_6.h5')
loss = [l1_loss, 'binary_crossentropy']
# loss = [perceptual_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss, loss_weights=loss_weights, optimizer=G_opt)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy', optimizer=D_opt)
# Start training
print("Start training")
for e in range(1, nb_epoch + 1):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
print('Epoch %s/%s' % (e, nb_epoch))
for b in range(1, n_batch_per_epoch + 1):
X_HR_batch, X_LR_batch = gen_batch(train_list, batch_size)
# Create a batch to feed the discriminator model
X_disc, y_disc = get_disc_batch(X_HR_batch, X_LR_batch,
generator_model, b)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen_target, X_gen = gen_batch(train_list, batch_size)
y_gen = np.zeros((X_gen.shape[0], 1), dtype=np.uint8)
y_gen[:, 0] = 1
def main():
start_time = time.time()
signal.signal(signal.SIGINT, InterruptHandler)
#signal.signal(signal.SIGKILL, InterruptHandler)
signal.signal(signal.SIGTERM, InterruptHandler)
parser = argparse.ArgumentParser(
prog='trainMLP.py',
description='Train MLP model for visual question answering')
parser.add_argument('--mlp-hidden-units',
type=int,
default=1024,
metavar='<mlp-hidden-units>')
parser.add_argument('--mlp-hidden-layers',
type=int,
default=3,
metavar='<mlp-hidden-layers>')
parser.add_argument('--dropout',
type=float,
default=0.5,
metavar='<dropout-rate>')
parser.add_argument('--mlp-activation',
type=str,
default='relu',
metavar='<activation-function>')
parser.add_argument('--num-epochs',
type=int,
default=100,
metavar='<num-epochs>')
parser.add_argument('--model-save-interval',
type=int,
default=5,
metavar='<interval>')
parser.add_argument('--batch-size',
type=int,
default=128,
metavar='<batch-size>')
args = parser.parse_args()
word_vec_dim = 300
img_dim = 4096
######################
# Load Data #
######################
print('Loading data...')
train_id_pairs, train_image_ids = LoadIds('train')
dev_id_pairs, dev_image_ids = LoadIds('dev')
train_choices = LoadChoices('train')
dev_choices = LoadChoices('dev')
train_answers = LoadAnswers('train')
dev_answers = LoadAnswers('dev')
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
print('-' * 100, file=sys.stderr)
print('Training Information', file=sys.stderr)
print('# of MLP hidden units: %i' % args.mlp_hidden_units, file=sys.stderr)
print('# of MLP hidden layers: %i' % args.mlp_hidden_layers,
file=sys.stderr)
print('Dropout: %f' % args.dropout, file=sys.stderr)
print('MLP activation function: %s' % args.mlp_activation, file=sys.stderr)
print('# of training epochs: %i' % args.num_epochs, file=sys.stderr)
print('Batch size: %i' % args.batch_size, file=sys.stderr)
print('# of train images: %i' % len(train_image_ids), file=sys.stderr)
print('# of dev images: %i' % len(dev_image_ids), file=sys.stderr)
print('-' * 100, file=sys.stderr)
######################
# Model Descriptions #
######################
# MLP model
model = Sequential()
model.add(Dense(output_dim=args.mlp_hidden_units, input_dim=img_dim))
model.add(Activation(args.mlp_activation))
model.add(Dropout(args.dropout))
for i in range(args.mlp_hidden_layers - 1):
model.add(Dense(args.mlp_hidden_units))
model.add(Activation(args.mlp_activation))
model.add(Dropout(args.dropout))
model.add(Dense(word_vec_dim))
model.add(Activation('softmax'))
json_string = model.to_json()
model_filename = 'models/mlp_units_%i_layers_%i' % (args.mlp_hidden_units,
args.mlp_hidden_layers)
open(model_filename + '.json', 'w').write(json_string)
# loss and optimizer
model.compile(loss='categorical_crossentropy', optimizer='adagrad')
print('Compilation finished.')
print('Time: %f s' % (time.time() - start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time() - start_time))
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# training batches
train_answer_batches = [
b for b in MakeBatches(train_answers['toks'],
args.batch_size,
fillvalue=train_answers['toks'][-1])
]
train_image_batches = [
b for b in MakeBatches(
train_image_ids, args.batch_size, fillvalue=train_image_ids[-1])
]
train_indices = list(range(len(train_image_ids)))
# validation batches
dev_answer_batches = [
b for b in MakeBatches(dev_answers['labs'],
args.batch_size,
fillvalue=dev_answers['labs'][-1])
]
dev_choice_batches = [
b for b in MakeBatches(
dev_choices, args.batch_size, fillvalue=dev_choices[-1])
]
dev_image_batches = [
b for b in MakeBatches(
dev_image_ids, args.batch_size, fillvalue=dev_image_ids[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Training #
######################
dev_accs = []
max_acc = -1
max_acc_epoch = -1
print('Training started...')
for k in range(args.num_epochs):
print('Epoch %i' % (k + 1), file=sys.stderr)
print('-' * 80)
print('Epoch %i' % (k + 1))
progbar = generic_utils.Progbar(len(train_indices) * args.batch_size)
# shuffle batch indices
random.shuffle(train_indices)
for i in train_indices:
X_image_batch = GetImagesMatrix(train_image_batches[i], img_map,
VGG_features)
Y_answer_batch = GetAnswersMatrix(train_answer_batches[i],
word_embedding, word_map)
loss = model.train_on_batch(X_image_batch, Y_answer_batch)
loss = loss[0].tolist()
progbar.add(args.batch_size, values=[('train loss', loss)])
if k % args.model_save_interval == 0:
model.save_weights(model_filename +
'_epoch_{:03d}.hdf5'.format(k + 1),
overwrite=True)
# evaluate on dev set
widgets = [
'Evaluating ',
Percentage(), ' ',
Bar(marker='#', left='[', right=']'), ' ',
ETA()
]
pbar = ProgressBar(widgets=widgets, redirect_stdout=True)
dev_correct = 0
for i in pbar(range(len(dev_image_batches))):
# feed forward
X_image_batch = GetImagesMatrix(dev_image_batches[i], img_map,
VGG_features)
prob = model.predict_proba(X_image_batch,
args.batch_size,
verbose=0)
# get word vecs of choices
choice_feats = GetChoicesTensor(dev_choice_batches[i],
word_embedding, word_map)
similarity = np.zeros((5, args.batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(
cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
dev_correct += np.count_nonzero(dev_answer_batches[i] == pred)
dev_acc = float(dev_correct) / len(dev_image_ids)
dev_accs.append(dev_acc)
print('Validation Accuracy: %f' % dev_acc)
print('Validation Accuracy: %f' % dev_acc, file=sys.stderr)
print('Time: %f s' % (time.time() - start_time))
print('Time: %f s' % (time.time() - start_time), file=sys.stderr)
if dev_acc > max_acc:
max_acc = dev_acc
max_acc_epoch = k
model.save_weights(model_filename + '_best.hdf5', overwrite=True)
model.save_weights(model_filename + '_epoch_{:03d}.hdf5'.format(k + 1))
print(dev_accs, file=sys.stderr)
print('Best validation accuracy: epoch#%i' % max_acc_epoch)
print('Training finished.')
print('Training finished.', file=sys.stderr)
print('Time: %f s' % (time.time() - start_time))
print('Time: %f s' % (time.time() - start_time), file=sys.stderr)
