def basic_model_properties(self, cf, variable_input_size):
# Define the input size, loss and metrics
if cf.dataset.class_mode == 'categorical':
if K.image_dim_ordering() == 'th':
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels)
loss = 'categorical_crossentropy'
metrics = ['accuracy']
elif cf.dataset.class_mode == 'detection':
# Check model, different detection nets may have different losses and metrics
if cf.model_name in ['yolo', 'tiny-yolo']:
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors)
metrics = [YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)]
elif cf.model_name == 'ssd300':
in_shape = (cf.target_size_train[0],
cf.target_size_train[1], cf.dataset.n_channels)
loss = MultiboxLoss(cf.dataset.n_classes, neg_pos_ratio=2.0).compute_loss
metrics = None
# TODO: Add metrics for SSD
# priors = pickle.load(open('prior_boxes_ssd300.pkl', 'rb'))
# metrics = [SSDMetrics(priors, cf.dataset.n_classes)]
else:
raise NotImplementedError
elif cf.dataset.class_mode == 'segmentation':
if K.image_dim_ordering() == 'th':
if variable_input_size:
in_shape = (cf.dataset.n_channels,
None,
None)
else:
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
else:
if variable_input_size:
in_shape = (None,
None,
cf.dataset.n_channels)
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels)
loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
else:
raise ValueError('Unknown problem type')
return in_shape, loss, metrics
def basic_model_properties(self, cf, variable_input_size):
# Define the input size, loss and metrics
if cf.dataset.class_mode == 'categorical':
if K.image_dim_ordering() == 'th':
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
else:
in_shape = (cf.target_size_train[0], cf.target_size_train[1],
cf.dataset.n_channels)
loss = 'categorical_crossentropy'
metrics = ['accuracy']
elif cf.dataset.class_mode == 'detection':
if cf.model_name in ['yolo', 'tiny-yolo']:
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
loss = YOLOLoss(in_shape, cf.dataset.n_classes,
cf.dataset.priors)
metrics = [
YOLOMetrics(in_shape, cf.dataset.n_classes,
cf.dataset.priors)
]
elif cf.model_name in [
'ssd300', 'ssd300_pretrained', 'ssd_resnet50'
]:
# TODO: in_shape ok for ssd?
in_shape = (cf.target_size_train[0], cf.target_size_train[1],
cf.dataset.n_channels)
# TODO: extract config parameters from MultiboxLoss
mboxloss = MultiboxLoss(cf.dataset.n_classes + 1,
alpha=1.0,
neg_pos_ratio=2.0,
background_label_id=0,
negatives_for_hard=100.0)
loss = mboxloss.compute_loss
metrics = [] # TODO: add mAP metric
elif cf.dataset.class_mode == 'segmentation':
if K.image_dim_ordering() == 'th':
if variable_input_size:
in_shape = (cf.dataset.n_channels, None, None)
else:
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
else:
if variable_input_size:
in_shape = (None, None, cf.dataset.n_channels)
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1], cf.dataset.n_channels)
loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
else:
raise ValueError('Unknown problem type')
return in_shape, loss, metrics
def basic_model_properties(self, cf, variable_input_size):
# Define the input size, loss and metrics
if cf.dataset.class_mode == 'categorical':
if K.image_dim_ordering() == 'th':
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
else:
in_shape = (cf.target_size_train[0], cf.target_size_train[1],
cf.dataset.n_channels)
loss = 'categorical_crossentropy'
metrics = ['accuracy']
elif cf.dataset.class_mode == 'detection':
if cf.model_name in ['yolo', 'tiny-yolo']:
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
loss = YOLOLoss(in_shape, cf.dataset.n_classes,
cf.dataset.priors)
metrics = [
YOLOMetrics(in_shape, cf.dataset.n_classes,
cf.dataset.priors)
]
elif cf.model_name == 'ssd':
if K.image_dim_ordering() == 'th':
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1], cf.dataset.n_channels)
loss = SSDLoss(in_shape, cf.dataset.n_classes + 1,
cf.dataset.priors) #+1 to include background
#metrics = [SSDMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)]
metrics = []
else:
raise ValueError('Unknown model')
elif cf.dataset.class_mode == 'segmentation':
if K.image_dim_ordering() == 'th':
if variable_input_size:
in_shape = (cf.dataset.n_channels, None, None)
else:
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
else:
if variable_input_size:
in_shape = (None, None, cf.dataset.n_channels)
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1], cf.dataset.n_channels)
loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
else:
raise ValueError('Unknown problem type')
return in_shape, loss, metrics
def basic_model_properties(self, cf, variable_input_size):
# Define the input size, loss and metrics
if cf.dataset.class_mode == 'categorical':
if K.image_dim_ordering() == 'th':
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels)
loss = 'categorical_crossentropy'
metrics = ['accuracy']
elif cf.dataset.class_mode == 'detection':
if "yolo" in cf.model_name:
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
# TODO detection : check model, different detection nets may have different losses and metrics
loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors)
metrics = [YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)]
elif "ssd" in cf.model_name:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels,)
loss = SSDLoss(cf.dataset.n_classes)
metrics = [SSDMetrics()]
elif cf.dataset.class_mode == 'segmentation':
if K.image_dim_ordering() == 'th':
if variable_input_size:
in_shape = (cf.dataset.n_channels, None, None)
else:
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
else:
if variable_input_size:
in_shape = (None, None, cf.dataset.n_channels)
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels)
loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
else:
raise ValueError('Unknown problem type')
return in_shape, loss, metrics
def basic_model_properties(self, cf, variable_input_size):
# Define the input size, loss and metrics
if cf.dataset.class_mode == 'categorical':
if K.image_dim_ordering() == 'th':
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels)
loss = 'categorical_crossentropy'
metrics = ['accuracy']
elif cf.dataset.class_mode == 'detection':
if cf.model_name == 'ssd':
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels,)
loss = MultiboxLoss(cf.dataset.n_classes, neg_pos_ratio=2.0).compute_loss
metrics = None
else: # YOLO
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors)
metrics = [YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)]
elif cf.dataset.class_mode == 'segmentation':
if K.image_dim_ordering() == 'th':
if variable_input_size:
in_shape = (cf.dataset.n_channels, None, None)
else:
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
else:
if variable_input_size:
in_shape = (None, None, cf.dataset.n_channels)
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels)
loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
else:
raise ValueError('Unknown problem type')
return in_shape, loss, metrics
def make_segmentor(self):
segmentor = build_segnet(self.img_shape,
self.n_classes,
l2_reg=0.,
init='glorot_uniform',
path_weights=None,
freeze_layers_from=None,
use_unpool=False,
basic=False)
lr = 1e-04
optimizer = RMSprop(lr=lr, rho=0.9, epsilon=1e-8, clipnorm=10)
print(
' Optimizer segmentor: rmsprop. Lr: {}. Rho: 0.9, epsilon=1e-8, '
'clipnorm=10'.format(lr))
the_loss = cce_flatt(self.cf.dataset.void_class,
self.cf.dataset.cb_weights)
metrics = [IoU(self.cf.dataset.n_classes, self.cf.dataset.void_class)]
segmentor.compile(loss=the_loss, metrics=metrics, optimizer=optimizer)
return segmentor
x_test -= x_train_mean
x_train /= (x_train_std + 1e-7)
x_test /= (x_train_std + 1e-7)
# plot data
if(not args.nodisplay):
for idx in range(25):
plt.subplot(5,10,2*idx+1)
plt.imshow(x_train[idx,:,:,0])
plt.subplot(5,10,2*idx+2)
plt.imshow(y_train[idx,:,:,0])
plt.show()
if(not args.nomodel):
loss = cce_flatt(void_class, None)
metrics = [IoU(n_classes, void_class)]
#opt = RMSprop(lr=0.001, clipnorm=10)
opt = Nadam(lr=0.002)
model = build_fcn8(in_shape, n_classes, 0.)
model.compile(loss=loss, metrics=metrics, optimizer=opt)
cb = [EarlyStopping(monitor='val_loss', min_delta = 0.0001, patience=2)]
model.fit(x_train, y_train, epochs=1000, batch_size=16, callbacks=cb, validation_data=(x_valid,y_valid))
score = model.evaluate(x_test, y_test) #, batch_size=128)
y_pred = model.predict(x_test)
print(score)
for sample in range(y_test.shape[0]):
def basic_model_properties(self, cf, variable_input_size):
# Define the input size, loss and metrics
if cf.dataset.class_mode == 'categorical':
if K.image_dim_ordering() == 'th':
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels)
loss = 'categorical_crossentropy'
metrics = ['accuracy']
elif cf.dataset.class_mode == 'detection':
if 'yolo' in cf.model_name:
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors)
metrics = [YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)]
elif cf.model_name == 'ssd':
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels)
loss = MultiboxLoss(cf.dataset.n_classes, neg_pos_ratio=2.0).compute_loss
metrics = [] # TODO: There is no metrics for the ssd model
else:
raise ValueError('Uknown "' + cf.model_name + '" name for the ' + cf.dataset.class_mode + ' problem type.'
'Only is implemented for: {yolo, tiny-yolo, ssd}')
elif cf.dataset.class_mode == 'segmentation':
if K.image_dim_ordering() == 'th':
if variable_input_size:
in_shape = (cf.dataset.n_channels, None, None)
else:
in_shape = (cf.dataset.n_channels,
cf.target_size_train[0],
cf.target_size_train[1])
else:
if variable_input_size:
in_shape = (None, None, cf.dataset.n_channels)
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1],
cf.dataset.n_channels)
loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
# if cf.model_name == 'fcn8':
# loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
# metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
#
# elif 'segnet' in cf.model_name:
# loss = 'categorical_crossentropy'
# metrics = []
#
# else:
# raise ValueError('Uknown "'+cf.model_name+'" name for the '+cf.dataset.class_mode+' problem type.'
# 'Only is implemented for: {fc8, segnet}')
else:
raise ValueError('Unknown problem type')
return in_shape, loss, metrics
