def getModel(C):
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=(C.num_rois, 4))
base_model = VGG16(input_tensor = img_input, weights='imagenet', include_top=False)
# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn = nn.rpn(base_model.layers[-2].output, num_anchors)
b1_feat = base_model.get_layer('block1_pool').output
b2_feat = base_model.get_layer('block2_pool').output
b4_feat = base_model.get_layer('block4_pool').output
# Fuse features from 1st, 3rd and 4th conv blocks
# classifier output: ([face_pred,pose_pred, gender_pred, viz_pred, landmark_pred, regr_pred])
classifier = nn.classifier(b1_feat, b2_feat, b4_feat, roi_input, C.num_rois, nb_classes=len(classes_count), trainable=True)
model_classifier = Model([img_input,roi_input],classifier)
model_rpn = Model(base_model.input, rpn[:2])
feat_1_inp = Input(shape=None,None,64)
feat_2_inp = Input(shape=None,None,128)
feat_4_inp = Input(shape=None,None,512)
classifier_only = nn.classifier(feat_1_inp, feat_2_inp, feat_4_inp, roi_input, C.num_rois, nb_classes=len(classes_count), trainable=True)
model_classifier_only = Model([feat1_inp, feat_2_inp, feat_4_inp, roi_input ], classifier_only )
model_classifier_only = Model( [ b1_feat, b2_feat, b4_feat ] , classifier)
model_classifier = Model([img_input, roi_input], classifier )
classifier_only =
model_classifier_only = Model([img_input,b1_feat, b2_feat, b4_feat, roi_input ], classifier )
# define the base network (resnet here, can be VGG, Inception, etc)
#shared_layers = nn.nn_base(img_input, trainable=True)
base_model = VGG16(input_tensor=img_input,
weights='imagenet',
include_top=False)
# for layer in base_model.layers:
# layer.trainable = False
b1_feat = base_model.get_layer('block1_conv2').output
b2_feat = base_model.get_layer('block2_pool').output
b4_feat = base_model.get_layer('block4_pool').output
# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn = nn.rpn(b1_feat, b2_feat, b4_feat, num_anchors)
# Fuse features from 1st, 3rd and 4th conv blocks
classifier = nn.classifier(b1_feat,
b2_feat,
b4_feat,
roi_input,
C.num_rois,
nb_classes=len(classes_count),
trainable=True)
# classifier = [face_out,pose_out, gender_out, viz_out, landmark_out, regr_out]
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
# bp()
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(C.num_rois, 4))
# define the base network (resnet here, can be VGG, Inception, etc)
#shared_layers = nn.nn_base(img_input, trainable=True)
base_model = VGG16(input_tensor=img_input,
weights='imagenet',
include_top=False)
# base_model = nn.alexnet(input_tensor = img_input, trainable=True)
# for layer in base_model.layers:
# layer.trainable = False
# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn = nn.rpn(base_model.layers[-2].output, num_anchors)
b1_feat = base_model.get_layer('block1_pool').output
b2_feat = base_model.get_layer('block2_pool').output
b4_feat = base_model.get_layer('block4_pool').output
# Fuse features from 1st, 3rd and 4th conv blocks
classifier = nn.classifier(b1_feat,
b2_feat,
b4_feat,
roi_input,
C.num_rois,
nb_classes=2,
trainable=True)
# classifier = [face_out,pose_out, gender_out, viz_out, landmark_out]
