def get_model(name, in_shape, n_classes, backend='resnet34'):
if name == 'fpn':
return FPN(backbone_name=backend,
input_shape=in_shape,
classes=n_classes,
encoder_weights=None)
if name == 'unet':
return Unet(backbone_name=backend,
input_shape=in_shape,
classes=n_classes,
encoder_weights=None)
if name == 'pspnet':
return PSPNet50(input_shape=in_shape, n_labels=n_classes)
if name == 'deeplab':
return Deeplabv3(input_shape=in_shape, classes=n_classes, weights=None)
if name == 'biard':
return biard_net(in_shape=in_shape, n_classes=n_classes)
raise ValueError("Unknown model name")
# summarize history for loss
matplotlib.pyplot.subplot(212)
matplotlib.pyplot.plot(history.history['loss'])
matplotlib.pyplot.plot(history.history['val_loss'])
matplotlib.pyplot.title('model loss')
matplotlib.pyplot.ylabel('loss')
matplotlib.pyplot.xlabel('epoch')
matplotlib.pyplot.legend(['train', 'test'], loc='upper left')
fig.savefig('depth.png', dpi=fig.dpi)
# Load dataset
pspnet_ini = PSPNet50(nb_classes=150,
input_shape=(640, 480),
weights='pspnet50_ade20k')
pspnet_ini.model.layers.pop()
layer_lambda = pspnet_ini.model.layers.pop()
pspnet_ini.model.layers.pop()
print(layer_lambda.get_config())
'''
layer_lambda = pspnet_ini.model.layers.pop()
layer_lambda.get_config()
'''
tf_resize = Input(shape=(640, 480))
kernel_size = (1, 1)
new_layer = Conv2D(256, (1, 1),
strides=(1, 1),
model = basename(dirname(args.checkpoint))
version = basename(args.checkpoint).split('-')[0]
root_result = "predictions/{}/{}/{}".format(model, version, args.scale)
print "Outputting to ", root_result
root_mask = os.path.join(root_result, 'category_mask')
root_prob = os.path.join(root_result, 'prob_mask')
root_maxprob = os.path.join(root_result, 'max_prob')
root_allprob = os.path.join(root_result, 'all_prob')
sess = tf.Session()
K.set_session(sess)
with sess.as_default():
print(args)
pspnet = PSPNet50(checkpoint=args.checkpoint)
for im in im_list:
print im
fn_maxprob = os.path.join(root_maxprob, im.replace('.jpg', '.h5'))
fn_mask = os.path.join(root_mask, im.replace('.jpg', '.png'))
fn_prob = os.path.join(root_prob, im)
fn_allprob = os.path.join(root_allprob, im.replace('.jpg', '.h5'))
if os.path.exists(fn_allprob):
print "Already done."
continue
# make paths if not exist
if not os.path.exists(dirname(fn_maxprob)):
elif args.scale == "medium":
maxside = 1024
elif args.scale == "big":
maxside = 2048
else:
maxside = None
project = "ade20k"
config = utils.get_config(project)
im_list = utils.open_im_list(config["im_list"])
datasource = DataSource(config)
data_generator = DataGenerator(im_list, datasource, maxside=maxside)
# Load checkpoint
checkpoint_dir = join("weights", "checkpoints", args.name)
if not isdir(checkpoint_dir):
makedirs(checkpoint_dir)
checkpoint, epoch = (None, 0)
if args.resume:
checkpoint, epoch = utils.get_latest_checkpoint(checkpoint_dir)
sess = tf.Session()
K.set_session(sess)
with sess.as_default():
print(args)
pspnet = PSPNet50(activation=args.activation, checkpoint=checkpoint)
train(pspnet, data_generator, checkpoint_dir, initial_epoch=epoch)
# EVALUATION_SCALES = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] # must be all floats!
# EVALUATION_SCALES = [0.15, 0.25, 0.5] # must be all floats!
sliding = False
flip = False
from ade20k_labels import labels
id = {label.id for label in labels if label.name == 'ceiling'}
#todo get label and idddddd
img = misc.imread('PSPNet-Keras-tensorflow-master/example_images/ade20k2.jpg')
#create the neural network
pspnet = PSPNet50(nb_classes=150,
input_shape=(473, 473),
weights='pspnet50_ade20k')
#Run over input data
class_scores = predict_multi_scale(img, pspnet, EVALUATION_SCALES, sliding,
flip)
print("Writing results...")
class_image = np.argmax(class_scores,
axis=2) #Get best classes (highest score!!)
pm = np.max(class_scores, axis=2)
colored_class_image = utils.color_class_image(class_image, 'ade20k')
# colored_class_image is [0.0-1.0] img is [0-255]
alpha_blended = 0.5 * colored_class_image + 0.5 * img