def __init__(self, config_file, model_name):
self.configuration = conf.ConfigurationFile(config_file, model_name)
#loading data
mean_file = os.path.join(self.configuration.get_data_dir(), "mean.dat")
shape_file = os.path.join(self.configuration.get_data_dir(),
"shape.dat")
#
self.input_shape = np.fromfile(shape_file, dtype=np.int32)
self.mean_image = np.fromfile(mean_file, dtype=np.float32)
self.mean_image = np.reshape(self.mean_image, self.input_shape)
#loading classifier model
# self.model = resnet.ResNetFeatureVector([3,4,6,3],[64,128,256,512], se_factor = 0)
input_image = tf.keras.Input(
(self.input_shape[0], self.input_shape[1], self.input_shape[2]),
name='input_image')
# self.model(input_image)
# self.model.summary()
# self.model.load_weights(self.configuration.get_checkpoint_file(), by_name = True, skip_mismatch = True)
#color model
model = resnet.ResNetFeatureVector([3, 4, 6, 3], [64, 128, 256, 512],
se_factor=0)
model(input_image)
model.summary()
model.load_weights(self.configuration.get_checkpoint_file(),
by_name=True,
skip_mismatch=True)
#create the sim-model with a customized layer
output1 = model.output
output2 = model.get_layer('backbone').get_layer('block_1').output
output2 = tf.keras.layers.GlobalAveragePooling2D()(output2)
output = tf.concat([output1, output2], axis=1)
self.model_color = tf.keras.Model(model.input, output)
self.model_color.summary()
self.model_color.save('color_model')
print('model was loaded OK')
#defining process arch
self.process_fun = imgproc.process_image
#loading catalog
self.ssearch_dir = os.path.join(self.configuration.get_search_dir(),
'ssearch')
catalog_file = os.path.join(self.ssearch_dir, 'catalog.txt')
assert os.path.exists(catalog_file), '{} does not exist'.format(
catalog_file)
print('loading catalog ...')
self.load_catalog(catalog_file)
print('loading catalog ok ...')
sys.stdout.flush()
self.enable_search = False
def __init__(self, config_file, model_name):
self.configuration = conf.ConfigurationFile(config_file, model_name)
#loading data
mean_file = os.path.join(self.configuration.get_data_dir(), "mean.dat")
shape_file = os.path.join(self.configuration.get_data_dir(),
"shape.dat")
#
self.input_shape = np.fromfile(shape_file, dtype=np.int32)
self.mean_image = np.fromfile(mean_file, dtype=np.float32)
self.mean_image = np.reshape(self.mean_image, self.input_shape)
#loading classifier model
model = resnet.ResNet([3, 4, 6, 3], [64, 128, 256, 512],
self.configuration.get_number_of_classes(),
se_factor=0)
input_image = tf.keras.Input(
(self.input_shape[0], self.input_shape[1], self.input_shape[2]),
name='input_image')
model(input_image)
model.summary()
model.load_weights(self.configuration.get_checkpoint_file(),
by_name=True,
skip_mismatch=True)
#create the sim-model with a customized layer
#you can change output_layer_name
output_layer_name = 'global_average_pooling2d'
output = model.get_layer(output_layer_name).output
self.sim_model = tf.keras.Model(model.input, output)
self.sim_model.summary()
print('sim_model was loaded OK')
#defining process arch
self.process_fun = imgproc.process_image
#loading catalog
self.ssearch_dir = os.path.join(self.configuration.get_data_dir(),
'ssearch')
catalog_file = os.path.join(self.ssearch_dir, 'catalog.txt')
assert os.path.exists(catalog_file), '{} does not exist'.format(
catalog_file)
print('loading catalog ...')
self.load_catalog(catalog_file)
print('loading catalog ok ...')
self.enable_search = False
img_array,
(width, height)) # resize image to match model's expected sizing
res = new_array.reshape(-1, height, width, 3)
res = res - mean_image
return res # return the image with shaping that TF wants.
disable_eager_execution()
model = tf.keras.models.load_model(
'C:/Users/andyb/PycharmProjects/kerasResnet/resnet50.model', compile=True)
configuration_file = "configs/cnn.config"
configuration = conf.ConfigurationFile(configuration_file, "RES")
shape_file = os.path.join(configuration.get_data_dir(), "shape.dat")
mean_file = os.path.join(configuration.get_data_dir(), "mean.dat")
input_shape = np.fromfile(shape_file, dtype=np.int32)
mean_image = np.fromfile(mean_file, dtype=np.float32)
mean_image = np.reshape(mean_image, input_shape)
img_name = "4090"
img_path = "C:/Users/andyb/PycharmProjects/kerasResnet/images390p/" + img_name + ".jpg"
img = image.load_img(img_path, target_size=(input_shape[0], input_shape[1]))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
import sys
sys.path.append("/data/practica/otra/")
import tensorflow as tf
from models import resnet
import utils.configuration as conf
import numpy as np
import argparse
if __name__ == '__main__' :
parser = argparse.ArgumentParser(description = "Train a simple mnist model")
parser.add_argument("-config", type = str, help = "<str> configuration file", required = True)
parser.add_argument("-name", type=str, help=" name of section in the configuration file", required = True)
parser.add_argument("-image_type", type=str, help=" sketch or photo", choices =['sketch', 'image'], required = True)
pargs = parser.parse_args()
configuration_file = pargs.config
configuration = conf.ConfigurationFile(configuration_file, pargs.name)
number_of_classes = configuration.get_number_of_classes()
shape_file = os.path.join(configuration.get_data_dir(),'sketches', 'shape.dat')
input_shape = np.fromfile(shape_file, dtype=np.int32)
if pargs.image_type == 'sketch' :
model = resnet.SiameseNetSketch([3,4,6,3],[64,128,256,512], False, se_factor = 0)
else:
model = resnet.SiameseNetImage([3,4,6,3],[64,128,256,512], False, se_factor = 0)
input_image = tf.keras.Input((input_shape[0], input_shape[1], input_shape[2]), name = 'input_' + pargs.image_type)
model(input_image)
model.summary()
assert configuration.use_checkpoint()
model.load_weights(configuration.get_checkpoint_file(), by_name = True, skip_mismatch = True)
