def test_vgg16(self):
from dlpy.applications import VGG16
pd.set_option('display.max_columns', 40)
model = VGG16(self.s)
model.print_summary()
res = self.s.fetch(table=model.model_name, sortby='_dllayerid_')
print(res)
# test random_crop and mutation
model1 = VGG16(self.s,
model_table='VGG16',
n_classes=1000,
n_channels=3,
width=224,
height=224,
scale=1,
offsets=None,
random_crop='unique',
random_flip='hv',
random_mutation='random',
include_top=True)
model1.print_summary()
res1 = self.s.fetch(table=model1.model_name, sortby='_dllayerid_')
print(res1)
self.assertEqual(res1['Fetch'].iloc[10, 3], 4)
self.assertEqual(res1['Fetch'].iloc[4, 3], 2)
def test_vgg16_2(self):
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
model1 = VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=(103.939, 116.779, 123.68),
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir+'VGG_ILSVRC_16_layers.caffemodel.h5',
include_top=True)
model2 = VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=None,
random_crop=None,
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir+'VGG_ILSVRC_16_layers.caffemodel.h5',
include_top=True)
self.assertRaises(ValueError,
lambda:VGG16(self.s, model_table='VGG16',
n_classes=1000, n_channels=3, width=224, height=224, scale=1,
offsets=None,
random_crop='wrong_val',
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir+'VGG_ILSVRC_16_layers.caffemodel.h5',
include_top=True))
def initialize_network(s):
model_name = request.form.get('model', 'VGG16')
resize_x = request.form.get('resizeX', 224)
resize_y = request.form.get('resizeY', 224)
import sys
sys.path.append(app.config.get('DLPY_PATH'))
from dlpy.applications import VGG16
try:
with Capturing() as dlpy_out:
model1 = VGG16(conn=s,
model_table='VGG16',
n_classes=None,
n_channels=3,
width=224,
height=224)
resp = dict(messages=dlpy_out)
session['current_network'] = 'VGG16'
session['current_network_tbl'] = 'VGG16'
except Exception as ex:
print(str(ex))
resp = dict(messages=['Error initializing model'])
return jsonify(resp)
def test_vgg16_2(self):
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
file_dependency = self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(self, "File, {}, not found.".format(file_dependency))
model1 = VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=(103.939, 116.779, 123.68),
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5',
include_top=True)
model1.print_summary()
model2 = VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=None,
random_crop=None,
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5',
include_top=True)
model2.print_summary()
self.assertRaises(ValueError,
lambda: VGG16(self.s, model_table='VGG16',
n_classes=1000, n_channels=3, width=224, height=224, scale=1,
offsets=None,
random_crop='wrong_val',
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5',
include_top=True))
# test random_crop and mutation
model3 = VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=None,
random_crop='unique',
random_flip='hv',
random_mutation='random',
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5',
include_top=True)
model3.print_summary()
def test_vgg16(self):
from dlpy.applications import VGG16
model = VGG16(self.s)
model.print_summary()