def test_resnet152_caffe(self):
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
model = ResNet152_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir+'ResNet-152-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop='unique')
model.print_summary()
model = ResNet152_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir+'ResNet-152-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop=None,
offsets=None)
model.print_summary()
self.assertRaises(ValueError,
lambda:ResNet152_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir+'ResNet-152-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop='wrong_val'))
def test_resnet152_caffe(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 + 'ResNet-152-model.caffemodel.h5'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(self, "File, {}, not found.".format(file_dependency))
model = ResNet152_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir + 'ResNet-152-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop='unique')
model.print_summary()
model = ResNet152_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir + 'ResNet-152-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop=None,
offsets=None)
model.print_summary()
self.assertRaises(ValueError,
lambda: ResNet152_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir + 'ResNet-152-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop='wrong_val'))
# test random_mutation and crop on VDMML 8.4
model = ResNet152_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir + 'ResNet-152-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop='RESIZETHENCROP',
random_mutation='random',
offsets=None)
model.print_summary()
def test_resnet152_2(self):
from dlpy.applications import ResNet152_Caffe
model = ResNet152_Caffe(self.s)
model.print_summary()