def test_object_table_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 + 'YOLOV2_MULTISIZE.sashdat'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(
self, "File, {}, not found.".format(file_dependency))
file_dependency = self.data_dir + 'YoloV2_Multisize_weights.sashdat'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(
self, "File, {}, not found.".format(file_dependency))
img_path = self.data_dir + 'imageCaptioning_images'
image_table = ImageTable.load_files(self.s, path=img_path)
image_table.resize(width=416)
word_embeddings = self.data_dir + 'no_file.txt'
detection_model = Model(self.s)
detection_model.load(self.data_dir + 'YOLOV2_MULTISIZE.sashdat')
detection_model.load_weights(self.data_dir +
'YoloV2_Multisize_weights.sashdat')
self.assertRaises(
DLPyError, lambda: create_embeddings_from_object_detection(
self.s, image_table, detection_model, word_embeddings))
def test_object_table_3(self):
if self.data_dir_local is None:
unittest.TestCase.skipTest(
self,
"DLPY_DATA_DIR_LOCAL is not set in the environment variables")
file_dependency = self.data_dir + 'YOLOV2_MULTISIZE.sashdat'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(
self, "File, {}, not found.".format(file_dependency))
file_dependency = self.data_dir + 'YoloV2_Multisize_weights.sashdat'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(
self, "File, {}, not found.".format(file_dependency))
captions_file = self.data_dir_local + 'image_captions.txt'
image_table = create_captions_table(self.s, captions_file)
word_embeddings = self.data_dir + 'word_embeddings.txt'
detection_model = Model(self.s)
detection_model.load(self.data_dir + 'YOLOV2_MULTISIZE.sashdat')
detection_model.load_weights(self.data_dir +
'YoloV2_Multisize_weights.sashdat')
self.assertRaises(
DLPyError, lambda: create_embeddings_from_object_detection(
self.s, image_table, detection_model, word_embeddings))
def test_captioning_table_1(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))
if self.data_dir_local is None:
unittest.TestCase.skipTest(
self,
"DLPY_DATA_DIR_LOCAL is not set in the environment variables")
img_path = self.data_dir + 'imageCaptioning_images'
image_table = ImageTable.load_files(self.s, path=img_path)
image_table.resize(width=224)
captions_file = self.data_dir_local + 'image_captions.txt'
features_model = VGG16(self.s,
width=224,
height=224,
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir +
'VGG_ILSVRC_16_layers.caffemodel.h5')
self.assertTrue(
create_captioning_table(self.s, image_table, features_model,
captions_file) is not None)
def test_resnet50_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-50-model.caffemodel.h5'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(self, "File, {}, not found.".format(file_dependency))
model = ResNet50_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir + 'ResNet-50-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop='unique')
model.print_summary()
model = ResNet50_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir + 'ResNet-50-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop=None,
offsets=None)
model.print_summary()
# test random_mutation and crop on VDMML 8.4
model = ResNet50_Caffe(self.s, n_channels=3, height=224, random_flip='HV',
pre_trained_weights_file=self.data_dir + 'ResNet-50-model.caffemodel.h5',
pre_trained_weights=True,
include_top=False,
n_classes=120,
random_crop='RESIZETHENCROP',
random_mutation='random',
offsets=None)
def test_inceptionv3_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 + 'InceptionV3_weights.kerasmodel.h5'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(self, "File, {}, not found.".format(file_dependency))
from dlpy.applications import InceptionV3
model = InceptionV3(self.s,
model_table='INCEPTIONV3',
n_classes=1000,
n_channels=3,
width=299,
height=299,
scale=1,
offsets=(103.939, 116.779, 123.68),
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'InceptionV3_weights.kerasmodel.h5',
include_top=True)
model.print_summary()
# test random_crop and mutation
model2 = InceptionV3(self.s, model_table='VGG16', n_classes=1000, n_channels=3,
width=299, height=299, scale=1,
offsets=None,
random_crop='unique',
random_flip='hv',
random_mutation='random',
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'InceptionV3_weights.kerasmodel.h5',
include_top=True)
model2.print_summary()
def test_resnet50_3(self):
from dlpy.applications import ResNet50_Caffe
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-50-model.caffemodel.h5'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(self, "File, {}, not found.".format(file_dependency))
reshape = Reshape(width=224, height=224, depth=3, order='WHD')
model = ResNet50_Caffe(self.s, reshape_after_input=reshape)
model.print_summary()
# test it with pretrained weights
model1 = ResNet50_Caffe(self.s, model_table='Resnet50', 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 + 'ResNet-50-model.caffemodel.h5',
include_top=True,
reshape_after_input=reshape)
res = model1.print_summary()
print(res)
self.assertEqual(res.iloc[1, 6][0], 224)
self.assertEqual(res.iloc[1, 6][1], 224)
self.assertEqual(res.iloc[1, 6][2], 3)
def test_captioning_table_3(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))
file_dependency = self.data_dir + 'YOLOV2_MULTISIZE.sashdat'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(
self, "File, {}, not found.".format(file_dependency))
file_dependency = self.data_dir + 'YoloV2_Multisize_weights.sashdat'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(
self, "File, {}, not found.".format(file_dependency))
if self.data_dir_local is None:
unittest.TestCase.skipTest(
self,
"DLPY_DATA_DIR_LOCAL is not set in the environment variables")
img_path = self.data_dir + 'imageCaptioning_images'
image_table = ImageTable.load_files(self.s, path=img_path)
image_table.resize(width=224)
captions_file = self.data_dir_local + 'image_captions.txt'
features_model = VGG16(self.s,
width=224,
height=224,
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir +
'VGG_ILSVRC_16_layers.caffemodel.h5')
detection_model = Model(self.s)
detection_model.load(self.data_dir + 'YOLOV2_MULTISIZE.sashdat')
detection_model.load_weights(self.data_dir +
'YoloV2_Multisize_weights.sashdat')
word_embeddings = self.data_dir_local + 'word_embeddings.txt'
self.assertTrue(
create_captioning_table(self.s,
image_table,
features_model,
captions_file,
obj_detect_model=detection_model,
word_embeddings_file=word_embeddings)
is not None)
def test_densenet121_onnx_2(self):
from dlpy.applications import DenseNet121_ONNX
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 + 'DenseNet121_ONNX.sashdat'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(self, "File, {}, not found.".format(file_dependency))
model = DenseNet121_ONNX(self.s,
model_file=file_dependency,
width=224, height=224,
n_classes=1000, include_top=True)
model.print_summary()
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_heat_map_analysis(self):
if self.data_dir is None:
unittest.TestCase.skipTest(self, 'DLPY_DATA_DIR is not set in the environment variables')
if not file_exist_on_server(self.s, self.data_dir + 'ResNet-50-model.caffemodel.h5'):
unittest.TestCase.skipTest(self, "File, {}, not found.".format(self.data_dir
+ 'ResNet-50-model.caffemodel.h5'))
from dlpy.applications import ResNet50_Caffe
from dlpy.images import ImageTable
pre_train_weight_file = os.path.join(self.data_dir, 'ResNet-50-model.caffemodel.h5')
my_im = ImageTable.load_files(self.s, self.data_dir+'giraffe_dolphin_small')
my_im_r = my_im.resize(width=224, inplace=False)
model = ResNet50_Caffe(self.s, model_table='ResNet50_Caffe',
n_classes=2, n_channels=3, width=224, height=224, scale=1,
random_flip='none', random_crop='none',
offsets=my_im_r.channel_means, pre_trained_weights=True,
pre_trained_weights_file=pre_train_weight_file,
include_top=False)
model.fit(data=my_im_r, mini_batch_size=1, max_epochs=1)
model.heat_map_analysis(data=my_im_r, mask_width=None, mask_height=None, step_size=None,
max_display=1)
self.assertRaises(ValueError, lambda:model.heat_map_analysis(mask_width=56, mask_height=56,
step_size=8, display=False))
self.assertRaises(ValueError, lambda:model.heat_map_analysis(data=my_im, mask_width=56,
mask_height=56, step_size=8, display=False))
try:
from numpy import array
except:
unittest.TestCase.skipTest(self, 'numpy is not installed')
self.assertRaises(ValueError, lambda:model.heat_map_analysis(data=array([]), mask_width=56,
mask_height=56, step_size=8, display=False))
