def test_detection_demo(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU') # it runs very slow in CPU
try_set_default_device(cntk_device(device_id))
from prepare_test_data import prepare_Grocery_data, prepare_alexnet_v0_model
grocery_path = prepare_Grocery_data()
prepare_alexnet_v0_model()
from FastRCNN.install_data_and_model import create_grocery_mappings
create_grocery_mappings(grocery_path)
from DetectionDemo import get_configuration
import utils.od_utils as od
cfg = get_configuration('FasterRCNN')
cfg["CNTK"].FORCE_DETERMINISTIC = True
cfg["CNTK"].DEBUG_OUTPUT = False
cfg["CNTK"].MAKE_MODE = False
cfg["CNTK"].FAST_MODE = False
cfg.CNTK.E2E_MAX_EPOCHS = 3
cfg.CNTK.RPN_EPOCHS = 2
cfg.CNTK.FRCN_EPOCHS = 2
cfg.IMAGE_WIDTH = 400
cfg.IMAGE_HEIGHT = 400
cfg["CNTK"].TRAIN_E2E = True
cfg.USE_GPU_NMS = False
cfg.VISUALIZE_RESULTS = False
cfg["DATA"].MAP_FILE_PATH = grocery_path
externalData = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' in os.environ
if externalData:
extPath = os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY']
cfg['BASE_MODEL_PATH'] = os.path.join(extPath, "PreTrainedModels",
"AlexNet", "v1",
"AlexNet_ImageNet_Caffe.model")
else:
cfg['BASE_MODEL_PATH'] = os.path.join(
abs_path,
*"../../../../PretrainedModels/AlexNet_ImageNet_Caffe.model".split(
"/"))
# train and test
eval_model = od.train_object_detector(cfg)
eval_results = od.evaluate_test_set(eval_model, cfg)
meanAP = np.nanmean(list(eval_results.values()))
print('meanAP={}'.format(meanAP))
assert meanAP > 0.01
# detect objects in single image
img_path = os.path.join(grocery_path, "testImages",
"WIN_20160803_11_28_42_Pro.jpg")
regressed_rois, cls_probs = od.evaluate_single_image(
eval_model, img_path, cfg)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs, cfg)
assert bboxes.shape[0] == labels.shape[0]
def test_fastrcnn_with_config_file(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU') # it runs very slow in CPU
try_set_default_device(cntk_device(device_id))
from A1_GenerateInputROIs import generate_input_rois
assert generate_input_rois(testing=True)
prepare_alexnet_v0_model()
from A2_RunWithBSModel import run_fastrcnn_with_config_file
assert run_fastrcnn_with_config_file(os.environ["TEST_CNTK_BINARY"])
def test_fastrcnn_with_config_file(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU') # it runs very slow in CPU
try_set_default_device(cntk_device(device_id))
from A1_GenerateInputROIs import generate_input_rois
assert generate_input_rois(testing=True)
prepare_alexnet_v0_model()
from A2_RunWithBSModel import run_fastrcnn_with_config_file
assert run_fastrcnn_with_config_file(os.environ["TEST_CNTK_BINARY"])
def test_detection_demo(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU') # it runs very slow in CPU
try_set_default_device(cntk_device(device_id))
from prepare_test_data import prepare_Grocery_data, prepare_alexnet_v0_model
grocery_path = prepare_Grocery_data()
prepare_alexnet_v0_model()
from FastRCNN.install_data_and_model import create_grocery_mappings
create_grocery_mappings(grocery_path)
from DetectionDemo import get_configuration
import utils.od_utils as od
cfg = get_configuration('FasterRCNN')
cfg["CNTK"].FORCE_DETERMINISTIC = True
cfg["CNTK"].DEBUG_OUTPUT = False
cfg["CNTK"].MAKE_MODE = False
cfg["CNTK"].FAST_MODE = False
cfg.CNTK.E2E_MAX_EPOCHS = 3
cfg.CNTK.RPN_EPOCHS = 2
cfg.CNTK.FRCN_EPOCHS = 2
cfg.IMAGE_WIDTH = 400
cfg.IMAGE_HEIGHT = 400
cfg["CNTK"].TRAIN_E2E = True
cfg.USE_GPU_NMS = False
cfg.VISUALIZE_RESULTS = False
cfg["DATA"].MAP_FILE_PATH = grocery_path
externalData = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' in os.environ
if externalData:
extPath = os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY']
cfg['BASE_MODEL_PATH'] = os.path.join(extPath, "PreTrainedModels", "AlexNet", "v1", "AlexNet_ImageNet_Caffe.model")
else:
cfg['BASE_MODEL_PATH'] = os.path.join(abs_path, *"../../../../PretrainedModels/AlexNet_ImageNet_Caffe.model".split("/"))
# train and test
eval_model = od.train_object_detector(cfg)
eval_results = od.evaluate_test_set(eval_model, cfg)
meanAP = np.nanmean(list(eval_results.values()))
print('meanAP={}'.format(meanAP))
assert meanAP > 0.01
# detect objects in single image
img_path = os.path.join(grocery_path, "testImages", "WIN_20160803_11_28_42_Pro.jpg")
regressed_rois, cls_probs = od.evaluate_single_image(eval_model, img_path, cfg)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs, cfg)
assert bboxes.shape[0] == labels.shape[0]
from cntk.cntk_py import DeviceKind_GPU
from cntk.device import try_set_default_device, gpu
from cntk.logging.graph import get_node_outputs
from cntk.ops.tests.ops_test_utils import cntk_device
from _cntk_py import force_deterministic_algorithms
force_deterministic_algorithms()
abs_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(abs_path)
sys.path.append(
os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image",
"Detection", "FasterRCNN"))
from prepare_test_data import prepare_Grocery_data, prepare_alexnet_v0_model
grocery_path = prepare_Grocery_data()
prepare_alexnet_v0_model()
from install_data_and_model import create_grocery_mappings
create_grocery_mappings(grocery_path)
win35_linux34 = pytest.mark.skipif(
not ((sys.platform == 'win32' and sys.version_info[:2] == (3, 5)) or
(sys.platform != 'win32' and sys.version_info[:2] == (3, 4))),
reason=
"it runs currently only in windows-py35 and linux-py34 due to precompiled cython modules"
)
@win35_linux34
def test_fasterrcnn_grocery_training_e2e(device_id):
from config import cfg
from cntk import load_model
from cntk.cntk_py import DeviceKind_GPU
from cntk.device import try_set_default_device, gpu
from cntk.logging.graph import get_node_outputs
from cntk.ops.tests.ops_test_utils import cntk_device
from _cntk_py import force_deterministic_algorithms
force_deterministic_algorithms()
abs_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(abs_path)
sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Detection"))
sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Detection", "FastRCNN"))
from prepare_test_data import prepare_Grocery_data, prepare_alexnet_v0_model
grocery_path = prepare_Grocery_data()
prepare_alexnet_v0_model()
from install_data_and_model import create_grocery_mappings
create_grocery_mappings(grocery_path)
win35 = pytest.mark.skipif(not (sys.platform == 'win32' and sys.version_info[:2] == (3,5)),
reason="it runs currently only in windows-py35 due to precompiled cython modules")
# TODO NaN on M60?
#win35_linux34 = pytest.mark.skipif(not ((sys.platform == 'win32' and sys.version_info[:2] == (3,5)) or
# (sys.platform != 'win32' and sys.version_info[:2] == (3,4))),
# reason="it runs currently only in windows-py35 and linux-py34 due to precompiled cython modules")
@win35
def test_fastrcnnpy_grocery_training(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU') # it runs very slow in CPU
