def test_native_fasterrcnn_eval(tmpdir, device_id):
from config import cfg
cfg["CNTK"].FORCE_DETERMINISTIC = True
cfg["CNTK"].DEBUG_OUTPUT = False
cfg["CNTK"].VISUALIZE_RESULTS = False
cfg["CNTK"].FAST_MODE = True
cfg["CNTK"].MAP_FILE_PATH = grocery_path
from FasterRCNN import set_global_vars
set_global_vars(False)
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))
# since we do not use a reader for evaluation we need unzipped data
externalData = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' in os.environ
if externalData:
extPath = os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY']
model_file = os.path.join(extPath, "PreTrainedModels", "AlexNet", "v0",
"AlexNet.model")
else:
model_file = os.path.join(
abs_path,
*"../../../../Examples/Image/PretrainedModels/AlexNet.model".split(
"/"))
from FasterRCNN import train_faster_rcnn_e2e, eval_faster_rcnn_mAP
np.random.seed(seed=3)
eval_model = train_faster_rcnn_e2e(model_file, debug_output=False)
meanAP_python = eval_faster_rcnn_mAP(eval_model)
cntk_py.always_allow_setting_default_device()
try_set_default_device(cpu())
from native_proposal_layer import clone_with_native_proposal_layer
model_with_native_pl = clone_with_native_proposal_layer(eval_model)
meanAP_native = eval_faster_rcnn_mAP(model_with_native_pl)
# 0.2067 (python) vs 0.2251 (native) -- the difference stems
# from different sorting algorithms: quicksort in python and
# heapsort in c++ (both are not stable).
assert abs(meanAP_python - meanAP_native) < 0.02
def test_fasterrcnn_grocery_training_4stage(device_id):
from config import cfg
cfg["CNTK"].FORCE_DETERMINISTIC = True
cfg["CNTK"].DEBUG_OUTPUT = False
cfg["CNTK"].VISUALIZE_RESULTS = False
cfg["CNTK"].FAST_MODE = True
cfg["CNTK"].MAP_FILE_PATH = grocery_path
from FasterRCNN import set_global_vars
set_global_vars(False)
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))
# since we do not use a reader for evaluation we need unzipped data
externalData = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' in os.environ
if externalData:
extPath = os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY']
model_file = os.path.join(extPath, "PreTrainedModels", "AlexNet", "v0",
"AlexNet.model")
else:
model_file = os.path.join(
abs_path,
*"../../../../Examples/Image/PretrainedModels/AlexNet.model".split(
"/"))
from FasterRCNN import train_faster_rcnn_alternating, eval_faster_rcnn_mAP
np.random.seed(seed=3)
eval_model = train_faster_rcnn_alternating(model_file, debug_output=False)
meanAP = eval_faster_rcnn_mAP(eval_model)
assert meanAP > 0.01
def evaluate(model_path):
# ProposalLayer currently only runs on the CPU
eval_device = C.cpu()
model = C.Function.load(model_path, device=eval_device)
set_global_vars(False)
return eval_faster_rcnn_mAP(model)
