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_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_transfer_learning(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU') # due to batch normalization in ResNet_18
try_set_default_device(cntk_device(device_id))
base_path = os.path.dirname(os.path.abspath(__file__))
externalData = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' in os.environ
if externalData:
extPath = os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY']
print("Reading data and model from %s" % extPath)
model_file = os.path.join(extPath, *"PreTrainedModels/ResNet/v1/ResNet_18.model".split("/"))
map_file = os.path.join(extPath, *"Image/CIFAR/v0/cifar-10-batches-py/test_map.txt".split("/"))
else:
model_file = os.path.join(base_path, *"../../../../Examples/Image/PretrainedModels/ResNet_18.model".split("/"))
map_file = os.path.join(base_path, *"../../../../Examples/Image/DataSets/CIFAR-10/test_map.txt".split("/"))
os.chdir(os.path.join(os.path.dirname(map_file), '..'))
feature_node_name = "features"
last_hidden_node_name = "z.x"
image_width = 224
image_height = 224
num_channels = 3
num_classes = 10
num_epochs = 10
num_train_images = 10
num_test_images = 2
node_outputs = get_node_outputs(load_model(model_file))
assert len(node_outputs) == 83
output_file = os.path.join(base_path, "tl_output.txt")
trained_model = train_model(model_file, feature_node_name, last_hidden_node_name,
image_width, image_height, num_channels, num_classes, map_file,
num_epochs=num_epochs, max_images=num_train_images, freeze=True)
# since we do not use a reader for evaluation we need unzipped data
grocery_path = prepare_Grocery_data()
eval_map_file = os.path.join(grocery_path, "test.txt")
os.chdir(grocery_path)
eval_test_images(trained_model, output_file, eval_map_file, image_width, image_height,
max_images=num_test_images, column_offset=1)
expected_output_file = os.path.join(base_path, "tl_expected_output.txt")
output = np.fromfile(output_file)
expected_output = np.fromfile(expected_output_file)
assert np.allclose(output, expected_output, atol=TOLERANCE_ABSOLUTE)
def test_transfer_learning(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU') # due to batch normalization in ResNet_18
try_set_default_device(cntk_device(device_id))
base_path = os.path.dirname(os.path.abspath(__file__))
externalData = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' in os.environ
if externalData:
extPath = os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY']
print("Reading data and model from %s" % extPath)
model_file = os.path.join(extPath, *"PreTrainedModels/ResNet/v1/ResNet_18.model".split("/"))
map_file = os.path.join(extPath, *"Image/CIFAR/v0/cifar-10-batches-py/test_map.txt".split("/"))
else:
model_file = os.path.join(base_path, *"../../../../PretrainedModels/ResNet_18.model".split("/"))
map_file = os.path.join(base_path, *"../../../../Examples/Image/DataSets/CIFAR-10/test_map.txt".split("/"))
os.chdir(os.path.join(os.path.dirname(map_file), '..'))
feature_node_name = "features"
last_hidden_node_name = "z.x"
image_width = 224
image_height = 224
num_channels = 3
num_classes = 10
num_epochs = 10
num_train_images = 10
num_test_images = 2
node_outputs = get_node_outputs(load_model(model_file))
assert len(node_outputs) == 83
output_file = os.path.join(base_path, "tl_output.txt")
trained_model = train_model(model_file, feature_node_name, last_hidden_node_name,
image_width, image_height, num_channels, num_classes, map_file,
num_epochs=num_epochs, max_images=num_train_images, freeze=True)
# since we do not use a reader for evaluation we need unzipped data
grocery_path = prepare_Grocery_data()
eval_map_file = os.path.join(grocery_path, "test.txt")
os.chdir(grocery_path)
eval_test_images(trained_model, output_file, eval_map_file, image_width, image_height,
max_images=num_test_images, column_offset=1)
expected_output_file = os.path.join(base_path, "tl_expected_output.txt")
output = np.fromfile(output_file)
expected_output = np.fromfile(expected_output_file)
assert np.allclose(output, expected_output, atol=TOLERANCE_ABSOLUTE)
import sys
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
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", "FastRCNN"))
from prepare_test_data import prepare_Grocery_data, prepare_alexnet_v0_model
grocery_path = prepare_Grocery_data()
python34_only = pytest.mark.skipif(sys.version_info[:2] != (3, 4),
reason="requires python 3.4")
linux_only = pytest.mark.skipif(sys.platform == 'win32',
reason="it runs currently only in linux")
@python34_only
@linux_only
def test_fastrcnn_grocery_visualization():
from A1_GenerateInputROIs import generate_input_rois
assert generate_input_rois(testing=True)
from B1_VisualizeInputROIs import generate_rois_visualization
assert generate_rois_visualization(testing=True)
import os
import pytest
import sys
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
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", "FastRCNN"))
from prepare_test_data import prepare_Grocery_data, prepare_alexnet_v0_model
grocery_path = prepare_Grocery_data()
python34_only = pytest.mark.skipif(sys.version_info[:2] != (3,4), reason="requires python 3.4")
linux_only = pytest.mark.skipif(sys.platform == 'win32', reason="it runs currently only in linux")
@python34_only
@linux_only
def test_fastrcnn_grocery_visualization():
from A1_GenerateInputROIs import generate_input_rois
assert generate_input_rois(testing=True)
from B1_VisualizeInputROIs import generate_rois_visualization
assert generate_rois_visualization(testing=True)
from B2_EvaluateInputROIs import evaluate_rois
assert evaluate_rois()
