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()