from datagen import genanchors print('tensorflow version: {}'.format(tf.__version__)) output_path = 'output' ishape = [512, 512, 3] ssize = [128, 128] asizes = [[32, 32]] total_classes = 1 resnet_settings = [[8, 8, 32], [24, [2, 2]]] nsm_iou_threshold = 0.2 nsm_score_threshold = 0.8 nsm_max_output_size = 100 a1box_2dtensor = tf.constant(value=genanchors(isize=ishape[:2], ssize=ssize, asizes=asizes), dtype='float32') # (h1*w1*k1, 4) a2box_2dtensor = tf.constant(value=genanchors( isize=[ishape[0] // 2, ishape[1] // 2], ssize=[ssize[0] // 2, ssize[1] // 2], asizes=asizes), dtype='float32') # (h2*w2*k2, 4) a3box_2dtensor = tf.constant(value=genanchors( isize=[ishape[0] // 4, ishape[1] // 4], ssize=[ssize[0] // 4, ssize[1] // 4], asizes=asizes), dtype='float32') # (h3*w3*k3, 4) a4box_2dtensor = tf.constant(value=genanchors( isize=[ishape[0] // 8, ishape[1] // 8], ssize=[ssize[0] // 8, ssize[1] // 8],
from datagen import genanchors print('tensorflow version: {}'.format(tf.__version__)) output_path = 'output' ishape = [512, 512, 3] ssize = [128, 128] asizes = [[32, 32]] total_classes = 1 resnet_settings = [[8, 8, 32], [24, [2, 2]]] nsm_iou_threshold = 0.2 nsm_score_threshold = 0.8 nsm_max_output_size = 100 a1box_2dtensor = tf.constant(value=genanchors(isize=ishape[:2], ssize=ssize, asizes=asizes), dtype='float32') # (h1*w1*k1, 4) a2box_2dtensor = tf.constant(value=genanchors(isize=[ishape[0]//2, ishape[1]//2], ssize=[ssize[0]//2, ssize[1]//2], asizes=asizes), dtype='float32') # (h2*w2*k2, 4) a3box_2dtensor = tf.constant(value=genanchors(isize=[ishape[0]//4, ishape[1]//4], ssize=[ssize[0]//4, ssize[1]//4], asizes=asizes), dtype='float32') # (h3*w3*k3, 4) a4box_2dtensor = tf.constant(value=genanchors(isize=[ishape[0]//8, ishape[1]//8], ssize=[ssize[0]//8, ssize[1]//8], asizes=asizes), dtype='float32') # (h4*w4*k4, 4) abox_2dtensor = tf.concat(values=[a1box_2dtensor, a2box_2dtensor, a3box_2dtensor, a4box_2dtensor], axis=0) model = build_robust_test_model( ishape=ishape, resnet_settings=resnet_settings, k=len(asizes), total_classes=total_classes, abox_2dtensor=abox_2dtensor, nsm_iou_threshold=nsm_iou_threshold, nsm_score_threshold=nsm_score_threshold, nsm_max_output_size=nsm_max_output_size)
combine = True if ishape[0] == 512 else False ssize = [ishape[0] / 4, ishape[1] / 4] asizes = [[32, 32]] total_classes = 1 resnet_settings = [[8, 8, 32], [24, [2, 2]]] iou_thresholds = [0.3, 0.5] anchor_sampling = 256 nsm_iou_threshold = 0.2 nsm_score_threshold = 0.8 total_epoches = 1000 nsm_max_output_size = 100 total_train_examples = 4 total_test_examples = 4 abox_2dtensor = tf.constant(value=genanchors(isize=ishape[:2], ssize=ssize, asizes=asizes), dtype='float32') # (h*w*k, 4) model = build_model(ishape=ishape, resnet_settings=resnet_settings, k=len(asizes), total_classes=total_classes, net_name=net_name) # model.summary() model.load_weights('{}/weights_.h5'.format(output_path), by_name=True) min_loss = 2**32 max_precision = 0 max_recall = 0 max_precision_recall = 0