From f43ee6ef94a54570316cb09faa8ee2d33f7b57a6 Mon Sep 17 00:00:00 2001 From: glenn-jocher Date: Mon, 1 Jul 2019 17:17:29 +0200 Subject: [PATCH] updates --- train.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/train.py b/train.py index 76969494..1b1e7182 100644 --- a/train.py +++ b/train.py @@ -307,7 +307,7 @@ def print_mutation(hyp, results): if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('--epochs', type=int, default=1, help='number of epochs') + parser.add_argument('--epochs', type=int, default=100, help='number of epochs') parser.add_argument('--batch-size', type=int, default=8, help='batch size') parser.add_argument('--accumulate', type=int, default=8, help='number of batches to accumulate before optimizing') parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path') @@ -320,8 +320,8 @@ if __name__ == '__main__': parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--notest', action='store_true', help='only test final epoch') parser.add_argument('--giou', action='store_true', help='use GIoU loss instead of xy, wh loss') - parser.add_argument('--evolve', action='store_true', help='run hyperparameter evolution') - parser.add_argument('--cloud_evolve', action='store_true', help='--evolve from a central source') + parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') + parser.add_argument('--cloud_evolve', action='store_true', help='evolve hyperparameters from a cloud source') parser.add_argument('--var', default=0, type=int, help='debug variable') opt = parser.parse_args() print(opt) @@ -347,7 +347,7 @@ if __name__ == '__main__': for _ in range(gen): # Get best hyperparamters x = np.loadtxt('evolve.txt', ndmin=2) - x = x[x[:, 2].argmax()] # select best mAP for fitness (col 2) + x = x[x[:, 2].argmax()] # select best mAP as genetic fitness (col 2) for i, k in enumerate(hyp.keys()): hyp[k] = x[i + 5] @@ -356,7 +356,7 @@ if __name__ == '__main__': s = [.2, .2, .2, .2, .2, .2, .2, .2, .2 * 0, .2 * 0, .05 * 0, .2 * 0] # fractional sigmas for i, k in enumerate(hyp.keys()): x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300) - hyp[k] *= float(x) # vary by about 30% 1sigma + hyp[k] *= float(x) # vary by 20% 1sigma # Clip to limits keys = ['lr0', 'iou_t', 'momentum', 'weight_decay']