From bce3dd03e82ec0c5c182643e6a047b219251fa03 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 12 Apr 2019 14:00:16 +0200 Subject: [PATCH] updates --- models.py | 2 +- train.py | 3 +-- utils/utils.py | 9 ++++++++- 3 files changed, 10 insertions(+), 4 deletions(-) diff --git a/models.py b/models.py index 2d88c47d..d36640a0 100755 --- a/models.py +++ b/models.py @@ -33,7 +33,7 @@ def create_modules(module_defs): if bn: modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters)) if module_def['activation'] == 'leaky': - modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1)) + modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1, inplace=True)) elif module_def['type'] == 'maxpool': kernel_size = int(module_def['size']) diff --git a/train.py b/train.py index c00ad68c..720e0b1e 100644 --- a/train.py +++ b/train.py @@ -49,7 +49,6 @@ def train( start_epoch = 0 best_loss = float('inf') nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255) - if resume: # Load previously saved model if transfer: # Transfer learning chkpt = torch.load(weights + 'yolov3.pt', map_location=device) @@ -74,7 +73,7 @@ def train( else: cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74') - # Set scheduler (reduce lr at epochs 218, 245, i.e. batches 400k, 450k) + # Scheduler (reduce lr at epochs 218, 245, i.e. batches 400k, 450k) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[218, 245], gamma=0.1, last_epoch=start_epoch - 1) diff --git a/utils/utils.py b/utils/utils.py index fa24b07f..bfdddeef 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -326,7 +326,6 @@ def build_targets(model, targets): return txy, twh, tcls, indices -# @profile def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5): """ Removes detections with lower object confidence score than 'conf_thres' @@ -383,6 +382,11 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5): dc = pred[pred[:, -1] == c] # select class c dc = dc[:min(len(dc), 100)] # limit to first 100 boxes: https://github.com/ultralytics/yolov3/issues/117 + # No NMS required if only 1 prediction + if len(dc) == 1: + det_max.append(dc) + continue + # Non-maximum suppression if nms_style == 'OR': # default # METHOD1 @@ -410,6 +414,9 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5): elif nms_style == 'MERGE': # weighted mixture box while len(dc): + if len(dc) == 1: + det_max.append(dc) + break i = bbox_iou(dc[0], dc) > nms_thres # iou with other boxes weights = dc[i, 4:5] dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum()