Hi there, thanks for building this great toolbox. I was trying to train my detector on the PASCAL VOC dataset. I noticed that the image normalization parameter - std values in ssd512_voc.py and ssd300_voc.py img_norm_cfg = dict(mean=[123...
_img_metas = { 'ori_shape': (480, 640, 3), 'img_shape': (480, 640, 3), 'pad_shape': (480, 640, 3), 'scale_factor': [1., 1., 1., 1.], 'flip': True, 'flip_direction': 'horizontal', 'img_norm_cfg': { 'mean': [123.675, 116.28 , 103.53 ], 'std': [58.395,...
weights = torch.load(weights_path, map_location='cpu') model.load_state_dict(weights) return model 这个密码正确吗?也许吧!对某些模型来说确实是正确的。例如,当模型没有规范层时,例如 torch.nn.BatchNorm2d;或者当模型需要为每个图像使用实际的 norm 统计信息时(例如,许多基于 pix2pix 的架构需要它)。
module_output.running_mean=module.running_mean module_output.running_var=module.running_var module_output.num_batches_tracked=module.num_batches_trackedforname,childinmodule.named_children():module_output.add_module(name,_convert_batchnorm(child))delmodulereturnmodule_outputdef_demo_mm_inputs(input_sh...
return (numerator / denominator).mean 看起来很不错,让我们做一个小小的检查: In [3]: ones = np.ones((1, 3, 10, 10)) ...: x1 = iou_continuous_loss(ones * 0.01, ones) ...: x2 = iou_continuous_loss(ones * 0.99, ones) ...
outlier_dset = self.dset_thin.new_view(np.array(removed_vectors)) outlier_centroid = np.mean( outlier_dset.shuffle(seed=np.random.randint(0, 2**63 - 1))[:16384][ "clip_embedding" ], axis=0, ) outlier_centroid_norm = np.linalg.norm(outlier_centroid) if outlier_centroid_norm > ...