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[i] AffinityNet@ResNet-50@Puzzle
[i] mean values is [0.485, 0.456, 0.406]
[i] std values is [0.229, 0.224, 0.225]
[i] The number of class is 20
[i] train_transform is Compose(
<tools.ai.augment_utils.RandomResize_For_Segmentation object at 0x79be15df3430>
<tools.ai.augment_utils.RandomHorizontalFlip_For_Segmentation object at 0x79be15df3400>
<tools.ai.augment_utils.Normalize_For_Segmentation object at 0x79be15df3490>
<tools.ai.augment_utils.RandomCrop_For_Segmentation object at 0x79be15df3550>
<tools.ai.augment_utils.Transpose_For_Segmentation object at 0x79be15df35b0>
<tools.ai.augment_utils.Resize_For_Mask object at 0x79be15df35e0>
)
[i] log_iteration : 33
[i] val_iteration : 330
[i] max_iteration : 990
[i] Architecture is resnet50
[i] Total Params: 23.63M
[i] iteration=33, learning_rate=0.0971, loss=0.5866, bg_loss=0.5234, fg_loss=0.7140, neg_loss=0.5546, time=21sec
[i] iteration=66, learning_rate=0.0941, loss=0.4096, bg_loss=0.3311, fg_loss=0.5322, neg_loss=0.3876, time=18sec
[i] iteration=99, learning_rate=0.0910, loss=0.3690, bg_loss=0.2907, fg_loss=0.4917, neg_loss=0.3468, time=18sec
[i] iteration=132, learning_rate=0.0880, loss=0.3705, bg_loss=0.2945, fg_loss=0.4954, neg_loss=0.3460, time=18sec
[i] iteration=165, learning_rate=0.0850, loss=0.3655, bg_loss=0.2897, fg_loss=0.4852, neg_loss=0.3436, time=19sec
[i] iteration=198, learning_rate=0.0819, loss=0.3529, bg_loss=0.2799, fg_loss=0.4650, neg_loss=0.3334, time=19sec
[i] iteration=231, learning_rate=0.0788, loss=0.3469, bg_loss=0.2809, fg_loss=0.4627, neg_loss=0.3220, time=19sec
[i] iteration=264, learning_rate=0.0757, loss=0.3695, bg_loss=0.2976, fg_loss=0.4803, neg_loss=0.3500, time=19sec
[i] iteration=297, learning_rate=0.0726, loss=0.3496, bg_loss=0.2698, fg_loss=0.4781, neg_loss=0.3252, time=19sec
[i] iteration=330, learning_rate=0.0695, loss=0.3384, bg_loss=0.2712, fg_loss=0.4460, neg_loss=0.3183, time=19sec
[i] iteration=363, learning_rate=0.0664, loss=0.3259, bg_loss=0.2599, fg_loss=0.4418, neg_loss=0.3010, time=21sec
[i] iteration=396, learning_rate=0.0632, loss=0.3375, bg_loss=0.2621, fg_loss=0.4632, neg_loss=0.3123, time=18sec
[i] iteration=429, learning_rate=0.0601, loss=0.3277, bg_loss=0.2583, fg_loss=0.4373, neg_loss=0.3076, time=18sec
[i] iteration=462, learning_rate=0.0569, loss=0.3313, bg_loss=0.2533, fg_loss=0.4549, neg_loss=0.3084, time=18sec
[i] iteration=495, learning_rate=0.0537, loss=0.3301, bg_loss=0.2494, fg_loss=0.4540, neg_loss=0.3085, time=19sec
[i] iteration=528, learning_rate=0.0505, loss=0.3229, bg_loss=0.2521, fg_loss=0.4341, neg_loss=0.3028, time=19sec
[i] iteration=561, learning_rate=0.0472, loss=0.3174, bg_loss=0.2464, fg_loss=0.4381, neg_loss=0.2925, time=19sec
[i] iteration=594, learning_rate=0.0439, loss=0.3270, bg_loss=0.2472, fg_loss=0.4452, neg_loss=0.3079, time=19sec
[i] iteration=627, learning_rate=0.0406, loss=0.3237, bg_loss=0.2511, fg_loss=0.4465, neg_loss=0.2987, time=19sec
[i] iteration=660, learning_rate=0.0373, loss=0.3258, bg_loss=0.2443, fg_loss=0.4472, neg_loss=0.3058, time=19sec
[i] iteration=693, learning_rate=0.0339, loss=0.3254, bg_loss=0.2507, fg_loss=0.4396, neg_loss=0.3056, time=21sec
[i] iteration=726, learning_rate=0.0305, loss=0.3242, bg_loss=0.2441, fg_loss=0.4472, neg_loss=0.3027, time=18sec
[i] iteration=759, learning_rate=0.0271, loss=0.3185, bg_loss=0.2413, fg_loss=0.4289, neg_loss=0.3019, time=19sec
[i] iteration=792, learning_rate=0.0236, loss=0.3287, bg_loss=0.2424, fg_loss=0.4540, neg_loss=0.3091, time=19sec
[i] iteration=825, learning_rate=0.0200, loss=0.3113, bg_loss=0.2352, fg_loss=0.4350, neg_loss=0.2875, time=19sec
[i] iteration=858, learning_rate=0.0164, loss=0.3110, bg_loss=0.2417, fg_loss=0.4271, neg_loss=0.2876, time=19sec
[i] iteration=891, learning_rate=0.0127, loss=0.3182, bg_loss=0.2422, fg_loss=0.4330, neg_loss=0.2987, time=19sec
[i] iteration=924, learning_rate=0.0089, loss=0.3107, bg_loss=0.2489, fg_loss=0.4280, neg_loss=0.2829, time=19sec
[i] iteration=957, learning_rate=0.0048, loss=0.3141, bg_loss=0.2364, fg_loss=0.4319, neg_loss=0.2940, time=19sec
[i] iteration=990, learning_rate=0.0002, loss=0.3115, bg_loss=0.2404, fg_loss=0.4173, neg_loss=0.2942, time=19sec
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