Thin-Plate-Spline-Motion-Model 的training log

00000000) perceptual - 116.65633; equivariance_value - 0.89652; warp_loss - 3.55680; output_each_loss - 2.09351; bg - 0.14203 00000001) perceptual - 105.36805; equivariance_value - 0.69519; warp_loss - 2.98258; output_each_loss - 1.72231; bg - 0.05199 00000002) perceptual - 99.62815; equivariance_value - 0.55770; warp_loss - 2.62713; output_each_loss - 1.58131; bg - 0.04725 00000003) perceptual - 96.11101; equivariance_value - 0.47373; warp_loss - 2.36628; output_each_loss - 1.50274; bg - 0.04705 00000004) perceptual - 93.39845; equivariance_value - 0.42779; warp_loss - 2.16927; output_each_loss - 1.44283; bg - 0.04693 00000005) perceptual - 90.92935; equivariance_value - 0.40312; warp_loss - 2.01604; output_each_loss - 1.40351; bg - 0.04675 00000006) perceptual - 89.29865; equivariance_value - 0.37523; warp_loss - 1.89265; output_each_loss - 1.37541; bg - 0.04665 00000007) perceptual - 87.33111; equivariance_value - 0.35618; warp_loss - 1.78275; output_each_loss - 1.34827; bg - 0.04666 00000008) perceptual - 85.82854; equivariance_value - 0.34024; warp_loss - 1.68894; output_each_loss - 1.32962; bg - 0.04620 00000009) perceptual - 84.98846; equivariance_value - 0.33100; warp_loss - 1.61929; output_each_loss - 1.32032; bg - 0.04699 00000010) perceptual - 83.71490; equivariance_value - 0.32019; warp_loss - 1.54614; output_each_loss - 1.30181; bg - 0.04809 00000011) perceptual - 82.50879; equivariance_value - 0.30821; warp_loss - 1.47911; output_each_loss - 1.28805; bg - 0.04902 00000012) perceptual - 81.30040; equivariance_value - 0.29838; warp_loss - 1.42535; output_each_loss - 1.27089; bg - 0.05106 00000013) perceptual - 80.22541; equivariance_value - 0.29103; warp_loss - 1.37416; output_each_loss - 1.25923; bg - 0.05248 00000014) perceptual - 79.30081; equivariance_value - 0.28644; warp_loss - 1.33174; output_each_loss - 1.24852; bg - 0.05334 00000015) perceptual - 78.71069; equivariance_value - 0.28078; warp_loss - 1.29883; output_each_loss - 1.24249; bg - 0.05455 00000016) perceptual - 78.03793; equivariance_value - 0.27591; warp_loss - 1.26411; output_each_loss - 1.23260; bg - 0.05521 00000017) perceptual - 77.08243; equivariance_value - 0.27134; warp_loss - 1.22706; output_each_loss - 1.22278; bg - 0.05583 00000018) perceptual - 76.35187; equivariance_value - 0.26779; warp_loss - 1.19564; output_each_loss - 1.21766; bg - 0.05636 00000019) perceptual - 75.97030; equivariance_value - 0.26265; warp_loss - 1.17140; output_each_loss - 1.21133; bg - 0.05700 00000020) perceptual - 75.10955; equivariance_value - 0.25777; warp_loss - 1.14276; output_each_loss - 1.20214; bg - 0.05767 00000021) perceptual - 74.63866; equivariance_value - 0.25183; warp_loss - 1.12050; output_each_loss - 1.19663; bg - 0.05750 00000022) perceptual - 73.94718; equivariance_value - 0.24957; warp_loss - 1.09815; output_each_loss - 1.18764; bg - 0.05759 00000023) perceptual - 73.65253; equivariance_value - 0.24534; warp_loss - 1.07981; output_each_loss - 1.18601; bg - 0.05829 00000024) perceptual - 73.21500; equivariance_value - 0.24310; warp_loss - 1.05986; output_each_loss - 1.18267; bg - 0.05891 00000025) perceptual - 72.67480; equivariance_value - 0.23854; warp_loss - 1.04068; output_each_loss - 1.17333; bg - 0.05896 00000026) perceptual - 72.46416; equivariance_value - 0.23743; warp_loss - 1.02392; output_each_loss - 1.17042; bg - 0.05884 00000027) perceptual - 72.00395; equivariance_value - 0.23445; warp_loss - 1.00834; output_each_loss - 1.16729; bg - 0.05900 00000028) perceptual - 71.63246; equivariance_value - 0.23115; warp_loss - 0.99045; output_each_loss - 1.16402; bg - 0.05886 00000029) perceptual - 71.20876; equivariance_value - 0.23038; warp_loss - 0.97354; output_each_loss - 1.15865; bg - 0.05891 00000030) perceptual - 70.96395; equivariance_value - 0.22707; warp_loss - 0.96229; output_each_loss - 1.15868; bg - 0.05934 00000031) perceptual - 70.55905; equivariance_value - 0.22541; warp_loss - 0.94752; output_each_loss - 1.15201; bg - 0.05897 00000032) perceptual - 70.47787; equivariance_value - 0.22371; warp_loss - 0.93265; output_each_loss - 1.15134; bg - 0.05860 00000033) perceptual - 69.85097; equivariance_value - 0.22054; warp_loss - 0.91786; output_each_loss - 1.14416; bg - 0.05802 00000034) perceptual - 69.85689; equivariance_value - 0.21896; warp_loss - 0.91032; output_each_loss - 1.14529; bg - 0.05859 00000035) perceptual - 67.99393; equivariance_value - 0.21669; warp_loss - 0.90973; output_each_loss - 1.11638; bg - 0.05894 00000036) perceptual - 67.53101; equivariance_value - 0.21572; warp_loss - 0.90077; output_each_loss - 1.10831; bg - 0.05894 00000037) perceptual - 67.22431; equivariance_value - 0.21434; warp_loss - 0.89288; output_each_loss - 1.10543; bg - 0.05909 00000038) perceptual - 66.85408; equivariance_value - 0.21344; warp_loss - 0.88227; output_each_loss - 1.09939; bg - 0.05895 00000039) perceptual - 66.56845; equivariance_value - 0.21251; warp_loss - 0.87417; output_each_loss - 1.09537; bg - 0.05860 00000040) perceptual - 66.21569; equivariance_value - 0.20956; warp_loss - 0.86704; output_each_loss - 1.09033; bg - 0.05904 00000041) perceptual - 66.11765; equivariance_value - 0.20908; warp_loss - 0.85676; output_each_loss - 1.08778; bg - 0.05858 00000042) perceptual - 65.84417; equivariance_value - 0.20781; warp_loss - 0.85228; output_each_loss - 1.08574; bg - 0.05833 00000043) perceptual - 65.56458; equivariance_value - 0.20681; warp_loss - 0.84177; output_each_loss - 1.08355; bg - 0.05842 00000044) perceptual - 65.51819; equivariance_value - 0.20570; warp_loss - 0.83641; output_each_loss - 1.08270; bg - 0.05857 00000045) perceptual - 65.11051; equivariance_value - 0.20398; warp_loss - 0.82812; output_each_loss - 1.07546; bg - 0.05821 00000046) perceptual - 64.97604; equivariance_value - 0.20252; warp_loss - 0.81967; output_each_loss - 1.07597; bg - 0.05807 00000047) perceptual - 64.96129; equivariance_value - 0.20104; warp_loss - 0.81444; output_each_loss - 1.07475; bg - 0.05827 00000048) perceptual - 64.78112; equivariance_value - 0.20050; warp_loss - 0.80841; output_each_loss - 1.07265; bg - 0.05811 00000049) perceptual - 64.33219; equivariance_value - 0.19987; warp_loss - 0.79829; output_each_loss - 1.06613; bg - 0.05787 00000050) perceptual - 64.12677; equivariance_value - 0.19780; warp_loss - 0.79063; output_each_loss - 1.06398; bg - 0.05746 00000051) perceptual - 63.99312; equivariance_value - 0.19577; warp_loss - 0.78358; output_each_loss - 1.06246; bg - 0.05730 00000052) perceptual - 63.93724; equivariance_value - 0.19540; warp_loss - 0.77756; output_each_loss - 1.06095; bg - 0.05740 00000053) perceptual - 63.61037; equivariance_value - 0.19468; warp_loss - 0.77097; output_each_loss - 1.05844; bg - 0.05729 00000054) perceptual - 63.55986; equivariance_value - 0.19390; warp_loss - 0.76553; output_each_loss - 1.05670; bg - 0.05725 00000055) perceptual - 63.53143; equivariance_value - 0.19267; warp_loss - 0.75834; output_each_loss - 1.05685; bg - 0.05790 00000056) perceptual - 63.47717; equivariance_value - 0.19209; warp_loss - 0.75340; output_each_loss - 1.05511; bg - 0.05727 00000057) perceptual - 63.38591; equivariance_value - 0.19145; warp_loss - 0.74774; output_each_loss - 1.05541; bg - 0.05733 00000058) perceptual - 62.99603; equivariance_value - 0.18964; warp_loss - 0.73929; output_each_loss - 1.04839; bg - 0.05677 00000059) perceptual - 62.89783; equivariance_value - 0.18956; warp_loss - 0.73474; output_each_loss - 1.05115; bg - 0.05714 00000060) perceptual - 62.71036; equivariance_value - 0.18936; warp_loss - 0.72863; output_each_loss - 1.04661; bg - 0.05668 00000061) perceptual - 62.76099; equivariance_value - 0.18828; warp_loss - 0.72682; output_each_loss - 1.04875; bg - 0.05668 00000062) perceptual - 62.55880; equivariance_value - 0.18748; warp_loss - 0.71827; output_each_loss - 1.04491; bg - 0.05671 00000063) perceptual - 62.39692; equivariance_value - 0.18609