From 67b18f082cae842ce7bb3b6ed7034a07cd3516df Mon Sep 17 00:00:00 2001 From: bishe <123456789@163.com> Date: Sun, 23 Feb 2025 16:06:28 +0800 Subject: [PATCH] debug --- checkpoints/ROMA_UNSB_001/loss_log.txt | 1 + .../roma_unsb_model.cpython-39.pyc | Bin 19431 -> 19480 bytes models/roma_unsb_model.py | 1 + 3 files changed, 2 insertions(+) diff --git a/checkpoints/ROMA_UNSB_001/loss_log.txt b/checkpoints/ROMA_UNSB_001/loss_log.txt index 3f966d3..ea5e856 100644 --- a/checkpoints/ROMA_UNSB_001/loss_log.txt +++ b/checkpoints/ROMA_UNSB_001/loss_log.txt @@ -2,3 +2,4 @@ ================ Training Loss (Sun Feb 23 15:52:29 2025) ================ ================ Training Loss (Sun Feb 23 16:00:07 2025) ================ ================ Training Loss (Sun Feb 23 16:02:40 2025) ================ +================ Training Loss (Sun Feb 23 16:05:19 2025) ================ diff --git a/models/__pycache__/roma_unsb_model.cpython-39.pyc b/models/__pycache__/roma_unsb_model.cpython-39.pyc index 870fbd6961245826832fc4e672d8a2f43d994f8f..340cd490ba975ba42f0f1153108c780419c5079f 100644 GIT binary patch delta 3385 zcmcInTWlN06`diMZ&IQ}QX(zcvh}hU(H0}gt!&GVRI9dSsuGnKO6i?yTN@hF-WpeTvWLCh+&@o8L~q{*rGjM88>`4!tc!mcUuF z=I_&j8^>^h|@{I%UB`eb!-#6!E^N7fvIAK^Mc z)WOElevc>PBlP$DYAnFN9>0e^&p(PMOS&iPHAvP4Kc8wnXEBKEYWUpid}xzwOVK9o ztwp=;H``PxO;wv}{~XcV4ZG?HlSk+b{5na679GF{7!I(U23)E$Lb^!Nsk-WRkf0ST zkxp;|H3+|u3UtGnwqPgSV|oo|g_<1|Vs=)51;Y?I3<6wcM3sPpLbu?=Aj5smf-LZK zHQE&0L1K18U$+4Zkhj8^+bX1NIp0kQjMHOoFI5Od^(4F1+yN8yR*3586zeF{`>qr{ zhDVhaq%vjKV0ur4AmU0RBut7R5Z85s&u4fIUl%d^RHy2|t0EO!46o{_`@<+{Emo3> ztzrrbOz{D(E&mCiwuFh{4-%urXfyk96`jOrHEhsz0M}>^8h*&kPUL5O6^ikq-|*n- zg>OieB1B~6239>6z>2Q2N)e)Zph2>?1WDQgXD_B{C9P0;}%I=G4GKtr^?p4Bk#oj4NeG4OS@x>=8^-p`O|kUumrDb2%)#b2E` zx^1(wv_j!XEAyVcpXzuPPq$|P?qpsxeF#tS%M*J`?2l-Rqjy3z$xMtT= zd9A=$2NJBhvdk{=~@5A(dlMNUd_w7%tDHV(A$cTMfeuRB^;4Gilz_Nlar_Ad?BNq9!97e zhG9L?c7HCjB&SX-^W*zR>0|tf{d3dLVRwg?(iL52>|ty7u7 zgd+$i5I&9YL*6+#5ZfqYA40fB?@maWd{Sfo#Fpix#;kmPau?0>rzZ!glYeJ&HgE+( z#OkEp`eg~yU^R5$^YpWS0)N!YK7bp03_;}PWwf05T)&6bUVwxrSI{(hM!sKxT;W4Y zxRzK-g@>f%YO6tPu-Ezf2fHIZ*!`w&xU0~utJ;5CgdV((17l*>P3yugUVxe_S4eBi zi>Z~R`e&QaBAy*;VL#^&PQ`6+fElg6ICa88pXKgDTW=SO*~$Kbl#2*r(_xb8li(L! zO~Oac>^O^6)76AKr!1aS75T{YoowB@xppWTUlX||&3#hQlk>8cIg_eu>qXT<+a>;^ zLs3sFw*QKg`k$H!9SvY}3pR%kg3wGja-%Y)ll=l*QBVEzvIB`i>~^$<5OyH+A@uY6 zX2OweXx^06H(`vi>XS2X(7+&uF$jd|r05yg zAaQiL^}+&Fw4BWAY!^DVtQI|%S)4z3>>ajY za6H6+JA3lr7f{x%2tP*nCBi!h;$vRy?F2S)`!U=#40FcDal~QDzdF}pVFhj;+wn=i zv&HFl?sWFE0x+W8@ITG+;?ij*^70$(^csTLNKU=LlJmRsdG>w&;jz=65=I;duX1B< LJ1y|9%pLm|^Q2Zg delta 3280 zcmcIneQZ#{>dE9gR*8( zWtP@zmSkSlbT$hrcDOpWXfLI6oOeb8Xe87%Tk=polT2ysFn_(X-CCche%$%FHS#?) z`aVK$6IP&fFTg)hIK`z%jGpGhkrX}7AB+ssWBj#9kQ&wZB8<`>@%_Cydc1m}_ng%B z2sqR%I+HUhdlH?WsNNmDYy!U{ef#Nes~7t&QF<4DW=)Zvtd8}&XxFD`HHF|q_y{0s zW5Xz4;>lP){e)la^Ye#ed#S-M$C5>ZWJ%Ulu!y_6VAVb47DY-^#jIGqMRc!WRjeU$ z51oLkgH&k21{OZU29n)?pJEG>PExQd4LLwi3Xl%afn_UPZHl!*4SU*zKDxggFzgjt z4pvCHy;1}-ScW7zfPkYMQY0{P=>BXNHwGGxlO`}Dpjp%yQyd3KxwAqFPQwYlU9jll z3MrXRbx{KAbeES@2u1QF+g0v?jh0r3V(k!Nl;|B73NFK?NYmyLEwb~ly(>%*G14#z zk%GG(*Ht=?;Wj*-q`XYADL@TkG8bA5w_>g9Lr7`r7+%8yuFEmTap|coi|ej7 zf)xp@)}oH@UYjqrOq)tp_B)unb{~7c+&(Tg8hL&n_y4bboF;{iMlaJ-@&D*$l6B*Y z2s${{?BJq>efA}Hu$76jY4*ZZ9bD{K*bM(i2iNReIvfh z@O1~|TCo*Mmcu9Z5#{REuxh_v&>AGWL1~40vausTVN-T9RMmKS3pQAGT%k(aNeN#- zg>Z9rt*xTe0GdGGJg!}l1BkPCQUr-ju>-84;P%MOI9O0MTH`?wyj^aZIPa*uC4~+V)&!Tvi z-!j^7t4X>%$IkK7qp==5JZn}xrD}P`0yr=y=M)*v;!FmXLIW7@(kN3 zOfq^Z$L_)vcOkrwN+*+5MNQ0Prc*44+I9plLI&Yy05vl%%f5r6ho$71#Fm7b&uB*n z;|_689o$0IJ)6UH(?=kR49?rujxH{MnqwxP z*5-1lBeV7ID8WTMDdfW5;E#^SEWZJ9MfH#42Tb$~->`M@E)mR1_Bon7fFMc;6I9kWyfH=^qwP;#T>0f>48Z6^(QB&G1Yyx%DPl=broqB#6`Z$qPb=JopocuA}_tSOTDedJWJC<12 z;4WwR9Xm2~kiWd+Cd(>Ne1nH~9^CY8WHEs73xu~3-bE1K*rLMYIK(1kSUU_8$A)ml zRg}Lr)ox-czqoVhI*+}@?zFG8FJ~%PM7!W$jk(>uZ diff --git a/models/roma_unsb_model.py b/models/roma_unsb_model.py index 0c88585..d56f29a 100644 --- a/models/roma_unsb_model.py +++ b/models/roma_unsb_model.py @@ -405,6 +405,7 @@ class RomaUnsbModel(BaseModel): self.mutil_real_A0_tokens = self.netPreViT(real_A0, self.atten_layers, get_tokens=True) self.mutil_real_A1_tokens = self.netPreViT(real_A1, self.atten_layers, get_tokens=True) + print(self.mutil_real_A0_tokens) self.mutil_real_A0_tokens = torch.tensor(self.mutil_real_A0_tokens, device=self.device) self.mutil_real_A1_tokens = torch.tensor(self.mutil_real_A1_tokens, device=self.device)