From 3ccde3b443f0be26606c25f1994295dfc2c9b92b Mon Sep 17 00:00:00 2001
From: Engin Eren <engin.eren@desy.de>
Date: Tue, 17 May 2022 14:58:30 +0200
Subject: [PATCH] forgot to add ECAL data into G.Penalty
---
wganHCAL.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/wganHCAL.py b/wganHCAL.py
index 0988c4c..ced8009 100644
--- a/wganHCAL.py
+++ b/wganHCAL.py
@@ -22,7 +22,7 @@ from models.generatorFull import Hcal_ecalEMB
from models.data_loaderFull import HDF5Dataset
from models.criticFull import CriticEMB
-def calc_gradient_penalty(netD, real_data, fake_data, real_label, BATCH_SIZE, device, layer, xsize, ysize):
+def calc_gradient_penalty(netD, real_dataECAL, real_data, fake_data, real_label, BATCH_SIZE, device, layer, xsize, ysize):
alpha = torch.rand(BATCH_SIZE, 1)
alpha = alpha.expand(BATCH_SIZE, int(real_data.nelement()/BATCH_SIZE)).contiguous()
@@ -36,7 +36,7 @@ def calc_gradient_penalty(netD, real_data, fake_data, real_label, BATCH_SIZE, de
interpolates = interpolates.to(device)
interpolates.requires_grad_(True)
- disc_interpolates = netD(interpolates.float(), real_label.float())
+ disc_interpolates = netD(real_dataECAL, interpolates.float(), real_label.float())
gradients = autograd.grad(outputs=disc_interpolates, inputs=interpolates,
@@ -72,7 +72,7 @@ def train(args, aD, aG, device, train_loader, optimizer_d, optimizer_g, epoch, e
disc_real = aD(real_dataECAL.float(), real_dataHCAL.float(), real_label.float())
## Calculate Gradient Penalty Term
- gradient_penalty = calc_gradient_penalty(aD, real_dataHCAL.float(), fake_dataHCAL, real_label, args.batch_size, device, layer=48, xsize=30, ysize=30)
+ gradient_penalty = calc_gradient_penalty(aD, real_dataECAL.float(), real_dataHCAL.float(), fake_dataHCAL, real_label, args.batch_size, device, layer=48, xsize=30, ysize=30)
## Critic fwd pass on Fake
disc_fake = aD(real_dataECAL, fake_dataHCAL.unsqueeze(1), real_label)
--
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