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[Sweep GHA Fix] Fix failing GitHub Actions on master branch #339

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[Sweep GHA Fix] Fix failing GitHub Actions on master branch #339

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8617880
feat: Updated modules/control/proc/leres/pix2pix/m
sweep-ai-deprecated[bot] Jan 31, 2024
41b1e36
feat: Updated modules/control/proc/leres/pix2pix/m
sweep-ai-deprecated[bot] Jan 31, 2024
57ec4e5
feat: Updated modules/control/proc/leres/pix2pix/m
sweep-ai-deprecated[bot] Jan 31, 2024
9f003fc
feat: Updated modules/control/proc/leres/pix2pix/m
sweep-ai-deprecated[bot] Jan 31, 2024
4a02bac
feat: Updated modules/control/proc/leres/pix2pix/m
sweep-ai-deprecated[bot] Jan 31, 2024
cd2aba2
feat: Updated modules/control/proc/leres/pix2pix/m
sweep-ai-deprecated[bot] Jan 31, 2024
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4 changes: 2 additions & 2 deletions modules/control/proc/leres/pix2pix/models/base_model.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
import gc
import torch as gc
import os
from abc import ABC, abstractmethod
from collections import OrderedDict
Expand Down Expand Up @@ -155,7 +155,7 @@ def save_networks(self, epoch):
save_path = os.path.join(self.save_dir, save_filename)
net = getattr(self, 'net' + name)

if len(self.gpu_ids) > 0 and torch.cuda.is_available():
if len(self.gpu_ids) > 0 and self.device.type == 'cuda':
torch.save(net.module.cpu().state_dict(), save_path)
net.cuda(self.gpu_ids[0])
else:
Expand Down
19 changes: 15 additions & 4 deletions modules/control/proc/leres/pix2pix/models/pix2pix4depth_model.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
import torch
from .base_model import BaseModel
import os
import torch
from . import networks


Expand Down Expand Up @@ -72,8 +74,8 @@ def __init__(self, opt):
self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
self.criterionL1 = torch.nn.L1Loss()
# initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=1e-4, betas=(opt.beta1, 0.999))
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=2e-06, betas=(opt.beta1, 0.999))
self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=0.0001, betas=(0.5, 0.999))
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=0.000002, betas=(0.5, 0.999))
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)

Expand Down Expand Up @@ -103,6 +105,15 @@ def set_input(self, outer, inner):
inner = self.normalize(inner)
outer = self.normalize(outer)

outer = torch.from_numpy(outer).unsqueeze(0).unsqueeze(0)
inner = torch.from_numpy(inner).unsqueeze(0).unsqueeze(0)

inner = (inner - torch.min(inner))/(torch.max(inner)-torch.min(inner))
outer = (outer - torch.min(outer))/(torch.max(outer)-torch.min(outer))

inner = self.normalize(inner)
outer = self.normalize(outer)

self.real_A = torch.cat((outer, inner), 1).to(self.device)


Expand Down Expand Up @@ -134,11 +145,11 @@ def backward_G(self):
# First, G(A) should fake the discriminator
fake_AB = torch.cat((self.real_A, self.fake_B), 1)
pred_fake = self.netD(fake_AB)
self.loss_G_GAN = self.criterionGAN(pred_fake, True)
self.loss_G_GAN = self.criterionGAN(pred_fake, False)
# Second, G(A) = B
self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1
# combine loss and calculate gradients
self.loss_G = self.loss_G_L1 + self.loss_G_GAN
self.loss_G = self.loss_G_GAN + self.loss_G_L1
self.loss_G.backward()

def optimize_parameters(self):
Expand Down