Enable AMP optimizations & write sample train images to folder.

codes/models/SRGAN_model.py

@@ -7,6 +7,10 @@ import models.networks as networks
 import models.lr_scheduler as lr_scheduler
 from .base_model import BaseModel
 from models.loss import GANLoss
+from apex import amp
+
+import torchvision.utils as utils
+import os
 
 logger = logging.getLogger('base')
 
@@ -101,6 +105,10 @@ class SRGANModel(BaseModel):
                                                 betas=(train_opt['beta1_D'], train_opt['beta2_D']))
             self.optimizers.append(self.optimizer_D)
 
+            # AMP
+            [self.netG, self.netD], [self.optimizer_G, self.optimizer_D] = \
+                amp.initialize([self.netG, self.netD], [self.optimizer_G, self.optimizer_D], opt_level=self.amp_level, num_losses=3)
+
             # schedulers
             if train_opt['lr_scheme'] == 'MultiStepLR':
                 for optimizer in self.optimizers:
@@ -132,6 +140,13 @@ class SRGANModel(BaseModel):
             self.var_ref = input_ref.to(self.device)
 
     def optimize_parameters(self, step):
+
+        if step % 50 == 0:
+            for i in range(self.var_L.shape[0]):
+                utils.save_image(self.var_H[i].cpu().detach(), os.path.join("E:\\4k6k\\temp\hr", "%05i_%02i.png" % (step, i)))
+                utils.save_image(self.var_L[i].cpu().detach(), os.path.join("E:\\4k6k\\temp\\lr", "%05i_%02i.png" % (step, i)))
+
+
         # G
         for p in self.netD.parameters():
             p.requires_grad = False
@@ -161,7 +176,8 @@ class SRGANModel(BaseModel):
                     self.cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2
             l_g_total += l_g_gan
 
-            l_g_total.backward()
+            with amp.scale_loss(l_g_total, self.optimizer_G, loss_id=0) as l_g_total_scaled:
+                l_g_total_scaled.backward()
             self.optimizer_G.step()
 
         # D
@@ -178,7 +194,8 @@ class SRGANModel(BaseModel):
             # fake
             pred_d_fake = self.netD(self.fake_H.detach())  # detach to avoid BP to G
             l_d_fake = self.cri_gan(pred_d_fake, False)
-            l_d_fake.backward()
+            with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled:
+                l_d_fake_scaled.backward()
         elif self.opt['train']['gan_type'] == 'ragan':
             # pred_d_real = self.netD(self.var_ref)
             # pred_d_fake = self.netD(self.fake_H.detach())  # detach to avoid BP to G
@@ -189,10 +206,12 @@ class SRGANModel(BaseModel):
             pred_d_fake = self.netD(self.fake_H.detach()).detach()
             pred_d_real = self.netD(self.var_ref)
             l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True) * 0.5
-            l_d_real.backward()
+            with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled:
+                l_d_real_scaled.backward()
             pred_d_fake = self.netD(self.fake_H.detach())
             l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real.detach()), False) * 0.5
-            l_d_fake.backward()
+            with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled:
+                l_d_fake_scaled.backward()
         self.optimizer_D.step()
 
         # set log

codes/models/base_model.py

@@ -9,6 +9,7 @@ class BaseModel():
     def __init__(self, opt):
         self.opt = opt
         self.device = torch.device('cuda' if opt['gpu_ids'] is not None else 'cpu')
+        self.amp_level = 'O0' if opt['amp_opt_level'] is None else opt['amp_opt_level']
         self.is_train = opt['is_train']
         self.schedulers = []
         self.optimizers = []