Merge remote-tracking branch 'origin/gan_lab' into gan_lab

.gitmodules

@@ -0,0 +1,6 @@
+[submodule "flownet2"]
+	path = flownet2
+	url = https://github.com/NVIDIA/flownet2-pytorch.git
+[submodule "codes/models/flownet2"]
+	path = codes/models/flownet2
+	url = https://github.com/NVIDIA/flownet2-pytorch.git

codes/models/ExtensibleTrainer.py

@@ -29,7 +29,8 @@ class ExtensibleTrainer(BaseModel):
         self.env = {'device': self.device,
                'rank': self.rank,
                'opt': opt,
-               'step': 0}
+               'step': 0,
+               'base_path': os.path.join(opt['path']['models'])}
 
         self.mega_batch_factor = 1
         if self.is_train:

codes/models/flownet2

@@ -0,0 +1 @@
+Subproject commit 2e9e010c98931bc7cef3eb063b195f1e0ab470ba

codes/models/networks.py

@@ -14,6 +14,8 @@ import models.archs.rcan as rcan
 from collections import OrderedDict
 import torchvision
 import functools
+from models.flownet2.models import FlowNet2
+
 
 logger = logging.getLogger('base')
 
@@ -94,6 +96,10 @@ def define_G(opt, net_key='network_G', scale=None):
         xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
         netG = ssg.SSGDeep(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
                                  init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
+    elif which_model == "flownet2":
+        args_dict = {}
+        args = munchify(args_dict)
+        netG = FlowNet2(args)
     elif which_model == "backbone_encoder":
         netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet'])
     elif which_model == "backbone_encoder_no_ref":

codes/models/steps/tecogan_losses.py

@@ -3,6 +3,9 @@ from models.layers.resample2d_package.resample2d import Resample2d
 from models.steps.recurrent import RecurrentController
 from models.steps.injectors import Injector
 import torch
+import os
+import os.path as osp
+import torchvision
 
 def create_teco_loss(opt, env):
     type = opt['type']
@@ -114,7 +117,6 @@ class TecoGanDiscriminatorLoss(ConfigurableLoss):
 class TecoGanGeneratorLoss(ConfigurableLoss):
     def __init__(self, opt, env):
         super(TecoGanGeneratorLoss, self).__init__(opt, env)
-        self.opt = opt
         self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
         # TecoGAN parameters
         self.image_flow_generator = opt['image_flow_generator']
@@ -130,6 +132,10 @@ class TecoGanGeneratorLoss(ConfigurableLoss):
             fake_sext = create_teco_discriminator_sextuplet(fake, i, flow_gen, self.resampler)
             d_fake = net(fake_sext)
 
+            if self.env['step'] % 100 == 0:
+                self.produce_teco_visual_debugs(fake_sext, 'fake', i)
+                self.produce_teco_visual_debugs(real_sext, 'real', i)
+
             if self.opt['gan_type'] in ['gan', 'pixgan']:
                 self.metrics.append(("d_fake", torch.mean(d_fake)))
                 l_fake = self.criterion(d_fake, True)
@@ -142,8 +148,16 @@ class TecoGanGeneratorLoss(ConfigurableLoss):
                            self.criterion(d_fake_diff, True))
             else:
                 raise NotImplementedError
+
         return l_total
 
+    def produce_teco_visual_debugs(self, sext, lbl, it):
+        base_path = osp.join(self.env['base_path'], "visual_dbg", "teco_sext", str(self.env['step']), lbl)
+        os.makedirs(base_path, exist_ok=True)
+        lbls = ['first', 'second', 'third', 'first_flow', 'second_flow', 'third_flow']
+        for i in range(6):
+            torchvision.utils.save_image(sext[:, i*3:(i+1)*3-1, :, :], osp.join(base_path, "%s_%s.png" % (lbls[i], it)))
+
 
 # This loss doesn't have a real entry - only fakes are used.
 class PingPongLoss(ConfigurableLoss):
@@ -159,4 +173,15 @@ class PingPongLoss(ConfigurableLoss):
             early = fake[i]
             late = fake[-i]
             l_total += self.criterion(early, late)
-        return l_total
+
+        if self.env['step'] % 100 == 0:
+            self.produce_teco_visual_debugs(fake)
+
+        return l_total
+
+    def produce_teco_visual_debugs(self, imglist):
+        base_path = osp.join(self.env['base_path'], "visual_dbg", "teco_pingpong", str(self.env['step']))
+        os.makedirs(base_path, exist_ok=True)
+        assert isinstance(imglist, list)
+        for i, img in enumerate(imglist):
+            torchvision.utils.save_image(img, osp.join(base_path, "%s.png" % (i, )))