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

codes/data/base_unsupervised_image_dataset.py

@@ -34,7 +34,7 @@ class BaseUnsupervisedImageDataset(data.Dataset):
                 for c in chunks:
                     c.reload(opt)
             else:
-                chunks = [ChunkWithReference(opt, d) for d in os.scandir(path) if d.is_dir()]
+                chunks = [ChunkWithReference(opt, d) for d in sorted(os.scandir(path), key=lambda e: e.name) if d.is_dir()]
                 # Prune out chunks that have no images
                 res = []
                 for c in chunks:

codes/data/multi_frame_dataset.py

@@ -23,6 +23,7 @@ class MultiFrameDataset(BaseUnsupervisedImageDataset):
                 frames_needed -= 1
                 search_idx -= 1
             else:
+                search_idx += 1
                 break
 
         # Now build num_frames starting from search_idx.
@@ -62,7 +63,7 @@ class MultiFrameDataset(BaseUnsupervisedImageDataset):
 if __name__ == '__main__':
     opt = {
         'name': 'amalgam',
-        'paths': ['F:\\4k6k\\datasets\\ns_images\\vixen\\full_video_256_tiled_with_ref'],
+        'paths': ['/content/fullvideo_256_tiled_test'],
         'weights': [1],
         'target_size': 128,
         'force_multiple': 32,
@@ -77,13 +78,14 @@ if __name__ == '__main__':
     ds = MultiFrameDataset(opt)
     import os
     os.makedirs("debug", exist_ok=True)
-    for i in range(100000, len(ds)):
+    for i in [3]:
         import random
-        o = ds[random.randint(0, 1000000)]
+        o = ds[i]
         k = 'GT'
         v = o[k]
         if 'path' not in k and 'center' not in k:
             fr, f, h, w = v.shape
             for j in range(fr):
                 import torchvision
-                torchvision.utils.save_image(v[j].unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, j))
+                base=osp.basename(o["GT_path"])
+                torchvision.utils.save_image(v[j].unsqueeze(0), "debug/%i_%s_%i__%s.png" % (i, k, j, base))

codes/models/ExtensibleTrainer.py

@@ -3,7 +3,8 @@ import os
 
 import torch
 from apex import amp
-from torch.nn.parallel import DataParallel, DistributedDataParallel
+from apex.parallel import DistributedDataParallel
+from torch.nn.parallel import DataParallel
 import torch.nn as nn
 
 import models.lr_scheduler as lr_scheduler
@@ -107,9 +108,7 @@ class ExtensibleTrainer(BaseModel):
         dnets = []
         for anet in amp_nets:
             if opt['dist']:
-                dnet = DistributedDataParallel(anet,
-                                               device_ids=[torch.cuda.current_device()],
-                                               find_unused_parameters=False)
+                dnet = DistributedDataParallel(anet, delay_allreduce=True)
             else:
                 dnet = DataParallel(anet)
             if self.is_train:
@@ -299,6 +298,8 @@ class ExtensibleTrainer(BaseModel):
     def load(self):
         for netdict in [self.netsG, self.netsD]:
             for name, net in netdict.items():
+                if not self.opt['networks'][name]['trainable']:
+                    continue
                 load_path = self.opt['path']['pretrain_model_%s' % (name,)]
                 if load_path is not None:
                     if self.rank <= 0:

codes/models/archs/StructuredSwitchedGenerator.py

@@ -476,6 +476,10 @@ class StackedSwitchGenerator5Layer(nn.Module):
 
     def update_for_step(self, step, experiments_path='.'):
         if self.attentions:
+            # All-reduce the attention norm.
+            for sw in self.switches:
+                sw.switch.reduce_norm_params()
+            
             temp = max(1, 1 + self.init_temperature *
                        (self.final_temperature_step - step) / self.final_temperature_step)
             self.set_temperature(temp)

codes/models/base_model.py

@@ -2,7 +2,7 @@ import os
 from collections import OrderedDict
 import torch
 import torch.nn as nn
-from torch.nn.parallel import DistributedDataParallel
+from apex.parallel import DistributedDataParallel
 import utils.util
 from apex import amp
 

codes/models/steps/tecogan_losses.py

@@ -7,6 +7,7 @@ import torch.nn.functional as F
 import os
 import os.path as osp
 import torchvision
+import torch.distributed as dist
 
 def create_teco_loss(opt, env):
     type = opt['type']
@@ -123,6 +124,8 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
         return {self.output: results}
 
     def produce_teco_visual_debugs(self, gen_input, it):
+        if dist.get_rank() > 0:
+            return
         base_path = osp.join(self.env['base_path'], "..", "visual_dbg", "teco_geninput", str(self.env['step']))
         os.makedirs(base_path, exist_ok=True)
         torchvision.utils.save_image(gen_input[:, :3], osp.join(base_path, "%s_img.png" % (it,)))
@@ -192,6 +195,8 @@ class TecoGanLoss(ConfigurableLoss):
         return l_total
 
     def produce_teco_visual_debugs(self, sext, lbl, it):
+        if dist.get_rank() > 0:
+            return
         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 = ['img_a', 'img_b', 'img_c', 'flow_a', 'flow_b', 'flow_c']
@@ -220,6 +225,8 @@ class PingPongLoss(ConfigurableLoss):
         return l_total
 
     def produce_teco_visual_debugs(self, imglist):
+        if dist.get_rank() > 0:
+            return
         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)

codes/switched_conv

@@ -1 +1 @@
-Subproject commit 004dda04e39e91c109fdec87b8fb9524f653f6d6
+Subproject commit a8c13a86ef22c5bd4e793e164fc5ebfceaad4b4b