add option 'find unused paramters' for trainer (PaddlePaddle#311)

AnnaTrainingG · May 12, 2021 · 94adf2c · 94adf2c
1 parent 8dc8647
commit 94adf2c
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Showing 7 changed files with 9 additions and 1 deletion.
diff --git a/configs/cyclegan_cityscapes.yaml b/configs/cyclegan_cityscapes.yaml
@@ -1,5 +1,6 @@
 epochs: 200
 output_dir: output_dir
+find_unused_parameters: True
 
 model:
   name: CycleGANModel

diff --git a/configs/cyclegan_horse2zebra.yaml b/configs/cyclegan_horse2zebra.yaml
@@ -1,5 +1,6 @@
 epochs: 200
 output_dir: output_dir
+find_unused_parameters: True
 
 model:
   name: CycleGANModel

diff --git a/configs/esrgan_x4_div2k.yaml b/configs/esrgan_x4_div2k.yaml
@@ -1,5 +1,6 @@
 total_iters: 250000
 output_dir: output_dir
+find_unused_parameters: True
 # tensor range for function tensor2img
 min_max:
   (0., 1.)

diff --git a/configs/makeup.yaml b/configs/makeup.yaml
@@ -1,6 +1,7 @@
 epochs: 100
 output_dir: tmp
 checkpoints_dir: checkpoints
+find_unused_parameters: True
 
 model:
   name: MakeupModel

diff --git a/configs/realsr_bicubic_noise_x4_df2k.yaml b/configs/realsr_bicubic_noise_x4_df2k.yaml
@@ -1,5 +1,6 @@
 total_iters: 60000
 output_dir: output_dir
+find_unused_parameters: True
 # tensor range for function tensor2img
 min_max:
   (0., 1.)

diff --git a/configs/realsr_kernel_noise_x4_dped.yaml b/configs/realsr_kernel_noise_x4_dped.yaml
@@ -1,5 +1,6 @@
 total_iters: 60000
 output_dir: output_dir
+find_unused_parameters: True
 # tensor range for function tensor2img
 min_max:
   (0., 1.)

diff --git a/ppgan/engine/trainer.py b/ppgan/engine/trainer.py
@@ -146,8 +146,10 @@ def __init__(self, cfg):
 
     def distributed_data_parallel(self):
         paddle.distributed.init_parallel_env()
+        find_unused_parameters = self.cfg.get('find_unused_parameters', False)
         for net_name, net in self.model.nets.items():
-            self.model.nets[net_name] = paddle.DataParallel(net)
+            self.model.nets[net_name] = paddle.DataParallel(
+                net, find_unused_parameters=find_unused_parameters)
 
     def learning_rate_scheduler_step(self):
         if isinstance(self.model.lr_scheduler, dict):