minor formatting error + incorrect use of args

Author: Andrey Ryabtsev

REAMDE_train.md

@@ -37,10 +37,10 @@ Cheers to the [unofficial Deep Image Matting repo.](https://github.com/foamliu/D
 [Please download our captured videos.](https://drive.google.com/drive/folders/1j3BMrRFhFpfzJAe6P2WDtfanoeSCLPiq?usp=sharing). We will show next how to finetune your model on `fixed-camera` captured videos. It will be similar for `hand-held` cameras, except you will need to align the captured background image to each frame of the video separately. (Take a hint from `test_pre_process.py` and use `alignImages()`.) 
 
 Data Pre-processing: 
-	- Extract frames for each video: `ffmpeg -i $NAME.mp4 $NAME/%04d_img.png -hide_banner`
-	- Run Segmentation (follow instructions on Deeplabv3+) : `python test_segmentation_deeplab.py -i $NAME`
-	- Target background for composition. For self-supervised learning we need some target backgrounds that has roughly similar lighting as the original videos. Either capture few videos of indoor/outdoor scenes without humans or use our captured background in the `background` folder.
-	- Create a .csv file `Video_data_train.csv` with each row as: `$image;$captured_back;$segmentation;$image+20frames;$image+2*20frames;$image+3*20frames;$image+4*20frames;$target_back`.
+- Extract frames for each video: `ffmpeg -i $NAME.mp4 $NAME/%04d_img.png -hide_banner`
+- Run Segmentation (follow instructions on Deeplabv3+) : `python test_segmentation_deeplab.py -i $NAME`
+- Target background for composition. For self-supervised learning we need some target backgrounds that has roughly similar lighting as the original videos. Either capture few videos of indoor/outdoor scenes without humans or use our captured background in the `background` folder.
+- Create a .csv file `Video_data_train.csv` with each row as: `$image;$captured_back;$segmentation;$image+20frames;$image+2*20frames;$image+3*20frames;$image+4*20frames;$target_back`.
 The process is automated by `prepare_real.py` -- take a look inside and change `background_path` and `path` before running.
 
 ### Training ###

train_real_fixed.py

@@ -65,14 +65,14 @@ def collate_filter_none(batch):
 
 print('\n[Phase 2] : Initialization')
 
-netB=ResnetConditionHR(input_nc=(3,3,1,4),output_nc=4,n_blocks1=args.n_blocks1,n_blocks2=args.n_blocks1)
+netB=ResnetConditionHR(input_nc=(3,3,1,4),output_nc=4,n_blocks1=args.n_blocks1,n_blocks2=args.n_blocks2)
 netB=nn.DataParallel(netB)
 netB.load_state_dict(torch.load(args.init_model))
 netB.cuda(); netB.eval()
 for param in netB.parameters(): #freeze netD
 	param.requires_grad = False
 
-netG=ResnetConditionHR(input_nc=(3,3,1,4),output_nc=4,n_blocks1=args.n_blocks1,n_blocks2=args.n_blocks1)
+netG=ResnetConditionHR(input_nc=(3,3,1,4),output_nc=4,n_blocks1=args.n_blocks1,n_blocks2=args.n_blocks2)
 netG.apply(conv_init)
 netG=nn.DataParallel(netG)
 netG.cuda()