All

pix2pixHD_attack/scripts/test_1024p.sh

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+#!/bin/bash
+################################ Testing ################################
+# labels only
+python test.py --name label2city_1024p --netG local --ngf 32 --resize_or_crop none $@

pix2pixHD_attack/scripts/test_1024p_feat.sh

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+################################ Testing ################################
+# first precompute and cluster all features
+python encode_features.py --name label2city_1024p_feat --netG local --ngf 32 --resize_or_crop none;
+# use instance-wise features
+python test.py --name label2city_1024p_feat ---netG local --ngf 32 --resize_or_crop none --instance_feat

pix2pixHD_attack/scripts/test_512p.sh

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+################################ Testing ################################
+# labels only
+python test.py --name label2city_512p

pix2pixHD_attack/scripts/test_512p_feat.sh

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+################################ Testing ################################
+# first precompute and cluster all features
+python encode_features.py --name label2city_512p_feat;
+# use instance-wise features
+python test.py --name label2city_512p_feat --instance_feat

pix2pixHD_attack/scripts/train_1024p_12G.sh

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+############## To train images at 2048 x 1024 resolution after training 1024 x 512 resolution models #############
+##### Using GPUs with 12G memory (not tested)
+# Using labels only
+python train.py --name label2city_1024p --netG local --ngf 32 --num_D 3 --load_pretrain checkpoints/label2city_512p/ --niter_fix_global 20 --resize_or_crop crop --fineSize 1024

pix2pixHD_attack/scripts/train_1024p_24G.sh

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+############## To train images at 2048 x 1024 resolution after training 1024 x 512 resolution models #############
+######## Using GPUs with 24G memory
+# Using labels only
+python train.py --name label2city_1024p --netG local --ngf 32 --num_D 3 --load_pretrain checkpoints/label2city_512p/ --niter 50 --niter_decay 50 --niter_fix_global 10 --resize_or_crop none

pix2pixHD_attack/scripts/train_1024p_feat_12G.sh

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+############## To train images at 2048 x 1024 resolution after training 1024 x 512 resolution models #############
+##### Using GPUs with 12G memory (not tested)
+# First precompute feature maps and save them
+python precompute_feature_maps.py --name label2city_512p_feat;
+# Adding instances and encoded features
+python train.py --name label2city_1024p_feat --netG local --ngf 32 --num_D 3 --load_pretrain checkpoints/label2city_512p_feat/ --niter_fix_global 20 --resize_or_crop crop --fineSize 896 --instance_feat --load_features

pix2pixHD_attack/scripts/train_1024p_feat_24G.sh

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+############## To train images at 2048 x 1024 resolution after training 1024 x 512 resolution models #############
+######## Using GPUs with 24G memory
+# First precompute feature maps and save them
+python precompute_feature_maps.py --name label2city_512p_feat;
+# Adding instances and encoded features
+python train.py --name label2city_1024p_feat --netG local --ngf 32 --num_D 3 --load_pretrain checkpoints/label2city_512p_feat/ --niter 50 --niter_decay 50 --niter_fix_global 10 --resize_or_crop none --instance_feat --load_features

pix2pixHD_attack/scripts/train_512p.sh

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+### Using labels only
+python train.py --name label2city_512p

pix2pixHD_attack/scripts/train_512p_feat.sh

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+### Adding instances and encoded features
+python train.py --name label2city_512p_feat --instance_feat

pix2pixHD_attack/scripts/train_512p_fp16.sh

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+### Using labels only
+ python -m torch.distributed.launch train.py --name label2city_512p --fp16

pix2pixHD_attack/scripts/train_512p_fp16_multigpu.sh

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+######## Multi-GPU training example #######
+python -m torch.distributed.launch train.py --name label2city_512p --batchSize 8 --gpu_ids 0,1,2,3,4,5,6,7 --fp16

pix2pixHD_attack/scripts/train_512p_multigpu.sh

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+######## Multi-GPU training example #######
+python train.py --name label2city_512p --batchSize 8 --gpu_ids 0,1,2,3,4,5,6,7