update

Author: zengxianyu

models/networks/inpaint_g.py

@@ -291,13 +291,13 @@ def __init__(self, opt, return_feat=False, return_pm=False):
         self.conv7_atrous = gen_conv(2*cnum, 4*cnum, 3, rate=2)
         self.conv8_atrous = gen_conv(2*cnum, 4*cnum, 3, rate=4)
         self.conv9_atrous = gen_conv(2*cnum, 4*cnum, 3, rate=8)
-        self.conv10_atrous = gen_conv(2*cnum, 4*cnum, 3, rate=16) #8
+        self.conv10_atrous = gen_conv(2*cnum, 4*cnum, 3, rate=16)
         self.conv11 = gen_conv(2*cnum, 4*cnum, 3, 1)
-        self.conv12 = gen_conv(2*cnum, 4*cnum, 3, 1) #4
+        self.conv12 = gen_conv(2*cnum, 4*cnum, 3, 1)
         self.conv13_upsample_conv = gen_deconv(2*cnum, 2*cnum)
-        self.conv14 = gen_conv(cnum, 2*cnum, 3, 1) #2
+        self.conv14 = gen_conv(cnum, 2*cnum, 3, 1)
         self.conv15_upsample_conv = gen_deconv(cnum, cnum)
-        self.conv16 = gen_conv(cnum//2, cnum//2, 3, 1) #1
+        self.conv16 = gen_conv(cnum//2, cnum//2, 3, 1)
         self.conv17 = gen_conv(cnum//4, 3, 3, 1, activation=None)
 
         # stage2
@@ -316,7 +316,7 @@ def __init__(self, opt, return_feat=False, return_pm=False):
         self.pmconv3 = gen_conv(cnum//2, 2*cnum, 3, 1)
         self.pmconv4_downsample = gen_conv(cnum, 4*cnum, 3, 2)
         self.pmconv5 = gen_conv(2*cnum, 4*cnum, 3, 1)
-        self.pmconv6 = gen_conv(2*cnum, 4*cnum, 3, 1,
+        self.pmconv6 = gen_conv(2*cnum, 4*cnum, 3, 1, 
                             activation=nn.ReLU())
         self.pmconv9 = gen_conv(2*cnum, 4*cnum, 3, 1)
         self.pmconv10 = gen_conv(2*cnum, 4*cnum, 3, 1)
@@ -396,28 +396,25 @@ def forward(self, x, mask):
         x = self.pmconv4_downsample(x)
         x = self.pmconv5(x)
         x = self.pmconv6(x)
-        pm = x
+        pm_return = x
+
         x = self.pmconv9(x)
         x = self.pmconv10(x)
+        pm = x
         x = torch.cat([x_hallu, pm], 1)
-        feat = x
 
         x = self.allconv11(x)
         x = self.allconv12(x)
         x = self.allconv13_upsample_conv(x)
-        feat_x2 = x
         x = self.allconv14(x)
         x = self.allconv15_upsample_conv(x)
-        feat_x4 = x
         x = self.allconv16(x)
         x = self.allconv17(x)
         x_stage2 = torch.tanh(x)
-        if self.return_feat:
-            return x_stage1, x_stage2, [feat, feat_x2, feat_x4]
-        elif self.return_pm:
-            return x_stage1, x_stage2, pm
-        else:
-            return x_stage1, x_stage2
+        if self.return_pm:
+            return x_stage1, x_stage2, pm_return
+
+        return x_stage1, x_stage2
 
 if __name__ == "__main__":
     pass

test.py

@@ -1,4 +1,5 @@
 import numpy as np
+import cv2
 import torch
 import data
 from options.test_options import TestOptions

train.sh

@@ -1,55 +1,55 @@
-#BSIZE0=48 # stage coarse
-#BSIZE=96 # 96:64G
+##BSIZE0=48 # stage coarse
+##BSIZE=96 # 96:64G
 BSIZE0=$((BSIZE/2))
 NWK=16
-PREFIX="--dataset_mode_train trainimage \
---gpu_ids 0,1 \
---name debug \
---dataset_mode_val valimage \
---train_image_dir ./datasets/places/places2 \
---train_image_list ./datasets/places/train_example.txt \
---path_objectshape_list ./datasets/object_shapes.txt \
---path_objectshape_base ./datasets/object_masks \
---val_image_dir ./datasets/places2sample1k_val/places2samples1k_crop256 \
---val_image_list ./datasets/places2sample1k_val/files.txt \
---val_mask_dir ./datasets/places2sample1k_val/places2samples1k_256_mask_square128 \
---no_vgg_loss \
---no_ganFeat_loss \
---load_size 640 \
---crop_size 256 \
---model inpaint \
---netG baseconv \
---netD deepfill \
---preprocess_mode scale_shortside_and_crop \
---validation_freq 10000 \
---niter 50 "
-python train.py \
-	${PREFIX} \
-	--batchSize ${BSIZE0} \
-	--nThreads ${NWK} \
-	--no_fine_loss \
-	--update_part coarse \
-	--no_gan_loss \
-	--freeze_D \
-	--niter 1 \
-	${EXTRA}
-python train.py \
-	${PREFIX} \
-	--batchSize ${BSIZE} \
-	--nThreads ${NWK} \
-	--update_part fine \
-	--continue_train \
-	--niter 2 \
-	${EXTRA}
-python train.py \
-	${PREFIX} \
-	--batchSize ${BSIZE} \
-	--nThreads ${NWK} \
-	--update_part all \
-	--continue_train \
-	--niter 4 \
-	${EXTRA}
-
+#PREFIX="--dataset_mode_train trainimage \
+#--gpu_ids 0,1 \
+#--name debug \
+#--dataset_mode_val valimage \
+#--train_image_dir ./datasets/places/places2 \
+#--train_image_list ./datasets/places/train_example.txt \
+#--path_objectshape_list ./datasets/object_shapes.txt \
+#--path_objectshape_base ./datasets/object_masks \
+#--val_image_dir ./datasets/places2sample1k_val/places2samples1k_crop256 \
+#--val_image_list ./datasets/places2sample1k_val/files.txt \
+#--val_mask_dir ./datasets/places2sample1k_val/places2samples1k_256_mask_square128 \
+#--no_vgg_loss \
+#--no_ganFeat_loss \
+#--load_size 640 \
+#--crop_size 256 \
+#--model inpaint \
+#--netG baseconv \
+#--netD deepfill \
+#--preprocess_mode scale_shortside_and_crop \
+#--validation_freq 10000 \
+#--niter 50 "
+#python train.py \
+#	${PREFIX} \
+#	--batchSize ${BSIZE0} \
+#	--nThreads ${NWK} \
+#	--no_fine_loss \
+#	--update_part coarse \
+#	--no_gan_loss \
+#	--freeze_D \
+#	--niter 1 \
+#	${EXTRA}
+#python train.py \
+#	${PREFIX} \
+#	--batchSize ${BSIZE} \
+#	--nThreads ${NWK} \
+#	--update_part fine \
+#	--continue_train \
+#	--niter 2 \
+#	${EXTRA}
+#python train.py \
+#	${PREFIX} \
+#	--batchSize ${BSIZE} \
+#	--nThreads ${NWK} \
+#	--update_part all \
+#	--continue_train \
+#	--niter 4 \
+#	${EXTRA}
+#
 PREFIX="--dataset_mode_train trainimage \
 --name debugarr0 \
 --gpu_ids 0,1 \

util/util.py

@@ -226,7 +226,7 @@ def load_network(net, label, epoch, opt):
         if k.startswith("module."):
             k=k.replace("module.","")
         new_dict[k] = v
-    net.load_state_dict(new_dict, strict=False)
+    net.load_state_dict(new_dict)
     return net