add mini_colorization dataset and remove imagepool from pix2pix

Author: junyanz

datasets/download_cyclegan_dataset.sh

@@ -1,6 +1,6 @@
 FILE=$1
 
-if [[ $FILE != "ae_photos" && $FILE != "apple2orange" && $FILE != "summer2winter_yosemite" &&  $FILE != "horse2zebra" && $FILE != "monet2photo" && $FILE != "cezanne2photo" && $FILE != "ukiyoe2photo" && $FILE != "vangogh2photo" && $FILE != "maps" && $FILE != "cityscapes" && $FILE != "facades" && $FILE != "iphone2dslr_flower" && $FILE != "ae_photos" && $FILE != "mini" && $FILE != "mini_pix2pix" ]]; then
+if [[ $FILE != "ae_photos" && $FILE != "apple2orange" && $FILE != "summer2winter_yosemite" &&  $FILE != "horse2zebra" && $FILE != "monet2photo" && $FILE != "cezanne2photo" && $FILE != "ukiyoe2photo" && $FILE != "vangogh2photo" && $FILE != "maps" && $FILE != "cityscapes" && $FILE != "facades" && $FILE != "iphone2dslr_flower" && $FILE != "ae_photos" && $FILE != "mini" && $FILE != "mini_pix2pix" && $FILE != "mini_colorization" ]]; then
     echo "Available datasets are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos"
     exit 1
 fi

models/__init__.py

@@ -12,10 +12,10 @@
     -- self.loss_names (str list):          specify the training losses that you want to plot and save.
     -- self.model_names (str list):         specify the images that you want to display and save.
     -- self.visual_names (str list):        define networks used in our training.
-    -- self.optimizers (optimzier list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
+    -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage.
 
 Now you can use the model class by specifying flag '--model dummy'.
-See our template model class 'template_model.py' for an example.
+See our template model class 'template_model.py' for more details.
 """
 
 import importlib

models/pix2pix_model.py

@@ -1,5 +1,4 @@
 import torch
-from util.image_pool import ImagePool
 from .base_model import BaseModel
 from . import networks
 
@@ -62,7 +61,6 @@ def __init__(self, opt):
                                           opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
 
         if self.isTrain:
-            self.fake_AB_pool = ImagePool(opt.pool_size)
             # define loss functions
             self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
             self.criterionL1 = torch.nn.L1Loss()
@@ -92,7 +90,7 @@ def forward(self):
     def backward_D(self):
         """Calculate GAN loss for the discriminator"""
         # Fake; stop backprop to the generator by detaching fake_B
-        fake_AB = self.fake_AB_pool.query(torch.cat((self.real_A, self.fake_B), 1))  # we use conditional GANs; we need to feed both input and output to the discriminator
+        fake_AB = torch.cat((self.real_A, self.fake_B), 1)  # we use conditional GANs; we need to feed both input and output to the discriminator
         pred_fake = self.netD(fake_AB.detach())
         self.loss_D_fake = self.criterionGAN(pred_fake, False)
         # Real