fix

main.py

@@ -38,10 +38,10 @@ logging.basicConfig(format='[%(asctime)s, %(levelname)s]: %(message)s', level=lo
 # from jactorch.parallel import JacDataParallel
 # from jactorch.train import TrainerEnv
 
-from model.const import MAX_VALUE
+from model.const import MAX_VALUE, ORIGIN_IMAGE_SIZE
 # from model.dataset import get_dataset_name_and_num_features, load_data
 from model.dataset import RAVENdataset, PGMdataset, ToTensor
-from model.utils import Model, Observer
+from model.model import Model
 # from model.train import Trainer
 # from model.utils import plot_curve, get_exp_name, get_image_title
 
@@ -343,17 +343,17 @@ def get_model(args):
 
 def get_dataloader():
     if args.dataset == 'PGM':
-        train = PGMdataset(args.dataset_path, "train", args.img_size, transform=transforms.Compose([ToTensor()]), shuffle = True)
-        valid = PGMdataset(args.dataset_path, "val", args.img_size, transform=transforms.Compose([ToTensor()]))
-        test = PGMdataset(args.dataset_path, "test", args.img_size, transform=transforms.Compose([ToTensor()]))
+        train = PGMdataset(args.dataset_path, "train", ORIGIN_IMAGE_SIZE, transform=transforms.Compose([ToTensor()]), shuffle = True)
+        valid = PGMdataset(args.dataset_path, "val", ORIGIN_IMAGE_SIZE, transform=transforms.Compose([ToTensor()]))
+        test = PGMdataset(args.dataset_path, "test", ORIGIN_IMAGE_SIZE, transform=transforms.Compose([ToTensor()]))
     elif args.dataset == 'RAVEN':
         # (I-)RAVEN      
         args.train_figure_configurations = [0,1,2,3,4,5,6]
         args.val_figure_configurations = args.train_figure_configurations
         args.test_figure_configurations = [0,1,2,3,4,5,6]
-        train = RAVENdataset(args.dataset_path, "train", args.train_figure_configurations, args.img_size, transform=transforms.Compose([ToTensor()]), shuffle = True)
-        valid = RAVENdataset(args.dataset_path, "val", args.val_figure_configurations, args.img_size, transform=transforms.Compose([ToTensor()]))
-        test = RAVENdataset(args.dataset_path, "test", args.test_figure_configurations, args.img_size, transform=transforms.Compose([ToTensor()]))
+        train = RAVENdataset(args.dataset_path, "train", args.train_figure_configurations, ORIGIN_IMAGE_SIZE, transform=transforms.Compose([ToTensor()]), shuffle = True)
+        valid = RAVENdataset(args.dataset_path, "val", args.val_figure_configurations, ORIGIN_IMAGE_SIZE, transform=transforms.Compose([ToTensor()]))
+        test = RAVENdataset(args.dataset_path, "test", args.test_figure_configurations, ORIGIN_IMAGE_SIZE, transform=transforms.Compose([ToTensor()]))
 
     trainloader = DataLoader(train, batch_size=args.batch_size, shuffle=True, num_workers=args.load_workers)
     validloader = DataLoader(valid, batch_size=args.batch_size, shuffle=False, num_workers=args.load_workers)

model/dataset.py

@@ -43,7 +43,7 @@ class PGMdataset(Dataset):
         
         resize_image = []
         for idx in range(0, 16):
-            resize_image.append(misc.imresize(image[idx,:,:], (self.img_size, self.img_size)))
+            resize_image.append(misc.imresize(image[idx,:,:], self.img_size))
         resize_image = np.stack(resize_image)
 
         if meta_target.dtype == np.int8:
@@ -98,7 +98,7 @@ class RAVENdataset(Dataset):
 
         resize_image = []
         for idx in range(0, 16):
-            resize_image.append(misc.imresize(image[idx,:,:], (self.img_size, self.img_size)))
+            resize_image.append(misc.imresize(image[idx,:,:], self.img_size))
         resize_image = np.stack(resize_image) 
     
         del data

model/model.py

@@ -15,13 +15,13 @@ import torch.nn as nn
 import torch.nn.functional as F
 import logging 
 
-from modules import MLPModel
+from .modules import MLPModel
 
 # from .baselines import SimpleModel, SharedModel
-from relation_model import RelationModel
-from utils import compute_entropy, compute_mi
-from visual_model import VisualModel
-from const import ORIGIN_IMAGE_SIZE, MAX_VALUE
+from .relation_model import RelationModel
+from .utils import compute_entropy, compute_mi
+from .visual_model import VisualModel
+from .const import ORIGIN_IMAGE_SIZE, MAX_VALUE
 
 
 __all__ = ['Model']

model/modules.py

@@ -15,7 +15,7 @@ import torch.nn.functional as F
 from torchvision.models.resnet import BasicBlock, Bottleneck
 import logging
 
-from const import ORIGIN_IMAGE_SIZE
+from .const import ORIGIN_IMAGE_SIZE
 
 
 __all__ = ['FCResBlock', 'Expert', 'Scorer', 'ConvBlock', 'ConvNet',

model/relation_model.py

@@ -13,8 +13,8 @@ import torch.nn.functional as F
 import logging
 
 
-from modules import FCResBlock, SharedGroupMLP, Expert, Scorer, MLPModel
-from utils import transform
+from .modules import FCResBlock, SharedGroupMLP, Expert, Scorer, MLPModel
+from .utils import transform
 
 
 __all__ = ['AnalogyModel']

model/visual_model.py

 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from modules import ResNetWrapper, ConvNet, MLPModel, SharedGroupMLP
-from const import ORIGIN_IMAGE_SIZE
+from .modules import ResNetWrapper, ConvNet, MLPModel, SharedGroupMLP
+from .const import ORIGIN_IMAGE_SIZE
 
 
 class VisualModel(nn.Module):

run.sh

+python main.py --dataset "RAVEN" --dataset_path "./dataset/I-RAVEN" -t center_single left_right up_down in_out distribute_four distribute_nine in_distri --use-gpu -v -lr 0.005 -vlr 0.005 -wd 0.01 -tsd 80 -fg 10 -nf 80 -chd 16 16 32 32 -rb -erb -vhd 128 -hd 64 32 -lhd 128
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