Regular refactoring

1 files changed, 5 insertions, 44 deletions

diff --git a/supervised/baseline.py b/supervised/baseline.py
index d5671b1..92d8d30 100644
--- a/supervised/baseline.py
+++ b/supervised/baseline.py
@@ -2,18 +2,17 @@ import os
 import random
 
 import torch
-from torch import nn, Tensor, optim
 from torch.nn import CrossEntropyLoss
 from torch.utils.data import DataLoader
 from torch.utils.tensorboard import SummaryWriter
 from torchvision.datasets import CIFAR10
-from torchvision.models import ResNet
-from torchvision.models.resnet import BasicBlock
 from torchvision.transforms import transforms, InterpolationMode
 from tqdm import tqdm
 
-from lars_optimizer import LARS
-from scheduler import LinearWarmupAndCosineAnneal
+from optimizers import LARS
+from schedulers import LinearWarmupAndCosineAnneal
+from supervised.datautils import color_distortion
+from supervised.models import CIFAR10ResNet50
 
 CODENAME = 'cifar10-resnet50-aug-lars-sched'
 DATASET_ROOT = 'dataset'
@@ -36,48 +35,10 @@ random.seed(SEED)
 torch.manual_seed(SEED)
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-
-class CIFAR10ResNet50(ResNet):
-    def __init__(self):
-        super(CIFAR10ResNet50, self).__init__(
-            block=BasicBlock, layers=[3, 4, 6, 3], num_classes=10
-        )
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
-                               stride=1, padding=1, bias=False)
-
-    def forward(self, x: Tensor) -> Tensor:
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        x = torch.flatten(x, 1)
-        x = self.fc(x)
-
-        return x
-
-
-def get_color_distortion(s=1.0):
-    # s is the strength of color distortion.
-    color_jitter = transforms.ColorJitter(0.8 * s, 0.8 * s, 0.8 * s, 0.2 * s)
-    rnd_color_jitter = transforms.RandomApply([color_jitter], p=0.8)
-    rnd_gray = transforms.RandomGrayscale(p=0.2)
-    color_distort = transforms.Compose([
-        rnd_color_jitter,
-        rnd_gray
-    ])
-    return color_distort
-
-
 train_transform = transforms.Compose([
     transforms.RandomResizedCrop(32, interpolation=InterpolationMode.BICUBIC),
     transforms.RandomHorizontalFlip(0.5),
-    get_color_distortion(0.5),
+    color_distortion(0.5),
     transforms.ToTensor()
 ])