Austin

.gitignore

@@ -1,4 +1,5 @@
 __pycache__
-.vscode
-
+sauce/
 training_summaries
+.*
+*.zip

README.md

@@ -1,4 +1,4 @@
-# ACM AI Projects Skeleton Code
+# 50Whales Skeleton Code
 
 ## Setup
 

constants.py

@@ -1,3 +1,4 @@
-EPOCHS = 100
-BATCH_SIZE = 32
-N_EVAL = 100
+EPOCHS = 1
+BATCH_SIZE = 16
+N_EVAL = 2
+DATA = "../50whales/sauce"

data/ImageAugment.py

@@ -0,0 +1,86 @@
+import torch
+from PIL import Image
+from PIL import ImageOps
+import pandas as pd
+import constants
+import torchvision
+import torchvision.transforms.functional
+import numpy as np
+
+
+class ImageAugment(torch.utils.data.Dataset):
+
+    def __init__(self, path):
+        self.path = path
+        self.data = pd.read_csv(constants.DATA + "/train.csv")
+        self.data.rename(columns=self.data.iloc[0]).drop(self.data.index[0])
+        self.images = self.data.iloc[:, 0]
+        self.labels = self.data.iloc[:, 1]
+        self.transition = list(set(self.labels))
+        self.whales = self.labels.replace(self.transition, list(range(5005)))
+
+        self.transform1 = torchvision.transforms.RandomResizedCrop(size = (448,224), scale = (0.5, 0.75))
+
+        self.transform2 = torchvision.transforms.ColorJitter()
+
+        self.transform3 = torchvision.transforms.RandomAffine(180)
+
+        self.augmentedimages = []
+        self.augmentedlabels = []
+
+
+    def __getitem__(self, index):
+        image = Image.open(constants.DATA + self.path + self.images[index])
+
+        label = self.whales[index]
+
+        image = image.resize((448, 224))
+        image = ImageOps.grayscale(image)
+
+        image = torchvision.transforms.ToTensor()(np.array(image))
+
+        return image, label
+
+
+    def __len__(self):
+        return len(self.labels)
+
+    def cutout(self, image):
+        size = 30
+        x = np.random.randint(448)
+        y = np.random.randint(224)
+        y1 = np.clip(y - size // 2, 0, 224)
+        y2 = np.clip(y + size // 2, 0, 224)
+        x1 = np.clip(x - size // 2, 0, 448)
+        x2 = np.clip(x + size // 2, 0, 448)
+        image[y1:y2, x1:x2] = 0
+
+
+
+    def augment(self, index):
+        image = Image.open(constants.DATA + self.path + self.images[index])
+
+        image = image.resize((448,224))
+
+        self.augmentedimages.append(torchvision.transforms.ToTensor()(np.array(self.transform1(image))))
+        self.augmentedlabels.append(index)
+
+        self.augmentedimages.append(torchvision.transforms.ToTensor()(np.array(self.transform2(image))))
+        self.augmentedlabels.append(index)
+
+        self.augmentedimages.append(torchvision.transforms.ToTensor()(np.array(self.transform2(image))))
+        self.augmentedlabels.append(index)
+
+        self.augmentedimages.append(torchvision.transforms.ToTensor()(np.array(self.transform3(image))))
+        self.augmentedlabels.append(index)
+
+        self.augmentedimages.append(torchvision.transforms.ToTensor()(np.array(self.transform3(image))))
+        self.augmentedlabels.append(index)
+
+        self.augmentedimages.append(torchvision.transforms.ToTensor()(np.array(image + torch.std(image)*torch.randn(image.size))))
+        self.augmentedlabels.append(index)
+
+        self.augmentedimages.append(torchvision.transforms.ToTensor()(np.array(self.cutout(self, image))))
+
+
+

data/StartingDataset.py

@@ -1,19 +1,35 @@
+import os
 import torch
+from PIL import Image
+from PIL import ImageOps
+import pandas as pd
+import constants
+import torchvision
 
 
 class StartingDataset(torch.utils.data.Dataset):
     """
     Dataset that contains 100000 3x224x224 black images (all zeros).
     """
 
-    def __init__(self):
-        pass
+    def __init__(self, path):
+        self.path = path
+        self.data = pd.read_csv(constants.DATA + "/train.csv")
+        self.data.rename(columns=self.data.iloc[0]).drop(self.data.index[0])
+        self.images = self.data.iloc[:, 0]
+        self.labels = self.data.iloc[:, 1]
+        self.transition = list(set(self.labels))
+        self.whales = self.labels.replace(self.transition, list(range(5005)))
 
     def __getitem__(self, index):
-        inputs = torch.zeros([3, 224, 224])
-        label = 0
+        image = Image.open(constants.DATA + self.path + self.images[index])
+        label = self.whales[index]
+
+        image = image.resize((448, 224))
+        image = ImageOps.grayscale(image)
+
+        return torchvision.transforms.functional.pil_to_tensor(image), label
 
-        return inputs, label
 
     def __len__(self):
-        return 10000
+        return len(self.labels)

data/tempCodeRunnerFile.py

@@ -0,0 +1,17 @@
+    def augment(self, index):
+        image, label = ImageAugment.__getitem__(index)
+
+        self.images.append(self.transform1(image))
+        self.labels.append(index)
+
+        self.images.append(self.transform2(image))
+        self.labels.append(index)
+
+        self.images.append(self.transform2(image))
+        self.labels.append(index)
+
+        self.images.append(self.transform3(image))
+        self.labels.append(index)
+
+        self.images.append(self.transform3(image))
+        self.labels.append(index)