feat: AutoAugment on RGB frames

stanford-action-recognition · May 30, 2021 · 15ba693 · 15ba693
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diff --git a/dataloaders/dataset.py b/dataloaders/dataset.py
@@ -7,22 +7,39 @@
 from torch.utils.data import Dataset
 from shutil import copytree
 import torch.nn.functional as F
+import torchvision.transforms as transforms
+from utils.autoaugment import ImageNetPolicy
 
 RESIZE_HEIGHT = 64
 RESIZE_WIDTH = 85
 CROP_SIZE = 60
 
+train_transform = transforms.Compose(
+    [
+        transforms.ToPILImage("RGB"),
+        # transforms.Resize((60, 60)),
+        # transforms.RandomResizedCrop(size=32, scale=(0.9, 1.1), ratio=(0.75, 1.33)),
+        # transforms.RandomRotation(degrees=15),
+        # transforms.ColorJitter(0.5, 0.5, 0.5, 0.1),
+        ImageNetPolicy(),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ]
+)
+
+
 def temporal_padding(buffer, clip_len):
     """Pad buffer to have temporal length of clip_len, Pad with 0"""
     if buffer.shape[0] > clip_len:
         pass
     else:
         pad_len = clip_len - buffer.shape[0] + 1
-        npad = ((pad_len, 0), (0, 0), (0, 0), (0,0))
-        buffer = np.pad(buffer, pad_width=npad, mode='constant', constant_values=0)
+        npad = ((pad_len, 0), (0, 0), (0, 0), (0, 0))
+        buffer = np.pad(buffer, pad_width=npad, mode="constant", constant_values=0)
     assert buffer.shape[0] - clip_len > 0, "Incorrect Padding"
     return buffer
 
+
 class RGBDataset(Dataset):
     r"""A Dataset for a folder of videos. Expects the directory structure to be
     directory->[train/val/test]->[class labels]->[videos]. Initializes with a list
@@ -115,6 +132,11 @@ def __getitem__(self, index):
             # Perform data augmentation
             buffer = self.randomflip(buffer)
         buffer = self.normalize(buffer)
+
+        for i in range(buffer.shape[0]):
+            img = train_transform(buffer[i].astype(int))
+            buffer[i] = img.cpu().detach().numpy().transpose((1, 2, 0))
+
         buffer = self.to_tensor(buffer)
         return torch.from_numpy(buffer), torch.from_numpy(labels)
 

diff --git a/utils/autoaugment.py b/utils/autoaugment.py
@@ -0,0 +1,230 @@
+# https://github.com/DeepVoltaire/AutoAugment/blob/master/autoaugment.py
+
+from PIL import Image, ImageEnhance, ImageOps
+import numpy as np
+import random
+
+
+class ImageNetPolicy(object):
+    """ Randomly choose one of the best 24 Sub-policies on ImageNet.
+        Example:
+        >>> policy = ImageNetPolicy()
+        >>> transformed = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     ImageNetPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+    def __init__(self, fillcolor=(128, 128, 128)):
+        self.policies = [
+            SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
+            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
+            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
+            SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
+            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
+
+            SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
+            SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
+            SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
+            SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
+            SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),
+
+            SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
+            SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
+            SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
+            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
+            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
+
+            SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
+            SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
+            SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
+            SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
+            SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),
+
+            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
+            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
+            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
+            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
+            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor)
+        ]
+
+
+    def __call__(self, img):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        return "AutoAugment ImageNet Policy"
+
+
+class CIFAR10Policy(object):
+    """ Randomly choose one of the best 25 Sub-policies on CIFAR10.
+        Example:
+        >>> policy = CIFAR10Policy()
+        >>> transformed = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     CIFAR10Policy(),
+        >>>     transforms.ToTensor()])
+    """
+    def __init__(self, fillcolor=(128, 128, 128)):
+        self.policies = [
+            SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
+            SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
+            SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
+            SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
+            SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
+
+            SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
+            SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
+            SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
+            SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
+            SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
+
+            SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
+            SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
+            SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
+            SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
+            SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
+
+            SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
+            SubPolicy(0.2, "equalize", 8, 0.6, "equalize", 4, fillcolor),
+            SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
+            SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
+            SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
+
+            SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
+            SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
+            SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
+            SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
+            SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor)
+        ]
+
+
+    def __call__(self, img):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        return "AutoAugment CIFAR10 Policy"
+
+
+class SVHNPolicy(object):
+    """ Randomly choose one of the best 25 Sub-policies on SVHN.
+        Example:
+        >>> policy = SVHNPolicy()
+        >>> transformed = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     SVHNPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+    def __init__(self, fillcolor=(128, 128, 128)):
+        self.policies = [
+            SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor),
+            SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor),
+            SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor),
+            SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor),
+            SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor),
+
+            SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor),
+            SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor),
+            SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor),
+            SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor),
+            SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor),
+
+            SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor),
+            SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor),
+            SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor),
+            SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor),
+            SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor),
+
+            SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor),
+            SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor),
+            SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor),
+            SubPolicy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor),
+            SubPolicy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor),
+
+            SubPolicy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor),
+            SubPolicy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor),
+            SubPolicy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor),
+            SubPolicy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor),
+            SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor)
+        ]
+
+
+    def __call__(self, img):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        return "AutoAugment SVHN Policy"
+
+
+class SubPolicy(object):
+    def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)):
+        ranges = {
+            "shearX": np.linspace(0, 0.3, 10),
+            "shearY": np.linspace(0, 0.3, 10),
+            "translateX": np.linspace(0, 150 / 331, 10),
+            "translateY": np.linspace(0, 150 / 331, 10),
+            "rotate": np.linspace(0, 30, 10),
+            "color": np.linspace(0.0, 0.9, 10),
+            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+            "solarize": np.linspace(256, 0, 10),
+            "contrast": np.linspace(0.0, 0.9, 10),
+            "sharpness": np.linspace(0.0, 0.9, 10),
+            "brightness": np.linspace(0.0, 0.9, 10),
+            "autocontrast": [0] * 10,
+            "equalize": [0] * 10,
+            "invert": [0] * 10
+        }
+
+        # from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
+        def rotate_with_fill(img, magnitude):
+            rot = img.convert("RGBA").rotate(magnitude)
+            return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(img.mode)
+
+        func = {
+            "shearX": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor),
+            "shearY": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor),
+            "translateX": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
+                fillcolor=fillcolor),
+            "translateY": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
+                fillcolor=fillcolor),
+            "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
+            "color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])),
+            "posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude),
+            "solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude),
+            "contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
+                1 + magnitude * random.choice([-1, 1])),
+            "sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
+                1 + magnitude * random.choice([-1, 1])),
+            "brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
+                1 + magnitude * random.choice([-1, 1])),
+            "autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
+            "equalize": lambda img, magnitude: ImageOps.equalize(img),
+            "invert": lambda img, magnitude: ImageOps.invert(img)
+        }
+
+        self.p1 = p1
+        self.operation1 = func[operation1]
+        self.magnitude1 = ranges[operation1][magnitude_idx1]
+        self.p2 = p2
+        self.operation2 = func[operation2]
+        self.magnitude2 = ranges[operation2][magnitude_idx2]
+
+
+    def __call__(self, img):
+        if random.random() < self.p1: img = self.operation1(img, self.magnitude1)
+        if random.random() < self.p2: img = self.operation2(img, self.magnitude2)
+        return img