Adding Preset Transforms in reference scripts (#3317)

* Adding presets in the classification reference scripts.

* Adding presets in the object detection reference scripts.

* Adding presets in the segmentation reference scripts.

* Adding presets in the video classification reference scripts.

* Moving flip at the end to align with image classification signature.

Author: datumbox

references/classification/README.md

@@ -124,22 +124,9 @@ Training converges at about 10 epochs.
 For post training quant, device is set to CPU. For training, the device is set to CUDA
 
 ### Command to evaluate quantized models using the pre-trained weights:
-For all quantized models except inception_v3:
+For all quantized models:
 ```
 python references/classification/train_quantization.py  --data-path='imagenet_full_size/' \
     --device='cpu' --test-only --backend='fbgemm' --model='<model_name>'
 ```
 
-For inception_v3, since it expects tensors with a size of N x 3 x 299 x 299, before running above command,
-need to change the input size of dataset_test in train.py to:
-```
-dataset_test = torchvision.datasets.ImageFolder(
-    valdir,
-    transforms.Compose([
-        transforms.Resize(342),
-        transforms.CenterCrop(299),
-        transforms.ToTensor(),
-        normalize,
-    ]))
-```
-

references/classification/presets.py

@@ -0,0 +1,37 @@
+from torchvision.transforms import autoaugment, transforms
+
+
+class ClassificationPresetTrain:
+    def __init__(self, crop_size, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), hflip_prob=0.5,
+                 auto_augment_policy=None, random_erase_prob=0.0):
+        trans = [transforms.RandomResizedCrop(crop_size)]
+        if hflip_prob > 0:
+            trans.append(transforms.RandomHorizontalFlip(hflip_prob))
+        if auto_augment_policy is not None:
+            aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy)
+            trans.append(autoaugment.AutoAugment(policy=aa_policy))
+        trans.extend([
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
+        ])
+        if random_erase_prob > 0:
+            trans.append(transforms.RandomErasing(p=random_erase_prob))
+
+        self.transforms = transforms.Compose(trans)
+
+    def __call__(self, img):
+        return self.transforms(img)
+
+
+class ClassificationPresetEval:
+    def __init__(self, crop_size, resize_size=256, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
+
+        self.transforms = transforms.Compose([
+            transforms.Resize(resize_size),
+            transforms.CenterCrop(crop_size),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
+        ])
+
+    def __call__(self, img):
+        return self.transforms(img)

references/classification/train.py

@@ -6,8 +6,8 @@
 import torch.utils.data
 from torch import nn
 import torchvision
-from torchvision import transforms
 
+import presets
 import utils
 
 try:
@@ -82,8 +82,7 @@ def _get_cache_path(filepath):
 def load_data(traindir, valdir, args):
     # Data loading code
     print("Loading data")
-    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                                     std=[0.229, 0.224, 0.225])
+    resize_size, crop_size = (342, 299) if args.model == 'inception_v3' else (256, 224)
 
     print("Loading training data")
     st = time.time()
@@ -93,22 +92,10 @@ def load_data(traindir, valdir, args):
         print("Loading dataset_train from {}".format(cache_path))
         dataset, _ = torch.load(cache_path)
     else:
-        trans = [
-            transforms.RandomResizedCrop(224),
-            transforms.RandomHorizontalFlip(),
-        ]
-        if args.auto_augment is not None:
-            aa_policy = transforms.AutoAugmentPolicy(args.auto_augment)
-            trans.append(transforms.AutoAugment(policy=aa_policy))
-        trans.extend([
-            transforms.ToTensor(),
-            normalize,
-        ])
-        if args.random_erase > 0:
-            trans.append(transforms.RandomErasing(p=args.random_erase))
         dataset = torchvision.datasets.ImageFolder(
             traindir,
-            transforms.Compose(trans))
+            presets.ClassificationPresetTrain(crop_size=crop_size, auto_augment_policy=args.auto_augment,
+                                              random_erase_prob=args.random_erase))
         if args.cache_dataset:
             print("Saving dataset_train to {}".format(cache_path))
             utils.mkdir(os.path.dirname(cache_path))
@@ -124,12 +111,7 @@ def load_data(traindir, valdir, args):
     else:
         dataset_test = torchvision.datasets.ImageFolder(
             valdir,
-            transforms.Compose([
-                transforms.Resize(256),
-                transforms.CenterCrop(224),
-                transforms.ToTensor(),
-                normalize,
-            ]))
+            presets.ClassificationPresetEval(crop_size=crop_size, resize_size=resize_size))
         if args.cache_dataset:
             print("Saving dataset_test to {}".format(cache_path))
             utils.mkdir(os.path.dirname(cache_path))

references/detection/presets.py

@@ -0,0 +1,21 @@
+import transforms as T
+
+
+class DetectionPresetTrain:
+    def __init__(self, hflip_prob=0.5):
+        trans = [T.ToTensor()]
+        if hflip_prob > 0:
+            trans.append(T.RandomHorizontalFlip(hflip_prob))
+
+        self.transforms = T.Compose(trans)
+
+    def __call__(self, img, target):
+        return self.transforms(img, target)
+
+
+class DetectionPresetEval:
+    def __init__(self):
+        self.transforms = T.ToTensor()
+
+    def __call__(self, img, target):
+        return self.transforms(img, target)

references/detection/train.py

@@ -32,8 +32,8 @@
 from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups
 from engine import train_one_epoch, evaluate
 
+import presets
 import utils
-import transforms as T
 
 
 def get_dataset(name, image_set, transform, data_path):
@@ -48,11 +48,7 @@ def get_dataset(name, image_set, transform, data_path):
 
 
 def get_transform(train):
-    transforms = []
-    transforms.append(T.ToTensor())
-    if train:
-        transforms.append(T.RandomHorizontalFlip(0.5))
-    return T.Compose(transforms)
+    return presets.DetectionPresetTrain() if train else presets.DetectionPresetEval()
 
 
 def main(args):

references/segmentation/presets.py

@@ -0,0 +1,32 @@
+import transforms as T
+
+
+class SegmentationPresetTrain:
+    def __init__(self, base_size, crop_size, hflip_prob=0.5, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
+        min_size = int(0.5 * base_size)
+        max_size = int(2.0 * base_size)
+
+        trans = [T.RandomResize(min_size, max_size)]
+        if hflip_prob > 0:
+            trans.append(T.RandomHorizontalFlip(hflip_prob))
+        trans.extend([
+            T.RandomCrop(crop_size),
+            T.ToTensor(),
+            T.Normalize(mean=mean, std=std),
+        ])
+        self.transforms = T.Compose(trans)
+
+    def __call__(self, img, target):
+        return self.transforms(img, target)
+
+
+class SegmentationPresetEval:
+    def __init__(self, base_size, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
+        self.transforms = T.Compose([
+            T.RandomResize(base_size, base_size),
+            T.ToTensor(),
+            T.Normalize(mean=mean, std=std),
+        ])
+
+    def __call__(self, img, target):
+        return self.transforms(img, target)

references/segmentation/train.py

@@ -8,7 +8,7 @@
 import torchvision
 
 from coco_utils import get_coco
-import transforms as T
+import presets
 import utils
 
 
@@ -30,18 +30,7 @@ def get_transform(train):
     base_size = 520
     crop_size = 480
 
-    min_size = int((0.5 if train else 1.0) * base_size)
-    max_size = int((2.0 if train else 1.0) * base_size)
-    transforms = []
-    transforms.append(T.RandomResize(min_size, max_size))
-    if train:
-        transforms.append(T.RandomHorizontalFlip(0.5))
-        transforms.append(T.RandomCrop(crop_size))
-    transforms.append(T.ToTensor())
-    transforms.append(T.Normalize(mean=[0.485, 0.456, 0.406],
-                                  std=[0.229, 0.224, 0.225]))
-
-    return T.Compose(transforms)
+    return presets.SegmentationPresetTrain(base_size, crop_size) if train else presets.SegmentationPresetEval(base_size)
 
 
 def criterion(inputs, target):

references/video_classification/presets.py

@@ -0,0 +1,40 @@
+import torch
+
+from torchvision.transforms import transforms
+from transforms import ConvertBHWCtoBCHW, ConvertBCHWtoCBHW
+
+
+class VideoClassificationPresetTrain:
+    def __init__(self, resize_size, crop_size, mean=(0.43216, 0.394666, 0.37645), std=(0.22803, 0.22145, 0.216989),
+                 hflip_prob=0.5):
+        trans = [
+            ConvertBHWCtoBCHW(),
+            transforms.ConvertImageDtype(torch.float32),
+            transforms.Resize(resize_size),
+        ]
+        if hflip_prob > 0:
+            trans.append(transforms.RandomHorizontalFlip(hflip_prob))
+        trans.extend([
+            transforms.Normalize(mean=mean, std=std),
+            transforms.RandomCrop(crop_size),
+            ConvertBCHWtoCBHW()
+        ])
+        self.transforms = transforms.Compose(trans)
+
+    def __call__(self, x):
+        return self.transforms(x)
+
+
+class VideoClassificationPresetEval:
+    def __init__(self, resize_size, crop_size, mean=(0.43216, 0.394666, 0.37645), std=(0.22803, 0.22145, 0.216989)):
+        self.transforms = transforms.Compose([
+            ConvertBHWCtoBCHW(),
+            transforms.ConvertImageDtype(torch.float32),
+            transforms.Resize(resize_size),
+            transforms.Normalize(mean=mean, std=std),
+            transforms.CenterCrop(crop_size),
+            ConvertBCHWtoCBHW()
+        ])
+
+    def __call__(self, x):
+        return self.transforms(x)

references/video_classification/train.py

@@ -7,13 +7,12 @@
 from torch import nn
 import torchvision
 import torchvision.datasets.video_utils
-from torchvision import transforms as T
 from torchvision.datasets.samplers import DistributedSampler, UniformClipSampler, RandomClipSampler
 
+import presets
 import utils
 
 from scheduler import WarmupMultiStepLR
-from transforms import ConvertBHWCtoBCHW, ConvertBCHWtoCBHW
 
 try:
     from apex import amp
@@ -112,21 +111,11 @@ def main(args):
     print("Loading data")
     traindir = os.path.join(args.data_path, args.train_dir)
     valdir = os.path.join(args.data_path, args.val_dir)
-    normalize = T.Normalize(mean=[0.43216, 0.394666, 0.37645],
-                            std=[0.22803, 0.22145, 0.216989])
 
     print("Loading training data")
     st = time.time()
     cache_path = _get_cache_path(traindir)
-    transform_train = torchvision.transforms.Compose([
-        ConvertBHWCtoBCHW(),
-        T.ConvertImageDtype(torch.float32),
-        T.Resize((128, 171)),
-        T.RandomHorizontalFlip(),
-        normalize,
-        T.RandomCrop((112, 112)),
-        ConvertBCHWtoCBHW()
-    ])
+    transform_train = presets.VideoClassificationPresetTrain((128, 171), (112, 112))
 
     if args.cache_dataset and os.path.exists(cache_path):
         print("Loading dataset_train from {}".format(cache_path))
@@ -154,14 +143,7 @@ def main(args):
     print("Loading validation data")
     cache_path = _get_cache_path(valdir)
 
-    transform_test = torchvision.transforms.Compose([
-        ConvertBHWCtoBCHW(),
-        T.ConvertImageDtype(torch.float32),
-        T.Resize((128, 171)),
-        normalize,
-        T.CenterCrop((112, 112)),
-        ConvertBCHWtoCBHW()
-    ])
+    transform_test = presets.VideoClassificationPresetEval((128, 171), (112, 112))
 
     if args.cache_dataset and os.path.exists(cache_path):
         print("Loading dataset_test from {}".format(cache_path))