Updated the learn2learn "image_classification_imagenette_mini" example

flash_examples/integrations/learn2learn/image_classification_imagenette_mini.py

@@ -14,71 +14,127 @@
 
 # adapted from https://github.com/learnables/learn2learn/blob/master/examples/vision/protonet_miniimagenet.py#L154
 
+"""## Train file https://www.dropbox.com/s/9g8c6w345s2ek03/mini-imagenet-cache-train.pkl?dl=1
+
+## Validation File
+https://www.dropbox.com/s/ip1b7se3gij3r1b/mini-imagenet-cache-validation.pkl?dl=1
+
+Followed by renaming the pickle files
+cp './mini-imagenet-cache-train.pkl?dl=1' './mini-imagenet-cache-train.pkl'
+cp './mini-imagenet-cache-validation.pkl?dl=1' './mini-imagenet-cache-validation.pkl'
+"""
+
 import warnings
+from dataclasses import dataclass
+from typing import Callable, Tuple, Union
 
 import kornia.augmentation as Ka
 import kornia.geometry as Kg
 import learn2learn as l2l
+import numpy as np
 import torch
 import torchvision
+import torchvision.transforms as T
+from PIL import Image
 from torch import nn
 
 import flash
 from flash.core.data.io.input import DataKeys
+from flash.core.data.io.input_transform import InputTransform
 from flash.core.data.transforms import ApplyToKeys, kornia_collate
 from flash.image import ImageClassificationData, ImageClassifier
 
 warnings.simplefilter("ignore")
 
 # download MiniImagenet
-train_dataset = l2l.vision.datasets.MiniImagenet(root="data", mode="train", download=True)
-val_dataset = l2l.vision.datasets.MiniImagenet(root="data", mode="validation", download=True)
+train_dataset = l2l.vision.datasets.MiniImagenet(root="./", mode="train", download=False)
+val_dataset = l2l.vision.datasets.MiniImagenet(root="./", mode="validation", download=False)
+
+
+@dataclass
+class ImageClassificationInputTransform(InputTransform):
+
+    image_size: Tuple[int, int] = (196, 196)
+    mean: Union[float, Tuple[float, float, float]] = (0.485, 0.456, 0.406)
+    std: Union[float, Tuple[float, float, float]] = (0.229, 0.224, 0.225)
 
-transform = {
-    "per_sample_transform": nn.Sequential(
-        ApplyToKeys(
+    def per_sample_transform(self):
+        return T.Compose(
+            [
+                ApplyToKeys(
+                    DataKeys.INPUT,
+                    T.Compose(
+                        [
+                            T.ToTensor(),
+                            Kg.Resize((196, 196)),
+                            # SPATIAL
+                            Ka.RandomHorizontalFlip(p=0.25),
+                            Ka.RandomRotation(degrees=90.0, p=0.25),
+                            Ka.RandomAffine(degrees=1 * 5.0, shear=1 / 5, translate=1 / 20, p=0.25),
+                            Ka.RandomPerspective(distortion_scale=1 / 25, p=0.25),
+                            # PIXEL-LEVEL
+                            Ka.ColorJitter(brightness=1 / 30, p=0.25),  # brightness
+                            Ka.ColorJitter(saturation=1 / 30, p=0.25),  # saturation
+                            Ka.ColorJitter(contrast=1 / 30, p=0.25),  # contrast
+                            Ka.ColorJitter(hue=1 / 30, p=0.25),  # hue
+                            Ka.RandomMotionBlur(kernel_size=2 * (4 // 3) + 1, angle=1, direction=1.0, p=0.25),
+                            Ka.RandomErasing(scale=(1 / 100, 1 / 50), ratio=(1 / 20, 1), p=0.25),
+                        ]
+                    ),
+                ),
+                ApplyToKeys(DataKeys.TARGET, torch.as_tensor),
+            ]
+        )
+
+    def train_per_sample_transform(self):
+        return T.Compose(
+            [
+                ApplyToKeys(
+                    DataKeys.INPUT,
+                    T.Compose(
+                        [
+                            T.ToTensor(),
+                            T.Resize(self.image_size),
+                            T.Normalize(self.mean, self.std),
+                            T.RandomHorizontalFlip(),
+                            T.ColorJitter(),
+                            T.RandomAutocontrast(),
+                            T.RandomPerspective(),
+                        ]
+                    ),
+                ),
+                ApplyToKeys("target", torch.as_tensor),
+            ]
+        )
+
+    def per_batch_transform_on_device(self):
+        return ApplyToKeys(
             DataKeys.INPUT,
-            nn.Sequential(
-                torchvision.transforms.ToTensor(),
-                Kg.Resize((196, 196)),
-                # SPATIAL
-                Ka.RandomHorizontalFlip(p=0.25),
-                Ka.RandomRotation(degrees=90.0, p=0.25),
-                Ka.RandomAffine(degrees=1 * 5.0, shear=1 / 5, translate=1 / 20, p=0.25),
-                Ka.RandomPerspective(distortion_scale=1 / 25, p=0.25),
-                # PIXEL-LEVEL
-                Ka.ColorJitter(brightness=1 / 30, p=0.25),  # brightness
-                Ka.ColorJitter(saturation=1 / 30, p=0.25),  # saturation
-                Ka.ColorJitter(contrast=1 / 30, p=0.25),  # contrast
-                Ka.ColorJitter(hue=1 / 30, p=0.25),  # hue
-                Ka.RandomMotionBlur(kernel_size=2 * (4 // 3) + 1, angle=1, direction=1.0, p=0.25),
-                Ka.RandomErasing(scale=(1 / 100, 1 / 50), ratio=(1 / 20, 1), p=0.25),
-            ),
-        ),
-        ApplyToKeys(DataKeys.TARGET, torch.as_tensor),
-    ),
-    "collate": kornia_collate,
-    "per_batch_transform_on_device": ApplyToKeys(
-        DataKeys.INPUT,
-        Ka.RandomHorizontalFlip(p=0.25),
-    ),
-}
+            Ka.RandomHorizontalFlip(p=0.25),
+        )
+
+    def collate(self):
+        return kornia_collate
+
 
 # construct datamodule
+
 datamodule = ImageClassificationData.from_tensors(
     train_data=train_dataset.x,
     train_targets=torch.from_numpy(train_dataset.y.astype(int)),
     val_data=val_dataset.x,
     val_targets=torch.from_numpy(val_dataset.y.astype(int)),
-    transform=transform,
+    train_transform=ImageClassificationInputTransform,
+    val_transform=ImageClassificationInputTransform,
+    batch_size=1,
 )
 
 model = ImageClassifier(
     backbone="resnet18",
     training_strategy="prototypicalnetworks",
     training_strategy_kwargs={
         "epoch_length": 10 * 16,
-        "meta_batch_size": 4,
+        "meta_batch_size": 1,
         "num_tasks": 200,
         "test_num_tasks": 2000,
         "ways": datamodule.num_classes,
@@ -92,9 +148,9 @@
 )
 
 trainer = flash.Trainer(
-    max_epochs=200,
-    gpus=2,
-    accelerator="ddp_shared",
+    max_epochs=1,
+    gpus=1,
     precision=16,
 )
+
 trainer.finetune(model, datamodule=datamodule, strategy="no_freeze")