test

data/dataset.py

@@ -8,11 +8,13 @@ from io import BytesIO
 from torch.utils.data import Dataset
 from torchvision.datasets.folder import default_loader
 from torchvision.datasets import ImageFolder
+from torchvision import transforms
+from torchvision.transforms import functional
 from pathlib import Path
 from collections import defaultdict
 
 
-class CARS(Dataset):
+class _CARS(Dataset):
     def __init__(self, root, loader=default_loader, transform=None):
         self.root = Path(root)
         self.transform = transform
@@ -32,7 +34,7 @@ class CARS(Dataset):
         sample = self.loader(self.root / "cars_train" / file_name)
         if self.transform is not None:
             sample = self.transform(sample)
-        return sample
+        return sample, target
 
 
 class ImprovedImageFolder(ImageFolder):
@@ -44,7 +46,7 @@ class ImprovedImageFolder(ImageFolder):
         assert len(self.classes_list) == len(self.classes)
 
     def __getitem__(self, item):
-        return super().__getitem__(item)[0]
+        return super().__getitem__(item)
 
 
 class LMDBDataset(Dataset):
@@ -61,16 +63,10 @@ class LMDBDataset(Dataset):
 
     def __getitem__(self, i):
         with self.db.begin(write=False) as txn:
-            sample = Image.open(BytesIO(txn.get("{}".format(i).encode())))
-            if sample.mode != "RGB":
-                sample = sample.convert("RGB")
+            sample, target = pickle.loads(txn.get("{}".format(i).encode()))
             if self.transform is not None:
-                try:
-                    sample = self.transform(sample)
-                except RuntimeError as re:
-                    print(sample.format, sample.size, sample.mode)
-                    raise re
-            return sample
+                sample = self.transform(sample)
+            return sample, target
 
 
 class EpisodicDataset(Dataset):
@@ -81,6 +77,24 @@ class EpisodicDataset(Dataset):
         self.num_set = num_set  # K
         self.num_episodes = num_episodes
 
+        self.t0 = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+        self.transform = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+
+    def apply_transform(self, img):
+        # img1 = self.transform(img)
+        # img2 = self.transform(img)
+        # return [self.t0(img), self.t0(functional.hflip(img))]
+        return [self.t0(img)]
+
     def __len__(self):
         return self.num_episodes
 
@@ -95,8 +109,11 @@ class EpisodicDataset(Dataset):
                 idx_list = torch.randperm(len(image_list))[:self.num_set * 2].tolist()
             else:
                 idx_list = torch.randint(high=len(image_list), size=(self.num_set * 2,)).tolist()
-            support_set_list.extend([self.origin[image_list[idx]] for idx in idx_list[:self.num_set]])
-            query_set_list.extend([self.origin[image_list[idx]] for idx in idx_list[self.num_set:]])
+            support = [self.origin[image_list[idx]][0] for idx in idx_list[:self.num_set]]
+            query = [self.origin[image_list[idx]][0] for idx in idx_list[:self.num_set]]
+
+            support_set_list.extend(sum(map(self.apply_transform, support), list()))
+            query_set_list.extend(sum(map(self.apply_transform, query), list()))
             target_list.extend([i] * self.num_set)
         return {
             "support": torch.stack(support_set_list),

data/lmdbify.py

@@ -1,30 +1,31 @@
 import os
 import pickle
-from io import BytesIO
 import argparse
-
+from PIL import Image
 import lmdb
-from data.dataset import CARS, ImprovedImageFolder
+from data.dataset import ImprovedImageFolder
 from tqdm import tqdm
 
 
 def content_loader(path):
-    with open(path, "rb") as f:
-        return f.read()
+    im = Image.open(path)
+    im = im.resize((256, 256))
+    if im.mode != "RGB":
+        im = im.convert("RGB")
+    return im
 
 
 def dataset_to_lmdb(dataset, lmdb_path):
     env = lmdb.open(lmdb_path, map_size=1099511627776*2, subdir=os.path.isdir(lmdb_path))
     with env.begin(write=True) as txn:
         for i in tqdm(range(len(dataset)), ncols=50):
-            txn.put("{}".format(i).encode(), bytearray(dataset[i]))
+            txn.put("{}".format(i).encode(), pickle.dumps(dataset[i]))
         txn.put(b"classes_list", pickle.dumps(dataset.classes_list))
         txn.put(b"__len__", pickle.dumps(len(dataset)))
 
 
 def transform(save_path, dataset_path):
     print(save_path, dataset_path)
-    # origin_dataset = CARS("/data/few-shot/STANFORD-CARS/", loader=content_loader)
     origin_dataset = ImprovedImageFolder(dataset_path, loader=content_loader)
     dataset_to_lmdb(origin_dataset, save_path)
 

loss/prototypical.py

@@ -0,0 +1,19 @@
+import torch
+
+
+def euclidean_dist(x, y):
+    """
+        Compute euclidean distance between two tensors
+    """
+    # x: B x N x D
+    # y: B x M x D
+    n = x.size(-2)
+    m = y.size(-2)
+    d = x.size(-1)
+    if d != y.size(-1):
+        raise Exception
+
+    x = x.unsqueeze(2).expand(x.size(0), n, m, d)  # B x N x M x D
+    y = y.unsqueeze(1).expand(x.size(0), n, m, d)
+
+    return torch.pow(x - y, 2).sum(-1)

test.py

@@ -49,12 +49,11 @@ def evaluate(query, target, support):
 
 def test(lmdb_path, import_path):
     dt = torchvision.transforms.Compose([
-        torchvision.transforms.Resize((256, 256)),
         torchvision.transforms.CenterCrop(224),
         torchvision.transforms.ToTensor(),
         torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     ])
-    origin_dataset = dataset.LMDBDataset(lmdb_path, transform=dt)
+    origin_dataset = dataset.LMDBDataset(lmdb_path, transform=None)
     N = 5
     K = 5
     episodic_dataset = dataset.EpisodicDataset(
@@ -65,8 +64,8 @@ def test(lmdb_path, import_path):
     )
     print(episodic_dataset)
 
-    data_loader = DataLoader(episodic_dataset, batch_size=20, pin_memory=False)
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    data_loader = DataLoader(episodic_dataset, batch_size=8, pin_memory=False)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     submit = import_module(f"submit.{import_path}")
 
@@ -78,12 +77,19 @@ def test(lmdb_path, import_path):
     with torch.no_grad():
         for item in tqdm(data_loader):
             item = convert_tensor(item, device, non_blocking=True)
-            # item["query"]: B x NK x 3 x W x H
-            # item["support"]: B x NK x 3 x W x H
+            # item["query"]: B x ANK x 3 x W x H
+            # item["support"]: B x ANK x 3 x W x H
             # item["target"]: B x NK
             batch_size = item["target"].size(0)
-            query_batch = extractor(item["query"].view([-1, *item["query"].shape[-3:]])).view(batch_size, N * K, -1)
-            support_batch = extractor(item["support"].view([-1, *item["query"].shape[-3:]])).view(batch_size, N, K, -1)
+            image_size = item["query"].shape[-3:]
+            A = int(item["query"].size(1) / (N * K))
+
+            query_batch = extractor(item["query"].view([-1, *image_size])).view(batch_size, N * K, A, -1)
+            support_batch = extractor(item["support"].view([-1, *image_size])).view(batch_size, N, K, A, -1)
+
+            query_batch = torch.mean(query_batch, -2)
+            support_batch = torch.mean(support_batch, -2)
+
             accs.append(evaluate(query_batch, item["target"], support_batch))
     print(torch.tensor(accs).mean().item())
 
@@ -91,11 +97,12 @@ def test(lmdb_path, import_path):
 if __name__ == '__main__':
     setup_seed(100)
     defined_path = [
-                    "/data/few-shot/lmdb/dogs/data.lmdb",
-                    "/data/few-shot/lmdb/flowers/data.lmdb",
-                    "/data/few-shot/lmdb/256-object/data.lmdb",
-                    "/data/few-shot/lmdb/dtd/data.lmdb",
-                    ]
+        "/data/few-shot/lmdb256/dogs.lmdb",
+        "/data/few-shot/lmdb256/flowers.lmdb",
+        "/data/few-shot/lmdb256/256-object.lmdb",
+        "/data/few-shot/lmdb256/dtd.lmdb",
+        "/data/few-shot/lmdb256/mini-imagenet-test.lmdb"
+    ]
     parser = argparse.ArgumentParser(description="test")
     parser.add_argument('-i', "--import_path", required=True)
     args = parser.parse_args()