Upload 2 files

dataset_info.json

@@ -597,6 +597,51 @@
     "download_size": 1165568633,
     "dataset_size": 3076928106
   },
+  "I2E-FashionMNIST": {
+    "features": [
+      {
+        "name": "file_path",
+        "dtype": "string"
+      },
+      {
+        "name": "label",
+        "dtype": {
+          "class_label": {
+            "names": {
+              "0": "0",
+              "1": "1",
+              "2": "2",
+              "3": "3",
+              "4": "4",
+              "5": "5",
+              "6": "6",
+              "7": "7",
+              "8": "8",
+              "9": "9"
+            }
+          }
+        }
+      },
+      {
+        "name": "data",
+        "dtype": "binary"
+      }
+    ],
+    "splits": [
+      {
+        "name": "train",
+        "num_bytes": 132648890,
+        "num_examples": 60000
+      },
+      {
+        "name": "validation",
+        "num_bytes": 22098890,
+        "num_examples": 10000
+      }
+    ],
+    "download_size": 68196022,
+    "dataset_size": 154747780
+  },
   "I2E-ImageNet": {
     "features": [
       {
@@ -1632,6 +1677,51 @@
     "download_size": 57961329620,
     "dataset_size": 133825742994
   },
+  "I2E-MNIST": {
+    "features": [
+      {
+        "name": "file_path",
+        "dtype": "string"
+      },
+      {
+        "name": "label",
+        "dtype": {
+          "class_label": {
+            "names": {
+              "0": "0",
+              "1": "1",
+              "2": "2",
+              "3": "3",
+              "4": "4",
+              "5": "5",
+              "6": "6",
+              "7": "7",
+              "8": "8",
+              "9": "9"
+            }
+          }
+        }
+      },
+      {
+        "name": "data",
+        "dtype": "binary"
+      }
+    ],
+    "splits": [
+      {
+        "name": "train",
+        "num_bytes": 132648890,
+        "num_examples": 60000
+      },
+      {
+        "name": "validation",
+        "num_bytes": 22098890,
+        "num_examples": 10000
+      }
+    ],
+    "download_size": 60473109,
+    "dataset_size": 154747780
+  },
   "I2E-Mini-ImageNet": {
     "features": [
       {

load_i2e_datasets.py

@@ -2,35 +2,42 @@ import io
 import torch
 import numpy as np
 from datasets import load_dataset
-from torch.utils.data import Dataset
+from torch.utils.data import Dataset, DataLoader
 
-
-# =================================================
-# Read and unpack function (Your proposed Refactor)
-# =================================================
+# ==================================================================
+# 1. Core Decoding Function (Handles the binary packing)
+# ==================================================================
 def unpack_event_data(item, use_io=True):
+    """
+    Decodes the custom binary format:
+    Header (8 bytes) -> Shape (T, C, H, W) -> Body (Packed Bits)
+    """
     if use_io:
         with io.BytesIO(item['data']) as f:
             raw_data = np.load(f)
     else:
         raw_data = np.load(item)
-    header_size = 4 * 2                                         # Parse Header (First 4*2=8 bytes)
-    shape_header = raw_data[:header_size].view(np.uint16)       # Assume shape is 4D (T, C, H, W)
-    original_shape = tuple(shape_header)
+        
+    header_size = 4 * 2      # Parse Header (First 8 bytes for 4 uint16 shape values)
+    shape_header = raw_data[:header_size].view(np.uint16)
+    original_shape = tuple(shape_header) # Returns (T, C, H, W)
     
-    packed_body = raw_data[header_size:]                        # Parse Body
-    unpacked = np.unpackbits(packed_body)                       # Unpack bits to binary array
+    packed_body = raw_data[header_size:]    # Parse Body & Bit-unpacking
+    unpacked = np.unpackbits(packed_body)
     
-    num_elements = np.prod(original_shape)                      # Extract valid bits (Handle padding)
+    num_elements = np.prod(original_shape)  # Extract valid bits (Handle padding)
     event_flat = unpacked[:num_elements]
+    event_data = event_flat.reshape(original_shape).astype(np.float32).copy()
     
-    event_data = event_flat.reshape(original_shape).astype(np.float32).copy()   # Reshape and convert to Float Tensor
     return torch.from_numpy(event_data)
 
-
+# ==================================================================
+# 2. Dataset Wrapper
+# ==================================================================
 class I2E_Dataset(Dataset):
-    def __init__(self, cache_dir, load_datasets, split='train', transform=None, target_transform=None):
-        self.ds = load_dataset('UESTC-BICS/I2E', load_datasets, split=split, cache_dir=cache_dir)
+    def __init__(self, cache_dir, config_name, split='train', transform=None, target_transform=None):
+        print(f"🚀 Loading {config_name} [{split}] from Hugging Face...")
+        self.ds = load_dataset('UESTC-BICS/I2E', config_name, split=split, cache_dir=cache_dir, keep_in_memory=False)
         self.transform = transform
         self.target_transform = target_transform
 
@@ -39,12 +46,30 @@ class I2E_Dataset(Dataset):
 
     def __getitem__(self, idx):
         item = self.ds[idx]
-        event = unpack_event_data(item)      # Returns [T, C, H, W] Tensor
+        event = unpack_event_data(item)
         label = item['label']
-
         if self.transform:
             event = self.transform(event)
         if self.target_transform:
             label = self.target_transform(label)
-        
-        return event, label
+        return event, label
+
+# ==================================================================
+# 3. Run Example
+# ==================================================================
+if __name__ == "__main__":
+    import os
+    os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'     # Use HF mirror server in some regions
+
+    DATASET_NAME = 'I2E-CIFAR10'                            # Choose your config: 'I2E-CIFAR10', 'I2E-ImageNet', etc.
+    MODEL_PATH = 'Your cache path here'                     # e.g., './hf_datasets_cache/'
+    
+    train_dataset = I2E_Dataset(MODEL_PATH, DATASET_NAME, split='train')
+    val_dataset = I2E_Dataset(MODEL_PATH, DATASET_NAME, split='validation')
+
+    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=32, persistent_workers=True)
+    val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=32, persistent_workers=True)
+
+    events, labels = next(iter(train_loader))
+    print(f"✅ Loaded Batch Shape: {events.shape}") # Expect: [32, T, 2, H, W]
+    print(f"✅ Labels: {labels}")