chore: import upstream snapshot with attribution

2026-07-13 13:24:13 +08:00
commit 1037506f2e
6050 changed files with 1731598 additions and 0 deletions
@@ -0,0 +1,57 @@
+# Copyright Howto100m authors.
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+import torch as th
+
+class Normalize(object):
+
+    def __init__(self, mean, std):
+        self.mean = th.FloatTensor(mean).view(1, 3, 1, 1)
+        self.std = th.FloatTensor(std).view(1, 3, 1, 1)
+
+    def __call__(self, tensor):
+        tensor = (tensor - self.mean) / (self.std + 1e-8)
+        return tensor
+
+class Preprocessing(object):
+
+    def __init__(self, type):
+        self.type = type
+        if type == '2d':
+            self.norm = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        elif type == '3d':
+            self.norm = Normalize(mean=[110.6, 103.2, 96.3], std=[1.0, 1.0, 1.0])
+        elif type == 'vmz':
+            self.norm = Normalize(mean=[110.201, 100.64, 95.997], std=[58.1489, 56.4701, 55.3324])
+
+    def _zero_pad(self, tensor, size):
+        n = size - len(tensor) % size
+        if n == size:
+            return tensor
+        else:
+            z = th.zeros(n, tensor.shape[1], tensor.shape[2], tensor.shape[3])
+            return th.cat((tensor, z), 0)
+
+    def __call__(self, tensor):
+        if self.type == '2d':
+            tensor = tensor / 255.0
+            tensor = self.norm(tensor)
+        elif self.type == 'vmz':
+            #tensor = self._zero_pad(tensor, 8)
+            tensor = self._zero_pad(tensor, 10)
+            tensor = self.norm(tensor)
+            #tensor = tensor.view(-1, 8, 3, 112, 112)
+            tensor = tensor.view(-1, 10, 3, 112, 112)
+            tensor = tensor.transpose(1, 2)
+        elif self.type == '3d':
+            tensor = self._zero_pad(tensor, 16)
+            tensor = self.norm(tensor)
+            tensor = tensor.view(-1, 16, 3, 112, 112)
+            tensor = tensor.transpose(1, 2)
+        elif self.type == 's3d':
+            tensor = tensor / 255.0
+            tensor = self._zero_pad(tensor, 30)
+            tensor = tensor.view(-1, 30, 3, 224, 224) # N x 30 x 3 x H x W
+            tensor = tensor.transpose(1, 2) # N x 3 x 30 x H x W
+        # for vae do nothing
+        return tensor