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python/test.py

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+import torch
+import torch.nn as nn
+from torch import Tensor
+import math
+
+'''import torch
+import torch.nn as nn
+
+import torch
+
+norm = torch.nn.LayerNorm(3)
+
+tensor = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).reshape((2, 3))
+
+out = norm(tensor)
+
+print(out)'''
+
+'''n_channel = 6
+norm = nn.LayerNorm(10)
+
+height = 10
+width = 10
+n_elements = height * width * n_channel
+
+t = torch.arange(0, n_elements, dtype=torch.float32).mul_(10.0 / n_elements).sin().reshape(1, n_channel, height, width)
+
+out = norm(t)
+print(out)'''
+
+def timestep_embedding(timesteps, dim, max_period=10000):
+  half = dim // 2
+  freqs = (-math.log(max_period) * torch.arange(half) / half).exp()
+  args = timesteps * freqs
+  return torch.cat( (args.cos(), args.sin()) ).reshape(1, -1)
+
+timesteps = Tensor([1, 2, 3]).reshape((3, 1))
+dim = 10
+res = timestep_embedding(timesteps, dim)
+
+print(res)
+
+'''n_group = 3
+n_channel = 6
+norm = nn.GroupNorm(n_group, n_channel)
+
+height = 10
+width = 10 
+n_elements = height * width * n_channel
+
+t = torch.arange(0, n_elements, dtype=torch.float32).mul_(10.0 / n_elements).sin().reshape(1, n_channel, height, width)
+
+out = norm(t)
+print(out.flatten())'''