Comments (1)
fbcotter
commented on May 21, 2024
Ah yes, this will happen with odd length signals. I think your input is quite small, particularly for a 3 scale transform, but let's consider a larger example and fewer scales:
j = 1
wave = 'db1'
mode = 'symmetric'
layer0 = DWTForward(J=j, wave=wave, mode=mode)
layer1 = DWTInverse(wave=wave, mode=mode)
test_input = torch.arange(27 * 9).reshape(1, 3, 9, 9).to(torch.float32)
low, high = layer0(test_input)
test_output = layer1((low, high))
print(test_input.shape, test_output.shape)
>> torch.Size([1, 3, 9, 9]) torch.Size([1, 3, 10, 10])Let's look at the size of low and high:
print(low.shape)
>> torch.Size([1, 3, 5, 5])
print(high[0].shape)
>> torch.Size([1, 3, 3, 5, 5])What's happening here? As we decimate by two as part of the transform then we need the signals to be even length, so the input is effectively padded, using the periodization mode you've selected (symmetric).
If we look at the output of the above reconstructed tensor, you'll see what happens:
import numpy as np
np.set_printoptions(linewidth=120, suppress=True, precision=2)
print(test_output.numpy())
[[[[ -0. 1. 2. 3. 4. 5. 6. 7. 8. 8.]
[ 9. 10. 11. 12. 13. 14. 15. 16. 17. 17.]
[ 18. 19. 20. 21. 22. 23. 24. 25. 26. 26.]
[ 27. 28. 29. 30. 31. 32. 33. 34. 35. 35.]
[ 36. 37. 38. 39. 40. 41. 42. 43. 44. 44.]
[ 45. 46. 47. 48. 49. 50. 51. 52. 53. 53.]
[ 54. 55. 56. 57. 58. 59. 60. 61. 62. 62.]
[ 63. 64. 65. 66. 67. 68. 69. 70. 71. 71.]
[ 72. 73. 74. 75. 76. 77. 78. 79. 80. 80.]
[ 72. 73. 74. 75. 76. 77. 78. 79. 80. 80.]]
[[ 81. 82. 83. 84. 85. 86. 87. 88. 89. 89.]
[ 90. 91. 92. 93. 94. 95. 96. 97. 98. 98.]
[ 99. 100. 101. 102. 103. 104. 105. 106. 107. 107.]
[108. 109. 110. 111. 112. 113. 114. 115. 116. 116.]
[117. 118. 119. 120. 121. 122. 123. 124. 125. 125.]
[126. 127. 128. 129. 130. 131. 132. 133. 134. 134.]
[135. 136. 137. 138. 139. 140. 141. 142. 143. 143.]
[144. 145. 146. 147. 148. 149. 150. 151. 152. 152.]
[153. 154. 155. 156. 157. 158. 159. 160. 161. 161.]
[153. 154. 155. 156. 157. 158. 159. 160. 161. 161.]]
[[162. 163. 164. 165. 166. 167. 168. 169. 170. 170.]
[171. 172. 173. 174. 175. 176. 177. 178. 179. 179.]
[180. 181. 182. 183. 184. 185. 186. 187. 188. 188.]
[189. 190. 191. 192. 193. 194. 195. 196. 197. 197.]
[198. 199. 200. 201. 202. 203. 204. 205. 206. 206.]
[207. 208. 209. 210. 211. 212. 213. 214. 215. 215.]
[216. 217. 218. 219. 220. 221. 222. 223. 224. 224.]
[225. 226. 227. 228. 229. 230. 231. 232. 233. 233.]
[234. 235. 236. 237. 238. 239. 240. 241. 242. 242.]
[234. 235. 236. 237. 238. 239. 240. 241. 242. 242.]]]]If you have to use odd length signals, you can crop the top left of the output. But otherwise it's good to have an input that's an integer multiple of 2^J
from pytorch_wavelets.
Related Issues (20)
- A bug when importing DWTForward HOT 1
- The order of the input parameters of the AFB2D function HOT 2
- Support for DoubleFloat HOT 1
- How to implement DTCWT to 1-D signals? HOT 10
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- Reconstructed wavelet HOT 1
- Export DWTForward to onnx HOT 1
- Make pypi release with 1D DWT
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- DWTInverse is not the inverse of DWTForward for small image sizes.
- ValueError: step must be greater than zero HOT 1
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- How does output yl reflect the two approximate subbands?
- How to change default tensor dtype in pytorch_wavelets HOT 2
- Why must kernels be fixed?
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- py37_cu102/fused/fused.so
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from pytorch_wavelets.