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debug_interp2_repro_bicubic.py
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import numpy as np
import PIL.Image
import torch
try:
import cv2
has_cv2 = True
except ImportError as e:
has_cv2 = False
print(e)
resampling_map = {"bilinear": PIL.Image.BILINEAR, "nearest": PIL.Image.NEAREST, "bicubic": PIL.Image.BICUBIC}
def main():
out_size = (24, 24)
resample = "bicubic"
align_corners = None if resample == "nearest" else False
mf = "channels_first"
# mf = "channels_last"
antialias = False
c = 1
# size = 48
# tensor_uint8 = torch.arange(c * size * size, dtype=torch.uint8).reshape(c, size, size)
tensor_uint8 = torch.tensor([
[ 12., 13., 14., 15., 16., 17., 18., 19.],
[ 60., 61., 62., 63., 64., 65., 66., 67.],
[108., 109., 110., 111., 112., 113., 114., 115.],
[156., 157., 158., 159., 160., 161., 162., 163.],
[204., 205., 206., 207., 208., 209., 210., 211.],
[252., 253., 254., 255., 0., 1., 2., 3.],
[ 44., 45., 46., 47., 48., 49., 50., 51.],
[ 92., 93., 94., 95., 96., 97., 98., 99.]
], dtype=torch.uint8)[None, ...]
print(tensor_uint8.shape)
out_size = (4, 4)
size = tensor_uint8.shape[-1]
tensor_float32 = tensor_uint8.float()
tensor_uint8 = tensor_uint8[None, ...]
tensor_float32 = tensor_float32[None, ...]
if mf == "channels_last":
tensor_uint8 = tensor_uint8.contiguous(memory_format=torch.channels_last)
tensor_float32 = tensor_float32.contiguous(memory_format=torch.channels_last)
print("Memory format:", mf)
print("Antialias:", antialias)
output_uint8 = torch.nn.functional.interpolate(
tensor_uint8, mode=resample, size=out_size, align_corners=align_corners, antialias=antialias
)
print("output_uint8: \n", output_uint8[0, 0, :, :])
output_float32 = torch.nn.functional.interpolate(
tensor_float32, mode=resample, size=out_size, align_corners=align_corners, antialias=antialias
)
if resample == "bicubic":
output_float32 = output_float32.clamp(min=0, max=255).round()
print("output_float32: \n", output_float32[0, 0, :, :])
abs_diff = torch.abs(output_float32 - output_uint8.float())
mae = torch.mean(abs_diff)
max_abs_err = torch.max(abs_diff)
print("PyTorch uint8 vs PyTorch float: Mean Absolute Error:", mae.item())
print("PyTorch uint8 vs PyTorch float: Max Absolute Error:", max_abs_err.item())
if has_cv2 and not antialias:
a_uint8 = tensor_uint8[0, ...].permute(1, 2, 0).contiguous().numpy()
a_float32 = a_uint8.astype("float32")
output_uint8_cv2 = cv2.resize(a_uint8, dsize=out_size, interpolation=cv2.INTER_CUBIC)
print("output_uint8_cv2: \n", output_uint8_cv2[:8, :8])
output_float32_cv2 = cv2.resize(a_float32, dsize=out_size, interpolation=cv2.INTER_CUBIC)
print("output_float32_cv2: \n", output_float32_cv2[:8, :8])
if resample == "bicubic":
output_float32_cv2 = np.clip(output_float32_cv2, 0, 255).round()
abs_diff = np.abs(output_float32_cv2 - output_uint8_cv2.astype("float32"))
mae = np.mean(abs_diff)
max_abs_err = np.max(abs_diff)
print("CV2 uint8 vs CV2 float: Mean Absolute Error:", mae.item())
print("CV2 uint8 vs CV2 float: Max Absolute Error:", max_abs_err.item())
# m = abs_diff > max_abs_err.item() - 1e-1
# print("Diff f32:\n", output_float32[m])
# print("Diff ui8:\n", output_uint8[m])
# print("Non-matched pixels:")
# indices = torch.nonzero(m)
# for idx in indices:
# print("out index:", idx)
# print("out f32:", output_float32[idx[0], idx[1], max(idx[2]-2,0):idx[2]+2, max(idx[3]-2,0):idx[3]+2])
# print("out ui8:", output_uint8[idx[0], idx[1], max(idx[2]-2,0):idx[2]+2, max(idx[3]-2,0):idx[3]+2])
# scale = size / min(out_size)
# idx = (idx * scale).to(torch.long)
# print("in index:", idx)
# print("f32:", tensor_float32[idx[0], idx[1], max(idx[2]-4,0):idx[2]+4, max(idx[3]-4,0):idx[3]+4])
# break
if __name__ == "__main__":
torch.set_num_threads(1)
print("")
print(f"Torch version: {torch.__version__}")
print(f"Torch config: {torch.__config__.show()}")
print(f"Num threads: {torch.get_num_threads()}")
print("")
print("PIL version: ", PIL.__version__)
main()