Remove opencv dependency and replace with torch logic

model_compression_toolkit/data_generation/pytorch/optimization_functions/image_initilization.py

@@ -15,11 +15,10 @@
 from functools import partial
 from typing import Tuple, Union, List, Callable, Dict
 
-import cv2
 from torch import Tensor
 from torchvision.transforms.transforms import _setup_size
 import torch
-import numpy as np
+import torch.nn.functional as F
 from torch.utils.data import Dataset, DataLoader
 
 from model_compression_toolkit.data_generation.common.enums import DataInitType
@@ -97,9 +96,8 @@ def diverse_sample(size: Tuple[int, ...]) -> Tensor:
     sample = random_std * torch.randn(size) + random_mean
 
     # filtering to make the image a bit smoother
-    kernel = np.ones((5, 5), np.float32) / 16
-    if sample.shape[1] < 500 and sample.shape[2] < 500:
-        sample = torch.from_numpy(cv2.filter2D(sample.float().detach().cpu().numpy(), -1, kernel))
+    kernel = torch.ones(NUM_INPUT_CHANNELS, NUM_INPUT_CHANNELS, 5, 5) / 16
+    sample = F.conv2d(sample, kernel, padding=1)
     return sample.float()
 
 def default_data_init_fn(

requirements.txt

@@ -2,7 +2,6 @@ networkx!=2.8.1
 tqdm
 Pillow
 numpy<2.0
-opencv-python
 scikit-image
 scikit-learn
 tensorboard