add usage of new transforms to eval script

mu_map/eval/measures.py

 import numpy as np
+import pandas as pd
 import torch
 
 from mu_map.dataset.default import MuMapDataset
 from mu_map.dataset.normalization import MeanNormTransform
+from mu_map.dataset.transform import SequenceTransform, PadCropTranform
 from mu_map.models.unet import UNet
 
 torch.set_grad_enabled(False)
@@ -22,22 +24,8 @@ model = UNet()
 model.load_state_dict(torch.load("trainings/01_cgan/snapshots/050_generator.pth", map_location=device))
 model = model.eval()
 
-dataset = MuMapDataset("data/initial/", transform_normalization=MeanNormTransform(), split_name="validation")
-
-def crop_or_pad(inputs: torch.Tensor, target_shape=32):
-    # crop
-    if inputs.shape[1] > target_shape:
-        c = inputs.shape[1] // 2
-        diff = target_shape // 2
-        inputs = inputs[:, c-diff:c+diff]
-    elif inputs.shape[1] < target_shape:
-        diff = target_shape - inputs.shape[1]
-        padding_front = math.ceil(diff / 2)
-        padding_back = math.floor(diff / 2)
-        padding = [0, 0, 0, 0, padding_front, padding_back]
-        inputs = torch.nn.functional.pad(inputs, padding, mode="constant", value=0)
-    return inputs
-
+transform_normalization = SequenceTransform(transforms=[MeanNormTransform(), PadCropTranform(dim=3, size=32)])
+dataset = MuMapDataset("data/initial/", transform_normalization=transform_normalization, split_name="validation")
 
 scores_mse = []
 scores_nmae = []