Remove unused utils functions

dan/utils.py

@@ -4,16 +4,6 @@ import numpy as np
 import torch
 from torch.distributions.uniform import Uniform
 
-# Layout string to token
-SEM_MATCHING_TOKENS_STR = {
-    "INTITULE": "ⓘ",
-    "DATE": "ⓓ",
-    "COTE_SERIE": "ⓢ",
-    "ANALYSE_COMPL": "ⓒ",
-    "PRECISIONS_SUR_COTE": "ⓟ",
-    "COTE_ARTICLE": "ⓐ",
-}
-
 # Layout begin-token to end-token
 SEM_MATCHING_TOKENS = {"ⓘ": "Ⓘ", "ⓓ": "Ⓓ", "ⓢ": "Ⓢ", "ⓒ": "Ⓒ", "ⓟ": "Ⓟ", "ⓐ": "Ⓐ"}
 
@@ -57,20 +47,6 @@ def pad_sequences_1D(data, padding_value):
     return padded_data
 
 
-def resize_max(img, max_width=None, max_height=None):
-    if max_width is not None and img.shape[1] > max_width:
-        ratio = max_width / img.shape[1]
-        new_h = int(np.floor(ratio * img.shape[0]))
-        new_w = int(np.floor(ratio * img.shape[1]))
-        img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
-    if max_height is not None and img.shape[0] > max_height:
-        ratio = max_height / img.shape[0]
-        new_h = int(np.floor(ratio * img.shape[0]))
-        new_w = int(np.floor(ratio * img.shape[1]))
-        img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
-    return img
-
-
 def pad_images(data, padding_value, padding_mode="br"):
     """
     data: list of numpy array
@@ -157,36 +133,6 @@ def pad_image(
     return output
 
 
-def pad_image_width_right(img, new_width, padding_value):
-    """
-    Pad img to right side with padding value to reach new_width as width
-    """
-    h, w, c = img.shape
-    pad_width = max((new_width - w), 0)
-    pad_right = np.ones((h, pad_width, c), dtype=img.dtype) * padding_value
-    img = np.concatenate([img, pad_right], axis=1)
-    return img
-
-
-def pad_image_width_random(img, new_width, padding_value, max_pad_left_ratio=1):
-    """
-    Randomly pad img to left and right sides with padding value to reach new_width as width
-    """
-    h, w, c = img.shape
-    pad_width = max((new_width - w), 0)
-    max_pad_left = int(max_pad_left_ratio * pad_width)
-    pad_left = (
-        randint(0, min(pad_width, max_pad_left))
-        if pad_width != 0 and max_pad_left > 0
-        else 0
-    )
-    pad_right = pad_width - pad_left
-    pad_left = np.ones((h, pad_left, c), dtype=img.dtype) * padding_value
-    pad_right = np.ones((h, pad_right, c), dtype=img.dtype) * padding_value
-    img = np.concatenate([pad_left, img, pad_right], axis=1)
-    return img
-
-
 def read_image(filename, scale=1.0):
     """
     Read image and rescale it