.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
       - id: isort
         args: ["--profile", "black"]
   - repo: https://github.com/ambv/black
-    rev: 22.12.0
+    rev: 23.1.0
     hooks:
     - id: black
   - repo: https://github.com/pycqa/flake8

dan/decoder.py

@@ -214,7 +214,6 @@ class GlobalDecoderLayer(Module):
         memory_key_padding_mask=None,
         predict_last_n_only=None,
     ):
-
         if memory_value is None:
             memory_value = memory_key
 

dan/manager/dataset.py

@@ -365,7 +365,6 @@ def apply_preprocessing(sample, preprocessings):
     resize_ratio = [1, 1]
     img = sample["img"]
     for preprocessing in preprocessings:
-
         if preprocessing["type"] == "dpi":
             ratio = preprocessing["target"] / preprocessing["source"]
             temp_img = img

dan/predict/attention.py

@@ -31,6 +31,27 @@ def split_text(text, level, word_separators, line_separators):
     return text_split, offset
 
 
+def compute_coverage(text: str, max_value: float, offset: int, attentions):
+    """
+    Aggregates attention maps for the current text piece (char, word, line)
+    :param text: Text piece selected with offset after splitting DAN prediction
+    :param max_value: Maximum "attention intensity" for parts of a text piece, used for normalization
+    :param offset: Offset value to get the relevant part of text piece
+    :param attentions: Attention weights of size (n_char, feature_height, feature_width)
+    """
+    _, height, width = attentions.shape
+
+    # blank vector to accumulate weights for the current text
+    coverage_vector = np.zeros((height, width))
+    for i in range(len(text)):
+        local_weight = cv2.resize(attentions[i + offset], (width, height))
+        coverage_vector = np.clip(coverage_vector + local_weight, 0, 1)
+
+    # Normalize coverage vector
+    coverage_vector = (coverage_vector / max_value * 255).astype(np.uint8)
+    return coverage_vector
+
+
 def plot_attention(
     image,
     text,
@@ -50,6 +71,7 @@ def plot_attention(
     :param scale: Scaling factor for the output gif image
     :param outname: Name of the gif image
     """
+
     height, width, _ = image.shape
     attention_map = []
 
@@ -64,20 +86,15 @@ def plot_attention(
     tot_len = 0
 
     max_value = weights.sum(0).max()
+
     for text_piece in text_list:
-        # blank vector to accumulate weights for the current word/line
-        coverage_vector = np.zeros((height, width))
-        for i in range(len(text_piece)):
-            local_weight = weights[i + tot_len]
-            local_weight = cv2.resize(local_weight, (width, height))
-            coverage_vector = np.clip(coverage_vector + local_weight, 0, 1)
+        # Accumulate weights for the current word/line and resize to original image size
+        coverage_vector = compute_coverage(text_piece, max_value, tot_len, weights)
+        coverage_vector = cv2.resize(coverage_vector, (width, height))
 
         # Keep track of text length
         tot_len += len(text_piece) + offset
 
-        # Normalize coverage vector
-        coverage_vector = (coverage_vector / max_value * 255).astype(np.uint8)
-
         # Blend coverage vector with original image
         blank_array = np.zeros((height, width)).astype(np.uint8)
         coverage_vector = Image.fromarray(

dan/schedulers.py

@@ -30,7 +30,7 @@ class DropoutScheduler:
                 self.init_teta_list_module(child)
 
     def update_dropout_rate(self):
-        for (module, p) in self.teta_list:
+        for module, p in self.teta_list:
             module.p = self.function(p, self.step_num, self.T)
 
 

dan/transforms.py

@@ -148,7 +148,6 @@ class ZoomRatio:
 
 class ElasticDistortion:
     def __init__(self, kernel_size=(7, 7), sigma=5, alpha=1):
-
         self.kernel_size = kernel_size
         self.sigma = sigma
         self.alpha = alpha

doc-requirements.txt

 black==22.12.0
 doc8==1.1.1
 mkdocs==1.4.2
-mkdocs-material==9.1.0
+mkdocs-material==9.1.2
 mkdocstrings==0.20.0
 mkdocstrings-python==0.8.3
 recommonmark==0.7.1

requirements.txt

 arkindex-client==1.0.11
-boto3==1.26.83
+boto3==1.26.90
 editdistance==0.6.2
 fontTools==4.38.0
 imageio==2.26.0
-mlflow==2.0.1
+mlflow==2.2.2
 networkx==3.0
 numpy==1.23.5
 opencv-python==4.7.0.72