move keras-specific classes to utils.keras, clean up imports

qurator/eynollah/eynollah.py

@@ -15,27 +15,27 @@ from typing import Optional
 import warnings
 from pathlib import Path
 from multiprocessing import Process, Queue, cpu_count
-import gc
 from PIL.Image import Image
 from ocrd import OcrdPage
-from ocrd_utils import getLogger
 import cv2
 import numpy as np
+from scipy.signal import find_peaks
+import matplotlib.pyplot as plt
+from scipy.ndimage import gaussian_filter1d
+
+from qurator.eynollah.utils.keras import PatchEncoder, Patches
+
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 stderr = sys.stderr
 sys.stderr = open(os.devnull, "w")
 import tensorflow as tf
-from tensorflow.python.keras import backend as K
-from tensorflow.keras.models import load_model
 sys.stderr = stderr
 tf.get_logger().setLevel("ERROR")
 warnings.filterwarnings("ignore")
-from scipy.signal import find_peaks
+
-import matplotlib.pyplot as plt
+load_model = tf.keras.models.load_model
-from scipy.ndimage import gaussian_filter1d
 # use tf1 compatibility for keras backend
-from tensorflow.compat.v1.keras.backend import set_session
+set_session = tf.compat.v1.keras.backend.set_session
-from tensorflow.keras import layers
 
 from .utils.contour import (
     filter_contours_area_of_image,
@@ -89,61 +89,8 @@ DPI_THRESHOLD = 298
 MAX_SLOPE = 999
 KERNEL = np.ones((5, 5), np.uint8)
 
-projection_dim = 64
+class Eynollah():
-patch_size = 1
+
-num_patches =21*21#14*14#28*28#14*14#28*28
-
-
-class Patches(layers.Layer):
-    def __init__(self, **kwargs):
-        super(Patches, self).__init__()
-        self.patch_size = patch_size
-
-    def call(self, images):
-        batch_size = tf.shape(images)[0]
-        patches = tf.image.extract_patches(
-            images=images,
-            sizes=[1, self.patch_size, self.patch_size, 1],
-            strides=[1, self.patch_size, self.patch_size, 1],
-            rates=[1, 1, 1, 1],
-            padding="VALID",
-        )
-        patch_dims = patches.shape[-1]
-        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
-        return patches
-    def get_config(self):
-
-        config = super().get_config().copy()
-        config.update({
-            'patch_size': self.patch_size,
-        })
-        return config
-    
-    
-class PatchEncoder(layers.Layer):
-    def __init__(self, **kwargs):
-        super(PatchEncoder, self).__init__()
-        self.num_patches = num_patches
-        self.projection = layers.Dense(units=projection_dim)
-        self.position_embedding = layers.Embedding(
-            input_dim=num_patches, output_dim=projection_dim
-        )
-
-    def call(self, patch):
-        positions = tf.range(start=0, limit=self.num_patches, delta=1)
-        encoded = self.projection(patch) + self.position_embedding(positions)
-        return encoded
-    def get_config(self):
-
-        config = super().get_config().copy()
-        config.update({
-            'num_patches': self.num_patches,
-            'projection': self.projection,
-            'position_embedding': self.position_embedding,
-        })
-        return config
-
-class Eynollah:
     def __init__(
         self,
         dir_models : str,
@@ -673,7 +620,7 @@ class Eynollah:
                 model = load_model(model_dir, compile=False)
                 self.models[model_dir] = model
             except:
-                model = load_model(model_dir , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
+                model = load_model(model_dir , compile=False, custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
                 self.models[model_dir] = model
 
 

qurator/eynollah/utils/keras.py

@@ -0,0 +1,58 @@
+import tensorflow as tf
+layers = tf.keras.layers
+
+PROJECTION_DIM = 64
+PATCH_SIZE = 1
+NUM_PATCHES = 21*21 #14*14#28*28#14*14#28*28
+
+class Patches(layers.Layer):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.patch_size = PATCH_SIZE
+
+    def call(self, inputs, *args, **kwargs):
+        batch_size = tf.shape(inputs)[0]
+        patches = tf.image.extract_patches(
+            images=inputs,
+            sizes=[1, self.patch_size, self.patch_size, 1],
+            strides=[1, self.patch_size, self.patch_size, 1],
+            rates=[1, 1, 1, 1],
+            padding="VALID",
+        )
+        patch_dims = patches.shape[-1]
+        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
+        return patches
+
+    def get_config(self):
+        config = super().get_config().copy()
+        config.update({
+            'patch_size': self.patch_size,
+        })
+        return config
+    
+    
+class PatchEncoder(layers.Layer):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.num_patches = NUM_PATCHES
+        self.projection = layers.Dense(units=PROJECTION_DIM)
+        self.position_embedding = layers.Embedding(input_dim=NUM_PATCHES, output_dim=PROJECTION_DIM)
+
+    def call(self, inputs, *args, **kwargs):
+        positions = tf.range(start=0, limit=self.num_patches, delta=1)
+        # XXX: pyright thinks self.projection(inputs) is None
+        encoded = self.projection(inputs) + self.position_embedding(positions)
+        return encoded
+
+    def get_config(self):
+        config = super().get_config().copy()
+        config.update({
+            'num_patches': self.num_patches,
+            'projection': self.projection,
+            'position_embedding': self.position_embedding,
+        })
+        return config
+
+