validating fashion_mnist train - kamangir/bolt#689

image_classifier/__init__.py

@@ -1,5 +1,5 @@
 name = "image_classifier"
 
-version = "1.1.57"
+version = "1.1.59"
 
 description = "fashion-mnist + hugging-face + awesome-bash-cli"

image_classifier/__main__.py

@@ -1,6 +1,6 @@
 import argparse
 from . import *
-from .classes import Image_Classifier
+from .classes import *
 from .funcs import *
 from abcli import file
 import abcli.logging
@@ -112,7 +112,7 @@ parser.add_argument(
 parser.add_argument(
     "--window_size",
     type=int,
-    default=28,
+    default=default_window_size,
 )
 args = parser.parse_args()
 

image_classifier/classes.py

@@ -12,15 +12,20 @@ import logging
 
 logger = logging.getLogger(__name__)
 
+default_window_size = 28
+
 
 class Image_Classifier(object):
     def __init__(self):
         self.class_names = []
         self.model = None
-        self.params = {"convnet": False}
 
-        self.object_name = ""
-        self.model_size = ""
+        self.params = {
+            "convnet": False,
+            "object_name": "",
+            "model_size": "",
+            "window_size": default_window_size,
+        }
 
     def load(self, model_path):
         success, self.class_names = file.load_json(f"{model_path}/class_names.json")
@@ -31,7 +36,9 @@ class Image_Classifier(object):
         if not success:
             return False
 
-        self.model_size = file.size(f"{model_path}/image_classifier/model")
+        self.params["object_name"] = path.name(model_path)
+
+        self.params["model_size"] = file.size(f"{model_path}/image_classifier/model")
 
         try:
             self.model = tf.keras.models.load_model(
@@ -43,15 +50,11 @@ class Image_Classifier(object):
             crash_report("image_classifier.load({}) failed".format(model_path))
             return False
 
-        self.window_size = int(
-            cache.read("{}.window_size".format(path.name(model_path)))
-        )
-
         logger.info(
             "{}.load({}x{}:{}): {}{} class(es): {}".format(
                 self.__class__.__name__,
-                self.window_size,
-                self.window_size,
+                self.params["window_size"],
+                self.params["window_size"],
                 path.name(model_path),
                 "convnet - " if self.params["convnet"] else "",
                 len(self.class_names),
@@ -60,8 +63,6 @@ class Image_Classifier(object):
         )
         self.model.summary()
 
-        self.object_name = path.name(model_path)
-
         return True
 
     def predict(self, test_images, test_labels, output_path="", page_count=-1):
@@ -169,7 +170,10 @@ class Image_Classifier(object):
         try:
             prediction = self.model.predict(
                 np.expand_dims(
-                    cv2.resize(frame, (self.window_size, self.window_size)) / 255.0,
+                    cv2.resize(
+                        frame, (self.params["window_size"], self.params["window_size"])
+                    )
+                    / 255.0,
                     axis=0,
                 )
             )
@@ -247,9 +251,11 @@ class Image_Classifier(object):
             crash_report("image_classifier.save({}) failed".format(model_path))
             return False
 
-        self.object_name = path.name(model_path)
+        self.params["object_name"] = path.name(model_path)
 
-        self.model_size = file.size("{}/image_classifier/model".format(model_path))
+        self.params["model_size"] = file.size(
+            "{}/image_classifier/model".format(model_path)
+        )
 
         if not file.save_json(
             "{}/class_names.json".format(model_path), self.class_names
@@ -266,8 +272,10 @@ class Image_Classifier(object):
             " | ".join(
                 [
                     "image_classifier",
-                    self.object_name,
-                    string.pretty_bytes(self.model_size) if self.model_size else "",
+                    self.params["object_name"],
+                    string.pretty_bytes(self.params["model_size"])
+                    if self.params["model_size"]
+                    else "",
                     string.pretty_shape(self.input_shape),
                     "/".join(string.shorten(self.class_names)),
                     "took {} / frame".format(
@@ -405,7 +413,6 @@ class Image_Classifier(object):
             test_images,
             np.argmax(test_labels, axis=1),
             model_path,
-            cache=True,
             page_count=10,
         )