add CustomMetric

@@ -12,3 +12,6 @@
 !.gitignore
 !.gitignore
 
 
 test.py
 test.py
+*.h5
+*.jpg
+*.out
\ No newline at end of file

@@ -4,3 +4,5 @@ CLASS_OTHER_FIRST = True
 
 
 CLASS_CN_LIST = [CLASS_OTHER_CN, '身份证', '营业执照', '经销商授权书', '个人授权书']
 CLASS_CN_LIST = [CLASS_OTHER_CN, '身份证', '营业执照', '经销商授权书', '个人授权书']
 
 
+OTHER_THRESHOLDS = 0.5
+

@@ -21,5 +21,5 @@ if __name__ == '__main__':
     batch_size = 128
     batch_size = 128
 
 
     m.train(dataset_dir, epoch, batch_size, ckpt_path, history_save_path,
     m.train(dataset_dir, epoch, batch_size, ckpt_path, history_save_path,
-            train_dir_name='train', validate_dir_name='test')
+            train_dir_name='train', validate_dir_name='test', thresholds=const.OTHER_THRESHOLDS)
 
 

@@ -9,6 +9,43 @@ import matplotlib.pyplot as plt
 from base_class import BaseModel
 from base_class import BaseModel
 
 
 
 
+class CustomMetric(metrics.Metric):
+
+    def __init__(self, thresholds=0.5, name="custom_metric", **kwargs):
+        super(CustomMetric, self).__init__(name=name, **kwargs)
+        self.thresholds = thresholds
+        self.true_positives = self.add_weight(name="ctp", initializer="zeros")
+        self.count = self.add_weight(name="count", initializer="zeros", dtype='int32')
+
+    def update_state(self, y_true, y_pred, sample_weight=None):
+        y_true_idx = tf.argmax(y_true, axis=1) + 1
+        y_true_is_other = tf.cast(tf.math.reduce_sum(y_true, axis=1), "int64")
+        y_true = tf.math.multiply(y_true_idx, y_true_is_other)
+
+        y_pred_idx = tf.argmax(y_pred, axis=1) + 1
+        y_pred_is_other = tf.cast(tf.math.greater_equal(tf.math.reduce_max(y_pred, axis=1), self.thresholds), 'int64')
+        y_pred = tf.math.multiply(y_pred_idx, y_pred_is_other)
+
+        print(y_true)
+        print(y_pred)
+
+        values = tf.cast(y_true, "int32") == tf.cast(y_pred, "int32")
+        values = tf.cast(values, "float32")
+        if sample_weight is not None:
+            sample_weight = tf.cast(sample_weight, "float32")
+            values = tf.multiply(values, sample_weight)
+        self.true_positives.assign_add(tf.reduce_sum(values))
+        self.count.assign_add(tf.shape(y_true)[0])
+
+    def result(self):
+        return self.true_positives / tf.cast(self.count, 'float32')
+
+    def reset_state(self):
+        # The state of the metric will be reset at the start of each epoch.
+        self.true_positives.assign(0.0)
+        self.count.assign(0)
+
+
 class F3Classification(BaseModel):
 class F3Classification(BaseModel):
 
 
     def __init__(self, class_name_list, class_other_first, *args, **kwargs):
     def __init__(self, class_name_list, class_other_first, *args, **kwargs):
@@ -18,6 +55,12 @@ class F3Classification(BaseModel):
         self.image_ext_set = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"}
         self.image_ext_set = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"}
 
 
     @staticmethod
     @staticmethod
+    def gpu_config():
+        gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
+        # print(gpus)
+        tf.config.set_visible_devices(devices=gpus[1], device_type='GPU')
+
+    @staticmethod
     def history_save(history, save_path):
     def history_save(history, save_path):
         acc = history.history['accuracy']
         acc = history.history['accuracy']
         val_acc = history.history['val_accuracy']
         val_acc = history.history['val_accuracy']
@@ -90,21 +133,21 @@ class F3Classification(BaseModel):
         # 1/10
         # 1/10
         if random.random() < 0.2:
         if random.random() < 0.2:
             image = tf.image.random_flip_left_right(image)
             image = tf.image.random_flip_left_right(image)
-        return image
+        return image, label
 
 
     @staticmethod
     @staticmethod
     def random_flip_up_down(image, label):
     def random_flip_up_down(image, label):
         # 1/10
         # 1/10
         if random.random() < 0.2:
         if random.random() < 0.2:
             image = tf.image.random_flip_up_down(image)
             image = tf.image.random_flip_up_down(image)
-        return image
+        return image, label
 
 
     @staticmethod
     @staticmethod
     def random_rot90(image, label):
     def random_rot90(image, label):
         # 1/10
         # 1/10
         if random.random() < 0.1:
         if random.random() < 0.1:
             image = tf.image.rot90(image, k=random.randint(1, 3))
             image = tf.image.rot90(image, k=random.randint(1, 3))
-        return image
+        return image, label
 
 
     @staticmethod
     @staticmethod
     # @tf.function
     # @tf.function
@@ -166,14 +209,17 @@ class F3Classification(BaseModel):
         return model
         return model
 
 
     def train(self, dataset_dir, epoch, batch_size, ckpt_path, history_save_path,
     def train(self, dataset_dir, epoch, batch_size, ckpt_path, history_save_path,
-              train_dir_name='train', validate_dir_name='test'):
+              train_dir_name='train', validate_dir_name='test', thresholds=0.5):
+
+        self.gpu_config()
+
         model = self.load_model()
         model = self.load_model()
         model.summary()
         model.summary()
 
 
         model.compile(
         model.compile(
             optimizer=optimizers.Adam(learning_rate=3e-4),
             optimizer=optimizers.Adam(learning_rate=3e-4),
-            loss=tfa.losses.SigmoidFocalCrossEntropy(),
-            metrics=['accuracy', ],
+            loss=tfa.losses.SigmoidFocalCrossEntropy(),  # TODO >>>
+            metrics=[CustomMetric(thresholds), ],
 
 
             loss_weights=None,
             loss_weights=None,
             weighted_metrics=None,
             weighted_metrics=None,
@@ -214,5 +260,25 @@ class F3Classification(BaseModel):
         self.history_save(history, history_save_path)
         self.history_save(history, history_save_path)
 
 
     def test(self):
     def test(self):
-        print(self.class_label_map)
-        print(self.class_count)
+        y_true = [
+            [0, 1, 0],
+            [0, 1, 0],
+            [0, 0, 1],
+            [0, 0, 0],
+        ]
+        y_pre = [
+            [0.1, 0.8, 0.9],  # TODO multi_label
+            [0.2, 0.8, 0.1],
+            [0.2, 0.1, 0.85],
+            [0.2, 0.4, 0.1],
+        ]
+
+        # x = tf.argmax(y_pre, axis=1)
+        # y = tf.reduce_sum(y_pre, axis=1)
+        # print(x)
+        # print(y)
+
+        # m = tf.keras.metrics.TopKCategoricalAccuracy(k=1)
+        m = CustomMetric(0.5)
+        m.update_state(y_true, y_pre)
+        print(m.result().numpy())