使用旧版候选区寻找方式。

2019-05-02 20:19:17 +08:00
parent ee83a373d3
commit 2f20367677
20 changed files with 12502 additions and 12395 deletions
--- a/tools/TrainCNN/backward.py
+++ b/tools/TrainCNN/backward.py
@@ -53,7 +53,7 @@ def save_para(folder, paras):
        save_bias(fp, paras[7])


-STEPS = 20000
+STEPS = 10000
 BATCH = 30
 LEARNING_RATE_BASE  = 0.01
 LEARNING_RATE_DECAY = 0.99
@@ -61,8 +61,6 @@ MOVING_AVERAGE_DECAY = 0.99


 def train(dataset, show_bar=False):
-    test_images, test_labels = dataset.all_test_sets()
-
    x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
    y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
    nodes, vars = forward.forward(x, 0.001)
@@ -104,7 +102,8 @@ def train(dataset, show_bar=False):

            if i % 100 == 0:
                if i % 1000 == 0:
-                    acc = sess.run(accuracy, feed_dict={x: test_images, y_: test_labels})
+                    test_samples, test_labels = dataset.sample_train_sets(5000)
+                    acc = sess.run(accuracy, feed_dict={x: test_samples, y_: test_labels})
                bar.set_postfix({"loss": loss_value, "acc": acc})


@@ -151,8 +150,8 @@ def train(dataset, show_bar=False):

        vars_val = sess.run(vars)
        save_para("/home/xinyang/Desktop/AutoAim/tools/para", vars_val)
-        nodes_val = sess.run(nodes, feed_dict={x:test_images})
-        return vars_val, nodes_val
+        nodes_val = sess.run(nodes, feed_dict={x:test_samples})
+        return vars_val, nodes_val, test_samples


 if __name__ == "__main__":
@@ -160,3 +159,4 @@ if __name__ == "__main__":
    dataset = generate.DataSet("/home/xinyang/Desktop/DataSets/box")
    print("Finish!")
    train(dataset, show_bar=True)
+    input("Press any key to continue...")
--- a/tools/TrainCNN/forward.py
+++ b/tools/TrainCNN/forward.py
@@ -64,8 +64,8 @@ def forward(x, regularizer=None):
        [CONV2_KERNAL_SIZE, CONV2_KERNAL_SIZE, CONV1_OUTPUT_CHANNELS, CONV2_OUTPUT_CHANNELS]
    )
    conv2_b = get_bias([CONV2_OUTPUT_CHANNELS])
-    conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(pool1, conv2_w), conv2_b))
-    pool2 = avg_pool_2x2(conv2)
+    conv2   = tf.nn.relu(tf.nn.bias_add(conv2d(pool1, conv2_w), conv2_b))
+    pool2   = avg_pool_2x2(conv2)
    vars.extend([conv2_w, conv2_b])
    nodes.extend([conv2, pool2])

--- a/tools/TrainCNN/generate.py
+++ b/tools/TrainCNN/generate.py
@@ -24,34 +24,40 @@ class DataSet:
        self.test_labels = []
        self.generate_data_sets(folder)

-    def file2nparray(self, name):
-        try:
-            image = cv2.imread(name)
-            image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
-            image = image.astype(np.float32)
-            return image / 255.0
-        except:
-            print(name)
-            sys.exit(-1)
-
-    def id2label(self, id):
-        a = np.zeros([OUTPUT_NODES])
-        a[id] = 1
-        return a[:]
-
    def generate_data_sets(self, folder):
+        def id2label(id):
+            a = np.zeros([OUTPUT_NODES])
+            a[id] = 1
+            return a[:]
+
+        def file2nparray(name):
+            try:
+                image = cv2.imread(name)
+                if image.shape[0] < 15:
+                    return None
+                elif image.shape[1] < 10:
+                    return None
+                image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
+                image = image.astype(np.float32)
+                return image / 255.0, id2label(int(name.split("/")[-2]))
+            except TypeError:
+                print(name)
+                sys.exit(-1)
+
        sets = []
        for i in range(OUTPUT_NODES):
            dir = "%s/%d" % (folder, i)
            files = os.listdir(dir)
            for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
                if file[-3:] == "jpg":
-                    if random.random() > 0.2:
-                        self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
-                        self.train_labels.append(self.id2label(i))
-                    else:
-                        self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
-                        self.test_labels.append(self.id2label(i))
+                    x = file2nparray("%s/%s" % (dir, file))
+                    if x is not None:
+                        if random.random() > 0.2:
+                            self.train_samples.append(x[0])
+                            self.train_labels.append(x[1])
+                        else:
+                            self.test_samples.append(x[0])
+                            self.test_labels.append(x[1])
        self.train_samples = np.array(self.train_samples)
        self.train_labels = np.array(self.train_labels)
        self.test_samples = np.array(self.test_samples)
@@ -67,6 +73,15 @@ class DataSet:
            labels.append(self.train_labels[id])
        return np.array(samples), np.array(labels)

+    def sample_test_sets(self, length):
+        samples = []
+        labels = []
+        for i in range(length):
+            id = random.randint(0, len(self.test_samples)-1)
+            samples.append(self.test_samples[id])
+            labels.append(self.test_labels[id])
+        return np.array(samples), np.array(labels)
+
    def all_train_sets(self):
        return self.train_samples[:], self.train_labels[:]

--- a/tools/para/conv1_b
+++ b/tools/para/conv1_b
@@ -1,7 +1,7 @@
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--- a/tools/para/conv1_w
+++ b/tools/para/conv1_w
@@ -2,453 +2,453 @@
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--- a/tools/para/conv2_b
+++ b/tools/para/conv2_b
@@ -1,11 +1,11 @@
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--- a/tools/para/conv2_w
+++ b/tools/para/conv2_w
--- a/tools/para/fc1_b
+++ b/tools/para/fc1_b
@@ -1,17 +1,17 @@
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--- a/tools/para/fc1_w
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--- a/tools/para/fc2_b
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--- a/tools/para/fc2_w
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