First impl for satanas car

src/tf_container/train_entry_point.py

@@ -16,6 +16,25 @@ from keras.layers import Activation, Dropout, Flatten, Dense
 from keras import callbacks
 from tensorflow.python.client import device_lib
 
+
+def get_data(root_dir, filename):
+    print('load data from file '+ filename)
+    d = json.load(open(os.path.join(root_dir, filename)))
+    if 'pilot/throttle' in d:
+        return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array'], d['pilot/throttle'], d['pilot/angle']]
+    else:
+        return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array']]
+
+
+numbers = re.compile(r'(\d+)')
+
+
+def unzip_file(root,f):
+    zip_ref = zipfile.ZipFile(os.path.join(root,f), 'r')
+    zip_ref.extractall(root)
+    zip_ref.close()
+
+
 def train():
     env = cs.TrainingEnvironment()
 
@@ -23,38 +42,24 @@ def train():
     os.system('mkdir -p logs')
 
     # ### Loading the files ###
-    # ** You need to copy all your files to the directory where you are runing this notebook into a folder named "data" **
+    # ** You need to copy all your files to the directory where you are runing this notebook **
+    # ** into a folder named "data"                                                          **
 
-    numbers = re.compile(r'(\d+)')
     data = []
-    def get_data(root,f):
-        d = json.load(open(os.path.join(root,f)))
-        if ('pilot/throttle' in d):
-            return [d['user/mode'],d['user/throttle'],d['user/angle'],root,d['cam/image_array'],d['pilot/throttle'],d['pilot/angle']]
-        else:
-            return [d['user/mode'],d['user/throttle'],d['user/angle'],root,d['cam/image_array']]
-    def numericalSort(value):
-        parts = numbers.split(value)
-        parts[1::2] = map(int, parts[1::2])
-        return parts
-    def unzip_file(root,f):
-        zip_ref = zipfile.ZipFile(os.path.join(root,f), 'r')
-        zip_ref.extractall(root)
-        zip_ref.close()
 
     for root, dirs, files in os.walk('/opt/ml/input/data/train'):
-        for f in files: 
+        for f in files:
             if f.endswith('.zip'):
                 unzip_file(root, f)
 
     for root, dirs, files in os.walk('/opt/ml/input/data/train'):
-        data.extend([get_data(root,f) for f in sorted(files, key=numericalSort) if f.startswith('record') and f.endswith('.json')])
+        data.extend([get_data(root,f) for f in sorted(files, key=str.lower) if f.startswith('record') and f.endswith('.json')])
 
     # Normalize / correct data
-    data = [d for d in data if d[1] > 0.1]
-    for d in data:
-        if d[1] < 0.2:
-            d[1] = 0.2
+    #data = [d for d in data if d[1] > 0.1]
+    #for d in data:
+    #    if d[1] < 0.2:
+    #        d[1] = 0.2
 
     # ### Loading throttle and angle ###
 
@@ -62,7 +67,8 @@ def train():
     throttle = [d[1] for d in data]
     angle_array = np.array(angle)
     throttle_array = np.array(throttle)
-    if (len(data[0]) > 5):
+
+    if len(data[0]) > 5:
         pilot_angle = [d[6] for d in data]
         pilot_throttle = [d[5] for d in data]
         pilot_angle_array = np.array(pilot_angle)
@@ -71,7 +77,6 @@ def train():
         pilot_angle = []
         pilot_throttle = []
 
-
     # ### Loading images ###
     images = np.array([img_to_array(load_img(os.path.join(d[3],d[4]))) for d in data],'f')
 
@@ -91,12 +96,12 @@ def train():
 
     logs = callbacks.TensorBoard(log_dir='logs', histogram_freq=0, write_graph=True, write_images=True)
     save_best = callbacks.ModelCheckpoint('/opt/ml/model/model_cat', monitor='angle_out_loss', verbose=1, save_best_only=True, mode='min')
-    early_stop = callbacks.EarlyStopping(monitor='angle_out_loss', 
-                                                    min_delta=.0005, 
-                                                    patience=10, 
-                                                    verbose=1, 
+    early_stop = callbacks.EarlyStopping(monitor='angle_out_loss',
+                                                    min_delta=.0005,
+                                                    patience=10,
+                                                    verbose=1,
                                                     mode='auto')
-    img_in = Input(shape=(120, 160, 3), name='img_in')                      # First layer, input layer, Shape comes from camera.py resolution, RGB
+    img_in = Input(shape=(128, 160, 3), name='img_in')                      # First layer, input layer, Shape comes from camera.py resolution, RGB
     x = img_in
     x = Convolution2D(24, (5,5), strides=(2,2), activation='relu')(x)       # 24 features, 5 pixel x 5 pixel kernel (convolution, feauture) window, 2wx2h stride, relu activation
     x = Convolution2D(32, (5,5), strides=(2,2), activation='relu')(x)       # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion
@@ -120,7 +125,7 @@ def train():
     angle_cat_array = np.array([linear_bin(a) for a in angle_array])
     model = Model(inputs=[img_in], outputs=[angle_out, throttle_out])
     model.compile(optimizer='adam',
-                loss={'angle_out': 'categorical_crossentropy', 
+                loss={'angle_out': 'categorical_crossentropy',
                         'throttle_out': 'mean_absolute_error'},
                 loss_weights={'angle_out': 0.9, 'throttle_out': .001})
     model.fit({'img_in':images},{'angle_out': angle_cat_array, 'throttle_out': throttle_array}, batch_size=32, epochs=100, verbose=1, validation_split=0.2, shuffle=True, callbacks=callbacks_list)