V1.4.0

Author: bkinman

README.md

@@ -47,11 +47,10 @@ If for some reason you choose not to use Anaconda, you must install the followin
 
 ## Implement the Segmentation Network
 1. Download the training dataset from above and extract to the project `data` directory.
-2. Complete `project_nn_lib.py`by following the TODOs in `project_nn_lib_template.py`
-4. Complete `train.py` by following the TODOs in `train_template.py`
-5. Train the network locally, or on [AWS](docs/aws_setup.md).
-6. Continue to experiment with the training data and network until you attain the score you desire.
-7. Once you are comfortable with performance on the training dataset, see how it performs in live simulation!
+2. Implement your solution in model_training.ipynb
+3. Train the network locally, or on [AWS](docs/aws_setup.md).
+4. Continue to experiment with the training data and network until you attain the score you desire.
+5. Once you are comfortable with performance on the training dataset, see how it performs in live simulation!
 
 ## Collecting Training Data ##
 A simple training dataset has been provided above in this repository. This dataset will allow you to verify that you're segmentation network is semi-functional. However, if you're interested in improving your score, you may be interested in collecting additional training data. To do, please see the following steps.

code/follower.py

@@ -53,6 +53,7 @@
 from utils import visualization
 from utils import scoring_utils
 from utils import sio_msgs
+from utils import model_tools
 
 import time
 
@@ -195,7 +196,7 @@ def sio_server():
 
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('model_file',
+    parser.add_argument('weight_file',
                         help='The model file to use for inference')
 
 
@@ -205,9 +206,8 @@ def sio_server():
 
     args = parser.parse_args()
 
-    model_path = os.path.join('..', 'data', 'weights', args.model_file)
-    model = keras.models.load_model(model_path)
-    image_hw = model.layers[0].input_shape[1]
+    model = model_tools.load_network(args.weight_file)
+    image_hw = model.layers[0].output_shape[1]
 
     if args.pred_viz: 
         overlay_plot = visualization.SideBySidePlot('Segmentation Overlay', image_hw)