upgrade to Python 3.8 and TensorFlow 2.7

README.md

@@ -50,7 +50,7 @@ Cam2BEV
 
 ## Installation
 
-We suggest to setup a **Python 3.7** virtual environment (e.g. by using _virtualenv_ or _conda_). Inside the virtual environment, users can then use _pip_ to install all package dependencies. The most important packages are _TensorFlow 2.1_ and _OpenCV 4.2_
+We suggest to setup a **Python 3.8** virtual environment (e.g. by using _virtualenv_ or _conda_). Inside the virtual environment, users can then use _pip_ to install all package dependencies. The most important packages are _TensorFlow 2.7_ and _OpenCV 4.5_
 ```bash
 pip install -r requirements.txt
 ```

model/train.py

@@ -168,17 +168,17 @@ def parse_sample(input_files, label_file):
 
 
 # create callbacks to be called after each epoch
+n_batches_train = len(files_train_label) // conf.batch_size
+n_batches_valid = len(files_valid_label)
 tensorboard_cb      = tf.keras.callbacks.TensorBoard(tensorboard_dir, update_freq="epoch", profile_batch=0)
-checkpoint_cb       = tf.keras.callbacks.ModelCheckpoint(os.path.join(checkpoint_dir, "e{epoch:03d}_weights.hdf5"), period=conf.save_interval, save_weights_only=True)
+checkpoint_cb       = tf.keras.callbacks.ModelCheckpoint(os.path.join(checkpoint_dir, "e{epoch:03d}_weights.hdf5"), save_freq=n_batches_train*conf.save_interval, save_weights_only=True)
 best_checkpoint_cb  = tf.keras.callbacks.ModelCheckpoint(os.path.join(checkpoint_dir, "best_weights.hdf5"), save_best_only=True, monitor="val_mean_io_u_with_one_hot_labels", mode="max", save_weights_only=True)
 early_stopping_cb   = tf.keras.callbacks.EarlyStopping(monitor="val_mean_io_u_with_one_hot_labels", mode="max", patience=conf.early_stopping_patience, verbose=1)
 callbacks = [tensorboard_cb, checkpoint_cb, best_checkpoint_cb, early_stopping_cb]
 
 
 # start training
 print("Starting training...")
-n_batches_train = len(files_train_label) // conf.batch_size
-n_batches_valid = len(files_valid_label)
 model.fit(dataTrain,
           epochs=conf.epochs, steps_per_epoch=n_batches_train,
           validation_data=dataValid, validation_freq=1, validation_steps=n_batches_valid,

model/utils.py

@@ -252,7 +252,7 @@ def wcce(y_true, y_pred):
 
 class MeanIoUWithOneHotLabels(tf.keras.metrics.MeanIoU):
 
-    def __call__(self, y_true, y_pred, sample_weight=None):
+    def update_state(self, y_true, y_pred, sample_weight=None):
         y_true = tf.argmax(y_true, axis=-1)
         y_pred = tf.argmax(y_pred, axis=-1)
-        return super().__call__(y_true, y_pred, sample_weight=sample_weight)
+        return super().update_state(y_true, y_pred, sample_weight)

preprocessing/ipm/ipm.py

@@ -155,7 +155,7 @@ def __init__(self, config):
 # setup masks to later clip invalid parts from transformed images (inefficient, but constant runtime)
 masks = []
 for config in cameraConfigs:
-  mask = np.zeros((outputRes[0], outputRes[1], 3), dtype=np.bool)
+  mask = np.zeros((outputRes[0], outputRes[1], 3), dtype=bool)
   for i in range(outputRes[1]):
     for j in range(outputRes[0]):
       theta = np.rad2deg(np.arctan2(-j + outputRes[0] / 2 - droneConfig["YCam"] * pxPerM[0], i - outputRes[1] / 2 + droneConfig["XCam"] * pxPerM[1]))

requirements.txt

@@ -1,51 +1,62 @@
-absl-py==0.9.0
-astor==0.8.1
-cachetools==4.0.0
-certifi==2019.11.28
-chardet==3.0.4
-ConfigArgParse==1.1
-cycler==0.10.0
-decorator==4.4.2
-gast==0.2.2
-google-auth==1.11.3
-google-auth-oauthlib==0.4.1
+ConfigArgParse==1.5.3
+matplotlib==3.5.0
+opencv-python==4.5.4.58
+pandas==1.3.4
+PyYAML==6.0
+scikit-image==0.18.3
+seaborn==0.11.2
+tensorflow==2.7.0
+tqdm==4.62.3
+
+absl-py==1.0.0
+astunparse==1.6.3
+cachetools==4.2.4
+certifi==2021.10.8
+charset-normalizer==2.0.7
+cycler==0.11.0
+flatbuffers==2.0
+fonttools==4.28.1
+gast==0.4.0
+google-auth==2.3.3
+google-auth-oauthlib==0.4.6
 google-pasta==0.2.0
-grpcio==1.27.2
-h5py==2.10.0
-idna==2.9
-imageio==2.8.0
-Keras-Applications==1.0.8
-Keras-Preprocessing==1.1.0
-kiwisolver==1.1.0
-Markdown==3.2.1
-matplotlib==3.2.0
-networkx==2.4
-numpy==1.18.1
-oauthlib==3.1.0
-opencv-python==4.2.0.32
-opt-einsum==3.2.0
-pandas==1.0.2
-Pillow==7.0.0
-protobuf==3.11.3
+grpcio==1.41.1
+h5py==3.5.0
+idna==3.3
+imageio==2.10.4
+keras==2.7.0
+Keras-Preprocessing==1.1.2
+kiwisolver==1.3.2
+libclang==12.0.0
+Markdown==3.3.4
+networkx==2.6.3
+numpy==1.21.4
+oauthlib==3.1.1
+opt-einsum==3.3.0
+packaging==21.2
+Pillow==8.4.0
+protobuf==3.19.1
 pyasn1==0.4.8
 pyasn1-modules==0.2.8
-pyparsing==2.4.6
-python-dateutil==2.8.1
-pytz==2019.3
-PyWavelets==1.1.1
-PyYAML==5.3
-requests==2.23.0
+pyparsing==3.0.6
+python-dateutil==2.8.2
+pytz==2021.3
+PyWavelets==1.2.0
+requests==2.26.0
 requests-oauthlib==1.3.0
-rsa==4.0
-scikit-image==0.16.2
-scipy==1.4.1
-seaborn==0.10.0
-six==1.14.0
-tensorboard==2.1.1
-tensorflow==2.1.0
-tensorflow-estimator==2.1.0
+rsa==4.7.2
+scipy==1.7.2
+setuptools-scm==6.3.2
+six==1.16.0
+tensorboard==2.7.0
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.0
+tensorflow-estimator==2.7.0
+tensorflow-io-gcs-filesystem==0.22.0
 termcolor==1.1.0
-tqdm==4.43.0
-urllib3==1.25.8
-Werkzeug==1.0.0
-wrapt==1.12.1
+tifffile==2021.11.2
+tomli==1.2.2
+typing-extensions==4.0.0
+urllib3==1.26.7
+Werkzeug==2.0.2
+wrapt==1.13.3