integration of old 2D-CNN model with DLB head

base/BboxModel.py

@@ -13,7 +13,7 @@ from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
 from tensorflow.keras.optimizers import Adam, Adamax
 
 from base.bbox_model_definition import bbox_model_for_generator, \
-    cnn_deep_loc_box, ae_model
+    cnn_deep_loc_box, ae_model, cnn_bbox_model_for_generator
 from base.hier_model_definition import hier_model_for_generator
 from base.custom_loss import bbox_loss, define_bbox_loss, define_circle_loss, \
     define_quantile_loss, \
@@ -89,6 +89,13 @@ class BboxModel(DnnModel):
                            'activation': elu,
                            'optimizer': Adamax})
 
+        elif self.type == "GRID_OVERLAP-BBOX_2D-CNN":
+            if 'loss' in params:
+                loss_func = define_yolo_loss_tanh_no_size(params['loss'])
+
+            params.update({'last_activation': tanh,
+                           'losses': loss_func})
+
         elif self.type == "CNNLoc_REG":
             params.update({'activation': elu,
                            'losses': mean_squared_error,
@@ -201,7 +208,8 @@ class BboxModel(DnnModel):
                 y_cols = 5 * math.ceil(
                     dp.floorplan_height / dp.grid_size) * math.ceil(
                     dp.floorplan_width / dp.grid_size)
-        if self.type == "GRID_OVERLAP-BBOX" or self.type == "GRID_OVERLAP-BBOX_CNN":
+
+        if self.type == "GRID_OVERLAP-BBOX" or self.type == "GRID_OVERLAP-BBOX_CNN" or self.type == "GRID_OVERLAP-BBOX_2D-CNN":
             y_cols = 5 * (math.ceil(
                 dp.floorplan_height / dp.grid_size) + 1) * (math.ceil(
                 dp.floorplan_width / dp.grid_size) + 1)
@@ -229,7 +237,12 @@ class BboxModel(DnnModel):
         if self.type == "GRID_OVERLAP-BBOX_CNN":
             ae_file = self.output_dir + "AE_bottleneck_{}_f{}.hdf5".format(
                 self.filename, self.data_provider.current_split_idx)
-            self.classifier = cnn_deep_loc_box(ae_file, y_cols, self.params)
+            self.classifier = cnn_deep_loc_box(ae_file, y_cols, params)
+
+        elif self.type == "GRID_OVERLAP-BBOX_2D-CNN":
+            x_cols, _ = dp.get_data_dims("CNN")
+            classifier_template = cnn_bbox_model_for_generator()
+            self.classifier = classifier_template(x_cols, y_cols, params)
 
         elif "BBOX" in self.type or "CIRCLE" in self.type or "QUANTILE" in self.type or "S_Q" in self.type:
             classifier_template = bbox_model_for_generator(metrics=[])
@@ -249,7 +262,8 @@ class BboxModel(DnnModel):
             self.classifier = classifier_template(
                 X_cols=x_cols, Y_cols=y_cols, params=params)
 
-        self.type = "regression"
+        if "2D-CNN" not in self.type:
+            self.type = "regression"
 
     def _train_model(self, save_weights_only=False, evaluate=True, autoencoder=False):
         """
@@ -264,7 +278,7 @@ class BboxModel(DnnModel):
 
         # calculate batch sizes (might be smaller than specified batch size if
         # not enough data supplied)
-        num_train, num_val, num_test = data_provider.get_train_val_test_num(area_labels=self.type != "regression")
+        num_train, num_val, num_test = data_provider.get_train_val_test_num(area_labels=False)
 
         train_bs = min(num_train, params['batch_size'])
         val_bs = min(num_val, train_bs)
@@ -323,10 +337,11 @@ class BboxModel(DnnModel):
             steps_per_epoch=train_steps_per_epoch,
             epochs=params['epochs'],
             callbacks=[earlyStopping, checkpoint],
-            verbose=1)
+            verbose=0)
 
         # evaluate model and store results in summary file
         if evaluate:
+            #self.type = "regression"
             self.evaluate_model(test_bs, chkpt_file=checkpoint_file_name)
 
     def collect_train_progress(self, test_bs):

base/bbox_model_definition.py

@@ -167,3 +167,36 @@ def cnn_deep_loc_box(ae_base_model_file, y_cols, params):
     )
 
     return position_model
+
+
+def cnn_bbox_model_for_generator():
+
+    def define_cnn_model_for_generator(input_shape, Y_cols,
+                                                  params) -> Sequential:
+
+        model = Sequential()
+
+        model.add(Conv2D(16, kernel_size=(3, 3),
+                         activation='relu',
+                         input_shape=input_shape, name="Conv2D_1", padding="same"))
+        model.add(Dropout(params['dropout']))
+
+        model.add(Conv2D(16, (3, 3), activation='relu', name="Conv2D_2", padding="same"))
+        model.add(MaxPooling2D(pool_size=(2, 2), padding="same"))
+        model.add(Dropout(params['dropout']))
+
+        model.add(Conv2D(8, (3, 3), activation='relu', name="Conv2D_3", padding="same"))
+        model.add(MaxPooling2D(pool_size=(2, 2), padding="same"))
+
+        model.add(Flatten())
+        model.add(Dense(128, activation='relu'))
+        #model.add(Dropout(0.25))
+        model.add(Dense(Y_cols, activation=params['last_activation']))
+
+        model.compile(loss=params['losses'],
+                      optimizer=params['optimizer'](
+                          lr=lr_normalizer(params['lr'], params['optimizer'])))
+
+        return model
+
+    return define_cnn_model_for_generator

base/data_provider_base_grid.py

@@ -495,7 +495,7 @@ class DataProviderGridBase(DataProviderBase):
             # augment only training data
             data, labels = self.get_augmented_train_data()
 
-        if model_type == "CNN":
+        if model_type == "CNN" or "2D-CNN" in model_type:
             data = self.transform_batch_to_image(data, pearsoncorr=False)
         elif model_type == "CNN-PSSD":
             data = self.transform_batch_to_pairwise_diff_image(data)

config/old_cnn_test.yml

+data:
+  # The data provider which should be used
+  provider: UJIndoorLocProvider
+  # File name of floor plan img
+  floor_plan_img: <test>.jpg
+  # (train, val, test) test=0.2 => 5 fold  # The number of temporal epochs into which the dataset is split
+  split_ratio: [0.7, 0.1, 0.2]
+  building: 0
+  floor: 0
+
+#
+# are used when not locally set for pipeline
+#
+
+global_params:
+
+  # number of experiment repetitions
+  repetitions: 1
+
+  preprocessing:
+    # Whether to standardize the RSS values
+    standardize: True
+    # Whether to assign labels with no matching area to closet area
+    assign_closest: False
+    # The floor number of the Lohan dataset, which should be used
+    #floor: 0
+    # The epoch number of the split dataset
+    #num_epochs: 10
+    #epoch: 5
+    # How to check for area matches of labels (to label positions with matching areas)
+    area_assignment: convex_hull
+    grid_size: 40
+
+  floor_plan:
+      # 'segmentation' => computes floor plan segmentation,
+      #  'regression' => uses DBSCAN to partitions labels into train, test split
+      type: floor_classification
+
+  model_params:
+    type: GRID_OVERLAP-BBOX_CNN # (DNN, CNN, kNN, SVM) supported (require different parameters)
+    #first_neuron: 512
+    #hidden_layers: 1
+    lr: 0.002
+    batch_size: 32
+    epochs: 200
+    #dropout: 0.7
+    #regularization_penalty: 0.0
+    augmentation: 0
+    #autoencoder: train
+    #pretrain: yes
+
+pipelines:
+
+#  - name: CNNLoc
+#    model_params:
+#      lr: 0.002
+#      type: CNNLoc_REG
+#      autoencoder: train
+
+#  - name: CNNLoc-DLB
+#    model_params:
+#      lr: 0.002
+#      batch_size: 32
+#      augmentation: 0
+#      autoencoder: train
+#
+#      loss:
+#        grid:
+#          scale: 100.0
+#        outside:
+#          scale: 1.0
+#          delta: [10.0, 15.0, 20.0]
+
+  - name: 2D-CNN-DLB
+    model_params:
+      type: GRID_OVERLAP-BBOX_2D-CNN # (DNN, CNN, kNN, SVM) supported (require different parameters)
+      #first_neuron: 512
+      #hidden_layers: [1,2,3]
+      lr: 0.7
+      batch_size: 32
+      epochs: 1
+      dropout: 0.5
+      #regularization_penalty: 0.0
+      augmentation: 0
+
+      loss:
+        grid:
+          scale: 100.0
+        outside:
+          scale: 1.0
+          delta: [10.0, 15.0, 20.0]
+
+  - name: 2D-CNN
+    model_params:
+      type: CNN # (DNN, CNN, kNN, SVM) supported (require different parameters)
+      pred: regression
+      #first_neuron: 512
+      #hidden_layers: [1,2,3]
+      lr: 0.7
+      batch_size: 32
+      #epochs: 200
+      #dropout: 0.5
+      #regularization_penalty: 0.0
+      augmentation: 0
+
+#  - name: DNN-DLB
+#    model_params:
+#      type: GRID_OVERLAP-BBOX # (DNN, CNN, kNN, SVM) supported (require different parameters)
+#      first_neuron: 512
+#      hidden_layers: [1,2]
+#      lr: 0.7
+#      batch_size: 32
+#      #epochs: 200
+#      dropout: 0.5
+#      regularization_penalty: 0.0
+#      augmentation: 0
+#
+#      loss:
+#        grid:
+#          scale: 100.0
+#        outside:
+#          scale: 1.0
+#          delta: [10.0, 15.0, 20.0]
+
+
+
+# base directories for file storage
+output:
+    model_dir: evaluation/uji/gpu/b0/f0/cnn/output/
+    summary_dir: evaluation/uji/gpu/b0/f0/cnn/summary/
+    img_folder: evaluation/uji/ # folder where floorplan images is located (if not present, will be downloaded)