-handle batch size issues

generator.py

@@ -51,6 +51,11 @@ def __getitem__(self, idx):
         batch_x = np.asarray([self.load_image(x_path) for x_path in batch_x_path])
         batch_x = self.transform_batch_images(batch_x)
         batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]
+        if batch_x.shape[0] < self.batch_size:
+            remaining = self.batch_size - batch_x.shape[0]
+            batch_x = np.concatenate((batch_x,batch_x[0:remaining]), axis = 0)
+            batch_x_path = np.concatenate((batch_x_path,batch_x_path[0:remaining]), axis = 0)
+            batch_y = np.concatenate((batch_y,batch_y[0:remaining]), axis = 0)
         return batch_x, batch_y, batch_x_path
 
     def load_image(self, image_file):
@@ -83,7 +88,7 @@ def get_y_true(self):
 
     def prepare_dataset(self):
         df = self.dataset_df.sample(frac=1., random_state=self.random_state)
-        self.x_path, self.y = df["Image Index"].as_matrix(), self.tokenizer_wrapper.tokenize_sentences(df[self.class_names].as_matrix())
+        self.x_path, self.y = df["Image Index"].values, self.tokenizer_wrapper.tokenize_sentences(df[self.class_names].values)
 
     def on_epoch_end(self):
         if self.shuffle:

requirements.txt

@@ -1,25 +1,9 @@
-absl-py==0.7.1
-astor==0.8.0
-boto==2.49.0
-boto3==1.9.214
-botocore==1.12.214
-certifi==2019.6.16
-chardet==3.0.4
-Click==7.0
-cycler==0.10.0
-decorator==4.4.0
 docutils==0.15.2
 efficientnet==1.0.0
-gast==0.2.2
-google-pasta==0.1.7
-h5py==2.9.0
-idna==2.8
 imageio==2.5.0
 imgaug==0.3.0
 jmespath==0.9.4
 joblib==0.14.1
-Keras-Applications==1.0.8
-Keras-Preprocessing==1.1.0
 kiwisolver==1.1.0
 lxml==4.4.1
 Markdown==3.1.1
@@ -29,29 +13,26 @@ nltk==3.4.5
 numpy==1.17.0
 opencv-python==4.1.0.25
 opencv-python-headless==4.1.2.30
-opt-einsum==3.1.0
 pandas==0.25.1
 Pillow==6.1.0
-protobuf==3.9.1
 psutil==5.6.7
 pyparsing==2.4.2
 python-dateutil==2.8.0
 python-docx==0.8.10
-pytz==2019.2
 PyWavelets==1.0.3
 requests==2.22.0
 s3transfer==0.2.1
 scikit-image==0.15.0
 scikit-learn==0.22.1
-Shapely==1.6.4.post2
-six==1.12.0
+Shapely==1.7.1
 smart-open==1.8.4
-tensorflow==2.1.0
+tensorflow==2.3.0
 termcolor==1.1.0
-Theano==1.0.4
 tqdm==4.41.1
 urllib3==1.25.3
-Werkzeug==0.15.5
-wrapt==1.11.2
-xdg==4.0.1
+boto3==1.10.50
+botocore==1.13.50
+pymc3==3.11.0
+theano==1.0.4
+theano-pymc==1.1.0
 git+https://github.com/Maluuba/nlg-eval.git@master

train.py

@@ -145,7 +145,7 @@ def train_step(tag_predictions, visual_features, target):
     print('Batches that took long: {}'.format(times_to_get_batch))
 
     ckpt_manager.save()
-    if epoch % FLAGS.epochs_to_evaluate == 0:
+    if epoch % FLAGS.epochs_to_evaluate == 0 and epoch > 0:
         print("Evaluating on test set..")
         train_enqueuer.stop()
         current_scores = evaluate_enqueuer(test_enqueuer, test_steps, FLAGS, encoder, decoder, tokenizer_wrapper,