fix training

Author: WenheLI

.gitignore

@@ -0,0 +1,5 @@
+data/
+data2/
+data3/
+logs/
+__pycache__/

cvae.py

@@ -17,7 +17,6 @@ def __init__(self, args):
         self.args = args
         self.logs_dir = args.logs_dir
         self.n_dim = args.n_dim
-        self.dimension = args.dimension
         self.image_size = args.image_size
         self.num_layers = args.num_layers
         self.filters = args.filters

downnload.py

@@ -0,0 +1,19 @@
+import urllib.request
+import os
+
+with open("./data/categories.txt", 'r') as f:
+    classes = f.readlines()
+
+classes = [c.replace('\n','').replace(' ','_') for c in classes]
+
+def download():
+  
+  base = 'https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/'
+  for c in classes:
+    cls_url = c.replace('_', '%20')
+    path = base+cls_url+'.npy'
+    print(path)
+    os.mkdir("data/"+c)
+    urllib.request.urlretrieve(path, 'data/'+c+'/'+c+'.npy')
+
+download()

train.py

@@ -3,7 +3,7 @@
 from keras.datasets import mnist
 from keras.utils import to_categorical
 from cvae import CVAE
-
+from utils import *
 
 
 def main():
@@ -37,8 +37,12 @@ def main():
 
     args = parser.parse_args()
 
-    (X_train, Y_train), (X_test, Y_test) = mnist.load_data()
+    # (train_features, train_labels), (validataion_features, validataion_labels) = get_files('data')
+    # training, validation = get_data(train_features, train_labels, validataion_features, validataion_labels)
+    (X_train, Y_train), (X_test, Y_test) = get_files('data')
 
+    # (X_train, Y_train), (X_test, Y_test) = mnist.load_data()
+    print(X_train.shape)
     X_train = np.reshape(X_train, [-1, args.image_size, args.image_size, args.image_depth])
     X_test = np.reshape(X_test, [-1, args.image_size, args.image_size, args.image_depth])
     X_train = X_train.astype('float32') / 255.

utils.py

@@ -10,7 +10,7 @@ def get_files(path):
     validation_features = None
     training_labels = None
     validation_labels = None
-    labels_value = None
+    labels_value = []
     count = 1
 
     #TODO: Load Img
@@ -34,10 +34,10 @@ def get_files(path):
                 training_labels = np.concatenate([training_labels, new_labels], axis=0)
 
             # concatenate arrays to get the validation data
-            if validataion_features is None:
-                validataion_features = np.copy(data[length-(length//10):,:,:,:])
+            if validation_features is None:
+                validation_features = np.copy(data[length-(length//10):,:,:,:])
             else:
-                validataion_features = np.concatenate([validataion_features ,data[length-(length//10):,:,:,:]), axis=0)
+                validation_features = np.concatenate((validation_features, data[length-(length//10):,:,:,:]), axis=0)
 
             # get validation data
             if validation_labels is None:
@@ -56,6 +56,7 @@ def get_files(path):
 # create training data
 def get_data(training_features, training_labels, validation_features, validation_labels):
     # get training data
+    print(training_features.shape, training_labels.shape)
     train_imgs = tf.constant(training_features)
     train_labels = tf.constant(training_labels)
 
@@ -68,12 +69,12 @@ def get_data(training_features, training_labels, validation_features, validation
 
     return training_data, validation_data
 
-(train_features, train_labels), (validataion_features, validataion_labels) = get_files('quickdraw_data')
-image_label_ds = get_data(train_features, train_labels, validataion_features, validataion_labels)
+# (train_features, train_labels), (validataion_features, validataion_labels) = get_files('quickdraw_data')
+# image_label_ds = get_data(train_features, train_labels, validataion_features, validataion_labels)
 
 
-print('image shape: ', image_label_ds.output_shapes[0])
-print('label shape: ', image_label_ds.output_shapes[1])
-print('types: ', image_label_ds.output_types)
-print()
-print(image_label_ds)
+# print('image shape: ', image_label_ds.output_shapes[0])
+# print('label shape: ', image_label_ds.output_shapes[1])
+# print('types: ', image_label_ds.output_types)
+# print()
+# print(image_label_ds)