pep8 styling

yenat · Nov 28, 2016 · 219302f · 219302f
1 parent 82dff4c
commit 219302f
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diff --git a/.travis.yml b/.travis.yml
@@ -10,6 +10,8 @@ install:
   - pip install numpy
   - pip install matplotlib
   - pip install colorlog
+  # install destin package
+  - pip install .
   # install TensorFlow from https://storage.googleapis.com/tensorflow/
   - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
       pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl;

diff --git a/data/make-mnist-movie.py b/data/make-mnist-movie.py
@@ -9,21 +9,21 @@
 from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets('mnist', one_hot=True)
 
-train_data = mnist.train.images 
+train_data = mnist.train.images
 
 print "👉 processed input data!"
 
 # Define the codec and create VideoWriter object
-out = cv2.VideoWriter('./mnist.mjpg',cv.FOURCC(*'MJPG'), 25, (28,28))
+out = cv2.VideoWriter('./mnist.mjpg', cv.FOURCC(*'MJPG'), 25, (28, 28))
 
 i = 0
 
 for frame in train_data:
     print "frame... " + str(i)
     i = i + 1
     frame = frame * 255.0
-    x = frame.reshape([28,28]).astype('uint8')
-    x = np.repeat(x,3,axis=1)
+    x = frame.reshape([28, 28]).astype('uint8')
+    x = np.repeat(x, 3, axis=1)
     x = x.reshape(28, 28, 3)
     out.write(x)
 

diff --git a/destin/__init__.py b/destin/__init__.py
@@ -32,4 +32,4 @@
 stream.setFormatter(formatter)
 log = logging.getLogger()
 log.setLevel(LOG_LEVEL)
-log.addHandler(stream)
+log.addHandler(stream)
diff --git a/tests/big_destin.py b/tests/big_destin.py
@@ -14,77 +14,69 @@
 
 with tf.Session() as sess:
     ae_bottom_a = AutoEncoderNode(
-            session = sess,
-            name="bottom_a",
-            hidden_dim=40
-
-        )
+        session=sess,
+        name="bottom_a",
+        hidden_dim=40
+    )
 
     ae_bottom_b = AutoEncoderNode(
-            session = sess,
-            name="bottom_b",
-            hidden_dim=40
-
-        )
+        session=sess,
+        name="bottom_b",
+        hidden_dim=40
+    )
 
     ae_bottom_c = AutoEncoderNode(
-            session = sess,
-            name="bottom_c",
-            hidden_dim=40
-
-        )
+        session=sess,
+        name="bottom_c",
+        hidden_dim=40
+    )
 
     ae_bottom_d = AutoEncoderNode(
-            session = sess,
-            name="bottom_d",
-            hidden_dim=40
-
-        )
+        session=sess,
+        name="bottom_d",
+        hidden_dim=40
+    )
 
     ae_bottom_e = AutoEncoderNode(
-            session = sess,
-            name="bottom_e",
-            hidden_dim=40
-
-        )
+        session=sess,
+        name="bottom_e",
+        hidden_dim=40
+    )
 
     ae_bottom_f = AutoEncoderNode(
-            session = sess,
-            name="bottom_f",
-            hidden_dim=40
-
-        )
+        session=sess,
+        name="bottom_f",
+        hidden_dim=40
+    )
 
     ae_bottom_g = AutoEncoderNode(
-            session = sess,
-            name="bottom_g",
-            hidden_dim=40
-
-        )
+        session=sess,
+        name="bottom_g",
+        hidden_dim=40
+    )
 
     ae_bottom_h = AutoEncoderNode(
-            session = sess,
-            name="bottom_h",
-            hidden_dim=40
-
-        )
+        session=sess,
+        name="bottom_h",
+        hidden_dim=40
+    )
 
     ae_top = AutoEncoderNode(
-            session = sess,
-            name="top",
-            hidden_dim=16
-        )
-
-    inputlayer = OpenCVInputLayer(output_size=(28,28), batch_size=250)
-    
-    ae_bottom_a.register_tensor(inputlayer.get_tensor_for_region([00,00,14,14]))
-    ae_bottom_b.register_tensor(inputlayer.get_tensor_for_region([00,14,14,14]))
-    ae_bottom_c.register_tensor(inputlayer.get_tensor_for_region([14,00,14,14]))
-    ae_bottom_d.register_tensor(inputlayer.get_tensor_for_region([14,14,14,14]))
-    ae_bottom_e.register_tensor(inputlayer.get_tensor_for_region([00,00,10,10]))
-    ae_bottom_f.register_tensor(inputlayer.get_tensor_for_region([00,14,14,10]))
-    ae_bottom_g.register_tensor(inputlayer.get_tensor_for_region([14,00,12,11]))
-    ae_bottom_h.register_tensor(inputlayer.get_tensor_for_region([14,14,11,9]))
+        session=sess,
+        name="top",
+        hidden_dim=16
+    )
+
+    inputlayer = OpenCVInputLayer(output_size=(28, 28), batch_size=250)
+
+    ae_bottom_a.register_tensor(inputlayer.get_tensor_for_region([00, 00, 14, 14]))
+    ae_bottom_b.register_tensor(inputlayer.get_tensor_for_region([00, 14, 14, 14]))
+    ae_bottom_c.register_tensor(inputlayer.get_tensor_for_region([14, 00, 14, 14]))
+    ae_bottom_d.register_tensor(inputlayer.get_tensor_for_region([14, 14, 14, 14]))
+    ae_bottom_e.register_tensor(inputlayer.get_tensor_for_region([00, 00, 10, 10]))
+    ae_bottom_f.register_tensor(inputlayer.get_tensor_for_region([00, 14, 14, 10]))
+    ae_bottom_g.register_tensor(inputlayer.get_tensor_for_region([14, 00, 12, 11]))
+    ae_bottom_h.register_tensor(inputlayer.get_tensor_for_region([14, 14, 11, 9]))
 
     ae_top.register_tensor(ae_bottom_a.get_output_tensor())
     ae_top.register_tensor(ae_bottom_b.get_output_tensor())
@@ -97,18 +89,18 @@
 
     ae_top.initialize_graph()
 
-    # initialize summary writer with graph 
+    # initialize summary writer with graph
     SummaryWriter().writer.add_graph(sess.graph)
     merged_summary_op = tf.merge_all_summaries()
 
-    merged_train_ops = [ae_bottom_a.train_op, ae_bottom_b.train_op, ae_bottom_c.train_op, ae_bottom_d.train_op, ae_bottom_e.train_op, ae_bottom_f.train_op, ae_bottom_g.train_op, ae_bottom_h.train_op]    
+    merged_train_ops = [ae_bottom_a.train_op, ae_bottom_b.train_op, ae_bottom_c.train_op, ae_bottom_d.train_op, ae_bottom_e.train_op, ae_bottom_f.train_op, ae_bottom_g.train_op, ae_bottom_h.train_op]
 
     iteration = 0
 
     def feed_callback(feed_dict):
         global iteration
         iteration += 1
-        
+
         for _ in xrange(50):
             sess.run(merged_train_ops, feed_dict=feed_dict)
             sess.run(ae_top.train_op, feed_dict=feed_dict)
@@ -117,7 +109,4 @@ def feed_callback(feed_dict):
         SummaryWriter().writer.add_summary(summary_str, iteration)
         SummaryWriter().writer.flush()
 
-
     inputlayer.feed_video(feed_callback, "data/mnist.mp4", frames=10000)
-
-
diff --git a/tests/profile.py b/tests/profile.py
@@ -15,32 +15,30 @@
 log.info("recording summaries to " + SummaryWriter().get_summary_folder())
 
 with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=4)) as sess:
-    inputlayer = OpenCVInputLayer(output_size=(28,28), batch_size=250)
+    inputlayer = OpenCVInputLayer(output_size=(28, 28), batch_size=250)
 
     ae_bottom_a = AutoEncoderNode(
-            session = sess,
-            name="bottom-a",
-            hidden_dim=100
-
-        )
+        session=sess,
+        name="bottom-a",
+        hidden_dim=100
+    )
 
     ae_bottom_b = AutoEncoderNode(
-            session = sess,
-            name="bottom-b",
-            hidden_dim=100
-
-        )
+        session=sess,
+        name="bottom-b",
+        hidden_dim=100
+    )
 
-    ae_bottom_a.register_tensor(inputlayer.get_tensor_for_region([0,0,28,28]))
+    ae_bottom_a.register_tensor(inputlayer.get_tensor_for_region([0, 0, 28, 28]))
     ae_bottom_a.initialize_graph()
 
-    ae_bottom_b.register_tensor(inputlayer.get_tensor_for_region([0,0,28,28]))
+    ae_bottom_b.register_tensor(inputlayer.get_tensor_for_region([0, 0, 28, 28]))
     ae_bottom_b.initialize_graph()
 
-    # initialize summary writer with graph 
+    # initialize summary writer with graph
     SummaryWriter().writer.add_graph(sess.graph)
-    merged_summary_op = tf.merge_all_summaries()          
-    
+    merged_summary_op = tf.merge_all_summaries()
+
     run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
     run_metadata = tf.RunMetadata()
 
@@ -50,27 +48,25 @@ def feed_callback(feed_dict):
         global iteration
         iteration += 1
 
-        #for _ in xrange(50):
-        summary_str, _,_ = sess.run([merged_summary_op, ae_bottom_a.train_op,ae_bottom_b.train_op], feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
-        #sess.run([ae_bottom_a.train_op,ae_bottom_b.train_op], feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
+        # for _ in xrange(50):
+        summary_str, _, _ = sess.run([merged_summary_op, ae_bottom_a.train_op, ae_bottom_b.train_op], feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
+        # sess.run([ae_bottom_a.train_op,ae_bottom_b.train_op], feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
 
         SummaryWriter().writer.add_summary(summary_str, iteration)
         SummaryWriter().writer.flush()
 
-
     inputlayer.feed_video(feed_callback, "data/mnist.mp4", frames=1000)
 
     SummaryWriter().writer.add_run_metadata(run_metadata, "run")
 
     tl = timeline.Timeline(run_metadata.step_stats)
     ctf = tl.generate_chrome_trace_format()
-    with open(SummaryWriter().get_output_folder('timelines')+"/timeline.json", 'w') as f:
+    with open(SummaryWriter().get_output_folder('timelines') + "/timeline.json", 'w') as f:
         f.write(ctf)
         log.info("📊 written timeline trace.")
 
-    #image = SummaryWriter().batch_of_1d_to_image_grid(ae_bottom_a.max_activations())
-    #SummaryWriter().image_summary(ae_bottom_a.name, image)
-
-    #plt.imshow(image, cmap = plt.get_cmap('gray'), interpolation='nearest')
-    #plt.axis('off')
-    #plt.show()
+    # image = SummaryWriter().batch_of_1d_to_image_grid(ae_bottom_a.max_activations())
+    # SummaryWriter().image_summary(ae_bottom_a.name, image)
+    # plt.imshow(image, cmap = plt.get_cmap('gray'), interpolation='nearest')
+    # plt.axis('off')
+    # plt.show()
diff --git a/tests/single_ae.py b/tests/single_ae.py
@@ -14,38 +14,35 @@
 
 with tf.Session() as sess:
     ae = AutoEncoderNode(
-            session = sess,
-            name="ae",
-            hidden_dim=40
-        )
+        session=sess,
+        name="ae",
+        hidden_dim=40
+    )
 
-    inputlayer = OpenCVInputLayer(output_size=(28,28), batch_size=250)
-    
-    ae.register_tensor(inputlayer.get_tensor_for_region([00,14,14,14]))
+    inputlayer = OpenCVInputLayer(output_size=(28, 28), batch_size=250)
+
+    ae.register_tensor(inputlayer.get_tensor_for_region([0, 14, 14, 14]))
 
     ae.initialize_graph()
 
-    # initialize summary writer with graph 
+    # initialize summary writer with graph
     SummaryWriter().writer.add_graph(sess.graph)
-    merged_summary_op = tf.merge_all_summaries()          
+    merged_summary_op = tf.merge_all_summaries()
 
     iteration = 0
 
     def feed_callback(feed_dict):
         global iteration
         iteration += 1
-
-        for _ in xrange(50):
-            sess.run(ae.train_op, feed_dict=feed_dict) 
-
-        #summary_str = merged_summary_op.eval(feed_dict=feed_dict)
-        #SummaryWriter().writer.add_summary(summary_str, iteration)
-        #SummaryWriter().writer.flush()
 
+        for _ in xrange(50):
+            sess.run(ae.train_op, feed_dict=feed_dict)
 
+        # summary_str = merged_summary_op.eval(feed_dict=feed_dict)
+        # SummaryWriter().writer.add_summary(summary_str, iteration)
+        # SummaryWriter().writer.flush()
 
     inputlayer.feed_video(feed_callback, "data/mnist.mp4", frames=50000)
 
     image = SummaryWriter().batch_of_1d_to_image_grid(ae.max_activations.eval())
-    SummaryWriter().image_summary(ae.name+"hoiho", image)
-
+    SummaryWriter().image_summary(ae.name, image)