Apply PEP 8 formatting (lanpa#235)

* autopep8 formatting for examples

Formats all demo_*.py files for better readability and code style

* autopep8 format all files in tensorboardX/

* autopep8 for examples/ again

* fix py2.7 encoding error for CI

demo.py

@@ -11,44 +11,50 @@
 sample_rate = 44100
 freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
 
-true_positive_counts=[75, 64, 21, 5, 0]
-false_positive_counts=[150, 105, 18, 0, 0]
-true_negative_counts=[0, 45, 132, 150, 150]
-false_negative_counts=[0, 11, 54, 70, 75]
-precision=[0.3333333, 0.3786982, 0.5384616, 1.0, 0.0]
-recall=[1.0, 0.8533334, 0.28, 0.0666667, 0.0]
+true_positive_counts = [75, 64, 21, 5, 0]
+false_positive_counts = [150, 105, 18, 0, 0]
+true_negative_counts = [0, 45, 132, 150, 150]
+false_negative_counts = [0, 11, 54, 70, 75]
+precision = [0.3333333, 0.3786982, 0.5384616, 1.0, 0.0]
+recall = [1.0, 0.8533334, 0.28, 0.0666667, 0.0]
 
 
 for n_iter in range(100):
-    s1 = torch.rand(1) # value to keep
+    s1 = torch.rand(1)  # value to keep
     s2 = torch.rand(1)
-    writer.add_scalar('data/scalar_systemtime', s1[0], n_iter) # data grouping by `slash`
-    writer.add_scalar('data/scalar_customtime', s1[0], n_iter, walltime=n_iter) # data grouping by `slash`
-    writer.add_scalars('data/scalar_group', {"xsinx":n_iter*np.sin(n_iter),
-                                             "xcosx":n_iter*np.cos(n_iter),
+    # data grouping by `slash`
+    writer.add_scalar('data/scalar_systemtime', s1[0], n_iter)
+    # data grouping by `slash`
+    writer.add_scalar('data/scalar_customtime', s1[0], n_iter, walltime=n_iter)
+    writer.add_scalars('data/scalar_group', {"xsinx": n_iter * np.sin(n_iter),
+                                             "xcosx": n_iter * np.cos(n_iter),
                                              "arctanx": np.arctan(n_iter)}, n_iter)
-    x = torch.rand(32, 3, 64, 64) # output from network
-    if n_iter%10==0:
+    x = torch.rand(32, 3, 64, 64)  # output from network
+    if n_iter % 10 == 0:
         x = vutils.make_grid(x, normalize=True, scale_each=True)
-        writer.add_image('Image', x, n_iter) # Tensor
-        #writer.add_image('astronaut', skimage.data.astronaut(), n_iter) # numpy
-        #writer.add_image('imread', skimage.io.imread('screenshots/audio.png'), n_iter) # numpy
-        x = torch.zeros(sample_rate*2)
+        writer.add_image('Image', x, n_iter)  # Tensor
+        # writer.add_image('astronaut', skimage.data.astronaut(), n_iter) # numpy
+        # writer.add_image('imread',
+        # skimage.io.imread('screenshots/audio.png'), n_iter) # numpy
+        x = torch.zeros(sample_rate * 2)
         for i in range(x.size(0)):
-            x[i] = np.cos(freqs[n_iter//10]*np.pi*float(i)/float(sample_rate)) # sound amplitude should in [-1, 1]
+            # sound amplitude should in [-1, 1]
+            x[i] = np.cos(freqs[n_iter // 10] * np.pi *
+                          float(i) / float(sample_rate))
         writer.add_audio('myAudio', x, n_iter)
-        writer.add_text('Text', 'text logged at step:'+str(n_iter), n_iter)
+        writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)
         writer.add_text('markdown Text', '''a|b\n-|-\nc|d''', n_iter)
         for name, param in resnet18.named_parameters():
-            if 'bn' not in name:            
+            if 'bn' not in name:
                 writer.add_histogram(name, param, n_iter)
-        writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter) #needs tensorboard 0.4RC or later
+        writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(
+            100), n_iter)  # needs tensorboard 0.4RC or later
         writer.add_pr_curve_raw('prcurve with raw data', true_positive_counts,
-            false_positive_counts,
-            true_negative_counts,
-            false_negative_counts,
-            precision,
-            recall, n_iter)
+                                false_positive_counts,
+                                true_negative_counts,
+                                false_negative_counts,
+                                precision,
+                                recall, n_iter)
 # export scalar data to JSON for external processing
 writer.export_scalars_to_json("./all_scalars.json")
 
@@ -66,14 +72,14 @@
 all_features = torch.cat((features, features_train))
 all_labels = torch.cat((label, labels_train))
 all_images = torch.cat((images, images_train))
-dataset_label = ['test']*100 + ['train']*100
+dataset_label = ['test'] * 100 + ['train'] * 100
 all_labels = list(zip(all_labels, dataset_label))
 
 writer.add_embedding(all_features, metadata=all_labels, label_img=all_images.unsqueeze(1),
                      metadata_header=['digit', 'dataset'], global_step=2)
 
 # VIDEO
-vid_images = dataset.train_data[:16*48]
+vid_images = dataset.train_data[:16 * 48]
 vid = vid_images.view(16, 1, 48, 28, 28)  # BxCxTxHxW
 
 writer.add_video('video', vid_tensor=vid)

examples/chainer/extension_logger/train_dcgan.py

@@ -14,6 +14,8 @@
 from visualize import out_generated_image
 from tensorboardX import SummaryWriter
 from writetnesorboard import LogTensorboard
+
+
 def main():
     parser = argparse.ArgumentParser(description='Chainer example: DCGAN')
     parser.add_argument('--batchsize', '-b', type=int, default=50,

examples/chainer/extension_logger/writetnesorboard.py

@@ -75,7 +75,8 @@ def __call__(self, trainer):
             summary.add({k: observation[k] for k in keys if k in observation})
         for k, v in observation.items():
             #self._logger.add_scalar(k, chainer.cuda.to_cpu(observation[k].data), trainer.updater.iteration)
-            self._logger.add_scalar(k, observation[k], trainer.updater.iteration)
+            self._logger.add_scalar(
+                k, observation[k], trainer.updater.iteration)
         if self._trigger(trainer):
             # output the result
             stats = self._summary.compute_mean()

examples/chainer/plain_logger/train_vae.py

@@ -105,8 +105,8 @@
                     (model.loss, ))
                 o.write(g.dump())
             print('graph generated')
-        writer.add_scalar('train/loss', model.loss, epoch*N+i)
-        writer.add_scalar('train/rec_loss', model.rec_loss, epoch*N+i)
+        writer.add_scalar('train/loss', model.loss, epoch * N + i)
+        writer.add_scalar('train/rec_loss', model.rec_loss, epoch * N + i)
         sum_loss += float(model.loss.data) * len(x.data)
         sum_rec_loss += float(model.rec_loss.data) * len(x.data)
 
@@ -123,10 +123,13 @@
             loss_func(x)
             sum_loss += float(model.loss.data) * len(x.data)
             sum_rec_loss += float(model.rec_loss.data) * len(x.data)
-            writer.add_scalar('test/loss', model.loss, epoch*N_test+i)
-            writer.add_scalar('test/rec_loss', model.rec_loss, epoch*N_test+i)
-            writer.add_image('reconstructed', model(x).reshape(-1,1,28,28), epoch*N_test+i)
-            writer.add_image('input', x.reshape(-1,1,28,28), epoch*N_test+i)
+            writer.add_scalar('test/loss', model.loss, epoch * N_test + i)
+            writer.add_scalar('test/rec_loss', model.rec_loss,
+                              epoch * N_test + i)
+            writer.add_image('reconstructed', model(
+                x).reshape(-1, 1, 28, 28), epoch * N_test + i)
+            writer.add_image('input', x.reshape(-1, 1, 28, 28),
+                             epoch * N_test + i)
             del model.loss
     print('test  mean loss={}, mean reconstruction loss={}'
           .format(sum_loss / N_test, sum_rec_loss / N_test))

examples/demo_beholder.py

@@ -21,23 +21,23 @@
 from __future__ import division
 from __future__ import print_function
 
-import argparse
-import sys
-import torch
 import numpy as np
 import tensorboardX.beholder as beholder_lib
-from collections import namedtuple
+import time
 
-LOG_DIRECTORY = '/tmp/beholder-demo'
+from collections import namedtuple
 
 
-import time
+LOG_DIRECTORY = '/tmp/beholder-demo'
 tensor_and_name = namedtuple('tensor_and_name', 'tensor, name')
 
+
 def beholder_pytorch():
     for i in range(1000):
-        fake_param = [tensor_and_name(np.random.randn(128, 768, 3), 'test'+str(i)) for i in range(5)]    
-        arrays = [tensor_and_name(np.random.randn(128, 768, 3), 'test'+str(i)) for i in range(5)]  
+        fake_param = [tensor_and_name(np.random.randn(128, 768, 3), 'test' + str(i))
+                      for i in range(5)]
+        arrays = [tensor_and_name(np.random.randn(128, 768, 3), 'test' + str(i))
+                  for i in range(5)]
         beholder = beholder_lib.Beholder(logdir=LOG_DIRECTORY)
         beholder.update(
             trainable=fake_param,
@@ -46,7 +46,6 @@ def beholder_pytorch():
         )
         time.sleep(0.1)
         print(i)
-
 
 
 if __name__ == '__main__':
@@ -55,4 +54,3 @@ def beholder_pytorch():
         os.makedirs(LOG_DIRECTORY)
     print(LOG_DIRECTORY)
     beholder_pytorch()
-

examples/demo_caffe2.py

@@ -47,12 +47,12 @@ def DownloadResource(url, path):
     print("Your data folder was not found!! This was generated: {}".format(data_folder))
 
 # Look for existing database: lmdb
-if os.path.exists(os.path.join(data_folder,"mnist-train-nchw-lmdb")):
+if os.path.exists(os.path.join(data_folder, "mnist-train-nchw-lmdb")):
     print("lmdb train db found!")
 else:
     db_missing = True
 
-if os.path.exists(os.path.join(data_folder,"mnist-test-nchw-lmdb")):
+if os.path.exists(os.path.join(data_folder, "mnist-test-nchw-lmdb")):
     print("lmdb test db found!")
 else:
     db_missing = True
@@ -64,7 +64,8 @@ def DownloadResource(url, path):
     try:
         DownloadResource(db_url, data_folder)
     except Exception as ex:
-        print("Failed to download dataset. Please download it manually from {}".format(db_url))
+        print(
+            "Failed to download dataset. Please download it manually from {}".format(db_url))
         print("Unzip it and place the two database folders here: {}".format(data_folder))
         raise ex
 
@@ -92,7 +93,7 @@ def AddInput(model, batch_size, db, db_type):
     # cast the data to float
     data = model.Cast(data_uint8, "data", to=core.DataType.FLOAT)
     # scale data from [0,255] down to [0,1]
-    data = model.Scale(data, data, scale=float(1./256))
+    data = model.Scale(data, data, scale=float(1. / 256))
     # don't need the gradient for the backward pass
     data = model.StopGradient(data, data)
     return data, label
@@ -117,7 +118,8 @@ def AddLeNetModel(model, data):
     conv2 = brew.conv(model, pool1, 'conv2', dim_in=20, dim_out=100, kernel=5)
     # Image size: 8 x 8 -> 4 x 4
     pool2 = brew.max_pool(model, conv2, 'pool2', kernel=2, stride=2)
-    # 50 * 4 * 4 stands for dim_out from previous layer multiplied by the image size
+    # 50 * 4 * 4 stands for dim_out from previous layer multiplied by the
+    # image size
     fc3 = brew.fc(model, pool2, 'fc3', dim_in=100 * 4 * 4, dim_out=500)
     relu = brew.relu(model, fc3, fc3)
     pred = brew.fc(model, relu, 'pred', 500, 10)
@@ -138,13 +140,14 @@ def AddTrainingOperators(model, softmax, label):
     loss = model.AveragedLoss(xent, "loss")
     # track the accuracy of the model
     AddAccuracy(model, softmax, label)
-    # use the average loss we just computed to add gradient operators to the model
+    # use the average loss we just computed to add gradient operators to the
+    # model
     model.AddGradientOperators([loss])
     # do a simple stochastic gradient descent
     ITER = brew.iter(model, "iter")
     # set the learning rate schedule
     LR = model.LearningRate(
-        ITER, "LR", base_lr=-0.1, policy="step", stepsize=1, gamma=0.999 )
+        ITER, "LR", base_lr=-0.1, policy="step", stepsize=1, gamma=0.999)
     # ONE is a constant value that is used in the gradient update. We only need
     # to create it once, so it is explicitly placed in param_init_net.
     ONE = model.param_init_net.ConstantFill([], "ONE", shape=[1], value=1.0)
@@ -179,6 +182,7 @@ def AddBookkeepingOperators(model):
     # demo, we will only show how to summarize the parameters and their
     # gradients.
 
+
 arg_scope = {"order": "NCHW"}
 train_model = model_helper.ModelHelper(name="mnist_train", arg_scope=arg_scope)
 data, label = AddInput(

examples/demo_custom_scalars.py

@@ -1,21 +1,23 @@
 from numpy.random import rand
 from tensorboardX import SummaryWriter
 import time
+
+
 with SummaryWriter() as writer:
     for n_iter in range(100):
         writer.add_scalar('twse/0050', rand(), n_iter)
         writer.add_scalar('twse/2330', rand(), n_iter)
         t = rand()
         writer.add_scalar('dow/aaa', t, n_iter)
-        writer.add_scalar('dow/bbb', t-1, n_iter)
-        writer.add_scalar('dow/ccc', t+1, n_iter)
+        writer.add_scalar('dow/bbb', t - 1, n_iter)
+        writer.add_scalar('dow/ccc', t + 1, n_iter)
         writer.add_scalar('nasdaq/aaa', rand(), n_iter)
         writer.add_scalar('nasdaq/bbb', rand(), n_iter)
         writer.add_scalar('nasdaq/ccc', rand(), n_iter)
 
-    layout = {'Taiwan':{'twse':['Multiline',['twse/0050', 'twse/2330']]},
-                'USA':{'dow':['Margin',['dow/aaa', 'dow/bbb', 'dow/ccc']],
-                    'nasdaq':['Margin',['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}
+    layout = {'Taiwan': {'twse': ['Multiline', ['twse/0050', 'twse/2330']]},
+              'USA': {'dow': ['Margin', ['dow/aaa', 'dow/bbb', 'dow/ccc']],
+                      'nasdaq': ['Margin', ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}
     writer.add_custom_scalars(layout)
 #    writer.add_custom_scalars(layout) second call has no effect
 
@@ -34,7 +36,7 @@
     for n_iter in range(100):
         t = rand()
         writer.add_scalar('dow/aaa', t, n_iter)
-        writer.add_scalar('dow/bbb', t-1, n_iter)
-        writer.add_scalar('dow/ccc', t+1, n_iter)
+        writer.add_scalar('dow/bbb', t - 1, n_iter)
+        writer.add_scalar('dow/ccc', t + 1, n_iter)
 
-    writer.add_custom_scalars_marginchart(['dow/aaa', 'dow/bbb', 'dow/ccc'])
+    writer.add_custom_scalars_marginchart(['dow/aaa', 'dow/bbb', 'dow/ccc'])