Fix matplotlib support

examples/keras_tensorboard.py

@@ -6,7 +6,9 @@
 # 2 seconds per epoch on a K520 GPU.
 from __future__ import print_function
 
-import io
+import argparse
+import tempfile
+import os
 import numpy as np
 
 from keras.callbacks import TensorBoard, ModelCheckpoint
@@ -15,8 +17,6 @@ from keras.models import Sequential
 from keras.layers.core import Dense, Activation
 from keras.optimizers import RMSprop
 from keras.utils import np_utils
-# TODO: test these methods binding
-from keras.models import load_model, save_model, model_from_json
 import tensorflow as tf
 from trains import Task
 
@@ -25,6 +25,7 @@ class TensorBoardImage(TensorBoard):
     @staticmethod
     def make_image(tensor):
         from PIL import Image
+        import io
         tensor = np.stack((tensor, tensor, tensor), axis=2)
         height, width, channels = tensor.shape
         image = Image.fromarray(tensor)
@@ -49,19 +50,17 @@ class TensorBoardImage(TensorBoard):
         self.writer.add_summary(summary, epoch)
 
 
-batch_size = 128
-nb_classes = 10
-nb_epoch = 6
+parser = argparse.ArgumentParser(description='Keras MNIST Example')
+parser.add_argument('--batch-size', type=int, default=128, help='input batch size for training (default: 64)')
+parser.add_argument('--epochs', type=int, default=6, help='number of epochs to train (default: 10)')
+args = parser.parse_args()
 
 # the data, shuffled and split between train and test sets
+nb_classes = 10
 (X_train, y_train), (X_test, y_test) = mnist.load_data()
 
-X_train = X_train.reshape(60000, 784)
-X_test = X_test.reshape(10000, 784)
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255.
-X_test /= 255.
+X_train = X_train.reshape(60000, 784).astype('float32')/255.
+X_test = X_test.reshape(10000, 784).astype('float32')/255.
 print(X_train.shape[0], 'train samples')
 print(X_test.shape[0], 'test samples')
 
@@ -91,21 +90,23 @@ model.compile(loss='categorical_crossentropy',
 
 # Connecting TRAINS
 task = Task.init(project_name='examples', task_name='Keras with TensorBoard example')
-# setting model outputs
+# Advanced: setting model class enumeration
 labels = dict(('digit_%d' % i, i) for i in range(10))
 task.set_model_label_enumeration(labels)
 
-board = TensorBoard(histogram_freq=1, log_dir='/tmp/histogram_example', write_images=False)
-model_store = ModelCheckpoint(filepath='/tmp/histogram_example/weight.{epoch}.hdf5')
+output_folder = os.path.join(tempfile.gettempdir(), 'keras_example')
+
+board = TensorBoard(histogram_freq=1, log_dir=output_folder, write_images=False)
+model_store = ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.{epoch}.hdf5'))
 
 # load previous model, if it is there
 try:
-    model.load_weights('/tmp/histogram_example/weight.1.hdf5')
+    model.load_weights(os.path.join(output_folder, 'weight.1.hdf5'))
 except:
     pass
 
 history = model.fit(X_train, Y_train,
-                    batch_size=batch_size, epochs=nb_epoch,
+                    batch_size=args.batch_size, epochs=args.epochs,
                     callbacks=[board, model_store],
                     verbose=1, validation_data=(X_test, Y_test))
 score = model.evaluate(X_test, Y_test, verbose=0)

examples/manual_reporting.py

@@ -7,7 +7,7 @@ from trains import Task
 
 task = Task.init(project_name='examples', task_name='Manual reporting')
 
-# example python logger
+# standard python logging
 logging.getLogger().setLevel('DEBUG')
 logging.debug('This is a debug message')
 logging.info('This is an info message')
@@ -23,7 +23,7 @@ except ImportError:
     pass
 
 # get TRAINS logger object for any metrics / reports
-logger = task.get_logger()
+logger = Task.current_task().get_logger()
 
 # log text
 logger.console("hello")
@@ -34,7 +34,7 @@ logger.report_scalar("example_scalar", "series A", iteration=1, value=200)
 
 # report histogram
 histogram = np.random.randint(10, size=10)
-logger.report_vector("example_histogram", "random histogram", iteration=1, values=histogram)
+logger.report_histogram("example_histogram", "random histogram", iteration=1, values=histogram)
 
 # report confusion matrix
 confusion = np.random.randint(10, size=(10, 10))

trains/backend_interface/metrics/reporter.py

@@ -2,6 +2,7 @@ import collections
 import json
 
 import six
+import numpy as np
 from threading import Thread, Event
 
 from ..base import InterfaceBase
@@ -157,8 +158,15 @@ class Reporter(InterfaceBase, AbstractContextManager, SetupUploadMixin, AsyncMan
         :param iter: Iteration number
         :type value: int
         """
+        try:
+            def default(o):
+                if isinstance(o, np.int64):
+                    return int(o)
+        except Exception:
+            default = None
+
         if isinstance(plot, dict):
-            plot = json.dumps(plot)
+            plot = json.dumps(plot, default=default)
         elif not isinstance(plot, six.string_types):
             raise ValueError('Plot should be a string or a dict')
         ev = PlotEvent(metric=self._normalize_name(title),

trains/binding/matplotlib_bind.py

@@ -1,3 +1,5 @@
+# -*- coding: utf-8 -*-
+
 import os
 import sys
 from tempfile import mkstemp
@@ -41,7 +43,8 @@ class PatchedMatplotlib:
         try:
             # we support matplotlib version 2.0.0 and above
             import matplotlib
-            if int(matplotlib.__version__.split('.')[0]) < 2:
+            matplot_major_version = int(matplotlib.__version__.split('.')[0])
+            if matplot_major_version < 2:
                 LoggerRoot.get_base_logger().warning(
                     'matplotlib binding supports version 2.0 and above, found version {}'.format(
                         matplotlib.__version__))
@@ -63,6 +66,7 @@ class PatchedMatplotlib:
                 PatchedMatplotlib._patched_original_plot = plt.show
                 PatchedMatplotlib._patched_original_imshow = plt.imshow
                 PatchedMatplotlib._patched_original_figure = figure.Figure.show
+
             plt.show = PatchedMatplotlib.patched_show
             figure.Figure.show = PatchedMatplotlib.patched_figure_show
             sys.modules['matplotlib'].pyplot.imshow = PatchedMatplotlib.patched_imshow
@@ -111,13 +115,26 @@ class PatchedMatplotlib:
 
     @staticmethod
     def patched_figure_show(self, *args, **kw):
+        if hasattr(self, '_trains_show'):
+            # flag will be cleared when calling clf() (object will be replaced)
+            return PatchedMatplotlib._patched_original_figure(self, *args, **kw)
+        try:
+            self._trains_show = True
+        except Exception:
+            pass
         PatchedMatplotlib._report_figure(set_active=False, specific_fig=self)
         ret = PatchedMatplotlib._patched_original_figure(self, *args, **kw)
         return ret
 
     @staticmethod
     def patched_show(*args, **kw):
-        PatchedMatplotlib._report_figure()
+        # noinspection PyBroadException
+        try:
+            figures = PatchedMatplotlib._get_output_figures(None, all_figures=True)
+            for figure in figures:
+                PatchedMatplotlib._report_figure(stored_figure=figure)
+        except Exception:
+            pass
         ret = PatchedMatplotlib._patched_original_plot(*args, **kw)
         if PatchedMatplotlib._current_task and running_remotely():
             # clear the current plot, because no one else will
@@ -155,10 +172,17 @@ class PatchedMatplotlib:
             else:
                 mpl_fig = specific_fig
 
+            # mark as processed, so nested calls to figure.show will do nothing
+            try:
+                mpl_fig._trains_show = True
+            except Exception:
+                pass
+
             # convert to plotly
             image = None
             plotly_fig = None
             image_format = 'jpeg'
+            fig_dpi = 300
             if not force_save_as_image:
                 image_format = 'svg'
                 # noinspection PyBroadException
@@ -168,12 +192,39 @@ class PatchedMatplotlib:
                         if matplotlylib:
                             renderer = matplotlylib.PlotlyRenderer()
                             matplotlylib.Exporter(renderer, close_mpl=False).run(fig)
+                            x_ticks = list(renderer.current_mpl_ax.get_xticklabels())
+                            if x_ticks:
+                                try:
+                                    # check if all values can be cast to float
+                                    values = [float(t.get_text().replace('−', '-')) for t in x_ticks]
+                                except:
+                                    try:
+                                        renderer.plotly_fig['layout']['xaxis1'].update({
+                                            'ticktext': [t.get_text() for t in x_ticks],
+                                            'tickvals': [t.get_position()[0] for t in x_ticks],
+                                        })
+                                    except:
+                                        pass
+                            y_ticks = list(renderer.current_mpl_ax.get_yticklabels())
+                            if y_ticks:
+                                try:
+                                    # check if all values can be cast to float
+                                    values = [float(t.get_text().replace('−', '-')) for t in y_ticks]
+                                except:
+                                    try:
+                                        renderer.plotly_fig['layout']['yaxis1'].update({
+                                            'ticktext': [t.get_text() for t in y_ticks],
+                                            'tickvals': [t.get_position()[1] for t in y_ticks],
+                                        })
+                                    except:
+                                        pass
                             return renderer.plotly_fig
 
                     plotly_fig = our_mpl_to_plotly(mpl_fig)
                 except Exception as ex:
                     # this was an image, change format to png
                     image_format = 'jpeg' if 'selfie' in str(ex) else 'png'
+                    fig_dpi = 300
 
             # plotly could not serialize the plot, we should convert to image
             if not plotly_fig:
@@ -183,13 +234,13 @@ class PatchedMatplotlib:
                     # first try SVG if we fail then fallback to png
                     buffer_ = BytesIO()
                     a_plt = specific_fig if specific_fig is not None else plt
-                    a_plt.savefig(buffer_, format=image_format, bbox_inches='tight', pad_inches=0, frameon=False)
+                    a_plt.savefig(buffer_, dpi=fig_dpi, format=image_format, bbox_inches='tight', pad_inches=0, frameon=False)
                     buffer_.seek(0)
                 except Exception:
                     image_format = 'png'
                     buffer_ = BytesIO()
                     a_plt = specific_fig if specific_fig is not None else plt
-                    a_plt.savefig(buffer_, format=image_format, bbox_inches='tight', pad_inches=0, frameon=False)
+                    a_plt.savefig(buffer_, dpi=fig_dpi, format=image_format, bbox_inches='tight', pad_inches=0, frameon=False)
                     buffer_.seek(0)
                 fd, image = mkstemp(suffix='.'+image_format)
                 os.write(fd, buffer_.read())
@@ -253,6 +304,17 @@ class PatchedMatplotlib:
 
         return
 
+    @staticmethod
+    def _get_output_figures(stored_figure, all_figures):
+        try:
+            from matplotlib import _pylab_helpers
+            if all_figures:
+                return list(_pylab_helpers.Gcf.figs.values())
+            else:
+                return [stored_figure] or [_pylab_helpers.Gcf.get_active()]
+        except Exception:
+            return []
+
     @staticmethod
     def __patched_draw_all(*args, **kwargs):
         recursion_guard = PatchedMatplotlib.__patched_draw_all_recursion_guard