Python 2.7 support

examples/keras_tensorboard.py

@@ -39,7 +39,7 @@ class TensorBoardImage(TensorBoard):
                                 encoded_image_string=image_string)
 
     def on_epoch_end(self, epoch, logs={}):
-        super().on_epoch_end(epoch, logs)
+        super(TensorBoardImage, self).on_epoch_end(epoch, logs)
         import tensorflow as tf
         images = self.validation_data[0]  # 0 - data; 1 - labels
         img = (255 * images[0].reshape(28, 28)).astype('uint8')

examples/pytorch_matplotlib.py

@@ -59,6 +59,7 @@ import torchvision.models as models
 import copy
 from trains import Task
 
+
 task = Task.init(project_name='examples', task_name='pytorch with matplotlib example', task_type=Task.TaskTypes.testing)
 
 
@@ -95,10 +96,6 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 #     with name ``images`` in your current working directory.
 
 # desired size of the output image
-
-STYLE_IMAGE_PATH = "./samples/picasso.jpg"
-CONTENT_IMAGE_PATH = "./samples/dancing.jpg"
-
 imsize = 512 if torch.cuda.is_available() else 128  # use small size if no gpu
 
 loader = transforms.Compose([
@@ -113,8 +110,8 @@ def image_loader(image_name):
     return image.to(device, torch.float)
 
 
-style_img = image_loader(STYLE_IMAGE_PATH)
-content_img = image_loader(CONTENT_IMAGE_PATH)
+style_img = image_loader("./samples/picasso.jpg")
+content_img = image_loader("./samples/dancing.jpg")
 
 assert style_img.size() == content_img.size(), \
     "we need to import style and content images of the same size"
@@ -169,7 +166,7 @@ imshow(content_img, title='Content Image')
 # computed at the desired layers and because of auto grad, all the
 # gradients will be computed. Now, in order to make the content loss layer
 # transparent we must define a ``forward`` method that computes the content
-# loss and then returns the layer’s input. The computed loss is saved as a
+# loss and then returns the layer's input. The computed loss is saved as a
 # parameter of the module.
 #
 
@@ -259,7 +256,7 @@ class StyleLoss(nn.Module):
 # Now we need to import a pre-trained neural network. We will use a 19
 # layer VGG network like the one used in the paper.
 #
-# PyTorch’s implementation of VGG is a module divided into two child
+# PyTorch's implementation of VGG is a module divided into two child
 # ``Sequential`` modules: ``features`` (containing convolution and pooling layers),
 # and ``classifier`` (containing fully connected layers). We will use the
 # ``features`` module because we need the output of the individual
@@ -299,7 +296,7 @@ class Normalization(nn.Module):
 ######################################################################
 # A ``Sequential`` module contains an ordered list of child modules. For
 # instance, ``vgg19.features`` contains a sequence (Conv2d, ReLU, MaxPool2d,
-# Conv2d, ReLU…) aligned in the right order of depth. We need to add our
+# Conv2d, ReLU...) aligned in the right order of depth. We need to add our
 # content loss and style loss layers immediately after the convolution
 # layer they are detecting. To do this we must create a new ``Sequential``
 # module that has content loss and style loss modules correctly inserted.
@@ -407,7 +404,7 @@ def get_input_optimizer(input_img):
 # Finally, we must define a function that performs the neural transfer. For
 # each iteration of the networks, it is fed an updated input and computes
 # new losses. We will run the ``backward`` methods of each loss module to
-# dynamicaly compute their gradients. The optimizer requires a “closure”
+# dynamicaly compute their gradients. The optimizer requires a "closure"
 # function, which reevaluates the modul and returns the loss.
 #
 # We still have one final constraint to address. The network may try to

trains/backend_interface/metrics/reporter.py

@@ -7,7 +7,7 @@ import six
 from ..base import InterfaceBase
 from ..setupuploadmixin import SetupUploadMixin
 from ...utilities.async_manager import AsyncManagerMixin
-from ...utilities.plotly import create_2d_histogram_plot, create_value_matrix, create_3d_surface, \
+from ...utilities.plotly_reporter import create_2d_histogram_plot, create_value_matrix, create_3d_surface, \
     create_2d_scatter_series, create_3d_scatter_series, create_line_plot, plotly_scatter3d_layout_dict
 from ...utilities.py3_interop import AbstractContextManager
 from .events import ScalarEvent, VectorEvent, ImageEvent, PlotEvent, ImageEventNoUpload

trains/config/cache.py

@@ -1,4 +1,7 @@
 import json
+
+import six
+
 from . import get_cache_dir
 from .defs import SESSION_CACHE_FILE
 
@@ -12,7 +15,8 @@ class SessionCache(object):
     @classmethod
     def _load_cache(cls):
         try:
-            with (get_cache_dir() / SESSION_CACHE_FILE).open("rt") as fp:
+            flag = 'rb' if six.PY2 else 'rt'
+            with (get_cache_dir() / SESSION_CACHE_FILE).open(flag) as fp:
                 return json.load(fp)
         except Exception:
             return {}
@@ -21,7 +25,8 @@ class SessionCache(object):
     def _store_cache(cls, cache):
         try:
             get_cache_dir().mkdir(parents=True, exist_ok=True)
-            with (get_cache_dir() / SESSION_CACHE_FILE).open("wt") as fp:
+            flag = 'wb' if six.PY2 else 'wt'
+            with (get_cache_dir() / SESSION_CACHE_FILE).open(flag) as fp:
                 json.dump(cache, fp)
         except Exception:
             pass

trains/config/default/__main__.py

@@ -1,3 +1,5 @@
+from __future__ import print_function
+
 from pyhocon import ConfigFactory
 from pathlib2 import Path
 from six.moves.urllib.parse import urlparse, urlunparse

trains/utilities/frameworks.py

@@ -4,7 +4,7 @@ import threading
 import weakref
 from collections import defaultdict
 from logging import ERROR, WARNING, getLogger
-from pathlib import Path
+from pathlib2 import Path
 
 import cv2
 import numpy as np
@@ -99,10 +99,11 @@ class PostImportHookPatching(object):
 
 def _patched_call(original_fn, patched_fn):
     def _inner_patch(*args, **kwargs):
-        ident = threading.get_ident()
+        ident = threading._get_ident() if six.PY2 else threading.get_ident()
         if ident in _recursion_guard:
             return original_fn(*args, **kwargs)
         _recursion_guard[ident] = 1
+        ret = None
         try:
             ret = patched_fn(original_fn, *args, **kwargs)
         except Exception as ex:

trains/utilities/matplotlib_bind.py

@@ -2,8 +2,10 @@ import sys
 
 import cv2
 import numpy as np
+import six
 from six import BytesIO
 
+from ..debugging.log import LoggerRoot
 from ..config import running_remotely
 
 
@@ -36,6 +38,9 @@ class PatchedMatplotlib:
             # we support matplotlib version 2.0.0 and above
             import matplotlib
             if int(matplotlib.__version__.split('.')[0]) < 2:
+                LoggerRoot.get_base_logger().warning(
+                    'matplotlib binding supports version 2.0 and above, found version {}'.format(
+                        matplotlib.__version__))
                 return False
 
             if running_remotely():
@@ -44,10 +49,13 @@ class PatchedMatplotlib:
                 import matplotlib.pyplot
                 sys.modules['matplotlib'].pyplot.switch_backend('agg')
             import matplotlib.pyplot as plt
-            import plotly.tools as tls
             from matplotlib import _pylab_helpers
-            PatchedMatplotlib._patched_original_plot = sys.modules['matplotlib'].pyplot.show
-            PatchedMatplotlib._patched_original_imshow = sys.modules['matplotlib'].pyplot.imshow
+            if six.PY2:
+                PatchedMatplotlib._patched_original_plot = staticmethod(sys.modules['matplotlib'].pyplot.show)
+                PatchedMatplotlib._patched_original_imshow = staticmethod(sys.modules['matplotlib'].pyplot.imshow)
+            else:
+                PatchedMatplotlib._patched_original_plot = sys.modules['matplotlib'].pyplot.show
+                PatchedMatplotlib._patched_original_imshow = sys.modules['matplotlib'].pyplot.imshow
             sys.modules['matplotlib'].pyplot.show = PatchedMatplotlib.patched_show
             # sys.modules['matplotlib'].pyplot.imshow = PatchedMatplotlib.patched_imshow
             # patch plotly so we know it failed us.
@@ -103,7 +111,6 @@ class PatchedMatplotlib:
         # noinspection PyBroadException
         try:
             import matplotlib.pyplot as plt
-            import plotly.tools as tls
             from plotly import optional_imports
             from matplotlib import _pylab_helpers
             # store the figure object we just created (if it is not already there)
@@ -136,7 +143,8 @@ class PatchedMatplotlib:
                 buffer_ = BytesIO()
                 plt.savefig(buffer_, format="png", bbox_inches='tight', pad_inches=0)
                 buffer_.seek(0)
-                image = cv2.imdecode(np.frombuffer(buffer_.getbuffer(), dtype=np.uint8), cv2.IMREAD_UNCHANGED)
+                buffer = buffer_.getbuffer() if not six.PY2 else buffer_.getvalue()
+                image = cv2.imdecode(np.frombuffer(buffer, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
 
             # check if we need to restore the active object
             if set_active and not _pylab_helpers.Gcf.get_active():