Merge pull request #4133 from taesupkim/issue_4056

theano/sandbox/conv.py

 from __future__ import print_function
 import sys
-print("DEPRECATION: theano.sandbox.conv no longer provides conv.  They have been moved to theano.tensor.nnet.conv", file=sys.stderr)
-from theano.tensor.nnet.conv import *
+print("DEPRECATION: theano.sandbox.conv no longer provides conv. "
+      "They have been moved to theano.tensor.nnet.conv", file=sys.stderr)

theano/sandbox/debug.py

-from __future__ import print_function
-
-from six import reraise
-from theano import gof
-import sys
-
-
-class DebugException(Exception):
-    pass
-
-
-class DebugLinker(gof.WrapLinker):
-
-    def __init__(self,
-                 linkers,
-                 debug_pre=None,
-                 debug_post=None,
-                 copy_originals=False,
-                 check_types=True,
-                 compare_variables=True,
-                 compare_fn=(lambda x, y: x == y)):
-        if debug_pre is None:
-            debug_pre = []
-        if debug_post is None:
-            debug_post = []
-        gof.WrapLinker.__init__(self,
-                                linkers=linkers,
-                                wrapper=self.wrapper)
-
-        self.fgraph = None
-
-        self.compare_fn = compare_fn
-
-        self.copy_originals = copy_originals
-        if check_types not in [None, True]:
-            self.check_types = check_types
-        if compare_variables not in [None, True]:
-            self.compare_variables = compare_variables
-
-        if not isinstance(debug_pre, (list, tuple)):
-            debug_pre = [debug_pre]
-        self.debug_pre = debug_pre
-
-        if not isinstance(debug_post, (list, tuple)):
-            debug_post = [debug_post]
-        self.debug_post = debug_post
-        if check_types is not None:
-            self.debug_post.append(self.check_types)
-        if compare_variables is not None:
-            self.debug_post.append(self.compare_variables)
-
-    def accept(self, fgraph, no_recycling=None):
-        if no_recycling is None:
-            no_recycling = []
-        return gof.WrapLinker.accept(self,
-                                     fgraph=fgraph,
-                                     no_recycling=no_recycling)
-
-    def store_value(self, i, node, *thunks):
-        th1 = thunks[0]
-        for r, oval in zip(node.outputs, th1.outputs):
-            r.step = i
-            r.value = oval[0]
-            if self.copy_originals:
-                r.original_value = copy(oval[0])
-
-    def check_types(self, i, node, *thunks):
-        for thunk, linker in zip(thunks, self.linkers):
-            for r in node.outputs:
-                try:
-                    r.type.filter(r.value, strict=True)
-                except TypeError as e:
-                    exc_type, exc_value, exc_trace = sys.exc_info()
-                    exc = DebugException(e, "The output %s was filled with data with the wrong type using linker " \
-                                         ("%s. This happened at step %i of the program." % (r, linker, i)) + \
-                                         "For more info, inspect this exception's 'original_exception', 'debugger', " \
-                                         "'output_at_fault', 'step', 'node', 'thunk' and 'linker' fields.")
-                    exc.debugger = self
-                    exc.original_exception = e
-                    exc.output_at_fault = r
-                    exc.step = i
-                    exc.node = node
-                    exc.thunk = thunk
-                    exc.linker = linker
-                    reraise(DebugException, exc, exc_trace)
-
-    def compare_variables(self, i, node, *thunks):
-        thunk0 = thunks[0]
-        linker0 = self.linkers[0]
-        for thunk, linker in zip(thunks[1:], self.linkers[1:]):
-            for o, output0, output in zip(node.outputs, thunk0.outputs, thunk.outputs):
-                if not self.compare_fn(output0[0], output[0]):
-                    exc = DebugException(("The variables from %s and %s for output %s are not the same. This happened at step %i." % (linker0, linker, o, step)) + \
-                                         "For more info, inspect this exception's 'debugger', 'output', 'output_value1', 'output_value2', " \
-                                         "'step', 'node', 'thunk1', 'thunk2', 'linker1' and 'linker2' fields.")
-                    exc.debugger = self
-                    exc.output = o
-                    exc.output_value1 = output0
-                    exc.output_value2 = output
-                    exc.step = i
-                    exc.node = node
-                    exc.thunk1 = thunk0
-                    exc.thunk2 = thunk
-                    exc.linker1 = linker0
-                    exc.linker2 = linker
-                    raise exc
-
-    def pre(self, f, inputs, order, thunk_groups):
-        fgraph = f.fgraph
-        for r in fgraph.variables:
-            if r.owner is None:
-                r.step = "value"  # this will be overwritten if r is an input
-            else:
-                r.step = None
-            r.value = None
-            r.original_value = None
-            if r.owner is None and r not in fgraph.inputs:
-                r.value = r.data
-                if self.copy_originals:
-                    r.original_value = copy(r.data)
-        for idx, (i, r) in enumerate(zip(inputs, fgraph.inputs)):
-            r.step = "input %i" % idx
-            r.value = i
-            if self.copy_originals:
-                r.original_value = copy(i)
-        for node, thunk_group in zip(order, thunk_groups):
-            node.step = None
-
-    def wrapper(self, i, node, *thunks):
-        try:
-            node.step = i
-            for f in self.debug_pre:
-                f(i, node, *thunks)
-            for thunk in thunks:
-                thunk()
-            self.store_value(i, node, *thunks)
-            for f in self.debug_post:
-                f(i, node, *thunks)
-        except Exception as e:
-            exc_type, exc_value, exc_trace = sys.exc_info()
-            if isinstance(e, DebugException):
-                raise
-            exc = DebugException(e, ("An exception occurred while processing node %s at step %i of the program." % (node, i)) + \
-                                 "For more info, inspect this exception's 'original_exception', 'debugger', 'step', 'node' and 'thunks' fields.")
-            exc.debugger = self
-            exc.original_exception = e
-            exc.step = i
-            exc.node = node
-            exc.thunks = thunks
-            reraise(DebugException, exc, exc_trace)
-
-
-def print_info(i, node, *thunks):
-    print("step %i, node %s" % (i, node))
-
-
-def print_from(i, node, *thunks):
-    print("parents:", ", ".join(str(input.step) for input in node.inputs))
-
-
-def print_input_shapes(i, node, *thunks):
-    shapes = []
-    for input in node.inputs:
-        if hasattr(input.value, 'shape'):
-            shapes.append(str(input.value.shape))
-        else:
-            shapes.append('N/A')
-    print("input shapes:", ", ".join(shapes))
-
-
-def print_input_types(i, node, *thunks):
-    print("input types:", ", ".join(str(type(input.value)) for input in node.inputs))
-
-
-def print_sep(i, node, *thunks):
-    print("===================================")
-
-import numpy
-
-
-def numpy_compare(a, b, tolerance=1e-6):
-    if isinstance(a, numpy.ndarray):
-        return (abs(a - b) <= tolerance).all()
-    else:
-        return a == b
-
-
-def numpy_debug_linker(pre, post=None):
-    if post is None:
-        post = []
-    return DebugLinker([gof.OpWiseCLinker],
-                       pre,
-                       post,
-                       compare_fn=numpy_compare)
-
-

theano/sandbox/fourier.py

@@ -12,7 +12,7 @@ from theano.gof import Op, Apply, generic
 
 
 class GradTodo(Op):
-
+    # TODO : need description for class
     __props__ = ()
 
     def make_node(self, x):
@@ -24,6 +24,7 @@ grad_todo = GradTodo()
 
 
 class FFT(Op):
+    # TODO : need description for parameters
     """
     Fast Fourier Transform.
 
@@ -44,7 +45,8 @@ class FFT(Op):
     # don't return the plan object in the 'buf' output
 
     half = False
-    """Only return the first half (positive-valued) of the frequency components."""
+    """Only return the first half (positive-valued) of the frequency
+    components."""
     __props__ = ("half", "inverse")
 
     def __init__(self, half=False, inverse=False):
@@ -82,11 +84,13 @@ class FFT(Op):
             M, N = fft.shape
             if axis == 0:
                 if (M % 2):
-                    raise ValueError('halfFFT on odd-length vectors is undefined')
+                    raise ValueError(
+                        'halfFFT on odd-length vectors is undefined')
                 spectrogram[0] = fft[0:M / 2, :]
             elif axis == 1:
                 if (N % 2):
-                    raise ValueError('halfFFT on odd-length vectors is undefined')
+                    raise ValueError(
+                        'halfFFT on odd-length vectors is undefined')
                 spectrogram[0] = fft[:, 0:N / 2]
             else:
                 raise NotImplementedError()
@@ -105,6 +109,7 @@ half_ifft = FFT(half=True, inverse=True)
 
 
 def dct_matrix(rows, cols, unitary=True):
+    # TODO : need description for parameters
     """
     Return a (rows x cols) matrix implementing a discrete cosine transform.
 
@@ -115,7 +120,8 @@ def dct_matrix(rows, cols, unitary=True):
     col_range = numpy.arange(cols)
     scale = numpy.sqrt(2.0 / cols)
     for i in xrange(rows):
-        rval[i] = numpy.cos(i * (col_range * 2 + 1) / (2.0 * cols) * numpy.pi) * scale
+        rval[i] = numpy.cos(
+            i * (col_range * 2 + 1) / (2.0 * cols) * numpy.pi) * scale
 
     if unitary:
         rval[0] *= numpy.sqrt(0.5)

theano/sandbox/minimal.py

@@ -9,17 +9,19 @@ from theano.tests import unittest_tools as utt
 
 
 class Minimal(gof.Op):
+    # TODO : need description for class
 
-    # if the Op has any attributes,
-    # consider using them in the eq function.  If two Apply nodes have the same inputs and the
-    # ops compare equal... then they will be MERGED so they had better have computed the same
-    # thing!
+    # if the Op has any attributes, consider using them in the eq function.
+    # If two Apply nodes have the same inputs and the ops compare equal...
+    # then they will be MERGED so they had better have computed the same thing!
 
     def __init__(self):
-        # If you put things here, think about whether they change the outputs computed by
-        # self.perform()
-        #  - If they do, then you should take them into consideration in __eq__ and __hash__
-        #  - If they do not, then you should not use them in __eq__ and __hash__
+        # If you put things here, think about whether they change the outputs
+        # computed by # self.perform()
+        #  - If they do, then you should take them into consideration in
+        #    __eq__ and __hash__
+        #  - If they do not, then you should not use them in
+        #    __eq__ and __hash__
 
         super(Minimal, self).__init__()
 

theano/sandbox/multinomial.py

@@ -16,6 +16,7 @@ if cuda_available:
 
 
 class MultinomialFromUniform(Op):
+    # TODO : need description for parameter 'odtype'
     """
     Converts samples from a uniform into sample from a multinomial.
 
@@ -197,7 +198,8 @@ class MultinomialFromUniform(Op):
 
 class MultinomialWOReplacementFromUniform(MultinomialFromUniform):
     """
-    Converts samples from a uniform into sample (without replacement) from a multinomial.
+    Converts samples from a uniform into sample (without replacement) from a
+    multinomial.
 
     """
 
@@ -347,8 +349,8 @@ class MultinomialWOReplacementFromUniform(MultinomialFromUniform):
         (z,) = outs
 
         if n_samples > pvals.shape[1]:
-            raise ValueError("Cannot sample without replacement n samples bigger "
-                             "than the size of the distribution.")
+            raise ValueError("Cannot sample without replacement n samples "
+                             "bigger than the size of the distribution.")
 
         if unis.shape[0] != pvals.shape[0] * n_samples:
             raise ValueError("unis.shape[0] != pvals.shape[0] * n_samples",
@@ -358,7 +360,8 @@ class MultinomialWOReplacementFromUniform(MultinomialFromUniform):
             odtype = 'int64'
         else:
             odtype = self.odtype
-        if z[0] is None or not numpy.all(z[0].shape == [pvals.shape[0], n_samples]):
+        if (z[0] is None or
+                not numpy.all(z[0].shape == [pvals.shape[0], n_samples])):
             z[0] = -1 * numpy.ones((pvals.shape[0], n_samples), dtype=odtype)
 
         nb_multi = pvals.shape[0]
@@ -374,7 +377,8 @@ class MultinomialWOReplacementFromUniform(MultinomialFromUniform):
                     cummul += pvals[n, m]
                     if (cummul > unis_n):
                         z[0][n, c] = m
-                        # set to zero and re-normalize so that it's not selected again
+                        # set to zero and re-normalize so that it's not
+                        # selected again
                         pvals[n, m] = 0.
                         pvals[n] /= pvals[n].sum()
                         break
@@ -562,6 +566,7 @@ class GpuMultinomialFromUniform(MultinomialFromUniform, GpuOp):
 
 @local_optimizer([MultinomialFromUniform])
 def local_gpu_multinomial(node):
+    # TODO : need description for function
     if type(node.op) is MultinomialFromUniform:
         if len(node.inputs) == 2:
             p, u = node.inputs

theano/sandbox/neighbourhoods.py

@@ -116,7 +116,8 @@ class NeighbourhoodsFromImages(Op):
         return dims, num_strides
 
     # for inverse mode
-    # "output" here actually referes to the Op's input shape (but it's inverse mode)
+    # "output" here actually referes to the Op's input shape (but it's inverse
+    # mode)
     def in_shape(self, output_shape):
         out_dims = list(output_shape[:self.n_dims_before])
         num_strides = []
@@ -168,7 +169,8 @@ class NeighbourhoodsFromImages(Op):
             for dim in self.dims_neighbourhoods:
                 prod *= dim
             if x.shape[-1] != prod:
-                raise ValueError("Last dimension of neighbourhoods (%s) is not"
+                raise ValueError(
+                    "Last dimension of neighbourhoods (%s) is not"
                     " the product of the neighbourhoods dimensions"
                     " (%s)" % (str(x.shape[-1]), str(prod)))
         else:
@@ -195,6 +197,7 @@ class NeighbourhoodsFromImages(Op):
         exec(self.code)
 
     def make_py_code(self):
+        # TODO : need description for method and return
         code = self._py_outerloops()
         for i in xrange(len(self.strides)):
             code += self._py_innerloop(i)
@@ -202,6 +205,7 @@ class NeighbourhoodsFromImages(Op):
         return code, builtins.compile(code, '<string>', 'exec')
 
     def _py_outerloops(self):
+        # TODO : need description for method, parameter and return
         code_before = ""
         for dim_idx in xrange(self.n_dims_before):
             code_before += ('\t' * (dim_idx)) + \
@@ -210,6 +214,7 @@ class NeighbourhoodsFromImages(Op):
         return code_before
 
     def _py_innerloop(self, inner_dim_no):
+        # TODO : need description for method, parameter and return
         base_indent = ('\t' * (self.n_dims_before + inner_dim_no * 2))
         code_before = base_indent + \
             "for stride_idx_%d in xrange(num_strides[%d]):\n" % \
@@ -229,10 +234,12 @@ class NeighbourhoodsFromImages(Op):
         return code_before
 
     def _py_flattened_idx(self):
+        # TODO : need description for method and return
         return "+".join(["neigh_strides[%d]*neigh_idx_%d" % (i, i)
                         for i in xrange(len(self.strides))])
 
     def _py_assignment(self):
+        # TODO : need description for method and return
         input_idx = "".join(["outer_idx_%d," % (i,)
                             for i in xrange(self.n_dims_before)])
         input_idx += "".join(["dim_%d_offset+neigh_idx_%d," %
@@ -259,6 +266,7 @@ class NeighbourhoodsFromImages(Op):
 
 
 class ImagesFromNeighbourhoods(NeighbourhoodsFromImages):
+    # TODO : need description for class, parameters
     def __init__(self, n_dims_before, dims_neighbourhoods,
                  strides=None, ignore_border=False):
         NeighbourhoodsFromImages.__init__(self, n_dims_before,

theano/sandbox/softsign.py

@@ -4,6 +4,7 @@ import theano.tensor
 
 
 class ScalarSoftsign(theano.scalar.UnaryScalarOp):
+    # TODO : need description for class
     @staticmethod
     def static_impl(x):
         return x / (1.0 + abs(x))

theano/sandbox/solve.py

@@ -24,7 +24,8 @@ class Solve(gof.Op):
     #     sym_pos, lower, overwrite_a, overwrite_b
 
     # TODO: Add C code that calls the underlying LAPACK routines
-    #      and keeps a memory workspace from call to call as a non-default Op output
+    #      and keeps a memory workspace from call to call as a non-default Op
+    #      output
 
     def __eq__(self, other):
         return type(self) == type(other)

theano/tests/test_flake8.py

@@ -92,13 +92,6 @@ whitelist_flake8 = [
     "tensor/nnet/tests/test_sigm.py",
     "scalar/__init__.py",
     "scalar/tests/test_basic.py",
-    "sandbox/__init__.py",
-    "sandbox/rng_mrg.py",
-    "sandbox/theano_object.py",
-    "sandbox/scan.py",
-    "sandbox/symbolic_module.py",
-    "sandbox/conv.py",
-    "sandbox/debug.py",
     "sandbox/tests/test_theano_object.py",
     "sandbox/tests/test_scan.py",
     "sandbox/tests/test_neighbourhoods.py",