Merge pull request #3721 from ballasn/cond2d_interface_update

theano/compile/mode.py

@@ -233,7 +233,7 @@ optdb.register('uncanonicalize', gof.EquilibriumDB(),
 
 # misc special cases for speed that are dependent on the device.
 optdb.register('specialize_device', gof.EquilibriumDB(),
-               48.6, 'fast_run')  # must be after gpu stuff at 48.5
+               48.6, 'fast_compile', 'fast_run')  # must be after gpu stuff at 48.5
 
 # especially constant merge
 optdb.register('merge2', gof.MergeOptimizer(),

theano/sandbox/cuda/dnn.py

@@ -11,7 +11,7 @@ from theano.gof.type import CDataType, Generic
 from theano.compile import optdb
 from theano.compile.ops import shape_i
 from theano.tensor.nnet import SoftmaxGrad
-from theano.tensor.nnet.abstract_conv2d import get_conv_output_shape
+from theano.tensor.nnet.abstract_conv import get_conv_output_shape
 from theano.tensor.signal.downsample import (
     DownsampleFactorMax, MaxPoolGrad, AveragePoolGrad)
 from theano.sandbox.cuda.type import CudaNdarrayType
@@ -30,7 +30,7 @@ from theano.sandbox.cuda import gpu_seqopt, register_opt
 
 from theano.sandbox.cuda.nvcc_compiler import NVCC_compiler
 
-from theano.tensor.nnet.abstract_conv2d import (AbstractConv2d,
+from theano.tensor.nnet.abstract_conv import (AbstractConv2d,
                                               AbstractConv2d_gradWeights,
                                               AbstractConv2d_gradInputs)
 

theano/sandbox/cuda/opt.py

@@ -75,7 +75,7 @@ from theano.tensor import slinalg
 from theano.tensor.nnet.Conv3D import Conv3D
 from theano.tests.breakpoint import PdbBreakpoint
 
-from theano.tensor.nnet.abstract_conv2d import (BaseAbstractConv2d,
+from theano.tensor.nnet.abstract_conv import (BaseAbstractConv2d,
                                               AbstractConv2d,
                                               AbstractConv2d_gradWeights,
                                               AbstractConv2d_gradInputs)

theano/sandbox/cuda/tests/test_abstractconv.py

@@ -4,7 +4,7 @@ import itertools
 
 import theano
 from theano.tests import unittest_tools as utt
-import theano.tensor.nnet.abstract_conv2d as conv
+import theano.tensor.nnet.abstract_conv as conv
 from theano.sandbox.cuda import float32_shared_constructor as gpu_shared
 from theano.compile import shared as cpu_shared
 from theano.sandbox.cuda.dnn import dnn_available, dnn_conv, dnn_gradweight, dnn_gradinput
@@ -14,7 +14,6 @@ import theano.sandbox.cuda as cuda
 if not cuda.cuda_available:
     raise SkipTest('Optional package cuda disabled')
 
-
 if theano.config.mode == 'FAST_COMPILE':
     mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
     mode_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
@@ -37,7 +36,6 @@ class TestConv2d(unittest.TestCase):
         self.filter_flip = [True, False]
 
     def get_output_shape(self, inputs_shape, filters_shape, subsample, border_mode):
-
         if border_mode == "valid":
             border_mode = (0, 0)
         if border_mode == "full":

theano/sandbox/gpuarray/dnn.py

@@ -12,7 +12,7 @@ from theano.gof.type import CDataType, Generic
 from theano.compile import optdb
 from theano.compile.ops import shape_i
 from theano.tensor.nnet import SoftmaxGrad
-from theano.tensor.nnet.abstract_conv2d import get_conv_output_shape
+from theano.tensor.nnet.abstract_conv import get_conv_output_shape
 from theano.tensor.signal.downsample import (
     DownsampleFactorMax, MaxPoolGrad, AveragePoolGrad)
 

theano/tensor/nnet/__init__.py

@@ -22,7 +22,7 @@ from .nnet import (
     prepend_scalar_to_each_row, relu, softmax, softmax_grad, softmax_graph,
     softmax_op, softmax_simplifier, softmax_with_bias)
 from . import opt
-from .conv import conv2d, ConvOp
+from .conv import ConvOp
 from .Conv3D import *
 from .ConvGrad3D import *
 from .ConvTransp3D import *
@@ -30,3 +30,91 @@ from .sigm import (softplus, sigmoid, sigmoid_inplace,
                    scalar_sigmoid, ultra_fast_sigmoid,
                    hard_sigmoid)
 from .bn import batch_normalization
+
+
+import warnings
+from .abstract_conv import conv2d as abstract_conv2d
+
+def conv2d(input, filters, input_shape=None, image_shape=None, filter_shape=None,
+           border_mode='valid', subsample=(1, 1), filter_flip=True,
+           **kargs):
+    """
+    This function will build the symbolic graph for convolving a mini-batch of a
+    stack of 2D inputs with a set of 2D filters. The implementation is modelled
+    after Convolutional Neural Networks (CNN).
+
+    :type input: symbolic 4D tensor
+    :param input: mini-batch of feature map stacks, of shape
+        (batch size, input channels, input rows, input columns).
+        See the optional parameter ``input_shape``.
+
+    :type filters: symbolic 4D tensor
+    :param filters: set of filters used in CNN layer of shape
+        (output channels, input channels, filter rows, filter columns).
+        See the optional parameter ``filter_shape``.
+
+    :type image_shape: None, tuple/list of len 4 of int or Constant variable
+    :param image_shape Deprecated, use input_shape instead
+
+    :type input_shape: None, tuple/list of len 4 of int or Constant variable
+    :param input_shape: The shape of the input parameter.
+        Optional, possibly used to choose an optimal implementation.
+        You can give ``None`` for any element of the list to specify that this
+        element is not known at compile time.
+
+    :type filter_shape: None, tuple/list of len 4 of int or Constant variable
+    :param filter_shape: The shape of the filters parameter.
+        Optional, possibly used to choose an optimal implementation.
+        You can give ``None`` for any element of the list to specify that this
+        element is not known at compile time.
+
+    :type border_mode: str, int or tuple of two int
+    :param border_mode: Either of the following:
+        * ``'valid'``: apply filter wherever it completely overlaps with the
+          input. Generates output of shape: input shape - filter shape + 1
+        * ``'full'``: apply filter wherever it partly overlaps with the input.
+          Generates output of shape: input shape + filter shape - 1
+        * ``'half'``: pad input with a symmetric border of ``filter rows // 2``
+          rows and ``filter columns // 2`` columns, then perform a valid
+          convolution. For filters with an odd number of rows and columns, this
+          leads to the output shape being equal to the input shape.
+        * ``int``: pad input with a symmetric border of zeros of the given
+          width, then perform a valid convolution.
+        * ``(int1, int2)``: pad input with a symmetric border of ``int1`` rows
+          and ``int2`` columns, then perform a valid convolution.
+
+    :type subsample: tuple of len 2
+    :param subsample: factor by which to subsample the output.
+        Also called strides elsewhere.
+
+    :type filter_flip: bool
+    :param filter_flip: If ``True``, will flip the filter rows and columns
+        before sliding them over the input. This operation is normally referred
+        to as a convolution, and this is the default. If ``False``, the filters
+        are not flipped and the operation is referred to as a cross-correlation.
+
+    :rtype: symbolic 4D tensor
+    :return: set of feature maps generated by convolutional layer. Tensor is
+        of shape (batch size, output channels, output rows, output columns)
+    """
+
+    if len(kargs.keys()) > 0:
+        warnings.warn(str(kargs.keys()) +
+                      " are now deprecated in "
+                      "`tensor.nnet.abstract_conv.conv2d` interface"
+                      " and will be ignored.")
+
+    if image_shape is not None:
+        warnings.warn("image_shape is no longer supported in "
+                      "`tensor.nnet.abstract_conv.conv2d` interface"
+                      " use input_shape instead.")
+        if input_shape is None:
+            input_shape = image_shape
+        else:
+            raise ValueError("input_shape and image_shape should not"
+                             " be provided at the same time.")
+
+    return abstract_conv2d(input, filters, input_shape, filter_shape,
+                           border_mode, subsample=(1, 1))
+
+

theano/tensor/nnet/abstract_conv2d.py → theano/tensor/nnet/abstract_conv.py

 """
 Define abstract conv2d interface
 """
-import logging
 
+import logging
 import theano
+
 from theano.tensor import as_tensor_variable
 from theano.gof import Apply, Op
 
 
 __docformat__ = "restructuredtext en"
-
 _logger = logging.getLogger("theano.tensor.nnet.conv2d")
 
 
@@ -187,6 +187,7 @@ class BaseAbstractConv2d(Op):
         element is not known at compile time.
         kshp is defined w.r.t the forward conv.
 
+
     :type border_mode: str, int or tuple of two int
     :param border_mode: Either of the following:
         * ``'valid'``: apply filter wherever it completely overlaps with the
@@ -219,6 +220,7 @@ class BaseAbstractConv2d(Op):
                  imshp=None, kshp=None,
                  border_mode="valid", subsample=(1, 1),
                  filter_flip=True):
+
         if isinstance(border_mode, int):
             border_mode = (border_mode, border_mode)
         if isinstance(border_mode, tuple):
@@ -297,6 +299,7 @@ class AbstractConv2d(BaseAbstractConv2d):
                                                self.border_mode,
                                                self.subsample,
                                                self.filter_flip)(
+
             bottom, top, weights.shape[-2:])
         return d_bottom, d_weights
 

theano/tensor/nnet/conv.py

@@ -21,7 +21,7 @@ from theano import OpenMPOp
 from theano.tensor import (as_tensor_variable, blas, get_scalar_constant_value,
                            patternbroadcast, NotScalarConstantError)
 from theano.gof import Apply
-from theano.tensor.nnet.abstract_conv2d import (get_conv_output_shape,
+from theano.tensor.nnet.abstract_conv import (get_conv_output_shape,
                                               get_conv_shape_1axis)
 
 try:

theano/tensor/nnet/corr.py

@@ -5,7 +5,7 @@ import theano
 from theano import Apply
 from theano import gof
 from theano.tensor import as_tensor_variable, TensorType
-from theano.tensor.nnet.abstract_conv2d import get_conv_output_shape
+from theano.tensor.nnet.abstract_conv import get_conv_output_shape
 from theano.tensor.blas_headers import blas_header_text
 from theano.tensor.blas import ldflags
 

theano/tensor/nnet/opt.py

@@ -13,7 +13,7 @@ from theano.tensor.nnet.blocksparse import (
     SparseBlockOuter,
     sparse_block_gemv_inplace,
     sparse_block_outer_inplace)
-from theano.tensor.nnet.abstract_conv2d import (AbstractConv2d,
+from theano.tensor.nnet.abstract_conv import (AbstractConv2d,
                                               AbstractConv2d_gradWeights,
                                               AbstractConv2d_gradInputs)
 from theano.tensor.opt import register_specialize_device

theano/tensor/nnet/tests/test_conv.py

@@ -450,29 +450,6 @@ class TestConv2D(utt.InferShapeTester):
                         print(t2 - t1, end=' ')
                     print()
 
-    def test_fail(self):
-        k = theano.shared(numpy.ones((1, 1, 3, 3), dtype='float32'))
-
-        im = T.ftensor4()
-        out = theano.function([im],
-                              T.nnet.conv2d(im, k, image_shape=(1, 1, 10, 10)))
-        self.assertRaises(ValueError, out, numpy.ones((1, 1, 20, 10),
-                                                      dtype='float32'))
-        out = theano.function([im],
-                              T.nnet.conv2d(im, k, filter_shape=(1, 1, 3, 2)))
-        self.assertRaises(ValueError, out, numpy.ones((1, 1, 10, 10),
-                                                      dtype='float32'))
-        out = theano.function([im],
-                              T.nnet.conv2d(im, k, filter_shape=(2, None,
-                                                                 None, None)))
-        self.assertRaises(ValueError, out, numpy.ones((1, 1, 10, 10),
-                                                      dtype='float32'))
-        out = theano.function([im],
-                              T.nnet.conv2d(im, k, image_shape=(1, None,
-                                                                None, None)))
-        self.assertRaises(ValueError, out, numpy.ones((2, 1, 10, 10),
-                                                      dtype='float32'))
-
     def test_infer_shape(self):
     # Note: infer_shape is incomplete and thus input and filter shapes
     # must be provided explicitly

theano/tensor/nnet/tests/test_conv3d2d.py

@@ -120,7 +120,7 @@ def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
 
     signals = numpy.random.rand(Ns, Ts, C, Hs, Ws).astype('float32')
     filters = numpy.random.rand(Nf, Tf, C, Hf, Wf).astype('float32')
-    utt.verify_grad(conv3d, [signals, filters], eps=1e-1)
+    utt.verify_grad(conv3d, [signals, filters], eps=1e-1, mode=mode)
 
     ### Additional Test that covers the case of patched implementation for filter with Tf=1
     Ns, Ts, C, Hs, Ws = 3, 10, 3, 32, 32
@@ -165,4 +165,4 @@ def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
 
     signals = numpy.random.rand(Ns, Ts, C, Hs, Ws).astype('float32')
     filters = numpy.random.rand(Nf, Tf, C, Hf, Wf).astype('float32')
-    utt.verify_grad(conv3d, [signals, filters], eps=1e-1)
+    utt.verify_grad(conv3d, [signals, filters], eps=1e-1, mode=mode)