address comments

python/mxnet/gluon/contrib/data/vision/transforms/bbox/bbox.py

@@ -277,7 +277,7 @@ def forward(self, img, bbox):
         if isinstance(self._fill, numeric_types):
             dst = F.full(shape=(oh, ow, c), val=self._fill, dtype=img.dtype)
         else:
-            fill = F.array(self._fill, dtype=img.dtype, ctx=img.context)
+            fill = F.array(self._fill, dtype=img.dtype, ctx=img.ctx)
             if not c == fill.size:
                 raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
             dst = F.tile(fill.reshape((1, c)), reps=(oh * ow, 1)).reshape((oh, ow, c))

python/mxnet/image/image.py

@@ -656,14 +656,14 @@ def imrotate(src, rotation_degrees, zoom_in=False, zoom_out=False):
     # when a scalar is passed we wrap it into an array
     if isinstance(rotation_degrees, Number):
         rotation_degrees = nd.array([rotation_degrees] * len(src),
-                                    ctx=src.context)
+                                    ctx=src.ctx)
 
     if len(src) != len(rotation_degrees):
         raise ValueError(
             "The number of images must be equal to the number of rotation angles"
         )
 
-    rotation_degrees = rotation_degrees.as_in_context(src.context)
+    rotation_degrees = rotation_degrees.as_in_context(src.ctx)
     rotation_rad = np.pi * rotation_degrees / 180
     # reshape the rotations angle in order to be broadcasted
     # over the `src` tensor
@@ -674,10 +674,10 @@ def imrotate(src, rotation_degrees, zoom_in=False, zoom_out=False):
     hscale = (float(h - 1) / 2)
     wscale = (float(w - 1) / 2)
     h_matrix = (
-        nd.repeat(nd.arange(h, ctx=src.context).astype('float32').reshape(h, 1), w, axis=1) - hscale
+        nd.repeat(nd.arange(h, ctx=src.ctx).astype('float32').reshape(h, 1), w, axis=1) - hscale
     ).expand_dims(axis=0)
     w_matrix = (
-        nd.repeat(nd.arange(w, ctx=src.context).astype('float32').reshape(1, w), h, axis=0) - wscale
+        nd.repeat(nd.arange(w, ctx=src.ctx).astype('float32').reshape(1, w), h, axis=0) - wscale
     ).expand_dims(axis=0)
     # perform rotation on the grid
     c_alpha = nd.cos(rotation_rad)
@@ -689,7 +689,7 @@ def imrotate(src, rotation_degrees, zoom_in=False, zoom_out=False):
     w_matrix_rot = w_matrix_rot / wscale
     h_matrix_rot = h_matrix_rot / hscale
 
-    h, w = nd.array([h], ctx=src.context), nd.array([w], ctx=src.context)
+    h, w = nd.array([h], ctx=src.ctx), nd.array([w], ctx=src.ctx)
     # compute the scale factor in case `zoom_in` or `zoom_out` are True
     if zoom_in or zoom_out:
         rho_corner = nd.sqrt(h * h + w * w)

python/mxnet/test_utils.py

@@ -584,7 +584,7 @@ def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=
     atol = get_atol(atol)
     use_np_allclose = isinstance(a, np.ndarray) and isinstance(b, np.ndarray)
     if not use_np_allclose:
-        if not (hasattr(a, 'context') and hasattr(b, 'context') and a.context == b.context and a.dtype == b.dtype):
+        if not (hasattr(a, 'ctx') and hasattr(b, 'ctx') and a.ctx == b.ctx and a.dtype == b.dtype):
             use_np_allclose = True
             if isinstance(a, mx.nd.NDArray):
                 a = a.asnumpy()

src/io/iter_prefetcher.h

@@ -99,7 +99,7 @@ class PrefetcherIter : public IIterator<DataBatch> {
         // copy data over
         for (size_t i = 0; i < batch.data.size(); ++i) {
           if ((*dptr)->data.at(i).shape() != batch.data[i].shape_) {
-            // perf warning, dynamic buffer might be slow
+            // TODO(zhreshold): memory pool for dynamic shaped data
             (*dptr)->data.at(i).ReshapeAndAlloc(batch.data[i].shape_);
           }
           CHECK_EQ((*dptr)->data.at(i).shape(), batch.data[i].shape_);

src/io/iter_sampler.cc

@@ -56,8 +56,7 @@ class SequentialSampler : public IIterator<DataInst> {
     param_.InitAllowUnknown(kwargs);
     indices_.resize(param_.length);
     std::iota(std::begin(indices_), std::end(indices_), 0);  // fill like arange
-    out_.data.resize(2);  // label required by DataBatch, we can use fake label here
-    out_.data[1] = TBlob(indices_.data(), TShape({1, }), cpu::kDevMask, 0);
+    out_.data.resize(1);
   }
 
   virtual void BeforeFirst(void) {
@@ -129,8 +128,7 @@ class RandomSampler : public IIterator<DataInst> {
     mshadow::Random<cpu> *ctx_rng = ResourceManager::Get()->Request(
       Context::CPU(), ResourceRequest::kRandom).get_random<cpu, real_t>(nullptr);
     rng_.reset(new common::RANDOM_ENGINE(ctx_rng->GetSeed() + param_.seed));
-    out_.data.resize(2);  // label required by DataBatch, we can use fake label here
-    out_.data[1] = TBlob(indices_.data(), TShape({1, }), cpu::kDevMask, 0);
+    out_.data.resize(1);
     BeforeFirst();
   }
 
@@ -164,7 +162,7 @@ class RandomSampler : public IIterator<DataInst> {
   /*! \brief data for next value */
   DataInst out_;
   /*! \brief random generator engine */
-  std::unique_ptr<common::RANDOM_ENGINE> rng_;
+  std::unique_ptr<std::mt19937> rng_;
   /*! \brief arguments */
   RandomSamplerParam param_;
 };  // class RandomSampler