ImageRec

Makefile

@@ -13,10 +13,19 @@ ifndef DMLC_CORE
 endif
 
 
+ifneq ($(USE_OPENMP_ITER), 1)
+	export NO_OPENMP = 1
+endif
+
+ifneq ($(USE_OPENMP_ITER), 1)
+	export NO_OPENMP = 1
+endif
+
 # use customized config file
 include $(config)
 include mshadow/make/mshadow.mk
 include $(DMLC_CORE)/make/dmlc.mk
+unexport NO_OPENMP
 
 # all tge possible warning tread
 WARNFLAGS= -Wall
@@ -39,10 +48,21 @@ endif
 
 # setup opencv
 ifeq ($(USE_OPENCV),1)
-	CFLAGS+= -DCXXNET_USE_OPENCV=1
+	CFLAGS+= -DMXNET_USE_OPENCV=1
 	LDFLAGS+= `pkg-config --libs opencv`
 else
-	CFLAGS+= -DCXXNET_USE_OPENCV=0
+	CFLAGS+= -DMXNET_USE_OPENCV=0
+endif
+
+# setup opencv
+ifeq ($(USE_OPENCV_DECODER),1)
+	CFLAGS+= -DMXNET_USE_OPENCV_DECODER=1
+else
+	CFLAGS+= -DMXNET_USE_OPENCV_DECODER=0
+endif
+
+ifeq ($(USE_OPENMP_ITER), 1)
+	CFLAGS += -fopenmp
 endif
 
 ifeq ($(USE_CUDNN), 1)
@@ -62,7 +82,7 @@ endif
 ENGINE=naive_engine.o
 BIN = tests/test_simple_engine
 OBJ = narray_function_cpu.o
-OBJCXX11 = narray.o c_api.o operator.o symbol.o storage.o static_graph.o graph_executor.o io.o iter_mnist.o $(ENGINE)
+OBJCXX11 = narray.o c_api.o operator.o symbol.o storage.o static_graph.o graph_executor.o io.o iter_mnist.o iter_image_recordio.o $(ENGINE)
 CUOBJ = narray_function_gpu.o
 SLIB = lib/libmxnet.so
 ALIB = lib/libmxnet.a
@@ -92,6 +112,7 @@ operator.o: src/operator/operator.cc
 c_api.o: src/c_api.cc
 io.o: src/io/io.cc
 iter_mnist.o: src/io/iter_mnist.cc src/io/*.h
+iter_image_recordio.o: src/io/iter_image_recordio.cc src/io/*.h
 
 # Rules for operators
 OPERATOR_HDR=$(wildcard src/operator/*-inl.h)

example/cifar10/cifar10.py

@@ -144,8 +144,109 @@ def RandomInit(narray):
 flatten = mx.symbol.Flatten(data=pool, name="flatten1")
 fc = mx.symbol.FullyConnected(data=flatten, num_hidden=10, name="fc1")
 loss = mx.symbol.Softmax(data=fc, name="softmax")
+args_list = loss.list_arguments()
 
 data_shape = (128, 3, 28, 28)
 arg_shapes, out_shapes, aux_shapes = loss.infer_shape(data=data_shape)
 
+arg_narrays = [mx.narray.create(shape, ctx=mx.Context("gpu")) for shape in arg_shapes]
+grad_narrays = [mx.narray.create(shape, ctx=mx.Context("gpu")) for shape in arg_shapes]
 
+inputs = dict(zip(args_list, arg_narrays))
+
+name2shape = dict(zip(args_list, arg_shapes))
+pred = mx.narray.create(out_shapes[0])
+
+np.random.seed(0)
+# set random weight
+for name, narray in inputs.items():
+    if "weight" in name:
+        tmp = mx.narray.create(name2shape[name])
+        tmp.numpy[:] = np.random.uniform(-0.07, 0.07, name2shape[name])
+        tmp.copyto(narray)
+    if "bias" in name:
+        narray[:] = 0.0
+
+# bind executer
+# TODO(bing): think of a better bind interface
+executor = loss.bind(mx.Context('gpu'), arg_narrays, grad_narrays)
+# update
+
+out_narray = executor.heads()[0]
+grad_narray = mx.narray.create(out_narray.shape)
+
+epoch = 9
+lr = 0.1
+wd = 0.0004
+
+def Update(grad, weight):
+    weight[:] -= lr * grad  / batch_size
+
+block = list(zip(grad_narrays, arg_narrays))
+
+#check data
+get_data.GetCifar10()
+train_dataiter = mx.io.ImageRecordIter(
+        path_imgrec="data/cifar/train.rec",
+        mean_img="data/cifar/cifar_mean.bin",
+        rand_crop=True,
+        rand_mirror=True,
+        input_shape=(3,28,28),
+        batch_size=128,
+        nthread=1)
+test_dataiter = mx.io.ImageRecordIter(
+        path_imgrec="data/cifar/test.rec",
+        mean_img="data/cifar/cifar_mean.bin",
+        rand_crop=True,
+        rand_mirror=True,
+        input_shape=(3,28,28),
+        batch_size=100,
+        nthread=1)
+
+tmp_label = mx.narray.create(name2shape["sm_label"])
+
+def test_cifar():
+    acc_train = 0.
+    acc_val = 0.
+    for i in range(epoch):
+        # train
+        print("Epoch %d" % i)
+        train_acc = 0.0
+        val_acc = 0.0
+        train_nbatch = 0
+        val_nbatch = 0
+        for data, label in train_dataiter:
+            data = data
+            tmp_label.numpy[:] = label.numpy.reshape(tmp_label.shape)
+            data.copyto(inputs["data"])
+            tmp_label.copyto(inputs["sm_label"])
+            executor.forward()
+            out_narray.copyto(pred)
+            train_acc += CalAcc(pred.numpy, label.numpy.flatten())
+            train_nbatch += 1
+            out_narray.copyto(grad_narray)
+            executor.backward([grad_narray])
+
+            for grad, weight in block:
+                Update(grad, weight)
+
+        # evaluate
+        for data, label in val_dataiter:
+            data = data
+            label = label.numpy.flatten()
+            data.copyto(inputs["data"])
+            executor.forward()
+            out_narray.copyto(pred)
+            val_acc += CalAcc(pred.numpy, label)
+            val_nbatch += 1
+        acc_train = train_acc / train_nbatch
+        acc_val = val_acc / val_nbatch
+        print("Train Acc: ", train_acc / train_nbatch)
+        print("Valid Acc: ", val_acc / val_nbatch)
+        train_dataiter.reset()
+        val_dataiter.reset()
+    assert(acc_train > 0.98)
+    assert(acc_val > 0.97)
+
+if __name__ == "__main__":
+    test_cifar()

include/mxnet/io.h

@@ -109,5 +109,21 @@ struct DataIteratorReg
   }                                                                     \
   DMLC_REGISTRY_REGISTER(::mxnet::DataIteratorReg, DataIteratorReg, name) \
   .set_body(__create__ ## DataIteratorType ## __)
+/*!
+ * \brief Macro to register chained Iterators
+ *
+ * \code
+ * // example of registering a imagerec iterator
+ * MXNET_REGISTER_IO_CHAINED_ITERATOR(ImageRec, ImageRecordIter, BatchIter)
+ * .describe("batched image record data iterator");
+ *
+ * \endcode
+ */
+#define MXNET_REGISTER_IO_CHAINED_ITER(name, ChainedDataIterType, HoldingDataIterType)          \
+  static ::mxnet::IIterator<DataBatch>* __create__ ## ChainedDataIteratorType ## __() { \
+    return new HoldingDataIterType(new ChainedDataIterType);                                    \
+  }                                                                     \
+  DMLC_REGISTRY_REGISTER(::mxnet::DataIteratorReg, DataIteratorReg, name) \
+  .set_body(__create__ ## ChainedDataIteratorType ## __)
 }  // namespace mxnet
 #endif  // MXNET_IO_H_

make/config.mk

@@ -27,8 +27,8 @@ USE_CUDA_PATH = NONE
 # whether use opencv during compilation
 # you can disable it, however, you will not able to use
 # imbin iterator
-USE_OPENCV = 0
-USE_OPENCV_DECODER = 0
+USE_OPENCV = 1
+USE_OPENCV_DECODER = 1
 # whether use CUDNN R3 library
 USE_CUDNN = 0
 # add the path to CUDNN libary to link and compile flag

src/common/utils.h

@@ -21,6 +21,11 @@ namespace common {
  * \brief Random Engine
  */
 typedef std::mt19937 RANDOM_ENGINE;
+// Get a double float, prnd is the pointer to a Random Engine
+#define NextDouble(prnd) std::generate_canonical<float, 10>(*prnd)
+// Get a random int in [0, range)
+#define NextUInt32(range, prnd) static_cast<uint32_t> \
+(floor(std::generate_canonical<float, 10>(*prnd) * range))
 
 /*!
  * \brief Helper functions.