Refactor ImageRecordIter (apache#14824)

* cpu optimized data loader

* Fix CI

* Fix CI

* Fix ci

* Fix doc

docs/architecture/note_data_loading.md

@@ -83,7 +83,7 @@ In MXNet, we rely on the binary recordIO format implemented in dmlc-core.
 In MXNet's binary RecordIO, we store each data instance as a record.
 **kMagic** is a *magic number* indicating the start of a record.
 **Lrecord** encodes length and a continue flag.
-In lrecord,  
+In lrecord,
 - cflag == 0: this is a complete record
 - cflag == 1: start of a multiple-records
 - cflag == 2: middle of multiple-records
@@ -228,7 +228,11 @@ dataiter = mx.io.ImageRecordIter(
     # Backend Parameter, preprocessing thread number
     preprocess_threads=4,
     # Backend Parameter, prefetch buffer size
-    prefetch_buffer=1)
+    prefetch_buffer=1,
+    # Optional, the device context which data loader optimized for, could be 'gpu' or 'cpu'
+    ctx="gpu",
+    # The out data type, could be 'float32' 'int8' or 'uint8'
+    dtype="float32")
 ```
 
 Generally, to create a data iterator, you need to provide five kinds of parameters:

example/quantization/README.md

@@ -54,10 +54,10 @@ The model would be automatically replaced in fusion and quantization format. It
 export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference 
-python imagenet_inference.py --symbol-file=./model/resnet50_v1-symbol.json --param-file=./model/resnet50_v1-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/resnet50_v1-symbol.json --param-file=./model/resnet50_v1-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
-python imagenet_inference.py --symbol-file=./model/resnet50_v1-quantized-5batches-naive-symbol.json --param-file=./model/resnet50_v1-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu  --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/resnet50_v1-quantized-5batches-naive-symbol.json --param-file=./model/resnet50_v1-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
 python imagenet_inference.py --symbol-file=./model/resnet50_v1-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
@@ -78,10 +78,10 @@ The model would be automatically replaced in fusion and quantization format. It
 export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference
-python imagenet_inference.py --symbol-file=./model/squeezenet1.0-symbol.json --param-file=./model/squeezenet1.0-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/squeezenet1.0-symbol.json --param-file=./model/squeezenet1.0-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
-python imagenet_inference.py --symbol-file=./model/squeezenet1.0-quantized-5batches-naive-symbol.json --param-file=./model/squeezenet1.0-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu  --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/squeezenet1.0-quantized-5batches-naive-symbol.json --param-file=./model/squeezenet1.0-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
 python imagenet_inference.py --symbol-file=./model/squeezenet1.0-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu  --benchmark=True
@@ -102,10 +102,10 @@ The model would be automatically replaced in fusion and quantization format. It
 export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference
-python imagenet_inference.py --symbol-file=./model/mobilenet1.0-symbol.json --param-file=./model/mobilenet1.0-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/mobilenet1.0-symbol.json --param-file=./model/mobilenet1.0-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
-python imagenet_inference.py --symbol-file=./model/mobilenet1.0-quantized-5batches-naive-symbol.json --param-file=./model/mobilenet1.0-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu  --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/mobilenet1.0-quantized-5batches-naive-symbol.json --param-file=./model/mobilenet1.0-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
 python imagenet_inference.py --symbol-file=./model/mobilenet1.0-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu  --benchmark=True
@@ -126,10 +126,10 @@ The model would be automatically replaced in fusion and quantization format. It
 export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference
-python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-symbol.json --param-file=./model/mobilenetv2_1.0-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-symbol.json --param-file=./model/mobilenetv2_1.0-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
-python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-quantized-5batches-naive-symbol.json --param-file=./model/mobilenetv2_1.0-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu  --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-quantized-5batches-naive-symbol.json --param-file=./model/mobilenetv2_1.0-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
 python imagenet_inference.py --symbol-file=./model/mobilenetv2_1.0-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu  --benchmark=True
@@ -150,10 +150,10 @@ The model would be automatically replaced in fusion and quantization format. It
 export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference
-python imagenet_inference.py --symbol-file=./model/inceptionv3-symbol.json --param-file=./model/inceptionv3-0000.params --image-shape=3,299,299 --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/inceptionv3-symbol.json --param-file=./model/inceptionv3-0000.params --image-shape=3,299,299 --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
-python imagenet_inference.py --symbol-file=./model/inceptionv3-quantized-5batches-naive-symbol.json --param-file=./model/inceptionv3-quantized-0000.params --image-shape=3,299,299 --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu  --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/inceptionv3-quantized-5batches-naive-symbol.json --param-file=./model/inceptionv3-quantized-0000.params --image-shape=3,299,299 --rgb-mean=123.68,116.779,103.939 --rgb-std=58.393,57.12,57.375 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
 python imagenet_inference.py --symbol-file=./model/inceptionv3-symbol.json --image-shape=3,299,299 --batch-size=64 --num-inference-batches=500 --ctx=cpu  --benchmark=True
@@ -175,10 +175,10 @@ The model would be automatically replaced in fusion and quantization format. It
 export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference 
-python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-symbol.json --param-file=./model/imagenet1k-resnet-152-0000.params --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-symbol.json --param-file=./model/imagenet1k-resnet-152-0000.params --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
-python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json --param-file=./model/imagenet1k-resnet-152-quantized-0000.params --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu  --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json --param-file=./model/imagenet1k-resnet-152-quantized-0000.params --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
 python imagenet_inference.py --symbol-file=./model/imagenet1k-resnet-152-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
@@ -200,10 +200,10 @@ The model would be automatically replaced in fusion and quantization format. It
 export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference 
-python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-symbol.json --param-file=./model/imagenet1k-inception-bn-0000.params --rgb-mean=123.68,116.779,103.939 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-symbol.json --param-file=./model/imagenet1k-inception-bn-0000.params --rgb-mean=123.68,116.779,103.939 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch INT8 Inference
-python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-quantized-5batches-naive-symbol.json --param-file=./model/imagenet1k-inception-bn-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu  --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-quantized-5batches-naive-symbol.json --param-file=./model/imagenet1k-inception-bn-quantized-0000.params --rgb-mean=123.68,116.779,103.939 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec --ctx=cpu
 
 # Launch dummy data Inference
 python imagenet_inference.py --symbol-file=./model/imagenet1k-inception-bn-symbol.json --batch-size=64 --num-inference-batches=500 --ctx=cpu --benchmark=True
@@ -244,7 +244,7 @@ Some tips on quantization configs:
 export MXNET_SUBGRAPH_BACKEND=MKLDNN
 
 # Launch FP32 Inference 
-python imagenet_inference.py --symbol-file=./model/custom-symbol.json --param-file=./model/custom-0000.params --rgb-mean=* --rgb-std=* --num-skipped-batches=* --batch-size=* --num-inference-batches=*--dataset=./data/* --ctx=cpu --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/custom-symbol.json --param-file=./model/custom-0000.params --rgb-mean=* --rgb-std=* --num-skipped-batches=* --batch-size=* --num-inference-batches=*--dataset=./data/* --ctx=cpu
 ```
 
 3. Then, you should add `rgb_mean`, `rgb_std` and `excluded_sym_names` in this script. Notice that you should exclude conv/pool layers that have negative data since Intel® MKL-DNN only supports `uint8` quantization temporarily. You should also exclude all fc layers in your model.
@@ -261,7 +261,7 @@ python imagenet_gen_qsym_mkldnn.py --model=custom --num-calib-batches=5 --calib-
 
 ```
 # Launch INT8 Inference 
-python imagenet_inference.py --symbol-file=./model/*.json --param-file=./model/*.params --rgb-mean=* --rgb-std=* --num-skipped-batches=* --batch-size=* --num-inference-batches=*--dataset=./data/* --ctx=cpu --data-nthreads=1
+python imagenet_inference.py --symbol-file=./model/*.json --param-file=./model/*.params --rgb-mean=* --rgb-std=* --num-skipped-batches=* --batch-size=* --num-inference-batches=*--dataset=./data/* --ctx=cpu
 
 # Launch dummy data Inference
 python imagenet_inference.py --symbol-file=./model/*.json --batch-size=* --num-inference-batches=500 --ctx=cpu --benchmark=True

example/quantization/imagenet_inference.py

@@ -217,45 +217,21 @@ def benchmark_score(symbol_file, ctx, batch_size, num_batches, data_layer_type,
         logger.info('Dataset for inference: %s' % dataset)
 
         # creating data iterator
-        if data_layer_type == 'int8':
-            data = mx.io.ImageRecordInt8Iter(path_imgrec=dataset,
-                                             label_width=1,
-                                             preprocess_threads=data_nthreads,
-                                             batch_size=batch_size,
-                                             data_shape=data_shape,
-                                             label_name=label_name,
-                                             rand_crop=False,
-                                             rand_mirror=False,
-                                             shuffle=args.shuffle_dataset,
-                                             shuffle_chunk_seed=args.shuffle_chunk_seed,
-                                             seed=args.shuffle_seed,
-                                             **combine_mean_std)
-        elif data_layer_type == 'uint8':
-            data = mx.io.ImageRecordUInt8Iter(path_imgrec=dataset,
-                                              label_width=1,
-                                              preprocess_threads=data_nthreads,
-                                              batch_size=batch_size,
-                                              data_shape=data_shape,
-                                              label_name=label_name,
-                                              rand_crop=False,
-                                              rand_mirror=False,
-                                              shuffle=args.shuffle_dataset,
-                                              shuffle_chunk_seed=args.shuffle_chunk_seed,
-                                              seed=args.shuffle_seed,
-                                              **combine_mean_std)
-        else:  #float32
-            data = mx.io.ImageRecordIter(path_imgrec=dataset,
-                                         label_width=1,
-                                         preprocess_threads=data_nthreads,
-                                         batch_size=batch_size,
-                                         data_shape=data_shape,
-                                         label_name=label_name,
-                                         rand_crop=False,
-                                         rand_mirror=False,
-                                         shuffle=args.shuffle_dataset,
-                                         shuffle_chunk_seed=args.shuffle_chunk_seed,
-                                         seed=args.shuffle_seed,
-                                         **combine_mean_std)
+        data = mx.io.ImageRecordIter(
+            path_imgrec=dataset,
+            label_width=1,
+            preprocess_threads=data_nthreads,
+            batch_size=batch_size,
+            data_shape=data_shape,
+            label_name=label_name,
+            rand_crop=False,
+            rand_mirror=False,
+            shuffle=args.shuffle_dataset,
+            shuffle_chunk_seed=args.shuffle_chunk_seed,
+            seed=args.shuffle_seed,
+            dtype=data_layer_type,
+            ctx=args.ctx,
+            **combine_mean_std)
 
         # loading model
         sym, arg_params, aux_params = load_model(symbol_file, param_file, logger)

src/io/image_iter_common.h

@@ -346,22 +346,32 @@ struct ImageDetNormalizeParam :  public dmlc::Parameter<ImageDetNormalizeParam>
 
 // Define prefetcher parameters
 struct PrefetcherParam : public dmlc::Parameter<PrefetcherParam> {
+  enum CtxType { kGPU = 0, kCPU};
   /*! \brief number of prefetched batches */
   size_t prefetch_buffer;
+
+  /*! \brief Context data loader optimized for */
+  int ctx;
+
   /*! \brief data type */
   dmlc::optional<int> dtype;
 
   // declare parameters
   DMLC_DECLARE_PARAMETER(PrefetcherParam) {
     DMLC_DECLARE_FIELD(prefetch_buffer).set_default(4)
         .describe("Maximum number of batches to prefetch.");
+    DMLC_DECLARE_FIELD(ctx).set_default(kGPU)
+        .add_enum("cpu", kCPU)
+        .add_enum("gpu", kGPU)
+        .describe("Context data loader optimized for.");
     DMLC_DECLARE_FIELD(dtype)
       .add_enum("float32", mshadow::kFloat32)
       .add_enum("float64", mshadow::kFloat64)
       .add_enum("float16", mshadow::kFloat16)
       .add_enum("int64", mshadow::kInt64)
       .add_enum("int32", mshadow::kInt32)
       .add_enum("uint8", mshadow::kUint8)
+      .add_enum("int8", mshadow::kInt8)
       .set_default(dmlc::optional<int>())
       .describe("Output data type. ``None`` means no change.");
   }