[RELAY][OP] Operators. pool2d, global_pool2d, batch_flatten, tanh, si…

…gmoid, floor, ceil, trunc, abs, negative, multiply, mod, pow, resize (apache#1813)

docs/langref/relay_op.rst

@@ -30,6 +30,13 @@ This level enables fully connected multi-layer perceptron.
    tvm.relay.expand_dims
    tvm.relay.concatenate
    tvm.relay.nn.softmax
+   tvm.relay.subtract
+   tvm.relay.multiply
+   tvm.relay.divide
+   tvm.relay.mod
+   tvm.relay.tanh
+   tvm.relay.sigmoid
+
 
 **Level 2: Convolutions**
 
@@ -39,10 +46,18 @@ This level enables typical convnet models.
    :nosignatures:
 
    tvm.relay.nn.conv2d
+   tvm.relay.nn.max_pool2d
+   tvm.relay.nn.avg_pool2d
+   tvm.relay.nn.global_max_pool2d
+   tvm.relay.nn.global_avg_pool2d
+   tvm.relay.nn.upsampling
+   tvm.relay.nn.batch_flatten
 
 
 **Level 3: Additional Math And Transform Operators**
 
+This level enables additional math and transform operators.
+
 .. autosummary::
    :nosignatures:
 
@@ -51,6 +66,13 @@ This level enables typical convnet models.
    tvm.relay.reshape
    tvm.relay.copy
    tvm.relay.transpose
+   tvm.relay.floor
+   tvm.relay.ceil
+   tvm.relay.trunc
+   tvm.relay.round
+   tvm.relay.abs
+   tvm.relay.negative
+
 
 **Level 4: Broadcast and Reductions**
 
@@ -67,9 +89,15 @@ This level enables typical convnet models.
    tvm.relay.less_equal
    tvm.relay.maximum
    tvm.relay.minimum
+   tvm.relay.pow
 
 **Level 5: Vision/Image Operators**
 
+.. autosummary::
+   :nosignatures:
+
+   tvm.relay.image.resize
+
 
 Level 1 Definitions
 -------------------
@@ -78,12 +106,38 @@ Level 1 Definitions
 .. autofunction:: tvm.relay.exp
 .. autofunction:: tvm.relay.sigmoid
 .. autofunction:: tvm.relay.add
+.. autofunction:: tvm.relay.subtract
+.. autofunction:: tvm.relay.multiply
+.. autofunction:: tvm.relay.divide
+.. autofunction:: tvm.relay.mod
+.. autofunction:: tvm.relay.tanh
+.. autofunction:: tvm.relay.sigmoid
+.. autofunction:: tvm.relay.concatenate
+.. autofunction:: tvm.relay.nn.softmax
 
 
 Level 2 Definitions
 -------------------
 .. autofunction:: tvm.relay.nn.conv2d
+.. autofunction:: tvm.relay.nn.max_pool2d
+.. autofunction:: tvm.relay.nn.avg_pool2d
+.. autofunction:: tvm.relay.nn.global_max_pool2d
+.. autofunction:: tvm.relay.nn.global_avg_pool2d
+.. autofunction:: tvm.relay.nn.upsampling
+.. autofunction:: tvm.relay.nn.batch_flatten
+
 
+Level 3 Definitions
+-------------------
+.. autofunction:: tvm.relay.floor
+.. autofunction:: tvm.relay.ceil
+.. autofunction:: tvm.relay.trunc
+.. autofunction:: tvm.relay.round
+.. autofunction:: tvm.relay.abs
+.. autofunction:: tvm.relay.negative
+.. autofunction:: tvm.relay.reshape
+.. autofunction:: tvm.relay.copy
+.. autofunction:: tvm.relay.transpose
 
 Level 4 Definitions
 -------------------
@@ -97,3 +151,8 @@ Level 4 Definitions
 .. autofunction:: tvm.relay.less_equal
 .. autofunction:: tvm.relay.maximum
 .. autofunction:: tvm.relay.minimum
+.. autofunction:: tvm.relay.pow
+
+Level 5 Definitions
+-------------------
+.. autofunction:: tvm.relay.image.resize

include/tvm/relay/attrs/image.h

@@ -0,0 +1,41 @@
+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file tvm/relay/attrs/image.h
+ * \brief Auxiliary attributes for image operators.
+ */
+#ifndef TVM_RELAY_ATTRS_IMAGE_H_
+#define TVM_RELAY_ATTRS_IMAGE_H_
+
+#include <tvm/attrs.h>
+#include <string>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief Attributes used in image resize operator */
+struct ResizeAttrs : public tvm::AttrsNode<ResizeAttrs> {
+  Array<IndexExpr> size;
+  std::string layout;
+  std::string method;
+  bool align_corners;
+
+  TVM_DECLARE_ATTRS(ResizeAttrs, "relay.attrs.ResizeAttrs") {
+    TVM_ATTR_FIELD(size).set_default(NullValue<Array<IndexExpr> >())
+        .describe("Output Size.");
+    TVM_ATTR_FIELD(layout).set_default("NCHW")
+        .describe("Dimension ordering of input data. Can be 'NCHW', 'NHWC', etc."
+                  "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                  "dimensions respectively. Resize is applied on the 'H' and"
+                  "'W' dimensions.");
+    TVM_ATTR_FIELD(method).set_default("BILINEAR")
+        .describe("Specify the mode to use for scaling."
+                  "NEAREST_NEIGHBOR -  Nearest Neighbor"
+                  "BILINEAR - Bilinear Interpolation");
+    TVM_ATTR_FIELD(align_corners).set_default(false)
+        .describe("Should be true to preserve the values at the corner pixels");
+  }
+};
+
+}  // namespace relay
+}  // namespace tvm
+#endif  // TVM_RELAY_ATTRS_IMAGE_H_

include/tvm/relay/attrs/nn.h

@@ -77,6 +77,102 @@ struct SoftmaxAttrs : public tvm::AttrsNode<SoftmaxAttrs> {
   }
 };
 
+/*! \brief Attributes for max pool operator */
+struct MaxPool2DAttrs : public tvm::AttrsNode<MaxPool2DAttrs> {
+  Array<IndexExpr> pool_size;
+  Array<IndexExpr> strides;
+  Array<IndexExpr> padding;
+  std::string layout;
+  bool ceil_mode;
+
+  TVM_DECLARE_ATTRS(MaxPool2DAttrs, "relay.attrs.MaxPool2DAttrs") {
+    TVM_ATTR_FIELD(pool_size)
+      .describe("Size of the pooling windows.");
+    TVM_ATTR_FIELD(strides).set_default(Array<IndexExpr>({1, 1}))
+      .describe("Specifies the strides of the convolution.");
+    TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
+      .describe("If padding is non-zero, then the input is implicitly zero-padded"
+                "Padding support both symmetric and asymmetric as"
+                "one int : same padding used on all sides"
+                "two int : bottom, right will use same padding as top, left"
+                "four int : padding width in the order of (top, left, bottom, right)");
+    TVM_ATTR_FIELD(layout).set_default("NCHW")
+      .describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
+                "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                "dimensions respectively. Convolution is applied on the 'H' and"
+                "'W' dimensions.");
+    TVM_ATTR_FIELD(ceil_mode).set_default(false)
+      .describe("When true, will use ceil instead of floor to compute the output shape.");
+  }
+};
+
+/*! \brief Attributes for avg pool operator */
+struct AvgPool2DAttrs : public tvm::AttrsNode<AvgPool2DAttrs> {
+  Array<IndexExpr> pool_size;
+  Array<IndexExpr> strides;
+  Array<IndexExpr> padding;
+  std::string layout;
+  bool ceil_mode;
+  bool count_include_pad;
+
+  TVM_DECLARE_ATTRS(AvgPool2DAttrs, "relay.attrs.AvgPool2DAttrs") {
+    TVM_ATTR_FIELD(pool_size)
+      .describe("Size of the pooling windows.");
+    TVM_ATTR_FIELD(strides).set_default(Array<IndexExpr>({1, 1}))
+      .describe("Specifies the strides of the convolution.");
+    TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
+      .describe("If padding is non-zero, then the input is implicitly zero-padded"
+                "Padding support both symmetric and asymmetric as"
+                "one int : same padding used on all sides"
+                "two int : bottom, right will use same padding as top, left"
+                "four int : padding width in the order of (top, left, bottom, right)");
+    TVM_ATTR_FIELD(layout).set_default("NCHW")
+      .describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
+                "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                "dimensions respectively. Convolution is applied on the 'H' and"
+                "'W' dimensions.");
+    TVM_ATTR_FIELD(ceil_mode).set_default(false)
+      .describe("When true, will use ceil instead of floor to compute the output shape.");
+    TVM_ATTR_FIELD(count_include_pad).set_default(false)
+      .describe("When true, will include padding to compute the average");
+  }
+};
+
+/*! \brief Attributes for global pool operator */
+struct GlobalPool2DAttrs : public tvm::AttrsNode<GlobalPool2DAttrs> {
+  std::string layout;
+
+  TVM_DECLARE_ATTRS(GlobalPool2DAttrs, "relay.attrs.GlobalPool2DAttrs") {
+    TVM_ATTR_FIELD(layout).set_default("NCHW")
+      .describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
+                "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                "dimensions respectively. Convolution is applied on the 'H' and"
+                "'W' dimensions.");
+  }
+};
+
+/*! \brief Attributes for upsampling operator */
+struct UpSamplingAttrs : public tvm::AttrsNode<UpSamplingAttrs> {
+  int scale;
+  std::string layout;
+  std::string method;
+
+  TVM_DECLARE_ATTRS(UpSamplingAttrs, "relay.attrs.UpSamplingAttrs") {
+    TVM_ATTR_FIELD(scale)
+        .describe("Should be true to preserve the values at the corner pixels");
+    TVM_ATTR_FIELD(layout).set_default("NCHW")
+        .describe("Dimension ordering of input data. Can be 'NCHW', 'NHWC', etc."
+                  "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                  "dimensions respectively. Upsampling is applied on the 'H' and"
+                  "'W' dimensions.");
+    TVM_ATTR_FIELD(method).set_default("NEAREST_NEIGHBOR")
+        .describe("Specify the mode to use for scaling."
+                  "NEAREST_NEIGHBOR -  Nearest Neighbor"
+                  "BILINEAR - Bilinear Interpolation");
+  }
+};
+
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_NN_H_

include/tvm/relay/attrs/vision.h

@@ -0,0 +1,17 @@
+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file tvm/relay/attrs/vision.h
+ * \brief Auxiliary attributes for vision operators.
+ */
+#ifndef TVM_RELAY_ATTRS_VISION_H_
+#define TVM_RELAY_ATTRS_VISION_H_
+
+#include <tvm/attrs.h>
+#include <string>
+
+namespace tvm {
+namespace relay {
+
+}  // namespace relay
+}  // namespace tvm
+#endif  // TVM_RELAY_ATTRS_VISION_H_

python/tvm/relay/__init__.py

@@ -1,4 +1,4 @@
-# pylint: disable=wildcard-import
+# pylint: disable=wildcard-import, redefined-builtin
 """The Relay IR namespace containing the IR definition and compiler."""
 from . import base
 from . import ty
@@ -10,8 +10,10 @@
 # Root operators
 from .op import Op
 from .op.tensor import *
-from . import nn
 from .op.transform import *
+from . import nn
+from . import vision
+from . import image
 
 # Span
 Span = base.Span

python/tvm/relay/image.py

@@ -0,0 +1,4 @@
+# pylint: disable=wildcard-import, unused-import, unused-wildcard-import
+"""Image nets related operators."""
+# Re-export in a specific file name so that autodoc can pick it up
+from .op.image import *

python/tvm/relay/op/__init__.py

@@ -1,13 +1,14 @@
-#pylint: disable=wildcard-import
+#pylint: disable=wildcard-import, redefined-builtin
 """Relay core operators."""
 # operator defs
 from .op import get, register, Op
 
 # Operators
 from .tensor import *
-from . import nn
 from .transform import *
-
+from . import nn
+from . import image
+from . import vision
 
 # operator registry
 from . import _tensor

python/tvm/relay/op/image/__init__.py

@@ -0,0 +1,4 @@
+# pylint: disable=wildcard-import
+"""Image network related operators."""
+from __future__ import absolute_import as _abs
+from .image import *

python/tvm/relay/op/image/_make.py

@@ -0,0 +1,4 @@
+"""Constructor APIs"""
+from ...._ffi.function import _init_api
+
+_init_api("relay.op.image._make", __name__)

python/tvm/relay/op/image/image.py

@@ -0,0 +1,42 @@
+"""Image operations."""
+from __future__ import absolute_import as _abs
+from . import _make
+
+def resize(data,
+           size,
+           layout="NCHW",
+           method="BILINEAR",
+           align_corners=False):
+    """Image resize operator.
+
+    This operator takes data as input and does 2D scaling to the given scale factor.
+    In the default case, where the data_layout is `NCHW`
+    with data of shape (n, c, h, w)
+    out will have a shape (n, c, size[0], size[1])
+
+    method indicates the algorithm to be used while calculating ghe out value
+    and method can be one of ("BILINEAR", "NEAREST_NEIGHBOR")
+
+    Parameters
+    ----------
+    data : relay.Expr
+        The input data to the operator.
+
+    size: Tuple of Expr
+        The out size to which the image will be resized.
+
+    layout : str, optional
+        Layout of the input.
+
+    method : str, optional
+        Scale method to used [NEAREST_NEIGHBOR, BILINEAR].
+
+    align_corners : int, optional
+        Should be true to preserve the values at the corner pixels
+
+    Returns
+    -------
+    result: relay.Expr
+        The resized result.
+    """
+    return _make.resize(data, size, layout, method, align_corners)