[FRONTEND][TF] Add conv3d (apache#4604)

* [FRONTEND][TF] Add conv3d

* fix high rtol

include/tvm/relay/attrs/nn.h

@@ -212,7 +212,11 @@ struct Conv3DAttrs : public tvm::AttrsNode<Conv3DAttrs> {
         .describe("Specifies the strides of the convolution.");
     TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0, 0}))
         .describe("If padding is non-zero, then the input is implicitly zero-padded"
-                  "on both sides for padding number of points");
+                  "Padding support both symmetric and asymmetric as"
+                  "one int : same padding used on all sides"
+                  "three int : back, bottom, right will use same padding as front, top, left"
+                  "six int : padding width in the order of (front, top, left, back, bottom,"
+                  "right)");
     TVM_ATTR_FIELD(dilation).set_default(Array<IndexExpr>({1, 1, 1}))
         .describe("Specifies the dilation rate to use for dilated convolution.");
     TVM_ATTR_FIELD(groups).set_default(1)

python/tvm/relay/frontend/tensorflow.py

@@ -66,16 +66,18 @@ def _impl(attr):
         kernel = attr['kernel_shape']
         if len(kernel) == 2:
             return prefix + '2d' + surfix
+        if len(kernel) == 3:
+            return prefix + '3d' + surfix
         raise tvm.error.OpAttributeInvalid(
-            'Only 2D kernels are supported for operator {}'.format(prefix + '2d'))
+            'Only 2D or 3D kernels are supported for operator {}'.format(prefix + '2d or 3d'))
     return _impl
 
 def _dimension_constraint():
     def _dim_check(attrs):
-        if len(attrs['kernel_shape']) == 2:
+        if len(attrs['kernel_shape']) in (2, 3):
             return True
         return False
-    return _dim_check, "Only 2d kernel supported."
+    return _dim_check, "Only 2d or 3d kernel supported."
 
 def _get_param(params, input_node):
     if isinstance(input_node, _expr.Constant):
@@ -425,6 +427,130 @@ def _impl(inputs, attr, params):
         return out
     return _impl
 
+def _conv3d(opname):
+    def _impl(inputs, attr, params):
+        attr['data_format'] = attr['data_format'].decode("utf-8")
+        flip_layout = False
+
+        inputs_data = inputs[0] if opname != 'conv_transpose' else inputs[2]
+
+        # NCDHW Layout require weights transpose
+        if attr['data_format'] == 'NCDHW':
+            tmp_shape = attr['_input_shapes'][inputs[1]]
+            tmp_shape = [tmp_shape[ii] for ii in (4, 3, 0, 1, 2)]
+            inputs[1] = _op.transpose(inputs[1], axes=(4, 3, 0, 1, 2))
+            attr['_input_shapes'][inputs[1]] = tmp_shape
+
+        input_shape = attr['_input_shapes'][inputs_data]
+        weights_shape = attr['_input_shapes'][inputs[1]]
+
+        if attr['_target_layout'] == "NCDHW" and attr['data_format'] == "NDHWC":
+            input_shape = [input_shape[ii] for ii in (0, 4, 1, 2, 3)]
+            inputs_data = _op.transpose(inputs_data, axes=(0, 4, 1, 2, 3))
+            weights_shape = [weights_shape[ii] for ii in (4, 3, 0, 1, 2)]
+            inputs[1] = _op.transpose(inputs[1], axes=(4, 3, 0, 1, 2))
+
+            attr['data_format'] = "NCDHW"
+            attr['strides'] = [attr['strides'][ii] for ii in (0, 4, 1, 2, 3)]
+            flip_layout = True
+
+        if attr['data_format'] == 'NDHWC':
+            kernel_d, kernel_h, kernel_w, _, _ = weights_shape
+            attr['kernel_shape'] = (kernel_d, kernel_h, kernel_w)
+            if opname == 'conv':
+                attr['channels'] = weights_shape[4]
+            elif opname == 'conv_transpose':
+                attr['channels'] = weights_shape[3]
+
+            if 'dilations' in attr:
+                attr['dilations'] =\
+                    (attr['dilations'][1], attr['dilations'][2], attr['dilations'][3])
+            attr['strides'] = (attr['strides'][1], attr['strides'][2], attr['strides'][3])
+        elif attr['data_format'] == 'NCDHW':
+            _, _, kernel_d, kernel_h, kernel_w = weights_shape
+            attr['kernel_shape'] = (kernel_d, kernel_h, kernel_w)
+            if opname == 'conv':
+                attr['channels'] = weights_shape[0]
+            elif opname == 'conv_transpose':
+                attr['channels'] = weights_shape[1]
+
+            if 'dilations' in attr:
+                attr['dilations'] =\
+                    (attr['dilations'][2], attr['dilations'][3], attr['dilations'][4])
+            attr['strides'] = (attr['strides'][2], attr['strides'][3], attr['strides'][4])
+        else:
+            msg = 'Value {} in attribute "data_format" of operator Conv is ' \
+                  'not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))
+
+        # Fix padding
+        attr['padding'] = attr['padding'].decode("utf-8")
+
+        if attr['padding'] == 'VALID':
+            attr['padding'] = [0, 0, 0]
+        elif attr['padding'] == 'SAME':
+            stride_d, stride_h, stride_w = attr['strides']
+            kernel_d, kernel_h, kernel_w = attr['kernel_shape']
+
+            pdata_shape = input_shape
+            if opname == 'conv_transpose' and len(attr['_output_shapes']) > 0:
+                pdata_shape = attr['_output_shapes'][0]
+
+            if attr['data_format'] == 'NDHWC':
+                in_d = pdata_shape[1]
+                in_h = pdata_shape[2]
+                in_w = pdata_shape[3]
+            else:
+                in_d = pdata_shape[2]
+                in_h = pdata_shape[3]
+                in_w = pdata_shape[4]
+
+            dilation_d = attr['dilations'][0]
+            dilation_h = attr['dilations'][1]
+            dilation_w = attr['dilations'][2]
+            dilated_kernel_d = (kernel_d - 1) * dilation_d + 1
+            dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
+            dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
+            pad_d = _get_pad_pair(in_d, dilated_kernel_d, stride_d)
+            pad_v = _get_pad_pair(in_h, dilated_kernel_h, stride_h)
+            pad_h = _get_pad_pair(in_w, dilated_kernel_w, stride_w)
+
+            attr['padding'] = [pad_d[0], pad_v[0], pad_h[0], pad_v[0], pad_v[1], pad_h[1]]
+
+        else:
+            msg = 'Value {} in attribute "padding" of operator Conv is not ' \
+                  'valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))
+
+        if 'kernel_layout' not in attr:
+            attr['kernel_layout'] = 'DHWIO' if attr['data_format'] == 'NDHWC' else 'OIDHW'
+
+        use_bias = len(inputs) == (3 if opname != 'conv_transpose' else 4)
+        channel_axis = 1 if attr['data_format'] == "NCDHW" else 3
+
+        # Ignore the new attributes from TF2.0, for now.
+        out = AttrCvt(
+            op_name=_dimension_picker('conv', \
+                surfix="_transpose" if opname == 'conv_transpose' else ""),
+            ignores=['explicit_paddings'],
+            transforms={
+                'kernel_shape': 'kernel_size',
+                'data_format': 'data_layout',
+                'dilations': ('dilation', (0, 0)),
+                'group': ('groups', 1)},
+            custom_check=_dimension_constraint())([inputs_data, inputs[1]], attr)
+
+        if use_bias:
+            out = _op.nn.bias_add(out,
+                                  inputs[2] if opname != 'conv_transpose' else inputs[3],
+                                  axis=channel_axis)
+
+        if flip_layout:
+            out = _op.transpose(out, axes=(0, 2, 3, 4, 1))
+
+        return out
+    return _impl
+
 def _decode_image():
     def _impl(inputs, attr, params):
         # Image decode wrapper: Expecting user to feed decoded input to next layer drop this layer.
@@ -1442,6 +1568,7 @@ def _impl(inputs, attr, params):
     'Concat'                            : _concat(),
     'ConcatV2'                          : _concatV2(),
     'Conv2D'                            : _conv('conv'),
+    'Conv3D'                            : _conv3d('conv'),
     'Conv2DBackpropInput'               : _conv('conv_transpose'),
     'CropAndResize'                     : _crop_and_resize(),
     'DecodeJpeg'                        : _decode_image(),

python/tvm/relay/op/nn/_nn.py

@@ -173,6 +173,7 @@ def _get_out_depth():
             assert len(weight_shape) == 5
             C, M, _, _, VC = weight_shape
             return C * VC * M
+
     if groups == 1:
         out = topi.nn.conv2d(
             inputs[0], inputs[1], strides, padding,
@@ -330,7 +331,7 @@ def compute_conv3d(attrs, inputs, out_type, target):
     out_dtype = (inputs[0].dtype if out_dtype in ("same", "")
                  else out_dtype)
 
-    assert layout in ["NCDHW"]
+    assert layout in ["NCDHW", "NDHWC"]
     (dilation_d, dilation_h, dilation_w) = dilation
     if dilation_d < 1 or dilation_h < 1 or dilation_w < 1:
         raise ValueError("dilation should be positive value")
@@ -353,6 +354,8 @@ def schedule_conv3d(attrs, outs, target):
     with target:
         if groups == 1 and layout == "NCDHW":
             return topi.generic.schedule_conv3d_ncdhw(outs)
+        elif groups == 1 and layout == "NDHWC":
+            return topi.generic.schedule_conv3d_ndhwc(outs)
 
     raise ValueError("No compatible schedule")
 

src/relay/op/nn/convolution.cc

@@ -38,7 +38,7 @@ namespace relay {
 TVM_REGISTER_NODE_TYPE(Conv2DAttrs);
 
 template<typename T>
-Array<Array<Layout> > Conv2DInferCorrectLayout(
+Array<Array<Layout> > ConvInferCorrectLayout(
     const Attrs& attrs,
     const Array<Layout>& new_in_layouts,
     const Array<Layout>& old_in_layouts,
@@ -105,7 +105,7 @@ with the layer input to produce a tensor of outputs.
 .add_argument("weight", "Tensor", "The weight tensor.")
 .set_support_level(2)
 .add_type_rel("Conv2D", Conv2DRel<Conv2DAttrs>)
-.set_attr<FInferCorrectLayout>("FInferCorrectLayout", Conv2DInferCorrectLayout<Conv2DAttrs>);
+.set_attr<FInferCorrectLayout>("FInferCorrectLayout", ConvInferCorrectLayout<Conv2DAttrs>);
 
 // relay.nn.conv3d
 TVM_REGISTER_NODE_TYPE(Conv3DAttrs);
@@ -163,7 +163,8 @@ with the layer input to produce a tensor of outputs.
 .add_argument("data", "Tensor", "The input tensor.")
 .add_argument("weight", "Tensor", "The weight tensor.")
 .set_support_level(2)
-.add_type_rel("Conv3D", Conv3DRel<Conv3DAttrs>);
+.add_type_rel("Conv3D", Conv3DRel<Conv3DAttrs>)
+.set_attr<FInferCorrectLayout>("FInferCorrectLayout", ConvInferCorrectLayout<Conv3DAttrs>);
 
 
 // relay.nn.conv2d_transpose
@@ -337,7 +338,7 @@ v            (batch_size, channels, out_height, out_width) if `layout` is `NCHW`
 .add_argument("weight", "Tensor", "The weight tensor.")
 .set_support_level(2)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
-                               Conv2DInferCorrectLayout<Conv2DTransposeAttrs>)
+                               ConvInferCorrectLayout<Conv2DTransposeAttrs>)
 .add_type_rel("Conv2DTranspose", Conv2DTransposeRel);
 
 
@@ -635,7 +636,7 @@ RELAY_REGISTER_OP("nn.contrib_conv2d_winograd_without_weight_transform")
 .set_support_level(10)
 .add_type_rel("Conv2DWinograd", Conv2DWinogradRel<Conv2DWinogradAttrs>)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
-        Conv2DInferCorrectLayout<Conv2DWinogradAttrs>);
+        ConvInferCorrectLayout<Conv2DWinogradAttrs>);
 
 // relay.nn.contrib_conv2d_winograd_weight_transform
 TVM_REGISTER_NODE_TYPE(Conv2DWinogradWeightTransformAttrs);
@@ -744,7 +745,7 @@ RELAY_REGISTER_OP("nn.contrib_conv2d_winograd_nnpack_without_weight_transform")
 .add_argument("weight", "Tensor", "The weight tensor.")
 .set_support_level(10)
 .add_type_rel("Conv2DWinogradNNPACKRel", Conv2DWinogradRel<Conv2DAttrs>)
-.set_attr<FInferCorrectLayout>("FInferCorrectLayout", Conv2DInferCorrectLayout<Conv2DAttrs>);
+.set_attr<FInferCorrectLayout>("FInferCorrectLayout", ConvInferCorrectLayout<Conv2DAttrs>);
 
 // relay.nn.contrib_conv2d_winograd_nnpack_weight_transform
 TVM_REGISTER_NODE_TYPE(Conv2DWinogradNNPACKWeightTransformAttrs);
@@ -854,7 +855,7 @@ RELAY_REGISTER_OP("nn.contrib_conv2d_NCHWc_int8")
 .set_support_level(10)
 .add_type_rel("Conv2DNCHWcInt8", Conv2DWinogradRel<Conv2DAttrs>)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
-        Conv2DInferCorrectLayout<Conv2DAttrs>);
+        ConvInferCorrectLayout<Conv2DAttrs>);
 
 // Positional relay function to create conv2d NCHWc operator
 // used by frontend FFI.
@@ -903,7 +904,7 @@ RELAY_REGISTER_OP("nn.contrib_conv2d_NCHWc")
 .set_support_level(10)
 .add_type_rel("Conv2DNCHWc", Conv2DWinogradRel<Conv2DAttrs>)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
-        Conv2DInferCorrectLayout<Conv2DAttrs>);
+        ConvInferCorrectLayout<Conv2DAttrs>);
 
 
 // Positional relay function to create depthwise conv2d NCHWc operator
@@ -953,7 +954,7 @@ RELAY_REGISTER_OP("nn.contrib_depthwise_conv2d_NCHWc")
 .set_support_level(10)
 .add_type_rel("Conv2D", Conv2DRel<Conv2DAttrs>)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
-        Conv2DInferCorrectLayout<Conv2DAttrs>);
+        ConvInferCorrectLayout<Conv2DAttrs>);
 
 
 bool DeformableConv2DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,

src/relay/op/nn/convolution.h

@@ -28,6 +28,8 @@
 #include <string>
 #include <utility>
 
+#include "../op_common.h"
+
 namespace tvm {
 namespace relay {
 
@@ -187,7 +189,7 @@ bool Conv3DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
                  param->kernel_size[1], param->kernel_size[2]}};
     }
 
-    /*wshape = trans_kernel_layout.BackwardShape(wshape); */
+    wshape = trans_kernel_layout.BackwardShape(wshape);
     channels = param->channels;
     dilated_ksize_z = 1 + (param->kernel_size[0] - 1) * param->dilation[0];
     dilated_ksize_y = 1 + (param->kernel_size[1] - 1) * param->dilation[1];
@@ -196,6 +198,7 @@ bool Conv3DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
     if (weight != nullptr) {
       weight_dtype = weight->dtype;
     }
+
     // assign result to reporter
     reporter->Assign(types[1], TensorTypeNode::make(wshape, weight_dtype));
   } else {
@@ -225,22 +228,24 @@ bool Conv3DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   // dilation
   Array<IndexExpr> oshape({dshape_ncdhw[0], channels, 0, 0, 0});
 
+  IndexExpr pad_d, pad_h, pad_w;
+  GetPaddingDepthHeightWidth(param->padding, &pad_d, &pad_h, &pad_w);
   if (!dshape_ncdhw[2].as<ir::Any>()) {
-    oshape.Set(2, indexdiv(dshape_ncdhw[2] + param->padding[0] * 2 - dilated_ksize_z,
+    oshape.Set(2, indexdiv(dshape_ncdhw[2] + pad_d - dilated_ksize_z,
                            param->strides[0]) + 1);
   } else {
     oshape.Set(2, dshape_ncdhw[2]);
   }
 
   if (!dshape_ncdhw[3].as<ir::Any>()) {
-    oshape.Set(3, indexdiv(dshape_ncdhw[3] + param->padding[1] * 2 - dilated_ksize_y,
+    oshape.Set(3, indexdiv(dshape_ncdhw[3] + pad_h - dilated_ksize_y,
                            param->strides[1]) + 1);
   } else {
     oshape.Set(3, dshape_ncdhw[3]);
   }
 
   if (!dshape_ncdhw[4].as<ir::Any>()) {
-    oshape.Set(4, indexdiv(dshape_ncdhw[4] + param->padding[2] * 2 - dilated_ksize_x,
+    oshape.Set(4, indexdiv(dshape_ncdhw[4] + pad_w - dilated_ksize_x,
                            param->strides[2]) + 1);
   } else {
     oshape.Set(4, dshape_ncdhw[4]);

src/relay/op/op_common.h

@@ -162,6 +162,45 @@ inline void GetPaddingWidth(const Array<IndexExpr>& padding, IndexExpr* pad_w) {
   }
 }
 
+/*! \brief A utility function to get padding height and width from a 1, 2, 4 ints tuple. */
+inline void GetPaddingHeightWidth(const Array<IndexExpr>& padding, IndexExpr* pad_h,
+                                  IndexExpr* pad_w) {
+  if (padding.size() == 1) {
+    *pad_h = padding[0] * 2;
+    *pad_w = padding[0] * 2;
+  } else if (padding.size() == 2) {
+    *pad_h = padding[0] * 2;
+    *pad_w = padding[1] * 2;
+  } else if (padding.size() == 4) {
+    *pad_h = padding[0] + padding[2];
+    *pad_w = padding[1] + padding[3];
+  } else {
+    CHECK_EQ(padding.size(), 4) << " Padding size should be 1, 2 or 4, but got "
+        << padding.size();
+  }
+}
+
+/*! \brief A utility function to get padding depth, height and width from a 1, 3, 6 ints tuple. */
+inline void GetPaddingDepthHeightWidth(const Array<IndexExpr>& padding, IndexExpr* pad_d,
+                                       IndexExpr* pad_h, IndexExpr* pad_w) {
+  if (padding.size() == 1) {
+    *pad_d = padding[0] * 2;
+    *pad_h = padding[0] * 2;
+    *pad_w = padding[0] * 2;
+  } else if (padding.size() == 3) {
+    *pad_d = padding[0] * 2;
+    *pad_h = padding[1] * 2;
+    *pad_w = padding[2] * 2;
+  } else if (padding.size() == 6) {
+    *pad_d = padding[0] + padding[3];
+    *pad_h = padding[1] + padding[4];
+    *pad_w = padding[2] + padding[5];
+  } else {
+    CHECK_EQ(padding.size(), 6) << " Padding size should be 1, 3 or 6, but got "
+        << padding.size();
+  }
+}
+
 }  // namespace relay
 }  // namespace tvm