Add MXnNet parser for box_decode

python/tvm/relay/frontend/mxnet.py

@@ -978,6 +978,42 @@ def _mx_box_nms(inputs, attrs):
     return nms_out
 
 
+def _mx_box_decode(inputs, attrs):
+    std0 = relay.const(attrs.get_float('std0', 1), "float32")
+    std1 = relay.const(attrs.get_float('std1', 1), "float32")
+    std2 = relay.const(attrs.get_float('std2', 1), "float32")
+    std3 = relay.const(attrs.get_float('std3', 1), "float32")
+    clip = attrs.get_float('clip', -1)
+    in_format = attrs.get_str('format', 'corner')
+
+    anchors = inputs[1] # (1, N, 4) encoded in corner or center
+    a = _op.split(anchors, indices_or_sections=4, axis=-1)
+    # Convert to format "center".
+    if in_format == "corner":
+        a_width = a[2] - a[0]
+        a_height = a[3] - a[1]
+        a_x = a[0] + a_width * relay.const(0.5, "float32")
+        a_y = a[1] + a_height * relay.const(0.5, "float32")
+    else:
+        a_x, a_y, a_width, a_height = a
+    data = inputs[0] # (B, N, 4) predicted bbox offset
+    p = _op.split(data, indices_or_sections=4, axis=-1)
+    ox = p[0] * std0 * a_width + a_x
+    oy = p[1] * std1 * a_height + a_y
+    dw = p[2] * std2
+    dh = p[3] * std3
+    if clip > 0:
+        clip = relay.const(clip, "float32")
+        dw = _op.minimum(dw, clip)
+        dh = _op.minimum(dh, clip)
+    dw = _op.exp(dw)
+    dh = _op.exp(dh)
+    ow = dw * a_width * relay.const(0.5, "float32")
+    oh = dh * a_height * relay.const(0.5, "float32")
+    out = _op.concatenate([ox - ow, oy - oh, ox + ow, oy + oh], axis=-1)
+    return out
+
+
 def _mx_l2_normalize(inputs, attrs):
     new_attrs = {}
     mode = attrs.get_str('mode', 'instance')
@@ -2220,6 +2256,7 @@ def impl(inputs, input_types):
     "_contrib_Proposal" : _mx_proposal,
     "_contrib_MultiProposal" : _mx_proposal,
     "_contrib_box_nms" : _mx_box_nms,
+    "_contrib_box_decode" : _mx_box_decode,
     "_contrib_DeformableConvolution" : _mx_deformable_convolution,
     "_contrib_AdaptiveAvgPooling2D" : _mx_adaptive_avg_pooling,
     "GridGenerator"                 : _mx_grid_generator,

tests/python/frontend/mxnet/test_forward.py

@@ -1306,6 +1306,28 @@ def verify(batch, seq_length, num_heads, head_dim):
     verify(3, 10, 6, 8)
 
 
+def test_forward_box_decode():
+    def verify(data_shape, anchor_shape, stds=[1, 1, 1, 1], clip=-1, in_format="corner"):
+        dtype = "float32"
+        data = np.random.uniform(low=-2, high=2, size=data_shape).astype(dtype)
+        anchors = np.random.uniform(low=-2, high=2, size=anchor_shape).astype(dtype)
+        ref_res = mx.nd.contrib.box_decode(mx.nd.array(data), mx.nd.array(anchors), stds[0], stds[1], stds[2], stds[3], clip, in_format)
+        mx_sym = mx.sym.contrib.box_decode(mx.sym.var("data"), mx.sym.var("anchors"), stds[0], stds[1], stds[2], stds[3], clip, in_format)
+        shape_dict = {"data": data_shape, "anchors": anchor_shape}
+        mod, _ = relay.frontend.from_mxnet(mx_sym, shape_dict)
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+                op_res = intrp.evaluate()(data, anchors)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-3, atol=1e-5)
+
+    verify((1, 10, 4), (1, 10, 4))
+    verify((4, 10, 4), (1, 10, 4))
+    verify((1, 10, 4), (1, 10, 4), stds=[2, 3, 0.5, 1.5])
+    verify((1, 10, 4), (1, 10, 4), clip=1)
+    verify((1, 10, 4), (1, 10, 4), in_format="center")
+
+
 if __name__ == '__main__':
     test_forward_mlp()
     test_forward_vgg()
@@ -1379,3 +1401,4 @@ def verify(batch, seq_length, num_heads, head_dim):
     test_forward_arange_like()
     test_forward_interleaved_matmul_selfatt_qk()
     test_forward_interleaved_matmul_selfatt_valatt()
+    test_forward_box_decode()