[Caffe Frontend] extending Eltwise to handle multiple inputs

python/tvm/relay/frontend/caffe.py

@@ -83,14 +83,13 @@ def convert_flatten(self, op):
     def convert_eltwise(self, op):
         """Convert Eltwise layer"""
         inputs = op.bottom
-        assert len(inputs) == 2, "input tensors length should be 2"
+        assert len(inputs) >= 2, "input tensors length should be larger than 2"
 
+        # gethering initial 2 input expressions
         lhs_expr = self.exp_tab.get_expr(inputs[0])
         rhs_expr = self.exp_tab.get_expr(inputs[1])
-
         lhs_shape = _infer_shape(lhs_expr)
         rhs_shape = _infer_shape(rhs_expr)
-
         assert lhs_shape == rhs_shape, "input tensors shape should be equal"
 
         eltwise_params = op.eltwise_param
@@ -100,6 +99,11 @@ def convert_eltwise(self, op):
 
         if eltwise_type_dict[eltwise_type] == "PROD":
             out = _op.multiply(lhs_expr, rhs_expr)
+            # for rest inputs
+            for i in range(len(inputs) - 2):
+                extra_expr = self.exp_tab.get_expr(inputs[i + 2])
+                assert _infer_shape(out) == _infer_shape(extra_expr)
+                out = _op.multiply(out, extra_expr)
         elif eltwise_type_dict[eltwise_type] == "SUM":
             if coeff:
                 left_coeff_expr = self.exp_tab.new_const(np.asarray(coeff[0], np.float32))
@@ -109,8 +113,23 @@ def convert_eltwise(self, op):
                 out = _op.add(lhs_expr_scale, rhs_expr_scale)
             else:
                 out = _op.add(lhs_expr, rhs_expr)
+            # for rest inputs
+            for i in range(len(inputs) - 2):
+                extra_expr = self.exp_tab.get_expr(inputs[i + 2])
+                assert _infer_shape(out) == _infer_shape(extra_expr)
+                if coeff:
+                    coeff_expr = self.exp_tab.new_const(np.asarray(coeff[i + 2], np.float32))
+                    extra_expr_scale = _op.multiply(extra_expr, coeff_expr)
+                    out = _op.add(out, extra_expr_scale)
+                else:
+                    out = _op.add(out, extra_expr)
         elif eltwise_type_dict[eltwise_type] == "MAX":
             out = _op.maximum(lhs_expr, rhs_expr)
+            # for rest inputs
+            for i in range(len(inputs) - 2):
+                extra_expr = self.exp_tab.get_expr(inputs[i + 2])
+                assert _infer_shape(out) == _infer_shape(extra_expr)
+                out = _op.maximum(out, extra_expr)
         else:
             raise tvm.error.OpNotImplemented(
                 "eltwise_type {} is not supported for frontend Caffe.".format(eltwise_type)

tests/python/frontend/caffe/test_forward.py

@@ -511,6 +511,45 @@ def test_forward_Eltwise():
         operation=1,
         coeff=[0.5, 1],
     )
+    _test_eltwise(
+        [
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+        ],
+        operation=0,
+    )
+    _test_eltwise(
+        [
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+        ],
+        operation=1,
+    )
+    _test_eltwise(
+        [
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+        ],
+        operation=2,
+    )
+    _test_eltwise(
+        [
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+            np.random.rand(1, 3, 10, 11).astype(np.float32),
+        ],
+        operation=1,
+        coeff=[0.5, 1, 0.2, 1.8, 3.1, 0.1],
+    )
 
 
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