diff --git a/extra/onnx.py b/extra/onnx.py
index bf31e9355b..8f838b00a2 100644
--- a/extra/onnx.py
+++ b/extra/onnx.py
@@ -195,6 +195,7 @@ def get_onnx_ops():
     return [pads[i]-pads[i]//2 for i in range(len(pads))] + [pads[i]//2 for i in range(len(pads))]
 
   def _resolve_pool_pads(x:Tensor, p_, k_, d_, s_, auto_pad:AUTO_PAD_OPTIONS):
+    if auto_pad == "VALID": return [0]*(len(k_)*2)
     i_, (s_,d_,p_) = x.shape[-len(k_):], (make_tuple(x, len(k_)*2) for x in (s_, d_, p_))
     if auto_pad == "NOTSET": return _onnx_pads_to_tiny_pads(p_ if len(p_)==len(k_)*2 else p_*2)
     o_ = [((i - (1 if auto_pad in ("SAME_UPPER", "SAME_LOWER") else k)) // s + 1) for i,k,s in zip(i_, k_, s_)]
@@ -673,7 +674,8 @@ def get_onnx_ops():
       x_sh = list(x.shape)
       ret_shape = x_sh[:axis] + list(indices.shape) + x_sh[axis+1:]
       if indices.ndim > 1: indices = indices.flatten()
-      indices = [_cached_to_python_const(indices)] if indices.shape == () else [x_sh[axis]+x if x<0 else x for x in _cached_to_python_const(indices)]
+      indices = [_cached_to_python_const(indices)] if indices.shape == () else _cached_to_python_const(indices)
+      indices = [x_sh[axis]+x if x<0 else x for x in indices]
       args = [[(0,x) if j != axis else (i,i+1) for j, x in enumerate(x_sh)] for i in indices] # type: ignore
       return x.shrink(arg=tuple(args[0])).cat(*[x.shrink(arg=tuple(arg)) for arg in args[1:]], dim=axis).reshape(ret_shape)
     # NOTE faster gather, fixed number of kernels, but exceeds limited kernels for openpilot
diff --git a/test/external/external_test_onnx_ops.py b/test/external/external_test_onnx_ops.py
index 1442a0a565..7e846695f9 100644
--- a/test/external/external_test_onnx_ops.py
+++ b/test/external/external_test_onnx_ops.py
@@ -27,6 +27,27 @@ class TestMainOnnxOps(TestOnnxOps):
     outputs = ["out"]
     self.helper_test_single_op("Reshape", inputs, attributes, outputs)
 
+  def test_conv(self):
+    # test VALID auto_pad
+    inputs = {
+      "x": np.random.randn(1, 3, 384, 384).astype(np.float32),
+      "w": np.random.randn(1152, 3, 14, 14).astype(np.float32),
+      "b": np.random.randn(1152).astype(np.float32)
+    }
+    attributes = {'auto_pad': 'VALID', 'dilations': (1, 1), 'group': 1, 'kernel_shape': (14, 14), 'strides': (14, 14)}
+    outputs = ["y"]
+    self.helper_test_single_op("Conv", inputs, attributes, outputs, atol=1e-4)
+
+  def test_gather(self):
+    # test const negative indices
+    inputs = {
+      "input": np.random.randn(1, 3, 3).astype(np.float32),
+      "indices": np.array(-2, dtype=np.int64),
+    }
+    attributes = {'axis': 1}
+    outputs = ["y"]
+    self.helper_test_single_op("Gather", inputs, attributes, outputs)
+
   def test_quantize_linear(self):
     test_cases = [
       {"test_case": "round_half_to_even", "qdtype": np.int8, "qzero_point": 0, "x": [-1.5, -0.5, 0.5, 1.5], "scale": 1.0},