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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/09/03 17:18:15 UTC
[GitHub] [incubator-tvm] zhiics commented on a change in pull request #6353: [RELAY][DYN] Dynamic UpSampling3D Op
zhiics commented on a change in pull request #6353:
URL: https://github.com/apache/incubator-tvm/pull/6353#discussion_r483136198
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File path: tests/python/relay/dyn/test_dynamic_op_level2.py
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@@ -52,30 +52,84 @@ def verify_upsampling(dshape, scale_h, scale_w, layout, method, align_corners=Fa
func = relay.Function([x, scale_h_var, scale_w_var], z)
for target, ctx in ctx_list():
- if "llvm" not in target: continue
- for kind in ["vm", "debug"]:
- mod = tvm.ir.IRModule.from_expr(func)
- intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
- op_res = intrp.evaluate()(x_data, np.array(scale_h).astype("float32"), np.array(scale_w).astype("float32"))
- tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-4, atol=1e-6)
-
- verify_upsampling((1, 16, 32, 32), 2.0, 2.0,"NCHW", "nearest_neighbor")
- verify_upsampling((1, 16, 32, 32), 2.0, 2.0, "NCHW", "bilinear", True)
- verify_upsampling((1, 16, 32, 32), 2.0, 2.0, "NHWC", "nearest_neighbor")
+ ##TODO(mbrookhart)(electriclilies): remove when VM supports heterogeneous execution
+ if "llvm" not in target: continue
+ for kind in ["vm", "debug"]:
+ mod = tvm.ir.IRModule.from_expr(func)
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+ op_res = intrp.evaluate()(x_data, np.array(scale_h).astype("float32"), np.array(scale_w).astype("float32"))
+ tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-4, atol=1e-6)
+
+ verify_upsampling((1, 16, 32, 32), 3, 2.0,"NCHW", "nearest_neighbor")
+ verify_upsampling((1, 16, 32, 32), 5, 2.0, "NCHW", "bilinear", True)
+ verify_upsampling((1, 16, 32, 32), 2.0, 6, "NHWC", "nearest_neighbor")
verify_upsampling((1, 16, 32, 32), 2.0, 2.0,"NHWC", "bilinear", True)
#tests upsampling type inference with scale_h passed in as a constant and scale_w as a variable
def test_dyn_upsampling_infer_type_const():
n, c, h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), te.size_var("w")
data = relay.var("data", relay.TensorType((n, c, h, w), "int8"))
- scale_h = relay.Var("scale_h", relay.TensorType((), "float32"))
scale_w = relay.Var("scale_w", relay.TensorType((), "float32"))
z = relay.nn.upsampling(data, 2.0, scale_w)
zz = run_infer_type(z)
assert zz.checked_type == relay.TensorType((n, c, relay.Any(), relay.Any()), "int8")
+def test_dyn_upsampling3d_run():
+ def verify_upsampling3d(dshape, scale_d, scale_h, scale_w, layout, method, coord_trans="half_pixel"):
+
+ if layout == "NCDHW":
+ (n, c, d, h, w) = dshape
+ x_data = np.random.uniform(size=(n, c, d, h, w)).astype("float32")
+
+ elif layout == "NDHWC":
+ (n, d, h, w, c) = dshape
+ x_data = np.random.uniform(size=(n, d, h, w, c)).astype("float32")
+
+ if method == "nearest_neighbor":
+ ref_res = tvm.topi.testing.upsampling3d_python(x_data, (scale_d, scale_h, scale_w), layout)
+ else:
+ ref_res = tvm.topi.testing.trilinear_resize3d_python(x_data, (int(round(d*scale_d)),
+ int(round(h*scale_h)),
+ int(round(w*scale_w))), layout)
+ x = relay.Var("x", relay.TensorType(dshape, "float32"))
+ scale_d_var = relay.var("scale_d", relay.TensorType((), "float32"))
+ scale_h_var = relay.var("scale_h", relay.TensorType((), "float32"))
+ scale_w_var = relay.var("scale_h", relay.TensorType((), "float32"))
+
+ z = relay.nn.upsampling3d(x, scale_d_var, scale_h_var, scale_w_var, method=method, layout=layout,
+ coordinate_transformation_mode=coord_trans)
+ zz = run_infer_type(z)
+ func = relay.Function([x, scale_d_var, scale_h_var, scale_w_var], z)
+
+ for target, ctx in ctx_list():
+ ##TODO(mbrookhart)(electriclilies): remove when VM supports heterogeneous execution
Review comment:
we can try gpu tests now
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