resolve conflicts

tests/python/relay/test_any.py

@@ -1121,6 +1121,85 @@ def test_any_ndarray_size():
     verify_any_ndarray_size((1, 2, 3, 4))
 
 
+def verify_any_resize(data_shape, scale, layout, static_data_shape, ref_out_shape):
+    mod = tvm.IRModule()
+    dtype = "float32"
+    data = relay.var('data', shape=data_shape, dtype=dtype)
+    if layout == "NHWC":
+        size = (data_shape[1] * scale, data_shape[2] * scale)
+    else:
+        size = (data_shape[2] * scale, data_shape[3] * scale)
+    y = relay.image.resize(data, size, layout)
+    mod["main"] = relay.Function([data], y)
+    data_np = np.random.uniform(size=static_data_shape).astype(dtype)
+    check_result([data_np], mod, ref_out_shape, assert_shape=True)
+
+@tvm.testing.uses_gpu
+def test_any_resize():
+    verify_any_resize(
+        data_shape=(relay.Any(), 4, 4, 4),
+        scale=2,
+        layout='NHWC',
+        static_data_shape=(1, 4, 4, 4),
+        ref_out_shape=(1, 8, 8, 4))
+    verify_any_resize(
+        data_shape=(relay.Any(), 8, 17, 20),
+        scale=3,
+        layout='NCHW',
+        static_data_shape=(2, 8, 17, 20),
+        ref_out_shape=(2, 8, 51, 60))
+
+
+def verify_any_grid_sample(data_shape, grid_shape, static_data_shape, ref_out_shape):
+    mod = tvm.IRModule()
+    dtype = "float32"
+    data = relay.var('data', shape=data_shape, dtype=dtype)
+    grid = relay.var('grid', shape=grid_shape, dtype=dtype)
+    y = relay.image.grid_sample(data, grid)
+    mod["main"] = relay.Function([data, grid], y)
+    data_np = np.random.uniform(size=static_data_shape).astype(dtype)
+    grid_np = np.random.uniform(size=grid_shape).astype(dtype)
+    check_result([data_np, grid_np], mod, ref_out_shape, assert_shape=True)
+
+@tvm.testing.uses_gpu
+def test_any_grid_sample():
+    verify_any_grid_sample(
+        data_shape=(relay.Any(), 4, 16, 32),
+        grid_shape=(4, 2, 8, 8),
+        static_data_shape=(4, 4, 16, 32),
+        ref_out_shape=(4, 4, 8, 8))
+    verify_any_grid_sample(
+        data_shape=(relay.Any(), 4, 16, 32),
+        grid_shape=(4, 2, 32, 32),
+        static_data_shape=(4, 4, 16, 32),
+        ref_out_shape=(4, 4, 32, 32))
+
+
+def verify_any_affine_grid(num_batch, static_num_batch, target_shape, ref_out_shape):
+    mod = tvm.IRModule()
+    dtype = "float32"
+    data_shape = (num_batch, 2, 3)
+    static_data_shape = (static_num_batch, 2, 3)
+    data = relay.var('data', shape=data_shape, dtype=dtype)
+    y = relay.image.affine_grid(data, target_shape)
+    mod["main"] = relay.Function([data], y)
+    data_np = np.random.uniform(size=static_data_shape).astype(dtype)
+    check_result([data_np], mod, ref_out_shape, assert_shape=True)
+
+@tvm.testing.uses_gpu
+def test_any_affine_grid():
+    verify_any_affine_grid(
+        num_batch=relay.Any(),
+        static_num_batch=1,
+        target_shape=(16, 32),
+        ref_out_shape=(1, 2, 16, 32))
+    verify_any_affine_grid(
+        num_batch=relay.Any(),
+        static_num_batch=8,
+        target_shape=(32, 32),
+        ref_out_shape=(8, 2, 32, 32))
+
+
 def test_any_consecutive_broadcast():
     dtype = "float32"
     data0 = relay.var("data0", shape=any_dims(2), dtype=dtype)