masahi commented on code in PR #11190:
URL: https://github.com/apache/tvm/pull/11190#discussion_r864452107
##########
tests/python/relay/test_op_level3.py:
##########
@@ -2066,5 +2066,170 @@ def verify_unique(n, dtype, is_dyn=False,
is_sorted=False, return_counts=False):
verify_unique(10, dtype, is_dyn, is_sorted, return_counts)
+class TestSTFT:
+ (
+ data_np,
+ n_fft,
+ hop_length,
+ win_length,
+ window_np,
+ normalized,
+ onesided,
+ ) = tvm.testing.parameters(
+ (
+ np.array([[1, 2, 3, 4, 5, 6]], dtype=np.float32),
+ 3,
+ 3,
+ 3,
+ np.array([4, 3, 2], dtype=np.int32),
+ False,
+ True,
+ ),
+ (
+ np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 5, 7, 8, 5, 6, 7, 3,
2]], dtype=np.float32),
+ 2,
+ 1,
+ 2,
+ np.array([1, 3], dtype=np.int32),
+ False,
+ True,
+ ),
+ (
+ np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 5, 7, 8, 5, 6, 7, 3,
2]], dtype=np.float32),
+ 2,
+ 1,
+ 2,
+ np.array([1, 3], dtype=np.int32),
+ True,
+ True,
+ ),
+ (
+ np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 5, 7, 8, 5, 6, 7, 3,
2]], dtype=np.float32),
+ 2,
+ 1,
+ 2,
+ np.array([1, 3], dtype=np.int32),
+ False,
+ False,
+ ),
+ )
+
+ @tvm.testing.fixture(cache_return_value=True)
+ def ref_res(
+ self,
+ data_np: np.ndarray,
+ n_fft: int,
+ hop_length: int,
+ win_length: int,
+ window_np,
+ normalized,
+ onesided,
+ ):
+ """
+ This function calculates the expected output of segment_sum operator
given the inputs.
+ """
+
+ def pad_window(window_np, n_fft):
+ shape = window_np.shape[-1]
+ lpad = int((n_fft - shape) // 2)
+ lengths = [(0, 0)] * len(window_np.shape)
+ lengths[-1] = (lpad, int(n_fft - shape - lpad))
+ if lpad < 0:
+ print("ERROR Padding")
+ return np.pad(window_np, lengths, mode="constant")
+
+ import math
+
+ if not onesided:
+ n_rows = n_fft
+ else:
+ n_rows = n_fft // 2 + 1
+ if window_np is None:
+ window_np = np.ones(win_length, dtype=np.int32)
+ window_np = pad_window(window_np, n_fft)
+
+ n_cols = (data_np.shape[-1] - n_fft) // hop_length + 1
+ np_result = np.zeros((data_np.shape[0], n_rows, n_cols, 2))
+
+ for batch in range(data_np.shape[0]):
+ for w in range(n_rows):
+ for m in range(n_cols):
+ for k in range(n_fft):
+ np_result[batch][w][m][0] += (
+ window_np[k]
+ * data_np[batch][m * hop_length + k]
+ * math.cos(2 * math.pi * w * k / n_fft)
+ )
+ np_result[batch][w][m][1] -= (
+ window_np[k]
+ * data_np[batch][m * hop_length + k]
+ * math.sin(2 * math.pi * w * k / n_fft)
+ )
+ if normalized:
+ np_result[batch][w][m][0] /= math.sqrt(n_fft)
+ np_result[batch][w][m][1] /= math.sqrt(n_fft)
+ return np_result
+
+ use_dyn = tvm.testing.parameter(True, False, ids=["dyn", "static"])
+
+ @tvm.testing.parametrize_targets("llvm", "cuda")
+ def test_stft(
+ self,
+ target,
+ dev,
+ ref_res: np.ndarray,
+ data_np: np.ndarray,
+ n_fft: int,
+ hop_length: int,
+ win_length: int,
+ window_np: np.ndarray,
+ normalized: bool,
+ onesided: bool,
+ use_dyn,
+ ):
+ if use_dyn:
+ data = relay.var(
+ "data",
+ relay.TensorType([relay.Any(), relay.Any()],
str(data_np.dtype)),
+ )
+ window = relay.var(
+ "window",
+ relay.TensorType([relay.Any()], str(window_np.dtype)),
+ )
+ backends = ["vm"]
+ else:
+ data = relay.var(
+ "data",
+ relay.TensorType(data_np.shape, str(data_np.dtype)),
+ )
+ window = relay.var(
+ "window",
+ relay.TensorType(window_np.shape, str(window_np.dtype)),
+ )
+ backends = ["graph", "vm"]
+
+ z = relay.op.stft(data, n_fft, hop_length, win_length, window,
normalized, onesided)
+ func = relay.Function([data, window], z)
+ verify_func2(
+ target, dev, func, [data_np, window_np], ref_res, rtol=1e-3,
atol=1e-3, kinds=backends
+ )
+
+
+def verify_func2(target, dev, func, data, ref_res, rtol=1e-5, atol=1e-7,
kinds=["vm"]):
Review Comment:
Why do you need this? It looks identical to `verify_func`.
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