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import hypothesis.strategies as st
import numpy as np
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
class TestTopK(serial.SerializedTestCase):
def top_k_ref(self, X, k, flatten_indices, axis=-1):
in_dims = X.shape
out_dims = list(in_dims)
out_dims[axis] = k
out_dims = tuple(out_dims)
if axis == -1:
axis = len(in_dims) - 1
prev_dims = 1
next_dims = 1
for i in range(axis):
prev_dims *= in_dims[i]
for i in range(axis + 1, len(in_dims)):
next_dims *= in_dims[i]
n = in_dims[axis]
X_flat = X.reshape((prev_dims, n, next_dims))
values_ref = np.ndarray(
shape=(prev_dims, k, next_dims), dtype=np.float32)
values_ref.fill(0)
indices_ref = np.ndarray(
shape=(prev_dims, k, next_dims), dtype=np.int64)
indices_ref.fill(-1)
flatten_indices_ref = np.ndarray(
shape=(prev_dims, k, next_dims), dtype=np.int64)
flatten_indices_ref.fill(-1)
for i in range(prev_dims):
for j in range(next_dims):
kv = []
for x in range(n):
val = X_flat[i, x, j]
y = x * next_dims + i * in_dims[axis] * next_dims + j
kv.append((val, x, y))
cnt = 0
for val, x, y in sorted(
kv, key=lambda x: (x[0], -x[1]), reverse=True):
values_ref[i, cnt, j] = val
indices_ref[i, cnt, j] = x
flatten_indices_ref[i, cnt, j] = y
cnt += 1
if cnt >= k or cnt >= n:
break
values_ref = values_ref.reshape(out_dims)
indices_ref = indices_ref.reshape(out_dims)
flatten_indices_ref = flatten_indices_ref.flatten()
if flatten_indices:
return (values_ref, indices_ref, flatten_indices_ref)
else:
return (values_ref, indices_ref)
@serial.given(
X=hu.tensor(),
flatten_indices=st.booleans(),
seed=st.integers(0, 10),
**hu.gcs
)
def test_top_k(self, X, flatten_indices, seed, gc, dc):
X = X.astype(dtype=np.float32)
np.random.seed(seed)
# `k` can be larger than the total size
k = np.random.randint(1, X.shape[-1] + 4)
output_list = ["Values", "Indices"]
if flatten_indices:
output_list.append("FlattenIndices")
op = core.CreateOperator("TopK", ["X"], output_list,
k=k, device_option=gc)
def bind_ref(X_loc):
return self.top_k_ref(X_loc, k, flatten_indices)
self.assertReferenceChecks(gc, op, [X], bind_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(bs=st.integers(1, 3), n=st.integers(1, 1), k=st.integers(1, 1),
flatten_indices=st.booleans(), **hu.gcs)
def test_top_k_1(self, bs, n, k, flatten_indices, gc, dc):
X = np.random.rand(bs, n).astype(dtype=np.float32)
output_list = ["Values", "Indices"]
if flatten_indices:
output_list.append("FlattenIndices")
op = core.CreateOperator("TopK", ["X"], output_list,
k=k, device_option=gc)
def bind_ref(X_loc):
return self.top_k_ref(X_loc, k, flatten_indices)
self.assertReferenceChecks(gc, op, [X], bind_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(bs=st.integers(1, 3), n=st.integers(1, 10000), k=st.integers(1, 1),
flatten_indices=st.booleans(), **hu.gcs)
def test_top_k_2(self, bs, n, k, flatten_indices, gc, dc):
X = np.random.rand(bs, n).astype(dtype=np.float32)
output_list = ["Values", "Indices"]
if flatten_indices:
output_list.append("FlattenIndices")
op = core.CreateOperator("TopK", ["X"], output_list,
k=k, device_option=gc)
def bind_ref(X_loc):
return self.top_k_ref(X_loc, k, flatten_indices)
self.assertReferenceChecks(gc, op, [X], bind_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(bs=st.integers(1, 3), n=st.integers(1, 10000),
k=st.integers(1, 1024), flatten_indices=st.booleans(), **hu.gcs)
def test_top_k_3(self, bs, n, k, flatten_indices, gc, dc):
X = np.random.rand(bs, n).astype(dtype=np.float32)
output_list = ["Values", "Indices"]
if flatten_indices:
output_list.append("FlattenIndices")
op = core.CreateOperator("TopK", ["X"], output_list,
k=k, device_option=gc)
def bind_ref(X_loc):
return self.top_k_ref(X_loc, k, flatten_indices)
self.assertReferenceChecks(gc, op, [X], bind_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(bs=st.integers(1, 3), n=st.integers(100, 10000),
flatten_indices=st.booleans(), **hu.gcs)
@settings(deadline=10000)
def test_top_k_4(self, bs, n, flatten_indices, gc, dc):
k = np.random.randint(n // 3, 3 * n // 4)
X = np.random.rand(bs, n).astype(dtype=np.float32)
output_list = ["Values", "Indices"]
if flatten_indices:
output_list.append("FlattenIndices")
op = core.CreateOperator("TopK", ["X"], output_list,
k=k, device_option=gc)
def bind_ref(X_loc):
return self.top_k_ref(X_loc, k, flatten_indices)
self.assertReferenceChecks(gc, op, [X], bind_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(bs=st.integers(1, 3), n=st.integers(1, 1024),
flatten_indices=st.booleans(), **hu.gcs)
def test_top_k_5(self, bs, n, flatten_indices, gc, dc):
k = n
X = np.random.rand(bs, n).astype(dtype=np.float32)
output_list = ["Values", "Indices"]
if flatten_indices:
output_list.append("FlattenIndices")
op = core.CreateOperator("TopK", ["X"], output_list,
k=k, device_option=gc)
def bind_ref(X_loc):
return self.top_k_ref(X_loc, k, flatten_indices)
self.assertReferenceChecks(gc, op, [X], bind_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(bs=st.integers(1, 3), n=st.integers(1, 5000),
flatten_indices=st.booleans(), **hu.gcs)
@settings(deadline=10000)
def test_top_k_6(self, bs, n, flatten_indices, gc, dc):
k = n
X = np.random.rand(bs, n).astype(dtype=np.float32)
output_list = ["Values", "Indices"]
if flatten_indices:
output_list.append("FlattenIndices")
op = core.CreateOperator("TopK", ["X"], output_list,
k=k, device_option=gc)
def bind_ref(X_loc):
return self.top_k_ref(X_loc, k, flatten_indices)
self.assertReferenceChecks(gc, op, [X], bind_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(X=hu.tensor(dtype=np.float32), k=st.integers(1, 5),
axis=st.integers(-1, 5), flatten_indices=st.booleans(),
**hu.gcs)
def test_top_k_axis(self, X, k, axis, flatten_indices, gc, dc):
dims = X.shape
if axis >= len(dims):
axis %= len(dims)
output_list = ["Values", "Indices"]
if flatten_indices:
output_list.append("FlattenIndices")
op = core.CreateOperator(
"TopK", ["X"], output_list, k=k, axis=axis, device_option=gc)
def bind_ref(X_loc):
return self.top_k_ref(X_loc, k, flatten_indices, axis)
self.assertReferenceChecks(gc, op, [X], bind_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(X=hu.tensor(dtype=np.float32), k=st.integers(1, 5),
axis=st.integers(-1, 5), **hu.gcs)
@settings(deadline=10000)
def test_top_k_grad(self, X, k, axis, gc, dc):
dims = X.shape
if axis >= len(dims):
axis %= len(dims)
input_axis = len(dims) - 1 if axis == -1 else axis
prev_dims = 1
next_dims = 1
for i in range(input_axis):
prev_dims *= dims[i]
for i in range(input_axis + 1, len(dims)):
next_dims *= dims[i]
X_flat = X.reshape((prev_dims, dims[input_axis], next_dims))
for i in range(prev_dims):
for j in range(next_dims):
# this try to make sure adding stepsize (0.05)
# will not change TopK selections at all
X_flat[i, :, j] = np.arange(dims[axis], dtype=np.float32) / 5
np.random.shuffle(X_flat[i, :, j])
X = X_flat.reshape(dims)
op = core.CreateOperator(
"TopK", ["X"], ["Values", "Indices"], k=k, axis=axis,
device_option=gc)
self.assertGradientChecks(gc, op, [X], 0, [0], stepsize=0.05)
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