1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
|
// Copyright 2019 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "ui/base/prediction/linear_resampling.h"
#include "base/test/scoped_feature_list.h"
#include "testing/gtest/include/gtest/gtest.h"
#include "ui/base/prediction/input_predictor_unittest_helpers.h"
#include "ui/base/ui_base_features.h"
namespace ui {
namespace test {
class LinearResamplingTest : public InputPredictorTest {
public:
explicit LinearResamplingTest() {}
LinearResamplingTest(const LinearResamplingTest&) = delete;
LinearResamplingTest& operator=(const LinearResamplingTest&) = delete;
void SetUp() override { predictor_ = std::make_unique<LinearResampling>(); }
void ValidatePredictorFrameBased(
const std::vector<double>& events_x,
const std::vector<double>& events_y,
const std::vector<double>& events_time_ms,
const std::vector<double>& prediction_time_ms,
const std::vector<double>& predicted_x,
const std::vector<double>& predicted_y,
const double vsync_frequency) {
// LinearResampling* predictor =
// dynamic_cast<LinearResampling*>(predictor_.get());
base::TimeDelta frame_interval = base::Seconds(1.0f / vsync_frequency);
predictor_->Reset();
std::vector<double> computed_x;
std::vector<double> computed_y;
size_t current_prediction_index = 0;
for (size_t i = 0; i < events_time_ms.size(); i++) {
InputPredictor::InputData data = {gfx::PointF(events_x[i], events_y[i]),
FromMilliseconds(events_time_ms[i])};
predictor_->Update(data);
if (predictor_->HasPrediction()) {
auto result = predictor_->GeneratePrediction(
FromMilliseconds(prediction_time_ms[current_prediction_index]),
frame_interval);
EXPECT_TRUE(result);
computed_x.push_back(result->pos.x());
computed_y.push_back(result->pos.y());
EXPECT_GT(result->time_stamp, base::TimeTicks());
current_prediction_index++;
}
}
EXPECT_TRUE(computed_x.size() == predicted_x.size());
if (computed_x.size() == predicted_x.size()) {
for (size_t i = 0; i < predicted_x.size(); i++) {
EXPECT_NEAR(computed_x[i], predicted_x[i], kEpsilon);
EXPECT_NEAR(computed_y[i], predicted_y[i], kEpsilon);
}
}
}
base::test::ScopedFeatureList feature_list;
};
// Test if the output name of the predictor is taking account of the
// equation order
TEST_F(LinearResamplingTest, GetName) {
EXPECT_EQ(predictor_->GetName(), features::kPredictorNameLinearResampling);
}
// Test that the number of events required to compute a prediction is correct
TEST_F(LinearResamplingTest, ShouldHavePrediction) {
LinearResampling predictor;
EXPECT_FALSE(predictor.HasPrediction());
// 1st event.
predictor.Update(
InputPredictor::InputData({gfx::PointF(0, 0), FromMilliseconds(0)}));
EXPECT_FALSE(predictor.HasPrediction());
// 2nd event.
predictor.Update(
InputPredictor::InputData({gfx::PointF(0, 0), FromMilliseconds(8)}));
EXPECT_TRUE(predictor.HasPrediction());
predictor.Reset();
EXPECT_FALSE(predictor.HasPrediction());
}
TEST_F(LinearResamplingTest, ResampleMinDelta) {
EXPECT_FALSE(predictor_->HasPrediction());
predictor_->Update(
InputPredictor::InputData({gfx::PointF(0, 0), FromMilliseconds(0)}));
predictor_->Update(
InputPredictor::InputData({gfx::PointF(0, 0), FromMilliseconds(1)}));
// No prediction when last_dt < kResampleMinDelta.
EXPECT_FALSE(predictor_->HasPrediction());
// Has prediction when last_dt >= kResampleMinDelta.
predictor_->Update(
InputPredictor::InputData({gfx::PointF(0, 0), FromMilliseconds(3)}));
EXPECT_TRUE(predictor_->HasPrediction());
predictor_->Update(
InputPredictor::InputData({gfx::PointF(0, 0), FromMilliseconds(15)}));
EXPECT_TRUE(predictor_->HasPrediction());
// Predictor is reset when dt > kMaxTimeDelta.
predictor_->Update(
InputPredictor::InputData({gfx::PointF(0, 0), FromMilliseconds(36)}));
EXPECT_FALSE(predictor_->HasPrediction());
}
TEST_F(LinearResamplingTest, ResamplingValue) {
std::vector<double> x = {10, 20, 30};
std::vector<double> y = {5, 25, 35};
std::vector<double> t = {15, 24, 32};
// Resample at frame_time = 33 ms, sample_time = 33-5 = 28ms.
// Resample at frame_time = 41 ms, sample_time = 41-5 = 36ms.
std::vector<double> pred_ts = {33, 41};
std::vector<double> pred_x = {24.44, 35};
std::vector<double> pred_y = {33.89, 40};
ValidatePredictor(x, y, t, pred_ts, pred_x, pred_y);
}
TEST_F(LinearResamplingTest, ResamplingMaxPrediction) {
std::vector<double> x = {10, 20};
std::vector<double> y = {5, 10};
std::vector<double> t = {10, 30};
// Resample at frame_time = 45 ms, with max_prediction =
// kResampleMaxPrediction, sample_time = 30 + 8ms = 38ms.
std::vector<double> pred_ts = {45};
std::vector<double> pred_x = {24};
std::vector<double> pred_y = {12};
ValidatePredictor(x, y, t, pred_ts, pred_x, pred_y);
}
TEST_F(LinearResamplingTest, ResamplingBoundLastDelta) {
std::vector<double> x = {10, 20};
std::vector<double> y = {5, 10};
std::vector<double> t = {10, 14};
// Resample at frame_time = 20 ms, sample time is bounded by 50% of the
// last time delta, result in 14 + 2ms = 16ms.
std::vector<double> pred_ts = {20};
std::vector<double> pred_x = {22.5};
std::vector<double> pred_y = {11.25};
ValidatePredictor(x, y, t, pred_ts, pred_x, pred_y);
}
// Test time interval in first order
TEST_F(LinearResamplingTest, TimeInterval) {
EXPECT_EQ(predictor_->TimeInterval(), kExpectedDefaultTimeInterval);
std::vector<double> x = {10, 20};
std::vector<double> y = {5, 25};
std::vector<double> t = {17, 33};
for (size_t i = 0; i < t.size(); i++) {
predictor_->Update({gfx::PointF(x[i], y[i]), FromMilliseconds(t[i])});
}
EXPECT_EQ(predictor_->TimeInterval(), base::Milliseconds(t[1] - t[0]));
}
// Tests resampling with the experimental latency if +3.3ms instead of
// the default -5ms.
TEST_F(LinearResamplingTest, ResamplingValueWithExperimentalLatencyTimeBased) {
base::FieldTrialParams params;
params["mode"] = ::features::kPredictionTypeTimeBased;
feature_list.Reset();
feature_list.InitAndEnableFeatureWithParameters(
features::kResamplingScrollEventsExperimentalPrediction, params);
std::vector<double> x = {10, 20, 30};
std::vector<double> y = {5, 25, 35};
std::vector<double> t = {15, 24, 32};
// Resample at `frame_time` = 24.7 ms, `sample_time` = 24.7+3.3 = 28ms.
// Resample at `frame_time` = 32.7 ms, `sample_time` = 32.7+3.3 = 36ms.
std::vector<double> pred_ts = {24.7, 32.7};
std::vector<double> pred_x = {24.44, 35};
std::vector<double> pred_y = {33.89, 40};
ValidatePredictor(x, y, t, pred_ts, pred_x, pred_y);
}
// Tests resampling with the experimental latency if +1ms (using switch) instead
// of the default -5ms.
TEST_F(LinearResamplingTest,
ResamplingValueWithExperimentalLatencyTimeBasedSwitch) {
base::FieldTrialParams params;
params["mode"] = ::features::kPredictionTypeTimeBased;
params["latency"] = "1.0";
feature_list.Reset();
feature_list.InitAndEnableFeatureWithParameters(
features::kResamplingScrollEventsExperimentalPrediction, params);
std::vector<double> x = {10, 20, 30};
std::vector<double> y = {5, 25, 35};
std::vector<double> t = {15, 24, 32};
// Resample at `frame_time` = 27 ms, `sample_time` = 27+1 = 28ms.
// Resample at `frame_time` = 35 ms, `sample_time` = 35+1 = 36ms.
std::vector<double> pred_ts = {27, 35};
std::vector<double> pred_x = {24.44, 35};
std::vector<double> pred_y = {33.89, 40};
ValidatePredictor(x, y, t, pred_ts, pred_x, pred_y);
}
// Tests resampling with the experimental latency if +0.5*`frame_interval`
// instead of the default -5ms.
TEST_F(LinearResamplingTest, ResamplingValueWithExperimentalLatencyFrameBased) {
base::FieldTrialParams params;
params["mode"] = ::features::kPredictionTypeFramesBased;
feature_list.Reset();
feature_list.InitAndEnableFeatureWithParameters(
features::kResamplingScrollEventsExperimentalPrediction, params);
std::vector<double> x = {10, 20, 30};
std::vector<double> y = {5, 25, 35};
std::vector<double> t = {15, 24, 32};
// Using 100Hz frequency => `frame_interval` = 10ms
// Resample at `frame_time` = 33 ms, `sample_time` = 28-5+0.5*10 = 28ms.
// Resample at `frame_time` = 41 ms, `sample_time` = 36-5+0.5*10 = 36ms.
std::vector<double> pred_ts = {28, 36};
std::vector<double> pred_x = {24.44, 35};
std::vector<double> pred_y = {33.89, 40};
ValidatePredictorFrameBased(x, y, t, pred_ts, pred_x, pred_y, 100);
}
// Tests resampling with the experimental latency if +0.5*`frame_interval`
// (using switch) instead of the default -5ms.
TEST_F(LinearResamplingTest,
ResamplingValueWithExperimentalLatencyFrameBasedSwitch) {
base::FieldTrialParams params;
params["mode"] = ::features::kPredictionTypeFramesBased;
params["latency"] = "1.0";
feature_list.Reset();
feature_list.InitAndEnableFeatureWithParameters(
features::kResamplingScrollEventsExperimentalPrediction, params);
std::vector<double> x = {10, 20, 30};
std::vector<double> y = {5, 25, 35};
std::vector<double> t = {15, 24, 32};
// Using 200Hz frequency => `frame_interval` = 5ms
// Resample at `frame_time` = 33 ms, `sample_time` = 28-5+1*5 = 28ms.
// Resample at `frame_time` = 41 ms, `sample_time` = 36-5+1*5 = 36ms.
std::vector<double> pred_ts = {28, 36};
std::vector<double> pred_x = {24.44, 35};
std::vector<double> pred_y = {33.89, 40};
ValidatePredictorFrameBased(x, y, t, pred_ts, pred_x, pred_y, 200);
}
} // namespace test
} // namespace ui
|
