| /external/libvpx/libvpx/vp9/common/ |
| D | vp9_scale.c | 82 sf->predict[0][0][0] = vpx_convolve_copy;
83 sf->predict[0][0][1] = vpx_convolve_avg;
84 sf->predict[0][1][0] = vpx_convolve8_vert;
85 sf->predict[0][1][1] = vpx_convolve8_avg_vert;
86 sf->predict[1][0][0] = vpx_convolve8_horiz;
87 sf->predict[1][0][1] = vpx_convolve8_avg_horiz;
90 sf->predict[0][0][0] = vpx_scaled_vert;
91 sf->predict[0][0][1] = vpx_scaled_avg_vert;
92 sf->predict[0][1][0] = vpx_scaled_vert;
93 sf->predict[0][1][1] = vpx_scaled_avg_vert;
[all …]
|
| /external/libvpx/libvpx/test/ |
| D | idct_test.cc | 36 predict = new Buffer<uint8_t>(4, 4, 3); in SetUp()
37 ASSERT_TRUE(predict != NULL); in SetUp()
38 ASSERT_TRUE(predict->Init()); in SetUp()
46 delete predict; in TearDown()
53 Buffer<uint8_t> *predict; member in __anonbe1d992a0111::IDCTTest
60 predict->Set(0); in TEST_P()
63 ASM_REGISTER_STATE_CHECK(UUT(input->TopLeftPixel(), predict->TopLeftPixel(), in TEST_P()
64 predict->stride(), output->TopLeftPixel(), in TEST_P()
78 predict->Set(0); in TEST_P()
81 ASM_REGISTER_STATE_CHECK(UUT(input->TopLeftPixel(), predict->TopLeftPixel(), in TEST_P()
[all …]
|
| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | base_preprocessing_layer_test.py | 168 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
183 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
198 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
217 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
233 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
249 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
265 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
281 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
284 self.assertAllEqual([[19], [20], [21]], model.predict([1., 2., 3.]))
302 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
[all …]
|
| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | convolutional_recurrent_test.py | 71 state = model.predict(inputs)
113 out1 = model.predict(np.ones_like(inputs))
118 out2 = model.predict(np.ones_like(inputs))
126 out3 = model.predict(np.ones_like(inputs))
131 out4 = model.predict(np.ones_like(inputs))
135 out5 = model.predict(np.ones_like(inputs))
194 reference_outputs = model.predict(test_inputs)
202 outputs = clone.predict(test_inputs)
|
| D | gru_v2_test.py | 116 model.predict(x_train)
178 y_1 = gru_model.predict(x_train)
181 y_2 = gru_model.predict(x_train)
189 y_3 = cudnn_model.predict(x_train)
192 y_4 = cudnn_model.predict(x_train)
226 y_ref = model.predict(x)
231 y = cloned_model.predict(x)
251 y_1 = cpu_model.predict(x_train)
258 y_2 = gpu_model.predict(x_train)
270 y_3 = canonical_model.predict(x_train)
[all …]
|
| D | lstm_v2_test.py | 272 state = model.predict(inputs)
290 model.predict(inputs)
350 y_1 = lstm_model.predict(x_train)
353 y_2 = lstm_model.predict(x_train)
359 y_3 = cudnn_model.predict(x_train)
362 y_4 = cudnn_model.predict(x_train)
457 y_ref = lstm_model.predict(x_train)
462 y = lstm_v2_model.predict(x_train)
490 model.predict(x_train)
521 y_ref = model.predict(x)
[all …]
|
| D | gru_test.py | 132 gru_model.predict(x_train)
172 out1 = model.predict(np.ones((num_samples, timesteps)))
178 out2 = model.predict(np.ones((num_samples, timesteps)))
186 out3 = model.predict(np.ones((num_samples, timesteps)))
191 out4 = model.predict(np.ones((num_samples, timesteps)))
195 out5 = model.predict(np.ones((num_samples, timesteps)))
204 out6 = model.predict(left_padded_input)
211 out7 = model.predict(right_padded_input)
|
| D | simplernn_test.py | 179 out1 = model.predict(np.ones((num_samples, timesteps)))
185 out2 = model.predict(np.ones((num_samples, timesteps)))
193 out3 = model.predict(np.ones((num_samples, timesteps)))
198 out4 = model.predict(np.ones((num_samples, timesteps)))
202 out5 = model.predict(np.ones((num_samples, timesteps)))
211 out6 = model.predict(left_padded_input)
218 out7 = model.predict(right_padded_input)
|
| D | pooling_test.py | 56 output = model.predict(model_input)
69 output_ragged = model.predict(ragged_data, steps=1)
75 output_dense = model.predict(dense_data, steps=1)
89 output_ragged = model.predict(ragged_data, steps=1)
94 output_dense = model.predict(dense_data, steps=1)
107 output_ragged = model.predict(ragged_data, steps=1)
|
| D | cudnn_recurrent_test.py | 94 state = model.predict(inputs)
121 out = model.predict(np.ones((num_samples, timesteps, input_size)))
221 out1 = model.predict(np.ones((num_samples, timesteps)))
227 out2 = model.predict(np.ones((num_samples, timesteps)))
235 out3 = model.predict(np.ones((num_samples, timesteps)))
240 out4 = model.predict(np.ones((num_samples, timesteps)))
244 out5 = model.predict(np.ones((num_samples, timesteps)))
319 self.assertAllClose(model.predict(inputs), cudnn_model.predict(inputs),
397 self.assertAllClose(model.predict(inputs), cudnn_model.predict(inputs),
|
| D | merge_test.py | 52 out = model.predict([x1, x2, x3])
85 out = model.predict([x1, x2])
120 out = model.predict([x1, x2, x3])
135 out = model.predict([x1, x2])
150 out = model.predict([x1, x2])
165 out = model.predict([x1, x2])
181 out = model.predict([x1, x2])
214 out = model.predict([x1, x2])
227 out = model.predict([x1, x2])
249 out_ragged = model.predict([ragged_data, ragged_data], steps=1)
[all …]
|
| D | recurrent_test.py | 198 y_np = model.predict(x_np)
206 y_np_2 = model.predict(x_np)
225 y_np = model.predict(x_np)
233 y_np_2 = model.predict(x_np)
380 y_np_1 = model.predict(x_np)
393 y_np_2 = model_2.predict(x_np)
418 y_np = model.predict([x_np, c_np])
427 y_np_2 = model.predict([x_np, c_np])
436 y_np_3 = model.predict([x_np, c_np])
476 y_np = model.predict([x_np, c_np])
[all …]
|
| D | lstm_test.py | 311 state = model.predict(inputs)
328 outputs = model.predict(inputs)
405 out1 = model.predict(np.ones((num_samples, timesteps)))
411 out2 = model.predict(np.ones((num_samples, timesteps)))
419 out3 = model.predict(np.ones((num_samples, timesteps)))
424 out4 = model.predict(np.ones((num_samples, timesteps)))
428 out5 = model.predict(np.ones((num_samples, timesteps)))
437 out6 = model.predict(left_padded_input)
444 out7 = model.predict(right_padded_input)
|
| D | wrappers_test.py | 186 y = model.predict(np.random.random((10, 3, 2)))
376 output_with_mask = model_1.predict(data, steps=1)
385 output = model_2.predict(data, steps=1)
411 output_ragged = model_1.predict(ragged_data, steps=1)
421 output_dense = model_2.predict(dense_data, steps=1)
443 output_ragged = model_1.predict(ragged_data, steps=1)
450 output_dense = model_2.predict(dense_data, steps=1)
517 y_ref = model.predict(x)
520 y = model.predict(x)
576 y_1 = model.predict(x)
[all …]
|
| /external/webp/src/enc/ |
| D | predictor_enc.c | 150 static uint8_t NearLosslessComponent(uint8_t value, uint8_t predict, in NearLosslessComponent() argument
152 const int residual = (value - predict) & 0xff; in NearLosslessComponent()
153 const int boundary_residual = (boundary - predict) & 0xff; in NearLosslessComponent()
189 static uint32_t NearLossless(uint32_t value, uint32_t predict, in NearLossless() argument
197 return VP8LSubPixels(value, predict); in NearLossless()
205 a = NearLosslessDiff((value >> 24) & 0xff, (predict >> 24) & 0xff); in NearLossless()
207 a = NearLosslessComponent(value >> 24, predict >> 24, 0xff, quantization); in NearLossless()
209 g = NearLosslessComponent((value >> 8) & 0xff, (predict >> 8) & 0xff, 0xff, in NearLossless()
214 new_green = ((predict >> 8) + g) & 0xff; in NearLossless()
221 (predict >> 16) & 0xff, 0xff - new_green, in NearLossless()
[all …]
|
| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | composite_tensor_support_test.py | 185 output = model.predict(input_data)
197 output = model.predict(input_data)
233 output = model.predict(input_data)
248 output = model.predict(input_data, batch_size=2)
263 output = model.predict(input_data)
283 output = model.predict(input_data, batch_size=2)
369 result = model.predict(input_data, **kwargs)
395 output = model.predict(input_data, steps=1)
401 output_2 = model.predict(input_data_2, steps=1)
449 output = model.predict(input_data, steps=1)
[all …]
|
| /external/tensorflow/tensorflow/python/keras/layers/preprocessing/ |
| D | text_vectorization_test.py | 321 output_dataset = model.predict(input_array)
341 output_dataset = model.predict(input_array)
359 output_dataset = model.predict(input_array)
378 output_dataset = model.predict(input_array)
402 output_dataset = model.predict(input_array)
424 output_dataset = model.predict(input_array)
458 output_dataset = model.predict(input_array)
497 output = model.predict(input_array)
511 output = model.predict(input_array)
536 output_dataset = model.predict(input_array)
[all …]
|
| D | categorical_encoding_test.py | 67 output_dataset = model.predict(input_array)
88 output_dataset = model.predict(input_array)
108 output_dataset = model.predict(input_array)
129 output_dataset = model.predict(input_array)
153 output_dataset = model.predict(input_array)
178 output_dataset = model.predict(input_array)
208 output_dataset = model.predict(input_array)
230 output_dataset = model.predict(input_array)
252 output_dataset = model.predict(input_array)
334 _ = model.predict(input_array)
|
| /external/tensorflow/tensorflow/python/keras/saving/saved_model/ |
| D | saved_model_test.py | 252 model.predict(np.random.random((1, 3)))
282 expected_predict = model.predict(input_arr)
290 actual_predict = loaded.predict(input_arr)
297 predict = loaded.predict(input_arr)
304 self.assertAllClose(predict, model.predict(input_arr))
402 def predict(inputs): function
418 'predict': predict,
421 'predict': predict,
427 model.predict(input_arr),
439 self.assertAllClose(model.predict(input_arr), outputs['predictions'])
[all …]
|
| /external/tensorflow/tensorflow/python/keras/saving/ |
| D | saved_model_experimental_test.py | 75 ref_y = model.predict(x)
81 y = loaded_model.predict(x)
93 ref_y = model.predict(x)
99 y = loaded_model.predict(x)
119 ref_y = model.predict(x)
125 y = loaded_model.predict(x)
140 ref_y = model.predict(x)
146 y = loaded_model.predict(x)
162 ref_y = model.predict(x)
173 y = loaded_model.predict(x)
[all …]
|
| D | hdf5_format_test.py | 70 ref_y = model.predict(x)
73 y = model.predict(x)
83 y = model.predict(x)
233 ref_y = model.predict(x)
241 y = model.predict(x)
275 ref_y = model.predict(x)
281 y = model.predict(x)
411 out = model.predict(x)
416 out2 = new_model.predict(x)
427 out = model.predict(x)
[all …]
|
| /external/tensorflow/tensorflow/python/keras/applications/ |
| D | imagenet_utils_test.py | 86 self.assertEqual(model.predict(x).shape, x.shape)
93 out1 = model1.predict(x)
101 out2 = model2.predict(x2)
111 self.assertEqual(model.predict(x[np.newaxis])[0].shape, x.shape)
118 out1 = model1.predict(x[np.newaxis])[0]
126 out2 = model2.predict(x2[np.newaxis])[0]
|
| /external/tensorflow/tensorflow/python/keras/wrappers/ |
| D | scikit_learn.py | 89 Sequential.fit, Sequential.predict, Sequential.predict_classes,
225 def predict(self, x, **kwargs): member in KerasClassifier
318 def predict(self, x, **kwargs): member in KerasRegressor
332 kwargs = self.filter_sk_params(Sequential.predict, kwargs)
333 return np.squeeze(self.model.predict(x, **kwargs))
|
| /external/tensorflow/tensorflow/python/keras/distribute/ |
| D | distribute_strategy_test.py | 461 model.predict(inputs)
462 model.predict(inputs, batch_size=8)
506 model.predict(inputs)
507 model.predict(inputs, batch_size=8)
576 model.predict(inputs)
577 model.predict(inputs, batch_size=8)
642 outs = model.predict(inputs)
722 predict_ground_truth = cpu_model.predict(inputs)
724 model_with_ds_strategy.predict(inputs, batch_size=4, steps=3),
730 model_with_ds_strategy.predict(inputs, batch_size=4),
[all …]
|
| /external/tensorflow/tensorflow/python/keras/tests/ |
| D | model_subclassing_compiled_test.py | 164 y_ref = model.predict(x)
167 y_new = model.predict(x)
191 y = model.predict(x)
263 model.predict([x1, x2])
287 y_ref_1, y_ref_2 = model.predict([x1, x2])
304 y1, y2 = model.predict([x1, x2])
311 y1, y2 = model.predict([x1, x2])
447 y = model.predict(x)
|