| /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 …]
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| /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 __anon795731860111::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 …]
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | base_preprocessing_layer_test.py | 185 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
199 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
213 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
231 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
246 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
261 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
276 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
291 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
294 self.assertAllEqual([[19], [20], [21]], model.predict([1., 2., 3.]))
311 self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.]))
[all …]
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| /external/tensorflow/tensorflow/python/keras/layers/preprocessing/ |
| D | category_encoding_test.py | 67 sp_output_dataset = model.predict(input_array, steps=1)
78 output_dataset = model.predict(input_array, steps=1)
102 output_dataset = model.predict(sparse_tensor_data, steps=1)
127 output_dataset = model.predict([sparse_tensor_data, sparse_weight_data],
152 sp_output_dataset = model.predict(sp_inp, steps=1)
163 output_dataset = model.predict(sp_inp, steps=1)
191 sp_output_dataset = model.predict([sp_inp, sp_weight], steps=1)
214 output_dataset = model.predict(input_array, steps=1)
233 sp_output_dataset = model.predict(input_array, steps=1)
244 output_dataset = model.predict(input_array, steps=1)
[all …]
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| D | text_vectorization_test.py | 476 output_dataset = model.predict(input_array)
494 output_dataset = model.predict(input_array)
514 output_dataset = model.predict(input_array)
532 output_dataset = model.predict(input_array)
551 output_dataset = model.predict(input_array)
575 output_dataset = model.predict(input_array)
599 output_dataset = model.predict(input_array)
621 output_dataset = model.predict(input_array)
655 output_dataset = model.predict(input_array)
712 output_dataset = model.predict(input_array)
[all …]
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| D | integer_lookup_test.py | 156 output_data = model.predict(input_array, steps=1)
172 output_dataset = model.predict(input_array)
202 output_data = model.predict(input_array, steps=1)
218 output_dataset = model.predict(input_array)
264 output_data = model.predict(input_array, steps=1)
280 output_dataset = model.predict(input_array)
313 output_dataset = model.predict(input_array)
332 output_dataset = model.predict(input_array)
347 output_dataset = model.predict(input_array)
362 output_dataset = model.predict(input_array)
[all …]
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| D | reduction_test.py | 65 output = model.predict(data)
108 output = model.predict([data, weights])
123 output = model.predict([data, weights])
157 output = model.predict(data)
200 output = model.predict([data, weights])
216 output = model.predict([data, weights])
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| D | discretization_test.py | 63 output_dataset = model.predict(input_array)
78 output_dataset = model.predict(input_array)
91 output_dataset = model.predict(input_array, steps=1)
108 output_dataset = model.predict(input_array)
123 output_dataset = model.predict(input_array)
136 output_dataset = model.predict(input_array, steps=1)
213 output_data = model.predict(test_data)
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| D | string_lookup_test.py | 153 output_data = model.predict(input_array)
166 output_data = model.predict(input_array)
185 output_data = model.predict(input_array)
198 output_data = model.predict(input_array)
236 output_data = model.predict(input_array)
252 output_data = model.predict(input_array)
276 output_data = model.predict(input_array)
293 output_data = model.predict(input_array)
308 output_data = model.predict(input_array)
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| D | index_lookup_test.py | 398 output_data = model.predict(input_array, steps=1)
424 output_data = model.predict(input_array, steps=1)
445 output_dataset = model.predict(input_array)
464 output_dataset = model.predict(input_array)
483 output_dataset = model.predict(input_array)
513 output_data = model.predict(input_array, steps=1)
539 output_data = model.predict(input_array, steps=1)
560 output_dataset = model.predict(input_array)
579 output_dataset = model.predict(input_array)
641 output_data = model.predict(input_array, steps=1)
[all …]
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | convolutional_recurrent_test.py | 70 state = model.predict(inputs)
112 out1 = model.predict(np.ones_like(inputs))
117 out2 = model.predict(np.ones_like(inputs))
125 out3 = model.predict(np.ones_like(inputs))
130 out4 = model.predict(np.ones_like(inputs))
134 out5 = model.predict(np.ones_like(inputs))
193 reference_outputs = model.predict(test_inputs)
201 outputs = clone.predict(test_inputs)
231 model.predict([x_1, x_2])
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| D | gru_v2_test.py | 119 model.predict(x_train)
185 y_1 = gru_model.predict(x_train)
188 y_2 = gru_model.predict(x_train)
196 y_3 = cudnn_model.predict(x_train)
199 y_4 = cudnn_model.predict(x_train)
233 y_ref = model.predict(x)
238 y = cloned_model.predict(x)
258 y_1 = cpu_model.predict(x_train)
265 y_2 = gpu_model.predict(x_train)
277 y_3 = canonical_model.predict(x_train)
[all …]
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| D | multi_head_attention_test.py | 105 masked_output_data = model.predict([from_data, to_data, mask_data])
109 unmasked_output_data = model.predict([from_data, to_data, null_mask_data])
120 masked_output_data = model.predict([from_data, to_data, to_data, mask_data])
121 unmasked_output_data = model.predict(
166 masked_output_data = model.predict([from_data, to_data, mask_data])
170 unmasked_output_data = model.predict([from_data, to_data, null_mask_data])
181 masked_output_data_score, masked_score = model.predict(
183 unmasked_output_data_score, unmasked_score = model.predict(
225 model.predict([query, value, mask_data]),
226 model.predict([query, value, null_mask_data]))
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| D | gru_test.py | 134 gru_model.predict(x_train)
176 out1 = model.predict(np.ones((num_samples, timesteps)))
182 out2 = model.predict(np.ones((num_samples, timesteps)))
190 out3 = model.predict(np.ones((num_samples, timesteps)))
195 out4 = model.predict(np.ones((num_samples, timesteps)))
199 out5 = model.predict(np.ones((num_samples, timesteps)))
208 out6 = model.predict(left_padded_input)
215 out7 = model.predict(right_padded_input)
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| D | simplernn_test.py | 186 out1 = model.predict(np.ones((num_samples, timesteps)))
192 out2 = model.predict(np.ones((num_samples, timesteps)))
200 out3 = model.predict(np.ones((num_samples, timesteps)))
205 out4 = model.predict(np.ones((num_samples, timesteps)))
209 out5 = model.predict(np.ones((num_samples, timesteps)))
218 out6 = model.predict(left_padded_input)
225 out7 = model.predict(right_padded_input)
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| D | lstm_v2_test.py | 276 state = model.predict(inputs)
294 model.predict(inputs)
357 y_1 = lstm_model.predict(x_train)
360 y_2 = lstm_model.predict(x_train)
366 y_3 = cudnn_model.predict(x_train)
369 y_4 = cudnn_model.predict(x_train)
470 y_ref = lstm_model.predict(x_train)
475 y = lstm_v2_model.predict(x_train)
503 model.predict(x_train)
534 y_ref = model.predict(x)
[all …]
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| D | pooling_test.py | 58 output = model.predict(model_input)
70 output_ragged = model.predict(ragged_data, steps=1)
76 output_dense = model.predict(dense_data, steps=1)
90 output_ragged = model.predict(ragged_data, steps=1)
95 output_dense = model.predict(dense_data, steps=1)
108 output_ragged = model.predict(ragged_data, steps=1)
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| D | recurrent_test.py | 192 y_np = model.predict(x_np)
200 y_np_2 = model.predict(x_np)
218 y_np = model.predict(x_np)
226 y_np_2 = model.predict(x_np)
367 y_np_1 = model.predict(x_np)
380 y_np_2 = model_2.predict(x_np)
404 y_np = model.predict([x_np, c_np])
413 y_np_2 = model.predict([x_np, c_np])
422 y_np_3 = model.predict([x_np, c_np])
460 y_np = model.predict([x_np, c_np])
[all …]
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| D | cudnn_recurrent_test.py | 95 state = model.predict(inputs)
122 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)))
320 self.assertAllClose(model.predict(inputs), cudnn_model.predict(inputs),
398 self.assertAllClose(model.predict(inputs), cudnn_model.predict(inputs),
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| /external/tensorflow/tensorflow/python/keras/integration_test/ |
| D | legacy_rnn_test.py | 70 predict = tf.placeholder(
77 loss = tf.losses.softmax_cross_entropy(predict, state)
82 [train_op, outputs, state], {inputs: x_train, predict: y_train})
103 predict = tf.placeholder(
110 loss = tf.losses.softmax_cross_entropy(predict, state)
115 [train_op, outputs, state], {inputs: x_train, predict: y_train})
136 predict = tf.placeholder(
145 loss = tf.losses.softmax_cross_entropy(predict, state[0])
150 [train_op, outputs, state], {inputs: x_train, predict: y_train})
175 predict = tf.placeholder(
[all …]
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| /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 …]
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| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | composite_tensor_support_test.py | 183 output = model.predict(input_data)
195 output = model.predict(input_data)
230 output = model.predict(input_data)
244 output = model.predict(input_data, batch_size=2)
258 output = model.predict(input_data)
277 output = model.predict(input_data, batch_size=2)
363 result = model.predict(input_data, **kwargs)
389 output = model.predict(input_data, steps=1)
395 output_2 = model.predict(input_data_2, steps=1)
442 output = model.predict(input_data, steps=1)
[all …]
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| /external/tensorflow/tensorflow/python/keras/saving/ |
| D | saved_model_experimental_test.py | 70 ref_y = model.predict(x)
76 y = loaded_model.predict(x)
87 ref_y = model.predict(x)
93 y = loaded_model.predict(x)
111 ref_y = model.predict(x)
117 y = loaded_model.predict(x)
131 ref_y = model.predict(x)
137 y = loaded_model.predict(x)
152 ref_y = model.predict(x)
161 y = loaded_model.predict(x)
[all …]
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| /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
264 probs = self.model.predict(x, **kwargs)
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))
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| /external/tensorflow/tensorflow/python/keras/applications/ |
| D | imagenet_utils_test.py | 91 self.assertEqual(model.predict(x).shape, x.shape)
98 out1 = model1.predict(x)
106 out2 = model2.predict(x2)
116 self.assertEqual(model.predict(x[np.newaxis])[0].shape, x.shape)
123 out1 = model1.predict(x[np.newaxis])[0]
131 out2 = model2.predict(x2[np.newaxis])[0]
|