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How to use reset_model method in Behave
Best Python code snippet using behave
test_base.py
Source:test_base.py 
...58 pt_outputs = [o.detach().numpy() for o in pt_outputs]59 for l, r in zip(ort_outputs, pt_outputs):60 assert np.allclose(l, r, atol=1e-4, rtol=1e-4, equal_nan=True)61 def test_conv_flatten_relu(self):62 reset_model(13)63 nps = [np.random.randn(1, 3, 224, 224).astype(np.float32)]64 inputs = Input(*nps)65 conv_node = Conv(inputs[0],66 np.random.randn(32, 3, 3, 3).astype(np.float32),67 np.random.randn(32).astype(np.float32))68 flatten_node = Flatten(conv_node)69 relu_node = Relu(flatten_node)70 Output(relu_node)71 self._run(list(zip(inputs, nps)))72 def test_conv_batchnorm_maxpool_flatten_add_relu(self):73 reset_model(13)74 nps = [np.random.randn(1, 3, 224, 224).astype(np.float32)]75 inputs = Input(*nps)76 conv_node = Conv(inputs[0],77 np.random.randn(32, 3, 3, 3).astype(np.float32),78 np.random.randn(32).astype(np.float32))79 bn_node = BatchNormalization(80 conv_node,81 np.ones(32,).astype(np.float32),82 np.zeros(32,).astype(np.float32),83 np.random.randn(32).astype(np.float32),84 np.abs(np.random.randn(32).astype(np.float32)),85 )86 max_pool_node = MaxPool(bn_node,87 kernel_shape=(3, 3),88 strides=(2, 2),89 pads=(0, 0, 1, 1))90 flatten_node = Flatten(max_pool_node, axis=1)91 add_node = Add(flatten_node, np.random.randn(1).astype(np.float32))92 relu_node = Relu(add_node)93 Output(relu_node)94 self._run(list(zip(inputs, nps)))95 def test_abs(self):96 reset_model(13)97 nps = [np.random.randn(1, 10).astype(np.float32)]98 inputs = Input(*nps)99 Output(Abs(inputs[0]))100 self._run(list(zip(inputs, nps)))101 def test_acos(self):102 reset_model(13)103 nps = [np.random.randn(5).astype(np.float32)]104 inputs = Input(*nps)105 Output(Acos(inputs[0]))106 self._run(list(zip(inputs, nps)))107 def test_acosh(self):108 reset_model(13)109 nps = [np.random.randn(5).astype(np.float32)]110 inputs = Input(*nps)111 Output(Acosh(inputs[0]))112 self._run(list(zip(inputs, nps)))113 def test_tanh(self):114 reset_model(13)115 nps = [np.random.randn(5).astype(np.float32)]116 inputs = Input(*nps)117 Output(Tanh(inputs[0]))118 self._run(list(zip(inputs, nps)))119 def test_add(self):120 reset_model(13)121 nps = [np.random.randn(1, 10).astype(np.float32)]122 inputs = Input(*nps)123 Output(Add(inputs[0], np.random.randn(1, 10).astype(np.float32)))124 self._run(list(zip(inputs, nps)))125 def test_sub(self):126 reset_model(13)127 nps = [np.random.randn(1, 10).astype(np.float32)]128 inputs = Input(*nps)129 Output(Sub(inputs[0], np.random.randn(1, 10).astype(np.float32)))130 self._run(list(zip(inputs, nps)))131 def test_and(self):132 reset_model(13)133 nps = [134 np.random.randint(low=0, high=1, size=(5,)).astype(bool),135 np.random.randint(low=0, high=1, size=(5,)).astype(bool)136 ]137 inputs = Input(*nps)138 Output(And(*inputs))139 self._run(list(zip(inputs, nps)))140 def test_argmax(self):141 reset_model(13)142 nps = [np.random.randn(1, 10).astype(np.float32)]143 inputs = Input(*nps)144 Output(ArgMax(inputs, axis=1))145 self._run(list(zip(inputs, nps)))146 def test_argmin(self):147 reset_model(13)148 nps = [np.random.randn(1, 10).astype(np.float32)]149 inputs = Input(*nps)150 Output(ArgMin(inputs, axis=1))151 self._run(list(zip(inputs, nps)))152 def test_asin(self):153 reset_model(13)154 nps = [np.random.randn(5).astype(np.float32)]155 inputs = Input(*nps)156 Output(Asin(inputs[0]))157 self._run(list(zip(inputs, nps)))158 def test_asinh(self):159 reset_model(13)160 nps = [np.random.randn(5).astype(np.float32)]161 inputs = Input(*nps)162 Output(Asinh(inputs[0]))163 self._run(list(zip(inputs, nps)))164 def test_atan(self):165 reset_model(13)166 nps = [np.random.randn(5).astype(np.float32)]167 inputs = Input(*nps)168 Output(Atan(inputs[0]))169 self._run(list(zip(inputs, nps)))170 def test_atanh(self):171 reset_model(13)172 nps = [np.random.randn(5).astype(np.float32)]173 inputs = Input(*nps)174 Output(Atanh(inputs[0]))175 self._run(list(zip(inputs, nps)))176 def test_avg_pool(self):177 reset_model(13)178 nps = [np.random.randn(1, 1, 5, 5).astype(np.float32)]179 inputs = Input(*nps)180 Output(AveragePool(inputs, kernel_shape=(3, 3), pads=(0, 0, 1, 1)))181 self._run(list(zip(inputs, nps)))182 def test_avg_pool_no_pad(self):183 reset_model(13)184 nps = [np.random.randn(1, 1, 6, 6).astype(np.float32)]185 inputs = Input(*nps)186 Output(AveragePool(inputs, kernel_shape=(3, 3)))187 self._run(list(zip(inputs, nps)))188 def test_bitshift_left(self):189 reset_model(13)190 nps = [np.array([1, 2]).astype(np.uint8), np.array([1, 2]).astype(np.uint8)]191 inputs = Input(*nps)192 Output(BitShift(*inputs, direction="LEFT"))193 self._run(list(zip(inputs, nps)))194 def test_bitshift_right(self):195 reset_model(13)196 nps = [np.array([1, 4]).astype(np.uint8), np.array([1, 1]).astype(np.uint8)]197 inputs = Input(*nps)198 Output(BitShift(*inputs, direction="RIGHT"))199 self._run(list(zip(inputs, nps)))200 def test_batch_normalization(self):201 reset_model(13)202 nps = [np.random.randn(1, 32, 3, 3).astype(np.float32)]203 inputs = Input(*nps)204 Output(BatchNormalization(205 inputs[0],206 np.ones(32,).astype(np.float32),207 np.zeros(32,).astype(np.float32),208 np.random.randn(32).astype(np.float32),209 np.abs(np.random.randn(32).astype(np.float32)),210 ),211 output_num=1)212 self._run(list(zip(inputs, nps)))213 def test_cast(self):214 reset_model(13)215 nps = [216 np.random.randn(1, 10).astype(np.float32),217 ]218 inputs = Input(*nps)219 Output(Cast(inputs, to=6))220 self._run(list(zip(inputs, nps)))221 def test_ceil(self):222 reset_model(13)223 nps = [np.random.randn(1, 10).astype(np.float32)]224 inputs = Input(*nps)225 Output(Ceil(inputs[0]))226 self._run(list(zip(inputs, nps)))227 def test_clip(self):228 reset_model(13)229 nps = [230 np.random.randn(1, 5).astype(np.float32),231 np.asarray(0).astype(np.float32)232 ]233 inputs = Input(*nps)234 Output(Clip(*inputs))235 self._run(list(zip(inputs, nps)))236 def test_concat(self):237 reset_model(13)238 nps = [239 np.random.randn(1, 10).astype(np.float32),240 np.random.randn(2, 10).astype(np.float32)241 ]242 inputs = Input(*nps)243 Output(Concat(inputs, axis=0))244 self._run(list(zip(inputs, nps)))245 def test_constant_add(self):246 reset_model(13)247 nps = [248 np.random.randn(1, 10).astype(np.float32),249 ]250 inputs = Input(*nps)251 constant_value = np.random.randn(1, 10).astype(np.float32)252 t = make_tensor("", 1, constant_value.shape, constant_value.flatten())253 Output(Add(inputs, Constant(value=t)))254 self._run(list(zip(inputs, nps)))255 def test_constant_of_shape(self):256 reset_model(13)257 nps = [258 np.array([2, 3]).astype(np.int64),259 ]260 inputs = Input(*nps)261 constant_value = np.random.randn(1).astype(np.float32)262 t = make_tensor("", 1, constant_value.shape, constant_value)263 Output(ConstantOfShape(inputs, value=t))264 self._run(list(zip(inputs, nps)))265 def test_conv(self):266 reset_model(13)267 nps = [np.random.randn(1, 3, 3, 3).astype(np.float32)]268 inputs = Input(*nps)269 Output(270 Conv(271 inputs[0],272 np.random.randn(32, 3, 3, 3).astype(np.float32),273 np.random.randn(32).astype(np.float32),274 ))275 self._run(list(zip(inputs, nps)))276 def test_conv_with_autopad_same(self):277 reset_model(13)278 x = np.array([[[279 [0., 1., 2., 3., 4.], # (1, 1, 5, 5) input tensor280 [5., 6., 7., 8., 9.],281 [10., 11., 12., 13., 14.],282 [15., 16., 17., 18., 19.],283 [20., 21., 22., 23., 24.]284 ]]]).astype(np.float32)285 W = np.array([[[286 [1., 1., 1.], # (1, 1, 3, 3) tensor for convolution weights287 [1., 1., 1.],288 [1., 1., 1.]289 ]]]).astype(np.float32)290 nps = [x]291 inputs = Input(*nps)292 Output(293 Conv(294 inputs[0],295 W,296 auto_pad='SAME_LOWER',297 kernel_shape=[3, 3],298 strides=[2, 2],299 ))300 self._run(list(zip(inputs, nps)))301 def test_conv_transpose(self):302 reset_model(13)303 nps = [np.random.randn(1, 3, 3, 3).astype(np.float32)]304 inputs = Input(*nps)305 Output(306 ConvTranspose(307 inputs[0],308 np.random.randn(3, 32, 3, 3).astype(np.float32),309 np.random.randn(32).astype(np.float32),310 ))311 self._run(list(zip(inputs, nps)))312 def test_conv_transpose_pads(self):313 reset_model(13)314 nps = [np.random.randn(1, 1, 3, 3).astype(np.float32)]315 inputs = Input(*nps)316 Output(317 ConvTranspose(inputs[0],318 np.random.randn(1, 2, 3, 3).astype(np.float32),319 strides=[3, 2],320 pads=[1, 2, 1, 2]))321 self._run(list(zip(inputs, nps)))322 def test_conv_transpose_dilations(self):323 reset_model(13)324 nps = [np.random.randn(1, 1, 3, 3).astype(np.float32)]325 inputs = Input(*nps)326 Output(327 ConvTranspose(328 inputs[0],329 np.random.randn(1, 1, 2, 2).astype(np.float32),330 dilations=[2, 2],331 ))332 self._run(list(zip(inputs, nps)))333 def test_conv_transpose_attributes(self):334 reset_model(13)335 nps = [np.random.randn(1, 1, 3, 3).astype(np.float32)]336 inputs = Input(*nps)337 Output(338 ConvTranspose(339 inputs[0],340 np.random.randn(1, 2, 3, 3).astype(np.float32),341 strides=[3, 2],342 output_shape=[10, 8],343 output_padding=[1, 1],344 ))345 self._run(list(zip(inputs, nps)))346 def test_cos(self):347 reset_model(13)348 nps = [np.random.randn(5).astype(np.float32)]349 inputs = Input(*nps)350 Output(Cos(inputs[0]))351 self._run(list(zip(inputs, nps)))352 def test_cosh(self):353 reset_model(13)354 nps = [np.random.randn(5).astype(np.float32)]355 inputs = Input(*nps)356 Output(Cosh(inputs[0]))357 self._run(list(zip(inputs, nps)))358 def test_flatten(self):359 reset_model(13)360 nps = [np.random.randn(1, 3, 3, 3).astype(np.float32)]361 inputs = Input(*nps)362 Output(Flatten(inputs))363 self._run(list(zip(inputs, nps)))364 def test_gather(self):365 reset_model(13)366 nps = [367 np.random.randn(6, 8, 3).astype(np.float32),368 np.random.randn(*[3, 2]).astype(np.int64),369 ]370 inputs = Input(*nps)371 Output(Gather(*inputs, axis=0))372 self._run(list(zip(inputs, nps)))373 def test_gather_axis_1(self):374 reset_model(13)375 nps = [376 np.random.randn(6, 3, 3).astype(np.float32),377 np.array([[0, 2], [0, 1], [2, 0]]).astype(np.int64),378 ]379 inputs = Input(*nps)380 Output(Gather(*inputs, axis=1))381 self._run(list(zip(inputs, nps)))382 def test_gather_nd(self):383 reset_model(13)384 nps = [385 np.random.randn(6, 8, 3).astype(np.float32),386 np.random.randn(*[3, 2]).astype(np.int64),387 ]388 inputs = Input(*nps)389 Output(GatherND(*inputs, batch_dims=0))390 self._run(list(zip(inputs, nps)))391 @pytest.mark.skip(reason="Not implemented for batch_dims != 0")392 def test_gather_nd_batch_dims_1(self):393 reset_model(13)394 nps = [395 np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]),396 np.array([[1], [0]]).astype(np.int64),397 ]398 inputs = Input(*nps)399 Output(GatherND(*inputs, batch_dims=1))400 self._run(list(zip(inputs, nps)))401 def test_gemm(self):402 reset_model(13)403 nps = [404 np.random.randn(2, 3).astype(np.float32),405 np.random.randn(3, 4).astype(np.float32),406 np.random.randn(2, 4).astype(np.float32),407 ]408 inputs = Input(*nps)409 Output(Gemm(*inputs))410 self._run(list(zip(inputs, nps)))411 def test_global_average_pool(self):412 reset_model(13)413 nps = [np.random.randn(2, 3, 4).astype(np.float32)]414 inputs = Input(*nps)415 Output(GlobalAveragePool(*inputs))416 self._run(list(zip(inputs, nps)))417 def test_mat_mul(self):418 reset_model(13)419 nps = [420 np.random.randn(5, 2, 3).astype(np.float32),421 np.random.randn(5, 3, 2).astype(np.float32)422 ]423 inputs = Input(*nps)424 Output(MatMul(*inputs))425 self._run(list(zip(inputs, nps)))426 def test_max(self):427 reset_model(13)428 nps = [429 np.random.randn(1, 10).astype(np.float32),430 np.random.randn(2, 10).astype(np.float32)431 ]432 inputs = Input(*nps)433 Output(Max(inputs))434 self._run(list(zip(inputs, nps)))435 def test_max_pool(self):436 reset_model(13)437 nps = [np.random.randn(1, 1, 5, 5).astype(np.float32)]438 inputs = Input(*nps)439 Output(MaxPool(inputs, kernel_shape=(3, 3), pads=(0, 0, 1, 1)),440 output_num=1)441 self._run(list(zip(inputs, nps)))442 def test_max_pool_pads(self):443 reset_model(13)444 nps = [np.random.randn(1, 1, 4, 4).astype(np.float32)]445 inputs = Input(*nps)446 Output(MaxPool(inputs, kernel_shape=(3, 3), pads=(1, 1, 1, 1)),447 output_num=1)448 self._run(list(zip(inputs, nps)))449 def test_mul(self):450 reset_model(13)451 nps = [452 np.random.randn(1, 2, 3).astype(np.float32),453 np.random.randn(1, 2, 3).astype(np.float32)454 ]455 inputs = Input(*nps)456 Output(Mul(*inputs))457 self._run(list(zip(inputs, nps)))458 def test_non_max_suppression_center_point_box_format(self):459 reset_model(13)460 boxes = np.array([[[0.5, 0.5, 1.0, 1.0], [0.5, 0.6, 1.0, 1.0],461 [0.5, 0.4, 1.0, 1.0], [0.5, 10.5, 1.0, 1.0],462 [0.5, 10.6, 1.0, 1.0], [0.5, 100.5, 1.0,463 1.0]]]).astype(np.float32)464 scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)465 max_output_boxes_per_class = np.array([3]).astype(np.int64)466 iou_threshold = np.array([0.5]).astype(np.float32)467 score_threshold = np.array([0.0]).astype(np.float32)468 selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0,469 5]]).astype(np.int64)470 inputs = Input(*[boxes, scores])471 Output(NonMaxSuppression(*inputs, max_output_boxes_per_class,472 iou_threshold))473 self._run(list(zip(inputs, [boxes, scores])))474 def test_non_max_suppression_flipped_coordinates(self):475 reset_model(13)476 boxes = np.array([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.1, 1.0, 1.1],477 [0.0, 0.9, 1.0, -0.1], [0.0, 10.0, 1.0, 11.0],478 [1.0, 10.1, 0.0, 11.1], [1.0, 101.0, 0.0,479 100.0]]]).astype(np.float32)480 scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)481 max_output_boxes_per_class = np.array([5]).astype(np.int64)482 iou_threshold = np.array([0.5]).astype(np.float32)483 score_threshold = np.array([0.0]).astype(np.float32)484 selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0,485 5]]).astype(np.int64)486 inputs = Input(*[boxes, scores])487 Output(NonMaxSuppression(*inputs, max_output_boxes_per_class,488 iou_threshold))489 self._run(list(zip(inputs, [boxes, scores])))490 def test_non_max_suppression_identical_boxes(self):491 reset_model(13)492 boxes = np.array([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0],493 [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0],494 [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0],495 [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0],496 [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0,497 1.0]]]).astype(np.float32)498 scores = np.array([[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9,499 0.9]]]).astype(np.float32)500 max_output_boxes_per_class = np.array([3]).astype(np.int64)501 iou_threshold = np.array([0.5]).astype(np.float32)502 score_threshold = np.array([0.0]).astype(np.float32)503 selected_indices = np.array([[0, 0, 0]]).astype(np.int64)504 inputs = Input(*[boxes, scores])505 Output(NonMaxSuppression(*inputs, max_output_boxes_per_class,506 iou_threshold))507 self._run(list(zip(inputs, [boxes, scores])))508 def test_non_max_suppression_limit_output_size(self):509 reset_model(13)510 boxes = np.array([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1],511 [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0],512 [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0,513 101.0]]]).astype(np.float32)514 scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)515 max_output_boxes_per_class = np.array([2]).astype(np.int64)516 iou_threshold = np.array([0.5]).astype(np.float32)517 score_threshold = np.array([0.0]).astype(np.float32)518 selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)519 inputs = Input(*[boxes, scores])520 Output(NonMaxSuppression(*inputs, max_output_boxes_per_class,521 iou_threshold))522 self._run(list(zip(inputs, [boxes, scores])))523 def test_non_max_suppression_single_box(self):524 reset_model(13)525 boxes = np.array([[[0.0, 0.0, 1.0, 1.0]]]).astype(np.float32)526 scores = np.array([[[0.9]]]).astype(np.float32)527 max_output_boxes_per_class = np.array([3]).astype(np.int64)528 iou_threshold = np.array([0.5]).astype(np.float32)529 score_threshold = np.array([0.0]).astype(np.float32)530 inputs = Input(*[boxes, scores])531 Output(NonMaxSuppression(*inputs, max_output_boxes_per_class,532 iou_threshold))533 self._run(list(zip(inputs, [boxes, scores])))534 def test_non_max_suppression_suppress_by_IOU(self):535 reset_model(13)536 boxes = np.array([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1],537 [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0],538 [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0,539 101.0]]]).astype(np.float32)540 scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)541 max_output_boxes_per_class = np.array([3]).astype(np.int64)542 iou_threshold = np.array([0.5]).astype(np.float32)543 score_threshold = np.array([0.0]).astype(np.float32)544 selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0,545 5]]).astype(np.int64)546 inputs = Input(*[boxes, scores])547 Output(NonMaxSuppression(*inputs, max_output_boxes_per_class,548 iou_threshold))549 self._run(list(zip(inputs, [boxes, scores])))550 def test_non_max_suppression_suppress_by_IOU_and_scores(self):551 reset_model(13)552 boxes = np.array([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1],553 [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0],554 [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0,555 101.0]]]).astype(np.float32)556 scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)557 max_output_boxes_per_class = np.array([3]).astype(np.int64)558 iou_threshold = np.array([0.5]).astype(np.float32)559 score_threshold = np.array([0.4]).astype(np.float32)560 selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)561 inputs = Input(*[boxes, scores])562 Output(563 NonMaxSuppression(*inputs, max_output_boxes_per_class, iou_threshold,564 score_threshold))565 self._run(list(zip(inputs, [boxes, scores])))566 def test_non_max_suppression_two_batches(self):567 reset_model(13)568 boxes = np.array([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1],569 [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0],570 [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0]],571 [[0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1],572 [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0],573 [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0,574 101.0]]]).astype(np.float32)575 scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]],576 [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)577 max_output_boxes_per_class = np.array([2]).astype(np.int64)578 iou_threshold = np.array([0.5]).astype(np.float32)579 score_threshold = np.array([0.0]).astype(np.float32)580 selected_indices = np.array([[0, 0, 3], [0, 0, 0], [1, 0, 3],581 [1, 0, 0]]).astype(np.int64)582 inputs = Input(*[boxes, scores])583 Output(NonMaxSuppression(*inputs, max_output_boxes_per_class,584 iou_threshold))585 self._run(list(zip(inputs, [boxes, scores])))586 def test_non_max_suppression_two_classes(self):587 reset_model(13)588 boxes = np.array([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1],589 [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0],590 [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0,591 101.0]]]).astype(np.float32)592 scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3],593 [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)594 max_output_boxes_per_class = np.array([2]).astype(np.int64)595 iou_threshold = np.array([0.5]).astype(np.float32)596 score_threshold = np.array([0.0]).astype(np.float32)597 selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 1, 3],598 [0, 1, 0]]).astype(np.int64)599 inputs = Input(*[boxes, scores])600 Output(NonMaxSuppression(*inputs, max_output_boxes_per_class,601 iou_threshold))602 self._run(list(zip(inputs, [boxes, scores])))603 def test_non_zero(self):604 reset_model(13)605 nps = [np.array([[1, 0], [1, 1]], dtype=bool)]606 inputs = Input(*nps)607 Output(NonZero(*inputs))608 self._run(list(zip(inputs, nps)))609 def test_pad(self):610 reset_model(13)611 nps = [612 np.random.randn(1, 2, 3).astype(np.float32),613 ]614 inputs = Input(*nps)615 Output(Pad(*inputs, np.array([0, 0, 1, 0, 0, 2])))616 self._run(list(zip(inputs, nps)))617 def test_pad_5D(self):618 reset_model(13)619 nps = [620 np.random.randn(1, 2, 3, 4, 5).astype(np.float32),621 ]622 inputs = Input(*nps)623 Output(624 Pad(*inputs, np.array([0, 0, 1, 2, 3, 0, 0, 2, 3, 4]),625 np.array([1.0]).astype(np.float32)))626 self._run(list(zip(inputs, nps)))627 def test_pad_reflect(self):628 reset_model(13)629 nps = [630 np.random.randn(1, 2, 3, 4).astype(np.float32),631 ]632 inputs = Input(*nps)633 Output(634 Pad(*inputs,635 np.array([0, 0, 1, 2, 0, 0, 2, 3]),636 np.array([1.0]).astype(np.float32),637 mode="reflect"))638 self._run(list(zip(inputs, nps)))639 def test_reciprocal(self):640 reset_model(13)641 nps = [np.random.randn(1, 10).astype(np.float32)]642 inputs = Input(*nps)643 Output(Reciprocal(inputs[0]))644 self._run(list(zip(inputs, nps)))645 def test_reduce_prod(self):646 reset_model(13)647 nps = [np.random.randn(1, 2, 3).astype(np.float32)]648 inputs = Input(*nps)649 Output(ReduceProd(inputs[0], axes=np.array((1, 2)).astype(np.int64)))650 self._run(list(zip(inputs, nps)))651 def test_reduce_sum(self):652 reset_model(13)653 nps = [np.random.randn(1, 2, 3).astype(np.float32)]654 inputs = Input(*nps)655 Output(ReduceSum(inputs[0], np.array((1, 2)).astype(np.int64)))656 self._run(list(zip(inputs, nps)))657 def test_relu(self):658 reset_model(13)659 nps = [np.random.randn(1, 5).astype(np.float32)]660 inputs = Input(*nps)661 Output(Relu(inputs))662 self._run(list(zip(inputs, nps)))663 def test_elu(self):664 reset_model(13)665 nps = [np.random.randn(1, 5).astype(np.float32)]666 inputs = Input(*nps)667 Output(Elu(inputs))668 self._run(list(zip(inputs, nps)))669 def test_reshape(self):670 reset_model(13)671 nps = [np.random.randn(4,).astype(np.float32)]672 inputs = Input(*nps)673 Output(Reshape(inputs[0], np.array((2, 2)).astype(np.int64)))674 self._run(list(zip(inputs, nps)))675 def test_resize_scales_nearest(self):676 reset_model(13)677 nps = [678 np.array([[[679 [1, 2, 3, 4],680 [5, 6, 7, 8],681 ]]], dtype=np.float32),682 np.array([], dtype=np.float32),683 ]684 inputs = Input(*nps)685 Output(686 Resize(687 *inputs,688 np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32),689 mode="nearest",690 ))691 self._run(list(zip(inputs, nps)))692 def test_resize_downsample_sizes_linear_pytorch_half_pixel(self):693 reset_model(13)694 nps = [695 np.array([[[696 [1, 2, 3, 4],697 [5, 6, 7, 8],698 [9, 10, 11, 12],699 [13, 14, 15, 16],700 ]]],701 dtype=np.float32),702 np.array([], dtype=np.float32),703 np.array([], dtype=np.float32),704 ]705 inputs = Input(*nps)706 Output(707 Resize(708 *inputs,709 np.array([1, 1, 3, 1], dtype=np.int64),710 mode='linear',711 # Changed from pytorch_half_pixel(1.8.1) to half_pixel(1.9.0) due to712 # https://github.com/pytorch/pytorch/issues/62237713 coordinate_transformation_mode='half_pixel'))714 self._run(list(zip(inputs, nps)))715 def test_resize_pt_nearest(self):716 reset_model(13)717 nps = [718 np.array([[[[1., 2., 0.], [3., 4., 0.], [0., 0., 0.]]]],719 dtype=np.float32),720 np.array([], dtype=np.float32),721 ]722 inputs = Input(*nps)723 Output(724 Resize(725 *inputs,726 np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32),727 mode="nearest",728 ))729 self._run(list(zip(inputs, nps)))730 def test_resize_pt_bilinear(self):731 reset_model(13)732 nps = [733 np.array([[[[1., 2., 0.], [3., 4., 0.], [0., 0., 0.]]]],734 dtype=np.float32),735 np.array([], dtype=np.float32),736 ]737 inputs = Input(*nps)738 Output(739 Resize(740 *inputs,741 np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32),742 mode="linear",743 ))744 self._run(list(zip(inputs, nps)))745 def test_resize_pt_bilinear_align_corners(self):746 reset_model(13)747 nps = [748 np.array([[[[1., 2., 0.], [3., 4., 0.], [0., 0., 0.]]]],749 dtype=np.float32),750 np.array([], dtype=np.float32),751 ]752 inputs = Input(*nps)753 Output(754 Resize(755 *inputs,756 np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32),757 mode="linear",758 coordinate_transformation_mode="align_corners",759 ))760 self._run(list(zip(inputs, nps)))761 def test_roi_align(self):762 reset_model(13)763 nps = [764 np.array(765 [[[766 [767 0.2764,768 0.7150,769 0.1958,770 0.3416,771 0.4638,772 0.0259,773 0.2963,774 0.6518,775 0.4856,776 0.7250,777 ],778 [779 0.9637,780 0.0895,781 0.2919,782 0.6753,783 0.0234,784 0.6132,785 0.8085,786 0.5324,787 0.8992,788 0.4467,789 ],790 [791 0.3265,792 0.8479,793 0.9698,794 0.2471,795 0.9336,796 0.1878,797 0.4766,798 0.4308,799 0.3400,800 0.2162,801 ],802 [803 0.0206,804 0.1720,805 0.2155,806 0.4394,807 0.0653,808 0.3406,809 0.7724,810 0.3921,811 0.2541,812 0.5799,813 ],814 [815 0.4062,816 0.2194,817 0.4473,818 0.4687,819 0.7109,820 0.9327,821 0.9815,822 0.6320,823 0.1728,824 0.6119,825 ],826 [827 0.3097,828 0.1283,829 0.4984,830 0.5068,831 0.4279,832 0.0173,833 0.4388,834 0.0430,835 0.4671,836 0.7119,837 ],838 [839 0.1011,840 0.8477,841 0.4726,842 0.1777,843 0.9923,844 0.4042,845 0.1869,846 0.7795,847 0.9946,848 0.9689,849 ],850 [851 0.1366,852 0.3671,853 0.7011,854 0.6234,855 0.9867,856 0.5585,857 0.6985,858 0.5609,859 0.8788,860 0.9928,861 ],862 [863 0.5697,864 0.8511,865 0.6711,866 0.9406,867 0.8751,868 0.7496,869 0.1650,870 0.1049,871 0.1559,872 0.2514,873 ],874 [875 0.7012,876 0.4056,877 0.7879,878 0.3461,879 0.0415,880 0.2998,881 0.5094,882 0.3727,883 0.5482,884 0.0502,885 ],886 ]]],887 dtype=np.float32,888 )889 ]890 inputs = Input(*nps)891 Output(892 RoiAlign(893 inputs[0],894 np.array([[0, 0, 9, 9], [0, 5, 4, 9], [5, 5, 9, 9]],895 dtype=np.float32),896 np.array([0, 0, 0], dtype=np.int64),897 spatial_scale=1.0,898 output_height=5,899 output_width=5,900 sampling_ratio=2,901 ))902 self._run(list(zip(inputs, nps)))903 def test_scatter_elements_with_axis(self):904 reset_model(13)905 nps = [np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)]906 inputs = Input(*nps)907 Output(908 ScatterElements(inputs[0],909 np.array([[1, 3]], dtype=np.int64),910 np.array([[1.1, 2.1]], dtype=np.float32),911 axis=1))912 self._run(list(zip(inputs, nps)))913 def test_scatter_elements_without_axis(self):914 reset_model(13)915 nps = [np.zeros((3, 3), dtype=np.float32)]916 inputs = Input(*nps)917 Output(918 ScatterElements(919 inputs[0], np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64),920 np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)))921 self._run(list(zip(inputs, nps)))922 def test_scatter_elements_with_negative_axis(self):923 reset_model(13)924 nps = [np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)]925 inputs = Input(*nps)926 Output(927 ScatterElements(inputs[0],928 np.array([[1, 3]], dtype=np.int64),929 np.array([[1.1, 2.1]], dtype=np.float32),930 axis=-1))931 self._run(list(zip(inputs, nps)))932 def test_scatter_elements(self):933 reset_model(13)934 nps = [np.zeros(shape=(10, 7, 7), dtype=np.float32)]935 inputs = Input(*nps)936 Output(937 ScatterElements(inputs[0],938 np.random.randint(low=0, high=5, size=(5, 7, 7)),939 np.random.randn(5, 7, 7).astype(np.float32),940 axis=0))941 self._run(list(zip(inputs, nps)))942 def test_shape(self):943 reset_model(13)944 nps = [np.random.randn(1, 2, 3, 4).astype(np.float32)]945 inputs = Input(*nps)946 Output(Shape(inputs[0]))947 self._run(list(zip(inputs, nps)))948 def test_sigmoid(self):949 reset_model(13)950 nps = [np.random.randn(1, 10).astype(np.float32)]951 inputs = Input(*nps)952 Output(Sigmoid(inputs[0]))953 self._run(list(zip(inputs, nps)))954 def test_slice(self):955 reset_model(13)956 nps = [np.random.randn(5, 5).astype(np.float32)]957 inputs = Input(*nps)958 Output(959 Slice(inputs[0],960 np.array((2, 3)).astype(np.int64),961 np.array((4, 5)).astype(np.int64)))962 self._run(list(zip(inputs, nps)))963 def test_softmax(self):964 reset_model(13)965 nps = [np.random.randn(1, 10).astype(np.float32)]966 inputs = Input(*nps)967 Output(Softmax(inputs[0]))968 self._run(list(zip(inputs, nps)))969 def test_split(self):970 reset_model(13)971 nps = [np.random.randn(1, 10).astype(np.float32)]972 inputs = Input(*nps)973 Output(Split(inputs, split=np.array([2, 8]), axis=1))974 self._run(list(zip(inputs, nps)))975 def test_sqrt(self):976 reset_model(13)977 nps = [np.random.randn(1, 10).astype(np.float32)]978 inputs = Input(*nps)979 Output(Sqrt(inputs[0]))980 self._run(list(zip(inputs, nps)))981 def test_squeeze(self):982 reset_model(13)983 nps = [np.random.randn(1, 10, 1, 1).astype(np.float32)]984 inputs = Input(*nps)985 Output(Squeeze(inputs[0], np.array(([2, 3]))))986 self._run(list(zip(inputs, nps)))987 def test_squeeze_no_axes(self):988 reset_model(13)989 nps = [np.random.randn(1, 10, 1, 1).astype(np.float32)]990 inputs = Input(*nps)991 Output(Squeeze(inputs[0]))992 self._run(list(zip(inputs, nps)))993 def test_topk(self):994 reset_model(13)995 nps = [np.random.randn(1, 2, 3, 4).astype(np.float32)]996 inputs = Input(*nps)997 Output(TopK(inputs[0], np.asarray([3])))998 self._run(list(zip(inputs, nps)))999 def test_topk_attrs(self):1000 reset_model(13)1001 nps = [np.random.randn(1, 1, 5, 1).astype(np.float32)]1002 inputs = Input(*nps)1003 Output(TopK(inputs[0], np.asarray([3]), axis=2, largest=0, sorted=1))1004 self._run(list(zip(inputs, nps)))1005 def test_transpose(self):1006 reset_model(13)1007 nps = [np.random.randn(1, 2, 3, 4).astype(np.float32)]1008 inputs = Input(*nps)1009 Output(Transpose(inputs[0], perm=[0, 2, 3, 1]))1010 self._run(list(zip(inputs, nps)))1011 def test_transpose_no_perm(self):1012 reset_model(13)1013 nps = [np.random.randn(1, 2, 3, 4).astype(np.float32)]1014 inputs = Input(*nps)1015 Output(Transpose(inputs[0]))1016 self._run(list(zip(inputs, nps)))1017 def test_unsqueeze(self):1018 reset_model(13)1019 nps = [np.random.randn(1, 2).astype(np.float32)]1020 inputs = Input(*nps)1021 Output(Unsqueeze(inputs[0], np.array(([2, 3]))))1022 self._run(list(zip(inputs, nps)))1023if __name__ == '__main__':...
model.py
Source:model.py
1import numpy as np2def map_values(data, mapping):3 # maps values of numpy array using a mapping dictionary4 return np.vectorize(mapping.get)(data)5def sign(x):6 # implements np.sign function as sets random choice of 1 or -1 for 0 results7 x_out = np.sign(x)8 if x_out == 0:9 x_out = np.random.choice([-1, 1])10 return x_out11# load data into training and test numpy array, and dictionary into dict()12directory = 'data/'13files = [directory + file for file in ['pa3dictionary.txt', 'pa3test.txt', 'pa3train.txt']]14row_to_word = dict(enumerate(list(np.loadtxt(files[0], dtype='str'))))15train = np.loadtxt(files[1], dtype='int', delimiter=' ')16test = np.loadtxt(files[2], dtype='int', delimiter=' ')17num_features = train.shape[1] - 118# map 2 / 1 labels to -1 / 119mapping = {2: -1, 1: 1}20train[:, -1] = map_values(train[:, -1], mapping)21test[:, -1] = map_values(test[:, -1], mapping)22# simple perceptron class23class SimplePerceptron:24 def __init__(self):25 self.num_features = None26 self.w = None27 def fit(self, labeled_data, reset_model=True):28 # fit model to labeled_data, where29 # labeled_data is 2d array, with columns as features and rows as samples30 # final column is label, must be -1 or 131 X = labeled_data[:, :-1]32 y = labeled_data[:, -1]33 if reset_model:34 self.num_features = X.size[1]35 self.w = np.zeros(self.num_features + 1)36 for i in range(y.size):37 x = np.append(X[i], [1]) # append 1 to the x, for bias term38 yhat = self.predict(x) # estimate class using current weights39 if y[i] != yhat: # if incorrect decision40 self.w = np.add(self.w, np.multiply(y[i], x)) # update weights41 def predict(self, x):42 return sign(np.dot(x, self.w))43 def test(self, labeled_data):44 # test classification of model using labeled_data, where45 # labeled_data is 2d array, with columns as features and rows as samples46 # final column is label, must be -1 or 147 X = labeled_data[:, :-1]48 y = labeled_data[:, -1]49 num_errors = 050 for i in range(y.size):51 x = np.append(X[i], [1]) # append 1 to the x, for bias term52 yhat = self.predict(x) # estimate class53 if y[i] != yhat: # if incorrect decision54 num_errors += 155 return num_errors / y.size56# train model over 5 passes of the training data and report training & test error each time57simple_perceptron = SimplePerceptron()58print('## Simple Perceptron ##')59num_passes = 560for i in range(num_passes):61 print('# Pass ' + str(i+1) + ' #')62 simple_perceptron.fit(train, reset_model=(i == 0)) # fit model and reset_model when i==063 print('Training error: ' + '{:.4f}'.format(simple_perceptron.test(train)))64 print('Testing error: ' + '{:.4f}'.format(simple_perceptron.test(test)))65 print('')66print('')67print('')68# kernelised perceptron class69class KernelisedPerceptron:70 def __init__(self):71 self.num_samples = None72 self.num_features = None73 self.alpha = None74 self.X_trained = None75 self.kernel = None76 def fit(self, labeled_data, reset_model=True, kernel=np.dot):77 # fit model to labeled_data using kernel, where78 # labeled_data is 2d array, with columns as features and rows as samples79 # final column is label, must be -1 or 180 X = labeled_data[:, :-1]81 y = labeled_data[:, -1]82 if reset_model:83 self.kernel = kernel84 self.X_trained = X85 self.num_samples, self.num_features = X.shape86 self.alpha = np.zeros(self.num_samples)87 for j in range(self.num_samples):88 yhat = self.predict(X[j, :])89 if y[j] != yhat:90 self.alpha[j] += y[j]91 def predict(self, x):92 # classify sample x using trained model93 yhat = 094 for i in range(self.num_samples):95 yhat += self.alpha[i] * self.kernel(self.X_trained[i, :], x)96 return sign(yhat)97 def test(self, labeled_data):98 # test classification using model on labeled_data, where99 # labeled_data is 2d array, with columns as features and rows as samples100 # final column is label, must be -1 or 1101 # returns error rate102 num_errors = 0103 X = labeled_data[:, :-1]104 y = labeled_data[:, -1]105 for j in range(self.num_samples):106 yhat = self.predict(X[j, :])107 if y[j] != yhat:108 num_errors += 1109 return num_errors / self.num_samples110# kernel functions as defined in exercise111def kernel_exp(x1, x2):112 return np.exp(-np.linalg.norm(x1-x2) / 20)113def kernel_poly(x1, x2):114 return (np.dot(x1, x2) + 10) ** 2115kernel_perceptron = KernelisedPerceptron()116kernels = [np.dot, kernel_exp, kernel_poly]117messages = ['## Kernalised - dot product ##',118 '## Kernalised - exponential ##',119 '## Kernalised - polynomial ##']120# for each kernel, train model over 5 passes of the training data and report training & test error each time121num_passes = 5122for message, kernel in zip(messages, kernels):123 print(message)124 for i in range(num_passes):125 print('# Pass ' + str(i+1) + ' #')126 kernel_perceptron.fit(train, reset_model=(i == 0), kernel=kernel) # fit model and reset_model when i==0127 print('Training error: ' + '{:.5f}'.format(kernel_perceptron.test(train)))128 print('Testing error: ' + '{:.5f}'.format(kernel_perceptron.test(test)))129 print('')130 print('')...
Find_Best_Learning_Rate.py
Source:Find_Best_Learning_Rate.py
1import os2from tensorflow.python.keras.optimizers import Adam3from tensorflow.python.keras.backend import get_session4def reset_model(model):5 print('Reset model')6 session = get_session()7 for i, layer in enumerate(model.layers):8 if hasattr(layer, 'kernel_initializer'):9 model.layers[i].kernel.initializer.run(session=session)10 return model11class Find_Best_Learning_Rate(object):12 def __init__(self, train_generator=None, validation_generator=None, Model_val=None, epochs=2, learning_rate=0, upper_bound=1, scale=1.1,reset_model=False,13 out_path=os.path.join('.','Learning_rates'),metrics=['accuracy'], optimizer=Adam, loss='categorical_crossentropy',num_workers=10, steps_per_epoch=None):14 self.steps_per_epoch = self.steps_per_epoch15 if steps_per_epoch is None:16 self.steps_per_epoch = len(train_generator)17 self.num_workers = num_workers18 self.reset_model = reset_model19 self.loss = loss20 self.train_generator = train_generator21 self.validation_generator = validation_generator22 self.epochs = epochs23 self.Model_val = Model_val24 self.out_path = out_path25 self.metrics = metrics26 self.optimizer = optimizer27 if not os.path.exists(out_path):28 os.makedirs(out_path)29 self.run_over_learning_rates(learning_rate=learning_rate, upper_bound=upper_bound, scale=scale)30 def run_for_learning_rate(self, learning_rate):31 out_path = os.path.join(self.out_path, str(learning_rate) + '.txt')32 if os.path.exists(out_path):33 return None # Do not redo a run34 if self.reset_model:35 self.Model_val = reset_model(self.Model_val)36 optimizer = self.optimizer(lr=learning_rate)37 self.Model_val.compile(optimizer, loss=self.loss, metrics=self.metrics)38 history = self.Model_val.fit_generator(generator=self.train_generator, workers=self.num_workers, use_multiprocessing=False,39 max_queue_size=200,shuffle=True, epochs=self.epochs, initial_epoch=0,40 validation_data=self.validation_generator, steps_per_epoch=self.steps_per_epoch)41 fid = open(out_path,'w+')42 for key in history.history.keys():43 if key.find('val') == 0: # Only get the validation data44 fid.write(key + ',')45 fid.write(str(history.history[key])+'\n')46 fid.close()47 return None48 def run_over_learning_rates(self, learning_rate, upper_bound, scale):49 while learning_rate < upper_bound:...
test_sentiment_classifier.py
Source:test_sentiment_classifier.py
...15 pos['training']16 ])17 c = 2 ** 718 classifier.c = c19 classifier.reset_model()20 n_er = self.validate(classifier, neg['validation'], 'negative')21 p_er = self.validate(classifier, pos['validation'], 'positive')22 total = Fraction(n_er.numerator + p_er.numerator,23 n_er.denominator + p_er.denominator)24 print(total)25 self.assertLess(total, 0.35)26 def test_validate_itself(self):27 """yields a zero error when it uses itself"""28 classifier = SentimentClassifier.build([29 'data/rt-polaritydata/rt-polarity.neg',30 'data/rt-polaritydata/rt-polarity.pos'31 ])32 c = 2 ** 733 classifier.c = c34 classifier.reset_model()35 n_er = self.validate(classifier,36 'data/rt-polaritydata/rt-polarity.neg',37 'negative')38 p_er = self.validate(classifier,39 'data/rt-polaritydata/rt-polarity.pos',40 'positive')41 total = Fraction(n_er.numerator + p_er.numerator,42 n_er.denominator + p_er.denominator)43 print(total)44 self.assertEqual(total, 0)45 def validate(self, classifier, file, sentiment):46 total = 047 misses = 048 with(io.open(file, errors='ignore')) as f:...
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