/dports/misc/tvm/incubator-tvm-0.6.1/tests/python/frontend/mxnet/model_zoo/ |
H A D | __init__.py | 25 num_class = 10 26 return mlp.get_symbol(num_class) 29 num_class = 10 30 return tvm.relay.testing.mlp.get_workload(1, num_class)[0] 34 num_class = 1000 35 return vgg.get_symbol(num_class, num_layers) 38 num_class = 1000 40 1, num_class, num_layers=num_layers)[0] 44 num_class = 1000 48 num_class = 1000 [all …]
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/frontend/mxnet/model_zoo/ |
H A D | __init__.py | 25 num_class = 10 26 return mlp.get_symbol(num_class) 30 num_class = 10 31 return tvm.relay.testing.mlp.get_workload(1, num_class)[0] 36 num_class = 1000 37 return vgg.get_symbol(num_class, num_layers) 41 num_class = 1000 42 return tvm.relay.testing.vgg.get_workload(1, num_class, num_layers=num_layers)[0] 47 num_class = 1000 48 return resnet.get_symbol(num_class, num_layers, "3,224,224") [all …]
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/dports/misc/py-tvm/incubator-tvm-0.6.1/tests/python/frontend/mxnet/model_zoo/ |
H A D | __init__.py | 25 num_class = 10 26 return mlp.get_symbol(num_class) 29 num_class = 10 30 return tvm.relay.testing.mlp.get_workload(1, num_class)[0] 34 num_class = 1000 35 return vgg.get_symbol(num_class, num_layers) 38 num_class = 1000 40 1, num_class, num_layers=num_layers)[0] 44 num_class = 1000 48 num_class = 1000 [all …]
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/dports/misc/py-gluoncv/gluon-cv-0.9.0/gluoncv/model_zoo/rcnn/ |
H A D | rcnn.py | 71 self.num_class = len(classes) 80 assert self.num_class > 0, "Invalid number of class : {}".format(self.num_class) 92 self.num_class + 1, weight_initializer=mx.init.Normal(0.01)) 94 self.num_class * 4, weight_initializer=mx.init.Normal(0.001)) 95 self.cls_decoder = MultiPerClassDecoder(num_class=self.num_class + 1) 169 self.num_class = len(classes) 208 self.num_class + 1, weight_initializer=mx.init.Normal(0.01), 211 self.num_class * 4, weight_initializer=mx.init.Normal(0.001), 213 self.cls_decoder = MultiPerClassDecoder(num_class=self.num_class + 1)
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/dports/misc/py-xgboost/xgboost-1.5.1/src/objective/ |
H A D | multiclass_obj.cu | 32 int num_class; member 35 DMLC_DECLARE_FIELD(num_class).set_lower_bound(1) in DMLC_DECLARE_PARAMETER() 58 CHECK(preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size())) in GetGradient() 61 << info.labels_.Size() * static_cast<size_t>(param_.num_class) << "\n" in GetGradient() 62 << "num_class: " << param_.num_class << "\n" in GetGradient() 65 const int nclass = param_.num_class; in GetGradient() 135 const int nclass = param_.num_class; in Transform()
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/dports/misc/xgboost/xgboost-1.5.1/src/objective/ |
H A D | multiclass_obj.cu | 32 int num_class; member 35 DMLC_DECLARE_FIELD(num_class).set_lower_bound(1) in DMLC_DECLARE_PARAMETER() 58 CHECK(preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size())) in GetGradient() 61 << info.labels_.Size() * static_cast<size_t>(param_.num_class) << "\n" in GetGradient() 62 << "num_class: " << param_.num_class << "\n" in GetGradient() 65 const int nclass = param_.num_class; in GetGradient() 135 const int nclass = param_.num_class; in Transform()
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/dports/misc/py-xgboost/xgboost-1.5.1/R-package/man/ |
H A D | predict.xgb.Booster.Rd | 70 For multiclass classification, either a \code{num_class * nrows(newdata)} vector or 71 a \code{(nrows(newdata), num_class)} dimension matrix is returned, depending on 79 For a multiclass case, a list of \code{num_class} elements is returned, where each element is 88 For a multiclass case, a list of \code{num_class} elements is returned, where each element is 92 normal prediction, the output is a 2-dimension array \code{(num_class, nrow(newdata))}. 162 num_class <- 3 166 objective = "multi:softprob", num_class = num_class) 167 # predict for softmax returns num_class probability numbers per case: 170 # reshape it to a num_class-columns matrix 171 pred <- matrix(pred, ncol=num_class, byrow=TRUE) [all …]
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/dports/misc/xgboost/xgboost-1.5.1/R-package/man/ |
H A D | predict.xgb.Booster.Rd | 70 For multiclass classification, either a \code{num_class * nrows(newdata)} vector or 71 a \code{(nrows(newdata), num_class)} dimension matrix is returned, depending on 79 For a multiclass case, a list of \code{num_class} elements is returned, where each element is 88 For a multiclass case, a list of \code{num_class} elements is returned, where each element is 92 normal prediction, the output is a 2-dimension array \code{(num_class, nrow(newdata))}. 162 num_class <- 3 166 objective = "multi:softprob", num_class = num_class) 167 # predict for softmax returns num_class probability numbers per case: 170 # reshape it to a num_class-columns matrix 171 pred <- matrix(pred, ncol=num_class, byrow=TRUE) [all …]
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/dports/misc/py-xgboost/xgboost-1.5.1/R-package/R/ |
H A D | callbacks.R | 492 if (env$num_class > 1) { 493 matrix(NA_real_, N, env$num_class) 718 num_class <- n - 1 functionVar 719 num_feat <- (length(dmp) - 4) / num_class 724 num_class <- NVL(model$params$num_class, 1) 731 num_class > 1 && 732 (class_index[1] < 0 || class_index[1] >= num_class)) 733 stop("class_index has to be within [0,", num_class - 1, "]") 736 if (!is.null(class_index) && num_class > 1) { 739 function(x) x[, seq(1 + class_index, by = num_class, length.out = num_feat)]) [all …]
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/dports/misc/xgboost/xgboost-1.5.1/R-package/R/ |
H A D | callbacks.R | 492 if (env$num_class > 1) { 493 matrix(NA_real_, N, env$num_class) 718 num_class <- n - 1 functionVar 719 num_feat <- (length(dmp) - 4) / num_class 724 num_class <- NVL(model$params$num_class, 1) 731 num_class > 1 && 732 (class_index[1] < 0 || class_index[1] >= num_class)) 733 stop("class_index has to be within [0,", num_class - 1, "]") 736 if (!is.null(class_index) && num_class > 1) { 739 function(x) x[, seq(1 + class_index, by = num_class, length.out = num_feat)]) [all …]
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/dports/misc/py-gluoncv/gluon-cv-0.9.0/gluoncv/model_zoo/yolo/ |
H A D | yolo_target.py | 24 def __init__(self, num_class, **kwargs): argument 26 self._num_class = num_class 162 def __init__(self, num_class, ignore_iou_thresh, **kwargs): argument 164 self._num_class = num_class 215 def __init__(self, num_class, ignore_iou_thresh, **kwargs): argument 217 self._num_class = num_class 218 self._dynamic_target = YOLOV3DynamicTargetGeneratorSimple(num_class, ignore_iou_thresh)
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/dports/graphics/vapoursynth-waifu2x-ncnn-vulkan/vapoursynth-waifu2x-ncnn-vulkan-r4/deps/ncnn/src/layer/x86/ |
H A D | yolov3detectionoutput_x86.cpp | 52 if (channels_per_box != 4 + 1 + num_class) in forward() 77 Mat scores = bottom_top_blobs.channel_range(p + 5, num_class); in forward() 94 for (int q = 0; q < num_class; q++) in forward() 108 float* end = ptr + num_class * cs; in forward() 111 float* end8 = ptr + (num_class & -8) * cs; in forward()
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/dports/graphics/waifu2x-ncnn-vulkan/waifu2x-ncnn-vulkan-20210521/src/ncnn/src/layer/x86/ |
H A D | yolov3detectionoutput_x86.cpp | 52 if (channels_per_box != 4 + 1 + num_class) in forward() 77 Mat scores = bottom_top_blobs.channel_range(p + 5, num_class); in forward() 94 for (int q = 0; q < num_class; q++) in forward() 108 float* end = ptr + num_class * cs; in forward() 111 float* end8 = ptr + (num_class & -8) * cs; in forward()
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/dports/benchmarks/vkpeak/vkpeak-20210430/ncnn/src/layer/x86/ |
H A D | yolov3detectionoutput_x86.cpp | 52 if (channels_per_box != 4 + 1 + num_class) in forward() 77 Mat scores = bottom_top_blobs.channel_range(p + 5, num_class); in forward() 94 for (int q = 0; q < num_class; q++) in forward() 108 float* end = ptr + num_class * cs; in forward() 111 float* end8 = ptr + (num_class & -8) * cs; in forward()
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/dports/misc/ncnn/ncnn-20211208/src/layer/x86/ |
H A D | yolov3detectionoutput_x86.cpp | 52 if (channels_per_box != 4 + 1 + num_class) in forward() 77 Mat scores = bottom_top_blobs.channel_range(p + 5, num_class); in forward() 94 for (int q = 0; q < num_class; q++) in forward() 108 float* end = ptr + num_class * cs; in forward() 111 float* end8 = ptr + (num_class & -8) * cs; in forward()
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/dports/graphics/realsr-ncnn-vulkan/realsr-ncnn-vulkan-20210210/src/ncnn/src/layer/x86/ |
H A D | yolov3detectionoutput_x86.cpp | 52 if (channels_per_box != 4 + 1 + num_class) in forward() 77 Mat scores = bottom_top_blobs.channel_range(p + 5, num_class); in forward() 94 for (int q = 0; q < num_class; q++) in forward() 108 float* end = ptr + num_class * cs; in forward() 111 float* end8 = ptr + (num_class & -8) * cs; in forward()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/ssd/ |
H A D | evaluate.py | 83 num_class = args.num_class variable 91 assert len(class_names) == num_class 102 evaluate_net(network, args.rec_path, num_class, args.num_batch,
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/ssd/ |
H A D | evaluate.py | 83 num_class = args.num_class variable 91 assert len(class_names) == num_class 102 evaluate_net(network, args.rec_path, num_class, args.num_batch,
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/image-classification/ |
H A D | symbol_resnet-v2.R | 83 resnet <- function(units, num_stage, filter_list, num_class, bottle_neck=TRUE, argument 120 fc1 <- mx.symbol.FullyConnected(data=flat, num_hidden=num_class, name='fc1') 125 get_symbol <- function(num_class, depth=18){ argument 154 num_class=num_class, bottle_neck=bottle_neck,
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/image-classification/ |
H A D | symbol_resnet-v2.R | 83 resnet <- function(units, num_stage, filter_list, num_class, bottle_neck=TRUE, argument 120 fc1 <- mx.symbol.FullyConnected(data=flat, num_hidden=num_class, name='fc1') 125 get_symbol <- function(num_class, depth=18){ argument 154 num_class=num_class, bottle_neck=bottle_neck,
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/dports/misc/py-xgboost/xgboost-1.5.1/R-package/tests/testthat/ |
H A D | test_model_compatibility.R | 40 testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 44 testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0) 48 testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0)
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/dports/misc/xgboost/xgboost-1.5.1/R-package/tests/testthat/ |
H A D | test_model_compatibility.R | 40 testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 44 testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0) 48 testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0)
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/tests/nightly/ |
H A D | test_tlocal_racecondition.py | 44 num_class = len(class_names) variable 63 return train_iter, val_iter, class_names, num_class 65 train_data, test_data, class_names, num_class = get_iterators( variable
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/dports/misc/mxnet/incubator-mxnet-1.9.0/tests/nightly/ |
H A D | test_tlocal_racecondition.py | 44 num_class = len(class_names) variable 63 return train_iter, val_iter, class_names, num_class 65 train_data, test_data, class_names, num_class = get_iterators( variable
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/dports/misc/py-gluoncv/gluon-cv-0.9.0/gluoncv/auto/estimators/image_classification/ |
H A D | image_classification.py | 76 if not self.classes or not self.num_class: 211 if not self.num_class: 214 assert len(self.classes) == self.num_class 246 kwargs = {'ctx': self.ctx, 'pretrained': False, 'classes': self.num_class} 270 new_fc_layer = gluon.nn.Dense(self.num_class, in_units=in_channels) 272 … new_fc_layer = gluon.nn.Conv2D(self.num_class, in_channels=in_channels, kernel_size=1) 288 …lf._cfg.train.teacher is not None and self._cfg.train.hard_weight < 1.0 and self.num_class == 1000: 290 … self.teacher = get_model(teacher_name, pretrained=True, classes=self.num_class, ctx=self.ctx) 346 acc_top5 = mx.metric.TopKAccuracy(min(5, self.num_class)) 392 topK = min(5, self.num_class)
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