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/third_party/mindspore/tests/st/fl/albert/src/
Dassessment_method.py28 def update(self, logits, labels): argument
31 logits = logits.asnumpy()
32 logit_id = np.argmax(logits, axis=-1)
48 def update(self, logits, labels): argument
50 logits = logits.asnumpy()
51 sorted_index = logits.argsort()
70 def update(self, logits, labels): argument
73 logits = logits.asnumpy()
74 logits = np.argmax(logits, axis=1)
76 self.logits_array = np.concatenate([self.logits_array, logits]).astype(np.bool)
[all …]
/third_party/mindspore/mindspore/nn/loss/
Dloss.py103 def construct(self, logits, labels): argument
117 def construct(self, logits, labels): argument
192 def construct(self, logits, labels): argument
193 _check_is_tensor('logits', logits, self.cls_name)
195 x = self.abs(logits - labels)
257 def construct(self, logits, labels): argument
258 _check_is_tensor('logits', logits, self.cls_name)
260 x = F.square(logits - labels)
309 def construct(self, logits, label): argument
310 rmse_loss = F.sqrt(self.MSELoss(logits, label))
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/third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/
Dcross_entropy_impl.cu24 __global__ void CrossEntropyWithSparseKernel(const T *logits, const S *labels, const size_t batch_s… in CrossEntropyWithSparseKernel() argument
29 T logit = logits[i * class_num + labels[i]]; in CrossEntropyWithSparseKernel()
40 __global__ void LargeBatchCrossEntropyWithSparseKernel(const T *logits, const S *labels, const size… in LargeBatchCrossEntropyWithSparseKernel() argument
45 T logit = logits[index * class_num + labels[index]]; in LargeBatchCrossEntropyWithSparseKernel()
54 __global__ void CrossEntropyGradWithSparseKernel(const T *logits, const S *labels, const size_t bat… in CrossEntropyGradWithSparseKernel() argument
59 grad[j * class_num + i] = (logits[j * class_num + i] - 1) / batch_size; in CrossEntropyGradWithSparseKernel()
61 grad[j * class_num + i] = logits[j * class_num + i] / batch_size; in CrossEntropyGradWithSparseKernel()
68 __global__ void CrossEntropyKernel(const T *logits, const S *labels, const size_t batch_size, const… in CrossEntropyKernel() argument
75 losses[index] -= logf((logits[i] <= 0 ? epsilon : logits[i])) * labels[i]; in CrossEntropyKernel()
76 dlogits[i] = logits[i] - labels[i]; in CrossEntropyKernel()
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Dsparse_cross_entropy_cuda_impl.cu22 __global__ void CalCrossEntropyKernel(const float *logits, T *labels, const int batch_size, const i… in CalCrossEntropyKernel() argument
27 float logit = logits[i * class_num + labels[i]]; in CalCrossEntropyKernel()
41 __global__ void CalCrossEntropyGradKernel(const float *logits, T *labels, const int batch_size, con… in CalCrossEntropyGradKernel() argument
46 grad[i * class_num + j] = (logits[i * class_num + j] - 1) / batch_size; in CalCrossEntropyGradKernel()
48 grad[i * class_num + j] = logits[i * class_num + j] / batch_size; in CalCrossEntropyGradKernel()
56 void CalCrossEntropy(const float *logits, T *labels, const int batch_size, const int class_num, flo… in CalCrossEntropy() argument
58 CalCrossEntropyKernel<<<1, 1, 0, cuda_stream>>>(logits, labels, batch_size, class_num, loss); in CalCrossEntropy()
63 void CalCrossEntropyGrad(const float *logits, T *labels, const int batch_size, const int class_num,… in CalCrossEntropyGrad() argument
65 …CalCrossEntropyGradKernel<<<GET_BLOCKS(class_num), GET_THREADS, 0, cuda_stream>>>(logits, labels, … in CalCrossEntropyGrad()
70 template void CalCrossEntropy<int>(const float *logits, int *labels, const int batch_size, const in…
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Dsigmoid_cross_entropy_with_logits_impl.cu20 __global__ void SigmoidCrossEntropyWithLogitsKernel(const size_t size, const T *logits, const S *la… in SigmoidCrossEntropyWithLogitsKernel() argument
22 const T reverse_factor = static_cast<T>(logits[i] >= 0); in SigmoidCrossEntropyWithLogitsKernel()
24 …log1p(exp(logits[i] - static_cast<T>(2) * reverse_factor * logits[i])) - logits[i] * (labels[i] - … in SigmoidCrossEntropyWithLogitsKernel()
29 void SigmoidCrossEntropyWithLogits(const size_t size, const T *logits, const S *labels, T *outputs, in SigmoidCrossEntropyWithLogits() argument
31 …WithLogitsKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, logits, labels, outputs); in SigmoidCrossEntropyWithLogits()
34 template void SigmoidCrossEntropyWithLogits<float, float>(const size_t size, const float *logits, c…
36 template void SigmoidCrossEntropyWithLogits<double, double>(const size_t size, const double *logits,
Dsigmoid_cross_entropy_with_logits_grad_impl.cu20 __global__ void SigmoidCrossEntropyWithLogitsGradKernel(const size_t size, const T *logits, const S… in SigmoidCrossEntropyWithLogitsGradKernel() argument
23 if (logits[i] >= 0) { in SigmoidCrossEntropyWithLogitsGradKernel()
24 …outputs[i] = (static_cast<T>(1.) / (static_cast<T>(1.) + exp(-logits[i])) - labels[i]) * dout_addr… in SigmoidCrossEntropyWithLogitsGradKernel()
26 const T exp_val = exp(logits[i]); in SigmoidCrossEntropyWithLogitsGradKernel()
33 void SigmoidCrossEntropyWithLogitsGrad(const size_t size, const T *logits, const S *labels, const T… in SigmoidCrossEntropyWithLogitsGrad() argument
35 …ntropyWithLogitsGradKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, logits, labels, in SigmoidCrossEntropyWithLogitsGrad()
39 …mplate void SigmoidCrossEntropyWithLogitsGrad<float, float>(const size_t size, const float *logits,
42 …ate void SigmoidCrossEntropyWithLogitsGrad<double, double>(const size_t size, const double *logits,
/third_party/mindspore/tests/st/ops/graph_kernel/
Dtest_sigmoid_cross_entropy_with_logits.py31 def construct(self, logits, labels): argument
32 return self.loss(logits, labels)
40 def construct(self, logits, labels, dout): argument
41 return self.sigmoid_cross_entropy_with_logits_grad(logits, labels, dout)
48 logits = Tensor(np.array([[1, 1, 2],
60 result_open_gk = sigmoid_cross_entropy_with_logits(logits, labels)
65 result_close_gk = sigmoid_cross_entropy_with_logits_beta(logits, labels)
74 logits = Tensor(np.array([[1, 1, 2],
87 result_open_gk = sigmoid_cross_entropy_with_logits_grad(logits, labels, dout)
92 result_close_gk = sigmoid_cross_entropy_with_logits_grad_beta(logits, labels, dout)
Dtest_softmax_cross_entropy_with_logits.py30 def construct(self, logits, labels): argument
31 return self.loss(logits, labels)
38 logits = Tensor(np.array([[1, 1, 10],
48 result_open_gk = softmax_cross_entropy_with_logits(logits, labels)
53 result_close_gk = softmax_cross_entropy_with_logits_beta(logits, labels)
/third_party/mindspore/mindspore/parallel/nn/
Dloss.py94 def construct(self, logits, label, input_mask): argument
95 self._check_input(logits, label, input_mask)
98 logits = F.cast(logits, mstype.float32)
100 _, logit_max = self.max(logits)
101 logit_sub = self.sub(logits, logit_max)
111 one_hot_label = self.onehot(label, F.shape(logits)[-1], self.on_value,
127 def _check_input(self, logits, label, input_mask): argument
129 _check_is_tensor('logits', logits, self.cls_name)
132 … _check_input_dtype(F.dtype(logits), "logits", [mstype.float32, mstype.float16], self.cls_name)
135 _check_input_shape(F.shape(logits), "logits", self.cls_name, 2)
/third_party/mindspore/mindspore/_extends/graph_kernel/expanders/
Dsigmoid_cross_entropy_with_logits.py24 logits, labels = self.inputs
30 const_one = graph_builder.value(logits.dtype, 1.0)
31 const_zero = graph_builder.value(logits.dtype, 0.0)
32 max_logits = graph_builder.emit('Maximum', [logits, const_zero])
33 logits_mul_labels = graph_builder.emit('Mul', [logits, labels])
34 abs_logits = graph_builder.emit('Abs', [logits])
Dsoftmax_cross_entropy_with_logits.py25 logits, label = self.inputs
29 … max_x = graph_builder.emit('ReduceMax', [logits], attrs={'reduce_axis': axis, 'keep_dims': True})
30 data_sub = graph_builder.emit('Sub', [logits, max_x])
34 const_eps = graph_builder.value(logits.dtype, 0.000001)
/third_party/mindspore/tests/st/networks/models/deeplabv3/src/
Ddeeplabv3.py389 logits = ()
393 logits = self.deeplabv3(x)
396 logits = self.deeplabv3(x1)
397 logits = self.sample_common(logits)
398 logits = self.expand_dims(logits, 2)
404 logits = self.concat((logits, logits_i))
405 logits = self.reduce_mean(logits, 2)
406 return logits
410 logits = self.deeplabv3(x1)
411 logits = self.sample_common(logits)
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Dlosses.py44 def construct(self, logits, labels): argument
45 logits = self.transpose(logits, (0, 2, 3, 1))
46 logits = self.reshape(logits, (-1, self.num))
50 losses = self.cross_entropy(logits, one_hot_labels)[0]
/third_party/mindspore/mindspore/lite/src/runtime/kernel/arm/fp32_grad/
Dsigmoid_cross_entropy_with_logits.cc39 auto logits = reinterpret_cast<float *>(in_tensors_.at(0)->MutableData()); in Execute() local
40 CHECK_NULL_RETURN(logits); in Execute()
52 if (logits[i] >= zero) { in Execute()
53 out[i] = log1pf(exp(logits[i] - two * logits[i])) - logits[i] * (labels[i] - one); in Execute()
55 out[i] = log1pf(exp(logits[i])) - logits[i] * labels[i]; in Execute()
/third_party/mindspore/tests/st/ops/gpu/
Dtest_softmax_cross_entropy_with_logits_op.py29 def construct(self, logits, labels): argument
30 return self.loss(logits, labels)
37 logits = Tensor(np.array([[1, 1, 10],
47 output = softmax_cross_entropy_with_logits(logits, labels)
54 output = softmax_cross_entropy_with_logits(logits, labels)
Dtest_sparse_softmax_cross_entropy_with_logits_op.py28 def construct(self, logits, labels): argument
29 return self.loss(logits, labels)
36 logits = Tensor(np.array([[1, 1, 10],
44 output = sparse_softmax_cross_entropy_with_logits(logits, labels)
51 output = sparse_softmax_cross_entropy_with_logits(logits, labels)
Dtest_sigmoid_cross_entropy_with_logits_op.py30 def construct(self, logits, labels): argument
31 return self.loss(logits, labels)
35 logits = Tensor(np.array([[1, 1, 2],
49 output = net(logits, labels)
55 output = net(logits, labels)
Dtest_sigmoid_cross_entropy_with_logits_grad_op.py30 def construct(self, logits, labels, dout): argument
31 return self.sigmoid_cross_entropy_with_logits_grad(logits, labels, dout)
35 logits = Tensor(np.array([[1, 1, 2],
51 output = net(logits, labels, dout)
57 output = net(logits, labels, dout)
/third_party/mindspore/tests/st/ops/cpu/
Dtest_softmax_with_cross_entropy_op.py32 logits = Tensor(np.array([[1, 1, 10],
35 self.logits = Parameter(initializer(logits, logits.shape), name='logits')
41 return self.SoftmaxWithCrossEntropy(self.logits, self.labels)
Dtest_softmax_cross_entropy_with_logits_op.py29 def construct(self, logits, labels): argument
30 return self.loss(logits, labels)
37 logits = Tensor(np.array([[1, 1, 10],
47 output = softmax_cross_entropy_with_logits(logits, labels)
Dtest_sigmoid_cross_entropy_with_logits_op.py30 def construct(self, logits, labels): argument
31 return self.loss(logits, labels)
38 logits = Tensor(np.array([[1, 1, 2],
52 output = sigmoid_cross_entropy_with_logits(logits, labels)
/third_party/mindspore/tests/ut/python/pynative_mode/vm/
Dtest_vm.py249 logits = 2.84806275 * np.ones([1, 10]).astype(np.float32)
250 y = vm.softmax(logits)
254 logits = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
255 y = vm.softmax(logits, axis=1)
263 …logits = np.array([[1, 2, 3, 4, 2, 1, 0, 2, 1, 1], [1, 2, 4, 1, 0, 5, 0, 2, 1, 3]], dtype=np.float…
265 loss, dx = vm.softmax_cross_entropy_with_logits(logits, labels)
266 print("logits.shape: ", logits.shape)
267 print("logits: ", logits)
268 print("softmax: ", vm.softmax(logits))
/third_party/mindspore/tests/ut/cpp/python_input/gtest_input/pipeline/infer/
Dprimitive_test.py39 def get_softmax_cross_entropy_with_logits(logits, labels): argument
40 return softmax_cross_entropy_with_logits(logits, labels)
50 def __call__(self, logits, labels): argument
64 def get_tensor_to_scalar(logits, labels): argument
65 return tensorToScalar(logits, labels)
/third_party/mindspore/tests/st/networks/models/bert/src/
Dcluener_evaluation.py48 logits = []
50 logits.extend(ele)
51 ids = logits
53 logits = model.predict(input_ids, input_mask, token_type_id, Tensor(1))
54 ids = logits.asnumpy()
/third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/
Dsoftmax_cross_entropy_with_logits_cpu_kernel.cc66 void SoftmaxCrossEntropyWithLogitsCPUKernel::ForwardPostExecute(const float *logits, const float *l… in ForwardPostExecute() argument
73 float logit = logf(logits[i * class_num_ + j] <= 0.0 ? epsilon : logits[i * class_num_ + j]); in ForwardPostExecute()
74 output2[i * class_num_ + j] = logits[i * class_num_ + j] - labels[i * class_num_ + j]; in ForwardPostExecute()
101 const auto *logits = reinterpret_cast<float *>(workspace[0]->addr); in Launch() local
104 ForwardPostExecute(logits, labels, output1, output2); in Launch()

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