| /third_party/mindspore/tests/st/quantization/resnet50_quant/ |
| D | test_resnet50_quant.py | 71 epoch_size = config.epoch_size
102 total_epochs=config.epoch_size,
119 model.train(epoch_size, dataset, callbacks=callbacks,
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| /third_party/mindspore/tests/st/quantization/mobilenetv2_quant/ |
| D | test_mobilenetv2_quant.py | 68 epoch_size = config.epoch_size
97 total_epochs=epoch_size + config.start_epoch,
110 model.train(epoch_size, dataset, callbacks=callback,
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| D | test_mobilenetv2_quant_gpu.py | 66 epoch_size = config.epoch_size
95 total_epochs=epoch_size + config.start_epoch,
108 model.train(epoch_size, dataset, callbacks=callback,
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| /third_party/mindspore/tests/ut/python/exec/ |
| D | test_train_with_lars.py | 74 def lr_gen(fn, epoch_size): argument
75 for i in range(epoch_size):
79 def me_train_tensor(net, input_np, label_np, epoch_size=2): argument
84 …opt = Momentum(get_net_trainable_reordered_params(net)[2], lr_gen(lambda i: 0.1, epoch_size), 0.9,…
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| D | test_train.py | 27 def lr_gen(fn, epoch_size): argument
28 for i in range(epoch_size):
32 def me_train_tensor(net, input_np, label_np, epoch_size=2): argument
35 …um(filter(lambda x: x.requires_grad, net.get_parameters()), lr_gen(lambda i: 0.1, epoch_size), 0.9,
42 for epoch in range(0, epoch_size):
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| /third_party/mindspore/tests/ut/python/parallel/ |
| D | test_full_batch.py | 70 epoch_size = 2
87 model.train(epoch_size, dataset, dataset_sink_mode=False)
98 epoch_size = 2
111 model.train(epoch_size, dataset, dataset_sink_mode=False)
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| D | test_reshape.py | 88 epoch_size = 2
102 model.train(epoch_size, dataset, dataset_sink_mode=False)
429 epoch_size = 2
440 model.train(epoch_size, dataset, dataset_sink_mode=False)
571 epoch_size = 2
583 model.train(epoch_size, dataset, dataset_sink_mode=False)
590 epoch_size = 2
603 model.train(epoch_size, dataset, dataset_sink_mode=False)
632 epoch_size = 2
644 model.train(epoch_size, dataset, dataset_sink_mode=False)
[all …]
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| D | test_bool_grad.py | 65 epoch_size = 1
77 model.train(epoch_size, dataset, dataset_sink_mode=False)
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| D | test_topk.py | 68 epoch_size = 2
72 model.train(epoch_size, dataset, dataset_sink_mode=False)
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| D | test_transpose.py | 72 epoch_size = 2
89 model.train(epoch_size, dataset, dataset_sink_mode=False)
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| D | test_gathernd.py | 69 epoch_size = 2
73 model.train(epoch_size, dataset, dataset_sink_mode=False)
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| D | test_auto_parallel_for_loop_simplify.py | 88 epoch_size = 2
93 model.train(epoch_size, dataset, dataset_sink_mode=False)
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| D | test_range.py | 79 epoch_size = 2
83 model.train(epoch_size, dataset, dataset_sink_mode=False)
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| D | test_select.py | 70 epoch_size = 2
74 model.train(epoch_size, dataset, dataset_sink_mode=False)
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| /third_party/mindspore/tests/st/auto_parallel/ |
| D | soft_entropy_loss_expand_parallel.py | 198 epoch_size = 6
200 model.train(epoch_size, dataset, callbacks=single_callback, dataset_sink_mode=False)
210 epoch_size = 6
212 model.train(epoch_size, dataset, callbacks=parallel_callback, dataset_sink_mode=False)
236 epoch_size = 6
238 model.train(epoch_size, dataset, callbacks=parallel_callback, dataset_sink_mode=False)
262 epoch_size = 6
264 model.train(epoch_size, dataset, callbacks=parallel_callback, dataset_sink_mode=False)
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| /third_party/mindspore/tests/st/tbe_networks/ |
| D | test_resnet_cifar_1p.py | 134 def train_process(epoch_size, num_classes, batch_size): argument
145 model.train(epoch_size, dataset, callbacks=[loss_cb])
155 epoch_size = 1
158 acc = train_process(epoch_size, num_classes, batch_size)
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| D | test_resnet_cifar_8p.py | 145 def train_process(q, device_id, epoch_size, num_classes, device_num, batch_size, enable_hccl): argument
170 model.train(epoch_size, dataset, callbacks=[loss_cb])
177 epoch_size = 1
185 … args=(q, device_id, epoch_size, num_classes, device_num, batch_size, enable_hccl)))
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| D | resnet_cifar.py | 131 epoch_size = args_opt.epoch_size variable
145 model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb, time_cb])
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| /third_party/mindspore/tests/st/mem_reuse/ |
| D | resnet_cifar_memreuse.py | 130 epoch_size = args_opt.epoch_size variable
138 dataset = create_dataset(epoch_size)
143 model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
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| D | resnet_cifar_normal.py | 130 epoch_size = args_opt.epoch_size variable
138 dataset = create_dataset(epoch_size)
143 model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
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| /third_party/mindspore/tests/st/ps/part_ps/ |
| D | test_ps_embedding_heterogeneous_conv2d_adam.py | 127 epoch_size=1, target='CPU', sparse=True): argument
134 self.epoch_size = epoch_size
150 model.train(self.epoch_size, dataset, dataset_sink_mode=False)
167 model.train(self.epoch_size, dataset, dataset_sink_mode=False)
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| /third_party/mindspore/tests/st/networks/models/resnet50/ |
| D | test_resnet50_imagenet.py | 132 def train_process(q, device_id, epoch_size, device_num, enable_hccl): argument
176 … warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size,
215 for epoch_idx in range(0, int(epoch_size / eval_interval)):
230 def train_process_thor(q, device_id, epoch_size, device_num, enable_hccl): argument
295 for epoch_idx in range(0, int(epoch_size / eval_interval)):
325 epoch_size = 2
333 args=(q, device_id, epoch_size, device_num, enable_hccl)))
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| /third_party/mindspore/tests/st/pynative/ |
| D | test_pynative_lenet.py | 140 epoch_size = 20
154 for epoch in range(0, epoch_size):
178 epoch_size = 20
193 for epoch in range(0, epoch_size):
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| /third_party/mindspore/tests/st/networks/models/bert/src/ |
| D | dataset.py | 26 def create_bert_dataset(epoch_size=1, device_num=1, rank=0, do_shuffle="true", data_dir=None, schem… argument
29 repeat_count = epoch_size
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| /third_party/mindspore/tests/st/networks/models/bert/bert_performance/ |
| D | test_bert_thor.py | 143 def train_process_bert_thor(q, device_id, epoch_size, device_num): argument
164 new_repeat_count = epoch_size * data_set.get_dataset_size() // data_sink_steps
199 epoch_size = 2
203 …process.append(Process(target=train_process_bert_thor, args=(q, device_id, epoch_size, device_num)…
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