# Copyright 2019 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ import os import pytest import numpy as np from mindspore import Tensor import mindspore.context as context from mindspore.ops import operations as P import mindspore.nn as nn from mindspore.nn import Dense, TrainOneStepCell, WithLossCell from mindspore.nn.optim import Momentum from mindspore.nn.metrics import Accuracy from mindspore.train import Model from mindspore.common import dtype as mstype from mindspore.common.initializer import initializer from mindspore.model_zoo.lenet import LeNet5 from mindspore.train.callback import LossMonitor import mindspore.dataset as ds import mindspore.dataset.transforms.vision.c_transforms as CV import mindspore.dataset.transforms.c_transforms as C from mindspore.dataset.transforms.vision import Inter context.set_context(mode=context.GRAPH_MODE, device_target="GPU") class LeNet(nn.Cell): def __init__(self): super(LeNet, self).__init__() self.relu = P.ReLU() self.batch_size = 1 weight1 = Tensor(np.ones([6, 3, 5, 5]).astype(np.float32) * 0.01) weight2 = Tensor(np.ones([16, 6, 5, 5]).astype(np.float32) * 0.01) self.conv1 = nn.Conv2d(3, 6, (5, 5), weight_init=weight1, stride=1, padding=0, pad_mode='valid') self.conv2 = nn.Conv2d(6, 16, (5, 5), weight_init=weight2, pad_mode='valid', stride=1, padding=0) self.pool = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="valid") self.reshape = P.Reshape() self.reshape1 = P.Reshape() self.fc1 = Dense(400, 120) self.fc2 = Dense(120, 84) self.fc3 = Dense(84, 10) def construct(self, input_x): output = self.conv1(input_x) output = self.relu(output) output = self.pool(output) output = self.conv2(output) output = self.relu(output) output = self.pool(output) output = self.reshape(output, (self.batch_size, -1)) output = self.fc1(output) output = self.fc2(output) output = self.fc3(output) return output def multisteplr(total_steps, gap, base_lr=0.9, gamma=0.1, dtype=mstype.float32): lr = [] for step in range(total_steps): lr_ = base_lr * gamma ** (step // gap) lr.append(lr_) return Tensor(np.array(lr), dtype) @pytest.mark.level0 @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_train_lenet(): epoch = 100 net = LeNet() momentum = initializer(Tensor(np.array([0.9]).astype(np.float32)), [1]) learning_rate = multisteplr(epoch, 30) optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum) criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) net_with_criterion = WithLossCell(net, criterion) train_network = TrainOneStepCell(net_with_criterion, optimizer) # optimizer train_network.set_train() losses = [] for i in range(epoch): data = Tensor(np.ones([net.batch_size, 3, 32, 32]).astype(np.float32) * 0.01) label = Tensor(np.ones([net.batch_size]).astype(np.int32)) loss = train_network(data, label) losses.append(loss) print(losses) def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1): """ create dataset for train or test """ # define dataset mnist_ds = ds.MnistDataset(data_path) resize_height, resize_width = 32, 32 rescale = 1.0 / 255.0 shift = 0.0 rescale_nml = 1 / 0.3081 shift_nml = -1 * 0.1307 / 0.3081 # define map operations resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) rescale_op = CV.Rescale(rescale, shift) hwc2chw_op = CV.HWC2CHW() type_cast_op = C.TypeCast(mstype.int32) # apply map operations on images mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers) # apply DatasetOps buffer_size = 10000 mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) mnist_ds = mnist_ds.repeat(repeat_size) return mnist_ds @pytest.mark.level0 @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_train_and_eval_lenet(): context.set_context(mode=context.GRAPH_MODE, device_target="GPU", enable_mem_reuse=False) network = LeNet5(10) net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9) model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) print("============== Starting Training ==============") ds_train = create_dataset(os.path.join('/home/workspace/mindspore_dataset/mnist', "train"), 32, 1) model.train(1, ds_train, callbacks=[LossMonitor()], dataset_sink_mode=True) print("============== Starting Testing ==============") ds_eval = create_dataset(os.path.join('/home/workspace/mindspore_dataset/mnist', "test"), 32, 1) acc = model.eval(ds_eval, dataset_sink_mode=True) print("============== Accuracy:{} ==============".format(acc))