# 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 numpy as np import os import pytest import mindspore.context as context import mindspore.dataset as ds import mindspore.dataset.transforms.c_transforms as C import mindspore.dataset.transforms.vision.c_transforms as CV import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype from mindspore.common.initializer import initializer from mindspore.dataset.transforms.vision import Inter from mindspore.model_zoo.lenet import LeNet5 from mindspore.nn import Dense, TrainOneStepCell, WithLossCell from mindspore.nn.metrics import Accuracy from mindspore.nn.optim import Momentum from mindspore.ops import operations as P from mindspore.train import Model from mindspore.train.callback import LossMonitor 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") 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))