# 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 numpy as np import pytest import mindspore.context as context import mindspore.dataset as ds import mindspore.dataset.transforms.c_transforms as C import mindspore.dataset.vision.c_transforms as CV import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype from mindspore.dataset.vision import Inter 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 from mindspore.common.initializer import TruncatedNormal context.set_context(mode=context.GRAPH_MODE, device_target="GPU") def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): """weight initial for conv layer""" weight = weight_variable() return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="valid") def fc_with_initialize(input_channels, out_channels): """weight initial for fc layer""" weight = weight_variable() bias = weight_variable() return nn.Dense(input_channels, out_channels, weight, bias) def weight_variable(): """weight initial""" return TruncatedNormal(0.02) class LeNet5(nn.Cell): def __init__(self, num_class=10, channel=1): super(LeNet5, self).__init__() self.num_class = num_class self.conv1 = conv(channel, 6, 5) self.conv2 = conv(6, 16, 5) self.fc1 = fc_with_initialize(16 * 5 * 5, 120) self.fc2 = fc_with_initialize(120, 84) self.fc3 = fc_with_initialize(84, self.num_class) self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.flatten = nn.Flatten() def construct(self, x): x = self.conv1(x) x = self.relu(x) x = self.max_pool2d(x) x = self.conv2(x) x = self.relu(x) x = self.max_pool2d(x) x = self.flatten(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.relu(x) x = self.fc3(x) return x 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 = 0.9 learning_rate = multisteplr(epoch, 30) optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum) criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') 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).asnumpy() losses.append(loss) assert losses[-1] < 0.01 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(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", 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(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("============== {} ==============".format(acc))