| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| # |
| |
| from singa import singa_wrap as singa |
| from singa import autograd |
| from singa import layer |
| from singa import tensor |
| from singa import device |
| from singa import opt |
| import numpy as np |
| import os |
| import sys |
| import gzip |
| import codecs |
| import time |
| |
| |
| class CNN: |
| |
| def __init__(self): |
| self.conv1 = layer.Conv2d(1, 20, 5, padding=0) |
| self.conv2 = layer.Conv2d(20, 50, 5, padding=0) |
| self.linear1 = layer.Linear(4 * 4 * 50, 500) |
| self.linear2 = layer.Linear(500, 10) |
| self.pooling1 = layer.MaxPool2d(2, 2, padding=0) |
| self.pooling2 = layer.MaxPool2d(2, 2, padding=0) |
| self.relu1 = layer.ReLU() |
| self.relu2 = layer.ReLU() |
| self.relu3 = layer.ReLU() |
| self.flatten = layer.Flatten() |
| |
| def forward(self, x): |
| y = self.conv1(x) |
| y = self.relu1(y) |
| y = self.pooling1(y) |
| y = self.conv2(y) |
| y = self.relu2(y) |
| y = self.pooling2(y) |
| y = self.flatten(y) |
| y = self.linear1(y) |
| y = self.relu3(y) |
| y = self.linear2(y) |
| return y |
| |
| |
| def check_dataset_exist(dirpath): |
| if not os.path.exists(dirpath): |
| print( |
| 'The MNIST dataset does not exist. Please download the mnist dataset using download_mnist.py (e.g. python3 download_mnist.py)' |
| ) |
| sys.exit(0) |
| return dirpath |
| |
| |
| def load_dataset(): |
| train_x_path = '/tmp/train-images-idx3-ubyte.gz' |
| train_y_path = '/tmp/train-labels-idx1-ubyte.gz' |
| valid_x_path = '/tmp/t10k-images-idx3-ubyte.gz' |
| valid_y_path = '/tmp/t10k-labels-idx1-ubyte.gz' |
| |
| train_x = read_image_file(check_dataset_exist(train_x_path)).astype( |
| np.float32) |
| train_y = read_label_file(check_dataset_exist(train_y_path)).astype( |
| np.float32) |
| valid_x = read_image_file(check_dataset_exist(valid_x_path)).astype( |
| np.float32) |
| valid_y = read_label_file(check_dataset_exist(valid_y_path)).astype( |
| np.float32) |
| return train_x, train_y, valid_x, valid_y |
| |
| |
| def read_label_file(path): |
| with gzip.open(path, 'rb') as f: |
| data = f.read() |
| assert get_int(data[:4]) == 2049 |
| length = get_int(data[4:8]) |
| parsed = np.frombuffer(data, dtype=np.uint8, offset=8).reshape((length)) |
| return parsed |
| |
| |
| def get_int(b): |
| return int(codecs.encode(b, 'hex'), 16) |
| |
| |
| def read_image_file(path): |
| with gzip.open(path, 'rb') as f: |
| data = f.read() |
| assert get_int(data[:4]) == 2051 |
| length = get_int(data[4:8]) |
| num_rows = get_int(data[8:12]) |
| num_cols = get_int(data[12:16]) |
| parsed = np.frombuffer(data, dtype=np.uint8, offset=16).reshape( |
| (length, 1, num_rows, num_cols)) |
| return parsed |
| |
| |
| def to_categorical(y, num_classes): |
| y = np.array(y, dtype="int") |
| n = y.shape[0] |
| categorical = np.zeros((n, num_classes)) |
| categorical[np.arange(n), y] = 1 |
| categorical = categorical.astype(np.float32) |
| return categorical |
| |
| |
| def accuracy(pred, target): |
| y = np.argmax(pred, axis=1) |
| t = np.argmax(target, axis=1) |
| a = y == t |
| return np.array(a, "int").sum() |
| |
| |
| # Function to all reduce NUMPY accuracy and loss from multiple devices |
| def reduce_variable(variable, dist_opt, reducer): |
| reducer.copy_from_numpy(variable) |
| dist_opt.all_reduce(reducer.data) |
| dist_opt.wait() |
| output = tensor.to_numpy(reducer) |
| return output |
| |
| |
| # Function to sychronize SINGA TENSOR initial model parameters |
| def synchronize(tensor, dist_opt): |
| dist_opt.all_reduce(tensor.data) |
| dist_opt.wait() |
| tensor /= dist_opt.world_size |
| |
| |
| # Data augmentation |
| def augmentation(x, batch_size): |
| xpad = np.pad(x, [[0, 0], [0, 0], [4, 4], [4, 4]], 'symmetric') |
| for data_num in range(0, batch_size): |
| offset = np.random.randint(8, size=2) |
| x[data_num, :, :, :] = xpad[data_num, :, offset[0]:offset[0] + 28, |
| offset[1]:offset[1] + 28] |
| if_flip = np.random.randint(2) |
| if (if_flip): |
| x[data_num, :, :, :] = x[data_num, :, :, ::-1] |
| return x |
| |
| |
| # Data partition |
| def data_partition(dataset_x, dataset_y, global_rank, world_size): |
| data_per_rank = dataset_x.shape[0] // world_size |
| idx_start = global_rank * data_per_rank |
| idx_end = (global_rank + 1) * data_per_rank |
| return dataset_x[idx_start:idx_end], dataset_y[idx_start:idx_end] |
| |
| |
| def train_mnist_cnn(DIST=False, |
| local_rank=None, |
| world_size=None, |
| nccl_id=None, |
| spars=0, |
| topK=False, |
| corr=True): |
| |
| # Define the hypermeters for the mnist_cnn |
| max_epoch = 10 |
| batch_size = 64 |
| sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5) |
| |
| # Prepare training and valadiation data |
| train_x, train_y, test_x, test_y = load_dataset() |
| IMG_SIZE = 28 |
| num_classes = 10 |
| train_y = to_categorical(train_y, num_classes) |
| test_y = to_categorical(test_y, num_classes) |
| |
| # Normalization |
| train_x = train_x / 255 |
| test_x = test_x / 255 |
| |
| if DIST: |
| # For distributed GPU training |
| sgd = opt.DistOpt(sgd, |
| nccl_id=nccl_id, |
| local_rank=local_rank, |
| world_size=world_size) |
| dev = device.create_cuda_gpu_on(sgd.local_rank) |
| |
| # Dataset partition for distributed training |
| train_x, train_y = data_partition(train_x, train_y, sgd.global_rank, |
| sgd.world_size) |
| test_x, test_y = data_partition(test_x, test_y, sgd.global_rank, |
| sgd.world_size) |
| world_size = sgd.world_size |
| else: |
| # For single GPU |
| dev = device.create_cuda_gpu() |
| world_size = 1 |
| |
| # Create model |
| model = CNN() |
| |
| tx = tensor.Tensor((batch_size, 1, IMG_SIZE, IMG_SIZE), dev, tensor.float32) |
| ty = tensor.Tensor((batch_size, num_classes), dev, tensor.int32) |
| num_train_batch = train_x.shape[0] // batch_size |
| num_test_batch = test_x.shape[0] // batch_size |
| idx = np.arange(train_x.shape[0], dtype=np.int32) |
| |
| if DIST: |
| #Sychronize the initial parameters |
| autograd.training = True |
| x = np.random.randn(batch_size, 1, IMG_SIZE, |
| IMG_SIZE).astype(np.float32) |
| y = np.zeros(shape=(batch_size, num_classes), dtype=np.int32) |
| tx.copy_from_numpy(x) |
| ty.copy_from_numpy(y) |
| out = model.forward(tx) |
| loss = autograd.softmax_cross_entropy(out, ty) |
| for p, g in autograd.backward(loss): |
| synchronize(p, sgd) |
| |
| # Training and evaulation loop |
| for epoch in range(max_epoch): |
| start_time = time.time() |
| np.random.shuffle(idx) |
| |
| if ((DIST == False) or (sgd.global_rank == 0)): |
| print('Starting Epoch %d:' % (epoch)) |
| |
| # Training phase |
| autograd.training = True |
| train_correct = np.zeros(shape=[1], dtype=np.float32) |
| test_correct = np.zeros(shape=[1], dtype=np.float32) |
| train_loss = np.zeros(shape=[1], dtype=np.float32) |
| |
| for b in range(num_train_batch): |
| x = train_x[idx[b * batch_size:(b + 1) * batch_size]] |
| x = augmentation(x, batch_size) |
| y = train_y[idx[b * batch_size:(b + 1) * batch_size]] |
| tx.copy_from_numpy(x) |
| ty.copy_from_numpy(y) |
| out = model.forward(tx) |
| loss = autograd.softmax_cross_entropy(out, ty) |
| train_correct += accuracy(tensor.to_numpy(out), y) |
| train_loss += tensor.to_numpy(loss)[0] |
| if DIST: |
| if (spars == 0): |
| sgd.backward_and_update(loss, threshold=50000) |
| else: |
| sgd.backward_and_sparse_update(loss, |
| spars=spars, |
| topK=topK, |
| corr=corr) |
| else: |
| sgd(loss) |
| |
| if DIST: |
| # Reduce the evaluation accuracy and loss from multiple devices |
| reducer = tensor.Tensor((1,), dev, tensor.float32) |
| train_correct = reduce_variable(train_correct, sgd, reducer) |
| train_loss = reduce_variable(train_loss, sgd, reducer) |
| |
| # Output the training loss and accuracy |
| if ((DIST == False) or (sgd.global_rank == 0)): |
| print('Training loss = %f, training accuracy = %f' % |
| (train_loss, train_correct / |
| (num_train_batch * batch_size * world_size)), |
| flush=True) |
| |
| # Evaluation phase |
| autograd.training = False |
| for b in range(num_test_batch): |
| x = test_x[b * batch_size:(b + 1) * batch_size] |
| y = test_y[b * batch_size:(b + 1) * batch_size] |
| tx.copy_from_numpy(x) |
| ty.copy_from_numpy(y) |
| out_test = model.forward(tx) |
| test_correct += accuracy(tensor.to_numpy(out_test), y) |
| |
| if DIST: |
| # Reduce the evaulation accuracy from multiple devices |
| test_correct = reduce_variable(test_correct, sgd, reducer) |
| |
| # Output the evaluation accuracy |
| if ((DIST == False) or (sgd.global_rank == 0)): |
| print('Evaluation accuracy = %f, Elapsed Time = %fs' % |
| (test_correct / (num_test_batch * batch_size * world_size), |
| time.time() - start_time), |
| flush=True) |
| |
| |
| if __name__ == '__main__': |
| |
| DIST = False |
| train_mnist_cnn(DIST=DIST) |
