Merge pull request #918 from NLGithubWP/add_resnet_10
Add resnet_cifar10 to cifar_distributed_cnn/autograd
diff --git a/examples/cifar_distributed_cnn/autograd/resnet_cifar10.py b/examples/cifar_distributed_cnn/autograd/resnet_cifar10.py
new file mode 100644
index 0000000..0d6379b
--- /dev/null
+++ b/examples/cifar_distributed_cnn/autograd/resnet_cifar10.py
@@ -0,0 +1,292 @@
+#
+# 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.
+#
+
+try:
+ import pickle
+except ImportError:
+ import cPickle as pickle
+
+from singa import singa_wrap as singa
+from singa import autograd
+from singa import tensor
+from singa import device
+from singa import opt
+from PIL import Image
+import numpy as np
+import os
+import sys
+import time
+
+
+def load_dataset(filepath):
+ with open(filepath, 'rb') as fd:
+ try:
+ cifar10 = pickle.load(fd, encoding='latin1')
+ except TypeError:
+ cifar10 = pickle.load(fd)
+ image = cifar10['data'].astype(dtype=np.uint8)
+ image = image.reshape((-1, 3, 32, 32))
+ label = np.asarray(cifar10['labels'], dtype=np.uint8)
+ label = label.reshape(label.size, 1)
+ return image, label
+
+
+def load_train_data(dir_path='cifar-10-batches-py', num_batches=5):
+ labels = []
+ batchsize = 10000
+ images = np.empty((num_batches * batchsize, 3, 32, 32), dtype=np.uint8)
+ for did in range(1, num_batches + 1):
+ fname_train_data = dir_path + "/data_batch_{}".format(did)
+ image, label = load_dataset(check_dataset_exist(fname_train_data))
+ images[(did - 1) * batchsize:did * batchsize] = image
+ labels.extend(label)
+ images = np.array(images, dtype=np.float32)
+ labels = np.array(labels, dtype=np.int32)
+ return images, labels
+
+
+def load_test_data(dir_path='cifar-10-batches-py'):
+ images, labels = load_dataset(check_dataset_exist(dir_path + "/test_batch"))
+ return np.array(images, dtype=np.float32), np.array(labels, dtype=np.int32)
+
+
+def check_dataset_exist(dirpath):
+ if not os.path.exists(dirpath):
+ print(
+ 'Please download the cifar10 dataset using download_data.py (e.g. python ~/singa/examples/cifar10/download_data.py py)'
+ )
+ sys.exit(0)
+ return dirpath
+
+
+def normalize_for_resnet(train_x, test_x):
+ mean = [0.4914, 0.4822, 0.4465]
+ std = [0.2023, 0.1994, 0.2010]
+ train_x /= 255
+ test_x /= 255
+ for ch in range(0, 2):
+ train_x[:, ch, :, :] -= mean[ch]
+ train_x[:, ch, :, :] /= std[ch]
+ test_x[:, ch, :, :] -= mean[ch]
+ test_x[:, ch, :, :] /= std[ch]
+ return train_x, test_x
+
+
+def resize_dataset(x, IMG_SIZE):
+ num_data = x.shape[0]
+ dim = x.shape[1]
+ X = np.zeros(shape=(num_data, dim, IMG_SIZE, IMG_SIZE), dtype=np.float32)
+ for n in range(0, num_data):
+ for d in range(0, dim):
+ X[n, d, :, :] = np.array(Image.fromarray(x[n, d, :, :]).resize(
+ (IMG_SIZE, IMG_SIZE), Image.BILINEAR),
+ dtype=np.float32)
+ return X
+
+
+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] + 32,
+ offset[1]:offset[1] + 32]
+ if_flip = np.random.randint(2)
+ if (if_flip):
+ x[data_num, :, :, :] = x[data_num, :, :, ::-1]
+ return x
+
+
+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()
+
+
+def to_categorical(y, num_classes):
+ y = np.array(y, dtype="int")
+ n = y.shape[0]
+ categorical = np.zeros((n, num_classes))
+ for i in range(0, n):
+ categorical[i, y[i]] = 1
+ categorical = categorical.astype(np.float32)
+ return categorical
+
+
+# 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 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_cifar10(DIST=False,
+ local_rank=None,
+ world_size=None,
+ nccl_id=None,
+ partial_update=False):
+
+ # Define the hypermeters for the train_cifar10
+ sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5)
+ max_epoch = 5
+ batch_size = 32
+
+ train_x, train_y = load_train_data()
+ test_x, test_y = load_test_data()
+ train_x, test_x = normalize_for_resnet(train_x, test_x)
+ IMG_SIZE = 224
+ num_classes = 10
+
+ 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
+
+ from resnet import resnet50
+ model = resnet50(num_classes=num_classes)
+
+ tx = tensor.Tensor((batch_size, 3, IMG_SIZE, IMG_SIZE), dev, tensor.float32)
+ ty = tensor.Tensor((batch_size,), 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, 3, IMG_SIZE,
+ IMG_SIZE).astype(np.float32)
+ y = np.zeros(shape=(batch_size,), dtype=np.int32)
+ tx.copy_from_numpy(x)
+ ty.copy_from_numpy(y)
+ out = model(tx)
+ loss = autograd.softmax_cross_entropy(out, ty)
+ param = []
+ for p, _ in autograd.backward(loss):
+ synchronize(p, sgd)
+ param.append(p)
+
+ 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)
+ x = resize_dataset(x, IMG_SIZE)
+ y = train_y[idx[b * batch_size:(b + 1) * batch_size]]
+ tx.copy_from_numpy(x)
+ ty.copy_from_numpy(y)
+ out = model(tx)
+ loss = autograd.softmax_cross_entropy(out, ty)
+ train_correct += accuracy(tensor.to_numpy(out),
+ to_categorical(y, num_classes)).astype(
+ np.float32)
+ train_loss += tensor.to_numpy(loss)[0]
+ if not partial_update:
+ sgd.backward_and_update(loss)
+ else:
+ sgd.backward_and_partial_update(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)
+
+ if partial_update:
+ # Sychronize parameters before evaluation phase
+ for p in param:
+ synchronize(p, sgd)
+
+ #Evaulation phase
+ autograd.training = False
+ for b in range(num_test_batch):
+ x = test_x[b * batch_size:(b + 1) * batch_size]
+ x = resize_dataset(x, IMG_SIZE)
+ y = test_y[b * batch_size:(b + 1) * batch_size]
+ tx.copy_from_numpy(x)
+ ty.copy_from_numpy(y)
+ out_test = model(tx)
+ test_correct += accuracy(tensor.to_numpy(out_test),
+ to_categorical(y, num_classes))
+
+ 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_cifar10(DIST=DIST)
