Add the implementations of the autograd resnet model for peft

examples/singa_peft/examples/autograd/resnet_cifar10.py

@@ -0,0 +1,153 @@
+#
+# 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 synchronize 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]