blob: 4994c2aaa714baa3158f4f8989a118636f99339e [file] [log] [blame]
#
# 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 device
from singa import opt
from singa import tensor
import argparse
import numpy as np
import time
from PIL import Image
import sys
sys.path.append(".")
print(sys.path)
import examples.cnn.model.cnn as cnn
from examples.cnn.data import cifar10
import model as cpl
def accuracy(pred, target):
# y is network output to be compared with ground truth (int)
y = np.argmax(pred, axis=1)
a = y == target
correct = np.array(a, "int").sum()
return correct
def resize_dataset(x, image_size):
num_data = x.shape[0]
dim = x.shape[1]
X = np.zeros(shape=(num_data, dim, image_size, image_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(
(image_size, image_size), Image.BILINEAR
),
dtype=np.float32,
)
return X
def run(
local_rank,
max_epoch,
batch_size,
sgd,
graph,
verbosity,
dist_option="plain",
spars=None,
):
dev = device.create_cuda_gpu_on(local_rank)
dev.SetRandSeed(0)
np.random.seed(0)
train_x, train_y, val_x, val_y = cifar10.load()
num_channels = train_x.shape[1]
data_size = np.prod(train_x.shape[1 : train_x.ndim]).item()
num_classes = (np.max(train_y) + 1).item()
backbone = cnn.create_model(num_channels=num_channels, num_classes=num_classes)
model = cpl.create_model(backbone, prototype_count=10, lamb=0.5, temp=10)
if backbone.dimension == 4:
tx = tensor.Tensor(
(batch_size, num_channels, backbone.input_size, backbone.input_size), dev
)
train_x = resize_dataset(train_x, backbone.input_size)
val_x = resize_dataset(val_x, backbone.input_size)
elif backbone.dimension == 2:
tx = tensor.Tensor((batch_size, data_size), dev)
np.reshape(train_x, (train_x.shape[0], -1))
np.reshape(val_x, (val_x.shape[0], -1))
ty = tensor.Tensor((batch_size,), dev, tensor.int32)
num_train_batch = train_x.shape[0] // batch_size
num_val_batch = val_x.shape[0] // batch_size
idx = np.arange(train_x.shape[0], dtype=np.int32)
model.set_optimizer(sgd)
model.compile([tx], is_train=True, use_graph=graph, sequential=True)
dev.SetVerbosity(verbosity)
for epoch in range(max_epoch):
print(f"Epoch {epoch}")
np.random.shuffle(idx)
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)
model.train()
for b in range(num_train_batch):
x = train_x[idx[b * batch_size : (b + 1) * batch_size]]
y = train_y[idx[b * batch_size : (b + 1) * batch_size]]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out, loss = model(tx, ty, dist_option, spars)
train_correct += accuracy(tensor.to_numpy(out), y)
train_loss += tensor.to_numpy(loss)[0]
print(
"Training loss = %f, training accuracy = %f"
% (train_loss, train_correct / (num_train_batch * batch_size)),
flush=True,
)
model.eval()
for b in range(num_val_batch):
x = val_x[b * batch_size : (b + 1) * batch_size]
y = val_y[b * batch_size : (b + 1) * batch_size]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out_test = model(tx, ty, dist_option="fp32", spars=None)
test_correct += accuracy(tensor.to_numpy(out_test), y)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Train a CPL model")
parser.add_argument(
"-m",
"--max-epoch",
default=20,
type=int,
help="maximum epochs",
dest="max_epoch",
)
parser.add_argument(
"-b", "--batch-size", default=64, type=int, help="batch size", dest="batch_size"
)
parser.add_argument(
"-l",
"--learning-rate",
default=0.005,
type=float,
help="initial learning rate",
dest="lr",
)
parser.add_argument(
"-i",
"--device-id",
default=0,
type=int,
help="which GPU to use",
dest="device_id",
)
parser.add_argument(
"-g",
"--disable-graph",
default="True",
action="store_false",
help="disable graph",
dest="graph",
)
parser.add_argument(
"-v",
"--log-verbosity",
default=0,
type=int,
help="logging verbosity",
dest="verbosity",
)
args = parser.parse_args()
print(args)
sgd = opt.SGD(lr=args.lr, momentum=0.9, weight_decay=1e-5)
run(
args.device_id, args.max_epoch, args.batch_size, sgd, args.graph, args.verbosity
)