tangwei12
7 years ago
commit
bf2c53ae0a
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import random
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.dataset import mnist, cifar, flowers, image
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def convert_2_recordio(py_reader, outfilepath, batch_size, shape_data,
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shape_label):
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num_batches = 0
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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reader = paddle.batch(py_reader(), batch_size=batch_size)
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feeder = fluid.DataFeeder(
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feed_list=[ # order is image and label
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fluid.layers.data(
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name='image', shape=shape_data),
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fluid.layers.data(
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name='label', shape=shape_label, dtype='int64'),
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],
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place=fluid.CPUPlace())
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num_batches = fluid.recordio_writer.convert_reader_to_recordio_file(
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outfilepath, reader, feeder)
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return num_batches
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def prepare_mnist(outpath, batch_size):
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outfilepath = os.path.join(outpath, "mnist.recordio")
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convert_2_recordio(mnist.train, outfilepath, batch_size, [784], [1])
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def prepare_cifar10(outpath, batch_size):
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outfilepath = os.path.join(outpath, "cifar.recordio")
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convert_2_recordio(cifar.train10, outfilepath, batch_size, [3, 32, 32], [1])
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def prepare_flowers(outpath, batch_size):
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outfilepath = os.path.join(outpath, "flowers.recordio")
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convert_2_recordio(flowers.train, outfilepath, batch_size, [3, 224, 224],
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[1])
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def default_mapper(sample):
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img, label = sample
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img = image.simple_transform(
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img, 256, 224, True, mean=[103.94, 116.78, 123.68])
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return img.flatten().astype('float32'), label
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def imagenet_train(data_dir):
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contents = os.listdir(data_dir)
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if set(contents) != set(
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["train", "train.txt", "val", "val_set", "val.txt", "unzip.sh"]):
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raise Exception("Imagenet data contents error!")
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img2label = dict()
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imgfilelist = []
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with open(os.path.join(data_dir, "train.txt")) as fn:
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while 1:
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l = fn.readline()
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if not l:
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break
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img, lbl = l[:-1].split(" ")
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img2label[img] = int(lbl)
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imgfilelist.append(img)
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# shuffle all, this is slow
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random.shuffle(imgfilelist)
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def train_reader():
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for idx, imgfile in enumerate(imgfilelist):
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data = image.load_image(
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os.path.join(data_dir, "train", imgfile.lower()))
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label = [img2label[imgfile], ]
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yield [data, label]
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return paddle.reader.map_readers(default_mapper, train_reader)
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def imagenet_test(data_dir):
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contents = os.listdir(data_dir)
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if set(contents) != set(
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["train", "train.txt", "val", "val_set", "val.txt", "unzip.sh"]):
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raise Exception("Imagenet data contents error!")
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img2label = dict()
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imgfilelist = []
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with open(os.path.join(data_dir, "val.txt")) as fn:
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while 1:
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l = fn.readline()
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if not l:
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break
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img, lbl = l[:-1].split(" ")
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img2label[img] = int(lbl)
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imgfilelist.append(img)
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def test_reader():
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for idx, imgfile in enumerate(imgfilelist):
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base_path = os.path.join(data_dir, "val", imgfile.split(".")[0])
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image_path = ".".join([base_path, "jpeg"])
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data = image.load_image(image_path)
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label = [img2label[imgfile], ]
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yield [data, label]
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return paddle.reader.map_readers(default_mapper, test_reader)
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# FIXME(wuyi): delete this when https://github.com/PaddlePaddle/Paddle/pull/11066 is merged
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def convert_reader_to_recordio_files(
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filename,
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batch_per_file,
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reader_creator,
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feeder,
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compressor=core.RecordIOWriter.Compressor.Snappy,
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max_num_records=1000,
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feed_order=None):
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if feed_order is None:
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feed_order = feeder.feed_names
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f_name, f_ext = os.path.splitext(filename)
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assert (f_ext == ".recordio")
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lines = []
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f_idx = 0
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counter = 0
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for idx, batch in enumerate(reader_creator()):
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lines.append(batch)
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if idx >= batch_per_file and idx % batch_per_file == 0:
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filename = "%s-%05d%s" % (f_name, f_idx, f_ext)
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with fluid.recordio_writer.create_recordio_writer(
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filename, compressor, max_num_records) as writer:
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for l in lines:
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res = feeder.feed(l)
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for each in feed_order:
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writer.append_tensor(res[each])
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writer.complete_append_tensor()
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counter += 1
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lines = []
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f_idx += 1
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print("written file: ", filename)
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return counter
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def prepare_imagenet(inpath, outpath, batch_size):
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r = paddle.batch(imagenet_train(inpath), batch_size=batch_size)
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feeder = fluid.DataFeeder(
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feed_list=[
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fluid.layers.data(
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name="image", shape=[3, 224, 224]), fluid.layers.data(
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name="label", shape=[1], dtype='int64')
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],
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place=fluid.CPUPlace())
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outpath = os.path.join(outpath, "imagenet.recordio")
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convert_reader_to_recordio_files(outpath, 10000, r, feeder)
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@ -0,0 +1,9 @@
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#!/bin/bash
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PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model resnet --device CPU --update_method pserver --iterations=10000 &
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sleep 15
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CUDA_VISIBLE_DEVICES=0,1 PADDLE_TRAINING_ROLE=TRAINER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model resnet --device GPU --update_method pserver --iterations=10000 --gpus 2 &
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CUDA_VISIBLE_DEVICES=2,3 PADDLE_TRAINING_ROLE=TRAINER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=1 python fluid_benchmark.py --model resnet --device GPU --update_method pserver --iterations=10000 --gpus 2 &
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