dali.py

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed 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 __future__ import division

import os

import numpy as np
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.ops as ops
import nvidia.dali.types as types
from nvidia.dali.plugin.paddle import DALIGenericIterator

import paddle
from paddle import fluid


class HybridTrainPipe(Pipeline):
    def __init__(self,
                 file_root,
                 file_list,
                 batch_size,
                 resize_shorter,
                 crop,
                 min_area,
                 lower,
                 upper,
                 interp,
                 mean,
                 std,
                 device_id,
                 shard_id=0,
                 num_shards=1,
                 random_shuffle=True,
                 num_threads=4,
                 seed=42):
        super(HybridTrainPipe, self).__init__(
            batch_size, num_threads, device_id, seed=seed)
        self.input = ops.FileReader(
            file_root=file_root,
            file_list=file_list,
            shard_id=shard_id,
            num_shards=num_shards,
            random_shuffle=random_shuffle)
        # set internal nvJPEG buffers size to handle full-sized ImageNet images
        # without additional reallocations
        device_memory_padding = 211025920
        host_memory_padding = 140544512
        self.decode = ops.ImageDecoderRandomCrop(
            device='mixed',
            output_type=types.RGB,
            device_memory_padding=device_memory_padding,
            host_memory_padding=host_memory_padding,
            random_aspect_ratio=[lower, upper],
            random_area=[min_area, 1.0],
            num_attempts=100)
        self.res = ops.Resize(
            device='gpu', resize_x=crop, resize_y=crop, interp_type=interp)
        self.cmnp = ops.CropMirrorNormalize(
            device="gpu",
            output_dtype=types.FLOAT,
            output_layout=types.NCHW,
            crop=(crop, crop),
            image_type=types.RGB,
            mean=mean,
            std=std)
        self.coin = ops.CoinFlip(probability=0.5)
        self.to_int64 = ops.Cast(dtype=types.INT64, device="gpu")

    def define_graph(self):
        rng = self.coin()
        jpegs, labels = self.input(name="Reader")
        images = self.decode(jpegs)
        images = self.res(images)
        output = self.cmnp(images.gpu(), mirror=rng)
        return [output, self.to_int64(labels.gpu())]

    def __len__(self):
        return self.epoch_size("Reader")


class HybridValPipe(Pipeline):
    def __init__(self,
                 file_root,
                 file_list,
                 batch_size,
                 resize_shorter,
                 crop,
                 interp,
                 mean,
                 std,
                 device_id,
                 shard_id=0,
                 num_shards=1,
                 random_shuffle=False,
                 num_threads=4,
                 seed=42):
        super(HybridValPipe, self).__init__(
            batch_size, num_threads, device_id, seed=seed)
        self.input = ops.FileReader(
            file_root=file_root,
            file_list=file_list,
            shard_id=shard_id,
            num_shards=num_shards,
            random_shuffle=random_shuffle)
        self.decode = ops.ImageDecoder(device="mixed", output_type=types.RGB)
        self.res = ops.Resize(
            device="gpu", resize_shorter=resize_shorter, interp_type=interp)
        self.cmnp = ops.CropMirrorNormalize(
            device="gpu",
            output_dtype=types.FLOAT,
            output_layout=types.NCHW,
            crop=(crop, crop),
            image_type=types.RGB,
            mean=mean,
            std=std)
        self.to_int64 = ops.Cast(dtype=types.INT64, device="gpu")

    def define_graph(self):
        jpegs, labels = self.input(name="Reader")
        images = self.decode(jpegs)
        images = self.res(images)
        output = self.cmnp(images)
        return [output, self.to_int64(labels.gpu())]

    def __len__(self):
        return self.epoch_size("Reader")


def build(settings, mode='train'):
    env = os.environ
    assert settings.get('use_gpu',
                        True) == True, "gpu training is required for DALI"
    #assert not settings.get('use_mix'), "mixup is not supported by DALI reader"
    assert not settings.get(
        'use_aa'), "auto augment is not supported by DALI reader"
    assert float(env.get('FLAGS_fraction_of_gpu_memory_to_use', 0.92)) < 0.9, \
        "Please leave enough GPU memory for DALI workspace, e.g., by setting" \
        " `export FLAGS_fraction_of_gpu_memory_to_use=0.8`"

    file_root = settings.TRAIN.data_dir
    bs = settings.TRAIN.batch_size if mode == 'train' else settings.VALID.batch_size
    print(bs, paddle.fluid.core.get_cuda_device_count())
    assert bs % paddle.fluid.core.get_cuda_device_count() == 0, \
        "batch size must be multiple of number of devices"
    batch_size = bs // paddle.fluid.core.get_cuda_device_count()

    image_mean = [0.485, 0.456, 0.406]
    image_std = [0.229, 0.224, 0.225]
    mean = [v * 255 for v in image_mean]
    std = [v * 255 for v in image_std]

    crop = 224  # settings.crop_size
    resize_shorter = 256  # settings.resize_short_size
    min_area = 0.08  # settings.lower_scale
    lower = 3. / 4.  # settings.lower_ratio
    upper = 4. / 3.  # settings.upper_ratio

    interp = 1  # settings.interpolation or 1  # default to linear
    interp_map = {
        0: types.INTERP_NN,  # cv2.INTER_NEAREST
        1: types.INTERP_LINEAR,  # cv2.INTER_LINEAR
        2: types.INTERP_CUBIC,  # cv2.INTER_CUBIC
        4: types.INTERP_LANCZOS3,  # XXX use LANCZOS3 for cv2.INTER_LANCZOS4
    }
    assert interp in interp_map, "interpolation method not supported by DALI"
    interp = interp_map[interp]

    if mode != 'train':
        p = fluid.framework.cuda_places()[0]
        place = fluid.core.Place()
        place.set_place(p)
        device_id = place.gpu_device_id()
        file_list = os.path.join(file_root, 'val_list.txt')
        if not os.path.exists(file_list):
            file_list = None
            file_root = os.path.join(file_root, 'val')
        pipe = HybridValPipe(
            file_root,
            file_list,
            batch_size,
            resize_shorter,
            crop,
            interp,
            mean,
            std,
            device_id=device_id)
        pipe.build()
        return DALIGenericIterator(
            pipe, ['feed_image', 'feed_label'],
            size=len(pipe),
            dynamic_shape=True,
            fill_last_batch=False,
            last_batch_padded=True)

    file_list = os.path.join(file_root, 'train_list.txt')
    if not os.path.exists(file_list):
        file_list = None
        file_root = os.path.join(file_root, 'train')

    if 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env:
        shard_id = int(env['PADDLE_TRAINER_ID'])
        num_shards = int(env['PADDLE_TRAINERS_NUM'])
        device_id = int(env['FLAGS_selected_gpus'])
        pipe = HybridTrainPipe(
            file_root,
            file_list,
            batch_size,
            resize_shorter,
            crop,
            min_area,
            lower,
            upper,
            interp,
            mean,
            std,
            device_id,
            shard_id,
            num_shards,
            seed=42 + shard_id)
        pipe.build()
        pipelines = [pipe]
        sample_per_shard = len(pipe) // num_shards
    else:
        pipelines = []
        places = fluid.framework.cuda_places()
        num_shards = len(places)
        for idx, p in enumerate(places):
            place = fluid.core.Place()
            place.set_place(p)
            device_id = place.gpu_device_id()
            pipe = HybridTrainPipe(
                file_root,
                file_list,
                batch_size,
                resize_shorter,
                crop,
                min_area,
                lower,
                upper,
                interp,
                mean,
                std,
                device_id,
                idx,
                num_shards,
                seed=42 + idx)
            pipe.build()
            pipelines.append(pipe)
        sample_per_shard = len(pipelines[0])

    return DALIGenericIterator(
        pipelines, ['feed_image', 'feed_label'], size=sample_per_shard)


def train(settings):
    return build(settings, 'train')


def val(settings):
    return build(settings, 'val')


def _to_Tensor(lod_tensor, dtype):
    data_tensor = fluid.layers.create_tensor(dtype=dtype)
    data = np.array(lod_tensor).astype(dtype)
    fluid.layers.assign(data, data_tensor)
    return data_tensor


def post_mix(settings, batch):
    batch_size = settings.TRAIN.batch_size // paddle.fluid.core.get_cuda_device_count(
    )

    batch_imgs = _to_Tensor(batch[0]['feed_image'], 'float32')
    batch_label = _to_Tensor(batch[0]['feed_label'], 'int64')
    alpha = 0.2
    idx = _to_Tensor(np.random.permutation(batch_size), 'int32')
    lam = np.random.beta(alpha, alpha)

    batch_imgs = lam * batch_imgs + (1 - lam) * paddle.fluid.layers.gather(
        batch_imgs, idx)

    # print(type(batch_label))
    feed = [{
        'feed_image': batch_imgs,
        'feed_y_a': batch_label,
        'feed_y_b': paddle.fluid.layers.gather(batch_label, idx),
        'feed_lam': _to_Tensor([lam] * batch_size, 'float32')
    }]

    return feed


def post_mix_numpy(settings, batch):
    batch_size = settings.TRAIN.batch_size // paddle.fluid.core.get_cuda_device_count(
    )

    batch_imgs = np.array(batch[0]['feed_image'])
    batch_label = np.array(batch[0]['feed_label'])
    alpha = 0.2
    idx = np.random.permutation(batch_size)
    lam = np.random.beta(alpha, alpha)

    batch_imgs = lam * batch_imgs + (1 - lam) * batch_imgs[idx]

    feed = [{
        'feed_image': batch_imgs,
        'feed_y_a': batch_label,
        'feed_y_b': batch_label[idx],
        'feed_lam': np.array([lam] * batch_size).astype('float32')
    }]

    return feed