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316 lines
11 KiB
Python
316 lines
11 KiB
Python
# Copyright (c) 2021 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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from __future__ import division
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import copy
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import os
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import numpy as np
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import nvidia.dali.ops as ops
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import nvidia.dali.types as types
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import paddle
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from nvidia.dali import fn
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from nvidia.dali.pipeline import Pipeline
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from nvidia.dali.plugin.base_iterator import LastBatchPolicy
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from nvidia.dali.plugin.paddle import DALIGenericIterator
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class HybridTrainPipe(Pipeline):
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def __init__(self,
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file_root,
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file_list,
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batch_size,
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resize_shorter,
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crop,
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min_area,
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lower,
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upper,
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interp,
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mean,
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std,
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device_id,
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shard_id=0,
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num_shards=1,
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random_shuffle=True,
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num_threads=4,
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seed=42,
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pad_output=False,
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output_dtype=types.FLOAT,
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dataset='Train'):
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super(HybridTrainPipe, self).__init__(
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batch_size, num_threads, device_id, seed=seed)
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self.input = ops.readers.File(
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file_root=file_root,
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file_list=file_list,
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shard_id=shard_id,
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num_shards=num_shards,
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random_shuffle=random_shuffle)
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# set internal nvJPEG buffers size to handle full-sized ImageNet images
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# without additional reallocations
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device_memory_padding = 211025920
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host_memory_padding = 140544512
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self.decode = ops.decoders.ImageRandomCrop(
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device='mixed',
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output_type=types.DALIImageType.RGB,
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device_memory_padding=device_memory_padding,
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host_memory_padding=host_memory_padding,
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random_aspect_ratio=[lower, upper],
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random_area=[min_area, 1.0],
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num_attempts=100)
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self.res = ops.Resize(
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device='gpu', resize_x=crop, resize_y=crop, interp_type=interp)
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self.cmnp = ops.CropMirrorNormalize(
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device="gpu",
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dtype=output_dtype,
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output_layout='CHW',
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crop=(crop, crop),
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mean=mean,
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std=std,
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pad_output=pad_output)
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self.coin = ops.random.CoinFlip(probability=0.5)
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self.to_int64 = ops.Cast(dtype=types.DALIDataType.INT64, device="gpu")
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def define_graph(self):
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rng = self.coin()
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jpegs, labels = self.input(name="Reader")
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images = self.decode(jpegs)
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images = self.res(images)
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output = self.cmnp(images.gpu(), mirror=rng)
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return [output, self.to_int64(labels.gpu())]
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def __len__(self):
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return self.epoch_size("Reader")
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class HybridValPipe(Pipeline):
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def __init__(self,
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file_root,
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file_list,
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batch_size,
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resize_shorter,
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crop,
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interp,
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mean,
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std,
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device_id,
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shard_id=0,
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num_shards=1,
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random_shuffle=False,
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num_threads=4,
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seed=42,
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pad_output=False,
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output_dtype=types.FLOAT):
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super(HybridValPipe, self).__init__(
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batch_size, num_threads, device_id, seed=seed)
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self.input = ops.readers.File(
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file_root=file_root,
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file_list=file_list,
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shard_id=shard_id,
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num_shards=num_shards,
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random_shuffle=random_shuffle)
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self.decode = ops.decoders.Image(device="mixed")
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self.res = ops.Resize(
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device="gpu", resize_shorter=resize_shorter, interp_type=interp)
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self.cmnp = ops.CropMirrorNormalize(
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device="gpu",
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dtype=output_dtype,
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output_layout='CHW',
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crop=(crop, crop),
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mean=mean,
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std=std,
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pad_output=pad_output)
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self.to_int64 = ops.Cast(dtype=types.DALIDataType.INT64, device="gpu")
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def define_graph(self):
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jpegs, labels = self.input(name="Reader")
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images = self.decode(jpegs)
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images = self.res(images)
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output = self.cmnp(images)
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return [output, self.to_int64(labels.gpu())]
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def __len__(self):
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return self.epoch_size("Reader")
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def dali_dataloader(config, mode, device, seed=None):
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assert "gpu" in device, "gpu training is required for DALI"
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device_id = int(device.split(':')[1])
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config_dataloader = config[mode]
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# mode = 'train' if mode.lower() == 'train' else 'eval'
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seed = 42 if seed is None else seed
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ops = [
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list(x.keys())[0]
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for x in config_dataloader["dataset"]["transform_ops"]
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]
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support_ops_train = [
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"DecodeImage", "NormalizeImage", "RandFlipImage", "RandCropImage"
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]
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support_ops_eval = [
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"DecodeImage", "ResizeImage", "CropImage", "NormalizeImage"
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]
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if mode.lower() == 'train':
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assert set(ops) == set(
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support_ops_train
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), "The supported trasform_ops for train_dataset in dali is : {}".format(
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",".join(support_ops_train))
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else:
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assert set(ops) == set(
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support_ops_eval
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), "The supported trasform_ops for eval_dataset in dali is : {}".format(
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",".join(support_ops_eval))
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env = os.environ
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# assert float(env.get('FLAGS_fraction_of_gpu_memory_to_use', 0.92)) < 0.9, \
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# "Please leave enough GPU memory for DALI workspace, e.g., by setting" \
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# " `export FLAGS_fraction_of_gpu_memory_to_use=0.8`"
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gpu_num = paddle.distributed.get_world_size()
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batch_size = config_dataloader["sampler"]["batch_size"]
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# assert batch_size % gpu_num == 0, \
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# "batch size must be multiple of number of devices"
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# batch_size = batch_size // gpu_num
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file_root = config_dataloader["dataset"]["image_root"]
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file_list = config_dataloader["dataset"]["cls_label_path"]
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interp = 1 # settings.interpolation or 1 # default to linear
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interp_map = {
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0: types.DALIInterpType.INTERP_NN, # cv2.INTER_NEAREST
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1: types.DALIInterpType.INTERP_LINEAR, # cv2.INTER_LINEAR
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2: types.DALIInterpType.INTERP_CUBIC, # cv2.INTER_CUBIC
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3: types.DALIInterpType.
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INTERP_LANCZOS3, # XXX use LANCZOS3 for cv2.INTER_LANCZOS4
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}
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output_dtype = (types.FLOAT16 if 'AMP' in config and
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config.AMP.get("use_pure_fp16", False) else types.FLOAT)
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assert interp in interp_map, "interpolation method not supported by DALI"
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interp = interp_map[interp]
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pad_output = False
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image_shape = config.get("image_shape", None)
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if image_shape and image_shape[0] == 4:
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pad_output = True
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transforms = {
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k: v
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for d in config_dataloader["dataset"]["transform_ops"]
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for k, v in d.items()
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}
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scale = transforms["NormalizeImage"].get("scale", 1.0 / 255)
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scale = eval(scale) if isinstance(scale, str) else scale
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mean = transforms["NormalizeImage"].get("mean", [0.485, 0.456, 0.406])
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std = transforms["NormalizeImage"].get("std", [0.229, 0.224, 0.225])
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mean = [v / scale for v in mean]
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std = [v / scale for v in std]
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if mode.lower() == "train":
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resize_shorter = 256
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crop = transforms["RandCropImage"]["size"]
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scale = transforms["RandCropImage"].get("scale", [0.08, 1.])
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ratio = transforms["RandCropImage"].get("ratio", [3.0 / 4, 4.0 / 3])
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min_area = scale[0]
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lower = ratio[0]
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upper = ratio[1]
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if 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env:
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shard_id = int(env['PADDLE_TRAINER_ID'])
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num_shards = int(env['PADDLE_TRAINERS_NUM'])
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device_id = int(env['FLAGS_selected_gpus'])
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pipe = HybridTrainPipe(
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file_root,
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file_list,
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batch_size,
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resize_shorter,
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crop,
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min_area,
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lower,
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upper,
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interp,
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mean,
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std,
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device_id,
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shard_id,
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num_shards,
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seed=seed + shard_id,
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pad_output=pad_output,
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output_dtype=output_dtype)
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pipe.build()
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pipelines = [pipe]
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# sample_per_shard = len(pipe) // num_shards
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else:
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pipe = HybridTrainPipe(
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file_root,
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file_list,
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batch_size,
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resize_shorter,
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crop,
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min_area,
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lower,
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upper,
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interp,
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mean,
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std,
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device_id=device_id,
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shard_id=0,
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num_shards=1,
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seed=seed,
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pad_output=pad_output,
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output_dtype=output_dtype)
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pipe.build()
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pipelines = [pipe]
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# sample_per_shard = len(pipelines[0])
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return DALIGenericIterator(
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pipelines, ['data', 'label'], reader_name='Reader')
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else:
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resize_shorter = transforms["ResizeImage"].get("resize_short", 256)
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crop = transforms["CropImage"]["size"]
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if 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env:
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shard_id = int(env['PADDLE_TRAINER_ID'])
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num_shards = int(env['PADDLE_TRAINERS_NUM'])
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device_id = int(env['FLAGS_selected_gpus'])
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pipe = HybridValPipe(
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file_root,
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file_list,
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batch_size,
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resize_shorter,
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crop,
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interp,
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mean,
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std,
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device_id=device_id,
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shard_id=shard_id,
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num_shards=num_shards,
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pad_output=pad_output,
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output_dtype=output_dtype)
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else:
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pipe = HybridValPipe(
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file_root,
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file_list,
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batch_size,
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resize_shorter,
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crop,
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interp,
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mean,
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std,
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device_id=device_id,
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pad_output=pad_output,
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output_dtype=output_dtype)
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pipe.build()
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return DALIGenericIterator(
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[pipe], ['data', 'label'], reader_name="Reader")
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