154 lines
No EOL
5.5 KiB
Python
154 lines
No EOL
5.5 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright (c) 2018, NVIDIA CORPORATION. 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 sys
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import tensorflow as tf
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import horovod.tensorflow as hvd
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from utils import image_processing
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from utils import hvd_utils
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from nvidia import dali
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import nvidia.dali.plugin.tf as dali_tf
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__all__ = ["get_synth_input_fn", "normalized_inputs"]
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class HybridPipe(dali.pipeline.Pipeline):
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def __init__(self,
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tfrec_filenames,
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tfrec_idx_filenames,
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height, width,
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batch_size,
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num_threads,
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device_id,
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shard_id,
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num_gpus,
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deterministic=False,
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dali_cpu=True,
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training=True):
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kwargs = dict()
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if deterministic:
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kwargs['seed'] = 7 * (1 + hvd.rank())
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super(HybridPipe, self).__init__(batch_size, num_threads, device_id, **kwargs)
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self.input = dali.ops.TFRecordReader(
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path=tfrec_filenames,
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index_path=tfrec_idx_filenames,
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random_shuffle=True,
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shard_id=shard_id,
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num_shards=num_gpus,
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initial_fill=10000,
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features={
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'image/encoded':dali.tfrecord.FixedLenFeature((), dali.tfrecord.string, ""),
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'image/class/label':dali.tfrecord.FixedLenFeature([1], dali.tfrecord.int64, -1),
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'image/class/text':dali.tfrecord.FixedLenFeature([ ], dali.tfrecord.string, ''),
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'image/object/bbox/xmin':dali.tfrecord.VarLenFeature(dali.tfrecord.float32, 0.0),
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'image/object/bbox/ymin':dali.tfrecord.VarLenFeature(dali.tfrecord.float32, 0.0),
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'image/object/bbox/xmax':dali.tfrecord.VarLenFeature(dali.tfrecord.float32, 0.0),
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'image/object/bbox/ymax':dali.tfrecord.VarLenFeature(dali.tfrecord.float32, 0.0)})
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if dali_cpu:
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self.decode = dali.ops.HostDecoder(device="cpu", output_type=dali.types.RGB)
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resize_device = "cpu"
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else:
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self.decode = dali.ops.nvJPEGDecoder(
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device="mixed",
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output_type=dali.types.RGB)
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resize_device = "gpu"
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if training:
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self.resize = dali.ops.RandomResizedCrop(
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device=resize_device,
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size=[height, width],
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interp_type=dali.types.INTERP_LINEAR,
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random_aspect_ratio=[0.8, 1.25],
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random_area=[0.1, 1.0],
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num_attempts=100)
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else:
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# Make sure that every image > 224 for CropMirrorNormalize
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self.resize = dali.ops.Resize (device=resize_device, resize_shorter=256)
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self.normalize = dali.ops.CropMirrorNormalize(
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device="gpu",
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output_dtype=dali.types.FLOAT,
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crop=(height, width),
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image_type=dali.types.RGB,
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mean=[121., 115., 100.],
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std=[70., 68., 71.],
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output_layout=dali.types.NHWC)
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self.uniform = dali.ops.Uniform(range=(0.0, 1.0))
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self.cast_float = dali.ops.Cast(device="gpu", dtype=dali.types.FLOAT)
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self.mirror = dali.ops.CoinFlip()
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self.iter = 0
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def define_graph(self):
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# Read images and labels
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inputs = self.input(name="Reader")
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images = inputs["image/encoded"]
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labels = inputs["image/class/label"].gpu()
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# Decode and augmentation
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images = self.decode(images)
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images = self.resize(images)
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images = self.normalize(images.gpu(), mirror=self.mirror())
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return (images, labels)
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class DALIPreprocessor(object):
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def __init__(self,
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filenames,
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idx_filenames,
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height, width,
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batch_size,
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num_threads,
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dtype=tf.uint8,
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dali_cpu=True,
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deterministic=False,
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training=False):
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device_id = hvd.local_rank()
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shard_id = hvd.rank()
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num_gpus = hvd.size()
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pipe = HybridPipe(
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tfrec_filenames=filenames,
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tfrec_idx_filenames=idx_filenames,
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height=height,
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width=width,
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batch_size=batch_size,
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num_threads=num_threads,
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device_id=device_id,
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shard_id=shard_id,
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num_gpus=num_gpus,
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deterministic=deterministic,
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dali_cpu=dali_cpu,
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training=training)
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daliop = dali_tf.DALIIterator()
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with tf.device("/gpu:0"):
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self.images, self.labels = daliop(
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pipeline=pipe,
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shapes=[(batch_size, height, width, 3), (batch_size, 1)],
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dtypes=[tf.float32, tf.int64],
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device_id=device_id)
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def get_device_minibatches(self):
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with tf.device("/gpu:0"):
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self.labels -= 1 # Change to 0-based (don't use background class)
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self.labels = tf.squeeze(self.labels)
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return self.images, self.labels |