354 lines
13 KiB
Python
354 lines
13 KiB
Python
# Copyright (c) 2021, 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 itertools
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import os
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import numpy as np
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import nvidia.dali.fn as fn
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import nvidia.dali.math as math
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import nvidia.dali.ops as ops
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import nvidia.dali.types as types
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from nvidia.dali.pipeline import Pipeline
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from nvidia.dali.plugin.pytorch import DALIGenericIterator
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def get_numpy_reader(files, shard_id, num_shards, seed, shuffle):
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return ops.readers.Numpy(
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seed=seed,
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files=files,
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device="cpu",
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read_ahead=True,
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shard_id=shard_id,
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pad_last_batch=True,
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num_shards=num_shards,
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dont_use_mmap=True,
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shuffle_after_epoch=shuffle,
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)
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class TrainPipeline(Pipeline):
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def __init__(self, batch_size, num_threads, device_id, **kwargs):
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super(TrainPipeline, self).__init__(batch_size, num_threads, device_id)
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self.dim = kwargs["dim"]
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self.internal_seed = kwargs["seed"]
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self.oversampling = kwargs["oversampling"]
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self.input_x = get_numpy_reader(
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num_shards=kwargs["gpus"],
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files=kwargs["imgs"],
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seed=kwargs["seed"],
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shard_id=device_id,
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shuffle=True,
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)
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self.input_y = get_numpy_reader(
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num_shards=kwargs["gpus"],
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files=kwargs["lbls"],
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seed=kwargs["seed"],
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shard_id=device_id,
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shuffle=True,
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)
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self.patch_size = kwargs["patch_size"]
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if self.dim == 2:
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self.patch_size = [kwargs["batch_size_2d"]] + self.patch_size
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self.crop_shape = types.Constant(np.array(self.patch_size), dtype=types.INT64)
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self.crop_shape_float = types.Constant(np.array(self.patch_size), dtype=types.FLOAT)
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def load_data(self):
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img, lbl = self.input_x(name="ReaderX"), self.input_y(name="ReaderY")
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img, lbl = fn.reshape(img, layout="CDHW"), fn.reshape(lbl, layout="CDHW")
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return img, lbl
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def random_augmentation(self, probability, augmented, original):
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condition = fn.cast(fn.random.coin_flip(probability=probability), dtype=types.DALIDataType.BOOL)
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neg_condition = condition ^ True
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return condition * augmented + neg_condition * original
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@staticmethod
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def slice_fn(img):
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return fn.slice(img, 1, 3, axes=[0])
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def biased_crop_fn(self, img, label):
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roi_start, roi_end = fn.segmentation.random_object_bbox(
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label,
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format="start_end",
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foreground_prob=self.oversampling,
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background=0,
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seed=self.internal_seed,
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device="cpu",
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cache_objects=True,
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)
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anchor = fn.roi_random_crop(label, roi_start=roi_start, roi_end=roi_end, crop_shape=[1, *self.patch_size])
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anchor = fn.slice(anchor, 1, 3, axes=[0]) # drop channels from anchor
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img, label = fn.slice(
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[img, label], anchor, self.crop_shape, axis_names="DHW", out_of_bounds_policy="pad", device="cpu"
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)
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return img.gpu(), label.gpu()
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def zoom_fn(self, img, lbl):
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scale = self.random_augmentation(0.15, fn.random.uniform(range=(0.7, 1.0)), 1.0)
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d, h, w = [scale * x for x in self.patch_size]
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if self.dim == 2:
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d = self.patch_size[0]
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img, lbl = fn.crop(img, crop_h=h, crop_w=w, crop_d=d), fn.crop(lbl, crop_h=h, crop_w=w, crop_d=d)
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img = fn.resize(img, interp_type=types.DALIInterpType.INTERP_CUBIC, size=self.crop_shape_float)
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lbl = fn.resize(lbl, interp_type=types.DALIInterpType.INTERP_NN, size=self.crop_shape_float)
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return img, lbl
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def noise_fn(self, img):
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img_noised = img + fn.random.normal(img, stddev=fn.random.uniform(range=(0.0, 0.33)))
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return self.random_augmentation(0.15, img_noised, img)
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def blur_fn(self, img):
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img_blurred = fn.gaussian_blur(img, sigma=fn.random.uniform(range=(0.5, 1.5)))
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return self.random_augmentation(0.15, img_blurred, img)
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def brightness_fn(self, img):
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brightness_scale = self.random_augmentation(0.15, fn.random.uniform(range=(0.7, 1.3)), 1.0)
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return img * brightness_scale
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def contrast_fn(self, img):
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min_, max_ = fn.reductions.min(img), fn.reductions.max(img)
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scale = self.random_augmentation(0.15, fn.random.uniform(range=(0.65, 1.5)), 1.0)
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img = math.clamp(img * scale, min_, max_)
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return img
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def flips_fn(self, img, lbl):
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kwargs = {
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"horizontal": fn.random.coin_flip(probability=0.33),
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"vertical": fn.random.coin_flip(probability=0.33),
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}
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if self.dim == 3:
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kwargs.update({"depthwise": fn.random.coin_flip(probability=0.33)})
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return fn.flip(img, **kwargs), fn.flip(lbl, **kwargs)
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def transpose_fn(self, img, lbl):
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img, lbl = fn.transpose(img, perm=(1, 0, 2, 3)), fn.transpose(lbl, perm=(1, 0, 2, 3))
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return img, lbl
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def define_graph(self):
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img, lbl = self.load_data()
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img, lbl = self.biased_crop_fn(img, lbl)
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img, lbl = self.zoom_fn(img, lbl)
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img, lbl = self.flips_fn(img, lbl)
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img = self.noise_fn(img)
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img = self.blur_fn(img)
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img = self.brightness_fn(img)
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img = self.contrast_fn(img)
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if self.dim == 2:
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img, lbl = self.transpose_fn(img, lbl)
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return img, lbl
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class EvalPipeline(Pipeline):
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def __init__(self, batch_size, num_threads, device_id, **kwargs):
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super(EvalPipeline, self).__init__(batch_size, num_threads, device_id)
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self.input_x = get_numpy_reader(
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files=kwargs["imgs"],
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shard_id=0,
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num_shards=1,
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seed=kwargs["seed"],
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shuffle=False,
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)
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self.input_y = get_numpy_reader(
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files=kwargs["lbls"],
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shard_id=0,
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num_shards=1,
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seed=kwargs["seed"],
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shuffle=False,
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)
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def define_graph(self):
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img, lbl = self.input_x(name="ReaderX").gpu(), self.input_y(name="ReaderY").gpu()
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img, lbl = fn.reshape(img, layout="CDHW"), fn.reshape(lbl, layout="CDHW")
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return img, lbl
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class BermudaPipeline(Pipeline):
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def __init__(self, batch_size, num_threads, device_id, **kwargs):
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super(BermudaPipeline, self).__init__(batch_size, num_threads, device_id)
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self.input_x = get_numpy_reader(
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files=kwargs["imgs"],
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shard_id=device_id,
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num_shards=kwargs["gpus"],
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seed=kwargs["seed"],
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shuffle=False,
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)
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self.input_y = get_numpy_reader(
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files=kwargs["lbls"],
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shard_id=device_id,
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num_shards=kwargs["gpus"],
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seed=kwargs["seed"],
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shuffle=False,
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)
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self.patch_size = kwargs["patch_size"]
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def crop_fn(self, img, lbl):
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img = fn.crop(img, crop=self.patch_size, out_of_bounds_policy="pad")
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lbl = fn.crop(lbl, crop=self.patch_size, out_of_bounds_policy="pad")
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return img, lbl
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def define_graph(self):
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img, lbl = self.input_x(name="ReaderX"), self.input_y(name="ReaderY")
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img, lbl = fn.reshape(img, layout="CDHW"), fn.reshape(lbl, layout="CDHW")
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img, lbl = self.crop_fn(img, lbl)
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return img, lbl
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class TestPipeline(Pipeline):
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def __init__(self, batch_size, num_threads, device_id, **kwargs):
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super(TestPipeline, self).__init__(batch_size, num_threads, device_id)
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self.input_x = get_numpy_reader(
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files=kwargs["imgs"],
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shard_id=device_id,
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num_shards=kwargs["gpus"],
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seed=kwargs["seed"],
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shuffle=False,
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)
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self.input_meta = get_numpy_reader(
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files=kwargs["meta"],
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shard_id=device_id,
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num_shards=kwargs["gpus"],
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seed=kwargs["seed"],
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shuffle=False,
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)
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def define_graph(self):
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img, meta = self.input_x(name="ReaderX").gpu(), self.input_meta(name="ReaderY").gpu()
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img = fn.reshape(img, layout="CDHW")
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return img, meta
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class BenchmarkPipeline(Pipeline):
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def __init__(self, batch_size, num_threads, device_id, **kwargs):
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super(BenchmarkPipeline, self).__init__(batch_size, num_threads, device_id)
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self.input_x = get_numpy_reader(
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files=kwargs["imgs"],
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shard_id=device_id,
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seed=kwargs["seed"],
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num_shards=kwargs["gpus"],
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shuffle=False,
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)
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self.input_y = get_numpy_reader(
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files=kwargs["lbls"],
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shard_id=device_id,
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num_shards=kwargs["gpus"],
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seed=kwargs["seed"],
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shuffle=False,
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)
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self.dim = kwargs["dim"]
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self.patch_size = kwargs["patch_size"]
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if self.dim == 2:
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self.patch_size = [kwargs["batch_size_2d"]] + self.patch_size
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def load_data(self):
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img, lbl = self.input_x(name="ReaderX").gpu(), self.input_y(name="ReaderY").gpu()
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img, lbl = fn.reshape(img, layout="CDHW"), fn.reshape(lbl, layout="CDHW")
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return img, lbl
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def transpose_fn(self, img, lbl):
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img, lbl = fn.transpose(img, perm=(1, 0, 2, 3)), fn.transpose(lbl, perm=(1, 0, 2, 3))
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return img, lbl
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def crop_fn(self, img, lbl):
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img = fn.crop(img, crop=self.patch_size, out_of_bounds_policy="pad")
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lbl = fn.crop(lbl, crop=self.patch_size, out_of_bounds_policy="pad")
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return img, lbl
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def define_graph(self):
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img, lbl = self.load_data()
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img, lbl = self.crop_fn(img, lbl)
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if self.dim == 2:
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img, lbl = self.transpose_fn(img, lbl)
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return img, lbl
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class LightningWrapper(DALIGenericIterator):
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def __init__(self, pipe, **kwargs):
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super().__init__(pipe, **kwargs)
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def __next__(self):
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out = super().__next__()
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out = out[0]
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return out
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def fetch_dali_loader(imgs, lbls, batch_size, mode, **kwargs):
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assert len(imgs) > 0, "Got empty list of images"
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if lbls is not None:
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assert len(imgs) == len(lbls), f"Got {len(imgs)} images but {len(lbls)} lables"
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if kwargs["benchmark"]: # Just to make sure the number of examples is large enough for benchmark run.
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nbs = kwargs["test_batches"] if mode == "test" else kwargs["train_batches"]
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if kwargs["dim"] == 3:
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nbs *= batch_size
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imgs = list(itertools.chain(*(100 * [imgs])))[: nbs * kwargs["gpus"]]
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lbls = list(itertools.chain(*(100 * [lbls])))[: nbs * kwargs["gpus"]]
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if mode == "eval": # To avoid padding for the multigpu evaluation.
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rank = int(os.getenv("LOCAL_RANK", "0"))
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imgs, lbls = np.array_split(imgs, kwargs["gpus"]), np.array_split(lbls, kwargs["gpus"])
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imgs, lbls = [list(x) for x in imgs], [list(x) for x in lbls]
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imgs, lbls = imgs[rank], lbls[rank]
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pipe_kwargs = {
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"imgs": imgs,
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"lbls": lbls,
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"dim": kwargs["dim"],
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"gpus": kwargs["gpus"],
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"seed": kwargs["seed"],
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"meta": kwargs["meta"],
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"patch_size": kwargs["patch_size"],
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"oversampling": kwargs["oversampling"],
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}
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if kwargs["benchmark"]:
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pipeline = BenchmarkPipeline
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output_map = ["image", "label"]
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dynamic_shape = False
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if kwargs["dim"] == 2:
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pipe_kwargs.update({"batch_size_2d": batch_size})
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batch_size = 1
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elif mode == "train":
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pipeline = TrainPipeline
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output_map = ["image", "label"]
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dynamic_shape = False
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if kwargs["dim"] == 2:
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pipe_kwargs.update({"batch_size_2d": batch_size // kwargs["nvol"]})
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batch_size = kwargs["nvol"]
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elif mode == "eval":
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pipeline = EvalPipeline
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output_map = ["image", "label"]
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dynamic_shape = True
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elif mode == "bermuda":
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pipeline = BermudaPipeline
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output_map = ["image", "label"]
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dynamic_shape = False
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else:
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pipeline = TestPipeline
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output_map = ["image", "meta"]
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dynamic_shape = True
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device_id = int(os.getenv("LOCAL_RANK", "0"))
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pipe = pipeline(batch_size, kwargs["num_workers"], device_id, **pipe_kwargs)
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return LightningWrapper(
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pipe,
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auto_reset=True,
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reader_name="ReaderX",
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output_map=output_map,
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dynamic_shape=dynamic_shape,
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)
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