Source code for cerebras.modelzoo.data.vision.segmentation.CityscapesDataProcessor

# Copyright 2022 Cerebras Systems.
#
# 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,
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# See the License for the specific language governing permissions and
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import torch
from torchvision import datasets, transforms

import cerebras.pytorch as cstorch
import cerebras.pytorch.distributed as dist
from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.data.vision.segmentation.UNetDataProcessor import (
    UNetDataProcessor,
)
from cerebras.modelzoo.data.vision.utils import create_worker_cache


[docs]class Cityscapes(datasets.Cityscapes): """Wrapper around torchvision.datasets.Cityscapes with sorted files for reproducibility""" def __init__(self, use_worker_cache=False, **kwargs): super(Cityscapes, self).__init__(**kwargs) if use_worker_cache and dist.is_streamer(): if not cstorch.use_cs(): raise RuntimeError( "use_worker_cache not supported for non-CS runs" ) else: self.root = create_worker_cache(self.root) self.images, self.targets = zip(*sorted(zip(self.images, self.targets)))
[docs]@registry.register_datasetprocessor("CityscapesDataProcessor") class CityscapesDataProcessor(UNetDataProcessor): def __init__(self, params): super(CityscapesDataProcessor, self).__init__(params) self.use_worker_cache = params["use_worker_cache"] self.image_shape = params["image_shape"] # of format (H, W, C) self._tiling_image_shape = self.image_shape # out format: (H, W, C) # Tiling param: # If `image_shape` < 1K x 2K, do not tile. # If `image_shape` > 1K x 2K in any dimension, # first resize image to min(img_shape, max_image_shape) # and then tile to target height and width specified in yaml self.max_image_shape = params.get("max_image_shape", [1024, 2048]) self.image_shape = self._update_image_shape() ( self.tgt_image_height, self.tgt_image_width, self.channels, ) = self.image_shape def _update_image_shape(self): # image_shape is of format (H, W, C) image_shape = [] for i in range(2): image_shape.append( min(self.image_shape[i], self.max_image_shape[i]) ) image_shape = ( image_shape + self.image_shape[-1:] ) # Output shape format (H, W, C) return image_shape def create_dataset(self, is_training): split = "train" if is_training else "val" dataset = Cityscapes( root=self.data_dir, split=split, mode="fine", target_type="semantic", transforms=self.transform_image_and_mask, use_worker_cache=self.use_worker_cache, ) return dataset def preprocess_mask(self, mask): # Refer to : # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py#L56-L99 # Mapping all classes with `ignoreInEval`=True(from above link) # to background class with id 0 def lookup_table(mask): # fmt: off lut = torch.tensor([ 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 5, 0, 0, 0, 6, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0, 0, 17, 18, 19, ], dtype=torch.uint8, ) # fmt: on return lut[mask] # Resize resize_pil_transform = transforms.Resize( [self.tgt_image_height, self.tgt_image_width], interpolation=transforms.InterpolationMode.NEAREST, ) # converts to (C, H, W) format. to_tensor_transform = transforms.PILToTensor() # Convert to long for lookup convert_to_long_transform = transforms.Lambda( lambda x: x.to(torch.long) ) # Map target ids based on lookup table lookup_table_transform = transforms.Lambda(lambda x: lookup_table(x)) # Convert to mp type convert_to_mp_type_transform = transforms.Lambda( lambda x: x.to(self.mp_type) ) tile_transform = self.get_tile_transform() transforms_list = [ resize_pil_transform, to_tensor_transform, convert_to_long_transform, lookup_table_transform, convert_to_mp_type_transform, tile_transform, ] mask = transforms.Compose(transforms_list)(mask) return mask