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# Copyright (c) Facebook, Inc. and its affiliates.
import copy
from itertools import count
import numpy as np
import torch
from fvcore.transforms import HFlipTransform
from torch import nn
from torch.nn.parallel import DistributedDataParallel
from detectron2.data.detection_utils import read_image
from detectron2.modeling import DatasetMapperTTA
__all__ = [
"SemanticSegmentorWithTTA",
]
class SemanticSegmentorWithTTA(nn.Module):
"""
A SemanticSegmentor with test-time augmentation enabled.
Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.
"""
def __init__(self, cfg, model, tta_mapper=None, batch_size=1):
"""
Args:
cfg (CfgNode):
model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.
tta_mapper (callable): takes a dataset dict and returns a list of
augmented versions of the dataset dict. Defaults to
`DatasetMapperTTA(cfg)`.
batch_size (int): batch the augmented images into this batch size for inference.
"""
super().__init__()
if isinstance(model, DistributedDataParallel):
model = model.module
self.cfg = cfg.clone()
self.model = model
if tta_mapper is None:
tta_mapper = DatasetMapperTTA(cfg)
self.tta_mapper = tta_mapper
self.batch_size = batch_size
def _batch_inference(self, batched_inputs):
"""
Execute inference on a list of inputs,
using batch size = self.batch_size, instead of the length of the list.
Inputs & outputs have the same format as :meth:`SemanticSegmentor.forward`
"""
outputs = []
inputs = []
for idx, input in zip(count(), batched_inputs):
inputs.append(input)
if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1:
with torch.no_grad():
outputs.extend(self.model(inputs))
inputs = []
return outputs
def __call__(self, batched_inputs):
"""
Same input/output format as :meth:`SemanticSegmentor.forward`
"""
def _maybe_read_image(dataset_dict):
ret = copy.copy(dataset_dict)
if "image" not in ret:
image = read_image(ret.pop("file_name"), self.model.input_format)
image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW
ret["image"] = image
if "height" not in ret and "width" not in ret:
ret["height"] = image.shape[1]
ret["width"] = image.shape[2]
return ret
return [self._inference_one_image(_maybe_read_image(x)) for x in batched_inputs]
def _inference_one_image(self, input):
"""
Args:
input (dict): one dataset dict with "image" field being a CHW tensor
Returns:
dict: one output dict
"""
augmented_inputs, tfms = self._get_augmented_inputs(input)
# 1: forward with all augmented images
outputs = self._batch_inference(augmented_inputs)
# Delete now useless variables to avoid being out of memory
del augmented_inputs
# 2: merge the results
# handle flip specially
new_outputs = []
for output, tfm in zip(outputs, tfms):
if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
new_outputs.append(output.pop("sem_seg").flip(dims=[2]))
else:
new_outputs.append(output.pop("sem_seg"))
del outputs
# to avoid OOM with torch.stack
final_predictions = new_outputs[0]
for i in range(1, len(new_outputs)):
final_predictions += new_outputs[i]
final_predictions = final_predictions / len(new_outputs)
del new_outputs
return {"sem_seg": final_predictions}
def _get_augmented_inputs(self, input):
augmented_inputs = self.tta_mapper(input)
tfms = [x.pop("transforms") for x in augmented_inputs]
return augmented_inputs, tfms
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