mirror of https://github.com/alibaba/EasyCV.git
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
|
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|||
|
|
|
|||
|
|
import torch
|
|||
|
|
from PIL import Image
|
|||
|
|
|
|||
|
|
from easycv.datasets.registry import DATASETS
|
|||
|
|
from easycv.datasets.shared.base import BaseDataset
|
|||
|
|
|
|||
|
|
|
|||
|
|
@DATASETS.register_module
|
|||
|
|
class ClsDataset(BaseDataset):
|
|||
|
|
"""Dataset for classification
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
data_source: data source to parse input data
|
|||
|
|
pipeline: transforms list
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
def __init__(self, data_source, pipeline):
|
|||
|
|
super(ClsDataset, self).__init__(data_source, pipeline)
|
|||
|
|
|
|||
|
|
def __getitem__(self, idx):
|
|||
|
|
|
|||
|
|
img, target = self.data_source.get_sample(idx)
|
|||
|
|
|
|||
|
|
if isinstance(img, list):
|
|||
|
|
# img is list, means contains multi img
|
|||
|
|
mlist = []
|
|||
|
|
for m in img:
|
|||
|
|
assert isinstance(m, Image.Image), \
|
|||
|
|
f'The output from the data source must be an Image, got: {type(m)}. \
|
|||
|
|
Please ensure that the list file does not contain labels.'
|
|||
|
|
|
|||
|
|
m = (self.pipeline(m)).unsqueeze(0)
|
|||
|
|
mlist.append(m)
|
|||
|
|
img_cat = torch.cat(mlist, dim=0)
|
|||
|
|
return dict(img=img_cat, gt_label=torch.tensor(target).long())
|
|||
|
|
else:
|
|||
|
|
img = self.pipeline(img)
|
|||
|
|
return dict(img=img, gt_label=target)
|
|||
|
|
|
|||
|
|
def evaluate(self, results, evaluators, logger=None, topk=(1, 5)):
|
|||
|
|
'''evaluate classification task
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
results: a dict of list of tensor, including prediction and groundtruth
|
|||
|
|
info, where prediction tensor is NxC，and the same with groundtruth labels.
|
|||
|
|
|
|||
|
|
evaluators: a list of evaluator
|
|||
|
|
|
|||
|
|
Return:
|
|||
|
|
eval_result: a dict of float, different metric values
|
|||
|
|
'''
|
|||
|
|
assert len(evaluators) == 1, \
|
|||
|
|
'classification evaluation only support one evaluator'
|
|||
|
|
gt_labels = results.pop('gt_labels')
|
|||
|
|
eval_res = evaluators[0].evaluate(results, gt_labels)
|
|||
|
|
|
|||
|
|
return eval_res
|