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evaluate_classifiers.py
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evaluate_classifiers.py
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#!/usr/bin/env python
import argparse
import pandas
import torch
from torchvision import transforms
from tqdm import tqdm
from models import DeepDermClassifier
from models import ModelDermClassifier
from models import ScanomaClassifier
from models import SSCDClassifier
from models import SIIMISICClassifier
import datasets
DEVICE = 'cuda'
OUTPATHS = {
'scanoma':
{'f17k': 'scanoma_f17k.csv',
'isic': 'scanoma_isic.csv',
'ddi': 'scanoma_ddi.csv'},
'sscd':
{'f17k': 'sscd_f17k.csv',
'isic': 'sscd_isic.csv',
'ddi': 'sscd_ddi.csv'},
'deepderm':
{'f17k': 'deepderm_f17k.csv',
'isic': 'deepderm_isic.csv',
'ddi': 'deepderm_ddi.csv'},
'modelderm':
{'f17k': 'modelderm_f17k.csv',
'isic': 'modelderm_isic.csv',
'ddi': 'modelderm_ddi.csv'},
'siimisic':
{'f17k': 'siimisic_f17k.csv',
'isic': 'siimisic_isic.csv',
'ddi': 'siimisic_ddi.csv'}
}
CLASSIFIER_CLASS = {
'scanoma': ScanomaClassifier,
'sscd': SSCDClassifier,
'deepderm': DeepDermClassifier,
'modelderm': ModelDermClassifier,
'siimisic': SIIMISICClassifier
}
DATASET_CLASS = {
'f17k': datasets.Fitzpatrick17kDataset,
'isic': datasets.ISICDataset,
'ddi': datasets.DDIDataset
}
def basic_test(model_name, dataset_name):
outpath = OUTPATHS[model_name][dataset_name]
cls = CLASSIFIER_CLASS[model_name]
dataset_class = DATASET_CLASS[dataset_name]
classifier = CLASSIFIER_CLASS[model_name]()
im_size = classifier.image_size
positive_index = classifier.positive_index
batch_size = 16
normalize = transforms.Normalize(mean=0.5,
std=0.5)
transform = transforms.Compose([
transforms.Resize(im_size),
transforms.CenterCrop(im_size),
transforms.ToTensor(),
normalize])
dataset = dataset_class(transform=transform)
#dataset.df = dataset.df.iloc[:500]
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
drop_last=False,
num_workers=1)
classifier.eval()
classifier.to(DEVICE)
try: classifier.enable_augment()
except AttributeError: pass
labels = []
preds = []
for ibatch, batch in enumerate(tqdm(dataloader)):
im, label = batch
im = im.to(DEVICE)
with torch.no_grad():
pred = classifier(im)[:, positive_index].detach().cpu().numpy()
labels += list(label.detach().cpu().numpy())
preds += list(pred)
d = {'ground_truth': labels, 'prediction': preds}
if 'fitzpatrick' in dataset.df.columns:
fitzpatrick_list = []
for i in range(len(dataset)):
fitzpatrick = dataset.df.fitzpatrick.iloc[i]
label = dataset._get_label(i)
assert label == labels[i]
fitzpatrick_list.append(fitzpatrick)
d['fitzpatrick'] = fitzpatrick_list
if 'skin_tone' in dataset.df.columns:
fitzpatrick_list = []
for i in range(len(dataset)):
skin_tone = dataset.df.skin_tone.iloc[i]
label = dataset._get_label(i)
assert label == labels[i]
fitzpatrick_list.append(skin_tone)
d['fitzpatrick'] = fitzpatrick_list
df = pandas.DataFrame(d)
df.to_csv(outpath)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--classifier', type=str, choices=CLASSIFIER_CLASS.keys())
parser.add_argument('--dataset', type=str, choices=DATASET_CLASS.keys())
args = parser.parse_args()
basic_test(args.classifier, args.dataset)
if __name__ == "__main__":
main()