reorder imports

monai_vila2d/eval/scripts/classification/expert_infer_chestxray14.py

@@ -13,20 +13,19 @@
 import argparse
 import os
 import warnings
-from copy import deepcopy
-from types import SimpleNamespace
 
+from copy import deepcopy
 from llava.conversation import conv_templates
 from llava.mm_utils import get_model_name_from_path
 from llava.model.builder import load_pretrained_model
 from monai.bundle.config_parser import ConfigParser
 from monai.utils import look_up_option
-
 from prompts import has_placeholder, replace, templates
-
-# from llava.eval.run_vila import eval_model
 from run_vila import eval_model
 from torchxray_cls import all_models, cls_models
+from types import SimpleNamespace
+
+
 
 cls = {1: "fracture", 2: "pneumothorax", 3: "lung opacity"}
 

monai_vila2d/eval/scripts/classification/expert_infer_chexpert.py

@@ -13,7 +13,7 @@
 import argparse
 import os
 import warnings
-from types import SimpleNamespace
+
 from copy import deepcopy
 
 from llava.conversation import conv_templates
@@ -23,10 +23,11 @@
 from monai.utils import look_up_option
 
 from prompts import has_placeholder, replace, templates
-
-# from llava.eval.run_vila import eval_model
 from run_vila import eval_model
 from torchxray_cls import all_models, cls_models
+from types import SimpleNamespace
+
+
 
 cls = {1: "atelectasis", 2: "cardiomegaly", 3: "consolidation", 4: "edema", 5: "pleural effusion"}
 

monai_vila2d/eval/scripts/classification/infer_chestxray14.py

@@ -14,11 +14,9 @@
 import os
 from types import SimpleNamespace
 
-from run_vila import eval_model
-# from llava.eval.run_vila import eval_model
 from llava.mm_utils import get_model_name_from_path
 from llava.model.builder import load_pretrained_model
-
+from run_vila import eval_model
 
 def batch_run(exp_id, mpath, conv_mode, folder_name):
     prompt = (

monai_vila2d/eval/scripts/classification/infer_chexpert.py

@@ -12,13 +12,13 @@
 # load chest x-ray and try different classification prompts
 import argparse
 import os
-from types import SimpleNamespace
 
-from run_vila import eval_model
-# from llava.eval.run_vila import eval_model
 from llava.mm_utils import get_model_name_from_path
 from llava.model.builder import load_pretrained_model
 
+from run_vila import eval_model
+from types import SimpleNamespace
+
 
 def batch_run(exp_id, mpath, conv_mode, folder_name):
     prompt = (

monai_vila2d/eval/scripts/classification/metric_chexpert.py

@@ -10,13 +10,13 @@
 # limitations under the License.
 
 import argparse
+import json
+import numpy as np
 import os
 import re
 import warnings
 
-import numpy as np
 from sklearn.metrics import f1_score
-import json
 
 classes = {"Atelectasis": "a", "Cardiomegaly": "b", "Consolidation": "c", "Edema": "d", "pleural_effusion": "e"}
 gt_ids = {"Atelectasis": -6, "Cardiomegaly": -12, "Consolidation": -8, "Edema": -9, "pleural_effusion": -4}

monai_vila2d/eval/scripts/classification/run_vila.py

@@ -11,10 +11,11 @@
 
 import argparse
 import os.path as osp
-from io import BytesIO
-
 import requests
 import torch
+
+from io import BytesIO
+
 from llava.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX
 from llava.conversation import SeparatorStyle, conv_templates
 from llava.mm_utils import KeywordsStoppingCriteria, get_model_name_from_path, process_images, tokenizer_image_token

monai_vila2d/eval/scripts/metric_pathvqa.py

@@ -9,9 +9,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import argparse
 import json
 import pickle
-import argparse
 
 def get_args():
     parser = argparse.ArgumentParser()

monai_vila2d/eval/scripts/metric_radvqa.py

@@ -10,9 +10,10 @@
 # limitations under the License.
 
 
+import argparse
 import json
 import jsonlines
-import argparse
+
 
 def get_args():
     parser = argparse.ArgumentParser()

monai_vila2d/eval/scripts/metric_slakevqa.py

@@ -10,9 +10,10 @@
 # limitations under the License.
 
 
+import argparse
 import json
 import jsonlines
-import argparse
+
 
 def get_args():
     parser = argparse.ArgumentParser()

monai_vila2d/eval/scripts/mimic_vqa/eval_pvalue.py

@@ -9,13 +9,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from pathlib import Path
 import numpy as np
 
-from glossary import normalize_word
 from evaluate_metrics import calculate_f1score
-from utils import load_json, load_jsonl
+from glossary import normalize_word
+from pathlib import Path
 from scipy.stats import wilcoxon
+from utils import load_json, load_jsonl
 
 def get_significance(instruct, prediction1, prediction2):
     gt = load_json(instruct)

monai_vila2d/eval/scripts/mimic_vqa/evaluate_metrics.py

@@ -10,8 +10,8 @@
 # limitations under the License.
 
 import math
-from utils import *
 from glossary import *
+from utils import *
 
 def bleu(candidate, references, n, weights):
 

monai_vila2d/eval/scripts/mimic_vqa/metric_mimicvqa.py

@@ -10,16 +10,13 @@
 # limitations under the License.
 
 
-import os
-import json
-# import jsonlines
 import argparse
-
-from pathlib import Path
+import json
 import numpy as np
 
-from glossary import normalize_word
 from evaluate_metrics import calculate_f1score
+from glossary import normalize_word
+from pathlib import Path
 from utils import load_json, load_jsonl
 
 

monai_vila2d/eval/scripts/mimic_vqa/utils.py

@@ -10,12 +10,13 @@
 # limitations under the License.
 
 
-from pathlib import Path
 import json
-from typing import Any, List, Tuple, Union, Optional
-from collections import defaultdict
 import math
 
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, List
+
 
 def load_json(path: Path) -> Any:
     """

monai_vila2d/eval/scripts/print_metrics.py

@@ -0,0 +1,100 @@
+# Copyright (c) MONAI Consortium
+# 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,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import json
+import os
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input", type=str, default=True)
+    return parser.parse_args()
+
+def load_json(file_path):
+    with open(file_path, 'r') as f:
+        return json.load(f)
+
+
+def main():
+
+    args = get_args()
+    results = {}
+
+
+    try:
+        json_data = load_json(os.path.join(args.input, 'radvqa/results.json'))
+        results['radvqa'] = json_data['accuracy']
+    except:
+        results['radvqa'] = '?'
+
+    try:
+        json_data = load_json(os.path.join(args.input, 'mimicvqa/results.json'))
+        results['mimicvqa'] = json_data['wavg_closed']
+    except:
+        results['mimicvqa'] = '?'
+
+    try:
+        json_data = load_json(os.path.join(args.input, 'slakevqa/results.json'))
+        results['slakevqa'] = json_data['accuracy']
+    except:
+        results['slakevqa'] = '?'
+
+    try:
+        json_data = load_json(os.path.join(args.input, 'pathvqa/results.json'))
+        results['pathvqa'] = json_data['accuracy']
+    except:
+        results['pathvqa'] = '?'
+
+
+    try:
+        json_data = load_json(os.path.join(args.input, 'report_mimiccxr/results.json'))
+        results['report_mimiccxr_bleu4'] = json_data['accuracy']['Bleu_4']
+        results['report_mimiccxr_rougel'] = json_data['accuracy']['ROUGE_L']
+
+    except:
+        results['report_mimiccxr_bleu4'] = '?'
+        results['report_mimiccxr_rougel'] = '?'
+
+
+    try:
+        json_data = load_json(os.path.join(args.input, 'chestxray14_class/results.json'))
+        results['chestxray14_class'] = json_data['f1']
+    except:
+        results['chestxray14_class'] = '?'
+
+
+    try:
+        json_data = load_json(os.path.join(args.input, 'chexpert_class/results.json'))
+        results['chexpert_class'] = json_data['f1']
+    except:
+        results['chexpert_class'] = '?'
+
+    try:
+        json_data = load_json(os.path.join(args.input, 'chestxray14_expert_class/results.json'))
+        results['chestxray14_expert_class'] = json_data['f1']
+    except:
+        results['chestxray14_expert_class'] = '?'
+
+    try:
+        json_data = load_json(os.path.join(args.input, 'chexpert_expert_class/results.json'))
+        results['chexpert_expert_class'] = json_data['f1']
+    except:
+        results['chexpert_expert_class'] = '?'
+
+    results = json.loads(json.dumps(results), parse_float=lambda x: round(100*float(x), 2)) # convert to percentage
+
+    print(json.dumps(results, indent = 4))
+    print([v for _, v in results.items()])
+
+if __name__ == "__main__":
+    main()
+
+

monai_vila2d/eval/scripts/report/compute_metrics_caption.py

@@ -11,9 +11,8 @@
 # limitations under the License.
 
 import argparse
-import os
 import json
-import sys
+import os
 from pycocotools.coco import COCO
 from pycocoevalcap.eval import COCOEvalCap
 

monai_vila2d/eval/scripts/report/eval.py

@@ -1,4 +1,3 @@
-__author__ = "tylin"
 # Copyright (c) MONAI Consortium
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -10,12 +9,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .tokenizer.ptbtokenizer import PTBTokenizer
 from .bleu.bleu import Bleu
+from .cider.cider import Cider
 from .meteor.meteor import Meteor
 from .rouge.rouge import Rouge
-from .cider.cider import Cider
-
+from .tokenizer.ptbtokenizer import PTBTokenizer
 
 class COCOEvalCap:
     def __init__(self, coco, cocoRes):

monai_vila2d/eval/scripts/report/report_mimiccxr_infer.py

@@ -10,14 +10,12 @@
 # limitations under the License.
 
 import argparse
-import re
-from io import BytesIO
-import os
 import os.path as osp
-
+import re
 import requests
 import torch
-from PIL import Image
+
+from io import BytesIO
 
 from llava.constants import (
     DEFAULT_IM_END_TOKEN,
@@ -36,6 +34,7 @@
 from llava.model.builder import load_pretrained_model
 from llava.utils import disable_torch_init
 
+from PIL import Image
 
 def load_filenames(file_path):
     with open(file_path, "r") as f: