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simclr_pad.py
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simclr_pad.py
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import os
import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score
from hydra import compose, initialize
from omegaconf import DictConfig
from classification.classification_module import ClassificationModule
from data_handling.xray import PadChestDataModule
from evaluation.helper_functions import run_inference
os.chdir("/vol/biomedic3/mb121/causal-contrastive/evaluation")
# Mapping from human readable run name to Weights&Biases run_id.
# Human readable name should be in format:
# for finetuning:
# {simclr/simclrcf/simclrcfaug}-{train_prop}-{seed}
# for linear probing
# {simclr/simclrcf/simclrcfaug}head-{train_prop}-{seed}
model_dict_normal = {
"supervised-0.1-11": "8vouetwb",
"supervised-0.1-22": "96rhabqe",
"supervised-0.1-33": "fyismsp9",
"supervised-1.0-22": "4oaaikt6",
"supervised-1.0-33": "n8h9oapi",
"supervised-1.0-11": "ghopk3ju",
"supervised-0.05-11": "yhhb9ytj",
"supervised-0.05-22": "bx6gi5ib",
"supervised-0.05-33": "wbhd0e7x",
"supervised-0.25-11": "jtwn5ni9",
"supervised-0.25-22": "yll5f08y",
"supervised-0.25-33": "mcyuc9hr",
"simclr-0.1-22": "n4q4a33f",
"simclr-0.1-33": "3z4co0f5",
"simclr-0.1-11": "u2hzkzi8",
"simclr-1.0-22": "4pe40g5x",
"simclr-1.0-33": "fph8u80n",
"simclr-1.0-11": "hqvqp4mh",
"simclrcfaug-0.1-33": "zqv28q1c",
"simclrcfaug-1.0-33": "hu8wg6s4",
"simclrcfaug-0.1-22": "8xf5b6ts",
"simclrcfaug-1.0-22": "e4k7i59o",
"simclrcfaug-0.1-11": "n7rp8bvc",
"simclrcfaug-1.0-11": "ghtxoeph",
"simclrcf-0.1-11": "597enhyw",
"simclrcf-0.1-22": "erjrh75z",
"simclrcf-1.0-22": "axb7nhet",
"simclrcf-1.0-33": "gcn4pmb5",
"simclrcf-1.0-11": "bjurudm1",
"simclrcf-0.1-33": "givxkg9r",
"simclrcf-0.05-11": "yuw1hej0",
"simclrcf-0.25-11": "zzr4utap",
"simclrcf-0.05-22": "46orb30r",
"simclrcf-0.25-22": "sg6h109z",
"simclrcf-0.05-33": "ues31163",
"simclrcf-0.25-33": "x20kayvz",
"simclr-0.05-11": "xuat4sf9",
"simclr-0.25-11": "03r51wrk",
"simclr-0.05-22": "noeakj31",
"simclr-0.25-22": "9k2na4p0",
"simclr-0.05-33": "qoum861b",
"simclr-0.25-33": "9ofn816x",
"simclrcfaug-0.05-11": "84ijd323",
"simclrcfaug-0.25-11": "2mveqyje",
"simclrcfaug-0.05-22": "9sg7nlcn",
"simclrcfaug-0.25-22": "7r316b1j",
"simclrcfaug-0.05-33": "edk3wfn3",
"simclrcfaug-0.25-33": "yuwmsfoz",
"simclrhead-0.25-33": "sjeg48br",
"simclrcfaughead-0.25-33": "n3r9fiz3",
"simclrcfaughead-0.1-33": "zjcf6740",
"simclrcfaughead-1.0-33": "hhmtknho",
"simclrhead-1.0-33": "maq90iwt",
"simclraughead-0.25-22": "6to2f7a0",
"simclrcfaughead-0.1-22": "4nfbi6tl",
"simclrcfaughead-1.0-22": "skb167qn",
"simclrhead-0.25-22": "cc2cdd76",
"simclrcfaughead-0.25-11": "ozkj5npc",
"simclrhead-0.1-22": "6hh5p3wu",
"simclrcfaughead-0.1-11": "g9i9fshy",
"simclrhead-1.0-22": "ujg8b6kr",
"simclrcfaughead-1.0-11": "fzb2gg18",
"simclrhead-0.25-11": "w5c1uilu",
"simclrhead-0.1-11": "kjy8kd4j",
"simclrhead-1.0-11": "h7b924ww",
"simclrcfhead-0.25-33": "8fbh0up5",
"simclrcfhead-0.1-33": "sfqu07v6",
"simclrcfhead-0.25-11": "crn58bt4",
"simclrcfhead-0.1-11": "ipxh9y6b",
"simclrcfhead-0.25-22": "yj99p9gk",
"simclrcfhead-1.0-11": "3cj3p5ha",
"simclrcfaughead-0.05-11": "w8o2l2bp",
"simclrcfaughead-0.05-22": "98wsobp9",
"simclrhead-0.05-33": "qjb0j9cp",
"simclrcfaughead-0.05-33": "93uflh60",
"simclrcfhead-0.05-33": "c2ssrqf7",
"simclrhead-0.05-22": "wzcayezk",
"simclrcfhead-0.05-22": "lvjitftj",
"simclrhead-0.05-11": "0t8nbhc7",
"simclrcfhead-0.05-11": "e7kw2tuf",
"simclrcfhead-1.0-33": "5et2yzej",
"simclrcfhead-1.0-22": "t0zmf1lp",
}
with initialize(version_base=None, config_path="../configs"):
cfg: DictConfig = compose(
config_name="config.yaml",
overrides=["experiment=base_padchestpneumo.yaml", "data.cache=True"],
)
print(cfg)
data_module = PadChestDataModule(config=cfg)
test_dataloader = data_module.test_dataloader()
df = pd.read_csv("../outputs/classification_padchestfinetunepneumo_results.csv")
for run_name, run_id in model_dict_normal.items():
already_in_df: bool = run_name in df.run_name.values
if run_id != "" and not already_in_df:
print(run_name)
model_to_evaluate = f"../outputs/run_{run_id}/best.ckpt"
classification_model = ClassificationModule.load_from_checkpoint(
model_to_evaluate, map_location="cuda:0", strict=False
).model.eval()
classification_model.cuda()
inference_results = run_inference(test_dataloader, classification_model)
scanners = inference_results["scanners"]
for sc in range(2):
sc_idx = np.where(scanners == sc)
targets = inference_results["targets"][sc_idx]
preds = np.argmax(inference_results["confs"], 1)[sc_idx]
confs = inference_results["confs"][sc_idx][:, 1]
res = {}
res["N_test"] = [targets.shape[0]]
res["Scanner"] = [sc]
res["run_name"] = run_name
res["ROC"] = [roc_auc_score(targets, confs)]
print(res)
df = pd.concat([df, pd.DataFrame(res, index=[0])], ignore_index=True)
df.to_csv(
"../outputs/classification_padchestfinetunepneumo_results.csv", index=False
)