def print_clf_table(bin_labels: bool, metrics: list):
config = get_config()
if bin_labels:
labels = ['Finding']
else:
labels = ['Lung Opacity', 'Pleural Effusion', 'Support Devices']
params = Params()
params.bin_labels = bin_labels
dir_clf = Path(os.getcwd()) / f'data/clfs/{config["dir_clf"]}'
clf_eval_results_path = dir_clf / f'clf_test_results{"_bin_label" if bin_labels else ""}.json'
with open(clf_eval_results_path, 'r') as json_file:
clf_eval_results = json.load(json_file)
mods = {'PA': 'F', 'Lateral': 'L', 'text': 'T'}
subsets = []
for L in range(1, len(mods) + 1):
for subset in combinations(mods, L):
subsets.append(''.join(subset))
df = pd.DataFrame(df_builder(clf_eval_results, metrics))
df = df.reset_index(drop=True)
df_tex = df.to_latex(index=False, escape=False)
df_tex = df_tex.replace(r'\toprule', '')
df_tex = df_tex.replace(r'\bottomrule', '')
print(bold_max_value(df, df_tex))
def get_random_perf():
config = get_config()
dir_clf = Path(__file__).parent.parent / f'data/clfs/{config["dir_clf"]}'
clf_result_path = f'{dir_clf}/clf_test_results_bin_label.json'
with open(clf_result_path, 'r') as jsonfile:
clf_results = json.load(jsonfile)
return clf_results['random_perf'][config['eval_metric']][0]
def print_lr_table(bin_labels: bool, metrics):
if bin_labels:
labels = ['Finding']
else:
labels = ['Lung Opacity', 'Pleural Effusion', 'Support Devices']
lr_eval_results_path = Path(os.getcwd(
)) / f'data/lr_eval_results{"_bin_label" if bin_labels else ""}.json'
with open(lr_eval_results_path, 'r') as json_file:
lr_eval_results = json.load(json_file)
mods = {'PA': 'F', 'Lateral': 'L', 'text': 'T'}
subsets = []
for L in range(1, len(mods) + 1):
for subset in combinations(mods, L):
subsets.append(''.join(subset))
config = get_config()
df = pd.DataFrame(df_builder(lr_eval_results, mods, metrics))
# df.set_index(['MODEL', 'LABEL'], inplace=True)
# df.sort_index(inplace=True)
#
# df_tex = df.to_latex(escape=False)
df = df.reset_index(drop=True)
df_tex = df.to_latex(index=False, escape=False)
df_tex = df_tex.replace(r'\toprule', '')
df_tex = df_tex.replace(r'\bottomrule', '')
print(bold_max_value(df, df_tex))
def print_rand_perf():
config = get_config()
dir_clf = Path(os.getcwd()) / f'data/clfs/{config["dir_clf"]}'
clf_eval_results_path = dir_clf / f'clf_test_results_bin_label.json'
with open(clf_eval_results_path, 'r') as json_file:
clf_eval_results = json.load(json_file)
return round(clf_eval_results['random_perf']['mean_AP_Finding'][0], 3)
# HK, 18.01.21
import json
from pathlib import Path
import numpy as np
import pandas as pd
import os
from scripts.utils import bold_max_value, get_random_perf
from prepare.utils import get_config
label = 'Finding'
config = get_config()
gen_eval_results_path = Path(os.getcwd()) / 'data/gen_eval_results.json'
with open(gen_eval_results_path, 'r') as json_file:
gen_eval_results = json.load(json_file)
mods_mapping = {'PA': 'F', 'Lateral': 'L', 'text': 'T'}
def df_builder(cond_values: dict):
label_row = {
'MODEL': 'MoPoE',
'Metric': config['eval_metric'].replace('_', ' ')
}
# label_row = {'MODEL': 'MoPoE', 'LABEL': label, 'rand perf': get_random_perf()}
for k, v in cond_values.items():
label_row[mods_mapping[k]] = np.round(v, 3)
yield label_row
def make_cond_gen_fig(nbr_samples=3):
import mimic
from mimic.utils import utils
from mimic.utils.experiment import MimicExperiment
from mimic.utils.filehandling import set_paths
log.info(
f'Starting generating cond gen fig with nbr_samples={nbr_samples}')
config = get_config()
# set seed
SEED = config['seed']
np.random.seed(SEED)
torch.manual_seed(SEED)
random.seed(SEED)
# experiment_dir = config['experiment_dir_bin']
experiment_dir = 'binary_labels-True_beta-0.01_weighted_sampler-False_class_dim-128_text_gen_lastlayer-softmax_2021_02_10_14_56_27_974859'
experiment_path = Path(
__file__).parent.parent / f'data/vae_model/{experiment_dir}'
flags_path = experiment_path / 'flags.rar'
FLAGS = torch.load(flags_path)
FLAGS.save_figure = True
FLAGS.dir_cond_gen = Path(__file__).parent.parent / 'data/cond_gen'
# FLAGS.text_gen_lastlayer = 'softmax'
FLAGS = set_paths(FLAGS)
FLAGS.use_clf = False
FLAGS.binary_labels = False
state_dict_path = experiment_path / 'checkpoints/0149/mm_vae'
mimic_experiment = MimicExperiment(flags=FLAGS)
mimic_experiment.mm_vae.to(FLAGS.device)
mimic_experiment.mm_vae.load_state_dict(
state_dict=torch.load(state_dict_path))
mimic_experiment.mm_vae.eval()
mimic_experiment.modalities['text'].plot_img_size = torch.Size(
[1, 256, 128])
samples = mimic_experiment.test_samples
model = mimic_experiment.mm_vae
mods = mimic_experiment.modalities
subsets = mimic_experiment.subsets
if not Path(mimic_experiment.flags.dir_cond_gen).exists():
Path(mimic_experiment.flags.dir_cond_gen).mkdir()
def create_cond_gen_plot(in_mods='Lateral_PA'):
subset = subsets[in_mods]
plot = {
**{f'in_{mod}': []
for mod in mimic_experiment.modalities},
**{f'out_{mod}': []
for mod in mimic_experiment.modalities}
}
for idx in range(nbr_samples):
sample = samples[idx]
i_batch = {
mod.name: sample[mod.name].unsqueeze(0)
for mod in subset
}
latents = model.inference(i_batch, num_samples=1)
c_in = latents['subsets'][in_mods]
c_rep = utils.reparameterize(mu=c_in[0], logvar=c_in[1])
cond_mod_in = {
'content': c_rep,
'style': {k: None
for k in mimic_experiment.modalities}
}
cond_gen_samples = model.generate_from_latents(cond_mod_in)
for mod_key, mod in mods.items():
plot[f'in_{mod_key}'].append(
mod.plot_data(mimic_experiment,
sample[mod_key].squeeze(0)))
plot[f'out_{mod_key}'].append(
mod.plot_data(mimic_experiment,
cond_gen_samples[mod_key].squeeze(0)))
rec = torch.Tensor()
# first concatenate all input images, then all the output images
for which, modalities in {'in': mods, 'out': mods}.items():
for mod in modalities:
for idx in range(nbr_samples):
if mod == 'text':
img = plot[f'{which}_{mod}'][idx].cpu().unsqueeze(0)
else:
img = plot[f'{which}_{mod}'][idx].cpu()
# pad the non text modalities such that they fit in a wider rectangle.
m = nn.ZeroPad2d((64, 64, 0, 0))
img = m(img.squeeze()).unsqueeze(0).unsqueeze(0)
rec = torch.cat((rec, img), 0)
out_path = Path(
mimic_experiment.flags.dir_cond_gen
) / f'{in_mods}{"_small" if nbr_samples < 5 else ""}.png'
log.info(f'Saving image to {out_path}')
_ = mimic.utils.plot.create_fig(out_path,
img_data=rec,
num_img_row=nbr_samples,
save_figure=True)
for in_mod in mimic_experiment.subsets:
if in_mod:
# for in_mod in ['Lateral_text']:
create_cond_gen_plot(in_mod)
def get_model_flags():
config = get_config()
flags_path = Path(os.getcwd(
)) / f'data/vae_model/{config["experiment_dir_bin"]}/flags.rar'
return torch.load(flags_path)