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consolidate_fine_tuning_results.py
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consolidate_fine_tuning_results.py
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import argparse
import matplotlib.pyplot as plt
import numpy as np
import os
import yaml
from consolidate_tracking_results import ANONYM_SUBJECT_MAP, consolidate_results_dataframes
from dataset import VocalTractMaskRCNNDataset
PINK = np.array([255, 0, 85, 255]) / 255
BLUE = np.array([0, 139, 231, 255]) / 255
cmap = plt.get_cmap("hsv")
num_subjects = len(ANONYM_SUBJECT_MAP)
SUBJECTS_COLORS = {
anonym_subject: cmap(i / num_subjects)
for i, (_, anonym_subject) in enumerate(ANONYM_SUBJECT_MAP.items())
}
def plot_fine_tuning(articulator, df, first_axis, second_axis=None, save_filepaths=None):
if save_filepaths is None:
save_filepaths = []
elif isinstance(save_filepaths, str):
save_filepaths = [save_filepaths]
fontsize = 28
labelsize = 22
lw = 2
alpha = 0.2
alpha_aux = 1.0
df_grouped = df.groupby("fine_tuning_size").agg("mean", "std").reset_index(drop=False)
fig = plt.figure(figsize=(10, 10))
ax1 = fig.gca()
ax1.set_ylim([0, 2.5])
ax1.set_ylabel(first_axis.replace("_", " "), fontsize=fontsize)
ax1.tick_params(axis="both", which="major", labelsize=labelsize)
ax1.plot(
df_grouped["fine_tuning_size"],
df_grouped[f"{first_axis}.mean"] - df_grouped[f"{first_axis}.std"],
color=PINK,
alpha=alpha
)
ax1 .plot(
df_grouped["fine_tuning_size"],
df_grouped[f"{first_axis}.mean"],
color=PINK,
lw=lw
)
ax1.plot(
df_grouped["fine_tuning_size"],
df_grouped[f"{first_axis}.mean"] + df_grouped[f"{first_axis}.std"],
color=PINK,
alpha=alpha
)
ax1.fill_between(
df_grouped["fine_tuning_size"],
df_grouped[f"{first_axis}.mean"] - df_grouped[f"{first_axis}.std"],
df_grouped[f"{first_axis}.mean"] + df_grouped[f"{first_axis}.std"],
color=PINK,
alpha=alpha
)
for subject, group in df.groupby("anonym_subject"):
ax1.plot(
group["fine_tuning_size"],
group[f"{first_axis}.mean"],
linestyle="dotted",
color=SUBJECTS_COLORS[subject],
alpha=alpha_aux
)
if second_axis is not None:
ax2 = ax1.twinx()
ax2.set_ylim([0, 1])
ax2.set_ylabel(second_axis.replace("_", " "), fontsize=fontsize)
ax2.tick_params(axis="both", which="major", labelsize=labelsize)
ax2.plot(
df_grouped["fine_tuning_size"],
df_grouped[f"{second_axis}.mean"] - df_grouped[f"{second_axis}.std"],
color=BLUE,
alpha=alpha
)
ax2.plot(
df_grouped["fine_tuning_size"],
df_grouped[f"{second_axis}.mean"],
color=BLUE,
lw=lw
)
ax2.plot(
df_grouped["fine_tuning_size"],
df_grouped[f"{second_axis}.mean"] + df_grouped[f"{second_axis}.std"],
color=BLUE,
alpha=alpha
)
ax2.fill_between(
df_grouped["fine_tuning_size"],
df_grouped[f"{second_axis}.mean"] - df_grouped[f"{second_axis}.std"],
df_grouped[f"{second_axis}.mean"] + df_grouped[f"{second_axis}.std"],
color=BLUE,
alpha=alpha
)
for subject, group in df.groupby("anonym_subject"):
ax2.plot(
group["fine_tuning_size"],
group[f"{second_axis}.mean"],
linestyle="dashdot",
color=SUBJECTS_COLORS[subject],
alpha=alpha_aux
)
ax1.set_xlabel("fine tuning size", fontsize=fontsize)
plt.tight_layout()
for filepath in save_filepaths:
plt.savefig(filepath)
plt.close()
def main(datadir, results_filepaths, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
groupby_cols = ["anonym_subject", "pred_class", "fine_tuning_size"]
df = consolidate_results_dataframes(results_filepaths)
df_grouped = df.groupby(groupby_cols).agg({
"p2cp_mean": ["mean", "std"],
"p2cp_rms": ["mean", "std"],
"jaccard_index": ["mean", "std"],
})
df_grouped.columns = df_grouped.columns.map('{0[0]}.{0[1]}'.format)
df_grouped = df_grouped.reset_index()
tabular_dir = os.path.join(save_dir, "tabular")
figures_dir = os.path.join(save_dir, "figures")
dirs_ = [tabular_dir, figures_dir]
for dir_ in dirs_:
if not os.path.exists(dir_):
os.makedirs(dir_)
df.to_csv(os.path.join(tabular_dir, "raw.csv"))
for articulator in df.pred_class.unique():
save_filepaths = [
os.path.join(figures_dir, f"{articulator}.png"),
os.path.join(figures_dir, f"{articulator}.pdf"),
]
df_articulator = df_grouped[df_grouped.pred_class == articulator]
closed_articulator = articulator in VocalTractMaskRCNNDataset.closed_articulators
plot_fine_tuning(
articulator,
df_articulator,
first_axis="p2cp_rms",
second_axis="jaccard_index" if closed_articulator else None,
save_filepaths=save_filepaths,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", dest="cfg_filepath")
args = parser.parse_args()
with open(args.cfg_filepath) as f:
cfg = yaml.safe_load(f.read())
main(**cfg)