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train_xgb.py
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import os
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
from models import Xgboost
from configs import XgbConfig
from utils import get_experiment_name, load_label_map
from augment import (
plus7rotation,
minus7rotation,
gaussSample,
cutout,
upsample,
downsample,
)
from tqdm.auto import tqdm
def flatten(arr, max_seq_len=200):
arr = np.array(arr)
arr = np.pad(arr, ((0, max_seq_len - arr.shape[0]), (0, 0)), "constant")
arr = arr.flatten()
return arr
def combine_xy(x, y):
x, y = np.array(x), np.array(y)
_, length = x.shape
x = x.reshape((-1, length, 1))
y = y.reshape((-1, length, 1))
return np.concatenate((x, y), -1).astype(np.float32)
def split_xy(data):
value_x, value_y = [], []
for row in data:
row = np.asarray(row)
if row.shape == ():
continue
value_x.append(row[:, 0])
value_y.append(row[:, 1])
value_x, value_y = np.asarray(value_x), np.asarray(value_y)
return value_x, value_y
def augment_sample(df, augs):
df = df.copy()
pose = combine_xy(df.pose_x, df.pose_y)
h1 = combine_xy(df.hand1_x, df.hand1_y)
h2 = combine_xy(df.hand2_x, df.hand2_y)
input_df = pd.DataFrame.from_dict(
{
"uid": df.uid,
"pose": pose.tolist(),
"hand1": h1.tolist(),
"hand2": h2.tolist(),
"label": df.label,
}
)
augmented_samples = []
for augmentation in augs:
df_augmented = augmentation(input_df)
pose_x, pose_y = split_xy(df_augmented.pose)
hand1_x, hand1_y = split_xy(df_augmented.hand1)
hand2_x, hand2_y = split_xy(df_augmented.hand2)
save_df = pd.Series(
{
"uid": df.uid + "_" + augmentation.__name__,
"label": df.label,
"pose_x": pose_x.tolist(),
"pose_y": pose_y.tolist(),
"hand1_x": hand1_x.tolist(),
"hand1_y": hand1_y.tolist(),
"hand2_x": hand2_x.tolist(),
"hand2_y": hand2_y.tolist(),
"n_frames": df.n_frames,
}
)
augmented_samples.append(save_df)
return pd.concat(augmented_samples, axis=0)
def preprocess(df, use_augs, label_map, mode):
feature_cols = ["pose_x", "pose_y", "hand1_x", "hand1_y", "hand2_x", "hand2_y"]
x, y = [], []
i = 0
no_of_videos = df.shape[0]
pbar = tqdm(total=no_of_videos, desc=f"Processing {mode} file....")
while i < no_of_videos:
if use_augs and mode == "train":
augs = [
plus7rotation,
minus7rotation,
gaussSample,
cutout,
upsample,
downsample,
]
augmented_rows = augment_sample(df.iloc[i], augs)
df = pd.concat([df, augmented_rows], axis=0)
row = df.loc[i, feature_cols]
flatten_features = np.hstack(list(map(flatten, row.values)))
x.append(flatten_features)
y.append(label_map[df.loc[i, "label"]])
i += 1
pbar.update(1)
x = np.stack(x)
y = np.array(y)
return x, y
def load_dataframe(files):
series = []
for file_path in files:
series.append(pd.read_json(file_path, typ="series"))
return pd.concat(series, axis=0)
def fit(args):
train_files = sorted(
glob.glob(
os.path.join(args.data_dir, f"{args.dataset}_train_keypoints", "*.json")
)
)
val_files = sorted(
glob.glob(
os.path.join(args.data_dir, f"{args.dataset}_val_keypoints", "*.json")
)
)
train_df = load_dataframe(train_files)
val_df = load_dataframe(val_files)
label_map = load_label_map(args.dataset)
x_train, y_train = preprocess(train_df, args.use_augs, label_map, "train")
x_val, y_val = preprocess(val_df, args.use_augs, label_map, "val")
config = XgbConfig()
model = Xgboost(config=config)
model.fit(x_train, y_train, x_val, y_val)
exp_name = get_experiment_name(args)
save_path = os.path.join(args.save_dir, exp_name, ".pickle.dat")
model.save(save_path)
def evaluate(args):
test_files = sorted(
glob.glob(
os.path.join(args.data_dir, f"{args.dataset}_test_keypoints", "*.json")
)
)
test_df = load_dataframe(test_files)
label_map = load_label_map(args.dataset)
x_test, y_test = preprocess(test_df, args.use_augs, label_map, "test")
exp_name = get_experiment_name(args)
config = XgbConfig()
model = Xgboost(config=config)
load_path = os.path.join(args.save_dir, exp_name, ".pickle.dat")
model.load(load_path)
print("### Model loaded ###")
test_preds = model(x_test)
print("Test accuracy:", accuracy_score(y_test, test_preds))