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evaluate.py
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evaluate.py
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# -*- coding: utf-8 -*-
""" MEye: Semantic Segmentation """
import argparse
import os
os.sys.path += ['expman', 'models/deeplab']
import expman
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.models import load_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, roc_curve, auc, precision_recall_curve, average_precision_score
from adabelief_tf import AdaBeliefOptimizer
from glob import glob
from tqdm import tqdm
from PIL import Image
from deeplabv3p.models.deeplabv3p_mobilenetv3 import hard_swish
from dataloader import get_loader, load_datasets
from utils import visualize, visualizable
def iou_coef(y_true, y_pred, smooth=0.001, thr=None):
y_pred = K.cast(y_pred > thr, 'float32') if thr is not None else y_pred
intersection = K.sum(K.abs(y_true * y_pred), axis=[1, 2, 3])
union = K.sum(y_true, [1, 2, 3]) + K.sum(y_pred, [1, 2, 3]) - intersection
iou = K.mean((intersection + smooth) / (union + smooth), axis=0)
return iou
def dice_coef(y_true, y_pred, smooth=0.001, thr=None):
y_pred = K.cast(y_pred > thr, 'float32') if thr is not None else y_pred
intersection = K.sum(y_true * y_pred, axis=[1, 2, 3])
union = K.sum(y_true, axis=[1, 2, 3]) + K.sum(y_pred, axis=[1, 2, 3])
dice = K.mean((2. * intersection + smooth) / (union + smooth), axis=0)
return dice
def _filter_by_closeness(a, eps=10e-3):
keep = []
prev = np.array([-1, -1])
for row in a.drop('thr', axis=1).values:
if (np.abs(prev - row) > eps).any():
keep.append(True)
prev = row
else:
keep.append(False)
return a[keep]
def _weighted_roc_pr(y_true, y_scores, label, outdir, simplify=False):
npos = y_true.sum()
nneg = len(y_true) - npos
pos_weight = nneg / npos
print(label, 'Tot:', len(y_true), 'P:', npos, 'N:', nneg, 'N/P:', pos_weight)
sample_weight = np.where(y_true, pos_weight, 1)
fpr, tpr, thr = roc_curve(y_true, y_scores, sample_weight=sample_weight)
auc_score = auc(fpr, tpr)
print(label, 'AuROC:', auc_score)
roc_metrics = pd.Series({'npos': npos, 'nneg': nneg, 'nneg_over_npos': pos_weight, 'roc_auc': auc_score})
roc_metrics_file = os.path.join(outdir, '{}_roc_metrics.csv'.format(label))
roc_metrics.to_csv(roc_metrics_file, index=False)
roc = pd.DataFrame({'fpr': fpr, 'tpr': tpr, 'thr': thr})
if simplify:
full_roc_file = os.path.join(outdir, '{}_roc_curve_full.csv.gz'.format(label))
roc.to_csv(full_roc_file, index=False)
roc = _filter_by_closeness(roc)
roc_file = os.path.join(outdir, '{}_roc_curve.csv'.format(label))
roc.to_csv(roc_file, index=False)
roc.plot(x='fpr', y='tpr', xlim=(0, 1), ylim=(0, 1))
roc_plot_file = os.path.join(outdir, '{}_roc.pdf'.format(label))
plt.savefig(roc_plot_file)
plt.close()
precision, recall, thr = precision_recall_curve(y_true, y_scores, sample_weight=sample_weight)
f1_score = 2 * precision * recall / (precision + recall)
pr_auc = auc(recall, precision)
pr_metrics = pd.Series({'npos': npos, 'nneg': nneg, 'nneg_over_npos': pos_weight, 'pr_auc': pr_auc})
pr_metrics_file = os.path.join(outdir, '{}_pr_metrics.csv'.format(label))
pr_metrics.to_csv(pr_metrics_file, index=False)
thr = np.append(thr, [thr[-1]])
pr = pd.DataFrame({'precision': precision, 'recall': recall, 'f1_score': f1_score, 'thr': thr})
if simplify:
full_pr_file = os.path.join(outdir, '{}_pr_curve_full.csv.gz'.format(label))
pr.to_csv(full_pr_file, index=False)
pr = _filter_by_closeness(pr)
pr_file = os.path.join(outdir, '{}_pr_curve.csv'.format(label))
pr.to_csv(pr_file, index=False)
pr.plot(x='recall', y='precision', xlim=(0, 1), ylim=(0, 1))
pr_plot_file = os.path.join(outdir, '{}_pr.pdf'.format(label))
plt.savefig(pr_plot_file)
plt.close()
print(label, 'AuPR:', pr_auc, 'AvgP:', average_precision_score(y_true, y_scores, sample_weight=sample_weight))
# https://github.com/tensorflow/tensorflow/issues/32809#issuecomment-768977280
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph
def get_flops(model):
concrete = tf.function(lambda inputs: model(inputs))
concrete_func = concrete.get_concrete_function(
[tf.TensorSpec([1, *inputs.shape[1:]]) for inputs in model.inputs])
frozen_func, graph_def = convert_variables_to_constants_v2_as_graph(concrete_func)
with tf.Graph().as_default() as graph:
tf.graph_util.import_graph_def(graph_def, name='')
run_meta = tf.compat.v1.RunMetadata()
opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()
flops = tf.compat.v1.profiler.profile(graph=graph, run_meta=run_meta, cmd="op", options=opts)
tf.compat.v1.reset_default_graph()
return flops.total_float_ops
def evaluate(exp, force=False):
ckpt_path = exp.path_to('best_model.h5')
custom_objects = {'AdaBeliefOptimizer': AdaBeliefOptimizer, 'iou_coef': iou_coef, 'dice_coef': dice_coef, 'hard_swish': hard_swish}
model = tf.keras.models.load_model(ckpt_path, custom_objects=custom_objects)
# get flops
flop_params_path = exp.path_to('flops_nparams.csv')
if force or not os.path.exists(flop_params_path):
model.compile()
tf.keras.models.save_model(model, 'tmp_model', overwrite=True, include_optimizer=False)
stripped_model = tf.keras.models.load_model('tmp_model')
flops = get_flops(stripped_model)
nparams = stripped_model.count_params()
del stripped_model
print('FLOPS:', flops)
print('#PARAMS:', nparams)
pd.DataFrame({'flops': flops, 'nparams': nparams}, index=[0]).to_csv(flop_params_path)
model.compile(loss='binary_crossentropy', metrics={'mask': [iou_coef, dice_coef], 'tags': 'binary_accuracy'})
params = exp.params
np.random.seed(params.seed)
tf.random.set_seed(params.seed)
data = load_datasets(params.data)
# TRAIN/VAL/TEST SPLIT
if params.split == 'subjects': # by SUBJECTS
# val_subjects = (6, 9, 11, 13, 16, 28, 30, 48, 49)
test_subjects = (3, 4, 19, 38, 45, 46, 51, 52)
# train_data = data[~data['sub'].isin(val_subjects + test_subjects)]
# val_data = data[data['sub'].isin(val_subjects)]
test_data = data[data['sub'].isin(test_subjects)]
elif params.split == 'random': # 70-20-10 %
_, valtest_data = train_test_split(data, test_size=.3, shuffle=True)
_, test_data = train_test_split(valtest_data, test_size=.33)
x_shape = (params.resolution, params.resolution, 1)
test_gen, test_categories = get_loader(test_data, batch_size=1, x_shape=x_shape)
prediction_dir = exp.path_to('test_pred')
os.makedirs(prediction_dir, exist_ok=True)
loss_per_sample = None
def _get_test_predictions(test_gen, model):
x_masks = []
y_masks, y_tags = [], []
pred_masks, pred_tags = [], []
loss_per_sample = []
for x, y in tqdm(test_gen, desc='TEST'):
sample_loss = model.test_on_batch(x, reset_metrics=True)
loss_per_sample.append(sample_loss)
p_mask, p_tags = model.predict_on_batch(x)
pred_masks.append(p_mask)
pred_tags.append(p_tags)
y_masks.append(y['mask'].numpy())
y_tags.append(y['tags'].numpy())
x_masks.append(x.numpy())
loss_per_sample = np.array(loss_per_sample)
pred_masks = np.concatenate(pred_masks)
pred_tags = np.concatenate(pred_tags)
y_masks = np.concatenate(y_masks)
y_tags = np.concatenate(y_tags)
x_masks = np.concatenate(x_masks)
return loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags
mask_metrics_path = exp.path_to('test_pred/mask_metrics.csv')
if force or not os.path.exists(mask_metrics_path):
if loss_per_sample is None:
loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags = _get_test_predictions(test_gen, model)
thrs = np.linspace(0, 1, 101)
ious = [iou_coef(y_masks, pred_masks, thr=thr).numpy() for thr in thrs]
dices = [dice_coef(y_masks, pred_masks, thr=thr).numpy() for thr in thrs]
best_thr = max(zip(dices, thrs))[1]
mask_metrics = pd.DataFrame({'iou': ious, 'dice': dices, 'thr': thrs})
print(mask_metrics.max(axis=0))
mask_metrics.to_csv(mask_metrics_path)
else:
mask_metrics = pd.read_csv(mask_metrics_path, index_col=0)
best_thr = mask_metrics.loc[mask_metrics.dice.idxmax(), 'thr']
if force:
if loss_per_sample is None:
loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags = _get_test_predictions(test_gen, model)
# _weighted_roc_pr(y_masks.ravel(), pred_masks.ravel(), 'all_pupil', prediction_dir, simplify=True)
_weighted_roc_pr(y_tags[:, 0], pred_tags[:, 0], 'all_eye', prediction_dir)
_weighted_roc_pr(y_tags[:, 1], pred_tags[:, 1], 'all_blink', prediction_dir)
filenames = ('top_samples.png', 'bottom_samples.png', 'random_samples.png')
if force or any(not os.path.exists(os.path.join(prediction_dir, f)) for f in filenames):
if loss_per_sample is None:
loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags = _get_test_predictions(test_gen, model)
k = 5
best_selector = []
worst_selector = []
random_selector = []
idx = np.arange(len(test_data))
for cat in np.unique(test_categories):
cat_outdir = os.path.join(prediction_dir, cat)
os.makedirs(cat_outdir, exist_ok=True)
selector = test_categories == cat
# _weighted_roc_pr(y_masks[selector].ravel(), pred_masks[selector].ravel(), '{}_pupil'.format(cat), cat_outdir, simplify=True)
_weighted_roc_pr(y_tags[selector, 0], pred_tags[selector, 0], '{}_eye'.format(cat), cat_outdir)
_weighted_roc_pr(y_tags[selector, 1], pred_tags[selector, 1], '{}_blink'.format(cat), cat_outdir)
cat_losses = loss_per_sample[selector, 1]
rank = cat_losses.argsort()
topk, bottomk = rank[:k], rank[-k:]
best_selector += idx[selector][topk].tolist()
worst_selector += idx[selector][bottomk].tolist()
random_selector += np.random.choice(idx[selector], k, replace=False).tolist()
# topk-bottomk images
selectors = (best_selector, worst_selector, random_selector)
for selector, outfile in zip(selectors, filenames):
combined_m = np.concatenate((pred_masks[selector], y_masks[selector]), axis=-1)[:, :, :, ::-1]
combined_t = np.concatenate((pred_tags[selector], y_tags[selector]), axis=-1)
combined_y = (combined_m, combined_t)
out = os.path.join(prediction_dir, outfile)
visualize(x_masks[selector], combined_y, out=out, thr=best_thr, n_cols=k, width=10)
for i, (xi, yi_mask) in enumerate(zip(x_masks[selector], combined_m)):
img = visualizable(xi, yi_mask, thr=best_thr)
img = (img * 255).astype(np.uint8)
out = os.path.join(prediction_dir, outfile[:-4])
os.makedirs(out, exist_ok=True)
out = os.path.join(out, f'{i}.png')
Image.fromarray(img).save(out)
def main(args):
for exp in expman.gather(args.run).filter(args.filter):
print(exp)
evaluate(exp, force=args.force)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Evaluate Run')
# data params
parser.add_argument('run', help='Run(s) directory')
parser.add_argument('-f', '--filter', default={}, type=expman.exp_filter)
parser.add_argument('--force', default=False, action='store_true', help='Force metrics recomputation')
args = parser.parse_args()
main(args)