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#!/usr/bin/env python | ||
import fire | ||
import matplotlib.pyplot as plt | ||
import numpy as np | ||
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# import pandas as pd | ||
# import pathlib | ||
# from sklearn.model_selection import train_test_split | ||
# import sys | ||
import tensorflow as tf | ||
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# from tqdm.auto import tqdm | ||
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import tails.models | ||
from tails.utils import log | ||
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def fnr_vs_fpr(predictions, ground_truth): | ||
rbbins = np.arange(-0.0001, 1.0001, 0.0001) | ||
h_b, e_b = np.histogram(predictions[ground_truth == 0], bins=rbbins, density=True) | ||
h_b_c = np.cumsum(h_b) | ||
h_r, e_r = np.histogram(predictions[ground_truth == 1], bins=rbbins, density=True) | ||
h_r_c = np.cumsum(h_r) | ||
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# h_b, e_b | ||
print(sum(ground_truth == 0), sum(ground_truth == 1)) | ||
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fig = plt.figure(figsize=(9, 4), dpi=200) | ||
ax = fig.add_subplot(111) | ||
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rb_thres = np.array(list(range(len(h_b)))) / len(h_b) | ||
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ax.plot( | ||
rb_thres, | ||
h_r_c / np.max(h_r_c), | ||
label="False Negative Rate (FNR)", | ||
linewidth=1.5, | ||
) | ||
ax.plot( | ||
rb_thres, | ||
1 - h_b_c / np.max(h_b_c), | ||
label="False Positive Rate (FPR)", | ||
linewidth=1.5, | ||
) | ||
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mmce = (h_r_c / np.max(h_r_c) + 1 - h_b_c / np.max(h_b_c)) / 2 | ||
ax.plot( | ||
rb_thres, | ||
mmce, | ||
"--", | ||
label="Mean misclassification error", | ||
color="gray", | ||
linewidth=1.5, | ||
) | ||
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ax.set_xlim([-0.05, 1.05]) | ||
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ax.set_xticks(np.arange(0, 1.1, 0.1)) | ||
ax.set_yticks(np.arange(0, 1.1, 0.1)) | ||
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# vals = ax.get_yticks() | ||
# ax.set_yticklabels(['{:,.0%}'.format(x) for x in vals]) | ||
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ax.set_yscale("log") | ||
ax.set_ylim([5e-4, 1]) | ||
vals = ax.get_yticks() | ||
ax.set_yticklabels( | ||
["{:,.1%}".format(x) if x < 0.01 else "{:,.0%}".format(x) for x in vals] | ||
) | ||
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# thresholds: | ||
# thrs = [0.5, ] | ||
thrs = [0.5, 0.7] | ||
for t in thrs: | ||
m_t = rb_thres < t | ||
fnr = np.array(h_r_c / np.max(h_r_c))[m_t][-1] | ||
fpr = np.array(1 - h_b_c / np.max(h_b_c))[m_t][-1] | ||
print(t, fnr * 100, fpr * 100) | ||
# ax.vlines(t_1, 0, 1.1) | ||
ax.vlines(t, 0, max(fnr, fpr)) | ||
ax.text( | ||
t - 0.05, | ||
max(fnr, fpr) + 0.01, | ||
f" {fnr*100:.1f}% FNR\n {fpr*100:.1f}% FPR", | ||
fontsize=10, | ||
) | ||
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ax.set_xlabel("$p_c$ score threshold") | ||
ax.set_ylabel("Cumulative percentage") | ||
ax.legend(loc="upper center") | ||
ax.grid(True, which="major", linewidth=0.5) | ||
ax.grid(True, which="minor", linewidth=0.3) | ||
plt.tight_layout() | ||
plt.show() | ||
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class TailsLoss(tf.keras.losses.BinaryCrossentropy): | ||
def __init__(self, w_1: float = 1, w_2: float = 1, **kwargs): | ||
super(TailsLoss, self).__init__(**kwargs) | ||
self.w_1 = w_1 | ||
self.w_2 = w_2 | ||
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def call(self, y_true, y_pred): | ||
output = tf.convert_to_tensor(y_pred[..., 0]) | ||
target = tf.cast(y_true[..., 0], output.dtype) | ||
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# l_1: binary crossentropy for the label | ||
l_1 = super(TailsLoss, self).call(target, output) | ||
w_1 = tf.cast(self.w_1, output.dtype) | ||
l_1 = tf.math.multiply(l_1, w_1) | ||
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# l_2: L1 loss | ||
l_2 = tf.norm(y_pred[..., 1:] - y_true[..., 1:], ord=1) | ||
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# l_2: L1 loss + L2 regularization | ||
# l_2 = tf.norm(y_pred[..., 1:] - y_true[..., 1:], ord=1) + \ | ||
# 1e-3 * tf.norm(y_pred[..., 1:] - y_true[..., 1:], ord=2) | ||
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l_2 = tf.math.multiply(l_2, target) | ||
l_2 = tf.math.divide(l_2, tf.math.reduce_sum(target)) | ||
w_2 = tf.cast(self.w_2, output.dtype) | ||
l_2 = tf.math.multiply(l_2, w_2) | ||
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return l_1 + l_2 | ||
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class LabelAccuracy(tf.keras.metrics.Metric): | ||
def __init__(self, name="label_accuracy", threshold=0.5, **kwargs): | ||
super(LabelAccuracy, self).__init__(name=name, **kwargs) | ||
self.total = self.add_weight(name="total", initializer="zeros") | ||
self.count = self.add_weight(name="count", initializer="zeros") | ||
self.threshold = float(threshold) | ||
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def update_state(self, y_true, y_pred, sample_weight=None): | ||
output = y_pred[..., 0] | ||
# target = tf.cast(y_true[..., 0], output.dtype) | ||
target = tf.cast(y_true[..., 0], tf.bool) | ||
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threshold = tf.cast(0.5, output.dtype) | ||
output = tf.cast(output > threshold, tf.bool) | ||
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# values = tf.cast(tf.math.equal(target, output), output.dtype) | ||
values = tf.cast(tf.math.equal(target, output), tf.float32) | ||
ones = tf.cast(tf.math.equal(target, target), tf.float32) | ||
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if sample_weight is not None: | ||
sample_weight = tf.cast(sample_weight, self.dtype) | ||
sample_weight = tf.broadcast_weights(sample_weight, values) | ||
values = tf.multiply(values, sample_weight) | ||
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self.count.assign_add(tf.math.reduce_sum(values, axis=-1)) | ||
self.total.assign_add(tf.math.reduce_sum(ones, axis=-1)) | ||
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def result(self): | ||
return tf.math.divide(self.count, self.total) | ||
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class PositionRootMeanSquarredError(tf.keras.metrics.Metric): | ||
def __init__(self, name="position_rmse", scaling_factor=1, **kwargs): | ||
super(PositionRootMeanSquarredError, self).__init__(name=name, **kwargs) | ||
self.total = self.add_weight(name="total", initializer="zeros") | ||
self.rmse = self.add_weight(name="rmse", initializer="zeros") | ||
self.scaling_factor = float(scaling_factor) | ||
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def update_state(self, y_true, y_pred, sample_weight=None): | ||
output = y_pred[..., 1:] | ||
target = tf.cast(y_true[..., 1:], output.dtype) | ||
label = tf.cast(y_true[..., 0], output.dtype) | ||
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rmse = tf.math.reduce_mean( | ||
tf.math.sqrt(tf.math.squared_difference(output, target)), axis=-1 | ||
) | ||
# only take positive examples into account: | ||
rmse = tf.math.multiply(rmse, label) | ||
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self.rmse.assign_add(tf.math.reduce_sum(rmse, axis=-1)) | ||
# only count the positive examples: | ||
self.total.assign_add(tf.math.reduce_sum(label, axis=-1)) | ||
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def result(self): | ||
sf = tf.constant(self.scaling_factor, dtype=self.rmse.dtype.base_dtype) | ||
return tf.math.multiply(sf, tf.math.divide(self.rmse, self.total)) | ||
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def train_and_eval( | ||
train_dataset, | ||
val_dataset, | ||
test_dataset, | ||
steps_per_epoch_train, | ||
steps_per_epoch_val, | ||
epochs, | ||
class_weight, | ||
model_name: str = "tails", | ||
tag="20210101", | ||
w_1: float = 1.2, | ||
w_2: float = 1, | ||
class_threshold: float = 0.5, | ||
scaling_factor=256, | ||
input_shape=(256, 256, 3), | ||
weights: str = None, | ||
save_model=False, | ||
verbose=False, | ||
**kwargs, | ||
): | ||
classifier = tails.models.DNN(name=model_name) | ||
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tails_loss = TailsLoss(name="loss", w_1=w_1, w_2=w_2) | ||
label_accuracy = LabelAccuracy(threshold=class_threshold) | ||
# convert position RMSE to pixels | ||
position_rmse = PositionRootMeanSquarredError(scaling_factor=scaling_factor) | ||
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learning_rate = kwargs.get("learning_rate", 3e-4) | ||
patience = kwargs.get("patience", 30) | ||
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classifier.setup( | ||
input_shape=input_shape, | ||
n_output_neurons=3, | ||
architecture="tails", | ||
loss=tails_loss, | ||
optimizer="adam", | ||
lr=learning_rate, # epsilon=1e-3, beta_1=0.7, | ||
metrics=[label_accuracy, position_rmse], | ||
patience=patience, | ||
monitor="val_position_rmse", | ||
restore_best_weights=True, | ||
callbacks=("early_stopping", "tensorboard"), | ||
tag=tag, | ||
logdir="logs", | ||
) | ||
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# pre-load weights? | ||
if weights is not None: | ||
classifier.model.load_weights(weights) | ||
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classifier.train( | ||
train_dataset, | ||
val_dataset, | ||
steps_per_epoch_train, | ||
steps_per_epoch_val, | ||
epochs=epochs, | ||
class_weight=class_weight, | ||
verbose=True, | ||
) | ||
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# evaluate | ||
stats = classifier.evaluate(test_dataset) | ||
if verbose: | ||
log(stats) | ||
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if save_model: | ||
classifier.model.save_weights(f"{model_name}-{tag}") | ||
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if __name__ == "__main__": | ||
fire.Fire(train_and_eval) |