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Merge 96b37a8f1b into 72eda9a24f

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Maria Novik 2023-07-17 21:39:59 -05:00 committed by GitHub
commit 53fc9c078c
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1 changed files with 20 additions and 20 deletions
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40
trust_and_safety_models/nsfw/nsfw_media.py
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@ -1,5 +1,6 @@
import logging
import kerastuner as kt
import math
import numpy as np
import pandas as pd
import random
@ -14,8 +15,9 @@ from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from google.cloud import storage
LOG = logging.getLogger(__name__)
physical_devices = tf.config.list_physical_devices('GPU')
physical_devices
tf.config.set_visible_devices([tf.config.PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU')], 'GPU')
tf.config.get_visible_devices('GPU')
@ -58,11 +60,9 @@ test_batch_size = 256
validation_batch_size = 256
do_resample = False
def class_func(features, label):
return label
resample_fn = tf.data.experimental.rejection_resample(
class_func, target_dist = [0.5, 0.5], seed=0
lambda _, x: x, target_dist = [0.5, 0.5], seed=0
)
train_glob = f"{input_root}/train/tfrecord/*.tfrecord"
train_files = tf.io.gfile.glob(train_glob)
@ -135,7 +135,7 @@ for example in tqdm(check_ds):
if label == 1:
pos_cnt += 1
cnt += 1
print(f'{cnt} train entries with {pos_cnt} positive')
LOG.info(f'{cnt} train entries with {pos_cnt} positive')
metrics = []
@ -264,7 +264,7 @@ bucket = client.get_bucket(...)
copy_local_directory_to_gcs(model_path, bucket, model_path)
copy_local_directory_to_gcs('tuner_dir', bucket, 'tuner_dir')
loaded_model = tf.keras.models.load_model(model_path)
print(history.history.keys())
LOG.info(history.history.keys())
plt.figure(figsize = (20, 5))
@ -319,7 +319,7 @@ for label in test_labels:
else:
n_test_neg +=1
print(f'n_test = {n_test}, n_pos = {n_test_pos}, n_neg = {n_test_neg}')
LOG.info(f'n_test = {n_test}, n_pos = {n_test_pos}, n_neg = {n_test_neg}')
n_test_sens_prev_pos = 0
n_test_sens_prev_neg = 0
@ -332,7 +332,7 @@ for label in test_sens_prev_labels:
else:
n_test_sens_prev_neg +=1
print(f'n_test_sens_prev = {n_test_sens_prev}, n_pos_sens_prev = {n_test_sens_prev_pos}, n_neg = {n_test_sens_prev_neg}')
LOG.info(f'n_test_sens_prev = {n_test_sens_prev}, n_pos_sens_prev = {n_test_sens_prev_pos}, n_neg = {n_test_sens_prev_neg}')
test_weights = np.ones(np.asarray(test_preds).shape)
@ -385,7 +385,7 @@ plt.subplot(1, 3, 2)
plt.plot(pr[2], pr[1][0:-1])
plt.xlabel("threshold")
plt.ylabel("recall")
plt.title("Keras", size=20)
plt.title("Keras", fontsize=20)
plt.subplot(1, 3, 3)
@ -406,7 +406,7 @@ precision, recall, thresholds = pr
auc_precision_recall = sklearn.metrics.auc(recall, precision)
print(auc_precision_recall)
LOG.info(auc_precision_recall)
plt.figure(figsize=(15, 10))
plt.plot(recall, precision)
@ -415,12 +415,12 @@ plt.xlabel("recall")
plt.ylabel("precision")
ptAt50 = get_point_for_recall(0.5, recall, precision)
print(ptAt50)
LOG.info(ptAt50)
plt.plot( [ptAt50[0],ptAt50[0]], [0,ptAt50[1]], 'r')
plt.plot([0, ptAt50[0]], [ptAt50[1], ptAt50[1]], 'r')
ptAt90 = get_point_for_recall(0.9, recall, precision)
print(ptAt90)
LOG.info(ptAt50)
plt.plot( [ptAt90[0],ptAt90[0]], [0,ptAt90[1]], 'b')
plt.plot([0, ptAt90[0]], [ptAt90[1], ptAt90[1]], 'b')
@ -428,8 +428,8 @@ ptAt50fmt = "%.4f" % ptAt50[1]
ptAt90fmt = "%.4f" % ptAt90[1]
aucFmt = "%.4f" % auc_precision_recall
plt.title(
f"Keras (nsfw MU test)\nAUC={aucFmt}\np={ptAt50fmt} @ r=0.5\np={ptAt90fmt} @ r=0.9\nN_train={...}} ({...} pos), N_test={n_test} ({n_test_pos} pos)",
size=20
f"Keras (nsfw MU test)\nAUC={aucFmt}\np={ptAt50fmt} @ r=0.5\np={ptAt90fmt} @ r=0.9\nN_train={...} ({...} pos), N_test={n_test} ({n_test_pos} pos)",
fontsize=20
)
plt.subplots_adjust(top=0.72)
plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_MU_test.pdf')
@ -437,7 +437,7 @@ plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_MU_test.pdf')
precision, recall, thresholds = pr_sens_prev
auc_precision_recall = sklearn.metrics.auc(recall, precision)
print(auc_precision_recall)
LOG.info(auc_precision_recall)
plt.figure(figsize=(15, 10))
plt.plot(recall, precision)
@ -446,12 +446,12 @@ plt.xlabel("recall")
plt.ylabel("precision")
ptAt50 = get_point_for_recall(0.5, recall, precision)
print(ptAt50)
LOG.info(ptAt50)
plt.plot( [ptAt50[0],ptAt50[0]], [0,ptAt50[1]], 'r')
plt.plot([0, ptAt50[0]], [ptAt50[1], ptAt50[1]], 'r')
ptAt90 = get_point_for_recall(0.9, recall, precision)
print(ptAt90)
LOG.info(ptAt90)
plt.plot( [ptAt90[0],ptAt90[0]], [0,ptAt90[1]], 'b')
plt.plot([0, ptAt90[0]], [ptAt90[1], ptAt90[1]], 'b')
@ -460,7 +460,7 @@ ptAt90fmt = "%.4f" % ptAt90[1]
aucFmt = "%.4f" % auc_precision_recall
plt.title(
f"Keras (nsfw sens prev test)\nAUC={aucFmt}\np={ptAt50fmt} @ r=0.5\np={ptAt90fmt} @ r=0.9\nN_train={...} ({...} pos), N_test={n_test_sens_prev} ({n_test_sens_prev_pos} pos)",
size=20
fontsize=20
)
plt.subplots_adjust(top=0.72)
plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_sens_prev_test.pdf')
plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_sens_prev_test.pdf')