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import gradio as gr
import tensorflow as tf
import numpy as np
import json
class Contrastive_Loss_2(tf.keras.losses.Loss):
def __init__(self, temperature=0.5, rate=0.5, name='Contrastive_Loss_2', **kwargs):
super(Contrastive_Loss_2, self).__init__(name=name, **kwargs)
self.temperature = temperature
self.rate = rate
self.cosine_sim = tf.keras.losses.CosineSimilarity(axis=-1, reduction=tf.keras.losses.Reduction.NONE)
# @tf.function
def call(self, z1, z2):
batch_size, n_dim = z1.shape
# Compute Euclid Distance loss
difference = z1 - z2 # (BxB) * z1 and z2 already applied soft max -> in the last axis, max dif will be 1
squared_norm = tf.reduce_sum(tf.square(difference), axis=1) # (B)
distance = tf.sqrt(squared_norm + 1e-8) # (B) * + epsilon to avoid Nan in gradient
mean_distance = tf.reduce_mean(distance) # () -> scalar
tf.debugging.check_numerics(mean_distance.numpy(), 'Distance contains NaN values.')
# print('distance: , ',mean_distance)
# Compute Consine Similarity loss
z = tf.concat((z1, z2), 0)
sim_ij = - self.cosine_sim(z[:batch_size], z[batch_size:]) # (B) -> batch_size pair
sim_ji = - self.cosine_sim(z[batch_size:], z[:batch_size]) # (B) -> batch_size pair
sim_pos = tf.concat((sim_ij,sim_ji), axis=0) # (2B) -> 2*batch_size positive pair
numerator = tf.math.exp(sim_pos / self.temperature) # (2B) -> 2*batch_size positive pair
sim_neg = - self.cosine_sim(tf.expand_dims(z, 1), z) # sim (Bx1xE, BxE) -> (2Bx2B)
mask = 1 - tf.eye(2*batch_size, dtype=tf.float32) # (2Bx2B)
sim_neg = mask * tf.math.exp(sim_neg / self.temperature) # (2Bx2B)
denominator = tf.math.reduce_sum(sim_neg, axis=-1) # (2B)
mean_cosine_similarity = tf.reduce_mean(- tf.math.log((numerator + 1e-11) / (denominator + 1e-11))) # () -> scalar
tf.debugging.check_numerics(mean_cosine_similarity.numpy(), 'Cosine contains NaN values.')
# print('similarity: , ',mean_cosine_similarity)
# Compute total loss with associated rate
total_loss = (1-self.rate)*mean_distance + self.rate*mean_cosine_similarity
tf.debugging.check_numerics(total_loss.numpy(), 'Total contains NaN values.')
return total_loss
model = tf.keras.models.load_model( filepath='contrastive_model.h5', custom_objects={'Contrastive_Loss_2': Contrastive_Loss_2}, compile=False)
with open("scene_labels.json") as labels_file:
labels = json.load(labels_file)
def classify_image(img):
img = tf.convert_to_tensor(img, dtype=tf.float32)
img = tf.expand_dims(img, axis=0)
probs = (model(img))[0]
return {labels[i]: float(probs[i]) for i in range(len(labels))}
image = gr.inputs.Image(shape=(256, 256))
label = gr.outputs.Label(num_top_classes=5)
examples = ['images/right/roundabout_028.jpg',
'images/right/airplane_002.jpg',
'images/right/baseball_diamond_018.jpg',
'images/right/meadow_019.jpg',
'images/right/ship_002.jpg',
'images/right/storage_tank_002.jpg',
'images/right/freeway_002.jpg',
'images/right/overpass_015.jpg',
'images/right/airport_002.jpg',
'images/right/beach_002.jpg',
'images/wrong/airport_020.jpg',
'images/wrong/palace_004.jpg',
'images/wrong/desert_199.jpg',
]
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)
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