import alex
sess = tf.InteractiveSession()
#batch_size = 64
batch_size = 24
#batch_size = 1
x_street = tf.placeholder("float", [batch_size, 227, 227, 3])
x_shop = tf.placeholder("float", [batch_size, 227, 227, 3])
if_pair = tf.placeholder("float", [
batch_size,
])
train_mode = tf.placeholder(tf.bool)
npy_path = '/ais/gobi4/fashion/bvlc_alexnet.npy'
#street_network = alex.ALEXNET(alex_npy_path=None, trainable=True)
street_network = alex.ALEXNET(alex_npy_path=npy_path, trainable=True)
#shop_network = alex.ALEXNET(alex_npy_path=None, trainable=True)
shop_network = alex.ALEXNET(alex_npy_path=npy_path, trainable=True)
street_network.build(rgb=x_street, flag="share", train_mode=train_mode)
shop_network.build(rgb=x_shop, flag="share", reuse=True, train_mode=train_mode)
y_street = street_network.relu6
y_shop = shop_network.relu6
dist_square_vec = tf.reduce_sum(tf.square(tf.sub(y_street, y_shop)), 1)
ones = tf.constant(1.0, dtype="float", shape=[
batch_size,
])
zero = tf.constant(0.0, dtype="float", shape=[
import skimage.transform
import input
import alex
import json
import bbox_input
import string
sess = tf.InteractiveSession()
x_shop = tf.placeholder("float", [1, 227, 227, 3])
train_mode = tf.placeholder(tf.bool)
#npy_path = '/ais/gobi4/fashion/retrieval/share_shop_alex.npy'
#npy_path = '/ais/gobi4/fashion/retrieval/shop_alex.npy'
npy_path = '/ais/gobi4/fashion/data/alex_full.npy'
#npy_path = '/ais/gobi4/fashion/bvlc_alexnet.npy'
shop_network = alex.ALEXNET(alex_npy_path=npy_path, trainable=False)
shop_network.build(rgb=x_shop, flag='shop', train_mode=train_mode)
y_shop = shop_network.relu6
sess.run(tf.initialize_all_variables())
#shop_path = '/ais/gobi4/fashion/retrieval/test_gallery.json'
shop_path = '/ais/gobi4/fashion/retrieval/alex_full_test_gallery.json'
img_path = '/ais/gobi4/fashion/data/Cross-domain-Retrieval/'
with open(shop_path, 'w') as jsonfile:
with open(
'/ais/gobi4/fashion/data/Cross-domain-Retrieval/list_test_pairs.txt',
'rb') as f:
data = f.readlines()
for line in data:
import numpy as np
import time
import inspect
import skimage
import skimage.io
import skimage.transform
import input
import alex
sess = tf.InteractiveSession()
batch_size = 32
x_street = tf.placeholder("float", [None, 227, 227, 3])
train_mode = tf.placeholder(tf.bool)
npy_path = '/ais/gobi4/fashion/retrieval/street_alex.npy'
street_network = alex.ALEXNET(npy_path=npy_path, trainable=False)
street_network.build(rgb=x_street, flag="street", train_mode=train_mode)
y_street = street_network.relu7
dist_pair = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(y_street, y_shop))))
dist_nopair = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(y_street, y_nopair))))
zero = tf.constant(0.0, dtype="float")
margin = tf.constant(0.3, dtype="float")
dist = tf.add(tf.sub(dist_pair, dist_nopair), margin)
pred = tf.less(dist, zero)
triplet_loss = tf.select(pred, zero, dist)
tf.scalar_summary('Loss', triplet_loss)
import alex
import input
sess = tf.InteractiveSession()
batch_size= 32
#batch_size = 4
x_street = tf.placeholder("float", [batch_size, 227, 227, 3])
x_shop = tf.placeholder("float", [batch_size, 227, 227, 3])
x_nopair = tf.placeholder("float", [batch_size, 227, 227, 3])
#y_ = tf.placeholder(tf.int32, shape=[None,])
train_mode = tf.placeholder(tf.bool)
npy_street = '/ais/gobi4/fashion/retrieval/street_alex.npy'
npy_shop = '/ais/gobi4/fashion/retrieval/shop_alex.npy'
npy_path = '/ais/gobi4/fashion/bvlc_alexnet.npy'
street_network = alex.ALEXNET(alex_npy_path=npy_street, trainable=True)
shop_network = alex.ALEXNET(alex_npy_path=npy_shop, trainable=True)
nopair_network = alex.ALEXNET(alex_npy_path=npy_shop, trainable=True)
street_network.build(rgb=x_street, flag="street", train_mode=train_mode)
shop_network.build(rgb=x_shop, flag="shop", train_mode=train_mode)
nopair_network.build(rgb=x_nopair, flag="shop", reuse=True, train_mode=train_mode)
y_street = street_network.relu6
y_shop = shop_network.relu6
y_nopair = nopair_network.relu6
#dist_pair_vec = tf.sqrt(tf.reduce_sum(tf.square(tf.sub(y_street, y_shop)), 1))
#dist_nopair_vec = tf.sqrt(tf.reduce_sum(tf.square(tf.sub(y_street, y_nopair)), 1))
dist_pair_vec = tf.reduce_sum(tf.square(tf.sub(y_street, y_shop)), 1)
dist_nopair_vec = tf.reduce_sum(tf.square(tf.sub(y_street, y_nopair)), 1)