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)