code stringlengths 17 6.64M |
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class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
self.initialized = False
def initialize(self):
self.parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders tra... |
class TestOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
self.parser.add_argument('--ntest', type=int, default=float('inf'), help='# of test examples.')
self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
... |
class TrainOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
self.parser.add_argument('--display_freq', type=int, default=100, help='frequency of showing training results on screen')
self.parser.add_argument('--display_single_pane_ncols', type=int, default=0, help='... |
class GetData(object):
"\n\n Download CycleGAN or Pix2Pix Data.\n\n Args:\n technique : str\n One of: 'cyclegan' or 'pix2pix'.\n verbose : bool\n If True, print additional information.\n\n Examples:\n >>> from util.get_data import GetData\n >>> gd = GetDa... |
class HTML():
def __init__(self, web_dir, title, reflesh=0):
self.title = title
self.web_dir = web_dir
self.img_dir = os.path.join(self.web_dir, 'images')
if (not os.path.exists(self.web_dir)):
os.makedirs(self.web_dir)
if (not os.path.exists(self.img_dir)):
... |
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if (self.pool_size > 0):
self.num_imgs = 0
self.images = []
def query(self, images):
if (self.pool_size == 0):
return Variable(images)
return_images = []
f... |
def tensor2im(image_tensor, imtype=np.uint8):
image_numpy = image_tensor[0].cpu().float().numpy()
if (image_numpy.shape[0] == 1):
image_numpy = np.tile(image_numpy, (3, 1, 1))
image_numpy = (((np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0) * 255.0)
return image_numpy.astype(imtype)
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def diagnose_network(net, name='network'):
mean = 0.0
count = 0
for param in net.parameters():
if (param.grad is not None):
mean += torch.mean(torch.abs(param.grad.data))
count += 1
if (count > 0):
mean = (mean / count)
print(name)
print(mean)
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def save_image(image_numpy, image_path):
image_pil = Image.fromarray(image_numpy)
image_pil.save(image_path)
|
def print_numpy(x, val=True, shp=False):
x = x.astype(np.float64)
if shp:
print('shape,', x.shape)
if val:
x = x.flatten()
print(('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))))
|
def mkdirs(paths):
if (isinstance(paths, list) and (not isinstance(paths, str))):
for path in paths:
mkdir(path)
else:
mkdir(paths)
|
def mkdir(path):
if (not os.path.exists(path)):
os.makedirs(path)
|
class Visualizer():
def __init__(self, opt):
self.display_id = opt.display_id
self.use_html = (opt.isTrain and (not opt.no_html))
self.win_size = opt.display_winsize
self.name = opt.name
self.opt = opt
self.saved = False
if (self.display_id > 0):
... |
def get_loader(config):
'Builds and returns Dataloader for MNIST and SVHN dataset.'
transform_list = []
if config.use_augmentation:
transform_list.append(transforms.RandomHorizontalFlip())
transform_list.append(transforms.RandomRotation(0.1))
transform_list.append(transforms.Scale(conf... |
def str2bool(v):
return (v.lower() in 'true')
|
def main(config):
(svhn_loader, mnist_loader, svhn_test_loader, mnist_test_loader) = get_loader(config)
solver = Solver(config, svhn_loader, mnist_loader)
cudnn.benchmark = True
if (not os.path.exists(config.model_path)):
os.makedirs(config.model_path)
if (not os.path.exists(config.sample_... |
def str2bool(v):
return (v.lower() in 'true')
|
def main(config):
(svhn_loader, mnist_loader, svhn_test_loader, mnist_test_loader) = get_loader(config)
solver = Solver(config, svhn_loader, mnist_loader)
cudnn.benchmark = True
if (not os.path.exists(config.model_path)):
os.makedirs(config.model_path)
if (not os.path.exists(config.sample_... |
def str2bool(v):
return (v.lower() in 'true')
|
def main(config):
(svhn_loader, mnist_loader, svhn_test_loader, mnist_test_loader) = get_loader(config)
solver = Solver(config, svhn_loader, mnist_loader)
cudnn.benchmark = True
if (not os.path.exists(config.model_path)):
os.makedirs(config.model_path)
if (not os.path.exists(config.sample_... |
def deconv(c_in, c_out, k_size, stride=2, pad=1, bn=True):
'Custom deconvolutional layer for simplicity.'
layers = []
layers.append(nn.ConvTranspose2d(c_in, c_out, k_size, stride, pad, bias=False))
if bn:
layers.append(nn.BatchNorm2d(c_out))
return nn.Sequential(*layers)
|
def conv(c_in, c_out, k_size, stride=2, pad=1, bn=True):
'Custom convolutional layer for simplicity.'
layers = []
layers.append(nn.Conv2d(c_in, c_out, k_size, stride, pad, bias=False))
if bn:
layers.append(nn.BatchNorm2d(c_out))
return nn.Sequential(*layers)
|
class G11(nn.Module):
def __init__(self, conv_dim=64):
super(G11, self).__init__()
self.conv1 = conv(1, conv_dim, 4)
self.conv1_svhn = conv(3, conv_dim, 4)
self.conv2 = conv(conv_dim, (conv_dim * 2), 4)
res_dim = (conv_dim * 2)
self.conv3 = conv(res_dim, res_dim, 3... |
class G22(nn.Module):
def __init__(self, conv_dim=64):
super(G22, self).__init__()
self.conv1 = conv(3, conv_dim, 4)
self.conv1_mnist = conv(1, conv_dim, 4)
self.conv2 = conv(conv_dim, (conv_dim * 2), 4)
res_dim = (conv_dim * 2)
self.conv3 = conv(res_dim, res_dim, ... |
class D1(nn.Module):
'Discriminator for mnist.'
def __init__(self, conv_dim=64, use_labels=False):
super(D1, self).__init__()
self.conv1 = conv(1, conv_dim, 4, bn=False)
self.conv2 = conv(conv_dim, (conv_dim * 2), 4)
self.conv3 = conv((conv_dim * 2), (conv_dim * 4), 4)
... |
class D2(nn.Module):
'Discriminator for svhn.'
def __init__(self, conv_dim=64, use_labels=False):
super(D2, self).__init__()
self.conv1 = conv(3, conv_dim, 4, bn=False)
self.conv2 = conv(conv_dim, (conv_dim * 2), 4)
self.conv3 = conv((conv_dim * 2), (conv_dim * 4), 4)
... |
def relu6(x):
return K.relu(x, max_value=6)
|
def load_model(input_shape=(224, 224, 3), n_veid=576, Mode='train', Weights_path='./weights'):
alpha = 1.0
depth_multiplier = 1
dropout = 0.001
gauss_size = 1024
input_layer = Input(shape=input_shape)
y = Input(shape=[n_veid])
x = _conv_block(input_layer, 32, alpha, strides=(2, 2))
x =... |
def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
channel_axis = (1 if (K.image_data_format() == 'channels_first') else (- 1))
filters = int((filters * alpha))
x = ZeroPadding2D(padding=(1, 1), name='conv1_pad')(inputs)
x = Conv2D(filters, kernel, padding='valid', use_bias=False,... |
def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1), block_id=1):
channel_axis = (1 if (K.image_data_format() == 'channels_first') else (- 1))
pointwise_conv_filters = int((pointwise_conv_filters * alpha))
x = ZeroPadding2D(padding=(1, 1), name=('conv_pad_%d... |
def display_output(output):
sys.stdout.write('\x1b[F')
print(output)
|
def eval(args):
e_common = E_common(args.sep, int((args.resize / 64)))
e_separate_A = E_separate_A(args.sep, int((args.resize / 64)))
e_separate_B = E_separate_B(args.sep, int((args.resize / 64)))
decoder = Decoder(int((args.resize / 64)))
if torch.cuda.is_available():
e_common = e_common.... |
class E_common(nn.Module):
def __init__(self, sep, size, dim=512):
super(E_common, self).__init__()
self.sep = sep
self.size = size
self.dim = dim
self.layer1 = []
self.layer2 = []
self.layer3 = []
self.layer4 = []
self.layer5 = []
s... |
class E_separate_A(nn.Module):
def __init__(self, sep, size):
super(E_separate_A, self).__init__()
self.sep = sep
self.size = size
self.layer1 = []
self.layer2 = []
self.layer3 = []
self.layer4 = []
self.layer5 = []
self.layer6 = []
... |
class E_separate_B(nn.Module):
def __init__(self, sep, size):
super(E_separate_B, self).__init__()
self.sep = sep
self.size = size
self.layer1 = []
self.layer2 = []
self.layer3 = []
self.layer4 = []
self.layer5 = []
self.layer6 = []
... |
class Decoder(nn.Module):
def __init__(self, size, dim=512):
super(Decoder, self).__init__()
self.size = size
self.dim = dim
self.layer1 = []
self.layer2 = []
self.layer3 = []
self.layer4 = []
self.layer5 = []
self.layer6 = []
self.l... |
class Disc(nn.Module):
def __init__(self, sep, size, dim=512):
super(Disc, self).__init__()
self.sep = sep
self.size = size
self.dim = dim
self.classify = nn.Sequential(nn.Linear((((dim - (2 * self.sep)) * self.size) * self.size), dim), nn.LeakyReLU(0.2, inplace=True), nn.... |
def l2normalize(v, eps=1e-12):
return (v / (v.norm() + eps))
|
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.name = name
self.power_iterations = power_iterations
if (not self._made_params()):
self._make_params()
... |
def preprocess_celeba(args):
if (not os.path.exists(args.dest)):
os.mkdir(args.dest)
allA = []
allB = []
with open(args.attributes) as f:
lines = f.readlines()
if (args.config == 'beard_glasses'):
for line in lines[2:]:
line = line.split()
if (male_n... |
def male_no_5_oclock(line):
return ((int(line[21]) == 1) and (int(line[1]) == (- 1)))
|
def beard(line):
return ((int(line[23]) == 1) or (int(line[17]) == 1) or (int(line[25]) == (- 1)))
|
def glasses(line):
return (int(line[16]) == 1)
|
def smile(line):
return (int(line[32]) == 1)
|
def blonde_hair(line):
return (int(line[10]) == 1)
|
def black_hair(line):
return (int(line[9]) == 1)
|
def preprocess_folders(args):
if (not os.path.exists(args.dest)):
os.mkdir(args.dest)
trainA = os.listdir(os.path.join(args.root, 'trainA'))
trainB = os.listdir(os.path.join(args.root, 'trainB'))
testA = os.listdir(os.path.join(args.root, 'testA'))
testB = os.listdir(os.path.join(args.root... |
def train(args):
if (not os.path.exists(args.out)):
os.makedirs(args.out)
_iter = 0
(domA_train, domB_train) = get_train_dataset(args)
size = (args.resize // 64)
dim = 512
e_common = E_common(args.sep, size, dim=dim)
e_separate_A = E_separate_A(args.sep, size)
e_separate_B = E_... |
def get_test_dataset(args, crop=None, resize=None):
if (crop is None):
crop = args.crop
if (resize is None):
resize = args.resize
comp_transform = transforms.Compose([transforms.CenterCrop(crop), transforms.Resize(resize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, ... |
def get_train_dataset(args, crop=None, resize=None):
if (crop is None):
crop = args.crop
if (resize is None):
resize = args.resize
comp_transform = transforms.Compose([transforms.CenterCrop(crop), transforms.Resize(resize), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transfor... |
def save_imgs(args, e_common, e_separate_A, e_separate_B, decoder, iters, size, BtoA=True, num_offsets=1):
' saves images of translation B -> A or A -> B'
(test_domA, test_domB) = get_test_imgs(args)
for k in range(num_offsets):
exps = []
for i in range((k * args.num_display), ((k + 1) * a... |
def get_test_imgs(args, crop=None, resize=None):
(domA_test, domB_test) = get_test_dataset(args, crop=crop, resize=resize)
domA_test_loader = torch.utils.data.DataLoader(domA_test, batch_size=64, shuffle=False, num_workers=6)
domB_test_loader = torch.utils.data.DataLoader(domB_test, batch_size=64, shuffle... |
def save_model(out_file, e_common, e_separate_A, e_separate_B, decoder, ae_opt, disc, disc_opt, iters):
state = {'e_common': e_common.state_dict(), 'e_separate_A': e_separate_A.state_dict(), 'e_separate_B': e_separate_B.state_dict(), 'decoder': decoder.state_dict(), 'ae_opt': ae_opt.state_dict(), 'disc': disc.sta... |
def load_model(load_path, e_common, e_separate_A, e_separate_B, decoder, ae_opt, disc, disc_opt):
state = torch.load(load_path)
e_common.load_state_dict(state['e_common'])
e_separate_A.load_state_dict(state['e_separate_A'])
e_separate_B.load_state_dict(state['e_separate_B'])
decoder.load_state_dic... |
def load_model_for_eval(load_path, e_common, e_separate_A, e_separate_B, decoder):
state = torch.load(load_path)
e_common.load_state_dict(state['e_common'])
e_separate_A.load_state_dict(state['e_separate_A'])
e_separate_B.load_state_dict(state['e_separate_B'])
decoder.load_state_dict(state['decode... |
def edges_loader(path, train=True):
image = Image.open(path).convert('RGB')
image_A = image.crop((0, 0, 256, 256))
image_B = image.crop((0, 256, 512, 256))
if train:
return image_A
else:
return image_B
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def default_loader(path):
return Image.open(path).convert('RGB')
|
class Logger():
def __init__(self, path):
self.full_path = ('%s/log.txt' % path)
self.log_file = open(self.full_path, 'w+')
self.log_file.close()
self.map = {}
def add_value(self, tag, value):
self.map[tag] = value
def log(self, iter):
self.log_file = ope... |
class CustomDataset(data.Dataset):
def __init__(self, path, transform=None, return_paths=False, loader=default_loader):
super(CustomDataset, self).__init__()
with open(path) as f:
imgs = [s.replace('\n', '') for s in f.readlines()]
if (len(imgs) == 0):
raise Runtim... |
def default_flist_reader(flist):
"\n flist format: impath label\nimpath label\n ...(same to caffe's filelist)\n "
imlist = []
with open(flist, 'r') as rf:
for line in rf.readlines():
impath = line.strip()
imlist.append(impath)
return imlist
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def is_image_file(filename):
return any((filename.endswith(extension) for extension in IMG_EXTENSIONS))
|
def save_stripped_imgs(args, e_common, e_separate_A, e_separate_B, decoder, iters, size, A=True):
(test_domA, test_domB) = get_test_imgs(args)
exps = []
zero_encoding = torch.full((1, ((args.sep * size) * size)), 0)
one_encoding = torch.full((1, ((args.sep * size) * size)), 1)
if torch.cuda.is_ava... |
def save_chosen_imgs(args, e_common, e_separate_A, e_separate_B, decoder, iters, listA, listB, BtoA=True):
' saves images of translation B -> A or A -> B'
(test_domA, test_domB) = get_test_imgs(args)
exps = []
for i in range(args.num_display):
with torch.no_grad():
if (i == 0):
... |
def interpolate_fixed_common(args, e_common, e_separate_A, e_separate_B, decoder, imgA1, imgA2, imgB1, imgB2, content_img):
(test_domA, test_domB) = get_test_imgs(args)
exps = []
common = e_common(test_domB[content_img].unsqueeze(0))
a1 = e_separate_A(test_domA[imgA1].unsqueeze(0))
a2 = e_separate... |
def interpolate_fixed_A(args, e_common, e_separate_A, e_separate_B, decoder, imgC1, imgC2, imgB1, imgB2, imgA):
(test_domA, test_domB) = get_test_imgs(args)
exps = []
c1 = e_common(test_domB[imgC1].unsqueeze(0))
c2 = e_common(test_domB[imgC2].unsqueeze(0))
a = e_separate_A(test_domA[imgA].unsqueez... |
def interpolate_fixed_B(args, e_common, e_separate_A, e_separate_B, decoder, imgC1, imgC2, imgA1, imgA2, imgB):
(test_domA, test_domB) = get_test_imgs(args)
exps = []
c1 = e_common(test_domB[imgC1].unsqueeze(0))
c2 = e_common(test_domB[imgC2].unsqueeze(0))
a1 = e_separate_A(test_domA[imgA1].unsque... |
class BUILD_NET_VGG16():
def __init__(self, vgg16_npy_path=None):
self.data_dict = np.load(vgg16_npy_path, encoding='latin1').item()
print('npy file loaded')
def build(self, rgb, ROIMap, NUM_CLASSES, keep_prob):
'\n load variable from npy to build the VGG\n\n :param rgb... |
def CheckVGG16(model_path):
TensorflowUtils.maybe_download_and_extract(model_path.split('/')[0], 'ftp://mi.eng.cam.ac.uk/pub/mttt2/models/vgg16.npy')
if (not os.path.isfile(model_path)):
print('Error: Cant find pretrained vgg16 model for network initiation. Please download model from:')
print(... |
def main(argv=None):
tf.reset_default_graph()
keep_prob = tf.placeholder(tf.float32, name='keep_probabilty')
image = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_image')
ROIMap = tf.placeholder(tf.int32, shape=[None, None, None, 1], name='ROIMap')
Net = BuildNetVgg16.BUILD_N... |
def GetIOU(Pred, GT, NumClasses, ClassNames=[], DisplyResults=False):
ClassIOU = np.zeros(NumClasses)
ClassWeight = np.zeros(NumClasses)
for i in range(NumClasses):
Intersection = np.float32(np.sum(((Pred == GT) * (GT == i))))
Union = ((np.sum((GT == i)) + np.sum((Pred == i))) - Intersecti... |
def main(argv=None):
keep_prob = tf.placeholder(tf.float32, name='keep_probabilty')
image = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_image')
ROIMap = tf.placeholder(tf.int32, shape=[None, None, None, 1], name='ROIMap')
Net = BuildNetVgg16.BUILD_NET_VGG16(vgg16_npy_path=model... |
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
return optimizer.apply_gradients(grads)
|
def main(argv=None):
tf.reset_default_graph()
keep_prob = tf.placeholder(tf.float32, name='keep_probabilty')
image = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_image')
ROIMap = tf.placeholder(tf.int32, shape=[None, None, None, 1], name='ROIMap')
GTLabel = tf.placeholder(tf... |
def get_model_data(dir_path, model_url):
maybe_download_and_extract(dir_path, model_url)
filename = model_url.split('/')[(- 1)]
filepath = os.path.join(dir_path, filename)
if (not os.path.exists(filepath)):
raise IOError('VGG Model not found!')
data = scipy.io.loadmat(filepath)
return ... |
def maybe_download_and_extract(dir_path, url_name, is_tarfile=False, is_zipfile=False):
if (not os.path.exists(dir_path)):
os.makedirs(dir_path)
filename = url_name.split('/')[(- 1)]
filepath = os.path.join(dir_path, filename)
if (not os.path.exists(filepath)):
def _progress(count, bl... |
def save_image(image, save_dir, name, mean=None):
'\n Save image by unprocessing if mean given else just save\n :param mean:\n :param image:\n :param save_dir:\n :param name:\n :return:\n '
if mean:
image = unprocess_image(image, mean)
misc.imsave(os.path.join(save_dir, (name ... |
def get_variable(weights, name):
init = tf.constant_initializer(weights, dtype=tf.float32)
var = tf.get_variable(name=name, initializer=init, shape=weights.shape)
return var
|
def weight_variable(shape, stddev=0.02, name=None):
initial = tf.truncated_normal(shape, stddev=stddev)
if (name is None):
return tf.Variable(initial)
else:
return tf.get_variable(name, initializer=initial)
|
def bias_variable(shape, name=None):
initial = tf.constant(0.0, shape=shape)
if (name is None):
return tf.Variable(initial)
else:
return tf.get_variable(name, initializer=initial)
|
def get_tensor_size(tensor):
from operator import mul
return reduce(mul, (d.value for d in tensor.get_shape()), 1)
|
def conv2d_basic(x, W, bias):
conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
return tf.nn.bias_add(conv, bias)
|
def conv2d_strided(x, W, b):
conv = tf.nn.conv2d(x, W, strides=[1, 2, 2, 1], padding='SAME')
return tf.nn.bias_add(conv, b)
|
def conv2d_transpose_strided(x, W, b, output_shape=None, stride=2):
if (output_shape is None):
output_shape = x.get_shape().as_list()
output_shape[1] *= 2
output_shape[2] *= 2
output_shape[3] = W.get_shape().as_list()[2]
conv = tf.nn.conv2d_transpose(x, W, output_shape, strides... |
def leaky_relu(x, alpha=0.0, name=''):
return tf.maximum((alpha * x), x, name)
|
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
|
def avg_pool_2x2(x):
return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
|
def local_response_norm(x):
return tf.nn.lrn(x, depth_radius=5, bias=2, alpha=0.0001, beta=0.75)
|
def batch_norm(x, n_out, phase_train, scope='bn', decay=0.9, eps=1e-05):
'\n Code taken from http://stackoverflow.com/a/34634291/2267819\n '
with tf.variable_scope(scope):
beta = tf.get_variable(name='beta', shape=[n_out], initializer=tf.constant_initializer(0.0), trainable=True)
gamma =... |
def process_image(image, mean_pixel):
return (image - mean_pixel)
|
def unprocess_image(image, mean_pixel):
return (image + mean_pixel)
|
def bottleneck_unit(x, out_chan1, out_chan2, down_stride=False, up_stride=False, name=None):
'\n Modified implementation from github ry?!\n '
def conv_transpose(tensor, out_channel, shape, strides, name=None):
out_shape = tensor.get_shape().as_list()
in_channel = out_shape[(- 1)]
... |
def add_to_regularization_and_summary(var):
if (var is not None):
tf.summary.histogram(var.op.name, var)
tf.add_to_collection('reg_loss', tf.nn.l2_loss(var))
|
def add_activation_summary(var):
if (var is not None):
tf.summary.histogram((var.op.name + '/activation'), var)
tf.summary.scalar((var.op.name + '/sparsity'), tf.nn.zero_fraction(var))
|
def add_gradient_summary(grad, var):
if (grad is not None):
tf.summary.histogram((var.op.name + '/gradient'), grad)
|
class BUILD_NET_VGG16():
def __init__(self, vgg16_npy_path=None):
self.data_dict = np.load(vgg16_npy_path, encoding='latin1').item()
print('npy file loaded')
def build(self, rgb, NUM_CLASSES, keep_prob):
'\n load variable from npy to build the VGG\n\n :param rgb: rgb im... |
def CheckVGG16(model_path):
TensorflowUtils.maybe_download_and_extract(model_path.split('/')[0], 'ftp://mi.eng.cam.ac.uk/pub/mttt2/models/vgg16.npy')
if (not os.path.isfile(model_path)):
print('Error: Cant find pretrained vgg16 model for network initiation. Please download model from:')
print(... |
def main(argv=None):
tf.reset_default_graph()
keep_prob = tf.placeholder(tf.float32, name='keep_probabilty')
image = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_image')
Net = BuildNetVgg16.BUILD_NET_VGG16(vgg16_npy_path=model_path)
Net.build(image, NUM_CLASSES, keep_prob)
... |
def GetIOU(Pred, GT, NumClasses, ClassNames=[], DisplyResults=False):
ClassIOU = np.zeros(NumClasses)
ClassWeight = np.zeros(NumClasses)
for i in range(NumClasses):
Intersection = np.float32(np.sum(((Pred == GT) * (GT == i))))
Union = ((np.sum((GT == i)) + np.sum((Pred == i))) - Intersecti... |
def main(argv=None):
keep_prob = tf.placeholder(tf.float32, name='keep_probabilty')
image = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_image')
Net = BuildNetVgg16.BUILD_NET_VGG16(vgg16_npy_path=model_path)
Net.build(image, NUM_CLASSES, keep_prob)
ValidReader = Data_Reader.... |
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
return optimizer.apply_gradients(grads)
|
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