def init_weights(self):
"""
Initialize weights.
"""
for conv in [self.conv1, self.conv2, self.conv3]:
init.xavier_uniform(conv.weight, gain=1)
init.constant(conv.bias, 0.1)
def __init__(
self,
last_conv_stride=1,
last_conv_dilation=1,
num_stripes=6,
local_conv_out_channels=256,
num_classes=0
):
super(PCBModel, self).__init__()
self.base = resnet50(
pretrained=True,
last_conv_stride=last_conv_stride,
last_conv_dilation=last_conv_dilation)
self.num_stripes = num_stripes
self.local_conv_list = nn.ModuleList()
for _ in range(num_stripes):
self.local_conv_list.append(nn.Sequential(
nn.Conv2d(2048, local_conv_out_channels, 1),
nn.BatchNorm2d(local_conv_out_channels),
nn.ReLU(inplace=True)
))
if num_classes > 0:
self.fc_list = nn.ModuleList()
for _ in range(num_stripes):
fc = nn.Linear(local_conv_out_channels, num_classes)
init.normal(fc.weight, std=0.001)
init.constant(fc.bias, 0)
self.fc_list.append(fc)
def weights_init_kaiming(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif classname.find('Linear') != -1:
init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif classname.find('BatchNorm2d') != -1:
init.normal(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def weights_init_orthogonal(m):
classname = m.__class__.__name__
print(classname)
if classname.find('Conv') != -1:
init.orthogonal(m.weight.data, gain=1)
elif classname.find('Linear') != -1:
init.orthogonal(m.weight.data, gain=1)
elif classname.find('BatchNorm2d') != -1:
init.normal(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def reset_parameters(self):
"""
Initialize parameters following the way proposed in the paper.
"""
init.orthogonal(self.weight_ih.data)
weight_hh_data = torch.eye(self.hidden_size)
weight_hh_data = weight_hh_data.repeat(1, 3)
self.weight_hh.data.set_(weight_hh_data)
# The bias is just set to zero vectors.
if self.use_bias:
init.constant(self.bias.data, val=0)
def __init__(self, depth, pretrained=True, cut_at_pooling=False,
num_features=0, norm=False, dropout=0, num_classes=0,
num_diff_features=0, iden_pretrain = False,
model_path='/media/hh/disc_d/hh/open-reid-master/pretrained model/resnet50.pth'):
super(ResNet, self).__init__()
self.depth = depth
self.pretrained = pretrained
self.cut_at_pooling = cut_at_pooling
self.iden_pretrain = iden_pretrain
# Construct base (pretrained) resnet
if depth not in ResNet.__factory:
raise KeyError("Unsupported depth:", depth)
# self.base = ResNet.__factory[depth](pretrained=pretrained)
self.base = baseresnet.ResNet(baseresnet.Bottleneck, [3, 4, 6, 3])
if pretrained is True:
self.base.load_state_dict(torch.load(model_path))
self.relu = nn.ReLU(inplace=True)
if not self.cut_at_pooling:
self.num_features = num_features
self.num_diff_features = num_diff_features
self.norm = norm
self.dropout = dropout
self.has_embedding = num_features > 0
self.num_classes = num_classes
out_planes = self.base.fc.in_features
# Append new layers
if self.has_embedding:
self.feat = nn.Linear(out_planes, self.num_features)
self.feat_bn = nn.BatchNorm1d(self.num_features)
init.kaiming_normal(self.feat.weight, mode='fan_out')
init.constant(self.feat.bias, 0)
init.constant(self.feat_bn.weight, 1)
init.constant(self.feat_bn.bias, 0)
else:
# Change the num_features to CNN output channels
self.num_features = out_planes
if self.dropout > 0:
self.drop = nn.Dropout(self.dropout)
if self.num_diff_features > 0:
self.diff_feat = nn.Linear(self.num_features, self.num_diff_features)
init.orthogonal(self.diff_feat.weight)
init.constant(self.diff_feat.bias, 0)
if self.num_classes > 0:
self.classifier = nn.Linear(self.num_features, self.num_classes)
# init.orthogonal(self.classifier.weight)
init.normal(self.classifier.weight, std=0.001)
init.constant(self.classifier.bias, 0)
if not self.pretrained:
self.reset_params()
def __init__(self, local_conv_out_channels=128, num_classes=None):
super(Model, self).__init__()
self.base = resnet50(pretrained=True)
planes = 2048
self.local_conv = nn.Conv2d(planes, local_conv_out_channels, 1)
self.local_bn = nn.BatchNorm2d(local_conv_out_channels)
self.local_relu = nn.ReLU(inplace=True)
if num_classes is not None:
self.fc = nn.Linear(planes, num_classes)
init.normal(self.fc.weight, std=0.001)
init.constant(self.fc.bias, 0)
def __init__(self, num_features=0, norm=False, dropout=0,
num_diff_features=0):
super(Trip_embedding, self).__init__()
self.num_features = num_features
self.num_diff_features = num_diff_features
self.norm = norm
self.dropout = dropout
if self.dropout > 0:
self.drop = nn.Dropout(self.dropout)
if self.num_diff_features > 0:
self.diff_feat = nn.Linear(self.num_features, self.num_diff_features)
init.orthogonal(self.diff_feat.weight)
init.constant(self.diff_feat.bias, 0)
def _prepare_tsn(self, num_class):
feature_dim = getattr(self.base_model, self.base_model.last_layer_name).in_features
if self.dropout == 0:
setattr(self.base_model, self.base_model.last_layer_name, nn.Linear(feature_dim, num_class))
self.new_fc = None
else:
setattr(self.base_model, self.base_model.last_layer_name, nn.Dropout(p=self.dropout))
self.new_fc = nn.Linear(feature_dim, num_class)
std = 0.001
if self.new_fc is None:
normal(getattr(self.base_model, self.base_model.last_layer_name).weight, 0, std)
constant(getattr(self.base_model, self.base_model.last_layer_name).bias, 0)
else:
normal(self.new_fc.weight, 0, std)
constant(self.new_fc.bias, 0)
return feature_dim
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0) and hasattr(m, 'weight'):
# print m.__class__.__name__
if init_type == 'gaussian':
init.normal(m.weight.data, 0.0, 0.02)
elif init_type == 'xavier':
init.xavier_normal(m.weight.data, gain=math.sqrt(2))
elif init_type == 'kaiming':
init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal(m.weight.data, gain=math.sqrt(2))
elif init_type == 'default':
pass
else:
assert 0, "Unsupported initialization: {}".format(init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant(m.bias.data, 0.0)
def init_func(m):
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
init.normal(m.weight.data, 0.0, gain)
elif init_type == 'xavier':
init.xavier_normal(m.weight.data, gain=gain)
elif init_type == 'kaiming':
init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal(m.weight.data, gain=gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1:
init.normal(m.weight.data, 1.0, gain)
init.constant(m.bias.data, 0.0)
def __init__(self, num_features=0):
super(Reconstruct, self).__init__()
self.num_features = num_features
self.fc_re = nn.Linear(self.num_features, 2048)
self.fc_rebn = nn.BatchNorm1d(2048)
init.kaiming_normal(self.fc_re.weight, mode='fan_out')
init.constant(self.fc_re.bias, 0)
# init.constant(self.fc_rebn.weight, 1)
# init.constant(self.fc_rebn.bias, 0)
self.upconv5 = nn.ConvTranspose2d(2048, 1024, 3, 2, 1)
self.upconv5_bn = nn.BatchNorm2d(1024)
self.upconv4 = nn.ConvTranspose2d(1024, 512, 3, 2, 1)
self.upconv4_bn = nn.BatchNorm2d(512)
self.upconv3 = nn.ConvTranspose2d(512, 256, 3, 2, 1)
self.upconv3_bn = nn.BatchNorm2d(256)
self.upconv2 = nn.ConvTranspose2d(256, 64, 3, 1, 1)
self.upconv2_bn = nn.BatchNorm2d(64)
self.upconv1 = nn.ConvTranspose2d(64, 3, 7, 2, 3)
def reset_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal(m.weight, mode='fan_out')
if m.bias is not None:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant(m.weight, 1)
init.constant(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal(m.weight, std=0.001)
if m.bias is not None:
init.constant(m.bias, 0)
def init_params(net):
'''Init layer parameters.'''
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal(m.weight, mode='fan_out')
if m.bias:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant(m.weight, 1)
init.constant(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal(m.weight, std=1e-3)
if m.bias:
init.constant(m.bias, 0)
def conv_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_uniform(m.weight, gain=np.sqrt(2))
init.constant(m.bias, 0)
elif classname.find('BatchNorm') != -1:
init.constant(m.weight, 1)
init.constant(m.bias, 0)
def reset_parameters(self):
"""
Initialize parameters following the way proposed in the paper.
"""
# The input-to-hidden weight matrix is initialized orthogonally.
init.orthogonal(self.weight_ih.data)
# The hidden-to-hidden weight matrix is initialized as an identity
# matrix.
weight_hh_data = torch.eye(self.hidden_size)
weight_hh_data = weight_hh_data.repeat(4, 1)
self.weight_hh.data.set_(weight_hh_data)
# The bias is just set to zero vectors.
init.constant(self.bias.data, val=0)
# Initialization of BN parameters.
self.bn_ih.reset_parameters()
self.bn_hh.reset_parameters()
self.bn_c.reset_parameters()
self.bn_ih.bias.data.fill_(0)
self.bn_hh.bias.data.fill_(0)
self.bn_ih.weight.data.fill_(0.1)
self.bn_hh.weight.data.fill_(0.1)
self.bn_c.weight.data.fill_(0.1)
def __init__(self, depth, pretrained=True, cut_at_pooling=False,
num_features=0, norm=False, dropout=0, num_classes=0):
super(ResNet, self).__init__()
self.depth = depth
self.pretrained = pretrained
self.cut_at_pooling = cut_at_pooling
# Construct base (pretrained) resnet
if depth not in ResNet.__factory:
raise KeyError("Unsupported depth:", depth)
self.base = ResNet.__factory[depth](pretrained=pretrained)
if not self.cut_at_pooling:
self.num_features = num_features
self.norm = norm
self.dropout = dropout
self.has_embedding = num_features > 0
self.num_classes = num_classes
out_planes = self.base.fc.in_features
# Append new layers
if self.has_embedding:
self.feat = nn.Linear(out_planes, self.num_features)
self.feat_bn = nn.BatchNorm1d(self.num_features)
init.kaiming_normal(self.feat.weight, mode='fan_out')
init.constant(self.feat.bias, 0)
init.constant(self.feat_bn.weight, 1)
init.constant(self.feat_bn.bias, 0)
else:
# Change the num_features to CNN output channels
self.num_features = out_planes
if self.dropout > 0:
self.drop = nn.Dropout(self.dropout)
if self.num_classes > 0:
self.classifier = nn.Linear(self.num_features, self.num_classes)
init.normal(self.classifier.weight, std=0.001)
init.constant(self.classifier.bias, 0)
if not self.pretrained:
self.reset_params()
def __init__(self, in_size, out_size, kernel_size=3,stride=1, padding=1, activation=nn.ReLU(), space_dropout=False):
super(UNetUpBlock, self).__init__()
self.conv0 = nn.Conv2d(in_size, out_size, 3, stride=1, padding=1)
self.conv = nn.Conv2d(in_size, out_size, kernel_size, stride=1, padding=1)
self.conv2 = nn.Conv2d(out_size, out_size, kernel_size,stride=1, padding=1)
init.xavier_normal(self.conv0.weight,gain=np.sqrt(2))
init.xavier_normal(self.conv.weight,gain=np.sqrt(2))
init.xavier_normal(self.conv2.weight,gain=np.sqrt(2))
init.constant(self.conv0.bias, 0.1)
init.constant(self.conv.bias, 0.1)
init.constant(self.conv2.bias, 0.1)
self.activation = activation
self.upsampler = nn.Upsample(scale_factor=2)
def __init__(self, in_size, out_size, kernel_size=3, stride=1, padding=1, activation = nn.ReLU(), downsample=True):
super(UNetConvBlock, self).__init__()
self.conv_down = nn.Conv2d(in_size, in_size, kernel_size, stride=2, padding=1)
self.conv = nn.Conv2d(in_size, out_size, kernel_size, stride=1, padding=padding)
self.conv2 = nn.Conv2d(out_size, out_size, kernel_size,stride=1, padding=1)
init.xavier_normal(self.conv_down.weight,gain=np.sqrt(2))
init.xavier_normal(self.conv.weight,gain=np.sqrt(2))
init.xavier_normal(self.conv2.weight,gain=np.sqrt(2))
init.constant(self.conv_down.bias,0.1)
init.constant(self.conv.bias, 0.1)
init.constant(self.conv2.bias, 0.1)
self.activation = activation
self.downsample = downsample
def init_params(net):
'''Init layer parameters.'''
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal(m.weight, mode='fan_out')
if m.bias:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant(m.weight, 1)
init.constant(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal(m.weight, std=1e-3)
if m.bias:
init.constant(m.bias, 0)
# _, term_width = os.popen('stty size', 'r').read().split()
# term_width = int(term_width)
#
# TOTAL_BAR_LENGTH = 65.
# last_time = time.time()
# begin_time = last_time
# def progress_bar(current, total, msg=None):
# global last_time, begin_time
# if current == 0:
# begin_time = time.time() # Reset for new bar.
#
# cur_len = int(TOTAL_BAR_LENGTH*current/total)
# rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1
#
# sys.stdout.write(' [')
# for i in range(cur_len):
# sys.stdout.write('=')
# sys.stdout.write('>')
# for i in range(rest_len):
# sys.stdout.write('.')
# sys.stdout.write(']')
#
# cur_time = time.time()
# step_time = cur_time - last_time
# last_time = cur_time
# tot_time = cur_time - begin_time
#
# L = []
# L.append(' Step: %s' % format_time(step_time))
# L.append(' | Tot: %s' % format_time(tot_time))
# if msg:
# L.append(' | ' + msg)
#
# msg = ''.join(L)
# sys.stdout.write(msg)
# for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):
# sys.stdout.write(' ')
#
# # Go back to the center of the bar.
# for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):
# sys.stdout.write('\b')
# sys.stdout.write(' %d/%d ' % (current+1, total))
#
# if current < total-1:
# sys.stdout.write('\r')
# else:
# sys.stdout.write('\n')
# sys.stdout.flush()
#
# def format_time(seconds):
# days = int(seconds / 3600/24)
# seconds = seconds - days*3600*24
# hours = int(seconds / 3600)
# seconds = seconds - hours*3600
# minutes = int(seconds / 60)
# seconds = seconds - minutes*60
# secondsf = int(seconds)
# seconds = seconds - secondsf
# millis = int(seconds*1000)
#
# f = ''
# i = 1
# if days > 0:
# f += str(days) + 'D'
# i += 1
# if hours > 0 and i <= 2:
# f += str(hours) + 'h'
# i += 1
# if minutes > 0 and i <= 2:
# f += str(minutes) + 'm'
# i += 1
# if secondsf > 0 and i <= 2:
# f += str(secondsf) + 's'
# i += 1
# if millis > 0 and i <= 2:
# f += str(millis) + 'ms'
# i += 1
# if f == '':
# f = '0ms'
# return f
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1,
out_channels=8,
kernel_size=(3, 3),
padding=(2, 2),
dilation=2)
self.conv2 = nn.Conv2d(in_channels=8,
out_channels=8,
kernel_size=(3, 3),
padding=(2, 2),
dilation=2)
self.conv3 = nn.Conv2d(in_channels=8,
out_channels=16,
kernel_size=(3, 3),
padding=(2, 2),
dilation=2)
# self.conv_attention1 = Attention_linear(16, 64)
self.conv_attention1 = Attention_nonlinear(16, 64, 64)
self.conv4 = nn.Conv2d(in_channels=16,
out_channels=16,
kernel_size=(3, 3),
padding=(2, 2),
dilation=2)
self.conv5 = nn.Conv2d(in_channels=16,
out_channels=32,
kernel_size=(3, 3),
padding=(2, 2),
dilation=2)
# self.conv_attention2 = Attention_linear(32, 64)
self.conv_attention2 = Attention_nonlinear(32, 64, 64)
self.conv6 = nn.Conv2d(in_channels=32,
out_channels=32,
kernel_size=(3, 3),
padding=(2, 2),
dilation=2)
# self.conv_attention3 = Attention_linear(32, 64)
self.conv_attention3 = Attention_nonlinear(32, 64, 64)
self.conv7 = nn.Conv2d(in_channels=32,
out_channels=64,
kernel_size=(3, 3),
padding=(2, 2),
dilation=2)
self.conv5_drop = nn.Dropout(p=.2)
self.fc1 = nn.Linear(in_features=80, out_features=32)
self.fc2 = nn.Linear(in_features=32, out_features=10)
self.fc3 = nn.Linear(in_features=10, out_features=2)
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
#init.xavier_normal(m.weight.data, gain=nn.init.calculate_gain('relu'))
init.xavier_uniform(m.weight.data,
gain=nn.init.calculate_gain('relu'))
#init.kaiming_uniform(m.weight.data)
init.constant(m.bias, .1)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def weights_init(net):
for m in net.modules():
if isinstance(m, torch.nn.Conv3d) or isinstance(
m, torch.nn.ConvTranspose3d):
init.kaiming_normal(m.weight)
init.constant(m.bias, 0.01)
def _layer_init(self, layer):
init.xavier_uniform(layer.weight, gain=1)
init.constant(layer.bias, val=0)
def _init_weights(self):
if cfg.MRCNN.CONV_INIT == 'GaussianFill':
init.normal(self.upconv5.weight, std=0.001)
elif cfg.MRCNN.CONV_INIT == 'MSRAFill':
init.kaiming_normal(self.upconv5.weight)
init.constant(self.upconv5.bias, 0)
def reset_parameters(self):
init.kaiming_normal(self.attend[0].weight.data)
init.constant(self.attend[0].bias.data, val=0)
def _init_weight(modules):
for m in modules:
if isinstance(m, (nn.Conv2d, nn.Conv3d)):
init.xavier_uniform(m.weight.data)
if m.bias is not None:
init.constant(m.bias.data, 0)
def weight_init(m):
if isinstance(m, nn.Conv2d):
init.xavier_uniform_(m.weight)
#init.xavier_normal(m.weight)
init.constant(m.bias, 0)
def weights_init(m):
if isinstance(m, nn.Conv2d):
init.xavier_uniform(m.weight, gain=numpy.sqrt(2.0))
init.constant(m.bias, 0.1)
def weights_init_bn(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('BatchNorm2d') != -1:
init.normal(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def __init__(self,
channels=3,
dimension=35,
activation=nn.ReLU(),
pretrained=True):
super(SqueezeSimplePredictor, self).__init__()
self.activation = activation
self.dimension = dimension
first_norm_layer = nn.BatchNorm2d(96)
final_norm_layer = nn.BatchNorm2d(dimension)
self.conv1 = nn.Conv2d(channels, 96, kernel_size=7, stride=2)
self.norm1 = first_norm_layer
self.downsample1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.fire1 = FireConvNorm(96, 16, 64, 64, activation=activation)
self.fire2 = FireConvNorm(128, 16, 64, 64, activation=activation)
self.fire3 = FireConvNorm(128, 32, 128, 128, activation=activation)
self.downsample2 = nn.MaxPool2d(kernel_size=3, stride=2)
self.fire4 = FireConvNorm(256, 32, 128, 128, activation=activation)
self.fire5 = FireConvNorm(256, 48, 192, 192, activation=activation)
self.fire6 = FireConvNorm(384, 48, 192, 192, activation=activation)
self.fire7 = FireConvNorm(384, 64, 256, 256, activation=activation)
self.downsample3 = nn.MaxPool2d(kernel_size=3, stride=2)
self.fire8 = FireConvNorm(512, 64, 256, 256, activation=activation)
if pretrained:
model = models.squeezenet1_0(pretrained=True).features
if channels == 3:
self.conv1 = model[0]
self.fire1.squeeze = model[3].squeeze
self.fire1.expand1x1 = model[3].expand1x1
self.fire1.expand3x3 = model[3].expand3x3
self.fire1.ConfigureNorm()
self.fire2.squeeze = model[4].squeeze
self.fire2.expand1x1 = model[4].expand1x1
self.fire2.expand3x3 = model[4].expand3x3
self.fire2.ConfigureNorm()
self.fire3.squeeze = model[5].squeeze
self.fire3.expand1x1 = model[5].expand1x1
self.fire3.expand3x3 = model[5].expand3x3
self.fire3.ConfigureNorm()
self.fire4.squeeze = model[7].squeeze
self.fire4.expand1x1 = model[7].expand1x1
self.fire4.expand3x3 = model[7].expand3x3
self.fire4.ConfigureNorm()
self.fire5.squeeze = model[8].squeeze
self.fire5.expand1x1 = model[8].expand1x1
self.fire5.expand3x3 = model[8].expand3x3
self.fire5.ConfigureNorm()
self.fire6.squeeze = model[9].squeeze
self.fire6.expand1x1 = model[9].expand1x1
self.fire6.expand3x3 = model[9].expand3x3
self.fire6.ConfigureNorm()
self.fire7.squeeze = model[10].squeeze
self.fire7.expand1x1 = model[10].expand1x1
self.fire7.expand3x3 = model[10].expand3x3
self.fire7.ConfigureNorm()
self.fire8.squeeze = model[12].squeeze
self.fire8.expand1x1 = model[12].expand1x1
self.fire8.expand3x3 = model[12].expand3x3
self.fire8.ConfigureNorm()
else:
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_uniform(m.weight)
if m.bias is not None:
init.constant(m.bias, 0)
self.predictor = nn.Sequential(
nn.Dropout(p=0),
nn.Conv2d(LATENT_DIM, dimension, kernel_size=1),
final_norm_layer,
activation,
nn.AvgPool2d(kernel_size=12, stride=1),
)
def create_network(self):
if self.stride_before_pool:
conv_stride = self.pool_time_stride
pool_stride = 1
else:
conv_stride = 1
pool_stride = self.pool_time_stride
pool_class_dict = dict(max=nn.MaxPool2d, mean=AvgPool2dWithConv)
first_pool_class = pool_class_dict[self.first_pool_mode]
later_pool_class = pool_class_dict[self.later_pool_mode]
model = nn.Sequential()
if self.split_first_layer:
model.add_module('dimshuffle', Expression(_transpose_time_to_spat))
model.add_module(
'conv_time',
nn.Conv2d(
1,
self.n_filters_time,
(self.filter_time_length, 1),
stride=1,
))
model.add_module(
'conv_spat',
nn.Conv2d(self.n_filters_time,
self.n_filters_spat, (1, self.in_chans),
stride=(conv_stride, 1),
bias=not self.batch_norm))
n_filters_conv = self.n_filters_spat
else:
model.add_module(
'conv_time',
nn.Conv2d(self.in_chans,
self.n_filters_time, (self.filter_time_length, 1),
stride=(conv_stride, 1),
bias=not self.batch_norm))
n_filters_conv = self.n_filters_time
if self.batch_norm:
model.add_module(
'bnorm',
nn.BatchNorm2d(n_filters_conv,
momentum=self.batch_norm_alpha,
affine=True,
eps=1e-5),
)
model.add_module('conv_nonlin', Expression(self.first_nonlin))
model.add_module(
'pool',
first_pool_class(kernel_size=(self.pool_time_length, 1),
stride=(pool_stride, 1)))
model.add_module('pool_nonlin', Expression(self.first_pool_nonlin))
def add_conv_pool_block(model, n_filters_before, n_filters,
filter_length, block_nr):
suffix = '_{:d}'.format(block_nr)
model.add_module('drop' + suffix, nn.Dropout(p=self.drop_prob))
model.add_module(
'conv' + suffix.format(block_nr),
nn.Conv2d(n_filters_before,
n_filters, (filter_length, 1),
stride=(conv_stride, 1),
bias=not self.batch_norm))
if self.batch_norm:
model.add_module(
'bnorm' + suffix,
nn.BatchNorm2d(n_filters,
momentum=self.batch_norm_alpha,
affine=True,
eps=1e-5))
model.add_module('nonlin' + suffix, Expression(self.later_nonlin))
model.add_module(
'pool' + suffix,
later_pool_class(kernel_size=(self.pool_time_length, 1),
stride=(pool_stride, 1)))
model.add_module('pool_nonlin' + suffix,
Expression(self.later_pool_nonlin))
add_conv_pool_block(model, n_filters_conv, self.n_filters_2,
self.filter_length_2, 2)
add_conv_pool_block(model, self.n_filters_2, self.n_filters_3,
self.filter_length_3, 3)
add_conv_pool_block(model, self.n_filters_3, self.n_filters_4,
self.filter_length_4, 4)
model.eval()
if self.final_conv_length == 'auto':
out = model(
np_to_var(
np.ones((1, self.in_chans, self.input_time_length, 1),
dtype=np.float32)))
n_out_time = out.cpu().data.numpy().shape[2]
self.final_conv_length = n_out_time
model.add_module(
'conv_classifier',
nn.Conv2d(self.n_filters_4,
self.n_classes, (self.final_conv_length, 1),
bias=True))
model.add_module('softmax', nn.LogSoftmax())
model.add_module('squeeze', Expression(_squeeze_final_output))
# Initialization, xavier is same as in our paper...
# was default from lasagne
init.xavier_uniform(model.conv_time.weight, gain=1)
# maybe no bias in case of no split layer and batch norm
if self.split_first_layer or (not self.batch_norm):
init.constant(model.conv_time.bias, 0)
if self.split_first_layer:
init.xavier_uniform(model.conv_spat.weight, gain=1)
if not self.batch_norm:
init.constant(model.conv_spat.bias, 0)
if self.batch_norm:
init.constant(model.bnorm.weight, 1)
init.constant(model.bnorm.bias, 0)
param_dict = dict(list(model.named_parameters()))
for block_nr in range(2, 5):
conv_weight = param_dict['conv_{:d}.weight'.format(block_nr)]
init.xavier_uniform(conv_weight, gain=1)
if not self.batch_norm:
conv_bias = param_dict['conv_{:d}.bias'.format(block_nr)]
init.constant(conv_bias, 0)
else:
bnorm_weight = param_dict['bnorm_{:d}.weight'.format(block_nr)]
bnorm_bias = param_dict['bnorm_{:d}.bias'.format(block_nr)]
init.constant(bnorm_weight, 1)
init.constant(bnorm_bias, 0)
init.xavier_uniform(model.conv_classifier.weight, gain=1)
init.constant(model.conv_classifier.bias, 0)
# Start in eval mode
model.eval()
return model
def __init__(self,
depth,
pretrained=True,
cut_at_pooling=False,
num_features=0,
norm=False,
dropout=0,
num_classes=0,
FCN=False,
radius=1.,
thresh=0.5):
super(ResNet, self).__init__()
self.depth = depth
self.pretrained = pretrained
self.cut_at_pooling = cut_at_pooling
self.FCN = FCN
# Construct base (pretrained) resnet
if depth not in ResNet.__factory:
raise KeyError("Unsupported depth:", depth)
self.base = ResNet.__factory[depth](pretrained=pretrained)
#==========================add dilation=============================#
if self.FCN:
for mo in self.base.layer4[0].modules():
if isinstance(mo, nn.Conv2d):
mo.stride = (1, 1)
#================append conv for FCN==============================#
self.num_features = num_features
self.num_classes = 702 #num_classes
self.dropout = dropout
out_planes = self.base.fc.in_features
self.local_conv = nn.Conv2d(out_planes,
self.num_features,
kernel_size=1,
padding=0,
bias=False)
init.kaiming_normal(self.local_conv.weight, mode='fan_out')
# init.constant(self.local_conv.bias,0)
self.feat_bn2d = nn.BatchNorm2d(
self.num_features) #may not be used, not working on caffe
init.constant(self.feat_bn2d.weight,
1) #initialize BN, may not be used
init.constant(self.feat_bn2d.bias,
0) # iniitialize BN, may not be used
# self.offset = ConvOffset2D(256)
##---------------------------stripe1----------------------------------------------#
self.instance0 = nn.Linear(self.num_features, self.num_classes)
init.normal(self.instance0.weight, std=0.001)
init.constant(self.instance0.bias, 0)
##---------------------------stripe1----------------------------------------------#
##---------------------------stripe1----------------------------------------------#
self.instance1 = nn.Linear(self.num_features, self.num_classes)
init.normal(self.instance1.weight, std=0.001)
init.constant(self.instance1.bias, 0)
##---------------------------stripe1----------------------------------------------#
##---------------------------stripe1----------------------------------------------#
self.instance2 = nn.Linear(self.num_features, self.num_classes)
init.normal(self.instance2.weight, std=0.001)
init.constant(self.instance2.bias, 0)
##---------------------------stripe1----------------------------------------------#
##---------------------------stripe1----------------------------------------------#
self.instance3 = nn.Linear(self.num_features, self.num_classes)
init.normal(self.instance3.weight, std=0.001)
init.constant(self.instance3.bias, 0)
##---------------------------stripe1----------------------------------------------#
##---------------------------stripe1----------------------------------------------#
self.instance4 = nn.Linear(self.num_features, self.num_classes)
init.normal(self.instance4.weight, std=0.001)
init.constant(self.instance4.bias, 0)
##---------------------------stripe1----------------------------------------------#
##---------------------------stripe1----------------------------------------------#
self.instance5 = nn.Linear(self.num_features, self.num_classes)
init.normal(self.instance5.weight, std=0.001)
init.constant(self.instance5.bias, 0)
self.drop = nn.Dropout(self.dropout)
elif not self.cut_at_pooling:
self.num_features = num_features
self.norm = norm
self.dropout = dropout
self.has_embedding = num_features > 0
self.num_classes = num_classes
self.radius = nn.Parameter(torch.FloatTensor([radius]))
self.thresh = nn.Parameter(torch.FloatTensor([thresh]))
out_planes = self.base.fc.in_features
# Append new layers
if self.has_embedding:
self.feat = nn.Linear(out_planes,
self.num_features,
bias=False)
self.feat_bn = nn.BatchNorm1d(self.num_features)
init.kaiming_normal(self.feat.weight, mode='fan_out')
else:
# Change the num_features to CNN output channels
self.num_features = out_planes
if self.dropout > 0:
self.drop = nn.Dropout(self.dropout)
if self.num_classes > 0:
self.classifier = nn.Linear(self.num_features,
self.num_classes,
bias=True)
init.normal(self.classifier.weight, std=0.001)
init.constant(self.classifier.bias, 0)
if not self.pretrained:
self.reset_params()
def _set_init(self, layer):
init.normal(layer.weight, mean=0., std=.1)
init.constant(layer.bias, 0.5)
def __init__(self, n_channels, init_scale):
super(L2NormScale, self).__init__()
self.n_channels = n_channels
self.init_scale = init_scale
self.weight = nn.Parameter(torch.Tensor(self.n_channels))
init.constant(self.weight, self.init_scale)
def weight_init(m):
if isinstance(m, nn.Conv2d):
init.xavier_normal(m.weight)
init.constant(m.bias, 0)
def reset_parameters(self):
init.kaiming_normal(self.encoder1[0].weight.data)
init.kaiming_normal(self.encoder1[2].weight.data)
init.constant(self.encoder1[0].bias.data, val=0)
init.constant(self.encoder1[2].bias.data, val=0)
init.kaiming_normal(self.encoder2[0].weight.data)
init.kaiming_normal(self.encoder2[2].weight.data)
init.constant(self.encoder2[0].bias.data, val=0)
init.constant(self.encoder2[2].bias.data, val=0)
init.kaiming_normal(self.encoder3[0].weight.data)
init.kaiming_normal(self.encoder3[2].weight.data)
init.constant(self.encoder3[0].bias.data, val=0)
init.constant(self.encoder3[2].bias.data, val=0)
init.kaiming_normal(self.encoder4[0].weight.data)
init.constant(self.encoder4[0].bias.data, val=0)
init.kaiming_normal(self.decoder1[0].weight.data)
init.kaiming_normal(self.decoder1[2].weight.data)
init.constant(self.decoder1[0].bias.data, val=0)
init.constant(self.decoder1[2].bias.data, val=0)
init.kaiming_normal(self.decoder2[0].weight.data)
init.kaiming_normal(self.decoder2[2].weight.data)
init.constant(self.decoder2[0].bias.data, val=0)
init.constant(self.decoder2[2].bias.data, val=0)
init.kaiming_normal(self.decoder3[0].weight.data)
init.kaiming_normal(self.decoder3[2].weight.data)
init.constant(self.decoder3[0].bias.data, val=0)
init.constant(self.decoder3[2].bias.data, val=0)
init.kaiming_normal(self.decoder4[0].weight.data)
init.constant(self.decoder4[0].bias.data, val=0)
def _set_init(self, layer):
init.normal(layer.weight, mean=0., std=.1)
init.constant(layer.bias, B_INIT)
def resnet50(num_classes=751, **kwargs):
pretrained = True
model = ResNet(Bottleneck,
layers=[3, 4, 6, 3],
num_classes=num_classes,
**kwargs)
if pretrained:
# if False:
print(
'***************************wgc will succeed! load model!********************************8'
)
updated_params = model_zoo.load_url(model_urls['resnet50'])
updated_params.pop('fc.weight')
updated_params.pop('fc.bias')
# print('updated_params:',updated_params)
# print('wgcwgcwgc:',type(updated_params))
new_params = model.state_dict()
new_params.update(updated_params)
model.load_state_dict(new_params)
# else:
if False:
print(
'***************************wgc will succeed! no pretrained********************************8'
)
for m in model.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal(m.weight, mode='fan_out')
if m.bias is not None:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant(m.weight, 1)
init.constant(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal(m.weight, std=0.001)
if m.bias is not None:
init.constant(m.bias, 0)
init.kaiming_normal(model.fc1.weight, mode='fan_out')
init.constant(model.fc1.bias, 0)
init.constant(model.bn2.weight, 1)
init.constant(model.bn2.bias, 0)
init.normal(model.fc2.weight, std=0.001)
init.constant(model.fc2.bias, 0)
return model
def _set_init(self, layer): # 参数初始化
init.normal(layer.weight, mean=0., std=.1)
init.constant(layer.bias, B_INIT)
def reset_parameters(self):
init.constant(self.weight, self.gamma)
def _construct(self, layer_config):
"""
Method to construct the layer from the layer_config dictionary parameters.
"""
self.linear = nn.Linear(layer_config['n_in'], layer_config['n_out'])
self.bn = lambda x: x
if 'batch_norm' in layer_config:
if layer_config['batch_norm']:
self.bn = nn.BatchNorm1d(layer_config['n_out'], momentum=0.99)
if 'weight_norm' in layer_config:
if layer_config['weight_norm']:
self.linear = nn.utils.weight_norm(self.linear, name='weight')
init_gain = 1.
if 'non_linearity' in layer_config:
non_linearity = layer_config['non_linearity']
if non_linearity is None or non_linearity == 'linear':
self.non_linearity = lambda x: x
elif non_linearity == 'relu':
self.non_linearity = nn.ReLU()
init_gain = init.calculate_gain('relu')
elif non_linearity == 'leaky_relu':
self.non_linearity = nn.LeakyReLU()
elif non_linearity == 'clipped_leaky_relu':
self.non_linearity = ClippedLeakyReLU(negative_slope=1. / 3,
clip_min=-3,
clip_max=3)
elif non_linearity == 'elu':
self.non_linearity = nn.ELU()
elif non_linearity == 'selu':
self.non_linearity = nn.SELU()
elif non_linearity == 'tanh':
self.non_linearity = nn.Tanh()
init_gain = init.calculate_gain('tanh')
elif non_linearity == 'sigmoid':
self.non_linearity = nn.Sigmoid()
else:
raise Exception('Non-linearity ' + str(non_linearity) +
' not found.')
else:
self.non_linearity = lambda x: x
self.dropout = lambda x: x
if 'dropout' in layer_config:
if layer_config['dropout'] is not None:
self.dropout = nn.Dropout(layer_config['dropout'])
if 'initialize' in layer_config:
initialize = layer_config['initialize']
if initialize == 'normal':
init.normal(self.linear.weight)
elif initialize == 'glorot_uniform':
init.xavier_uniform(self.linear.weight, gain=init_gain)
elif initialize == 'glorot_normal':
init.xavier_normal(self.linear.weight, gain=init_gain)
elif initialize == 'kaiming_uniform':
init.kaiming_uniform(self.linear.weight)
elif initialize == 'kaiming_normal':
init.kaiming_normal(self.linear.weight)
elif initialize == 'orthogonal':
init.orthogonal(self.linear.weight, gain=init_gain)
elif initialize == '':
pass
else:
raise Exception('Parameter initialization ' + str(initialize) +
' not found.')
if 'batch_norm' in layer_config:
if layer_config['batch_norm']:
init.normal(self.bn.weight, 1, 0.02)
init.constant(self.bn.bias, 0.)
else:
init.xavier_normal(self.linear.weight, gain=init_gain)
init.constant(self.linear.bias, 0.)
def __init__(self,
max_num_nodes,
input_dim,
hidden_dim,
embedding_dim,
label_dim,
num_layers,
assign_hidden_dim,
assign_ratio=0.25,
assign_num_layers=-1,
num_pooling=1,
pred_hidden_dims=[50],
concat=True,
bn=True,
dropout=0.0,
linkpred=True,
assign_input_dim=-1,
args=None):
'''
Args:
num_layers: number of gc layers before each pooling
num_nodes: number of nodes for each graph in batch
linkpred: flag to turn on link prediction side objective
'''
super(SoftPoolingGcnEncoder,
self).__init__(input_dim,
hidden_dim,
embedding_dim,
label_dim,
num_layers,
pred_hidden_dims=pred_hidden_dims,
concat=concat,
args=args)
add_self = not concat
self.num_pooling = num_pooling
self.linkpred = linkpred
# GC
self.conv_first_after_pool = []
self.conv_block_after_pool = []
self.conv_last_after_pool = []
for i in range(num_pooling):
# use self to register the modules in self.modules()
self.conv_first2, self.conv_block2, self.conv_last2 = self.build_conv_layers(
self.pred_input_dim,
hidden_dim,
embedding_dim,
num_layers,
add_self,
normalize=True,
dropout=dropout)
self.conv_first_after_pool.append(self.conv_first2)
self.conv_block_after_pool.append(self.conv_block2)
self.conv_last_after_pool.append(self.conv_last2)
# assignment
if assign_num_layers == -1:
assign_num_layers = num_layers
if assign_input_dim == -1:
assign_input_dim = input_dim
assign_dim = int(max_num_nodes * assign_ratio)
self.assign_conv_first, self.assign_conv_block, self.assign_conv_last = self.build_conv_layers(
assign_input_dim,
assign_hidden_dim,
assign_dim,
assign_num_layers,
add_self,
normalize=True)
assign_pred_input_dim = assign_hidden_dim * (
num_layers - 1) + assign_dim if concat else assign_dim
self.assign_pred = self.build_pred_layers(assign_pred_input_dim, [],
assign_dim,
num_aggs=1)
self.pred_model = self.build_pred_layers(self.pred_input_dim *
(num_pooling + 1),
pred_hidden_dims,
label_dim,
num_aggs=self.num_aggs)
for m in self.modules():
if isinstance(m, GraphConv):
m.weight.data = init.xavier_uniform(
m.weight.data, gain=nn.init.calculate_gain('relu'))
if m.bias is not None:
m.bias.data = init.constant(m.bias.data, 0.0)
def __init__(self,
growth_rate=32,
block_config=(6, 12, 24, 16),
num_init_features=64,
bn_size=4,
drop_rate=0,
hidden_num=2048,
cut_at_pooling=False,
num_features=0,
norm=False,
dropout=0,
num_classes=0):
super(DenseNet, self).__init__()
self.cut_at_pooling = cut_at_pooling
# First convolution
self.features = nn.Sequential(
OrderedDict([
('conv0',
nn.Conv2d(3,
num_init_features,
kernel_size=7,
stride=2,
padding=3,
bias=False)),
('norm0', nn.BatchNorm2d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))
# Each denseblock
num_features = num_init_features
for i, num_layers in enumerate(block_config):
if i < 2:
block = _DenseBlock(num_layers=num_layers,
num_input_features=num_features,
bn_size=bn_size,
growth_rate=growth_rate,
drop_rate=drop_rate)
self.features.add_module('denseblock%d' % (i + 1), block)
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
trans = _Transition(num_input_features=num_features,
num_output_features=num_features // 2)
self.features.add_module('transition%d' % (i + 1), trans)
num_features = num_features // 2
self.hidden = nn.Sequential(
OrderedDict([
('norm2', nn.BatchNorm2d(num_features)),
('relu2', nn.ReLU(inplace=True)),
('conv3',
nn.Conv2d(num_features,
num_features,
kernel_size=3,
stride=1,
padding=1,
bias=False)),
('norm3', nn.BatchNorm2d(num_features)),
('relu3', nn.ReLU(inplace=True)),
('pool3', nn.MaxPool2d(kernel_size=2, stride=2, padding=1)),
('conv4',
nn.Conv2d(num_features,
num_features,
kernel_size=3,
stride=1,
padding=1,
bias=False)),
('norm4', nn.BatchNorm2d(num_features)),
('relu4', nn.ReLU(inplace=True)),
('pool4', nn.MaxPool2d(kernel_size=2, stride=2, padding=1)),
]))
self.embedding = nn.Sequential(
OrderedDict([('feat', nn.Linear(3 * 6 * 512, hidden_num)),
('feat_bn', nn.BatchNorm2d(hidden_num)),
('feat_relu', nn.ReLU(inplace=True))]))
if not self.cut_at_pooling:
self.num_features = num_features
self.norm = norm
self.dropout = dropout
self.has_embedding = num_features > 0
self.num_classes = num_classes
out_planes = self.base.classifier.in_features
# Append new layers
if self.has_embedding:
self.feat = nn.Linear(out_planes, self.num_features)
self.feat_bn = nn.BatchNorm1d(self.num_features)
init.kaiming_normal(self.feat.weight, mode='fan_out')
init.constant(self.feat.bias, 0)
init.constant(self.feat_bn.weight, 1)
init.constant(self.feat_bn.bias, 0)
else:
# Change the num_features to CNN output channels
self.num_features = out_planes
if self.dropout > 0:
self.drop = nn.Dropout(self.dropout)
if self.num_classes > 0:
self.classifier_bn = nn.BatchNorm1d(self.num_features)
self.classifier = nn.Linear(self.num_features,
self.num_classes)
init.constant(self.classifier_bn.weight, 1)
init.constant(self.classifier_bn.bias, 0)
init.normal(self.classifier.weight, std=0.001)
init.constant(self.classifier.bias, 0)
def weights_init_classifier(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
init.normal(m.weight.data, std=0.001)
init.constant(m.bias.data, 0.0)
def __init__(self, dim, output_padding, numpen, slope=0.0):
super(extractconvSDAE, self).__init__()
self.in_dim = dim[0]
self.nlayers = len(dim) - 1
self.reluslope = slope
self.numpen = numpen
self.enc, self.dec = [], []
self.benc, self.bdec = [], []
for i in range(self.nlayers):
if i == self.nlayers - 1:
self.enc.append(nn.Linear(dim[i] * numpen, dim[i + 1]))
self.benc.append(nn.BatchNorm2d(dim[i + 1]))
self.dec.append(
nn.ConvTranspose2d(dim[i + 1],
dim[i],
kernel_size=numpen,
stride=1))
self.bdec.append(nn.BatchNorm2d(dim[i]))
elif i == 0:
self.enc.append(
nn.Conv2d(dim[i],
dim[i + 1],
kernel_size=4,
stride=2,
padding=1))
self.benc.append(nn.BatchNorm2d(dim[i + 1]))
self.dec.append(
nn.ConvTranspose2d(dim[i + 1],
dim[i],
kernel_size=4,
stride=2,
padding=1,
output_padding=output_padding[i]))
self.bdec.append(nn.BatchNorm2d(dim[i]))
else:
self.enc.append(
nn.Conv2d(dim[i],
dim[i + 1],
kernel_size=5,
stride=2,
padding=2))
self.benc.append(nn.BatchNorm2d(dim[i + 1]))
self.dec.append(
nn.ConvTranspose2d(dim[i + 1],
dim[i],
kernel_size=5,
stride=2,
padding=2,
output_padding=output_padding[i]))
self.bdec.append(nn.BatchNorm2d(dim[i]))
setattr(self, 'enc_{}'.format(i), self.enc[-1])
setattr(self, 'benc_{}'.format(i), self.benc[-1])
setattr(self, 'dec_{}'.format(i), self.dec[-1])
setattr(self, 'bdec_{}'.format(i), self.bdec[-1])
self.base = []
self.bbase = []
for i in range(self.nlayers):
self.base.append(nn.Sequential(*self.enc[:i]))
self.bbase.append(nn.Sequential(*self.benc[:i]))
# initialization
for m in self.modules():
if isinstance(m, nn.Linear):
init.normal(m.weight, std=1e-2)
if m.bias.data is not None:
init.constant(m.bias, 0)
elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
init.kaiming_normal(m.weight, mode='fan_out')
if m.bias.data is not None:
init.constant(m.bias, 0)
def weights_init(m):
classname = m.__class__.__name__
if 'Linear' in classname:
init.xavier_normal(m.weight.data)
init.constant(m.bias, 0.0)
def init_weight(self, m):
for each_module in m:
if "Linear" in each_module.__class__.__name__:
init.xavier_normal(each_module.weight)
init.constant(each_module.bias, 0.)
from retinanet import RetinaNet
print('Loading pretrained ResNet50 model..')
d = torch.load('./model/resnet50.pth')
print('Loading into FPN50..')
fpn = FPN50()
dd = fpn.state_dict()
for k in d.keys():
if not k.startswith('fc'): # skip fc layers
dd[k] = d[k]
print('Saving RetinaNet..')
net = RetinaNet()
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.normal(m.weight, mean=0, std=0.01)
if m.bias is not None:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
pi = 0.01
init.constant(net.cls_head[-1].bias, -math.log((1-pi)/pi))
net.fpn.load_state_dict(dd)
torch.save(net.state_dict(), 'net.pth')
print('Done!')
def weights_init_classifier(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
init.normal(m.weight.data, std=0.001)
init.constant(m.bias.data, 0.0)
def __init__(self):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(1,32,kernel_size=3,stride=2,padding=1) # 256x256
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1) # 128x128
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) # 64x64
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) # 32x32
self.conv5 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1) # 16x16
self.conv6 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1) # 8x8
self.conv7 = nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1) # 4x4
self.conv8 = nn.Conv2d(512, 1024, kernel_size=4, stride=1, padding=0) # 1x1
self.bn1 = nn.BatchNorm2d(32)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(128)
self.bn4 = nn.BatchNorm2d(256)
self.bn5 = nn.BatchNorm2d(256)
self.bn6 = nn.BatchNorm2d(512)
self.bn7 = nn.BatchNorm2d(512)
self.sigmoid = nn.Sigmoid()
self.lrelu = nn.LeakyReLU(negative_slope=0.2)
init.xavier_normal(self.conv1.weight, gain=np.sqrt(2))
init.constant(self.conv1.bias, 0.1)
init.xavier_normal(self.conv2.weight, gain=np.sqrt(2))
init.constant(self.conv2.bias, 0.1)
init.xavier_normal(self.conv3.weight, gain=np.sqrt(2))
init.constant(self.conv3.bias, 0.1)
init.xavier_normal(self.conv4.weight, gain=np.sqrt(2))
init.constant(self.conv4.bias, 0.1)
init.xavier_normal(self.conv5.weight, gain=np.sqrt(2))
init.constant(self.conv5.bias, 0.1)
init.xavier_normal(self.conv6.weight, gain=np.sqrt(2))
init.constant(self.conv6.bias, 0.1)
init.xavier_normal(self.conv7.weight, gain=np.sqrt(2))
init.constant(self.conv7.bias, 0.1)
init.xavier_normal(self.conv8.weight, gain=np.sqrt(2))
init.constant(self.conv8.bias, 0.1)
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
init.orthogonal(m.weight)
init.constant(m.bias, 0.1)
def _layer_init(self, layer):
init.xavier_uniform(layer.weight)
init.constant(layer.bias, 0.1)
def init_weights_xavier(model):
if isinstance(model, nn.Conv2d):
init.xavier_normal(model.weight)
init.constant(model.bias, 0)
def xavier_weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_uniform(m.weight, gain=np.sqrt(2))
init.constant(m.bias, 0.1)
def __init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv1d):
kaiming_normal(m.weight, mode='fan_in')
if m.bias is not None:
constant(m.bias, 0)
