CLASS torch.nn.BatchNorm2d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

def set_bn_eval(m):
    classname = m.__class__.__name__
    if classname.find('BatchNorm') != -1:
      m.eval()
model.apply(set_bn_eval)

def train(self, mode=True):
        """
        Override the default train() to freeze the BN parameters
        """
        super(MyNet, self).train(mode)
        if self.freeze_bn:
            print("Freezing Mean/Var of BatchNorm2D.")
            if self.freeze_bn_affine:
                print("Freezing Weight/Bias of BatchNorm2D.")
        if self.freeze_bn:
            for m in self.backbone.modules():
                if isinstance(m, nn.BatchNorm2d):
                    m.eval()
                    if self.freeze_bn_affine:
                        m.weight.requires_grad = False
                        m.bias.requires_grad = False

import torch
import torch.nn as nn
from torch.nn import init
from torchvision import models
from torch.autograd import Variable
from apex.fp16_utils import *
def fix_bn(m):
    classname = m.__class__.__name__
    if classname.find('BatchNorm') != -1:
        m.eval()
model = models.resnet50(pretrained=True)
model.cuda()
model = network_to_half(model)
model.train()
model.apply(fix_bn) # fix batchnorm
input = Variable(torch.FloatTensor(8, 3, 224, 224).cuda().half())
output = model(input)
output_mean = torch.mean(output)
output_mean.backward()

def fix_bn(m):
    classname = m.__class__.__name__
    if classname.find('BatchNorm') != -1:
        m.eval().half()

net_dropped = torch.nn.Sequential(
    torch.nn.Linear(1, N_HIDDEN),
    torch.nn.Dropout(0.5),  # drop 50% of the neuron
    torch.nn.ReLU(),
    torch.nn.Linear(N_HIDDEN, N_HIDDEN),
    torch.nn.Dropout(0.5),  # drop 50% of the neuron
    torch.nn.ReLU(),
    torch.nn.Linear(N_HIDDEN, 1),
)
for t in range(500):
    pred_drop = net_dropped(x)
    loss_drop = loss_func(pred_drop, y)
    optimizer_drop.zero_grad()
    loss_drop.backward()
    optimizer_drop.step()
    if t % 10 == 0:
        # change to eval mode in order to fix drop out effect
        net_dropped.eval()  # parameters for dropout differ from train mode
        test_pred_drop = net_dropped(test_x)
        # change back to train mode
        net_dropped.train()

            if self.do_bn:
                bn = nn.BatchNorm1d(10, momentum=0.5)
                setattr(self, 'bn%i' % i, bn)   # IMPORTANT set layer to the Module
                self.bns.append(bn)
    for epoch in range(EPOCH):
        print('Epoch: ', epoch)
        for net, l in zip(nets, losses):
            net.eval()              # set eval mode to fix moving_mean and moving_var
            pred, layer_input, pre_act = net(test_x)
            net.train()             # free moving_mean and moving_var
        plot_histogram(*layer_inputs, *pre_acts)  

tf_is_training = tf.placeholder(tf.bool, None)  # to control dropout when training and testing
# dropout net
d1 = tf.layers.dense(tf_x, N_HIDDEN, tf.nn.relu)
d1 = tf.layers.dropout(d1, rate=0.5, training=tf_is_training)   # drop out 50% of inputs
d2 = tf.layers.dense(d1, N_HIDDEN, tf.nn.relu)
d2 = tf.layers.dropout(d2, rate=0.5, training=tf_is_training)   # drop out 50% of inputs
d_out = tf.layers.dense(d2, 1)
for t in range(500):
    sess.run([o_train, d_train], {tf_x: x, tf_y: y, tf_is_training: True})  # train, set is_training=True
    if t % 10 == 0:
        # plotting
        plt.cla()
        o_loss_, d_loss_, o_out_, d_out_ = sess.run(
            [o_loss, d_loss, o_out, d_out], {tf_x: test_x, tf_y: test_y, tf_is_training: False} # test, set is_training=False
        )
# pytorch
    def add_layer(self, x, out_size, ac=None):
        x = tf.layers.dense(x, out_size, kernel_initializer=self.w_init, bias_initializer=B_INIT)
        self.pre_activation.append(x)
        # the momentum plays important rule. the default 0.99 is too high in this case!
        if self.is_bn: x = tf.layers.batch_normalization(x, momentum=0.4, training=tf_is_train)    # when have BN
        out = x if ac is None else ac(x)
        return out

 # !! IMPORTANT !! the moving_mean and moving_variance need to be updated,
        # pass the update_ops with control_dependencies to the train_op
        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
        with tf.control_dependencies(update_ops):
            self.train = tf.train.AdamOptimizer(LR).minimize(self.loss)