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License: MIT License
HRNet 的Tensorflow实现
License: MIT License
你好,请问为什么1-4阶段的’NUM_MODULES‘参数是1,1,4,3,是指使用了4个阶段3吗?
How can i visualize the results?
many thanks
for k in range(len(temp_layer)):
fuse = 0
fuse = fuse + temp_layer[k]
Please have a look.
Thanks
在您的blog中有提到HR Net 是由stem、stage1、stage2、stage3、stage4組合而成,但是您在tensorflow實現中並未提到Stem部份,請問是由哪邊實現呢?
Hello,
Can you upload somewhere the trained models?
THank you!
您好! 请问 下面的配置是起什么使用的? 代码中并没有使用到,是什么原因?
{'PRETRAINED_LAYERS': ['*'],
'STEM_INPLANES':64,
'FINAL_CONV_KERNEL':1,
'WITH_HEAD':True},
def Bottleneck(x, is_training,block_name, outplanes, stride=1, downsample=None):
residual = x
with tf.variable_scope(block_name + '11_1'):
name = block_name + 'conv1'
out = conv(x, 1, outplanes, name=name, stride=stride)
out = batch_norm(out,is_training)
out = tf.nn.relu(out)
with tf.variable_scope(block_name + '33_2'):
name = block_name + 'conv2'
out = conv(x, 3, outplanes, name=name, stride=stride)
out = batch_norm(out,is_training)
out = tf.nn.relu(out)
with tf.variable_scope(block_name + '11_3'):
name = block_name + 'conv3'
out = conv(out, 1, outplanes * 4, name=name, stride=stride)
out = batch_norm(out,is_training)
if downsample is not None:
with tf.variable_scope(block_name + 'downsample'):
residual = downsample(x,1,outplanes * 4, 'stage_dawnSample',stride = stride)
residual = batch_norm(residual, is_training)
out = out + residual
out = tf.nn.relu(out)
return out
请问,bottleneck模块中,33卷积的输入为什么是x?我按照原理来理解这里的输入应该是上一个11卷积的输出,这么设置是有什么原因么?谢谢~
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