Python lasagne.layers.Upscale2DLayer() Examples
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code examples of lasagne.layers.Upscale2DLayer().
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Example #1
| Source File: models.py From diagnose-heart with MIT License | 5 votes |
|
def build_fcn_segmenter(input_var, shape, version=2):
ret = {}
if version == 2:
ret['input'] = la = InputLayer(shape, input_var)
ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=8, filter_size=7))
ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=16, filter_size=3))
ret['pool%d'%len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=32, filter_size=3))
ret['pool%d'%len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3))
ret['pool%d'%len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3))
ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3,
pad='full'))
ret['ups%d'%len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3,
pad='full'))
ret['ups%d'%len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=32, filter_size=7,
pad='full'))
ret['ups%d'%len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=16, filter_size=3,
pad='full'))
ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=8, filter_size=7))
ret['output'] = la = Conv2DLayer(la, num_filters=1, filter_size=7,
pad='full', nonlinearity=nn.nonlinearities.sigmoid)
return ret, nn.layers.get_output(ret['output']), \
nn.layers.get_output(ret['output'], deterministic=True)
Example #2
| Source File: deep_conv_ae_spsparse_alt36.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def build_autoencoder_network():
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 180, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 120, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=90.0, alpha=0.5, beta=init.Constant(0.1), tight=100.0, name="mask_map");
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(20,20), stride=20, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Conv2DLayer(glblf, 3, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Upscale2DLayer(glblf, scale_factor=20);
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
mask_var = lasagne.layers.get_output(mask_map);
output_var = lasagne.layers.get_output(network);
return network, input_var, mask_var, output_var;
Example #3
| Source File: deep_conv_ae_spsparse_alt35.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def build_autoencoder_network():
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 140, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 160, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 180, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 200, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 160, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 120, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 120, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=99.9, alpha=0.5, beta=init.Constant(0.1), tight=100.0, name="mask_map");
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(layer, 200, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 180, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 160, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 140, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(20,20), stride=20, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Conv2DLayer(glblf, 3, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Upscale2DLayer(glblf, scale_factor=20);
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
mask_var = lasagne.layers.get_output(mask_map);
output_var = lasagne.layers.get_output(network);
return network, input_var, mask_var, output_var;
Example #4
| Source File: deep_conv_ae_spsparse_alt34_recon.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def build_autoencoder_network():
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 140, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=99.9, alpha=0.5, beta=init.Constant(0.1), tight=100.0, name="mask_map");
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(layer, 140, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(20,20), stride=20, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Conv2DLayer(glblf, 3, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Upscale2DLayer(glblf, scale_factor=20);
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
layers.set_all_param_values(network, pickle.load(open(filename_model_ae, 'rb')));
feat_var = lasagne.layers.get_output(feat_map, deterministic=True);
mask_var = lasagne.layers.get_output(mask_map, deterministic=True);
outp_var = lasagne.layers.get_output(network, deterministic=True);
return network, input_var, feat_var, mask_var, outp_var;
Example #5
| Source File: deep_conv_ae_spsparse_alt34.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def build_autoencoder_network():
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 140, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=99.9, alpha=0.5, beta=init.Constant(0.1), tight=100.0, name="mask_map");
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(layer, 140, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(20,20), stride=20, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Conv2DLayer(glblf, 3, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Upscale2DLayer(glblf, scale_factor=20);
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
mask_var = lasagne.layers.get_output(mask_map);
output_var = lasagne.layers.get_output(network);
return network, input_var, mask_var, output_var;
Example #6
| Source File: generator.py From salgan with MIT License | 4 votes |
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def build_decoder(net):
net['uconv5_3']= ConvLayer(net['conv5_3'], 512, 3, pad=1)
print "uconv5_3: {}".format(net['uconv5_3'].output_shape[1:])
net['uconv5_2'] = ConvLayer(net['uconv5_3'], 512, 3, pad=1)
print "uconv5_2: {}".format(net['uconv5_2'].output_shape[1:])
net['uconv5_1'] = ConvLayer(net['uconv5_2'], 512, 3, pad=1)
print "uconv5_1: {}".format(net['uconv5_1'].output_shape[1:])
net['upool4'] = Upscale2DLayer(net['uconv5_1'], scale_factor=2)
print "upool4: {}".format(net['upool4'].output_shape[1:])
net['uconv4_3'] = ConvLayer(net['upool4'], 512, 3, pad=1)
print "uconv4_3: {}".format(net['uconv4_3'].output_shape[1:])
net['uconv4_2'] = ConvLayer(net['uconv4_3'], 512, 3, pad=1)
print "uconv4_2: {}".format(net['uconv4_2'].output_shape[1:])
net['uconv4_1'] = ConvLayer(net['uconv4_2'], 512, 3, pad=1)
print "uconv4_1: {}".format(net['uconv4_1'].output_shape[1:])
net['upool3'] = Upscale2DLayer(net['uconv4_1'], scale_factor=2)
print "upool3: {}".format(net['upool3'].output_shape[1:])
net['uconv3_3'] = ConvLayer(net['upool3'], 256, 3, pad=1)
print "uconv3_3: {}".format(net['uconv3_3'].output_shape[1:])
net['uconv3_2'] = ConvLayer(net['uconv3_3'], 256, 3, pad=1)
print "uconv3_2: {}".format(net['uconv3_2'].output_shape[1:])
net['uconv3_1'] = ConvLayer(net['uconv3_2'], 256, 3, pad=1)
print "uconv3_1: {}".format(net['uconv3_1'].output_shape[1:])
net['upool2'] = Upscale2DLayer(net['uconv3_1'], scale_factor=2)
print "upool2: {}".format(net['upool2'].output_shape[1:])
net['uconv2_2'] = ConvLayer(net['upool2'], 128, 3, pad=1)
print "uconv2_2: {}".format(net['uconv2_2'].output_shape[1:])
net['uconv2_1'] = ConvLayer(net['uconv2_2'], 128, 3, pad=1)
print "uconv2_1: {}".format(net['uconv2_1'].output_shape[1:])
net['upool1'] = Upscale2DLayer(net['uconv2_1'], scale_factor=2)
print "upool1: {}".format(net['upool1'].output_shape[1:])
net['uconv1_2'] = ConvLayer(net['upool1'], 64, 3, pad=1,)
print "uconv1_2: {}".format(net['uconv1_2'].output_shape[1:])
net['uconv1_1'] = ConvLayer(net['uconv1_2'], 64, 3, pad=1)
print "uconv1_1: {}".format(net['uconv1_1'].output_shape[1:])
net['output'] = ConvLayer(net['uconv1_1'], 1, 1, pad=0,nonlinearity=sigmoid)
print "output: {}".format(net['output'].output_shape[1:])
return net
