szha closed pull request #11044: [MXNET-470] Gluon Style Transfer Example
URL: https://github.com/apache/incubator-mxnet/pull/11044
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diff --git a/example/gluon/style_transfer/main.py
b/example/gluon/style_transfer/main.py
index 7fcc927f9cb..cab8211bc9c 100644
--- a/example/gluon/style_transfer/main.py
+++ b/example/gluon/style_transfer/main.py
@@ -44,7 +44,8 @@ def train(args):
utils.ToTensor(ctx),
])
train_dataset = data.ImageFolder(args.dataset, transform)
- train_loader = gluon.data.DataLoader(train_dataset,
batch_size=args.batch_size, last_batch='discard')
+ train_loader = gluon.data.DataLoader(train_dataset,
batch_size=args.batch_size,
+ last_batch='discard')
style_loader = utils.StyleLoader(args.style_folder, args.style_size,
ctx=ctx)
print('len(style_loader):',style_loader.size())
# models
@@ -79,7 +80,7 @@ def train(args):
xc = utils.subtract_imagenet_mean_preprocess_batch(x.copy())
f_xc_c = vgg(xc)[1]
with autograd.record():
- style_model.setTarget(style_image)
+ style_model.set_target(style_image)
y = style_model(x)
y = utils.subtract_imagenet_mean_batch(y)
@@ -92,7 +93,8 @@ def train(args):
gram_y = net.gram_matrix(features_y[m])
_, C, _ = gram_style[m].shape
gram_s = F.expand_dims(gram_style[m],
0).broadcast_to((args.batch_size, 1, C, C))
- style_loss = style_loss + 2 * args.style_weight *
mse_loss(gram_y, gram_s[:n_batch, :, :])
+ style_loss = style_loss + 2 * args.style_weight * \
+ mse_loss(gram_y, gram_s[:n_batch, :, :])
total_loss = content_loss + style_loss
total_loss.backward()
@@ -115,7 +117,8 @@ def train(args):
if (batch_id + 1) % (4 * args.log_interval) == 0:
# save model
- save_model_filename = "Epoch_" + str(e) + "iters_" +
str(count) + "_" + str(time.ctime()).replace(' ', '_') + "_" + str(
+ save_model_filename = "Epoch_" + str(e) + "iters_" + \
+ str(count) + "_" + str(time.ctime()).replace(' ', '_') +
"_" + str(
args.content_weight) + "_" + str(args.style_weight) +
".params"
save_model_path = os.path.join(args.save_model_dir,
save_model_filename)
style_model.save_params(save_model_path)
@@ -142,7 +145,7 @@ def evaluate(args):
style_model = net.Net(ngf=args.ngf)
style_model.load_params(args.model, ctx=ctx)
# forward
- style_model.setTarget(style_image)
+ style_model.set_target(style_image)
output = style_model(content_image)
utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda)
diff --git a/example/gluon/style_transfer/models/download_model.py
b/example/gluon/style_transfer/models/download_model.py
index ef70e6a346c..8d0a855a3db 100644
--- a/example/gluon/style_transfer/models/download_model.py
+++ b/example/gluon/style_transfer/models/download_model.py
@@ -15,7 +15,17 @@
# specific language governing permissions and limitations
# under the License.
+import os
+import zipfile
+import shutil
from mxnet.test_utils import download
-download('https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/models/21styles-32f7205c.params',
'models/21styles.params')
+zip_file_path = 'models/msgnet_21styles.zip'
+download('https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/models/msgnet_21styles-2cb88353.zip',
zip_file_path)
+with zipfile.ZipFile(zip_file_path) as zf:
+ zf.extractall()
+
+os.remove(zip_file_path)
+
+shutil.move('msgnet_21styles-2cb88353.params', 'models/21styles.params')
diff --git a/example/gluon/style_transfer/net.py
b/example/gluon/style_transfer/net.py
index a33ce427fbd..2ca992a8ee1 100644
--- a/example/gluon/style_transfer/net.py
+++ b/example/gluon/style_transfer/net.py
@@ -152,10 +152,6 @@ def __init__(self, in_channels, out_channels, kernel_size,
stride, upsample=None):
super(UpsampleConvLayer, self).__init__()
self.upsample = upsample
- """
- if upsample:
- self.upsample_layer =
torch.nn.UpsamplingNearest2d(scale_factor=upsample)
- """
self.reflection_padding = int(np.floor(kernel_size / 2))
self.conv2d = nn.Conv2D(in_channels=in_channels,
channels=out_channels,
@@ -165,10 +161,6 @@ def __init__(self, in_channels, out_channels, kernel_size,
def forward(self, x):
if self.upsample:
x = F.UpSampling(x, scale=self.upsample, sample_type='nearest')
- """
- if self.reflection_padding != 0:
- x = self.reflection_pad(x)
- """
out = self.conv2d(x)
return out
@@ -222,16 +214,16 @@ def __init__(self, input_nc=3, output_nc=3, ngf=64,
self.model.add(ConvLayer(16*expansion, output_nc, kernel_size=7,
stride=1))
- def setTarget(self, Xs):
+ def set_target(self, Xs):
F = self.model1(Xs)
G = self.gram(F)
- self.ins.setTarget(G)
+ self.ins.set_target(G)
def forward(self, input):
return self.model(input)
-class Inspiration(HybridBlock):
+class Inspiration(Block):
""" Inspiration Layer (from MSG-Net paper)
tuning the featuremap with target Gram Matrix
ref https://arxiv.org/abs/1703.06953
@@ -243,17 +235,14 @@ def __init__(self, C, B=1):
self.weight = self.params.get('weight', shape=(1,C,C),
init=mx.initializer.Uniform(),
allow_deferred_init=True)
- self.gram = self.params.get('gram', shape=(B,C,C),
- init=mx.initializer.Uniform(),
- allow_deferred_init=True,
- lr_mult=0)
+ self.gram = F.random.uniform(shape=(B, C, C))
- def setTarget(self, target):
- self.gram.set_data(target)
+ def set_target(self, target):
+ self.gram = target
def forward(self, X):
# input X is a 3D feature map
- self.P = F.batch_dot(F.broadcast_to(self.weight.data(),
shape=(self.gram.shape)), self.gram.data())
+ self.P = F.batch_dot(F.broadcast_to(self.weight.data(),
shape=(self.gram.shape)), self.gram)
return F.batch_dot(F.SwapAxis(self.P,1,2).broadcast_to((X.shape[0],
self.C, self.C)), X.reshape((0,0,X.shape[2]*X.shape[3]))).reshape(X.shape)
def __repr__(self):
@@ -305,26 +294,3 @@ def forward(self, X):
relu4_3 = h
return [relu1_2, relu2_2, relu3_3, relu4_3]
-
-
-def test_InstanceNorm():
- import torch
- from torch import nn as nn2
- from torch.autograd import Variable
- tx = Variable(torch.Tensor(1, 2, 200, 300).uniform_(0,1))
- tlayer = nn2.InstanceNorm2d(2)
- ty = tlayer(tx)
-
- mlayer = InstanceNorm(2)
- ctx = mx.cpu(0)
- mlayer.initialize(ctx=ctx)
- mmx = (mx.nd.array(tx.data.numpy())).as_in_context(ctx)
- my = mlayer(mmx)
- print('tx',tx)
- print('mmx',mmx)
- print('ty',ty)
- print('my',my)
-
-if __name__ == "__main__":
- test_InstanceNorm()
-
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