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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/14 19:22:01 UTC
[GitHub] piiswrong closed pull request #9063: [example] fix neural-style end2end example
piiswrong closed pull request #9063: [example] fix neural-style end2end example
URL: https://github.com/apache/incubator-mxnet/pull/9063
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diff --git a/example/neural-style/end_to_end/README.md b/example/neural-style/end_to_end/README.md
index 2f19bf51ab..4a228c199b 100644
--- a/example/neural-style/end_to_end/README.md
+++ b/example/neural-style/end_to_end/README.md
@@ -1,20 +1,17 @@
# End to End Neural Art
-This is an implementation of blog: [http://dmlc.ml/mxnet/2016/06/20/end-to-end-neural-style.html](http://dmlc.ml/mxnet/2016/06/20/end-to-end-neural-style.html)
-
-
-We will release a Multi-GPU training code soon.
+Please refer to this [blog](http://dmlc.ml/mxnet/2016/06/20/end-to-end-neural-style.html) for details of how it is implemented.
## How to use
-1. First use `download.sh` to download pre-trained model and sample inputs
+1. First use `../download.sh` to download pre-trained model and sample inputs.
-2. Then prepare training dataset according to the blog
+2. Prepare training dataset. Put image samples to `../data/` (one file for each image sample). The pretrained model here was trained by 26k images sampled from [MIT Place dataset](http://places.csail.mit.edu).
-3. Modify [boost_train.py](boost_train.py)
+3. Use `boost_train.py` for training.
## Pretrained Model
-Weight [https://github.com/dmlc/web-data/raw/master/mxnet/art/model.zip](https://github.com/dmlc/web-data/raw/master/mxnet/art/model.zip)
-Inference [boost_inference.py](boost_inference.py)
+- Model: [https://github.com/dmlc/web-data/raw/master/mxnet/art/model.zip](https://github.com/dmlc/web-data/raw/master/mxnet/art/model.zip)
+- Inference script: `boost_inference.py`
diff --git a/example/neural-style/end_to_end/basic.py b/example/neural-style/end_to_end/basic.py
index 1763e884b9..eae64a6e68 100644
--- a/example/neural-style/end_to_end/basic.py
+++ b/example/neural-style/end_to_end/basic.py
@@ -14,10 +14,6 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
-
-import sys
-sys.path.insert(0, "../../mxnet/python/")
-
import mxnet as mx
import numpy as np
import model_vgg19 as vgg
diff --git a/example/neural-style/end_to_end/boost_inference.py b/example/neural-style/end_to_end/boost_inference.py
index 0ec8308f30..86ab000b08 100644
--- a/example/neural-style/end_to_end/boost_inference.py
+++ b/example/neural-style/end_to_end/boost_inference.py
@@ -14,10 +14,6 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
-
-import sys
-sys.path.insert(0, "../mxnet/python")
-
import mxnet as mx
import numpy as np
@@ -31,8 +27,6 @@
model_prefix = "./model/"
ctx = mx.gpu(0)
-
-
# generator
gens = [gen_v4.get_module("g0", dshape, ctx),
gen_v3.get_module("g1", dshape, ctx),
@@ -41,15 +35,10 @@
for i in range(len(gens)):
gens[i].load_params("./model/%d/v3_0002-0026000.params" % i)
-content_np = data_processing.PreprocessContentImage("../IMG_4343.jpg", min(dshape[2:]), dshape)
+content_np = data_processing.PreprocessContentImage("../input/IMG_4343.jpg", min(dshape[2:]), dshape)
data = [mx.nd.array(content_np)]
for i in range(len(gens)):
gens[i].forward(mx.io.DataBatch([data[-1]], [0]), is_train=False)
new_img = gens[i].get_outputs()[0]
data.append(new_img.copyto(mx.cpu()))
data_processing.SaveImage(new_img.asnumpy(), "out_%d.jpg" % i)
-
-
-import os
-os.system("rm -rf out.zip")
-os.system("zip out.zip out_*")
diff --git a/example/neural-style/end_to_end/boost_train.py b/example/neural-style/end_to_end/boost_train.py
index fa525e7e52..4f25b4304c 100644
--- a/example/neural-style/end_to_end/boost_train.py
+++ b/example/neural-style/end_to_end/boost_train.py
@@ -14,10 +14,6 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
-
-import sys
-sys.path.insert(0, "../../mxnet/python")
-
import mxnet as mx
import numpy as np
@@ -27,7 +23,7 @@
import gen_v4
# params
-vgg_params = mx.nd.load("./vgg19.params")
+vgg_params = mx.nd.load("../model/vgg19.params")
style_weight = 1.2
content_weight = 10
dshape = (1, 3, 384, 384)
@@ -36,7 +32,7 @@
ctx = mx.gpu(0)
# init style
-style_np = data_processing.PreprocessStyleImage("../starry_night.jpg", shape=dshape)
+style_np = data_processing.PreprocessStyleImage("../input/starry_night.jpg", shape=dshape)
style_mod = basic.get_style_module("style", dshape, ctx, vgg_params)
style_mod.forward(mx.io.DataBatch([mx.nd.array(style_np)], [0]), is_train=False)
style_array = [arr.copyto(mx.cpu()) for arr in style_mod.get_outputs()]
@@ -119,7 +115,10 @@ def get_tv_grad_executor(img, ctx, tv_weight):
loss_grad_array = []
data_array = []
path = data_root + file_list[idx]
- content_np = data_processing.PreprocessContentImage(path, min(dshape[2:]), dshape)
+ try:
+ content_np = data_processing.PreprocessContentImage(path, min(dshape[2:]), dshape)
+ except:
+ logging.warn("Fail to load an input image. Skip.")
data = mx.nd.array(content_np)
data_array.append(data)
# get content
diff --git a/example/neural-style/end_to_end/gen_v3.py b/example/neural-style/end_to_end/gen_v3.py
index 7962e68da2..a11e5989b0 100644
--- a/example/neural-style/end_to_end/gen_v3.py
+++ b/example/neural-style/end_to_end/gen_v3.py
@@ -18,14 +18,6 @@
# coding: utf-8
-# In[1]:
-
-import sys
-sys.path.insert(0, "../../mxnet/python")
-
-
-# In[2]:
-
import mxnet as mx
import numpy as np
@@ -48,8 +40,6 @@ def Deconv(data, num_filter, im_hw, kernel=(7, 7), pad=(2, 2), stride=(2, 2), cr
sym = mx.sym.Activation(sym, act_type="tanh")
return sym
-# In[70]:
-
def get_generator(prefix, im_hw):
data = mx.sym.Variable("%s_data" % prefix)
conv1 = Conv(data, 64) # 192
diff --git a/example/neural-style/end_to_end/gen_v4.py b/example/neural-style/end_to_end/gen_v4.py
index fb4e6d1e16..30f534cd76 100644
--- a/example/neural-style/end_to_end/gen_v4.py
+++ b/example/neural-style/end_to_end/gen_v4.py
@@ -18,14 +18,6 @@
# coding: utf-8
-# In[1]:
-
-import sys
-sys.path.insert(0, "../mxnet/python")
-
-
-# In[2]:
-
import mxnet as mx
import numpy as np
@@ -46,8 +38,6 @@ def Deconv(data, num_filter, kernel=(6, 6), pad=(2, 2), stride=(2, 2), out=False
sym = mx.sym.Activation(sym, act_type="tanh")
return sym
-# In[70]:
-
def get_generator(prefix, im_hw):
data = mx.sym.Variable("%s_data" % prefix)
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