DeepTen model (#85)

* deepten model

fixes https://github.com/zhanghang1989/PyTorch-Encoding/issues/77
fixes https://github.com/zhanghang1989/PyTorch-Encoding/issues/32

docs/source/experiments/segmentation.rst

@@ -68,7 +68,7 @@ Test Pre-trained Model
     </code>
 
     <code xml:space="preserve" id="cmd_enc50_ade" style="display: none; text-align: left; white-space: pre-wrap">
-    CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset ade20k --model encnetv2 --aux --se-loss
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset ADE20K --model EncNet --aux --se-loss
     </code>
 
 Quick Demo

docs/source/experiments/texture.rst

@@ -13,25 +13,20 @@ Test Pre-trained Model
 
 - Clone the GitHub repo::
 
-    git clone git@github.com:zhanghang1989/PyTorch-Encoding.git
+    git clone https://github.com/zhanghang1989/PyTorch-Encoding
 
 - Install PyTorch Encoding (if not yet). Please follow the installation guide `Installing PyTorch Encoding <../notes/compile.html>`_.
 
-- Download the `MINC-2500 <http://opensurfaces.cs.cornell.edu/publications/minc/>`_ dataset to ``$HOME/data/minc-2500/`` folder. Download pre-trained model (training `curve`_ as bellow, pre-trained on train-1 split using single training size of 224, with an error rate of :math:`19.98\%` using single crop on test-1 set)::
+- Download the `MINC-2500 <http://opensurfaces.cs.cornell.edu/publications/minc/>`_ dataset to ``$HOME/data/minc-2500/`` folder. Download pre-trained model (pre-trained on train-1 split using single training size of 224, with an error rate of :math:`19.70\%` using single crop on test-1 set)::
 
     cd PyTorch-Encoding/experiments/recognition
-    bash model/download_models.sh
-
-.. _curve:
-
-.. image:: ../_static/img/deep_ten_curve.svg
-    :width: 70%
+    python model/download_models.py
 
 - Test pre-trained model on MINC-2500::
 
-    >>> python main.py --dataset minc --model deepten --nclass 23 --resume model/minc.pth.tar --eval
+    python main.py --dataset minc --model deepten --nclass 23 --resume deepten_minc.pth --eval
     # Teriminal Output:
-    #[======================================== 23/23 ===================================>...]    Step: 104ms | Tot: 3s256ms | Loss: 0.719 | Err: 19.983% (1149/5750) 
+    # Loss: 1.005 | Err: 19.704% (1133/5750): 100%|████████████████████| 23/23 [00:18<00:00,  1.26it/s]
 
 
 Train Your Own Model
@@ -39,7 +34,7 @@ Train Your Own Model
 
 - Example training command for training above model::
 
-    python main.py --model deepten --nclass 23 --model deepten --batch-size 64 --lr 0.01 --epochs 60 
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --dataset minc --model deepten --nclass 23 --model deepten --batch-size 512 --lr 0.004 --epochs 80 --lr-step 60
 
 - Detail training options::
 

encoding/models/__init__.py

 from .model_zoo import get_model
+from .model_store import get_model_file
 from .base import *
 from .fcn import *
 from .psp import *

encoding/models/model_store.py

@@ -9,6 +9,7 @@ from ..utils import download, check_sha1
 _model_sha1 = {name: checksum for checksum, name in [
     ('853f2fb07aeb2927f7696e166b215609a987fd44', 'resnet50'),
     ('5be5422ad7cb6a2e5f5a54070d0aa9affe69a9a4', 'resnet101'),
+    ('6cb047cda851de6aa31963e779fae5f4c299056a', 'deepten_minc'),
     ('fc8c0b795abf0133700c2d4265d2f9edab7eb6cc', 'fcn_resnet50_ade'),
     ('eeed8e582f0fdccdba8579e7490570adc6d85c7c', 'fcn_resnet50_pcontext'),
     ('54f70c772505064e30efd1ddd3a14e1759faa363', 'psp_resnet50_ade'),

experiments/recognition/main.py

@@ -94,11 +94,11 @@ def main():
             loss.backward()
             optimizer.step()
 
-            train_loss += loss.data[0]
+            train_loss += loss.data.item()
             pred = output.data.max(1)[1] 
             correct += pred.eq(target.data).cpu().sum()
             total += target.size(0)
-            err = 100-100.*correct/total
+            err = 100.0 - 100.0 * correct / total
             tbar.set_description('\rLoss: %.3f | Err: %.3f%% (%d/%d)' % \
                 (train_loss/(batch_idx+1), err, total-correct, total))
 
@@ -122,7 +122,7 @@ def main():
                 correct += pred.eq(target.data).cpu().sum().item()
                 total += target.size(0)
 
-            err = 100-100.0*correct/total
+            err = 100.0 - 100.0 * correct / total
             tbar.set_description('Loss: %.3f | Err: %.3f%% (%d/%d)'% \
                 (test_loss/(batch_idx+1), err, total-correct, total))
 

experiments/recognition/model/deepten.py

@@ -14,7 +14,7 @@ import torch.nn as nn
 from torch.autograd import Variable
 
 import encoding
-import torchvision.models as resnet
+import encoding.dilated.resnet as resnet
 
 class Net(nn.Module):
     def __init__(self, args):
@@ -23,11 +23,11 @@ class Net(nn.Module):
         self.backbone = args.backbone
         # copying modules from pretrained models
         if self.backbone == 'resnet50':
-            self.pretrained = resnet.resnet50(pretrained=True)
+            self.pretrained = resnet.resnet50(pretrained=True, dilated=False)
         elif self.backbone == 'resnet101':
-            self.pretrained = resnet.resnet101(pretrained=True)
+            self.pretrained = resnet.resnet101(pretrained=True, dilated=False)
         elif self.backbone == 'resnet152':
-            self.pretrained = resnet.resnet152(pretrained=True)
+            self.pretrained = resnet.resnet152(pretrained=True, dilated=False)
         else:
             raise RuntimeError('unknown backbone: {}'.format(self.backbone))
         n_codes = 32

experiments/recognition/model/download_models.py

+import encoding
+import shutil
+
+encoding.models.get_model_file('deepten_minc', root='./')
+shutil.move('deepten_minc-6cb047cd.pth', 'deepten_minc.pth')

experiments/recognition/model/mynn.py

@@ -66,7 +66,7 @@ class Bottleneck(nn.Module):
         conv_block = []
         conv_block += [norm_layer(inplanes),
                        nn.ReLU(inplace=True),
-                       nn.Conv2d(inplanes, planes, kernel_size=1, 
+                       nn.Conv2d(inplanes, planes, kernel_size=1,
                                  stride=1, bias=False)]
         conv_block += [norm_layer(planes),
                        nn.ReLU(inplace=True),