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Perform data augmentation in training and normalize correctly at test… #9

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4 changes: 2 additions & 2 deletions image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@ def load_data(img_path,train = True):
img = Image.open(img_path).convert('RGB')
gt_file = h5py.File(gt_path)
target = np.asarray(gt_file['density'])
if False:
if train:
crop_size = (img.size[0]/2,img.size[1]/2)
if random.randint(0,9)<= -1:

Expand Down Expand Up @@ -40,4 +40,4 @@ def load_data(img_path,train = True):
target = cv2.resize(target,(target.shape[1]/8,target.shape[0]/8),interpolation = cv2.INTER_CUBIC)*64


return img,target
return img,target
25 changes: 11 additions & 14 deletions val.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -18,11 +18,12 @@
"from scipy.ndimage.filters import gaussian_filter \n",
"import scipy\n",
"import json\n",
"import torchvision.transforms.functional as F\n",
"from torchvision import datasets, transforms\n",
"from matplotlib import cm as CM\n",
"from image import *\n",
"from model import CSRNet\n",
"import torch\n",
"\n",
"%matplotlib inline"
]
},
Expand All @@ -34,11 +35,11 @@
},
"outputs": [],
"source": [
"from torchvision import datasets, transforms\n",
"transform=transforms.Compose([\n",
" transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225]),\n",
" ])"
"transform = transforms.Compose([\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225])\n",
"])"
]
},
{
Expand Down Expand Up @@ -326,17 +327,13 @@
"source": [
"mae = 0\n",
"for i in xrange(len(img_paths)):\n",
" img = 255.0 * F.to_tensor(Image.open(img_paths[i]).convert('RGB'))\n",
"\n",
" img[0,:,:]=img[0,:,:]-92.8207477031\n",
" img[1,:,:]=img[1,:,:]-95.2757037428\n",
" img[2,:,:]=img[2,:,:]-104.877445883\n",
" img = img.cuda()\n",
" #img = transform(Image.open(img_paths[i]).convert('RGB')).cuda()\n",
" img = transform(Image.open(img_paths[i]).convert('RGB')).cuda()\n",
" gt_file = h5py.File(img_paths[i].replace('.jpg','.h5').replace('images','ground_truth'),'r')\n",
" groundtruth = np.asarray(gt_file['density'])\n",
" \n",
" output = model(img.unsqueeze(0))\n",
" mae += abs(output.detach().cpu().sum().numpy()-np.sum(groundtruth))\n",
" \n",
" print i,mae\n",
"print mae/len(img_paths)"
]
Expand All @@ -358,7 +355,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
"version": "2.7.15"
}
},
"nbformat": 4,
Expand Down