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Merged
Cadene merged 1 commit into from
Nov 18, 2017
Merged

Make training commands work #23

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16 changes: 8 additions & 8 deletions extract.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,14 +26,14 @@
help='dir dataset to download or/and load images')
parser.add_argument('--data_split', default='train', type=str,
help='Options: (default) train | val | test')
parser.add_argument('--arch', '-a', default='resnet152',
parser.add_argument('--arch', '-a', default='fbresnet152',
choices=convnets.model_names,
help='model architecture: ' +
' | '.join(convnets.model_names) +
' (default: fbresnet152)')
parser.add_argument('--workers', default=4, type=int,
parser.add_argument('--workers', default=4, type=int,
help='number of data loading workers (default: 4)')
parser.add_argument('--batch_size', '-b', default=80, type=int,
parser.add_argument('--batch_size', '-b', default=80, type=int,
help='mini-batch size (default: 80)')
parser.add_argument('--mode', default='both', type=str,
help='Options: att | noatt | (default) both')
Expand All @@ -56,7 +56,7 @@ def main():
if args.dataset == 'coco':
if 'coco' not in args.dir_data:
raise ValueError('"coco" string not in dir_data')
dataset = datasets.COCOImages(args.data_split, dict(dir=args.dir_data),
dataset = datasets.COCOImages(args.data_split, dict(dir=args.dir_data),
transform=transforms.Compose([
transforms.Scale(args.size),
transforms.CenterCrop(args.size),
Expand All @@ -68,7 +68,7 @@ def main():
raise ValueError('train split is required for vgenome')
if 'vgenome' not in args.dir_data:
raise ValueError('"vgenome" string not in dir_data')
dataset = datasets.VisualGenomeImages(args.data_split, dict(dir=args.dir_data),
dataset = datasets.VisualGenomeImages(args.data_split, dict(dir=args.dir_data),
transform=transforms.Compose([
transforms.Scale(args.size),
transforms.CenterCrop(args.size),
Expand Down Expand Up @@ -122,7 +122,7 @@ def extract(data_loader, model, path_file, mode):

nb_regions = output_att.size(2) * output_att.size(3)
output_noatt = output_att.sum(3).sum(2).div(nb_regions).view(-1, 2048)

batch_size = output_att.size(0)
if mode == 'both' or mode == 'att':
hdf5_att[idx:idx+batch_size] = output_att.data.cpu().numpy()
Expand All @@ -141,7 +141,7 @@ def extract(data_loader, model, path_file, mode):
i, len(data_loader),
batch_time=batch_time,
data_time=data_time,))

hdf5_file.close()

# Saving image names in the same order than extraction
Expand All @@ -154,4 +154,4 @@ def extract(data_loader, model, path_file, mode):


if __name__ == '__main__':
main()
main()
3 changes: 2 additions & 1 deletion options/vqa/mutan_att_trainval.yaml
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Expand Up @@ -12,8 +12,9 @@ vqa:
samplingans: True
coco:
dir: data/coco
arch: fbresnet152torch
arch: fbresnet152
mode: att
size: 448
model:
arch: MutanAtt
dim_v: 2048
Expand Down
9 changes: 6 additions & 3 deletions vqa/datasets/coco.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,18 +23,21 @@ class COCOImages(AbstractImagesDataset):
def __init__(self, data_split, opt, transform=None, loader=default_loader):
self.split_name = split_name(data_split)
super(COCOImages, self).__init__(data_split, opt, transform, loader)
self.dir_split = os.path.join(self.dir_raw, self.split_name)
self.dir_split = self.get_dir_data()
self.dataset = ImagesFolder(self.dir_split, transform=self.transform, loader=self.loader)
self.name_to_index = self._load_name_to_index()

def get_dir_data(self):
return os.path.join(self.dir_raw, self.split_name)

def _raw(self):
if self.data_split in ['train', 'val']:
os.system('wget http://msvocds.blob.core.windows.net/coco2014/{}.zip -P {}'.format(self.split_name, self.dir_raw))
elif self.data_split == 'test':
os.execute('wget http://msvocds.blob.core.windows.net/coco2015/test2015.zip -P '+self.dir_raw)
os.system('wget http://msvocds.blob.core.windows.net/coco2015/test2015.zip -P '+self.dir_raw)
else:
assert False, 'data_split {} not exists'.format(self.data_split)
os.execute('unzip '+os.path.join(self.dir_raw, self.split_name+'.zip')+' -d '+self.dir_raw)
os.system('unzip '+os.path.join(self.dir_raw, self.split_name+'.zip')+' -d '+self.dir_raw)

def _load_name_to_index(self):
self.name_to_index = {name:index for index, name in enumerate(self.dataset.imgs)}
Expand Down
11 changes: 7 additions & 4 deletions vqa/datasets/images.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ def __init__(self, root, transform=None, loader=default_loader):

def __getitem__(self, index):
item = {}
item['name'] = self.imgs[index]
item['name'] = self.imgs[index]
item['path'] = os.path.join(self.root, item['name'])
if self.loader is not None:
item['visual'] = self.loader(item['path'])
Expand All @@ -57,11 +57,14 @@ def __init__(self, data_split, opt, transform=None, loader=default_loader):
self.opt = opt
self.transform = transform
self.loader = loader

self.dir_raw = os.path.join(self.opt['dir'], 'raw')
if not os.path.exists(self.dir_raw):

if not os.path.exists(self.get_dir_data()):
self._raw()

def get_dir_data(self):
return self.dir_raw

def get_by_name(self, image_name):
index = self.name_to_index[image_name]
return self[index]
Expand All @@ -73,4 +76,4 @@ def __getitem__(self, index):
raise NotImplementedError

def __len__(self):
raise NotImplementedError
raise NotImplementedError
32 changes: 21 additions & 11 deletions vqa/models/convnets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@
import torch
import torch.nn as nn
import torchvision.models as pytorch_models

import sys
sys.path.append('vqa/external/pretrained-models.pytorch')
import pretrainedmodels as torch7_models
Expand All @@ -21,7 +20,21 @@
def factory(opt, cuda=True, data_parallel=True):
opt = copy.copy(opt)

# forward_* will be better handle in futur release
class WrapperModule(nn.Module):
def __init__(self, net, forward_fn):
super(WrapperModule, self).__init__()
self.net = net
self.forward_fn = forward_fn

def forward(self, x):
return self.forward_fn(self.net, x)

def __getattr__(self, attr):
try:
return super(WrapperModule, self).__getattr__(attr)
except AttributeError:
return getattr(self.net, attr)

def forward_resnet(self, x):
x = self.conv1(x)
x = self.bn1(x)
Expand Down Expand Up @@ -58,26 +71,23 @@ def forward_resnext(self, x):
if opt['arch'] in pytorch_resnet_names:
model = pytorch_models.__dict__[opt['arch']](pretrained=True)

convnet = model # ugly hack in case of DataParallel wrapping
model.forward = lambda x: forward_resnet(convnet, x)
model = WrapperModule(model, forward_resnet) # ugly hack in case of DataParallel wrapping

elif opt['arch'] == 'fbresnet152':
model = torch7_models.__dict__[opt['arch']](num_classes=1000,
pretrained='imagenet')

convnet = model # ugly hack in case of DataParallel wrapping
model.forward = lambda x: forward_resnet(convnet, x)
model = WrapperModule(model, forward_resnet) # ugly hack in case of DataParallel wrapping

elif opt['arch'] in torch7_resnet_names:
model = torch7_models.__dict__[opt['arch']](num_classes=1000,
pretrained='imagenet')

convnet = model # ugly hack in case of DataParallel wrapping
model.forward = lambda x: forward_resnext(convnet, x)

model = WrapperModule(model, forward_resnext) # ugly hack in case of DataParallel wrapping

else:
raise ValueError

if data_parallel:
model = nn.DataParallel(model).cuda()
if not cuda:
Expand All @@ -86,4 +96,4 @@ def forward_resnext(self, x):
if cuda:
model.cuda()

return model
return model