def _get_resnet(self):
my_resnet = getattr(resnet, 'resnet101')()
my_resnet.load_state_dict(torch.load(self._resnet_model_path))
my_resnet = myResnet(my_resnet)
my_resnet.eval()
return my_resnet
def main(params):
print(device)
net = getattr(resnet, params['model'])()
net.load_state_dict(
torch.load(os.path.join(params['model_root'],
params['model'] + '.pth')))
my_resnet = myResnet(net)
#my_resnet.cuda()
my_resnet.to(device)
my_resnet.eval()
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
N = len(imgs)
seed(123) # make reproducible
dir_fc = params['output_dir'] + '_fc'
dir_att = params['output_dir'] + '_att'
if not os.path.isdir(dir_fc):
os.mkdir(dir_fc)
if not os.path.isdir(dir_att):
os.mkdir(dir_att)
resume_flag = False
for i, img in enumerate(imgs):
# load the image
#I = skimage.io.imread(os.path.join(params['images_root'], img['filepath'], img['filename']))
print('The image path is', img['file_path'])
# if img['file_path'] == '/media/hdd/data/imcaption/retina_dataset_resize/resize/1_2_826_0_1_3680043_9_5115_636252259520332334/1_3_6_1_4_1_33437_10_4_11578754_13134123662_18471_4_1_0_0.png':
# resume_flag = True
# if resume_flag == True:
I = skimage.io.imread(img['file_path'])
I = I[:, :, 0:3]
# print('The size of image I', I.shape)
# plt.imshow(I[:,:,0:3])
# plt.show()
# handle grayscale input images
if len(I.shape) == 2:
I = I[:, :, np.newaxis]
I = np.concatenate((I, I, I), axis=2)
I = I.astype('float32') / 255.0
I = torch.from_numpy(I.transpose([2, 0, 1])).to(device) #.cuda()
I = preprocess(I)
with torch.no_grad():
tmp_fc, tmp_att = my_resnet(I, params['att_size'])
# write to pkl
np.save(os.path.join(dir_fc, str(img['imgid'])),
tmp_fc.data.cpu().float().numpy())
np.savez_compressed(os.path.join(dir_att, str(img['imgid'])),
feat=tmp_att.data.cpu().float().numpy())
if i % 10 == 0:
print('processing %d/%d (%.2f%% done)' % (i, N, i * 100.0 / N))
print('wrote ', params['output_dir'])
def main(params):
net = getattr(resnet, params['model'])()
net.load_state_dict(
torch.load(os.path.join(params['model_root'],
params['model'] + '.pth')))
my_resnet = myResnet(net)
my_resnet.cuda()
my_resnet.eval()
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
N = len(imgs)
seed(123) # make reproducible
dir_fc = params['output_dir'] + '_fc'
dir_att = params['output_dir'] + '_att'
if not os.path.isdir(dir_fc):
os.mkdir(dir_fc)
if not os.path.isdir(dir_att):
os.mkdir(dir_att)
for i, img in enumerate(imgs):
# load the image
I = skimage.io.imread(
os.path.join(params['images_root'], img['filepath'],
img['filename']))
# handle grayscale input images
if len(I.shape) == 2:
I = I[:, :, np.newaxis]
I = np.concatenate((I, I, I), axis=2)
I = I.astype('float32') / 255.0
I = torch.from_numpy(I.transpose([2, 0, 1])).cuda()
I = preprocess(I)
with torch.no_grad():
tmp_fc, tmp_att = my_resnet(I, params['att_size'])
# write to pkl
np.save(os.path.join(dir_fc, str(img['cocoid'])),
tmp_fc.data.cpu().float().numpy())
np.savez_compressed(os.path.join(dir_att, str(img['cocoid'])),
feat=tmp_att.data.cpu().float().numpy())
if i % 1000 == 0:
print(
'Grid Feature Preprocess:',
'processing {}/{} images ({:.2f%} done)'.format(
i, N, i * 100.0 / N))
print('wrote ', params['output_dir'])
#with torch.cuda.device():
#args = get_args()
#net = WDSR_B(args)
# net = models.densenet161()
'''
################################
# Build dataloader
################################
loader = DataLoader(opt)
opt.vocab_size = loader.vocab_size
opt.seq_length = loader.seq_length
##########################
# Build model
##########################
opt.vocab = loader.get_vocab()
model = models.setup(opt).cuda()
del opt.vocab
'''
cnn_model = 'resnet101'
my_resnet = getattr(resnet, cnn_model)()
my_resnet.load_state_dict(
torch.load('data/imagenet_weights/' + cnn_model + '.pth'))
net = myResnet(my_resnet)
flops, params = get_model_complexity_info(net, (3, 640, 427),
as_strings=True,
print_per_layer_stat=True)
# flops, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True, print_per_layer_stat=True)
print('{:<30} {:<8}'.format('Computational complexity: ', flops))
print('{:<30} {:<8}'.format('Number of parameters: ', params))
# 144P(256×144) 240p(426×240) 360P(640×360) 480P(854×480)
# slice_sizes = [512,512,512,198,198]
# devices = [50,50,50,50,500]
# for folder, slice_size, device in zip(model_folder,slice_sizes,devices):
# print(folder)
# model = ResNet50_1d(args.slice_size,args.devices)
# Input arguments and options
parser = argparse.ArgumentParser()
# Input paths
parser.add_argument('--cnn_model',
type=str,
default='resnet101',
help='resnet101, resnet152')
opts.add_eval_options(parser)
opts.add_diversity_opts(parser)
opt = parser.parse_args()
cnn_model = 'resnet101'
my_resnet = getattr(resnet, cnn_model)()
my_resnet.load_state_dict(
torch.load('data/imagenet_weights/' + cnn_model + '.pth'))
model = myResnet(my_resnet)
input = torch.randn(3, 640, 480)
model.train(False)
model.eval()
macs, params = profile(model, inputs=(input, ))
# flops, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True, print_per_layer_stat=True)
print('{:<30} {:<8}'.format('Computational complexity: ',
macs / pow(10, 9))) # GMACs
print('{:<30} {:<8}'.format('Number of parameters: ',
params / pow(10, 6))) # M
from PIL import Image
params = {}
params['input_json'] = 'data/dataset_retina_resize.json'
params['output_json'] = 'data/retina_resize'
params['images_root'] = ''
params['att_size'] = 14
params['model'] = 'resnet101'
params['model_root'] = 'data/imagenet_weights'
newsize = (256, 256)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = getattr(resnet, params['model'])()
net.load_state_dict(
torch.load(os.path.join(params['model_root'], params['model'] + '.pth')))
my_resnet = myResnet(net)
#my_resnet.cuda()
my_resnet.to(device)
my_resnet.eval()
file_path = '/media/hdd/data/imcaption/retina_dataset_resize/resize/1_2_826_0_1_3680043_9_5115_636252259520332334/1_3_6_1_4_1_33437_10_4_11578754_13134123662_18471_4_1_0_0.png'
# I = skimage.io.imread(file_path)
# Resample image
original_image_path = '/media/hdd/data/imcaption/retina_dataset_resize/out/1_2_826_0_1_3680043_9_5115_636252259520332334/1_3_6_1_4_1_33437_10_4_11578754_13134123662_18471_4_1_0_0.png'
image = Image.open(original_image_path)
# print('image size before resizing', image.size)
reim = image.resize(newsize)
# im.save(imgPath)
# print('image size after resizing', reim.size)
imsavepath = '/media/hdd/data/imcaption/retina_dataset_resize/resize/1_2_826_0_1_3680043_9_5115_636252259520332334/1_3_6_1_4_1_33437_10_4_11578754_13134123662_18471_4_1_0_0.png'
