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Add Apple Silicon support #47

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Add Apple Silicon support #47

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49 changes: 25 additions & 24 deletions main_test_bsrgan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,6 @@
# from utils import utils_model
from models.network_rrdbnet import RRDBNet as net


"""
Spyder (Python 3.6-3.7)
PyTorch 1.4.0-1.8.1
Expand All @@ -33,46 +32,49 @@


def main():

utils_logger.logger_info('blind_sr_log', log_path='blind_sr_log.log')
logger = logging.getLogger('blind_sr_log')

# print(torch.__version__) # pytorch version
# print(torch.version.cuda) # cuda version
# print(torch.backends.cudnn.version()) # cudnn version
# print(torch.__version__) # pytorch version
# print(torch.version.cuda) # cuda version
# print(torch.backends.cudnn.version()) # cudnn version

testsets = 'testsets' # fixed, set path of testsets
testsets = 'testsets' # fixed, set path of testsets
testset_Ls = ['RealSRSet'] # ['RealSRSet','DPED']

model_names = ['RRDB','ESRGAN','FSSR_DPED','FSSR_JPEG','RealSR_DPED','RealSR_JPEG']
model_names = ['BSRGAN'] # 'BSRGANx2' for scale factor 2


model_names = ['RRDB', 'ESRGAN', 'FSSR_DPED', 'FSSR_JPEG', 'RealSR_DPED', 'RealSR_JPEG']
model_names = ['BSRGAN'] # 'BSRGANx2' for scale factor 2

save_results = True
sf = 4
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if torch.has_mps:
device = torch.device('mps')
else:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

for model_name in model_names:
if model_name in ['BSRGANx2']:
sf = 2
model_path = os.path.join('model_zoo', model_name+'.pth') # set model path
model_path = os.path.join('model_zoo', model_name + '.pth') # set model path
logger.info('{:>16s} : {:s}'.format('Model Name', model_name))

# torch.cuda.set_device(0) # set GPU ID
logger.info('{:>16s} : {:<d}'.format('GPU ID', torch.cuda.current_device()))
torch.cuda.empty_cache()
try:
logger.info('{:>16s} : {:<d}'.format('GPU ID', torch.cuda.current_device()))
torch.cuda.empty_cache()
except:
logger.info('{:>16s} : {:<s}'.format('GPU ID', 'None'))

# --------------------------------
# define network and load model
# --------------------------------
model = net(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=sf) # define network

# model_old = torch.load(model_path)
# state_dict = model.state_dict()
# for ((key, param),(key2, param2)) in zip(model_old.items(), state_dict.items()):
# state_dict[key2] = param
# model.load_state_dict(state_dict, strict=True)
# model_old = torch.load(model_path)
# state_dict = model.state_dict()
# for ((key, param),(key2, param2)) in zip(model_old.items(), state_dict.items()):
# state_dict[key2] = param
# model.load_state_dict(state_dict, strict=True)

model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
Expand All @@ -84,8 +86,8 @@ def main():
for testset_L in testset_Ls:

L_path = os.path.join(testsets, testset_L)
#E_path = os.path.join(testsets, testset_L+'_'+model_name)
E_path = os.path.join(testsets, testset_L+'_results_x'+str(sf))
# E_path = os.path.join(testsets, testset_L+'_'+model_name)
E_path = os.path.join(testsets, testset_L + '_results_x' + str(sf))
util.mkdir(E_path)

logger.info('{:>16s} : {:s}'.format('Input Path', L_path))
Expand All @@ -99,7 +101,7 @@ def main():
# --------------------------------
idx += 1
img_name, ext = os.path.splitext(os.path.basename(img))
logger.info('{:->4d} --> {:<s} --> x{:<d}--> {:<s}'.format(idx, model_name, sf, img_name+ext))
logger.info('{:->4d} --> {:<s} --> x{:<d}--> {:<s}'.format(idx, model_name, sf, img_name + ext))

img_L = util.imread_uint(img, n_channels=3)
img_L = util.uint2tensor4(img_L)
Expand All @@ -115,9 +117,8 @@ def main():
# --------------------------------
img_E = util.tensor2uint(img_E)
if save_results:
util.imsave(img_E, os.path.join(E_path, img_name+'_'+model_name+'.png'))
util.imsave(img_E, os.path.join(E_path, img_name + '_' + model_name + '.png'))


if __name__ == '__main__':

main()