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Make examples work with Metal Performance Shaders #22

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Make examples work with Metal Performance Shaders #22

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7 changes: 6 additions & 1 deletion main_test_fbcnn_color.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,8 +44,13 @@ def main():
logger = logging.getLogger(logger_name)
logger.info('--------------- quality factor: {:d} ---------------'.format(quality_factor))

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
border = 0
if torch.cuda.is_available():
device = torch.device('cuda')
elif torch.backends.mps.is_available():
device = torch.device('mps')
else:
device = torch.device('cpu')


# ----------------------------------------
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16 changes: 13 additions & 3 deletions main_test_fbcnn_color_real.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,8 +42,13 @@ def main():
utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log'))
logger = logging.getLogger(logger_name)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
border = 0
if torch.cuda.is_available():
device = torch.device('cuda')
elif torch.backends.mps.is_available():
device = torch.device('mps')
else:
device = torch.device('cpu')


# ----------------------------------------
Expand Down Expand Up @@ -83,7 +88,7 @@ def main():

#img_E,QF = model(img_L, torch.tensor([[0.6]]))
img_E,QF = model(img_L)
QF = 1- QF
QF = 1 - QF
img_E = util.tensor2single(img_E)
img_E = util.single2uint(img_E)
logger.info('predicted quality factor: {:d}'.format(round(float(QF*100))))
Expand All @@ -93,7 +98,12 @@ def main():
for QF_set in QF_control:
logger.info('Flexible control by QF = {:d}'.format(QF_set))
# from IPython import embed; embed()
qf_input = torch.tensor([[1-QF_set/100]]).cuda() if device == torch.device('cuda') else torch.tensor([[1-QF_set/100]])
if device == torch.device('cuda'):
qf_input = torch.tensor([[1-QF_set/100]]).cuda()
elif device == torch.device('mps'):
qf_input = torch.tensor([[1-QF_set/100]]).to('mps')
else:
qf_input = torch.tensor([[1-QF_set/100]])
img_E,QF = model(img_L, qf_input)
QF = 1- QF
img_E = util.tensor2single(img_E)
Expand Down
7 changes: 6 additions & 1 deletion main_test_fbcnn_gray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,8 +43,13 @@ def main():
logger = logging.getLogger(logger_name)
logger.info('--------------- quality factor: {:d} ---------------'.format(quality_factor))

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
border = 0
if torch.cuda.is_available():
device = torch.device('cuda')
elif torch.backends.mps.is_available():
device = torch.device('mps')
else:
device = torch.device('cpu')


# ----------------------------------------
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7 changes: 6 additions & 1 deletion main_test_fbcnn_gray_doublejpeg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,8 +44,13 @@ def main():
logger = logging.getLogger(logger_name)
logger.info('--------------- QF1={:d}, QF2={:d} ---------------'.format(qf1,qf2))

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
border = 0
if torch.cuda.is_available():
device = torch.device('cuda')
elif torch.backends.mps.is_available():
device = torch.device('mps')
else:
device = torch.device('cpu')


# ----------------------------------------
Expand Down