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Revert "MPS Upscalers Fix"

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This reverts commit 768b95394a8500da639b947508f78296524f1836.
This commit is contained in:
brkirch 2022-11-07 19:25:43 -05:00
parent 98947d173e
commit abfa22c16f
4 changed files with 4 additions and 12 deletions
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9
modules/devices.py
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@ -94,12 +94,3 @@ def autocast(disable=False):
return contextlib.nullcontext() return contextlib.nullcontext()
return torch.autocast("cuda") return torch.autocast("cuda")
# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
def mps_contiguous(input_tensor, device):
return input_tensor.contiguous() if device.type == 'mps' else input_tensor
def mps_contiguous_to(input_tensor, device):
return mps_contiguous(input_tensor, device).to(device)

2
modules/esrgan_model.py
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@ -199,7 +199,7 @@ def upscale_without_tiling(model, img):
img = img[:, :, ::-1] img = img[:, :, ::-1]
img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255 img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
img = torch.from_numpy(img).float() img = torch.from_numpy(img).float()
img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan) img = img.unsqueeze(0).to(devices.device_esrgan)
with torch.no_grad(): with torch.no_grad():
output = model(img) output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy() output = output.squeeze().float().cpu().clamp_(0, 1).numpy()

3
modules/scunet_model.py
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@ -54,8 +54,9 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
img = img[:, :, ::-1] img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255 img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float() img = torch.from_numpy(img).float()
img = devices.mps_contiguous_to(img.unsqueeze(0), device) img = img.unsqueeze(0).to(device)
img = img.to(device)
with torch.no_grad(): with torch.no_grad():
output = model(img) output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy() output = output.squeeze().float().cpu().clamp_(0, 1).numpy()

2
modules/swinir_model.py
View File

@ -111,7 +111,7 @@ def upscale(
img = img[:, :, ::-1] img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255 img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float() img = torch.from_numpy(img).float()
img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir) img = img.unsqueeze(0).to(devices.device_swinir)
with torch.no_grad(), precision_scope("cuda"): with torch.no_grad(), precision_scope("cuda"):
_, _, h_old, w_old = img.size() _, _, h_old, w_old = img.size()
h_pad = (h_old // window_size + 1) * window_size - h_old h_pad = (h_old // window_size + 1) * window_size - h_old