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Added support for Apple silicon #36

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68b695e
FTR: Add support for MPS on Apple silicon
Scoobadood Aug 12, 2023
32598cc
DOC: Updated the README
Scoobadood Aug 12, 2023
d404db3
FIX: Updated scripts to accept device.
Scoobadood Aug 12, 2023
f0137bf
DOC: Retructure readme
Scoobadood Aug 12, 2023
af9c0cd
DOCS: Simplified setup of conda env.
Scoobadood Aug 12, 2023
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17 changes: 17 additions & 0 deletions README.md
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Expand Up @@ -51,6 +51,23 @@ conda install pip
pip install -r requirements.txt
```

## Running on Apple Silicon
If you want to run this code on Apple's silicon, use the following:

```
conda create --name pips python=3.9
source activate pips
conda install pytorch torchvision torchaudio -c pytorch
conda install pip
pip install -r requirements.txt
```

At the time of this fix, MPS does not support all operators required for this code to run so you will also need:

`export PYTORCH_ENABLE_MPS_FALLBACK=1`



## Demo

To download our reference model, download the weights from [Hugging Face. ![](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-blue)](https://huggingface.co/aharley/pips)
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27 changes: 17 additions & 10 deletions chain_demo.py
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Expand Up @@ -18,8 +18,8 @@
random.seed(125)
np.random.seed(125)

def run_model(model, rgbs, N, sw):
rgbs = rgbs.cuda().float() # B, S, C, H, W
def run_model(model, rgbs, N, sw, device):
rgbs = rgbs.to(device).float() # B, S, C, H, W

B, S, C, H, W = rgbs.shape
rgbs_ = rgbs.reshape(B*S, C, H, W)
Expand All @@ -31,17 +31,17 @@ def run_model(model, rgbs, N, sw):
# try to pick a point on the dog, so we get an interesting trajectory
# x = torch.randint(-10, 10, size=(1, N), device=torch.device('cuda')) + 468
# y = torch.randint(-10, 10, size=(1, N), device=torch.device('cuda')) + 118
x = torch.ones((1, N), device=torch.device('cuda')) * 450.0
y = torch.ones((1, N), device=torch.device('cuda')) * 100.0
x = torch.ones((1, N), device=device) * 450.0
y = torch.ones((1, N), device=device) * 100.0
xy0 = torch.stack([x, y], dim=-1) # B, N, 2
_, S, C, H, W = rgbs.shape

trajs_e = torch.zeros((B, S, N, 2), dtype=torch.float32, device='cuda')
trajs_e = torch.zeros((B, S, N, 2), dtype=torch.float32, device=device)
for n in range(N):
# print('working on keypoint %d/%d' % (n+1, N))
cur_frame = 0
done = False
traj_e = torch.zeros((B, S, 2), dtype=torch.float32, device='cuda')
traj_e = torch.zeros((B, S, 2), dtype=torch.float32, device=device)
traj_e[:,0] = xy0[:,n] # B, 1, 2 # set first position
feat_init = None
while not done:
Expand All @@ -51,7 +51,7 @@ def run_model(model, rgbs, N, sw):
S_local = rgb_seq.shape[1]
rgb_seq = torch.cat([rgb_seq, rgb_seq[:,-1].unsqueeze(1).repeat(1,8-S_local,1,1,1)], dim=1)

outs = model(traj_e[:,cur_frame].reshape(1, -1, 2), rgb_seq, iters=6, feat_init=feat_init, return_feat=True)
outs = model(traj_e[:,cur_frame].reshape(1, -1, 2), rgb_seq, device, iters=6, feat_init=feat_init, return_feat=True)
preds = outs[0]
vis = outs[2] # B, S, 1
feat_init = outs[3]
Expand Down Expand Up @@ -143,10 +143,17 @@ def main():

global_step = 0

model = Pips(stride=4).cuda()
if torch.cuda.is_available():
device = torch.device('cuda')
elif torch.backends.mps.is_available():
device = torch.device('mps')
else:
device = torch.device('cpu')

model = Pips(stride=4).to(device)
parameters = list(model.parameters())
if init_dir:
_ = saverloader.load(init_dir, model)
_ = saverloader.load(init_dir, model, device=device)
global_step = 0
model.eval()

Expand Down Expand Up @@ -179,7 +186,7 @@ def main():
iter_start_time = time.time()

with torch.no_grad():
trajs_e = run_model(model, rgbs, N, sw_t)
trajs_e = run_model(model, rgbs, N, sw_t, device)

iter_time = time.time()-iter_start_time
print('%s; step %06d/%d; rtime %.2f; itime %.2f' % (
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23 changes: 16 additions & 7 deletions demo.py
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Expand Up @@ -18,8 +18,8 @@
random.seed(125)
np.random.seed(125)

def run_model(model, rgbs, N, sw):
rgbs = rgbs.cuda().float() # B, S, C, H, W
def run_model(model, rgbs, N, sw, device):
rgbs = rgbs.to(device).float() # B, S, C, H, W

B, S, C, H, W = rgbs.shape
rgbs_ = rgbs.reshape(B*S, C, H, W)
Expand All @@ -30,14 +30,14 @@ def run_model(model, rgbs, N, sw):

# pick N points to track; we'll use a uniform grid
N_ = np.sqrt(N).round().astype(np.int32)
grid_y, grid_x = utils.basic.meshgrid2d(B, N_, N_, stack=False, norm=False, device='cuda')
grid_y, grid_x = utils.basic.meshgrid2d(B, N_, N_, stack=False, norm=False, device=device)
grid_y = 8 + grid_y.reshape(B, -1)/float(N_-1) * (H-16)
grid_x = 8 + grid_x.reshape(B, -1)/float(N_-1) * (W-16)
xy = torch.stack([grid_x, grid_y], dim=-1) # B, N_*N_, 2
_, S, C, H, W = rgbs.shape

print_stats('rgbs', rgbs)
preds, preds_anim, vis_e, stats = model(xy, rgbs, iters=6)
preds, preds_anim, vis_e, stats = model(xy, rgbs, device, iters=6, )
trajs_e = preds[-1]
print_stats('trajs_e', trajs_e)

Expand Down Expand Up @@ -111,10 +111,19 @@ def main():

global_step = 0

model = Pips(stride=4).cuda()
# Pick a device
if torch.cuda.is_available():
device = torch.device('cuda')
elif torch.backends.mps.is_available():
device = torch.device('mps')
else:
device = torch.device('cpu')


model = Pips(stride=4).to(device)
parameters = list(model.parameters())
if init_dir:
_ = saverloader.load(init_dir, model)
_ = saverloader.load(init_dir, model, device=device)
global_step = 0
model.eval()

Expand Down Expand Up @@ -147,7 +156,7 @@ def main():
iter_start_time = time.time()

with torch.no_grad():
trajs_e = run_model(model, rgbs, N, sw_t)
trajs_e = run_model(model, rgbs, N, sw_t, device)

iter_time = time.time()-iter_start_time
print('%s; step %06d/%d; rtime %.2f; itime %.2f' % (
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4 changes: 2 additions & 2 deletions nets/pips.py
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Expand Up @@ -425,8 +425,8 @@ def __init__(self, S=8, stride=8):
nn.Linear(self.latent_dim, 1),
)

def forward(self, xys, rgbs, coords_init=None, feat_init=None, iters=3, trajs_g=None, vis_g=None, valids=None, sw=None, return_feat=False, is_train=False):
total_loss = torch.tensor(0.0).cuda()
def forward(self, xys, rgbs, device, coords_init=None, feat_init=None, iters=3, trajs_g=None, vis_g=None, valids=None, sw=None, return_feat=False, is_train=False):
total_loss = torch.tensor(0.0).to(device)

B, N, D = xys.shape
assert(D==2)
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4 changes: 2 additions & 2 deletions saverloader.py
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Expand Up @@ -22,7 +22,7 @@ def save(ckpt_dir, optimizer, model, global_step, scheduler=None, model_ema=None
torch.save(ckpt, model_path)
print("saved a checkpoint: %s" % (model_path))

def load(ckpt_dir, model, optimizer=None, scheduler=None, model_ema=None, step=0, model_name='model', ignore_load=None):
def load(ckpt_dir, model, device, optimizer=None, scheduler=None, model_ema=None, step=0, model_name='model', ignore_load=None):
print('reading ckpt from %s' % ckpt_dir)
if not os.path.exists(ckpt_dir):
print('...there is no full checkpoint here!')
Expand Down Expand Up @@ -55,7 +55,7 @@ def load(ckpt_dir, model, optimizer=None, scheduler=None, model_ema=None, step=0
# 3. load the new state dict
model.load_state_dict(model_dict, strict=False)
else:
checkpoint = torch.load(path)
checkpoint = torch.load(path, map_location='mps')
model.load_state_dict(checkpoint['model_state_dict'], strict=False)

if optimizer is not None:
Expand Down