def main(model, res=(512, ), pyramids=None, up_pyramid=False, max_depth=None):
from hyperseg.utils.obj_factory import obj_factory
from hyperseg.utils.utils import set_device
from hyperseg.utils.img_utils import create_pyramid
assert len(
res
) <= 2, f'res must be either a single number or a pair of numbers: "{res}"'
res = res * 2 if len(res) == 1 else res
device, gpus = set_device()
model = obj_factory(model).to(device)
x = torch.rand(1, 3, *res).to(device)
x = create_pyramid(x, pyramids) if pyramids is not None else x
if up_pyramid:
x.append(
F.interpolate(x[0],
scale_factor=2,
mode='bilinear',
align_corners=False)) # Upsample x2
# Run profile
flops_summary, params_summary, meta_params_summary = profile(
model, inputs=(x, ), max_depth=max_depth)
print_summary(flops_summary, params_summary, meta_params_summary)
def main(model='hyperseg.models.hyperseg_v0_1.hyperseg_efficientnet',
res=(512, ),
pyramids=None,
train=False):
from hyperseg.utils.obj_factory import obj_factory
from hyperseg.utils.utils import set_device
from hyperseg.utils.img_utils import create_pyramid
assert len(
res
) <= 2, f'res must be either a single number or a pair of numbers: "{res}"'
res = res * 2 if len(res) == 1 else res
device, gpus = set_device()
model = obj_factory(model).to(device).train(train)
x = torch.rand(2, 3, *res).to(device)
x = create_pyramid(x, pyramids) if pyramids is not None else x
pred = model(x)
print(pred.shape)
def main(model='hyperseg.models.hyperseg_v1_0.hypergen_efficientnet', res=(512,),
pyramids=None,
train=False):
from hyperseg.utils.obj_factory import obj_factory
from hyperseg.utils.utils import set_device
from hyperseg.utils.img_utils import create_pyramid
from tqdm import tqdm
assert len(res) <= 2, f'res must be either a single number or a pair of numbers: "{res}"'
res = res * 2 if len(res) == 1 else res
torch.set_grad_enabled(False)
torch.backends.cudnn.benchmark = True
device, gpus = set_device()
model = obj_factory(model).to(device).train(train)
x = torch.rand(1, 3, *res).to(device)
x = create_pyramid(x, pyramids) if pyramids is not None else x
pred = model(x)
print(pred.shape)
def main(model, res=(512, ), pyramids=None, max_depth=None):
from hyperseg.utils.obj_factory import obj_factory
from hyperseg.utils.utils import set_device
from hyperseg.utils.img_utils import create_pyramid
assert len(
res
) <= 2, f'res must be either a single number or a pair of numbers: "{res}"'
res = res * 2 if len(res) == 1 else res
device, gpus = set_device()
model = obj_factory(model).to(device)
x = torch.rand(1, 3, *res).to(device)
x = create_pyramid(x, pyramids) if pyramids is not None else x
# Run profile
flops_summary, params_summary = profile(model,
inputs=(x, ),
max_depth=max_depth)
print_summary(flops_summary, params_summary)