def setup_flower_shutter(
rawdata='/lustre/projects/project-broaddus/rawdata/artifacts/flower.tif',
savedir='/lustre/projects/project-broaddus/denoise_experiments/flower/e01/flower_test'
):
savedir = Path(savedir)
wipe_dirs(savedir)
init_dirs(savedir)
img = imread(rawdata)
pmin, pmax = 2, 99.6
print(f"pmin = {pmin}; pmax = {pmax}")
img = normalize3(img, pmin, pmax).astype(np.float32, copy=False)
data = img.reshape((-1, 4, 256, 4, 256)).transpose(
(0, 1, 3, 2, 4)).reshape((-1, 1, 256, 256))
d = SimpleNamespace()
d.net = torch_models.Unet2_2d(16, [[1], [1]], finallayer=nn.ReLU)
d.net.load_state_dict(
torch.load(
'/lustre/projects/project-broaddus/denoise_experiments/flower/models/net_randinit.pt'
))
d.net.apply(init_weights)
d.savedir = savedir
d.x1_all = torch.from_numpy(data).float()
return d
def predict_full():
"make movies scrolling through z"
net = torch_models.Unet2_2d(16, [[1], [1]], finallayer=nn.ReLU).cuda()
# Rob Jenkin (Alana) 540 692 0113
net.load_state_dict(
torch.load(
'/lustre/projects/project-broaddus/denoise/flower/e01/flower3_6/models/net600.pt'
))
img = imread(
f'/lustre/projects/project-broaddus/devseg_data/raw/artifacts/flower.tif'
)
# pmin, pmax = np.random.uniform(1,3), np.random.uniform(99.5,99.8)
pmin, pmax = 2, 99.6
img = normalize3(img, pmin, pmax, axs=(1, 2)).astype(np.float32,
copy=False)
pimg = []
for x in img:
# x = torch.from_numpy(x).cuda()
# x = net(x[None])
x = apply_net_tiled(net, x[None])
pimg.append(x)
pimg = np.array(pimg)
# return img, net, pimg
# pimg = apply_net_tiled(net,img[:,None])
imsave(pimg, savedir / f'pred_flower.tif')
def setup_flower_shutter(rawdata, savedir):
savedir = Path(savedir)
wipe_dirs(savedir)
init_dirs(savedir)
data = imread(rawdata)
data = normalize3(data, 2, 99.6) ## normalize across all dims?
d = SimpleNamespace()
d.net = torch_models.Unet2_2d(16, [[1], [1]], finallayer=nn.ReLU)
d.net.load_state_dict(
torch.load(
'/lustre/projects/project-broaddus/denoise_experiments/flower/models/net_randinit.pt'
))
# d.net.apply(init_weights);
d.savedir = savedir
d.xs = torch.from_numpy(data).float()
d.xs = d.xs.reshape(100, 4, 256, 4, 256,
1).permute(0, 1, 3, 5, 2, 4) #.reshape((-1,256,256))
d.ys = d.xs.mean(0)
io.imsave(savedir / 'xs.png',
collapse2(d.xs[0, :, :, 0].numpy(), "12yx", "1y,2x"))
io.imsave(savedir / 'ys.png',
collapse2(d.ys[:, :, 0].numpy(), "12yx", "1y,2x"))
d.cuda = False
return d
def predict_on_full_2d_stack(rawdata, savedir, weights):
savedir = Path(savedir)
net = torch_models.Unet2_2d(16, [[1], [1]], finallayer=nn.ReLU).cuda()
net.load_state_dict(torch.load(weights))
img = imread(rawdata)
pmin, pmax = 2, 99.6
img = normalize3(img, pmin, pmax, axs=(1, 2)).astype(np.float32,
copy=False)
pimg = []
for x in img:
x = apply_net_tiled_2d(net, x[None])
pimg.append(x)
pimg = np.array(pimg)
imsave(pimg.astype(np.float16), savedir / f'pred.tif', compress=9)
def setup(params={}):
wipe_dirs(savedir)
init_dirs(savedir)
# dg = datagen(savedir=savedir); data = dg.data;
# data = np.load('/lustre/projects/project-broaddus/devseg_data/cl_datagen/grid/data_shutter.npz')['data']
data = np.load(
'/lustre/projects/project-broaddus/denoise/flower/e01/data_flower3.npz'
)['data']
# data = np.load('/lustre/projects/project-broaddus/denoise/flower/e02/data_flower.npz')['data']
d = SimpleNamespace()
d.net = torch_models.Unet2_2d(16, [[1], [1]], finallayer=nn.ReLU).cuda()
d.net.load_state_dict(
torch.load(
'/lustre/projects/project-broaddus/denoise/flower/models/net_randinit.pt'
))
# d.net.apply(init_weights);
d.net2 = torch_models.Unet2_2d(16, [[1], [1]], finallayer=nn.ReLU).cuda()
d.net2.load_state_dict(
torch.load(
'/lustre/projects/project-broaddus/denoise/flower/models/net_randinit.pt'
))
# d.net2.apply(init_weights);
d.savedir = savedir
# d.net.load_state_dict(torch.load('/lustre/projects/project-broaddus/devseg_data/cl_datagen/d000/jj000/net250.pt'))
# torch.save(d.net.state_dict(), '/lustre/projects/project-broaddus/devseg_data/cl_datagen/rsrc/net_random_init_unet2.pt')
d.x1_all = torch.from_numpy(data).float().cuda()
return d