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Running
on
Zero
File size: 4,362 Bytes
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import torch
import numpy as np
import os
EPS = 1e-6
def sub2ind(height, width, y, x):
return y*width + x
def ind2sub(height, width, ind):
y = ind // width
x = ind % width
return y, x
def get_lr_str(lr):
lrn = "%.1e" % lr # e.g., 5.0e-04
lrn = lrn[0] + lrn[3:5] + lrn[-1] # e.g., 5e-4
return lrn
def strnum(x):
s = '%g' % x
if '.' in s:
if x < 1.0:
s = s[s.index('.'):]
s = s[:min(len(s),4)]
return s
def assert_same_shape(t1, t2):
for (x, y) in zip(list(t1.shape), list(t2.shape)):
assert(x==y)
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def print_stats(name, tensor):
shape = tensor.shape
tensor = tensor.detach().cpu().numpy()
print('%s (%s) min = %.2f, mean = %.2f, max = %.2f' % (name, tensor.dtype, np.min(tensor), np.mean(tensor), np.max(tensor)), shape)
def normalize_single(d):
# d is a whatever shape torch tensor
dmin = torch.min(d)
dmax = torch.max(d)
d = (d-dmin)/(EPS+(dmax-dmin))
return d
def normalize(d):
# d is B x whatever. normalize within each element of the batch
out = torch.zeros(d.size(), dtype=d.dtype, device=d.device)
B = list(d.size())[0]
for b in list(range(B)):
out[b] = normalize_single(d[b])
return out
def meshgrid2d(B, Y, X, stack=False, norm=False, device='cuda', on_chans=False):
# returns a meshgrid sized B x Y x X
grid_y = torch.linspace(0.0, Y-1, Y, device=torch.device(device))
grid_y = torch.reshape(grid_y, [1, Y, 1])
grid_y = grid_y.repeat(B, 1, X)
grid_x = torch.linspace(0.0, X-1, X, device=torch.device(device))
grid_x = torch.reshape(grid_x, [1, 1, X])
grid_x = grid_x.repeat(B, Y, 1)
if norm:
grid_y, grid_x = normalize_grid2d(
grid_y, grid_x, Y, X)
if stack:
# note we stack in xy order
# (see https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.grid_sample)
if on_chans:
grid = torch.stack([grid_x, grid_y], dim=1)
else:
grid = torch.stack([grid_x, grid_y], dim=-1)
return grid
else:
return grid_y, grid_x
def gridcloud2d(B, Y, X, norm=False, device='cuda'):
# we want to sample for each location in the grid
grid_y, grid_x = meshgrid2d(B, Y, X, norm=norm, device=device)
x = torch.reshape(grid_x, [B, -1])
y = torch.reshape(grid_y, [B, -1])
# these are B x N
xy = torch.stack([x, y], dim=2)
# this is B x N x 2
return xy
def reduce_masked_mean(x, mask, dim=None, keepdim=False, broadcast=False):
# x and mask are the same shape, or at least broadcastably so < actually it's safer if you disallow broadcasting
# returns shape-1
# axis can be a list of axes
if not broadcast:
for (a,b) in zip(x.size(), mask.size()):
if not a==b:
print('some shape mismatch:', x.shape, mask.shape)
assert(a==b) # some shape mismatch!
# assert(x.size() == mask.size())
prod = x*mask
if dim is None:
numer = torch.sum(prod)
denom = EPS+torch.sum(mask)
else:
numer = torch.sum(prod, dim=dim, keepdim=keepdim)
denom = EPS+torch.sum(mask, dim=dim, keepdim=keepdim)
mean = numer/denom
return mean
def reduce_masked_median(x, mask, keep_batch=False):
# x and mask are the same shape
assert(x.size() == mask.size())
device = x.device
B = list(x.shape)[0]
x = x.detach().cpu().numpy()
mask = mask.detach().cpu().numpy()
if keep_batch:
x = np.reshape(x, [B, -1])
mask = np.reshape(mask, [B, -1])
meds = np.zeros([B], np.float32)
for b in list(range(B)):
xb = x[b]
mb = mask[b]
if np.sum(mb) > 0:
xb = xb[mb > 0]
meds[b] = np.median(xb)
else:
meds[b] = np.nan
meds = torch.from_numpy(meds).to(device)
return meds.float()
else:
x = np.reshape(x, [-1])
mask = np.reshape(mask, [-1])
if np.sum(mask) > 0:
x = x[mask > 0]
med = np.median(x)
else:
med = np.nan
med = np.array([med], np.float32)
med = torch.from_numpy(med).to(device)
return med.float()
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