def test_variational_mode(self, devices):
"""Tests forward function of source encoder.
Arguments:
devices: GPU device information.
"""
device = devices.device
batch_size = 2
n_tiles_h = 3
n_tiles_w = 5
max_detections = 4
ptile_slen = 10
n_bands = 2
tile_slen = 2
background = (10.0, 20.0)
# get encoder
star_encoder: DetectionEncoder = DetectionEncoder(
LogBackgroundTransform(),
channel=8,
dropout=0,
spatial_dropout=0,
hidden=64,
ptile_slen=ptile_slen,
tile_slen=tile_slen,
n_bands=n_bands,
mean_detections=0.48,
max_detections=max_detections,
).to(device)
with torch.no_grad():
star_encoder.eval()
# simulate image padded tiles
images = torch.randn(
batch_size,
n_bands,
ptile_slen + (n_tiles_h - 1) * tile_slen,
ptile_slen + (n_tiles_w - 1) * tile_slen,
device=device,
)
background_tensor = torch.tensor(background, device=device)
background_tensor = background_tensor.reshape(1, -1, 1, 1).expand(*images.shape)
images *= background_tensor.sqrt()
images += background_tensor
image_ptiles = get_images_in_tiles(
torch.cat((images, background_tensor), dim=1),
star_encoder.tile_slen,
star_encoder.ptile_slen,
)
image_ptiles = rearrange(image_ptiles, "n nth ntw b h w -> (n nth ntw) b h w")
var_params = star_encoder.encode(image_ptiles)
catalog = star_encoder.variational_mode(var_params)
assert catalog["n_sources"].size() == torch.Size([batch_size * n_tiles_h * n_tiles_w])
correct_locs_shape = torch.Size([batch_size * n_tiles_h * n_tiles_w, max_detections, 2])
assert catalog["locs"].shape == correct_locs_shape
def validation_step(self, batch, batch_idx):
"""Pytorch lightning method."""
batch_size = len(batch["images"])
out = self._get_loss(batch)
# log all losses
self.log("val/loss", out["loss"], batch_size=batch_size)
self.log("val/counter_loss",
out["counter_loss"].mean(),
batch_size=batch_size)
self.log("val/locs_loss",
out["locs_loss"].mean(),
batch_size=batch_size)
self.log("val/star_params_loss",
out["star_params_loss"].mean(),
batch_size=batch_size)
catalog_dict = {
"locs": batch["locs"][:, :, :, 0:self.max_detections],
"log_fluxes": batch["log_fluxes"][:, :, :, 0:self.max_detections],
"galaxy_bools": batch["galaxy_bools"][:, :, :,
0:self.max_detections],
"n_sources": batch["n_sources"].clamp(max=self.max_detections),
}
true_tile_catalog = TileCatalog(self.tile_slen, catalog_dict)
true_full_catalog = true_tile_catalog.to_full_params()
image_ptiles = get_images_in_tiles(
torch.cat((batch["images"], batch["background"]), dim=1),
self.tile_slen,
self.ptile_slen,
)
image_ptiles = rearrange(image_ptiles,
"n nth ntw b h w -> (n nth ntw) b h w")
dist_params = self.encode(image_ptiles)
est_catalog_dict = self.variational_mode(dist_params)
est_tile_catalog = TileCatalog.from_flat_dict(
true_tile_catalog.tile_slen,
true_tile_catalog.n_tiles_h,
true_tile_catalog.n_tiles_w,
est_catalog_dict,
)
est_full_catalog = est_tile_catalog.to_full_params()
metrics = self.val_detection_metrics(true_full_catalog,
est_full_catalog)
self.log("val/precision", metrics["precision"], batch_size=batch_size)
self.log("val/recall", metrics["recall"], batch_size=batch_size)
self.log("val/f1", metrics["f1"], batch_size=batch_size)
self.log("val/avg_distance",
metrics["avg_distance"],
batch_size=batch_size)
return batch
def get_map_estimate(
image_encoder: DetectionEncoder, images, background, slen: int, wlen: int = None
):
# return full estimate of parameters in full image.
# NOTE: slen*wlen is size of the image without border padding
if wlen is None:
wlen = slen
assert isinstance(slen, int) and isinstance(wlen, int)
# check image compatibility
border1 = (images.shape[-2] - slen) / 2
border2 = (images.shape[-1] - wlen) / 2
assert border1 == border2, "border paddings on each dimension differ."
assert slen % image_encoder.tile_slen == 0, "incompatible slen"
assert wlen % image_encoder.tile_slen == 0, "incompatible wlen"
assert border1 == image_encoder.border_padding, "incompatible border"
# obtained estimates per tile, then on full image.
image_ptiles = get_images_in_tiles(
torch.cat((images, background), dim=1),
image_encoder.tile_slen,
image_encoder.ptile_slen,
)
_, n_tiles_h, n_tiles_w, _, _, _ = image_ptiles.shape
image_ptiles = rearrange(image_ptiles, "n nth ntw b h w -> (n nth ntw) b h w")
var_params = image_encoder.encode(image_ptiles)
tile_cutoff = 25**2
var_params2 = image_encoder.encode(image_ptiles[:tile_cutoff])
assert torch.allclose(
var_params["n_source_log_probs"][:tile_cutoff],
var_params2["n_source_log_probs"],
atol=1e-5,
)
assert torch.allclose(
var_params["per_source_params"][:tile_cutoff],
var_params2["per_source_params"],
atol=1e-5,
)
tile_map_dict = image_encoder.variational_mode(var_params)
tile_map = TileCatalog.from_flat_dict(
image_encoder.tile_slen, n_tiles_h, n_tiles_w, tile_map_dict
)
full_map = tile_map.to_full_params()
tile_map_tilde = full_map.to_tile_params(image_encoder.tile_slen, tile_map.max_sources)
assert tile_map.equals(tile_map_tilde, exclude=("n_source_log_probs",), atol=1e-5)
return full_map
def _make_ptile_loader(self, image: Tensor, background: Tensor,
n_tiles_h: int):
img_bg = torch.cat((image, background), dim=1).to(self.device)
n_images = image.shape[0]
for start_b in range(0, n_images, self.n_images_per_batch):
for row in range(0, n_tiles_h, self.n_rows_per_batch):
end_b = start_b + self.n_images_per_batch
end_row = row + self.n_rows_per_batch
start_h = row * self.detection_encoder.tile_slen
end_h = end_row * self.detection_encoder.tile_slen + 2 * self.border_padding
img_bg_cropped = img_bg[start_b:end_b, :, start_h:end_h, :]
image_ptiles = get_images_in_tiles(
img_bg_cropped,
self.detection_encoder.tile_slen,
self.detection_encoder.ptile_slen,
)
yield image_ptiles.reshape(-1, *image_ptiles.shape[-3:])
def _get_loss(self, batch):
images: Tensor = batch["images"]
background: Tensor = batch["background"]
tile_catalog = TileCatalog(self.tile_slen, {
k: v
for k, v in batch.items() if k not in {"images", "background"}
})
image_ptiles = get_images_in_tiles(
torch.cat((images, background), dim=1),
self.tile_slen,
self.ptile_slen,
)
image_ptiles = rearrange(image_ptiles,
"n nth ntw b h w -> (n nth ntw) b h w")
locs = rearrange(tile_catalog.locs,
"n nth ntw ns hw -> 1 (n nth ntw) ns hw")
galaxy_params, pq_divergence = self._encode(image_ptiles, locs)
# draw fully reconstructed image.
# NOTE: Assume recon_mean = recon_var per poisson approximation.
tile_catalog["galaxy_params"] = rearrange(
galaxy_params,
"ns (n nth ntw) ms d -> (ns n) nth ntw ms d",
nth=tile_catalog.n_tiles_h,
ntw=tile_catalog.n_tiles_w,
)
recon_mean = self.image_decoder.render_images(tile_catalog)
recon_mean = rearrange(recon_mean, "(ns n) c h w -> ns n c h w", ns=1)
recon_mean += background.unsqueeze(0)
assert not torch.any(torch.isnan(recon_mean))
assert not torch.any(torch.isinf(recon_mean))
recon_losses = -Normal(recon_mean, recon_mean.sqrt()).log_prob(
images.unsqueeze(0))
if self.crop_loss_at_border:
bp = self.border_padding * 2
recon_losses = recon_losses[:, :, :, bp:(-bp), bp:(-bp)]
assert not torch.any(torch.isnan(recon_losses))
assert not torch.any(torch.isinf(recon_losses))
# For divergence loss, we only evaluate tiles with a galaxy in them
galaxy_bools = rearrange(tile_catalog["galaxy_bools"],
"n nth ntw ms 1 -> 1 (n nth ntw) ms")
divergence_loss = (pq_divergence * galaxy_bools).sum()
return recon_losses.sum() - divergence_loss
def test_sample(self, devices):
device = devices.device
max_detections = 4
ptile_slen = 10
n_bands = 2
tile_slen = 2
n_samples = 5
background = (10.0, 20.0)
background_tensor = torch.tensor(background).view(1, -1, 1, 1).to(device)
images = (
torch.randn(1, n_bands, 4 * ptile_slen, 4 * ptile_slen).to(device)
* background_tensor.sqrt()
+ background_tensor
)
background_tensor = background_tensor.expand(*images.shape)
star_encoder: DetectionEncoder = DetectionEncoder(
LogBackgroundTransform(),
channel=8,
dropout=0,
spatial_dropout=0,
hidden=64,
ptile_slen=ptile_slen,
tile_slen=tile_slen,
n_bands=n_bands,
mean_detections=0.48,
max_detections=max_detections,
).to(device)
image_ptiles = get_images_in_tiles(
torch.cat((images, background_tensor), dim=1),
star_encoder.tile_slen,
star_encoder.ptile_slen,
)
image_ptiles = rearrange(image_ptiles, "n nth ntw b h w -> (n nth ntw) b h w")
var_params = star_encoder.encode(image_ptiles)
star_encoder.sample(var_params, n_samples)
def get_prediction(self, batch: Dict[str, Tensor]):
"""Return loss, accuracy, binary probabilities, and MAP classifications for given batch."""
galaxy_bools = batch["galaxy_bools"].reshape(-1)
locs = rearrange(batch["locs"], "n nth ntw ms hw -> 1 (n nth ntw) ms hw")
image_ptiles = get_images_in_tiles(
torch.cat((batch["images"], batch["background"]), dim=1),
self.tile_slen,
self.ptile_slen,
)
image_ptiles = rearrange(image_ptiles, "n nth ntw b h w -> (n nth ntw) b h w")
galaxy_probs = self.forward(image_ptiles, locs)
galaxy_probs = galaxy_probs.reshape(-1)
tile_is_on_array = get_is_on_from_n_sources(batch["n_sources"], self.max_sources)
tile_is_on_array = tile_is_on_array.reshape(-1)
# we need to calculate cross entropy loss, only for "on" sources
loss = BCELoss(reduction="none")(galaxy_probs, galaxy_bools) * tile_is_on_array
loss = loss.sum()
# get predictions for calculating metrics
pred_galaxy_bools = (galaxy_probs > 0.5).float() * tile_is_on_array
correct = ((pred_galaxy_bools.eq(galaxy_bools)) * tile_is_on_array).sum()
total_n_sources = batch["n_sources"].sum()
acc = correct / total_n_sources
# finally organize quantities and return as a dictionary
pred_star_bools = (1 - pred_galaxy_bools) * tile_is_on_array
return {
"loss": loss,
"acc": acc,
"galaxy_bools": pred_galaxy_bools,
"star_bools": pred_star_bools,
"galaxy_probs": galaxy_probs,
}
def validation_epoch_end(self,
outputs,
kind="validation",
max_n_samples=16):
# pylint: disable=too-many-statements
"""Pytorch lightning method."""
batch: Dict[str, Tensor] = outputs[-1]
if self.n_bands > 1:
return
n_samples = min(
int(math.sqrt(len(batch["n_sources"])))**2, max_n_samples)
nrows = int(n_samples**0.5) # for figure
catalog_dict = {
"locs": batch["locs"][:, :, :, 0:self.max_detections],
"log_fluxes": batch["log_fluxes"][:, :, :, 0:self.max_detections],
"galaxy_bools": batch["galaxy_bools"][:, :, :,
0:self.max_detections],
"star_bools": batch["star_bools"][:, :, :, 0:self.max_detections],
"n_sources": batch["n_sources"].clamp(max=self.max_detections),
}
true_tile_catalog = TileCatalog(self.tile_slen, catalog_dict)
true_cat = true_tile_catalog.to_full_params()
image_ptiles = get_images_in_tiles(
torch.cat((batch["images"], batch["background"]), dim=1),
self.tile_slen,
self.ptile_slen,
)
image_ptiles = rearrange(image_ptiles,
"n nth ntw b h w -> (n nth ntw) b h w")
dist_params = self.encode(image_ptiles)
est_catalog_dict = self.variational_mode(dist_params)
est_tile_catalog = TileCatalog.from_flat_dict(
true_tile_catalog.tile_slen,
true_tile_catalog.n_tiles_h,
true_tile_catalog.n_tiles_w,
est_catalog_dict,
)
est_cat = est_tile_catalog.to_full_params()
# setup figure and axes.
fig, axes = plt.subplots(nrows=nrows, ncols=nrows, figsize=(12, 12))
axes = axes.flatten() if nrows > 1 else [axes]
images = batch["images"]
assert images.shape[-2] == images.shape[-1]
bp = self.border_padding
for idx, ax in enumerate(axes):
true_n_sources = true_cat.n_sources[idx].item()
n_sources = est_cat.n_sources[idx].item()
ax.set_xlabel(f"True num: {true_n_sources}; Est num: {n_sources}")
# add white border showing where centers of stars and galaxies can be
ax.axvline(bp, color="w")
ax.axvline(images.shape[-1] - bp, color="w")
ax.axhline(bp, color="w")
ax.axhline(images.shape[-2] - bp, color="w")
# plot image first
image = images[idx, 0].cpu().numpy()
vmin = image.min().item()
vmax = image.max().item()
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
im = ax.matshow(image, vmin=vmin, vmax=vmax, cmap="viridis")
fig.colorbar(im, cax=cax, orientation="vertical")
true_cat.plot_plocs(ax,
idx,
"galaxy",
bp=bp,
color="r",
marker="x",
s=20)
true_cat.plot_plocs(ax,
idx,
"star",
bp=bp,
color="c",
marker="x",
s=20)
est_cat.plot_plocs(ax,
idx,
"all",
bp=bp,
color="b",
marker="+",
s=30)
if idx == 0:
ax.scatter(None,
None,
color="r",
marker="x",
s=20,
label="t.gal")
ax.scatter(None,
None,
color="c",
marker="x",
s=20,
label="t.star")
ax.scatter(None,
None,
color="b",
marker="+",
s=30,
label="p.source")
ax.legend(
bbox_to_anchor=(0.0, 1.2, 1.0, 0.102),
loc="lower left",
ncol=2,
mode="expand",
borderaxespad=0.0,
)
fig.tight_layout()
if self.logger:
if kind == "validation":
title = f"Epoch:{self.current_epoch}/Validation Images"
self.logger.experiment.add_figure(title, fig)
elif kind == "testing":
self.logger.experiment.add_figure("Test Images", fig)
else:
raise NotImplementedError()
plt.close(fig)
def _get_loss(self, batch: Dict[str, Tensor]):
true_catalog = {
"locs":
rearrange(
batch["locs"][:, :, :, 0:self.max_detections],
"n nth ntw ns hw -> (n nth ntw) ns hw",
),
"log_fluxes":
rearrange(
batch["log_fluxes"][:, :, :, 0:self.max_detections],
"n nth ntw ns b -> (n nth ntw) ns b",
),
"galaxy_bools":
rearrange(
batch["galaxy_bools"][:, :, :, 0:self.max_detections],
"n nth ntw ns 1 -> (n nth ntw) ns 1",
),
"n_sources":
rearrange(batch["n_sources"].clamp(max=self.max_detections),
"n nth ntw -> (n nth ntw)"),
}
true_catalog["is_on_array"] = get_is_on_from_n_sources(
true_catalog["n_sources"], self.max_detections)
image_ptiles = get_images_in_tiles(
torch.cat((batch["images"], batch["background"]), dim=1),
self.tile_slen,
self.ptile_slen,
)
image_ptiles = rearrange(image_ptiles,
"n nth ntw b h w -> (n nth ntw) b h w")
dist_params = self.encode(image_ptiles)
nslp_flat = rearrange(dist_params["n_source_log_probs"],
"n_ptiles ns -> n_ptiles ns")
counter_loss = F.nll_loss(nslp_flat,
true_catalog["n_sources"].reshape(-1),
reduction="none")
pred = self._encode_for_n_sources(
dist_params["per_source_params"],
rearrange(true_catalog["n_sources"], "n_ptiles -> 1 n_ptiles"),
)
locs_log_probs_all = _get_params_logprob_all_combs(
true_catalog["locs"],
pred["loc_mean"].squeeze(0),
pred["loc_sd"].squeeze(0),
)
star_params_log_probs_all = _get_params_logprob_all_combs(
true_catalog["log_fluxes"],
pred["log_flux_mean"].squeeze(0),
pred["log_flux_sd"].squeeze(0),
)
(locs_loss, star_params_loss) = _get_min_perm_loss(
locs_log_probs_all,
star_params_log_probs_all,
rearrange(true_catalog["galaxy_bools"],
"n_ptiles ns 1 -> n_ptiles ns"),
true_catalog["is_on_array"],
)
loss_vec = locs_loss * (locs_loss.detach() <
1e6).float() + counter_loss + star_params_loss
loss = loss_vec.mean()
return {
"loss": loss,
"counter_loss": counter_loss,
"locs_loss": locs_loss,
"star_params_loss": star_params_loss,
}
def get_images_in_ptiles(self, images):
"""Run get_images_in_ptiles with correct tile_slen and ptile_slen."""
return get_images_in_tiles(images, self.detection_encoder.tile_slen,
self.detection_encoder.ptile_slen)
def _make_plots(self, batch, n_samples=5):
# validate worst reconstruction images.
n_samples = min(len(batch["n_sources"]), n_samples)
samples = np.random.choice(len(batch["n_sources"]),
n_samples,
replace=False)
keys = [
"images",
"background",
"locs",
"galaxy_bools",
"star_bools",
"fluxes",
"log_fluxes",
"n_sources",
]
for k in keys:
batch[k] = batch[k][samples]
# extract non-params entries so that 'get_full_params' to works.
images = batch["images"]
background = batch["background"]
tile_locs = batch["locs"]
# obtain map estimates
image_ptiles = get_images_in_tiles(
torch.cat((images, background), dim=1),
self.tile_slen,
self.ptile_slen,
)
_, n_tiles_h, n_tiles_w, _, _, _ = image_ptiles.shape
image_ptiles = rearrange(image_ptiles,
"n nth ntw b h w -> (n nth ntw) b h w")
locs = rearrange(tile_locs, "n nth ntw ns hw -> 1 (n nth ntw) ns hw")
z, _ = self._encode(image_ptiles, locs)
galaxy_params = rearrange(
z,
"ns (n nth ntw) ms d -> (ns n) nth ntw ms d",
ns=1,
nth=n_tiles_h,
ntw=n_tiles_w,
)
tile_est = TileCatalog(
self.tile_slen,
{
"n_sources": batch["n_sources"],
"locs": batch["locs"],
"galaxy_bools": batch["galaxy_bools"],
"star_bools": batch["star_bools"],
"fluxes": batch["fluxes"],
"log_fluxes": batch["log_fluxes"],
"galaxy_params": galaxy_params,
},
)
est = tile_est.to_full_params()
# draw all reconstruction images.
# render_images automatically accounts for tiles with no galaxies.
recon_images = self.image_decoder.render_images(tile_est)
recon_images += background
residuals = (images - recon_images) / torch.sqrt(recon_images)
# draw worst `n_samples` examples as measured by absolute avg. residual error.
worst_indices = residuals.abs().mean(dim=(1, 2, 3)).argsort(
descending=True)[:n_samples]
if self.crop_loss_at_border:
bp = self.border_padding * 2
residuals[:, :, :bp, :] = 0.0
residuals[:, :, -bp:, :] = 0.0
residuals[:, :, :, :bp] = 0.0
residuals[:, :, :, -bp:] = 0.0
figsize = (12, 4 * n_samples)
fig, axes = plt.subplots(nrows=n_samples,
ncols=3,
figsize=figsize,
squeeze=False)
for i, idx in enumerate(worst_indices):
true_ax = axes[i, 0]
recon_ax = axes[i, 1]
res_ax = axes[i, 2]
# add titles to axes in the first row
if i == 0:
true_ax.set_title("Truth", size=18)
recon_ax.set_title("Reconstruction", size=18)
res_ax.set_title("Residual", size=18)
# vmin, vmax should be shared between reconstruction and true images.
if self.max_flux_valid_plots is None:
vmax = np.ceil(
max(images[idx].max().item(),
recon_images[idx].max().item()))
else:
vmax = self.max_flux_valid_plots
vmin = np.floor(
min(images[idx].min().item(), recon_images[idx].min().item()))
vrange = (vmin, vmax)
# plot!
labels = ("t. gal", None, "t. star", None)
# Plotting only works on square images
assert images.shape[-2] == images.shape[-1]
slen = images.shape[-1] - 2 * self.border_padding
bp = self.border_padding
image = images[i, 0].cpu().numpy()
plocs = est.plocs[i].cpu().numpy().reshape(-1, 2)
probs = est["galaxy_bools"][i].cpu().numpy().reshape(-1)
plot_image(fig, true_ax, image, vrange=vrange, colorbar=True)
plot_locs(true_ax, bp, slen, plocs, probs, cmap="cool")
plot_image(fig, recon_ax, image, vrange=vrange, colorbar=True)
plot_locs(recon_ax, bp, slen, plocs, probs, cmap="cool")
residuals_idx = residuals[idx, 0].cpu().numpy()
res_vmax = np.ceil(residuals_idx.max())
res_vmin = np.floor(residuals_idx.min())
if self.crop_loss_at_border:
bp = (recon_images.shape[-1] - slen) // 2
eff_slen = slen - bp
for b in (bp, bp * 2):
recon_ax.axvline(b, color="w")
recon_ax.axvline(b + eff_slen, color="w")
recon_ax.axhline(b, color="w")
recon_ax.axhline(b + eff_slen, color="w")
plot_image(fig, res_ax, residuals_idx, vrange=(res_vmin, res_vmax))
if self.crop_loss_at_border:
for b in (bp, bp * 2):
res_ax.axvline(b, color="w")
res_ax.axvline(b + eff_slen, color="w")
res_ax.axhline(b, color="w")
res_ax.axhline(b + eff_slen, color="w")
if i == 0:
add_legend(true_ax, labels)
fig.tight_layout()
if self.logger:
self.logger.experiment.add_figure(
f"Epoch:{self.current_epoch}/Worst Validation Images", fig)
plt.close(fig)