high-resolution images & a 2d discrete variational factor

.pylintrc

@@ -51,7 +51,7 @@ disable=too-many-ancestors,
 	unused-argument,
 
 	# flake8 recommends against f-strings
-	consider-using-f-string,
+	logging-fstring-interpolation,
 
 	# flake8 already checks for lambda expressions, which are OK at times
 	unnecessary-lambda-assignment,

bliss/cached_dataset.py

@@ -93,6 +93,7 @@ def __call__(self, datum_in):
 
 class ChunkingSampler(Sampler):
     def __init__(self, dataset: Dataset) -> None:
+        super().__init__()
         # please don't pass dataset to the following __init__()
         # according to https://pytorch.org/docs/stable/data.html#torch.utils.data.Sampler
         # the parameter `data_source` has been deprecated

bliss/catalog.py

@@ -82,6 +82,38 @@
             [tiles_to_crop, self.n_tiles_w - tiles_to_crop],
         )
 
+    def filter_by_ploc_box(self, box_origin: torch.Tensor, box_len: float):
+        assert box_origin[0] + box_len < self.height, "invalid box"
+        assert box_origin[1] + box_len < self.width, "invalid box"
+
+        box_origin_tensor = box_origin.view(1, 1, 2).to(device=self.device)
+        box_end_tensor = (box_origin + box_len).view(1, 1, 2).to(device=self.device)
+
+        plocs_mask = torch.all(
+            (self["plocs"] < box_end_tensor) & (self["plocs"] > box_origin_tensor), dim=2
+        )
+
+        plocs_mask_indexes = plocs_mask.nonzero()
+        plocs_inverse_mask_indexes = (~plocs_mask).nonzero()
+        plocs_full_mask_indexes = torch.cat((plocs_mask_indexes, plocs_inverse_mask_indexes), dim=0)
+        _, index_order = plocs_full_mask_indexes[:, 0].sort(stable=True)
+        plocs_full_mask_sorted_indexes = plocs_full_mask_indexes[index_order.tolist(), :]
+
+        d = {}
+        new_max_sources = plocs_mask.sum(dim=1).max()
+        for k, v in self.items():
+            if k == "n_sources":
+                d[k] = plocs_mask.sum(dim=1)
+            else:
+                d[k] = v[
+                    plocs_full_mask_sorted_indexes[:, 0].tolist(),
+                    plocs_full_mask_sorted_indexes[:, 1].tolist(),
+                ].view(-1, self.max_sources, v.shape[-1])[:, :new_max_sources, :]
+
+        d["plocs"] -= box_origin_tensor
+
+        return FullCatalog(box_len, box_len, d)
+
 
 class TileCatalog(BaseTileCatalog):
     galaxy_params = [
@@ -335,9 +367,8 @@
         ns11 = rearrange(self["n_sources"], "b ht wt -> b ht wt 1 1")
         for k, v in self.items():
             if k == "n_sources":
+                assert not disjoint or ((v == 0) | (other[k] == 0)).all()
                 d[k] = v + other[k]
-                if disjoint:
-                    assert d[k].max() <= 1
             else:
                 if disjoint:
                     d1 = v
@@ -734,9 +765,7 @@
 
         return TileCatalog(tile_params)
 
-    # pylint: enable=R0912,R0915
-
-    def filter_by_ploc_box(self, box_origin: torch.Tensor, box_len: float):
+    def filter_by_ploc_box(self, box_origin: torch.Tensor, box_len: float, exclude_box=False):
         assert box_origin[0] + box_len <= self.height, "invalid box"
         assert box_origin[1] + box_len <= self.width, "invalid box"
 
@@ -747,6 +776,9 @@
             (self["plocs"] < box_end_tensor) & (self["plocs"] > box_origin_tensor), dim=2
         )
 
+        if exclude_box:
+            plocs_mask = ~plocs_mask
+
         plocs_mask_indexes = plocs_mask.nonzero()
         plocs_inverse_mask_indexes = (~plocs_mask).nonzero()
         plocs_full_mask_indexes = torch.cat((plocs_mask_indexes, plocs_inverse_mask_indexes), dim=0)
@@ -764,6 +796,8 @@
                     plocs_full_mask_sorted_indexes[:, 1].tolist(),
                 ].view(-1, self.max_sources, v.shape[-1])[:, :new_max_sources, :]
 
-        d["plocs"] -= box_origin_tensor
+        if exclude_box:
+            return FullCatalog(self.height, self.width, d)
 
+        d["plocs"] -= box_origin_tensor
         return FullCatalog(box_len, box_len, d)

bliss/conf/base_config.yaml

@@ -17,28 +17,30 @@ paths:
     cached_data: /data/scratch/regier/sdss_like
     output: ${oc.env:HOME}/bliss_output
 
-# this prior is sdss-like; the flux parameters were fit using SDSS catalogs
+# this prior is sdss-like; the parameters were fit using SDSS catalogs
 prior:
     _target_: bliss.simulator.prior.CatalogPrior
     survey_bands: [u, g, r, i, z]  # SDSS available band filters
     reference_band: 2  # SDSS r-band
-    star_color_model_path: ${simulator.decoder.survey.dir_path}/color_models/star_gmm_nmgy.pkl
-    gal_color_model_path: ${simulator.decoder.survey.dir_path}/color_models/gal_gmm_nmgy.pkl
-    n_tiles_h: 20
-    n_tiles_w: 20
-    batch_size: 64
+    star_color_model_path: ${paths.sdss}/color_models/star_gmm_nmgy.pkl
+    gal_color_model_path: ${paths.sdss}/color_models/gal_gmm_nmgy.pkl
+    n_tiles_h: 68  # cropping 2 tiles from each side
+    n_tiles_w: 68  # cropping 2 tiles from each side
+    batch_size: 8
     max_sources: 1
-    mean_sources: 0.01  # 0.0025 is more realistic for SDSS but training takes more iterations
+    mean_sources: 0.0025
     min_sources: 0
     prob_galaxy: 0.5144
-    star_flux_exponent: 0.4689157382430609
-    star_flux_truncation: 613313.768995269
-    star_flux_loc: -0.5534648001193676
-    star_flux_scale: 1.1846035501201129
-    galaxy_flux_exponent: 1.5609458661807678
-    galaxy_flux_truncation: 28790.449063519092
-    galaxy_flux_loc: -3.29383532288203
-    galaxy_flux_scale: 3.924799999613338
+    star_flux:
+        exponent: 0.4689157382430609
+        truncation: 613313.768995269
+        loc: -0.5534648001193676
+        scale: 1.1846035501201129
+    galaxy_flux:
+        exponent: 1.5609458661807678
+        truncation: 28790.449063519092
+        loc: -3.29383532288203
+        scale: 3.924799999613338
     galaxy_a_concentration: 0.39330758068481686
     galaxy_a_loc: 0.8371888967872619
     galaxy_a_scale: 4.432725319432478
@@ -51,20 +53,11 @@ decoder:
     with_dither: true
     with_noise: true
 
-simulator:
-    _target_: bliss.simulator.simulated_dataset.SimulatedDataset
-    prior: ${prior}
-    decoder: ${decoder}
-    n_batches: 128
-    num_workers: 32
-    valid_n_batches: 10  # 256
-    fix_validation_set: true
-
 cached_simulator:
     _target_: bliss.cached_dataset.CachedSimulatedDataModule
-    batch_size: 64
+    batch_size: 16
     splits: 0:80/80:90/90:100  # train/val/test splits as percent ranges
-    num_workers: 8
+    num_workers: 4
     cached_data_path: ${paths.cached_data}
     train_transforms:
         - _target_: bliss.data_augmentation.RotateFlipTransform
@@ -140,23 +133,42 @@ variational_factors:
       nll_gating:
           _target_: bliss.encoder.variational_dist.GalaxyGating
 
+# these are in nanomaggies
+sdss_mag_zero_point: 1e9
+sdss_flux_cutoffs:
+    - 1.4928
+    - 1.9055
+    - 2.7542
+    - 3.9811
+    - 5.7544
+    - 8.3176
+    - 12.0227
+    - 17.3780
+    - 25.1189
+
 metrics:
     detection_performance:
         _target_: bliss.encoder.metrics.DetectionPerformance
-        base_flux_bin_cutoffs: [200, 400, 600, 800, 1000]
-        mag_zero_point: 3631e9  # for DC2
+        base_flux_bin_cutoffs: ${sdss_flux_cutoffs}
+        mag_zero_point: ${sdss_mag_zero_point}
         report_bin_unit: mag
+        exclude_last_bin: true
+        ref_band: 2
     source_type_accuracy:
         _target_: bliss.encoder.metrics.SourceTypeAccuracy
-        base_flux_bin_cutoffs: [200, 400, 600, 800, 1000]
-        mag_zero_point: 3631e9  # for DC2
+        base_flux_bin_cutoffs: ${sdss_flux_cutoffs}
+        mag_zero_point: ${sdss_mag_zero_point}
         report_bin_unit: mag
+        exclude_last_bin: true
+        ref_band: 2
     flux_error:
         _target_: bliss.encoder.metrics.FluxError
         survey_bands: ${encoder.survey_bands}
-        base_flux_bin_cutoffs: [200, 400, 600, 800, 1000]
-        mag_zero_point: 3631e9  # for DC2
+        base_flux_bin_cutoffs: ${sdss_flux_cutoffs}
+        mag_zero_point: ${sdss_mag_zero_point}
         report_bin_unit: mag
+        exclude_last_bin: true
+        ref_band: 2
 
 image_normalizers:
     psf:
@@ -173,7 +185,7 @@ encoder:
     _target_: bliss.encoder.encoder.Encoder
     survey_bands: [u, g, r, i, z]
     reference_band: 2  # SDSS r-band
-    tile_slen: ${simulator.decoder.tile_slen}
+    tile_slen: ${decoder.tile_slen}
     optimizer_params:
         lr: 1e-3
     scheduler_params:
@@ -201,7 +213,7 @@ encoder:
               frequency: 1
               restrict_batch: 0
               tiles_to_crop: 0
-              tile_slen: ${simulator.decoder.tile_slen}
+              tile_slen: ${decoder.tile_slen}
     use_double_detect: false
     use_checkerboard: false
     n_sampler_colors: 4
@@ -278,11 +290,13 @@ surveys:
 mode: train
 
 generate:
-    n_image_files: 64
-    n_batches_per_file: 16
-    simulator: ${simulator}
+    prior: ${prior}
+    decoder: ${decoder}
+    tiles_to_crop: 2
+    n_image_files: 512
+    n_batches_per_file: 32  # multiply by prior.batch_size to get total number of images
+    n_processes: 16  # using more isn't necessarily faster
     cached_data_path: ${paths.cached_data}
-    file_prefix: dataset
     store_full_catalog: false
 
 train:

bliss/encoder/convnet_layers.py

@@ -6,11 +6,15 @@
 
 
 class ConvBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
+    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, gn=True):
         super().__init__()
+        assert kernel_size % 2 == 1, "kernel size must be odd"
+        padding = (kernel_size - 1) // 2
         self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
         # seems to work about as well as BatchNorm2d
-        self.norm = nn.GroupNorm(out_channels // 8, out_channels)
+        n_groups = out_channels // 8
+        use_gn = gn and n_groups >= 16
+        self.norm = nn.GroupNorm(n_groups, out_channels) if use_gn else nn.Identity()
         self.activation = nn.SiLU(inplace=True)
 
     def forward(self, x):
@@ -27,11 +31,12 @@ def forward(self, x):
 
 
 class Bottleneck(nn.Module):
-    def __init__(self, c1, c2, shortcut=True, e=0.5):
+    def __init__(self, c1, c2, shortcut=True, e=0.5, gn=True, spatial=True):
         super().__init__()
         ch = int(c2 * e)
-        self.cv1 = ConvBlock(c1, ch, kernel_size=1, padding=0)
-        self.cv2 = ConvBlock(ch, c2, kernel_size=3, padding=1, stride=1)
+        self.cv1 = ConvBlock(c1, ch, kernel_size=1, gn=gn)
+        ks = 3 if spatial else 1
+        self.cv2 = ConvBlock(ch, c2, kernel_size=ks, stride=1, gn=gn)
         self.add = shortcut and c1 == c2
 
     def forward(self, x):
@@ -40,13 +45,15 @@ def forward(self, x):
 
 
 class C3(nn.Module):
-    def __init__(self, c1, c2, n=1, shortcut=True, e=0.5):
+    def __init__(self, c1, c2, n=1, shortcut=True, e=0.5, gn=True, spatial=True):
         super().__init__()
         ch = int(c2 * e)
-        self.cv1 = ConvBlock(c1, ch, kernel_size=1, padding=0)
-        self.cv2 = ConvBlock(c1, ch, kernel_size=1, padding=0)
-        self.cv3 = ConvBlock(2 * ch, c2, kernel_size=1, padding=0)
-        self.m = nn.Sequential(*(Bottleneck(ch, ch, shortcut, e=1.0) for _ in range(n)))
+        self.cv1 = ConvBlock(c1, ch, kernel_size=1, gn=gn)
+        self.cv2 = ConvBlock(c1, ch, kernel_size=1, gn=gn)
+        self.cv3 = ConvBlock(2 * ch, c2, kernel_size=1, gn=gn)
+        self.m = nn.Sequential(
+            *(Bottleneck(ch, ch, shortcut, e=1.0, spatial=spatial) for _ in range(n)),
+        )
 
     def forward(self, x):
         return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))