# Evaluation settings shared by the five systems: csFID-10k (the paper's image metric) and the sampling of the test read.

fid:
  n_truth: 10000                   # truth realizations per test condition (the first 10,000 members of the truth bank)
  n_generated: 10000               # generated samples per model and test condition
  backend: clean-fid
  clean_fid_version: "0.1.35"      # checked at run time
  mode: clean                      # clean-fid "clean" mode: one PIL bicubic resize to 299 x 299
  inception:
    file: inception-2015-12-05.pt  # the TF Inception-v3 weights distributed with clean-fid (sha256 checked)
    sha256: f58cb9b6ec323ed63459aa4fb441fe750cfe39fafad6da5cb504a16f19e958f4
    feature_layer: pool3           # 2048-D, used by cFID / cKID
  resize: {size: 299, filter: bicubic, backend: PIL, antialias: true, once: true}
  kid: {num_subsets: 100, max_subset_size: 1000}
  clip_quantile: 0.9999            # image range: c = Q_0.9999(|x|) over the training fields (records/<system>/fid_adapter.json)
  feature_batch: 200
  primary_metric: csfid            # csFID: the Frechet distance of the spatial Inception feature below
  secondary_metrics: [cfid, ckid]
  spatial:
    layer: mixed_6/conv            # the first branch of the mixed_6 block, before concatenation (192 x 17 x 17)
    channels: 7                    # the first 7 channels
    grid: 17
    dim: 2023                      # 17 * 17 * 7, flattened NHWC, no spatial pooling
    flatten: NHWC
    pooling: none
    reference: "Nash et al., ICML 2021; openai/guided-diffusion evaluations/evaluator.py FID_SPATIAL_NAME"

sampling:
  crn_base_seed: 777               # the noise of test condition gid is drawn from the stream seeded with 777 * 100003 + gid
  sample_chunk: 2000               # the 10,000 samples of a condition are drawn in chunks of 2,000 from that stream