upload code/RAEv2/sample_perclass_kermany.py

code/RAEv2/sample_perclass_kermany.py

@@ -0,0 +1,64 @@
+import os, sys, math, dataclasses, csv
+import numpy as np, torch
+from PIL import Image
+from omegaconf import OmegaConf
+from configs.stage2 import Stage2Config
+from stage2.transport import create_sampler, create_transport
+from utils.guidance_utils import get_model_forward_fn
+from utils.model_utils import instantiate_from_config
+
+CFG_PATH = sys.argv[1]
+OUTDIR   = sys.argv[2]
+NPER     = int(sys.argv[3]) if len(sys.argv) > 3 else 2000
+BATCH    = int(sys.argv[4]) if len(sys.argv) > 4 else 50
+ALLCLASSES = ["CNV", "DME", "DRUSEN", "NORMAL"]
+CLASSES  = sys.argv[5].split(",") if len(sys.argv) > 5 else ALLCLASSES   # subset (names)
+TAG      = sys.argv[6] if len(sys.argv) > 6 else "all"
+device = torch.device("cuda", 0)
+torch.set_grad_enabled(False)
+torch.backends.cuda.matmul.allow_tf32 = True; torch.backends.cudnn.allow_tf32 = True
+
+config = OmegaConf.to_object(OmegaConf.merge(OmegaConf.structured(Stage2Config), OmegaConf.load(CFG_PATH)))
+config.post_process()
+latent_size = tuple(config.misc.latent_size)
+rae = instantiate_from_config(config.stage_1).to(device).eval()
+config.prepare_model_params()
+model = instantiate_from_config(config.stage_2).to(device).eval()
+model_fn, sample_model_kwargs = get_model_forward_fn(model, config.guidance)
+use_guidance = config.guidance.any_guidance_active
+null_label = config.misc.num_classes
+tds = math.sqrt((config.misc.time_dist_shift_dim or math.prod(latent_size)) / config.misc.time_dist_shift_base)
+transport = create_transport(config=config.transport, time_dist_shift=tds)
+sampler = create_sampler(transport, guidance_config=config.guidance)
+sample_fn = sampler.sample_ode(**dataclasses.asdict(config.sampler))
+print(f"setup ok; latent={latent_size} use_guidance={use_guidance} ckpt={config.stage_2.ckpt}", flush=True)
+
+os.makedirs(OUTDIR, exist_ok=True)
+rows = []
+for c in CLASSES:
+    ci = ALLCLASSES.index(c)  # GLOBAL class index for conditioning
+    cdir = os.path.join(OUTDIR, "images", c); os.makedirs(cdir, exist_ok=True)
+    done = 0
+    while done < NPER:
+        n = min(BATCH, NPER - done)
+        z = torch.randn(n, *latent_size, device=device)
+        context = torch.full((n,), ci, device=device, dtype=torch.long)
+        if use_guidance:
+            z = torch.cat([z, z], dim=0)
+            context = torch.cat([context, torch.full((n,), null_label, device=device, dtype=torch.long)], dim=0)
+        mk = dict(context=context, attn_mask=None, **sample_model_kwargs)
+        with torch.autocast("cuda", dtype=torch.bfloat16):
+            samples = sample_fn(z, model_fn, **mk)[-1]
+            if use_guidance:
+                samples = samples.chunk(2, dim=0)[0]
+            imgs = rae.decode(samples).clamp(0, 1)
+        arr = imgs.mul(255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()
+        for j in range(n):
+            rel = f"images/{c}/{c}_{done+j:05d}.png"
+            Image.fromarray(arr[j]).save(os.path.join(OUTDIR, rel))
+            rows.append((os.path.join(OUTDIR, rel), c))
+        done += n
+    print(f"{c}: {done} (std={arr.std():.0f})", flush=True)
+with open(os.path.join(OUTDIR, f"synth_{TAG}.csv"), "w", newline="") as f:
+    w = csv.writer(f); w.writerow(["image_path", "dx"]); w.writerows(rows)
+print(f"SAMPLE_PERCLASS_DONE tag={TAG} n={len(rows)}", flush=True)