File size: 5,806 Bytes
c61c435 f8c73f9 c61c435 f8c73f9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | from __future__ import annotations
import io
import threading
from pathlib import Path
from adam.config import ConfigManager
from adam.executor import ToolContext
from adam.registry import ToolSpec
from adam.tools import ddpm_adapter
def _context(root: Path, logs: list[str]) -> ToolContext:
running = threading.Event()
running.set()
return ToolContext(
root=root,
job_id="DDPMTEST",
tool=ToolSpec("ddpm_trainer", "DDPM Trainer", "test", "Training", "train_ddpm"),
cancel_event=threading.Event(),
run_event=running,
progress_callback=lambda *_args: None,
log_callback=logs.append,
)
def test_resolution_change_branches_from_pipeline_instead_of_resuming_checkpoint(tmp_path: Path, monkeypatch) -> None:
trainer_root = tmp_path / "DDPM"
model = trainer_root / "output" / "Anime"
checkpoint = model / "checkpoint-40"
dataset = tmp_path / "dataset"
for folder in (checkpoint / "unet", model / "unet", model / "scheduler", dataset):
folder.mkdir(parents=True)
(trainer_root / "train.py").write_text("# fake trainer", encoding="utf-8")
(model / "model_index.json").write_text("{}", encoding="utf-8")
(checkpoint / "optimizer.bin").write_bytes(b"optimizer")
(checkpoint / "scheduler.bin").write_bytes(b"scheduler")
(checkpoint / "unet" / "diffusion_pytorch_model.safetensors").write_bytes(b"weights")
(checkpoint / "unet" / "config.json").write_text('{"sample_size": 64}', encoding="utf-8")
for index in range(2):
(dataset / f"{index}.png").write_bytes(b"image")
ConfigManager(tmp_path).update({"tool_folders": {"ddpm_trainer": str(trainer_root)}})
monkeypatch.setattr(ddpm_adapter.importlib.util, "find_spec", lambda _name: object())
commands: list[list[str]] = []
class FakeProcess:
stdout = io.StringIO("PROGRESS_JSON:{\"event\":\"done\"}\n")
returncode = 0
def poll(self):
return 0
def terminate(self):
self.returncode = -15
def fake_popen(command, **_kwargs):
commands.append(command)
return FakeProcess()
monkeypatch.setattr(ddpm_adapter.subprocess, "Popen", fake_popen)
logs: list[str] = []
result = ddpm_adapter.train_ddpm(
_context(tmp_path, logs),
dataset_dir=str(dataset),
model_name="Anime",
epochs=5,
output_dir=str(trainer_root / "output" / "Anime V2"),
resume_from=str(checkpoint),
resolution=256,
)
command = commands[0]
assert "--pretrained_model_path" in command
assert str(model.resolve()) in command
assert "--resume_from_checkpoint" not in command
assert result["output_folder"] != str(model.resolve())
assert any("Changing DDPM canvas from 64x64 to 256x256" in line for line in logs)
def test_widescreen_training_uses_native_256_by_144_canvas(tmp_path: Path, monkeypatch) -> None:
trainer_root = tmp_path / "DDPM"
output = trainer_root / "output" / "Wide"
dataset = tmp_path / "dataset"
dataset.mkdir(parents=True)
trainer_root.mkdir(parents=True)
(trainer_root / "train.py").write_text("# fake trainer", encoding="utf-8")
for index in range(2):
(dataset / f"{index}.png").write_bytes(b"image")
ConfigManager(tmp_path).update({"tool_folders": {"ddpm_trainer": str(trainer_root)}})
monkeypatch.setattr(ddpm_adapter.importlib.util, "find_spec", lambda _name: object())
commands: list[list[str]] = []
class FakeProcess:
stdout = io.StringIO("PROGRESS_JSON:{\"event\":\"done\"}\n")
returncode = 0
def poll(self):
return 0
monkeypatch.setattr(
ddpm_adapter.subprocess,
"Popen",
lambda command, **_kwargs: commands.append(command) or FakeProcess(),
)
ddpm_adapter.train_ddpm(
_context(tmp_path, []),
dataset_dir=str(dataset),
model_name="Wide",
epochs=1,
output_dir=str(output),
resolution=256,
training_aspect_ratio="16:9 (Widescreen)",
resize_mode="fit",
)
command = commands[0]
assert command[command.index("--resolution_width") + 1] == "256"
assert command[command.index("--resolution_height") + 1] == "144"
assert command[command.index("--resize_mode") + 1] == "fit"
def test_video_dataset_uses_nested_accepted_frames_only(tmp_path: Path, monkeypatch) -> None:
trainer_root = tmp_path / "DDPM"
output = trainer_root / "output" / "Video"
dataset = tmp_path / "Video Dataset"
accepted = dataset / "frames" / "video_0001"
rejected = dataset / "rejected" / "video_0001"
accepted.mkdir(parents=True)
rejected.mkdir(parents=True)
trainer_root.mkdir(parents=True)
(trainer_root / "train.py").write_text("# fake trainer", encoding="utf-8")
for index in range(2):
(accepted / f"accepted_{index}.png").write_bytes(b"image")
(rejected / "rejected_0.png").write_bytes(b"image")
ConfigManager(tmp_path).update({"tool_folders": {"ddpm_trainer": str(trainer_root)}})
monkeypatch.setattr(ddpm_adapter.importlib.util, "find_spec", lambda _name: object())
commands: list[list[str]] = []
class FakeProcess:
stdout = io.StringIO("PROGRESS_JSON:{\"event\":\"done\"}\n")
returncode = 0
def poll(self):
return 0
monkeypatch.setattr(
ddpm_adapter.subprocess,
"Popen",
lambda command, **_kwargs: commands.append(command) or FakeProcess(),
)
ddpm_adapter.train_ddpm(
_context(tmp_path, []), dataset_dir=str(dataset), model_name="Video",
epochs=1, output_dir=str(output),
)
command = commands[0]
assert command[command.index("--train_data_dir") + 1] == str((dataset / "frames").resolve())
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