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Author SHA1 Message Date
jalf a5dc093a15 Delete generator/notebooks/_build.py 2026-05-15 00:25:44 +01:00
Johnny Fernandes 1ed2b7a7a0 Final final 2026-05-14 23:19:26 +01:00
Johnny Fernandes afd26f47d2 Final polish 2026-05-14 21:16:03 +01:00
754 changed files with 4149 additions and 83044 deletions
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README.md
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@@ -1,4 +1,4 @@
# Deep Learning Face Project
# Deep learning face project
This repository contains a two-part deep learning project on the
DeepFakeFace (DFF) dataset:
@@ -10,7 +10,7 @@ The project is written as an experimental report. The notebooks are the main
deliverable: they show the pipeline, the intermediate failures, the ablations,
the decisions, and the final models. Read them in order.
## Project Story
## Project story
The work follows the same principle in both parts: start with a simple
baseline, inspect what fails, change one important factor at a time, and keep
@@ -19,7 +19,7 @@ the evidence tied to saved logs and saved artifacts.
For the **classifier**, the story moves from dataset understanding to
preprocessing, baseline models, controlled ablations, Grad-CAM inspection,
stronger model families, and data scaling. The final practical classifier is a
ResNet50-style pipeline using face crops, 224x224 inputs, ImageNet/default
ResNet50-style pipeline using face crops, 224×224 inputs, ImageNet/default
normalization, and no stochastic augmentation at validation/test time.
For the **generator**, the story starts with raw baseline failures, then locks
@@ -28,14 +28,14 @@ GAN, VAE, and DDPM. The final comparison keeps quality versus speed central:
DDPM gives the best saved FID and visual quality, GAN is the best
quality-speed compromise, and VAE is the fastest but smoothest option.
## How To Read The Project
## How to read the project
Start with the classifier notebooks, then read the generator notebooks. The
generator has one linear setup stage followed by three parallel branches:
GAN, VAE, and DDPM. Those branches are numbered in reading order, but they are
conceptually parallel experiments after the pipeline is selected.
### Classifier Notebooks
### Classifier notebooks
Read these first:
@@ -57,7 +57,7 @@ Read these first:
7. `classifier/notebooks/07_phase4_data_scaling_analysis.ipynb`
Data scaling for strong backbones and the final classifier decision.
### Generator Notebooks
### Generator notebooks
Read these after the classifier:
@@ -67,12 +67,12 @@ Read these after the classifier:
Controlled pipeline ablations: resolution, alignment, augmentation, and
raw/aligned mixing.
3. `generator/notebooks/03_gan_stability_progression.ipynb`
GAN branch: DCGAN -> WGAN-GP -> spectral normalization + GroupNorm +
self-attention -> 128x128 check.
GAN branch: DCGAN → WGAN-GP → spectral normalization + GroupNorm +
self-attention → 128×128 check.
4. `generator/notebooks/04_vae_loss_progression.ipynb`
VAE branch: MSE + KL -> perceptual loss -> PatchGAN adversarial loss.
VAE branch: MSE + KL → perceptual loss → PatchGAN adversarial loss.
5. `generator/notebooks/05_ddpm_recipe_progression.ipynb`
DDPM branch: linear schedule -> cosine schedule -> v-prediction -> wider
DDPM branch: linear schedule → cosine schedule → v-prediction → wider
backbone.
6. `generator/notebooks/06_final_family_comparison.ipynb`
Final comparison of the selected GAN, VAE, and DDPM recipes under saved
@@ -81,7 +81,7 @@ Read these after the classifier:
Curated final sample examples from saved outputs. This is qualitative
showcase material, not a replacement for FID.
## What The Notebooks Do
## What the notebooks do
The notebooks are analysis/report chapters. They load existing configs, logs,
figures, saved sample grids, checkpoints, and prediction summaries. They are
@@ -97,7 +97,7 @@ When a notebook shows a plot or image grid, the surrounding markdown explains:
This is important because the project is evaluated not only by final
performance, but by the documented evolution of the solution.
## Repository Layout
## Repository layout
```text
DRL_PROJ/
@@ -106,6 +106,7 @@ DRL_PROJ/
notebooks/ classifier report notebooks
outputs/ saved logs, figures, Grad-CAM panels, checkpoints
src/ classifier data, models, training, evaluation
tests/ unit and smoke tests
tools/ facecrop, Grad-CAM, inference, reevaluation helpers
generator/
@@ -113,6 +114,7 @@ DRL_PROJ/
notebooks/ generator report notebooks and notebook builder
outputs/ saved logs, sample grids, final showcase artifacts
src/ generator data, models, training, metrics
tests/ unit and smoke tests
tools/ sampling and utility scripts
data/ original DFF dataset root, not committed
@@ -121,11 +123,11 @@ DRL_PROJ/
pipeline/ optional remote/GPU orchestration helpers
```
## Rebuilding The Generator Notebooks
## Rebuilding the generator notebooks
The generator notebooks are generated from a single source file:
```powershell
```bash
cd generator/notebooks
python _build.py
```
@@ -133,24 +135,120 @@ python _build.py
That builder writes the numbered generator notebooks listed above. It uses
existing saved logs and artifacts; it does not train models.
## Running The Code
## Setup
Create an environment and install the project requirements:
Create a conda environment and install the project requirements:
```powershell
python -m venv .venv
.\.venv\Scripts\python.exe -m pip install --upgrade pip setuptools wheel
.\.venv\Scripts\python.exe -m pip install -r requirements.txt
```bash
conda create -n drl python=3.12
conda activate drl
python -m pip install --upgrade pip setuptools wheel
python -m pip install -r requirements.txt
```
Use **Python 3.12**; some dependencies (for example `facenet-pytorch`) are
unreliable on 3.13+.
The raw dataset should be placed under `data/`. Preprocessed crops are stored
under `cropped/`. These folders are intentionally not committed.
under `cropped/`. These folders are intentionally not committed. To download
and extract the dataset:
Execution entry points exist in `classifier/run.py` and `generator/run.py` for
reproducibility, but the report notebooks should be read as analysis over
already saved results.
```bash
python classifier/tools/fetch_ds.py
python classifier/tools/fetch_ds.py --data-dir /path/to/DFF
```
## Final Takeaway
Expected layout under the data root: `wiki/<identity>/*.jpg`,
`inpainting/...`, `text2img/...`, `insight/...`.
## Classifier — training
From the repository root:
```bash
# CPU (slow but valid)
python classifier/run.py classifier/configs/phase4/p4_convnext_tiny_100pct.json
# GPU when CUDA is available
python classifier/run.py classifier/configs/phase4/p4_convnext_tiny_100pct.json --use-gpu
```
Training uses 5-fold stratified group cross-validation. Per-fold checkpoints
are saved as `classifier/outputs/models/{run_name}_fold{k}_best.pt` (and
`_final.pt`). Override data or output locations with `--data-dir` and
`--output-root`.
**Primary delivery model** (best Phase 4 detector): config
`classifier/configs/phase4/p4_convnext_tiny_100pct.json` with per-fold
weights `classifier/outputs/models/p4_convnext_tiny_100pct_fold*_best.pt`.
## Classifier — inference
Classify a single image as real or fake:
```bash
python classifier/tools/inference.py image.jpg classifier/configs/phase4/p4_convnext_tiny_100pct.json
```
This loads the config and the matching checkpoint, runs the image through the
model, and prints a result like:
```
Image : image.jpg
Model : p4_convnext_tiny_100pct (convnext_tiny)
Device: cuda
Result: FAKE (confidence: 74.7%)
P(fake): 0.7466 P(real): 0.2534
```
If you omit `--checkpoint`, the tool automatically looks for a saved
checkpoint under `classifier/outputs/models/` — first the single-run
`{run_name}_best.pt`, then CV fold files `{run_name}_fold{k}_best.pt`, then
`{run_name}_fold{k}_final.pt`. To use a specific fold:
```bash
python classifier/tools/inference.py image.jpg classifier/configs/phase4/p4_convnext_tiny_100pct.json \
--checkpoint classifier/outputs/models/p4_convnext_tiny_100pct_fold0_best.pt
```
## Generator — training
From the repository root:
```bash
python generator/run.py generator/configs/phase0/p0_vae.json
python generator/run.py generator/configs/phase0/p0_ddpm.json
```
Generator training expects real-face images (default source is `wiki`); use
`--data-dir` to point at your dataset tree. Checkpoints are saved under
`generator/outputs/models/{run_name}_final_ema.pt` (EMA shadow) and
`{run_name}_best_ema.pt` (lowest-FID snapshot).
## Generator — inference (sampling)
Generate 4×4 sample grids from Phase 5 EMA checkpoints:
```bash
python generator/tools/sampling.py --models p5_gan p5_vae p5_ddpm --samples 10
```
Options:
- `--models` — which models to sample from (`p5_gan`, `p5_vae`, `p5_ddpm`;
defaults to all three).
- `--samples` — number of grids per model (default 10).
- `--output-dir` — where to write the PNGs (default
`generator/outputs/samples/final_comparison/`).
- `--truncation` — optional latent truncation for the GAN (lower = less
diversity but sharper).
- `--device` — `cuda` or `cpu` (default: auto-detect).
Each grid is a 4×4 PNG of 16 images sampled from the model's EMA weights.
GAN samples are drawn from random latent vectors, VAE samples decode from the
learned prior, and DDPM samples use 50-step DDIM.
## Final takeaway
The project is best understood as a sequence of controlled decisions:
classifier/configs/smoke/smoke.json
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{
"extends": "../shared.json",
"run_name": "smoke",
"backbone": "simple_cnn",
"cnn_preset": "micro",
"dropout": 0.0,
"epochs": 1,
"cv_folds": 2,
"image_size": 64,
"batch_size": 8,
"num_workers": 0,
"early_stopping_patience": 0,
"subsample": 1.0,
"augment": false,
"lr": 0.001,
"T_max": 1,
"data_dir": "data"
}
classifier/notebooks/01_eda.ipynb
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classifier/notebooks/02_preprocessing.ipynb
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classifier/notebooks/04_ablation_questions.ipynb
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classifier/notebooks_classifier.zip
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classifier/outputs/analysis/gradcam/gradcam_artifact_summary.csv
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run,n_candidates,n_images,heldout_source,candidate_auc,candidate_acc,panel,expanded_panel,fine_panel
p1_simplecnn_baseline,192,10,,0.8378182870370371,0.7395833333333334,classifier\outputs\gradcam\p1_simplecnn_baseline\panel.png,,
p1_resnet18_baseline,192,10,,0.9769965277777778,0.9270833333333334,classifier\outputs\gradcam\p1_resnet18_baseline\panel.png,,
p2a_t1_original,192,10,,0.9984085648148149,0.9895833333333334,classifier\outputs\gradcam\p2a_t1_original\panel.png,,
p2a_t2_real_norm,192,10,,0.9939236111111112,0.9791666666666666,classifier\outputs\gradcam\p2a_t2_real_norm\panel.png,,
p2a_t3_holdout_text2img,240,10,text2img,0.9264322916666667,0.775,classifier\outputs\gradcam\p2a_t3_holdout_text2img\panel.png,,
p2a_t3_holdout_inpainting,240,10,inpainting,0.9819878472222222,0.9333333333333333,classifier\outputs\gradcam\p2a_t3_holdout_inpainting\panel.png,,
p2a_t3_holdout_insight,240,10,insight,0.9549696180555556,0.7625,classifier\outputs\gradcam\p2a_t3_holdout_insight\panel.png,,
p2b_simplecnn_224,192,10,,0.8207465277777778,0.7447916666666666,classifier\outputs\gradcam\p2b_simplecnn_224\panel.png,,
p2b_resnet18_224,192,10,,0.9984085648148149,0.9895833333333334,classifier\outputs\gradcam\p2b_resnet18_224\panel.png,,
p2c_simplecnn_facecrop,192,10,,0.8058449074074073,0.7552083333333334,classifier\outputs\gradcam\p2c_simplecnn_facecrop\panel.png,,
p2c_resnet18_facecrop,192,16,,0.9911747685185185,0.890625,classifier\outputs\gradcam\p2c_resnet18_facecrop\panel.png,classifier\outputs\gradcam\p2c_resnet18_facecrop\panel_expanded.png,classifier\outputs\gradcam\p2c_resnet18_facecrop\panel_fine_layer3.png
p2d_simplecnn_aug,192,10,,0.7565104166666666,0.65625,classifier\outputs\gradcam\p2d_simplecnn_aug\panel.png,,
p2d_resnet18_aug,192,10,,0.9733796296296297,0.9270833333333334,classifier\outputs\gradcam\p2d_resnet18_aug\panel.png,,
p2e_simplecnn_facecrop_aug,192,10,,0.7358217592592592,0.6510416666666666,classifier\outputs\gradcam\p2e_simplecnn_facecrop_aug\panel.png,,
p2e_resnet18_facecrop_aug,192,10,,0.9910300925925927,0.9322916666666666,classifier\outputs\gradcam\p2e_resnet18_facecrop_aug\panel.png,,
1 run n_candidates n_images heldout_source candidate_auc candidate_acc panel expanded_panel fine_panel
2 p1_simplecnn_baseline 192 10 0.8378182870370371 0.7395833333333334 classifier\outputs\gradcam\p1_simplecnn_baseline\panel.png
3 p1_resnet18_baseline 192 10 0.9769965277777778 0.9270833333333334 classifier\outputs\gradcam\p1_resnet18_baseline\panel.png
4 p2a_t1_original 192 10 0.9984085648148149 0.9895833333333334 classifier\outputs\gradcam\p2a_t1_original\panel.png
5 p2a_t2_real_norm 192 10 0.9939236111111112 0.9791666666666666 classifier\outputs\gradcam\p2a_t2_real_norm\panel.png
6 p2a_t3_holdout_text2img 240 10 text2img 0.9264322916666667 0.775 classifier\outputs\gradcam\p2a_t3_holdout_text2img\panel.png
7 p2a_t3_holdout_inpainting 240 10 inpainting 0.9819878472222222 0.9333333333333333 classifier\outputs\gradcam\p2a_t3_holdout_inpainting\panel.png
8 p2a_t3_holdout_insight 240 10 insight 0.9549696180555556 0.7625 classifier\outputs\gradcam\p2a_t3_holdout_insight\panel.png
9 p2b_simplecnn_224 192 10 0.8207465277777778 0.7447916666666666 classifier\outputs\gradcam\p2b_simplecnn_224\panel.png
10 p2b_resnet18_224 192 10 0.9984085648148149 0.9895833333333334 classifier\outputs\gradcam\p2b_resnet18_224\panel.png
11 p2c_simplecnn_facecrop 192 10 0.8058449074074073 0.7552083333333334 classifier\outputs\gradcam\p2c_simplecnn_facecrop\panel.png
12 p2c_resnet18_facecrop 192 16 0.9911747685185185 0.890625 classifier\outputs\gradcam\p2c_resnet18_facecrop\panel.png classifier\outputs\gradcam\p2c_resnet18_facecrop\panel_expanded.png classifier\outputs\gradcam\p2c_resnet18_facecrop\panel_fine_layer3.png
13 p2d_simplecnn_aug 192 10 0.7565104166666666 0.65625 classifier\outputs\gradcam\p2d_simplecnn_aug\panel.png
14 p2d_resnet18_aug 192 10 0.9733796296296297 0.9270833333333334 classifier\outputs\gradcam\p2d_resnet18_aug\panel.png
15 p2e_simplecnn_facecrop_aug 192 10 0.7358217592592592 0.6510416666666666 classifier\outputs\gradcam\p2e_simplecnn_facecrop_aug\panel.png
16 p2e_resnet18_facecrop_aug 192 10 0.9910300925925927 0.9322916666666666 classifier\outputs\gradcam\p2e_resnet18_facecrop_aug\panel.png
classifier/outputs/analysis/gradcam/gradcam_evolution_panels.csv
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panel,path
architecture capacity,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_architecture_capacity.png
resnet pipeline,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_resnet_pipeline.png
simplecnn pipeline,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_simplecnn_pipeline.png
1 panel path
2 architecture capacity c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_architecture_capacity.png
3 resnet pipeline c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_resnet_pipeline.png
4 simplecnn pipeline c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_simplecnn_pipeline.png
classifier/outputs/analysis/gradcam/p1_resnet18_baseline_summary.json
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"decision": "enable",
"evidence": "Best Phase 2 run is p2c_resnet18_facecrop with AUC 0.9755.",
"confidence": "medium-high"
},
{
"choice": "augmentation",
"decision": "disable for current 20% setting",
"evidence": "p2e_resnet18_facecrop_aug reaches AUC 0.9737, below facecrop-only 0.9755; SimpleCNN drops sharply.",
"confidence": "low"
},
{
"choice": "normalization",
"decision": "ImageNet/default",
"evidence": "real_norm is only +0.0018 AUC and is less aligned with pretrained ImageNet weights.",
"confidence": "medium"
},
{
"choice": "source generalization",
"decision": "report as limitation and diagnostic target",
"evidence": "Holding out text2img and insight drops pairwise AUC to 0.7595 and 0.8421.",
"confidence": "high"
}
]
}
classifier/outputs/analysis/phase4_story_summary.json
-104
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@@ -1,104 +0,0 @@
{
"phase": "phase4",
"best_auc_run": "p4_convnext_tiny_100pct",
"best_auc": 0.9954,
"selected_detector_run": "p4_convnext_tiny_100pct",
"selected_detector_reason": "Lowest false positive rate on real images and best balanced accuracy at 100% data.",
"notes": [
"20% rows come from the matching Phase 3 runs and act as scaling anchors.",
"50% and 100% rows come from Phase 4 logs.",
"ConvNeXt-Tiny is the AUC winner and also the practical detector when false positives on real images matter most."
],
"summary_rows": [
{
"model": "ResNet50",
"scale_pct": 20,
"run": "p3_resnet50",
"auc": 0.9857046296296297,
"accuracy": 0.9532083333333334,
"f1": 0.9687705256330055,
"balanced_accuracy": 0.9385277777777778,
"macro_f1": 0.9377251247311893
},
{
"model": "ResNet50",
"scale_pct": 50,
"run": "p4_resnet50_50pct",
"auc": 0.9921022333333335,
"accuracy": 0.9607166666666667,
"f1": 0.973649056957159,
"balanced_accuracy": 0.9536555555555555,
"macro_f1": 0.9482411270477236
},
{
"model": "ResNet50",
"scale_pct": 100,
"run": "p4_resnet50_100pct",
"auc": 0.9949671268518518,
"accuracy": 0.9692166666666667,
"f1": 0.9793757797895071,
"balanced_accuracy": 0.9638111111111111,
"macro_f1": 0.9593474615494658
},
{
"model": "EfficientNet-B0",
"scale_pct": 20,
"run": "p3_efficientnet_b0",
"auc": 0.9844867592592592,
"accuracy": 0.9450000000000001,
"f1": 0.9628492625462333,
"balanced_accuracy": 0.9397222222222222,
"macro_f1": 0.9284971237471479
},
{
"model": "EfficientNet-B0",
"scale_pct": 50,
"run": "p4_efficientnet_b0_50pct",
"auc": 0.9911186185185186,
"accuracy": 0.9576499999999999,
"f1": 0.9714437706354593,
"balanced_accuracy": 0.9541444444444445,
"macro_f1": 0.9446884831225546
},
{
"model": "EfficientNet-B0",
"scale_pct": 100,
"run": "p4_efficientnet_b0_100pct",
"auc": 0.9949471324074075,
"accuracy": 0.96835,
"f1": 0.9787227370502695,
"balanced_accuracy": 0.9656777777777779,
"macro_f1": 0.9584469550394183
},
{
"model": "ConvNeXt-Tiny",
"scale_pct": 20,
"run": "p3_convnext_tiny",
"auc": 0.9867751388888889,
"accuracy": 0.9473333333333332,
"f1": 0.9650225390458838,
"balanced_accuracy": 0.9261666666666667,
"macro_f1": 0.9292641404681354
},
{
"model": "ConvNeXt-Tiny",
"scale_pct": 50,
"run": "p4_convnext_tiny_50pct",
"auc": 0.9926127555555556,
"accuracy": 0.96065,
"f1": 0.9735230102651047,
"balanced_accuracy": 0.9562111111111111,
"macro_f1": 0.9484169329127863
},
{
"model": "ConvNeXt-Tiny",
"scale_pct": 100,
"run": "p4_convnext_tiny_100pct",
"auc": 0.9953774120370371,
"accuracy": 0.9679166666666668,
"f1": 0.9783980914742021,
"balanced_accuracy": 0.9662444444444445,
"macro_f1": 0.9579703600254157
}
]
}
classifier/tests/smoke_test.py
+90
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@@ -0,0 +1,90 @@
"""
End-to-end smoke test: synthetic DFF layout -> short CV train -> inference.
Run from the classifier package root:
cd classifier && conda activate drl && python -m unittest tests.smoke_test -v
"""
import io
import subprocess
import sys
import unittest
from pathlib import Path
from PIL import Image
_CLASSIFIER_ROOT = Path(__file__).resolve().parents[1]
_SMOKE_CFG = _CLASSIFIER_ROOT / "configs" / "smoke" / "smoke.json"
def _write_synthetic_dff(data_root: Path, *, n_identities: int = 16) -> None:
"""Minimal DeepFakeFace-style tree: wiki + three fake sources, shared basenames per identity."""
sources = ("wiki", "inpainting", "text2img", "insight")
for i in range(n_identities):
stem = f"id{i:03d}"
for src in sources:
d = data_root / src / f"person_{i:03d}"
d.mkdir(parents=True, exist_ok=True)
buf = io.BytesIO()
Image.new(
"RGB",
(96, 96),
color=(min(20 + i * 11, 255), 80, 120 if src == "wiki" else 40),
).save(buf, format="JPEG")
# Same filename stem across all sources → one CV group per identity (matches DFF).
(d / f"{stem}.jpg").write_bytes(buf.getvalue())
class SmokeTrainInferTests(unittest.TestCase):
def test_local_smoke_train_then_inference(self):
import tempfile
with tempfile.TemporaryDirectory() as td:
tmp = Path(td)
data_dir = tmp / "data"
out_root = tmp / "outputs"
_write_synthetic_dff(data_dir)
sys.path.insert(0, str(_CLASSIFIER_ROOT))
import run as train_run
train_run.main(
str(_SMOKE_CFG),
data_dir_override=str(data_dir),
output_root=str(out_root),
use_gpu=False,
)
models_dir = out_root / "models"
ck_fold0 = models_dir / "smoke_fold0_best.pt"
if not ck_fold0.is_file():
ck_fold0 = models_dir / "smoke_fold0_final.pt"
self.assertTrue(
ck_fold0.is_file(),
f"Expected fold-0 checkpoint under {models_dir}",
)
probe = tmp / "probe.jpg"
probe.write_bytes((data_dir / "wiki" / "person_000" / "id000.jpg").read_bytes())
cmd = [
sys.executable,
str(_CLASSIFIER_ROOT / "tools" / "inference.py"),
str(probe),
str(_SMOKE_CFG),
"--checkpoint",
str(ck_fold0),
]
proc = subprocess.run(
cmd,
cwd=str(_CLASSIFIER_ROOT),
capture_output=True,
text=True,
timeout=300,
)
self.assertEqual(proc.returncode, 0, proc.stderr + proc.stdout)
self.assertIn("P(fake)", proc.stdout)
if __name__ == "__main__":
unittest.main()
classifier/tools/artifact_chk.py
+3 -2
View File
@@ -18,8 +18,9 @@ def parse_args():
def iter_config_paths(config_root: Path):
for sub in ("phase1", "phase2"):
yield from sorted((config_root / sub).glob("*.json"))
for sub in sorted(config_root.iterdir()):
if sub.is_dir() and sub.name not in ("smoke",):
yield from sorted(sub.glob("*.json"))
def main():
classifier/tools/fetch_ds.py
+2 -2
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@@ -2,8 +2,8 @@
Download the DeepFakeFace dataset from HuggingFace and extract it.
Usage:
python tools/download_data.py
python tools/download_data.py --data-dir /mnt/data/DFF
python tools/fetch_ds.py
python tools/fetch_ds.py --data-dir /mnt/data/DFF
"""
import argparse
import zipfile
classifier/tools/inference.py
+23 -9
View File
@@ -19,9 +19,27 @@ from PIL import Image
from src.models import get_model, load_checkpoint
from src.preprocessing import get_transforms
from src.utils import load_config
# Defaults checkpoint to outputs/models/{run_name}_best.pt when not supplied
def _default_checkpoint(cfg: dict, checkpoint_path: Path | None) -> Path:
"""Resolve checkpoint: explicit path, single-fold `*_best.pt`, or CV `*_fold{k}_best.pt` / `*_final.pt`."""
run_name = cfg["run_name"]
models_dir = ROOT / "outputs" / "models"
if checkpoint_path is not None:
return Path(checkpoint_path)
candidates: list[Path] = [models_dir / f"{run_name}_best.pt"]
for k in range(32):
candidates.append(models_dir / f"{run_name}_fold{k}_best.pt")
for k in range(32):
candidates.append(models_dir / f"{run_name}_fold{k}_final.pt")
for p in candidates:
if p.is_file():
return p
return models_dir / f"{run_name}_best.pt"
# Defaults checkpoint under outputs/models/ (single-run or CV best/final).
def predict(image_path, config_path, checkpoint_path=None):
image_path = Path(image_path)
config_path = Path(config_path)
@@ -35,10 +53,9 @@ def predict(image_path, config_path, checkpoint_path=None):
sys.exit(1)
try:
with open(config_path) as f:
cfg = json.load(f)
except json.JSONDecodeError as e:
print(f"Error: Invalid JSON in config: {e}")
cfg = load_config(str(config_path))
except (json.JSONDecodeError, OSError, ValueError) as e:
print(f"Error: Failed to load config: {e}")
sys.exit(1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -50,10 +67,7 @@ def predict(image_path, config_path, checkpoint_path=None):
print(f"Error: Failed to build model: {e}")
sys.exit(1)
if checkpoint_path is None:
checkpoint_path = ROOT / "outputs" / "models" / f"{cfg['run_name']}_best.pt"
else:
checkpoint_path = Path(checkpoint_path)
checkpoint_path = _default_checkpoint(cfg, Path(checkpoint_path) if checkpoint_path else None)
if not checkpoint_path.exists():
print(f"Error: Checkpoint not found: {checkpoint_path}")
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generator/configs/smoke/smoke.json
+19
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@@ -0,0 +1,19 @@
{
"extends": "../phase0/_base_phase0.json",
"run_name": "smoke",
"model": "vae",
"latent_dim": 32,
"ngf": 16,
"epochs": 1,
"batch_size": 8,
"lr": 0.001,
"beta_kl": 0.1,
"lambda_perceptual": 0,
"lambda_adversarial": 0,
"subsample": 1.0,
"sample_interval": 1,
"fid_interval": 999,
"fid_n_real": 64,
"num_workers": 0,
"ema_decay": 0.9
}
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generator/tests/__init__.py
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generator/tests/smoke_test.py
+78
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@@ -0,0 +1,78 @@
"""
Smoke test: tiny wiki-only dataset -> 1 VAE epoch -> decode / sample (inference).
Run from the generator package root:
cd generator && conda activate drl && python -m unittest tests.smoke_test -v
"""
import io
import sys
import unittest
from pathlib import Path
from unittest.mock import patch
import torch
from PIL import Image
_GEN_ROOT = Path(__file__).resolve().parents[1]
_SMOKE_CFG = _GEN_ROOT / "configs" / "smoke" / "smoke.json"
def _write_wiki_only(data_root: Path, *, n_folders: int = 12) -> None:
"""Minimal layout for GeneratorDataset: data/wiki/<id>/*.jpg"""
for i in range(n_folders):
d = data_root / "wiki" / f"person_{i:03d}"
d.mkdir(parents=True, exist_ok=True)
buf = io.BytesIO()
Image.new("RGB", (64, 64), color=(30 + i * 15, 100, 180)).save(buf, format="JPEG")
(d / "face.jpg").write_bytes(buf.getvalue())
class SmokeGeneratorTrainSampleTests(unittest.TestCase):
def test_vae_one_epoch_then_sample(self):
import tempfile
if not _SMOKE_CFG.is_file():
self.skipTest("smoke config missing")
with tempfile.TemporaryDirectory() as td:
tmp = Path(td)
data_dir = tmp / "data"
out_root = tmp / "outputs"
_write_wiki_only(data_dir)
sys.path.insert(0, str(_GEN_ROOT))
import run as gen_run
# Avoid torch.compile in short smoke runs (simpler state_dict / fewer edge cases).
with patch("torch.compile", lambda m, **kwargs: m):
gen_run.main(
str(_SMOKE_CFG),
data_dir_override=str(data_dir),
output_root=str(out_root),
)
models_dir = out_root / "models"
ckpt = models_dir / "smoke_final_ema.pt"
self.assertTrue(ckpt.is_file(), f"Expected EMA checkpoint at {ckpt}")
from src.models import get_model
from src.utils import load_config
cfg = load_config(str(_SMOKE_CFG))
vae, kind = get_model(cfg)
self.assertEqual(kind, "vae")
device = torch.device("cpu")
state = torch.load(ckpt, map_location=device, weights_only=True)
vae.load_state_dict(state, strict=True)
vae.eval()
with torch.no_grad():
grid = vae.sample(4, device)
self.assertEqual(grid.shape, (4, 3, 64, 64))
self.assertTrue(torch.isfinite(grid).all())
if __name__ == "__main__":
unittest.main()
generator/tools/sampling.py
+2 -2
View File
@@ -25,8 +25,8 @@ PHASE5_RUNS = {
def _load_cfg(name: str) -> dict:
with open(CFG_DIR / PHASE5_RUNS[name]["config"]) as f:
return json.load(f)
from src.utils import load_config
return load_config(str(CFG_DIR / PHASE5_RUNS[name]["config"]))
def _load_model(name: str, cfg: dict, device: torch.device):
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presentation_inputs/classifier/configs/phase1/p1_resnet18_baseline.json
-10
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@@ -1,10 +0,0 @@
{
"run_name": "p1_resnet18_baseline",
"backbone": "resnet18",
"pretrained": true,
"epochs": 15,
"image_size": 128,
"subsample": 0.2,
"augment": false,
"data_dir": "data"
}
presentation_inputs/classifier/configs/phase1/p1_simplecnn_baseline.json
-11
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@@ -1,11 +0,0 @@
{
"run_name": "p1_simplecnn_baseline",
"backbone": "simple_cnn",
"cnn_preset": "medium",
"dropout": 0.0,
"epochs": 15,
"image_size": 128,
"subsample": 0.2,
"augment": false,
"data_dir": "data"
}
presentation_inputs/classifier/configs/phase2/p2a_t1_original.json
-11
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@@ -1,11 +0,0 @@
{
"run_name": "p2a_t1_original",
"backbone": "resnet18",
"pretrained": true,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"augment": false,
"data_dir": "data",
"normalization": "imagenet"
}
presentation_inputs/classifier/configs/phase2/p2a_t2_real_norm.json
-5
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@@ -1,5 +0,0 @@
{
"extends": "p2a_t1_original.json",
"run_name": "p2a_t2_real_norm",
"normalization": "real_norm"
}
presentation_inputs/classifier/configs/phase2/p2a_t3_holdout_inpainting.json
-15
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@@ -1,15 +0,0 @@
{
"extends": "p2a_t1_original.json",
"run_name": "p2a_t3_holdout_inpainting",
"train_sources": [
"wiki",
"text2img",
"insight"
],
"eval_sources": [
"wiki",
"text2img",
"insight",
"inpainting"
]
}
presentation_inputs/classifier/configs/phase2/p2a_t3_holdout_insight.json
-15
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@@ -1,15 +0,0 @@
{
"extends": "p2a_t1_original.json",
"run_name": "p2a_t3_holdout_insight",
"train_sources": [
"wiki",
"text2img",
"inpainting"
],
"eval_sources": [
"wiki",
"text2img",
"inpainting",
"insight"
]
}
presentation_inputs/classifier/configs/phase2/p2a_t3_holdout_text2img.json
-15
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@@ -1,15 +0,0 @@
{
"extends": "p2a_t1_original.json",
"run_name": "p2a_t3_holdout_text2img",
"train_sources": [
"wiki",
"inpainting",
"insight"
],
"eval_sources": [
"wiki",
"inpainting",
"insight",
"text2img"
]
}
presentation_inputs/classifier/configs/phase2/p2b_resnet18_224.json
-10
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@@ -1,10 +0,0 @@
{
"run_name": "p2b_resnet18_224",
"backbone": "resnet18",
"pretrained": true,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"augment": false,
"data_dir": "data"
}
presentation_inputs/classifier/configs/phase2/p2b_simplecnn_224.json
-11
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@@ -1,11 +0,0 @@
{
"run_name": "p2b_simplecnn_224",
"backbone": "simple_cnn",
"cnn_preset": "medium",
"dropout": 0.0,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"augment": false,
"data_dir": "data"
}
presentation_inputs/classifier/configs/phase2/p2c_resnet18_facecrop.json
-10
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@@ -1,10 +0,0 @@
{
"run_name": "p2c_resnet18_facecrop",
"backbone": "resnet18",
"pretrained": true,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"augment": false,
"data_dir": "cropped/classifier"
}
presentation_inputs/classifier/configs/phase2/p2c_simplecnn_facecrop.json
-11
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@@ -1,11 +0,0 @@
{
"run_name": "p2c_simplecnn_facecrop",
"backbone": "simple_cnn",
"cnn_preset": "medium",
"dropout": 0.0,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"augment": false,
"data_dir": "cropped/classifier"
}
presentation_inputs/classifier/configs/phase2/p2d_resnet18_aug.json
-22
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@@ -1,22 +0,0 @@
{
"run_name": "p2d_resnet18_aug",
"backbone": "resnet18",
"pretrained": true,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"data_dir": "data",
"augment": {
"hflip_p": 0.5,
"rotation_degrees": 10,
"brightness": 0.2,
"contrast": 0.2,
"saturation": 0.1,
"hue": 0.02,
"grayscale_p": 0.1,
"blur_p": 0.1,
"erase_p": 0.2,
"noise_p": 0.3,
"noise_std": 0.04
}
}
presentation_inputs/classifier/configs/phase2/p2d_simplecnn_aug.json
-23
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@@ -1,23 +0,0 @@
{
"run_name": "p2d_simplecnn_aug",
"backbone": "simple_cnn",
"cnn_preset": "medium",
"dropout": 0.0,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"data_dir": "data",
"augment": {
"hflip_p": 0.5,
"rotation_degrees": 10,
"brightness": 0.2,
"contrast": 0.2,
"saturation": 0.1,
"hue": 0.02,
"grayscale_p": 0.1,
"blur_p": 0.1,
"erase_p": 0.2,
"noise_p": 0.3,
"noise_std": 0.04
}
}
presentation_inputs/classifier/configs/phase2/p2e_resnet18_facecrop_aug.json
-22
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@@ -1,22 +0,0 @@
{
"run_name": "p2e_resnet18_facecrop_aug",
"backbone": "resnet18",
"pretrained": true,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"data_dir": "cropped/classifier",
"augment": {
"hflip_p": 0.5,
"rotation_degrees": 10,
"brightness": 0.2,
"contrast": 0.2,
"saturation": 0.1,
"hue": 0.02,
"grayscale_p": 0.1,
"blur_p": 0.1,
"erase_p": 0.2,
"noise_p": 0.3,
"noise_std": 0.04
}
}
presentation_inputs/classifier/configs/phase2/p2e_simplecnn_facecrop_aug.json
-23
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@@ -1,23 +0,0 @@
{
"run_name": "p2e_simplecnn_facecrop_aug",
"backbone": "simple_cnn",
"cnn_preset": "medium",
"dropout": 0.0,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"data_dir": "cropped/classifier",
"augment": {
"hflip_p": 0.5,
"rotation_degrees": 10,
"brightness": 0.2,
"contrast": 0.2,
"saturation": 0.1,
"hue": 0.02,
"grayscale_p": 0.1,
"blur_p": 0.1,
"erase_p": 0.2,
"noise_p": 0.3,
"noise_std": 0.04
}
}
presentation_inputs/classifier/configs/phase3/_base.json
-8
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@@ -1,8 +0,0 @@
{
"pretrained": true,
"epochs": 15,
"image_size": 224,
"subsample": 0.2,
"augment": false,
"data_dir": "cropped/classifier"
}
presentation_inputs/classifier/configs/phase3/p3_convnext_tiny.json
-5
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@@ -1,5 +0,0 @@
{
"extends": "_base.json",
"run_name": "p3_convnext_tiny",
"backbone": "convnext_tiny"
}
presentation_inputs/classifier/configs/phase3/p3_efficientnet_b0.json
-5
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@@ -1,5 +0,0 @@
{
"extends": "_base.json",
"run_name": "p3_efficientnet_b0",
"backbone": "efficientnet_b0"
}
presentation_inputs/classifier/configs/phase3/p3_mobilenetv3_small.json
-5
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@@ -1,5 +0,0 @@
{
"extends": "_base.json",
"run_name": "p3_mobilenetv3_small",
"backbone": "mobilenet_v3_small"
}
presentation_inputs/classifier/configs/phase3/p3_resnet34.json
-5
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@@ -1,5 +0,0 @@
{
"extends": "_base.json",
"run_name": "p3_resnet34",
"backbone": "resnet34"
}
presentation_inputs/classifier/configs/phase3/p3_resnet50.json
-5
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@@ -1,5 +0,0 @@
{
"extends": "_base.json",
"run_name": "p3_resnet50",
"backbone": "resnet50"
}
presentation_inputs/classifier/configs/phase4/_base.json
-7
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@@ -1,7 +0,0 @@
{
"pretrained": true,
"epochs": 15,
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presentation_inputs/classifier/configs/phase4/p4_convnext_tiny_100pct.json
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{
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presentation_inputs/classifier/configs/phase4/p4_convnext_tiny_50pct.json
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presentation_inputs/classifier/configs/phase4/p4_efficientnet_b0_100pct.json
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presentation_inputs/classifier/configs/phase4/p4_efficientnet_b0_50pct.json
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presentation_inputs/classifier/configs/phase4/p4_resnet50_50pct.json
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presentation_inputs/classifier/configs/shared.json
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presentation_inputs/classifier/outputs/analysis/gradcam/gradcam_artifact_summary.csv
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simplecnn pipeline,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_simplecnn_pipeline.png
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presentation_inputs/classifier/outputs/analysis/gradcam/p1_resnet18_baseline_summary.json
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presentation_inputs/classifier/outputs/analysis/gradcam/p2e_simplecnn_facecrop_aug_summary.json
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presentation_inputs/classifier/outputs/analysis/phase2_story_summary.json
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presentation_inputs/classifier/outputs/analysis/phase4_story_summary.json
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