{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a0000001",
   "metadata": {},
   "source": [
    "# Phase 2 — GAN Evolution Analysis\n",
    "\n",
    "Traces the GAN improvement story, each step motivated by the failure of the previous:\n",
    "\n",
    "| Step | Model | Key change | Expected failure |\n",
    "|------|-------|------------|------------------|\n",
    "| 2.1 | DCGAN 64×64 | Baseline on best pipeline | Mode collapse, training instability |\n",
    "| 2.2 | WGAN-GP | Wasserstein loss + GP | Texture artifacts, limited coherence |\n",
    "| 2.3 | WGAN-GP + SN + GroupNorm + Attn | Principled Lipschitz + long-range deps | Possible underfitting at 64×64 |\n",
    "| 2.4 | 2.3 @ 128×128 | Scale resolution | ? |\n",
    "\n",
    "All runs use the best pipeline from Phase 1: MTCNN-aligned crops, H-flip + rotation + colour jitter, aligned-only dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0000002",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "plt.rcParams.update({\"figure.dpi\": 120, \"font.size\": 10})\n",
    "\n",
    "OUTPUTS = Path(\"../outputs\")\n",
    "LOGS    = OUTPUTS / \"logs\"\n",
    "SAMPLES = OUTPUTS / \"samples\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0000003",
   "metadata": {},
   "source": [
    "## 1. Load experiment logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0000004",
   "metadata": {},
   "outputs": [],
   "source": [
    "run_names = [\"p2_1_dcgan\", \"p2_2_wgan\", \"p2_3_wgan_sn_attn\", \"p2_4_wgan_sn_attn_128\"]\n",
    "run_labels = {\n",
    "    \"p2_1_dcgan\":              \"2.1 DCGAN\",\n",
    "    \"p2_2_wgan\":               \"2.2 WGAN-GP\",\n",
    "    \"p2_3_wgan_sn_attn\":       \"2.3 WGAN-GP+SN+Attn\",\n",
    "    \"p2_4_wgan_sn_attn_128\":   \"2.4 +128×128\",\n",
    "}\n",
    "\n",
    "runs = {}\n",
    "for name in run_names:\n",
    "    log_path = LOGS / f\"{name}.json\"\n",
    "    if log_path.exists():\n",
    "        with open(log_path) as f:\n",
    "            runs[name] = json.load(f)\n",
    "    else:\n",
    "        print(f\"  Missing: {log_path}\")\n",
    "\n",
    "print(f\"Loaded {len(runs)}/{len(run_names)} experiments:\")\n",
    "for name in run_names:\n",
    "    status = \"✓\" if name in runs else \"✗\"\n",
    "    print(f\"  {status} {name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0000005",
   "metadata": {},
   "source": [
    "## 2. FID Comparison Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0000006",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fid(run, epoch):\n",
    "    fid = run[\"history\"][\"fid\"]\n",
    "    return fid.get(str(epoch), fid.get(epoch, None))\n",
    "\n",
    "rows = []\n",
    "for name in run_names:\n",
    "    if name not in runs:\n",
    "        continue\n",
    "    r   = runs[name]\n",
    "    cfg = r[\"config\"]\n",
    "    h   = r[\"history\"]\n",
    "    rows.append({\n",
    "        \"Step\": run_labels[name],\n",
    "        \"Model\": cfg.get(\"model\"),\n",
    "        \"Size\": f\"{cfg.get('image_size', 64)}×{cfg.get('image_size', 64)}\",\n",
    "        \"FID@25\":  get_fid(r, 25),\n",
    "        \"FID@50\":  get_fid(r, 50),\n",
    "        \"FID@75\":  get_fid(r, 75),\n",
    "        \"FID@100\": get_fid(r, 100),\n",
    "    })\n",
    "\n",
    "df = pd.DataFrame(rows)\n",
    "df.style.format({c: \"{:.1f}\" for c in [\"FID@25\", \"FID@50\", \"FID@75\", \"FID@100\"] if c in df})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0000007",
   "metadata": {},
   "source": [
    "## 3. FID Curves — Evolution Story"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0000008",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(11, 5))\n",
    "colors = [\"#5B8DB8\", \"#E8705A\", \"#6ABF69\", \"#B86FB8\"]\n",
    "\n",
    "for i, name in enumerate(run_names):\n",
    "    if name not in runs:\n",
    "        continue\n",
    "    fid_dict = runs[name][\"history\"][\"fid\"]\n",
    "    epochs   = sorted(int(k) for k in fid_dict)\n",
    "    fids     = [fid_dict[str(e)] for e in epochs]\n",
    "    ax.plot(epochs, fids, \"o-\", label=f\"{run_labels[name]} (FID@100={fid_dict.get('100', '?'):.1f})\",\n",
    "            color=colors[i], linewidth=2, markersize=8)\n",
    "\n",
    "ax.set_xlabel(\"Epoch\")\n",
    "ax.set_ylabel(\"FID (lower is better)\")\n",
    "ax.set_title(\"Phase 2 — FID Curves: DCGAN → WGAN-GP → +SN+Attn → 128×128\")\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0000009",
   "metadata": {},
   "source": [
    "## 4. Training Dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0000010",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate DCGAN (has g_loss/d_loss/d_real/d_fake) from WGAN (has g_loss/w_dist/gp)\n",
    "dcgan_names = [n for n in run_names if n in runs and runs[n][\"config\"].get(\"model\") == \"dcgan\"]\n",
    "wgan_names  = [n for n in run_names if n in runs and runs[n][\"config\"].get(\"model\") != \"dcgan\"]\n",
    "\n",
    "if dcgan_names:\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n",
    "    for name in dcgan_names:\n",
    "        h = runs[name][\"history\"]\n",
    "        epochs = range(1, len(h[\"g_loss\"]) + 1)\n",
    "        axes[0].plot(epochs, h[\"g_loss\"], label=run_labels[name], linewidth=1.2)\n",
    "        axes[1].plot(epochs, h[\"d_loss\"], label=run_labels[name], linewidth=1.2)\n",
    "    axes[0].set_title(\"DCGAN — Generator Loss (BCE)\")\n",
    "    axes[1].set_title(\"DCGAN — Discriminator Loss\")\n",
    "    for ax in axes:\n",
    "        ax.set_xlabel(\"Epoch\"); ax.set_ylabel(\"Loss\"); ax.legend(fontsize=8)\n",
    "    plt.suptitle(\"Phase 2.1 — DCGAN Training Dynamics\", fontweight=\"bold\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "if wgan_names:\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n",
    "    cmap = plt.cm.Set1\n",
    "    for i, name in enumerate(wgan_names):\n",
    "        h = runs[name][\"history\"]\n",
    "        epochs = range(1, len(h[\"g_loss\"]) + 1)\n",
    "        c = cmap(i / max(len(wgan_names), 1))\n",
    "        axes[0].plot(epochs, h[\"g_loss\"],  label=run_labels[name], color=c, linewidth=1.2)\n",
    "        axes[1].plot(epochs, h[\"w_dist\"],  label=run_labels[name], color=c, linewidth=1.2)\n",
    "        axes[2].plot(epochs, h[\"gp\"],      label=run_labels[name], color=c, linewidth=1.2)\n",
    "    axes[0].set_title(\"Generator Loss (−E[D(G(z))])\")\n",
    "    axes[1].set_title(\"Wasserstein Distance Est. (↑ better)\")\n",
    "    axes[2].set_title(\"Gradient Penalty\")\n",
    "    for ax in axes:\n",
    "        ax.set_xlabel(\"Epoch\"); ax.legend(fontsize=8)\n",
    "    plt.suptitle(\"Phase 2.2–2.4 — WGAN-GP Training Dynamics\", fontweight=\"bold\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0000011",
   "metadata": {},
   "source": [
    "## 5. Sample Image Grids — Epoch 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0000012",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(18, 5))\n",
    "\n",
    "for idx, name in enumerate(run_names):\n",
    "    ax = axes[idx]\n",
    "    img_path = SAMPLES / name / \"epoch_0100.png\"\n",
    "    if img_path.exists():\n",
    "        fid = get_fid(runs[name], 100) if name in runs else None\n",
    "        ax.imshow(mpimg.imread(str(img_path)))\n",
    "        ax.set_title(f\"{run_labels[name]}\\nFID@100={fid:.1f}\" if fid else run_labels[name], fontsize=8)\n",
    "    else:\n",
    "        ax.text(0.5, 0.5, \"Not yet run\", ha=\"center\", va=\"center\", transform=ax.transAxes)\n",
    "        ax.set_title(run_labels[name], fontsize=8)\n",
    "    ax.axis(\"off\")\n",
    "\n",
    "fig.suptitle(\"Phase 2 — Epoch 100 Sample Grids (4×4)\", fontsize=13, fontweight=\"bold\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0000013",
   "metadata": {},
   "source": [
    "## 6. Progression: Epoch 10 → 50 → 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0000014",
   "metadata": {},
   "outputs": [],
   "source": [
    "check_epochs = [10, 50, 100]\n",
    "\n",
    "for name in run_names:\n",
    "    if name not in runs:\n",
    "        continue\n",
    "    fig, axes = plt.subplots(1, len(check_epochs), figsize=(12, 4))\n",
    "    for ax, ep in zip(axes, check_epochs):\n",
    "        img_path = SAMPLES / name / f\"epoch_{ep:04d}.png\"\n",
    "        if img_path.exists():\n",
    "            ax.imshow(mpimg.imread(str(img_path)))\n",
    "            fid = get_fid(runs[name], ep)\n",
    "            ax.set_title(f\"Ep {ep}\" + (f\"\\nFID={fid:.1f}\" if fid else \"\"), fontsize=9)\n",
    "        else:\n",
    "            ax.text(0.5, 0.5, f\"Ep {ep}\\n(pending)\", ha=\"center\", va=\"center\", transform=ax.transAxes)\n",
    "        ax.axis(\"off\")\n",
    "    fig.suptitle(f\"{run_labels[name]} — Training Progression\", fontsize=11, fontweight=\"bold\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0000015",
   "metadata": {},
   "source": [
    "## 7. Step-by-step Pairwise Comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0000016",
   "metadata": {},
   "outputs": [],
   "source": [
    "transitions = [\n",
    "    (\"2.1→2.2: BCE→Wasserstein\",      \"p2_1_dcgan\",           \"p2_2_wgan\"),\n",
    "    (\"2.2→2.3: +SN+GroupNorm+Attn\",   \"p2_2_wgan\",            \"p2_3_wgan_sn_attn\"),\n",
    "    (\"2.3→2.4: 64→128 resolution\",    \"p2_3_wgan_sn_attn\",    \"p2_4_wgan_sn_attn_128\"),\n",
    "]\n",
    "\n",
    "for title, name_a, name_b in transitions:\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    for ax, name in zip(axes, [name_a, name_b]):\n",
    "        img_path = SAMPLES / name / \"epoch_0100.png\"\n",
    "        if img_path.exists():\n",
    "            fid = get_fid(runs[name], 100) if name in runs else None\n",
    "            ax.imshow(mpimg.imread(str(img_path)))\n",
    "            ax.set_title(f\"{run_labels[name]}\\nFID@100 = {fid:.1f}\" if fid else run_labels[name], fontsize=10)\n",
    "        else:\n",
    "            ax.text(0.5, 0.5, \"Pending\", ha=\"center\", va=\"center\", transform=ax.transAxes)\n",
    "            ax.set_title(run_labels[name], fontsize=10)\n",
    "        ax.axis(\"off\")\n",
    "    fig.suptitle(title, fontsize=12, fontweight=\"bold\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0000017",
   "metadata": {},
   "source": [
    "## 8. Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0000018",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=\" * 70)\n",
    "print(\"PHASE 2 — GAN EVOLUTION SUMMARY\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "for name in run_names:\n",
    "    if name not in runs:\n",
    "        print(f\"\\n  {run_labels[name]}: NOT YET RUN\")\n",
    "        continue\n",
    "    fid100 = get_fid(runs[name], 100)\n",
    "    fid50  = get_fid(runs[name], 50)\n",
    "    print(f\"\\n  {run_labels[name]}:\")\n",
    "    print(f\"    FID@50  = {fid50:.1f}\" if fid50 else \"    FID@50  = ?\")\n",
    "    print(f\"    FID@100 = {fid100:.1f}\" if fid100 else \"    FID@100 = ?\")\n",
    "\n",
    "print(\"\\n\" + \"=\" * 70)\n",
    "print(\"Best model for Phase 3/4 comparison: fill in after runs complete.\")\n",
    "print(\"=\" * 70)"
   ]
  }
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