Repository: DiffuseMonkey/PyTorch-FWD (language: python)

Issue:
Cleanup



Discussion / hints:

Fix patch:
diff --git a/.flake8 b/.flake8
index bdee32d..d310bf5 100644
--- a/.flake8
+++ b/.flake8
@@ -22,6 +22,7 @@ ignore =
     DAR201
     # flake and pylance disagree on linebreaks in strings.
     N400
+    N806
 exclude =
     .tox,
     .git,
diff --git a/config/celeba.py b/config/celeba.py
deleted file mode 100644
index 5dabb03..0000000
--- a/config/celeba.py
+++ /dev/null
@@ -1,399 +0,0 @@
-"""Config for CelebA and CelebHQ datasets."""
-
-from typing import Any, Dict
-
-celeba_path: str = None
-celebahq_path: str = None
-
-
-class CELEBA64:
-    def __init__(self) -> None:
-        self.dataset_config: Dict[str, Any] = {
-            "random_flip": True,
-            "num_workers": 48,  # Feel free to change this
-            "dataset": "CELEBA",
-            "resize": 64,
-            "mean": [x / 255.0 for x in [129.058, 108.485, 97.622]],
-            "std": [x / 255.0 for x in [78.338, 73.131, 72.970]],
-        }
-
-        self.model_config: Dict[str, Any] = {
-            "in_c": 3,
-            "out_c": 3,
-            "model_c": 128,
-            "num_res_blocks": 2,
-            "attn_res": tuple([16]),
-            "dropout": 0.0,
-            "channel_mult": tuple([1, 2, 2, 2, 4]),
-            "num_classes": 1000,
-            "num_heads": 4,
-            "num_heads_ups": 4,
-            "use_scale_shift_norm": True,
-            "input_size": 64,
-        }
-
-        self.optimizer_config: Dict[str, Any] = {
-            "lr": 2e-4,
-            "clip_grad_norm": 1.0,  # TODO: Check this again.
-        }
-
-        self.data_dir: str = celeba_path
-
-
-class CELEBAHQ64:
-    def __init__(self) -> None:
-        self.dataset_config: Dict[str, Any] = {
-            "random_flip": True,
-            "num_workers": 48,  # Feel free to change this
-            "dataset": "CELEBAHQ",
-            "resize": 64,
-            "mean": [x / 255.0 for x in [131.810, 106.258, 92.634]],
-            "std": [x / 255.0 for x in [76.332, 69.183, 67.954]],
-        }
-
-        self.model_config: Dict[str, Any] = {
-            "in_c": 3,
-            "out_c": 3,
-            "model_c": 128,
-            "num_res_blocks": 2,
-            "attn_res": tuple([16, 8]),
-            "dropout": 0.0,
-            "channel_mult": tuple([1, 2, 2, 4]),
-            "num_classes": 6217,
-            "num_heads": 4,
-            "num_heads_ups": 4,
-            "use_scale_shift_norm": True,
-            "input_size": 64,
-        }
-
-        self.optimizer_config: Dict[str, Any] = {
-            "lr": 1e-4,
-            "clip_grad_norm": 1.0,  # TODO: Check this again.
-        }
-
-        self.data_dir: str = celebahq_path
-
-
-class CELEBAHQ128:
-    def __init__(self) -> None:
-        self.dataset_config: Dict[str, Any] = {
-            "random_flip": True,
-            "num_workers": 48,  # Feel free to change this
-            "dataset": "CELEBAHQ",
-            "resize": None,
-            "mean": [x / 255.0 for x in [131.810, 106.258, 92.634]],
-            "std": [x / 255.0 for x in [76.332, 69.183, 67.954]],
-        }
-
-        self.model_config: Dict[str, Any] = {
-            "in_c": 3,
-            "out_c": 3,
-            "model_c": 128,
-            "num_res_blocks": 3,
-            "attn_res": tuple([16, 8]),
-            "dropout": 0.0,
-            "channel_mult": tuple([1, 2, 2, 4, 4]),
-            "num_classes": 6217,
-            "num_heads": 4,
-            "num_heads_ups": 4,
-            "use_scale_shift_norm": True,
-            "input_size": 128,
-        }
-
-        self.optimizer_config: Dict[str, Any] = {
-            "lr": 1e-4,
-            "clip_grad_norm": 1.0,  # TODO: Check this again.
-        }
-
-        self.data_dir: str = celebahq_path
-
-        self.norm_weights = {
-            "haar_l3": [
-                0.18777889013290405,
-                0.9710429906845093,
-                0.9707638621330261,
-                0.9931913614273071,
-                0.9864241480827332,
-                0.9870322942733765,
-                0.9970768094062805,
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-                0.9981922507286072,
-                0.998842716217041,
-                0.9990741610527039,
-                0.9933870434761047,
-                0.9941969513893127,
-                0.9986422061920166,
-                0.9984362721443176,
-                0.9984094500541687,
-                0.9981328248977661,
-                0.9993311166763306,
-                0.9989345073699951,
-                0.9993430376052856,
-                0.9992878437042236,
-                0.9994747042655945,
-                0.9996297359466553,
-                0.9996768832206726,
-                0.9994822144508362,
-                0.9979738593101501,
-                0.9979062080383301,
-                0.9931361079216003,
-                0.9985382556915283,
-                0.9975590109825134,
-                0.9980301856994629,
-                0.9993646740913391,
-                0.9990947842597961,
-                0.9994685053825378,
-                0.9995315670967102,
-                0.997765302658081,
-                0.9985148906707764,
-                0.9987520575523376,
-                0.999146044254303,
-                0.9995391964912415,
-                0.9992204904556274,
-                0.9997932314872742,
-                0.9995009899139404,
-                0.9996944069862366,
-                0.9996473789215088,
-                0.9997705817222595,
-                0.999836802482605,
-                0.999863862991333,
-                0.999751627445221,
-                0.9991602301597595,
-                0.9990562796592712,
-                0.9999058842658997,
-                0.9997252821922302,
-                0.9998801946640015,
-                0.9997596740722656,
-                0.9994721412658691,
-                0.9992935657501221,
-                0.9998179078102112,
-                0.9997860789299011,
-            ],
-            "haar_l2": [
-                0.0800747275352478,
-                0.9780480861663818,
-                0.9814229607582092,
-                0.9969100952148438,
-                0.9897764325141907,
-                0.9968819618225098,
-                0.9986848831176758,
-                0.9966328144073486,
-                0.9913052320480347,
-                0.9985143542289734,
-                0.996516227722168,
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-                0.9993835687637329,
-                0.998535692691803,
-                0.9995526075363159,
-                0.998953104019165,
-            ],
-            "db2_l2": [
-                0.03834044933319092,
-                0.9883729815483093,
-                0.9925981760025024,
-                0.9972652196884155,
-                0.9974744319915771,
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-                0.9994933009147644,
-                0.9967793822288513,
-                0.9986762404441833,
-                0.999838650226593,
-                0.9998192191123962,
-                0.9995104670524597,
-                0.9993636608123779,
-            ],
-            "db2_l3": [
-                0.10848993062973022,
-                0.9738131761550903,
-                0.9853153824806213,
-                0.995220959186554,
-                0.9928703904151917,
-                0.9934724569320679,
-                0.998015284538269,
-                0.9969393610954285,
-                0.9960914850234985,
-                0.99883633852005,
-                0.9957753419876099,
-                0.9969750642776489,
-                0.9993109107017517,
-                0.9990206360816956,
-                0.9989132285118103,
-                0.9982315301895142,
-                0.998688280582428,
-                0.9984326362609863,
-                0.9997441172599792,
-                0.999107837677002,
-                0.9989015460014343,
-                0.9977681636810303,
-                0.9984432458877563,
-                0.998947262763977,
-                0.9998968243598938,
-                0.9997488856315613,
-                0.9998785853385925,
-                0.9993495941162109,
-                0.9996901154518127,
-                0.999739944934845,
-                0.9990999102592468,
-                0.9994240999221802,
-                0.9987577795982361,
-                0.999631941318512,
-                0.9991722106933594,
-                0.9993965029716492,
-                0.9998793601989746,
-                0.9997659921646118,
-                0.9998008608818054,
-                0.9997014403343201,
-                0.9985272884368896,
-                0.9981991052627563,
-                0.9989141821861267,
-                0.9988254308700562,
-                0.9993411302566528,
-                0.9994937181472778,
-                0.9994916915893555,
-                0.9993698000907898,
-                0.9999447464942932,
-                0.9999421238899231,
-                0.9999526739120483,
-                0.9998918771743774,
-                0.9999220371246338,
-                0.9999322295188904,
-                0.9999356865882874,
-                0.9998976588249207,
-                0.9999099373817444,
-                0.9996792674064636,
-                0.9999274015426636,
-                0.9997396469116211,
-                0.9997406005859375,
-                0.9997643828392029,
-                0.9996375441551208,
-                0.9997612833976746,
-            ],
-            "sym2_l2": [
-                0.03834044933319092,
-                0.9883729815483093,
-                0.9925981760025024,
-                0.9972652196884155,
-                0.9974744319915771,
-                0.9964457750320435,
-                0.9991838932037354,
-                0.9986961483955383,
-                0.998141884803772,
-                0.9994933009147644,
-                0.9967793822288513,
-                0.9986762404441833,
-                0.999838650226593,
-                0.9998192191123962,
-                0.9995104670524597,
-                0.9993636608123779,
-            ],
-            "sym2_l3": [
-                0.10848993062973022,
-                0.9738131761550903,
-                0.9853153824806213,
-                0.995220959186554,
-                0.9928703904151917,
-                0.9934724569320679,
-                0.998015284538269,
-                0.9969393610954285,
-                0.9960914850234985,
-                0.99883633852005,
-                0.9957753419876099,
-                0.9969750642776489,
-                0.9993109107017517,
-                0.9990206360816956,
-                0.9989132285118103,
-                0.9982315301895142,
-                0.998688280582428,
-                0.9984326362609863,
-                0.9997441172599792,
-                0.999107837677002,
-                0.9989015460014343,
-                0.9977681636810303,
-                0.9984432458877563,
-                0.998947262763977,
-                0.9998968243598938,
-                0.9997488856315613,
-                0.9998785853385925,
-                0.9993495941162109,
-                0.9996901154518127,
-                0.999739944934845,
-                0.9990999102592468,
-                0.9994240999221802,
-                0.9987577795982361,
-                0.999631941318512,
-                0.9991722106933594,
-                0.9993965029716492,
-                0.9998793601989746,
-                0.9997659921646118,
-                0.9998008608818054,
-                0.9997014403343201,
-                0.9985272884368896,
-                0.9981991052627563,
-                0.9989141821861267,
-                0.9988254308700562,
-                0.9993411302566528,
-                0.9994937181472778,
-                0.9994916915893555,
-                0.9993698000907898,
-                0.9999447464942932,
-                0.9999421238899231,
-                0.9999526739120483,
-                0.9998918771743774,
-                0.9999220371246338,
-                0.9999322295188904,
-                0.9999356865882874,
-                0.9998976588249207,
-                0.9999099373817444,
-                0.9996792674064636,
-                0.9999274015426636,
-                0.9997396469116211,
-                0.9997406005859375,
-                0.9997643828392029,
-                0.9996375441551208,
-                0.9997612833976746,
-            ],
-        }
-
-
-class CELEBAHQ256:
-    def __init__(self) -> None:
-        self.dataset_config: Dict[str, Any] = {
-            "random_flip": True,
-            "num_workers": 48,  # Feel free to change this
-            "dataset": "CELEBAHQ",
-            "resize": None,
-            "mean": [x / 255.0 for x in [131.810, 106.258, 92.634]],
-            "std": [x / 255.0 for x in [76.332, 69.183, 67.954]],
-        }
-
-        self.model_config: Dict[str, Any] = {
-            "in_c": 3,
-            "out_c": 3,
-            "model_c": 128,
-            "num_res_blocks": 3,
-            "attn_res": tuple([16, 8]),
-            "dropout": 0.0,
-            "channel_mult": tuple([1, 2, 2, 4, 4]),
-            "num_classes": 6217,
-            "num_heads": 4,
-            "num_heads_ups": 4,
-            "use_scale_shift_norm": True,
-            "input_size": 256,
-        }
-
-        self.optimizer_config: Dict[str, Any] = {
-            "lr": 1e-4,
-            "clip_grad_norm": 1.0,  # TODO: Check this again.
-        }
-
-        self.data_dir: str = (
-            "/p/scratch/holistic-vid-westai/veeramacheneni2_scratch/CelebAMask-HQ/data256x256/"
-        )
diff --git a/config/cifar10.py b/config/cifar10.py
deleted file mode 100644
index 67ff5a1..0000000
--- a/config/cifar10.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""Config for CIFAR10."""
-
-from typing import Any, Dict
-
-
-class CIFAR10:
-    def __init__(self) -> None:
-        self.dataset_config: Dict[str, Any] = {
-            "random_flip": True,
-            "num_workers": 12,  # Feel free to change this
-            "dataset": "CIFAR10",
-            "resize": None,
-            #            "mean": [x / 255.0 for x in [125.3, 123.0, 113.9]],
-            #            "std": [x / 255.0 for x in [63.0, 62.1, 66.7]],
-            "mean": [0.5, 0.5, 0.5],
-            "std": [0.5, 0.5, 0.5],
-        }
-
-        self.model_config: Dict[str, Any] = {
-            "in_c": 3,
-            "out_c": 3,
-            "model_c": 128,
-            "num_res_blocks": 2,
-            "attn_res": tuple([16]),
-            "dropout": 0.1,
-            "channel_mult": tuple([1, 2, 2, 2]),
-            "num_classes": 10,
-            "num_heads": 4,
-            "num_heads_ups": 4,
-            "use_scale_shift_norm": True,
-            "input_size": 32,
-        }
-
-        self.optimizer_config: Dict[str, Any] = {"lr": 2e-4, "clip_grad_norm": 1.0}
-
-        self.data_dir: str = ""
-
-        self.norm_weights = {
-            "db2_l2": [
-                0.020704030990600586,
-                0.9917393922805786,
-                0.9957547783851624,
-                0.9992679357528687,
-                0.9989995360374451,
-                0.9983512759208679,
-                0.9998998641967773,
-                0.9998476505279541,
-                0.9987537860870361,
-                0.9998438358306885,
-                0.9974440336227417,
-                0.9997131824493408,
-                0.9999409914016724,
-                0.9999169111251831,
-                0.9999364018440247,
-                0.9998863935470581,
-            ],
-            "haar_l2": [
-                0.030076563358306885,
-                0.9893319606781006,
-                0.9943220615386963,
-                0.9990335702896118,
-                0.9947222471237183,
-                0.9989709258079529,
-                0.9995564818382263,
-                0.9998138546943665,
-                0.9968331456184387,
-                0.9995421767234802,
-                0.9985470175743103,
-                0.9997920393943787,
-                0.9997794032096863,
-                0.9998888373374939,
-                0.9999101758003235,
-                0.9998796582221985,
-            ],
-            "sym2_l2": [
-                0.020704030990600586,
-                0.9917393922805786,
-                0.9957547783851624,
-                0.9992679357528687,
-                0.9989995360374451,
-                0.9983512759208679,
-                0.9998998641967773,
-                0.9998476505279541,
-                0.9987537860870361,
-                0.9998438358306885,
-                0.9974440336227417,
-                0.9997131824493408,
-                0.9999409914016724,
-                0.9999169111251831,
-                0.9999364018440247,
-                0.9998863935470581,
-            ],
-        }
diff --git a/data/CelebAHQ/identity_CelebAHQ.txt b/data/CelebAHQ/identity_CelebAHQ.txt
deleted file mode 100644
index a00f396..0000000
--- a/data/CelebAHQ/identity_CelebAHQ.txt
+++ /dev/null
@@ -1,1 +0,0 @@
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diff --git a/noxfile.py b/noxfile.py
index bc7e9b2..842f6d7 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -10,8 +10,6 @@ def format(session):
     session.install("isort")
     session.run("isort", "src", "noxfile.py")
     session.run("black", "src", "noxfile.py")
-    session.run("isort", "config", "noxfile.py")
-    session.run("black", "config", "noxfile.py")
     session.run("isort", "scripts", "noxfile.py")
     session.run("black", "scripts", "noxfile.py")
 
@@ -52,6 +50,7 @@ def mypy(session):
 
 @nox.session(name="test")
 def test(session):
+    """Run long pytest."""
     session.install("-r", "requirements.txt")
     session.chdir("tests")
     session.run("pytest")
diff --git a/paper_plots.ipynb b/paper_plots.ipynb
deleted file mode 100644
index 5fcf20d..0000000
--- a/paper_plots.ipynb
+++ /dev/null
@@ -1,500 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scripts.fid.inception import InceptionV3\n",
-    "import glob\n",
-    "import os\n",
-    "from PIL import Image\n",
-    "import tikzplotlib\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "from torchvision import transforms\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scripts.fid.fid import calculate_frechet_distance\n",
-    "from src.freq_math import fourier_power_divergence, wavelet_packet_power_divergence"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img_test1 = Image.open('/home/lveerama/HPCA/CelebA-HQ/data256x256/00001.jpg')\n",
-    "img_test2 = Image.open('/home/lveerama/HPCA/CelebA-HQ/data256x256/00002.jpg')\n",
-    "img_test3 = Image.open('/home/lveerama/HPCA/CelebA-HQ/data256x256/00003.jpg')\n",
-    "img_test4 = Image.open('/home/lveerama/HPCA/CelebA-HQ/data256x256/00004.jpg')\n",
-    "img_test5 = Image.open('/home/lveerama/HPCA/CelebA-HQ/data256x256/00005.jpg')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def rotate_values(img_test, angle, n=10, crop=64):\n",
-    "    imgr_test = transforms.functional.rotate(img_test, angle, expand=False)\n",
-    "    img_test = transforms.functional.center_crop(img_test, output_size=crop)\n",
-    "    imgr_test = transforms.functional.center_crop(imgr_test, output_size=crop)\n",
-    "    # plt.figure()\n",
-    "    # plt.subplot(1, 2, 1)\n",
-    "    # plt.imshow(img_test)\n",
-    "    # plt.axis('off')\n",
-    "    # plt.subplot(1, 2, 2)\n",
-    "    # plt.imshow(imgr_test)\n",
-    "    # plt.axis('off')\n",
-    "    # plt.show()\n",
-    "    img_tensor = transforms.ToTensor()(img_test)\n",
-    "    imgr_tensor = transforms.ToTensor()(imgr_test)\n",
-    "    img_tensor = torch.stack([img_tensor]*n, dim=0)\n",
-    "    imgr_tensor = torch.stack([imgr_tensor]*n, dim=0)\n",
-    "    # print(img_tensor.shape, imgr_tensor.shape)\n",
-    "    bidx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]\n",
-    "    model = InceptionV3([bidx])\n",
-    "    op1 = model(img_tensor)[0]\n",
-    "    op2 = model(imgr_tensor)[0]\n",
-    "    onp1 = op1.squeeze(3).squeeze(2).cpu().numpy()\n",
-    "    onp2 = op2.squeeze(3).squeeze(2).cpu().numpy()\n",
-    "    m1, s1 = np.mean(onp1, axis=0), np.cov(onp1, rowvar=False)\n",
-    "    m2, s2 = np.mean(onp2, axis=0), np.cov(onp2, rowvar=False)\n",
-    "    fid = calculate_frechet_distance(m1, s1, m2, s2)\n",
-    "    fa, fb = fourier_power_divergence(img_tensor, imgr_tensor)\n",
-    "    pa, pb = wavelet_packet_power_divergence(img_tensor, imgr_tensor, level=2)\n",
-    "    return fid, (fa.item(), fb.item()), (pa.item(), pb.item())\n",
-    "\n",
-    "\n",
-    "def gaussian_values(img_test, factor, n=10):\n",
-    "    img_tensor = transforms.ToTensor()(img_test)\n",
-    "    imgr_tensor = (1 - factor) * img_tensor + (factor) * torch.rand_like(img_tensor)\n",
-    "    # imgr_tensor = transforms.ToTensor()(imgr_test)\n",
-    "    img_tensor = torch.stack([img_tensor]*n, dim=0)\n",
-    "    imgr_tensor = torch.stack([imgr_tensor]*n, dim=0)\n",
-    "    # print(img_tensor.shape, imgr_tensor.shape)\n",
-    "    bidx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]\n",
-    "    model = InceptionV3([bidx])\n",
-    "    op1 = model(img_tensor)[0]\n",
-    "    op2 = model(imgr_tensor)[0]\n",
-    "    onp1 = op1.squeeze(3).squeeze(2).cpu().numpy()\n",
-    "    onp2 = op2.squeeze(3).squeeze(2).cpu().numpy()\n",
-    "    m1, s1 = np.mean(onp1, axis=0), np.cov(onp1, rowvar=False)\n",
-    "    m2, s2 = np.mean(onp2, axis=0), np.cov(onp2, rowvar=False)\n",
-    "    fid = calculate_frechet_distance(m1, s1, m2, s2)\n",
-    "    fa, fb = fourier_power_divergence(img_tensor, imgr_tensor)\n",
-    "    pa, pb = wavelet_packet_power_divergence(img_tensor, imgr_tensor, level=2)\n",
-    "    return fid, (fa.item(), fb.item()), (pa.item(), pb.item())\n",
-    "\n",
-    "def gaussianBlur_values(img_test, kernel, n=10):\n",
-    "    imgr_test = transforms.functional.gaussian_blur(img_test, kernel)\n",
-    "    img_tensor = transforms.ToTensor()(img_test)\n",
-    "    imgr_tensor = transforms.ToTensor()(imgr_test)\n",
-    "    img_tensor = torch.stack([img_tensor]*n, dim=0)\n",
-    "    imgr_tensor = torch.stack([imgr_tensor]*n, dim=0)\n",
-    "    # print(img_tensor.shape, imgr_tensor.shape)\n",
-    "    bidx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]\n",
-    "    model = InceptionV3([bidx])\n",
-    "    op1 = model(img_tensor)[0]\n",
-    "    op2 = model(imgr_tensor)[0]\n",
-    "    onp1 = op1.squeeze(3).squeeze(2).cpu().numpy()\n",
-    "    onp2 = op2.squeeze(3).squeeze(2).cpu().numpy()\n",
-    "    m1, s1 = np.mean(onp1, axis=0), np.cov(onp1, rowvar=False)\n",
-    "    m2, s2 = np.mean(onp2, axis=0), np.cov(onp2, rowvar=False)\n",
-    "    fid = calculate_frechet_distance(m1, s1, m2, s2)\n",
-    "    fa, fb = fourier_power_divergence(img_tensor, imgr_tensor)\n",
-    "    pa, pb = wavelet_packet_power_divergence(img_tensor, imgr_tensor, level=2)\n",
-    "    return fid, (fa.item(), fb.item()), (pa.item(), pb.item())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Angle: 0, Angle: 45, Angle: 90, Angle: 135, Angle: 180, Angle: 225, Angle: 270, Angle: 315, Angle: 360, \n",
-      "Angle: 0, Angle: 45, Angle: 90, Angle: 135, Angle: 180, Angle: 225, Angle: 270, Angle: 315, Angle: 360, \n"
-     ]
-    }
-   ],
-   "source": [
-    "angles = [0, 45, 90, 135, 180, 225, 270, 315, 360]\n",
-    "all_fids = []\n",
-    "all_ffts = []\n",
-    "all_packets = []\n",
-    "for img_test in [img_test1, img_test2]:#, img_test3, img_test4, img_test5]:\n",
-    "    fids = []\n",
-    "    ffts = []\n",
-    "    packets = []\n",
-    "    for ang in angles:\n",
-    "        fid, fft, packs = rotate_values(img_test, ang)#\n",
-    "\n",
-    "        print(f\"Angle: {ang}\", end=', ')\n",
-    "        fids.append(fid)\n",
-    "        ffts.append(sum(fft)/2.)\n",
-    "        packets.append(sum(packs)/2.)\n",
-    "    print()\n",
-    "    all_fids.append(fids)\n",
-    "    all_ffts.append(ffts)\n",
-    "    all_packets.append(packets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_mean_var(list_):\n",
-    "    y = np.stack(list_, axis=0)\n",
-    "    mean_y = np.mean(y, axis=0)\n",
-    "    std_y = np.std(y, axis=0)\n",
-    "    return mean_y, std_y\n",
-    "\n",
-    "def stand(list_):\n",
-    "    return (list_ - np.min(list_))/(np.max(list_) - np.min(list_))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "muf, stdf = compute_mean_var(all_fids)\n",
-    "mufft, stdfft = compute_mean_var(all_ffts)\n",
-    "muw, stdw = compute_mean_var(all_packets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10, 7))\n",
-    "plt.plot(angles, muf, label='FID')\n",
-    "plt.fill_between(angles, muf-stdf, muf+stdf, alpha=0.5)\n",
-    "plt.plot(angles, mufft, label='PSKL_FFT')\n",
-    "plt.fill_between(angles, mufft-stdfft, mufft+stdfft, alpha=0.7)\n",
-    "plt.plot(angles, muw, label='PSKL_Packet')\n",
-    "plt.fill_between(angles, muw-stdw, muw+stdw, alpha=0.3)\n",
-    "plt.xticks(angles)\n",
-    "plt.xlabel('Angles')\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "# plt.savefig('metrics_v_angles.pdf', dpi=600, bbox_inches='tight')\n",
-    "# plt.show()\n",
-    "tikzplotlib.save(\"metrics_v_angles.tex\")\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Factor: 0, 0.25, 0.5, 0.75, 1, \n",
-      "Factor: 0, 0.25, 0.5, 0.75, 1, \n",
-      "Factor: 0, 0.25, 0.5, 0.75, 1, \n",
-      "Factor: 0, 0.25, 0.5, 0.75, 1, \n",
-      "Factor: 0, 0.25, 0.5, 0.75, 1, \n"
-     ]
-    }
-   ],
-   "source": [
-    "gauss_factors = [0, 0.25, 0.5, 0.75, 1]\n",
-    "all_fids = []\n",
-    "all_ffts = []\n",
-    "all_packets = []\n",
-    "for img_test in [img_test1, img_test2, img_test3, img_test4, img_test5]:\n",
-    "    fids = []\n",
-    "    ffts = []\n",
-    "    packets = []\n",
-    "    print(\"Factor:\", end=' ')\n",
-    "    for fac in gauss_factors:\n",
-    "        fid, fft, packs = gaussian_values(img_test, fac)\n",
-    "\n",
-    "        print(f\"{fac}\", end=', ')\n",
-    "        fids.append(fid)\n",
-    "        ffts.append(sum(fft)/2.)\n",
-    "        packets.append(sum(packs)/2.)\n",
-    "    print()\n",
-    "    all_fids.append(fids)\n",
-    "    all_ffts.append(ffts)\n",
-    "    all_packets.append(packets)\n",
-    "muf, stdf = compute_mean_var(all_fids)\n",
-    "mufft, stdfft = compute_mean_var(all_ffts)\n",
-    "muw, stdw = compute_mean_var(all_packets)\n",
-    "plt.figure(figsize=(10, 7))\n",
-    "plt.plot(gauss_factors, muf, label='FID')\n",
-    "plt.fill_between(gauss_factors, muf-stdf, muf+stdf, alpha=0.5)\n",
-    "plt.plot(gauss_factors, mufft, label='PSKL_FFT')\n",
-    "plt.fill_between(gauss_factors, mufft-stdfft, mufft+stdfft, alpha=0.7)\n",
-    "plt.plot(gauss_factors, muw, label='PSKL_Packet')\n",
-    "plt.fill_between(gauss_factors, muw-stdw, muw+stdw, alpha=0.3)\n",
-    "plt.xticks(gauss_factors)\n",
-    "plt.xlabel('Gaussian Factor')\n",
-    "plt.grid()\n",
-    "plt.legend()\n",
-    "# plt.savefig('metrics_v_gaussian.pdf', dpi=600, bbox_inches='tight')\n",
-    "# plt.show()\n",
-    "tikzplotlib.save(\"metrics_v_gaussian.tex\")\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Kernel Size: 1, 3, 5, 7, 9, 11, \n",
-      "Kernel Size: 1, 3, 5, 7, 9, 11, \n",
-      "Kernel Size: 1, 3, 5, 7, 9, 11, \n",
-      "Kernel Size: 1, 3, 5, 7, 9, 11, \n",
-      "Kernel Size: 1, 3, 5, 7, 9, 11, \n"
-     ]
-    }
-   ],
-   "source": [
-    "kernel_size = [1, 3, 5, 7, 9, 11]\n",
-    "all_fids = []\n",
-    "all_ffts = []\n",
-    "all_packets = []\n",
-    "for img_test in [img_test1, img_test2, img_test3, img_test4, img_test5]:\n",
-    "    fids = []\n",
-    "    ffts = []\n",
-    "    packets = []\n",
-    "    print(\"Kernel Size:\", end=' ')\n",
-    "    for ker in kernel_size:\n",
-    "        fid, fft, packs = gaussianBlur_values(img_test, ker)\n",
-    "\n",
-    "        print(f\"{ker}\", end=', ')\n",
-    "        fids.append(fid)\n",
-    "        ffts.append(sum(fft)/2.)\n",
-    "        packets.append(sum(packs)/2.)\n",
-    "    print()\n",
-    "    all_fids.append(fids)\n",
-    "    all_ffts.append(ffts)\n",
-    "    all_packets.append(packets)\n",
-    "muf, stdf = compute_mean_var(all_fids)\n",
-    "mufft, stdfft = compute_mean_var(all_ffts)\n",
-    "muw, stdw = compute_mean_var(all_packets)\n",
-    "plt.figure(figsize=(10, 7))\n",
-    "plt.plot(kernel_size, muf, label='FID')\n",
-    "plt.fill_between(kernel_size, muf-stdf, muf+stdf, alpha=0.5)\n",
-    "plt.plot(kernel_size, mufft, label='PSKL_FFT')\n",
-    "plt.fill_between(kernel_size, mufft-stdfft, mufft+stdfft, alpha=0.7)\n",
-    "plt.plot(kernel_size, muw, label='PSKL_Packet')\n",
-    "plt.fill_between(kernel_size, muw-stdw, muw+stdw, alpha=0.3)\n",
-    "plt.xticks(kernel_size)\n",
-    "plt.xlabel('Gaussian Kernel size')\n",
-    "plt.grid()\n",
-    "plt.legend()\n",
-    "# plt.savefig('metrics_v_gaussianBlur.pdf', dpi=600, bbox_inches='tight')\n",
-    "# plt.show()\n",
-    "tikzplotlib.save(\"metrics_v_gaussianBlur.tex\")\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comp_path = './compression_samples/'\n",
-    "os.makedirs(comp_path, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "png_path = os.path.join(comp_path, 'png/')\n",
-    "os.makedirs(png_path, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for idx, img in enumerate([img_test1, img_test2, img_test3, img_test4, img_test5]):\n",
-    "    fn = os.path.join(png_path, f'{idx+1:05d}.png')\n",
-    "    img.save(fn)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comp_rate = [10, 25, 50, 75, 100]\n",
-    "for rate in comp_rate:\n",
-    "    compr_path =os.path.join(comp_path, f'{rate}/')\n",
-    "    os.makedirs(compr_path, exist_ok=True)\n",
-    "\n",
-    "    for idx, img in enumerate([img_test1, img_test2, img_test3, img_test4, img_test5]):\n",
-    "        fn = os.path.join(compr_path, f'{idx+1:05d}.jpg')\n",
-    "        img.save(fn, quality=rate)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def format_values(img_test, imgr_test, n=10):\n",
-    "    img_tensor = transforms.ToTensor()(img_test)\n",
-    "    imgr_tensor = transforms.ToTensor()(imgr_test)\n",
-    "    img_tensor = torch.stack([img_tensor]*n, dim=0)\n",
-    "    imgr_tensor = torch.stack([imgr_tensor]*n, dim=0)\n",
-    "    # print(img_tensor.shape, imgr_tensor.shape)\n",
-    "    bidx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]\n",
-    "    model = InceptionV3([bidx])\n",
-    "    op1 = model(img_tensor)[0]\n",
-    "    op2 = model(imgr_tensor)[0]\n",
-    "    onp1 = op1.squeeze(3).squeeze(2).cpu().numpy()\n",
-    "    onp2 = op2.squeeze(3).squeeze(2).cpu().numpy()\n",
-    "    m1, s1 = np.mean(onp1, axis=0), np.cov(onp1, rowvar=False)\n",
-    "    m2, s2 = np.mean(onp2, axis=0), np.cov(onp2, rowvar=False)\n",
-    "    fid = calculate_frechet_distance(m1, s1, m2, s2)\n",
-    "    fa, fb = fourier_power_divergence(img_tensor, imgr_tensor)\n",
-    "    pa, pb = wavelet_packet_power_divergence(img_tensor, imgr_tensor, level=2)\n",
-    "    return fid, (fa.item(), fb.item()), (pa.item(), pb.item())\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_imgs(rate):\n",
-    "    compr_path =os.path.join(comp_path, f'{rate}/')\n",
-    "    img_nms = glob.glob(f'{compr_path}*.jpg') if isinstance(rate, int) else glob.glob(f'{compr_path}*.png')\n",
-    "    assert len(img_nms) == 5\n",
-    "    imgs = []\n",
-    "    for nm in img_nms:\n",
-    "        imgs.append(Image.open(nm))\n",
-    "    return imgs\n",
-    "comp_imgs = {\n",
-    "    10: read_imgs(10),\n",
-    "    25: read_imgs(25),\n",
-    "    50: read_imgs(50),\n",
-    "    75: read_imgs(75),\n",
-    "    100: read_imgs(100)\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Rate: 10, 25, 50, 75, 100, \n",
-      "Rate: 10, 25, 50, 75, 100, \n",
-      "Rate: 10, 25, 50, 75, 100, \n",
-      "Rate: 10, 25, 50, 75, 100, \n",
-      "Rate: 10, 25, 50, 75, 100, \n"
-     ]
-    }
-   ],
-   "source": [
-    "rates = [10, 25, 50, 75, 100]\n",
-    "all_fids = []\n",
-    "all_ffts = []\n",
-    "all_packets = []\n",
-    "for idx, img_test in enumerate([img_test1, img_test2, img_test3, img_test4, img_test5]):\n",
-    "    fids = []\n",
-    "    ffts = []\n",
-    "    packets = []\n",
-    "    print(\"Rate:\", end=' ')\n",
-    "    for rate in rates:\n",
-    "        fid, fft, packs = format_values(img_test, comp_imgs[rate][idx])\n",
-    "\n",
-    "        print(f\"{rate}\", end=', ')\n",
-    "        fids.append(fid)\n",
-    "        ffts.append(sum(fft)/2.)\n",
-    "        packets.append(sum(packs)/2.)\n",
-    "    print()\n",
-    "    all_fids.append(fids)\n",
-    "    all_ffts.append(ffts)\n",
-    "    all_packets.append(packets)\n",
-    "muf, stdf = compute_mean_var(all_fids)\n",
-    "mufft, stdfft = compute_mean_var(all_ffts)\n",
-    "muw, stdw = compute_mean_var(all_packets)\n",
-    "plt.figure(figsize=(10, 7))\n",
-    "plt.plot(rates, muf, label='FID')\n",
-    "plt.fill_between(rates, muf-stdf, muf+stdf, alpha=0.5)\n",
-    "plt.plot(rates, mufft, label='PSKL_FFT')\n",
-    "plt.fill_between(rates, mufft-stdfft, mufft+stdfft, alpha=0.7)\n",
-    "plt.plot(rates, muw, label='PSKL_Packet')\n",
-    "plt.fill_between(rates, muw-stdw, muw+stdw, alpha=0.3)\n",
-    "plt.xticks(rates)\n",
-    "plt.xlabel('JPEG Compression rate')\n",
-    "plt.grid()\n",
-    "plt.gca().legend()\n",
-    "# plt.savefig('metrics_v_comprate.pdf', dpi=600, bbox_inches='tight')\n",
-    "# plt.show()\n",
-    "tikzplotlib.save(\"metrics_v_comprate.tex\")\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mlpytorch2",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/scripts/celebA_fid_experiment.py b/scripts/celebA_fid_experiment.py
new file mode 100644
index 0000000..fff7f7c
--- /dev/null
+++ b/scripts/celebA_fid_experiment.py
@@ -0,0 +1,115 @@
+import numpy as np
+import glob
+import torchvision
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Dataset
+
+from scripts.celebA_retrain_inception import label_map
+from scripts.fid.fid import calculate_frechet_distance
+
+import pytorch_fid.fid_score
+import PIL
+
+from tqdm import tqdm
+
+class SimpleLoader(Dataset):
+
+    def __init__(self, data_dir, type='jpg'):
+        self.data_dir = data_dir
+        self.file_list = glob.glob(data_dir + f"/*.{type}")
+        self.transforms = torchvision.transforms.Compose([
+            torchvision.transforms.Resize((299, 299)),
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize([x / 255.0 for x in [129.058, 108.485, 97.622]],
+                                             [x / 255.0 for x in [78.338, 73.131, 72.970]])
+            ])
+
+    def __len__(self):
+        return len(self.file_list)
+
+    def __getitem__(self, index) -> torch.Tensor:
+        # img 0 does not exist.
+        img_path = self.file_list[index]
+        with PIL.Image.open(img_path) as im:
+            tensor = self.transforms(im)
+        return {'img': tensor}
+
+class Identity(nn.Module):
+    def __init__(self):
+        super(Identity, self).__init__()
+        
+    def forward(self, x):
+        return x
+
+
+if __name__ == '__main__':
+    net = torchvision.models.Inception3()
+    net.fc = nn.Linear(2048, 40)
+    net = nn.DataParallel(net.cuda())
+
+    print('loading pretrained network')
+    net_weights = torch.load('../weights/inception_converged_celeba/inception_converged_99.pth')
+
+    # block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]
+    # model = InceptionV3([block_idx])
+
+    clean_weights = {}
+    for key, weight in net_weights.items():
+        new_key = ".".join(key.split('.')[1:])
+        clean_weights[new_key] = weight
+
+    missing, unexpected = net.load_state_dict(clean_weights)
+    print("missing", missing, "unxepected", unexpected)
+
+    net.module.fc = Identity()
+    # net.module.avgpool = Identity()
+    net.eval()
+
+    # celebA - acc
+    # data = ImageLoader('../data/celeba/CelebA')
+
+
+
+    def get_mu_sigma(data_loader):
+        acts = []
+        for img_batch in tqdm(data_loader):
+            with torch.no_grad():
+                out = net(img_batch['img'].cuda())
+            acts.append(out.cpu().numpy())
+
+        acts_stack = np.concatenate(acts)
+        mu = np.mean(acts_stack, axis=0)
+        sigma = np.cov(acts_stack, rowvar=False)
+        return mu, sigma
+
+    # agri-data
+    original_loader =  DataLoader(SimpleLoader('../data/agri/dataset256/images/'), batch_size=128, num_workers=6)
+    ddgan_loader = DataLoader(SimpleLoader('../data/agri/generated_samples/agri'), batch_size=128, num_workers=6)
+    pggan_loader = DataLoader(SimpleLoader('../data/agri/out', type='png'), batch_size=128, num_workers=6)
+
+    agri_mu, agri_sigma = get_mu_sigma(original_loader)
+    ddgan_mu, ddgan_sigma = get_mu_sigma(ddgan_loader)
+    pggan_mu, pggan_sigma = get_mu_sigma(pggan_loader)
+
+    fid_agri_ddgan = pytorch_fid.fid_score.calculate_frechet_distance(agri_mu, agri_sigma, ddgan_mu, ddgan_sigma)
+    fid_agri_pggan = pytorch_fid.fid_score.calculate_frechet_distance(agri_mu, agri_sigma, pggan_mu, pggan_sigma)
+
+    print(f"Fid agri-DDGAN: {fid_agri_ddgan}, FID agri-PGGAN: {fid_agri_pggan}")
+
+    # celebA-HQ
+    original_loader =  DataLoader(SimpleLoader('../data/celeba_hq/celeba_hq_256'), batch_size=128, num_workers=6)
+    ddgan_loader = DataLoader(SimpleLoader('../data/celeba_hq/DDGAN/celeba_256'), batch_size=128, num_workers=6)
+    pggan_loader = DataLoader(SimpleLoader('../data/celeba_hq/pggan_celebahq_generated', type='png'), batch_size=128, num_workers=6)
+
+    celebA_mu, celebA_sigma = get_mu_sigma(original_loader)
+    ddgan_mu, ddgan_sigma = get_mu_sigma(ddgan_loader)
+    pggan_mu, pggan_sigma = get_mu_sigma(pggan_loader)
+
+    fid_celebA_ddgan = pytorch_fid.fid_score.calculate_frechet_distance(celebA_mu, celebA_sigma, ddgan_mu, ddgan_sigma)
+    fid_celebA_pggan = pytorch_fid.fid_score.calculate_frechet_distance(celebA_mu, celebA_sigma, pggan_mu, pggan_sigma)
+
+    print(f"Fid CelebA-DDGAN: {fid_celebA_ddgan}, FID CelebA-PGGAN: {fid_celebA_pggan}")
+
+    breakpoint()
+
diff --git a/scripts/celebA_retrain_inception.py b/scripts/celebA_retrain_inception.py
new file mode 100644
index 0000000..69bd8e8
--- /dev/null
+++ b/scripts/celebA_retrain_inception.py
@@ -0,0 +1,140 @@
+import PIL.Image
+import numpy as np
+
+import torch
+import torchvision.transforms
+import pandas
+import PIL
+from typing import Any
+from torch import nn
+import torch.optim as optim
+import tqdm
+
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+
+import torchvision
+
+label_map = {pos: key for pos, key in enumerate([
+    '5_o_Clock_Shadow',
+    'Arched_Eyebrows',
+    'Attractive',
+    'Bags_Under_Eyes',
+    'Bald',
+    'Bangs',
+    'Big_Lips',
+    'Big_Nose',
+    'Black_Hair',
+    'Blond_Hair',
+    'Blurry',
+    'Brown_Hair',
+    'Bushy_Eyebrows',
+    'Chubby',
+    'Double_Chin',
+    'Eyeglasses',
+    'Goatee',
+    'Gray_Hair',
+    'Heavy_Makeup',
+    'High_Cheekbones',
+    'Male',
+    'Mouth_Slightly_Open',
+    'Mustache',
+    'Narrow_Eyes',
+    'No_Beard',
+    'Oval_Face',
+    'Pale_Skin',
+    'Pointy_Nose',
+    'Receding_Hairline',
+    'Rosy_Cheeks',
+    'Sideburns',
+    'Smiling',
+    'Straight_Hair',
+    'Wavy_Hair',
+    'Wearing_Earrings',
+    'Wearing_Hat',
+    'Wearing_Lipstick',
+    'Wearing_Necklace',
+    'Wearing_Necktie',
+    'Young'
+])}
+
+class ImageLoader(Dataset):
+    
+    def __init__(self, data_dir):
+        self.data_dir = data_dir
+        self.annos = np.array(pandas.read_csv(f"{data_dir}/Anno/list_attr_celeba.txt"))
+        self.annos_dict = {int(row[0].split()[0].split(".")[0]): row[0].split()[1:] for row in self.annos[1:]}
+        self.transforms = torchvision.transforms.Compose([
+            torchvision.transforms.Resize((299, 299)),
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize([x / 255.0 for x in [129.058, 108.485, 97.622]],
+                                             [x / 255.0 for x in [78.338, 73.131, 72.970]])
+            ])
+
+    def __len__(self):
+        return len(self.annos_dict) - 1
+
+    def __getitem__(self, index) -> torch.Tensor:
+        # img 0 does not exist.
+        index = index + 1
+        img_no_str = '0'*(6 - len(f"{index:d}")) + f"{index:d}"
+        img_path = f"{self.data_dir}/Img/img_align_celeba/{img_no_str}.jpg"
+        with PIL.Image.open(img_path) as im:
+            tensor = self.transforms(im)
+        anno = torch.tensor([int(attribute) for attribute in self.annos_dict[index]])
+        anno = (anno + 1) // 2
+        return {'img': tensor, 'anno': anno}
+
+
+if __name__ == "__main__":
+    dataset = ImageLoader(data_dir='/home/lveerama/CelebA/')
+
+    net = torchvision.models.Inception3()
+    net.fc = nn.Linear(2048, 40)
+    # pass
+    epochs = 100
+    test_size = 128
+    lr = 0.001
+    comp_stats = True
+
+    train_set, test_set = torch.utils.data.random_split(dataset, [len(dataset) - test_size, test_size])
+    train_loader = DataLoader(train_set, batch_size=512, shuffle=True, num_workers=16, prefetch_factor=2)
+    test_loader = DataLoader(test_set, batch_size=128, num_workers=5)
+
+    net = nn.DataParallel(net.cuda())
+
+    net = torch.compile(net)
+    # net = net.cuda()
+    optimizer = optim.Adam(net.parameters(), lr=lr)
+    cost = nn.BCEWithLogitsLoss()
+
+    
+    for e in range(epochs):
+        val_acc = 0.0
+        for test_batch in test_loader:
+            yhat = net(test_batch['img'].cuda()).logits
+            yhat = torch.nn.functional.sigmoid(yhat)
+            acc = torch.mean((test_batch['anno'].cuda() == (yhat > 0.9)).type(torch.float32) )
+            print(f"Epoch, {e}, acc { acc.item():2.4f}")
+
+        bar = tqdm.tqdm(train_loader)
+        for batch in bar:
+            optimizer.zero_grad()
+            x = batch['img'].cuda()
+            y = batch['anno'].cuda().type(torch.float32)
+
+            yhat = net(x).logits
+
+            cost_val = cost(yhat, y)
+
+            cost_val.backward()
+            optimizer.step()
+
+            train_acc = torch.mean((batch['anno'].cuda() == (yhat > 0.9)).type(torch.float32) )
+            bar.set_description(f"train, cost: {cost_val.item():2.4f}, acc: {train_acc.cpu().item():2.4f}")
+
+        if e > 20:
+            for g in optimizer.param_groups:
+                g['lr'] = 1e-4
+        torch.save(net.state_dict(), f'./saved_models/inception_converged_{e}.pth')
+    # torch.save(net, '/tmp/test/incelption_converged.pth')
diff --git a/scripts/ddpm_ddim_scheduler.py b/scripts/ddpm_ddim_scheduler.py
deleted file mode 100644
index 703fe9a..0000000
--- a/scripts/ddpm_ddim_scheduler.py
+++ /dev/null
@@ -1,118 +0,0 @@
-"""Generate images for following specified models."""
-
-import argparse
-import math
-import os
-import sys
-import time
-
-import numpy as np
-import torch
-from diffusers import DDIMScheduler, DDPMScheduler, UNet2DModel
-from torch.distributed import destroy_process_group, init_process_group
-from torch.nn.parallel import DistributedDataParallel as DDP
-from tqdm import tqdm
-
-model_id = {
-    "cifar10-32": ["google/ddpm-cifar10-32", 50000],
-    "celebahq-256": ["google/ddpm-celebahq-256", 30000],
-    "church-256": ["google/ddpm-church-256", 30000],
-    "bedroom-256": ["google/ddpm-bedroom-256", 30000],
-}
-
-
-class Sampler:
-    def __init__(
-        self, model, global_seed, scheduler, img_size, batch_size, sample_path
-    ):
-        self.local_rank = int(os.environ["LOCAL_RANK"])
-        self.global_rank = int(os.environ["RANK"])
-        self.model = model.to(self.local_rank)
-        self.model = DDP(self.model, device_ids=[self.local_rank])
-        self.seed = global_seed + self.global_rank
-        self.scheduler = scheduler
-        self.noise_img_shape = (batch_size, 3, img_size, img_size)
-        self.sample_path = sample_path
-
-    def sample(self, total_batches):
-        with torch.no_grad():
-            for batch in range(total_batches):
-                noise = torch.randn(self.noise_img_shape).to(self.local_rank)
-                input = noise
-                for t in tqdm(self.scheduler.timesteps):
-                    noisy_res = self.model(input, t).sample
-                    prev_noisy_sample = self.scheduler.step(
-                        noisy_res, t, input
-                    ).prev_sample
-                    input = prev_noisy_sample
-                # images = (input / 2 + 0.5).clamp(0, 1)
-                np_images = input.cpu().permute(0, 2, 3, 1).numpy()
-                # print(f"[GPU: {self.global_rank}], Img shape: {np_images.shape}")
-                str_time = str(time.time()).replace(".", "_")
-                fname = f"batch_{str_time}.npz"
-                fpath = os.path.join(self.sample_path, fname)
-                np.savez(fpath, x=np_images)
-                print(f"Saved batch at {fname}", flush=True)
-
-
-def main(scheduler_nm: str, dataset: str, input_shape: int):
-    global model_id
-    torch.backends.cuda.matmul.allow_tf32 = True
-    init_process_group(backend="nccl")
-
-    try:
-        model_name, num_samples = model_id[f"{dataset.lower()}-{input_shape}"]
-    except Exception as e:
-        print("Model doesn't exist for given dataset and input shape.")
-        print(f"Supported datasets with image shapes are {model_id.keys()}")
-        sys.exit(0)
-    batch_size = 50
-
-    diffusion_sampler = (
-        DDIMScheduler if scheduler_nm.upper() == "DDIM" else DDPMScheduler
-    )
-    scheduler = diffusion_sampler.from_pretrained(model_name)
-    model = UNet2DModel.from_pretrained(model_name)
-    scheduler.set_timesteps(1000)
-    total_batches = int(
-        math.ceil(num_samples / (batch_size * torch.distributed.get_world_size()))
-    )
-    global_rank = int(os.environ["RANK"])
-    sample_path = f"./sample_imgs_{dataset}_{input_shape}_{scheduler_nm}"
-    os.makedirs(sample_path, exist_ok=True)
-    if global_rank == 0:
-        print(f"Running {total_batches} number of batches for sampling", flush=True)
-        print(f"Saving the sampled images at {sample_path}", flush=True)
-
-    sampler = Sampler(
-        model, 0, scheduler, model.config.sample_size, batch_size, sample_path
-    )
-    sampler.sample(total_batches)
-    destroy_process_group()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        prog="DDPM_DDIM_Scheduler",
-        description="Sanple DDPM and DDIM on various datasets",
-    )
-    parser.add_argument(
-        "--dataset", type=str, required=True, help="Select the dataset."
-    )
-    parser.add_argument(
-        "--input-shape",
-        type=int,
-        required=True,
-        help="Input size either width or height.",
-    )
-    parser.add_argument(
-        "--scheduler",
-        type=str,
-        default="DDIM",
-        help="Select between DDIM and DDPM sampling.",
-    )
-    args = parser.parse_args()
-    scheduler = args.scheduler
-    dataset = args.dataset
-    input_shape = args.input_shape
-    main(scheduler, dataset, input_shape)
diff --git a/scripts/fid/fid.py b/scripts/fid/fid.py
index 294cfe1..fe30bb1 100644
--- a/scripts/fid/fid.py
+++ b/scripts/fid/fid.py
@@ -167,7 +167,6 @@ def get_activations(files, model, batch_size=50, dims=2048, device="cpu", resize
     pred_arr = np.empty((len(files), dims))
 
     start_idx = 0
-
     for batch in tqdm(dataloader):
         batch = batch.to(device)
 
diff --git a/scripts/fid_run.py b/scripts/fid_run.py
deleted file mode 100644
index 0ca77cd..0000000
--- a/scripts/fid_run.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import numpy as np
-import torch
-from PIL import Image
-from scipy import datasets
-
-from scripts.fid.fid import calculate_frechet_distance
-from scripts.fid.inception import InceptionV3
-
-
-def compute_activation(img_tensor, model):
-    op1 = model(img_tensor)[0]
-    onp1 = op1.squeeze(3).squeeze(2).cpu().numpy()
-    return onp1
-
-
-if __name__ == "__main__":
-    face = datasets.face()
-    face = Image.fromarray(face).resize((299, 299))
-    face = np.expand_dims(face, 0).astype(np.float32) / 255.0
-
-    face2 = np.round(face, 2)
-    face3 = np.round(face, 3)
-
-    prepare = lambda tensor: torch.permute(torch.tensor(tensor), [0, 3, 1, 2])
-
-    face = prepare(face)
-    face2 = prepare(face2)
-    face3 = prepare(face3)
-
-    bidx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]
-    model = InceptionV3([bidx])
-    model = model  # .to('cuda:0')
-    model.eval()
-
-    with torch.no_grad():
-        act = model(face)[0].squeeze([-2, -1]).numpy()
-        act2 = model(face2)[0].squeeze([-2, -1]).numpy()
-        act3 = model(face3)[0].squeeze([-2, -1]).numpy()
-
-    def compute_fid(activation1, activation2):
-        m1, s1 = np.mean(activation1, axis=0), np.cov(activation1, rowvar=False)
-        m2, s2 = np.mean(activation2, axis=0), np.cov(activation2, rowvar=False)
-        return calculate_frechet_distance(m1, s1, m2, s2)
-
-    fid2 = compute_fid(act, act2)
-    fid3 = compute_fid(act, act3)
-
-    pass
diff --git a/scripts/sample.py b/scripts/sample.py
deleted file mode 100644
index d3f15e1..0000000
--- a/scripts/sample.py
+++ /dev/null
@@ -1,66 +0,0 @@
-"""Sample based on the dataset."""
-
-import os
-
-import torch
-import torch.distributed as dist
-
-from scripts.train import instantiate_model, load_config
-from src.dataloader import get_dataloaders, get_distributed_dataloader
-from src.sampler import Sampler
-from src.util import _sampler_args
-
-
-def main():
-    global args
-    print(args)
-    config = load_config(args.dataset)
-    if args.allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-        torch.backends.cudnn.allow_tf32 = True
-
-    dist.init_process_group(backend="nccl")
-    torch.manual_seed(args.seed)
-    model = instantiate_model(config.model_config)
-
-    if config.data_dir is None:
-        raise ValueError(
-            "Datapath is None, please set the datapath in corresponding config file."
-        )
-    if not os.path.exists(config.data_dir) and ("CIFAR" not in args.dataset):
-        raise ValueError(
-            "Data directory doesnot exist, please provide proper path in corresponding config file."
-        )
-
-    data_set, _ = get_dataloaders(
-        dataset_name=args.dataset,
-        batch_size=args.batch_size,
-        val_size=20,
-        data_path=config.data_dir,
-        only_datasets=True,
-    )
-    dataloader, _ = get_distributed_dataloader(
-        dataset=data_set,
-        world_size=dist.get_world_size(),
-        global_seed=args.seed,
-        batch_size=args.batch_size,
-        num_workers=config.dataset_config["num_workers"],
-    )
-    path_nm = args.ckpt_path.split / ("/")[-2]
-    save_path = f"./sample_imgs_{args.sampler}_{path_nm}/"
-    os.makedirs(save_path, exist_ok=True)
-
-    args.batch_size = args.batch_size // dist.get_world_size()
-    sampler = Sampler(
-        model=model,
-        args=args,
-        save_path=save_path,
-        std=config.dataset_config["std"],
-        mean=config.dataset_config["mean"],
-    )
-    sampler.conditional_sample(batched_labels=dataloader)
-
-
-if __name__ == "__main__":
-    args = _sampler_args()
-    main()
diff --git a/scripts/train.py b/scripts/train.py
deleted file mode 100644
index 22d698f..0000000
--- a/scripts/train.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import datetime
-import os
-from typing import Any, Dict
-
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-
-# from src.improved_UNet import Improv_UNet
-from diffusers import UNet2DModel
-from torch.utils.tensorboard import SummaryWriter
-
-from config import celeba, cifar10
-from src.dataloader import get_dataloaders, get_distributed_dataloader
-from src.nn import get_loss_fn
-from src.trainer import Trainer
-from src.util import _get_global_rank, _get_local_rank, _parse_args
-
-
-def load_config(dataset_name: str) -> Any:
-    """Load the configurations from config folder.
-
-    Args:
-        dataset_name (str): Name of the dataset
-
-    Returns:
-        Any: Class containing the configuration details.
-    """
-    dataset_name = dataset_name.upper()
-    config_module = cifar10 if dataset_name == "CIFAR10" else celeba
-    config = getattr(config_module, dataset_name)()
-    return config
-
-
-def instantiate_model(dataset: Dict[str, Any]) -> nn.Module:
-    """Instantiate the UNet model.
-
-    Args:
-        model_config (Dict[str, Any]): Dictionary containing the model configuration details.
-
-    Returns:
-        nn.Module: Instantiate the model.
-    """
-    # def instantiate_model(model_config: Dict[str, Any]) -> nn.Module:
-    #     """Instantiate the UNet model.
-
-    #     Args:
-    #         model_config (Dict[str, Any]): Dictionary containing the model configuration details.
-
-    #     Returns:
-    #         nn.Module: Instantiate the model.
-    #     """
-    # attn_res = []
-    # for value in model_config["attn_res"]:
-    #     attn_res.append(model_config["input_size"] // int(value))
-
-    # model = Improv_UNet(
-    #     in_channels=model_config["in_c"],
-    #     model_channels=model_config["model_c"],
-    #     out_channels=model_config["out_c"],
-    #     num_res_blocks=model_config["num_res_blocks"],
-    #     attention_resolutions=attn_res,
-    #     dropout=model_config["dropout"],
-    #     channel_mult=model_config["channel_mult"],
-    #     num_classes=model_config["num_classes"],
-    #     num_heads=model_config["num_heads"],
-    #     num_heads_upsample=model_config["num_heads_ups"],
-    #     use_scale_shift_norm=model_config["use_scale_shift_norm"],
-    # )
-    # return model
-    name = None
-    if "cifar10" in dataset.lower():
-        name = "google/ddpm-cifar10-32"
-    elif "celeba" in dataset.lower():
-        name = "google/ddpm-celebahq-256"
-    elif "church" in dataset.lower():
-        name = "google/ddpm-church-256"
-    elif "bedroom" in dataset.lower():
-        name = "google/ddpm-bedroom-256"
-    else:
-        raise NotImplementedError("Improper dataset.")
-    model = UNet2DModel.from_pretrained(name)
-    return model
-
-
-def main():
-    """DDP Training for DDPM/DDIM diffusion models."""
-    global args
-    config = load_config(args.dataset)
-    if args.allow_tf32:
-        print(
-            "TF32 matmul are activated. \
-              (Note: This improves training on Ampere GPU devices but affects accuracy a little.)"
-        )
-        torch.backends.cuda.matmul.allow_tf32 = True
-        torch.backends.cudnn.allow_tf32 = True
-
-    # Initialize the models
-    dist.init_process_group(backend="nccl")
-    torch.manual_seed(args.seed)
-    torch.autograd.set_detect_anomaly(True)
-    local_rank = _get_local_rank()
-    global_rank = _get_global_rank() if args.distribute else local_rank
-    # model = instantiate_model(config.model_config)
-    model = instantiate_model(args.dataset)
-
-    if config.data_dir is None:
-        raise ValueError(
-            "Datapath is None, please set the datapath in corresponding config file."
-        )
-    if (not os.path.exists(config.data_dir)) and ("CIFAR" not in args.dataset):
-        raise ValueError(
-            "Data directory doesnot exist, please provide proper path in corresponding config file."
-        )
-
-    # Dataloading
-    train_set, _ = get_dataloaders(
-        dataset_name=args.dataset,
-        batch_size=args.batch_size,
-        val_size=20,
-        data_path=config.data_dir,
-        only_datasets=True,
-    )
-    train_loader, train_sampler = get_distributed_dataloader(
-        dataset=train_set,
-        world_size=dist.get_world_size(),
-        global_seed=args.seed,
-        batch_size=args.batch_size,
-        num_workers=config.dataset_config["num_workers"],
-    )
-    # val_loader, _ = get_distributed_dataloader(
-    #     dataset=val_set,
-    #     world_size=dist.get_world_size(),
-    #     global_seed=args.seed,
-    #     batch_size=args.batch_size,
-    #     num_workers=config.dataset_config["num_workers"],
-    # )
-
-    # model utils intialization
-    weights = None
-    if args.packet_norm_type is not None:
-        if "weight" in args.packet_norm_type:
-            weights = config.norm_weights[f"{args.wavelet.lower()}_l{args.max_level}"]
-    loss_fn = get_loss_fn(args, weights=weights)
-    args.clip_grad_norm = (
-        config.optimizer_config["clip_grad_norm"]
-        if args.clip_grad_norm == 0
-        else args.clip_grad_norm
-    )
-    if args.lr is None:
-        print(
-            "User defined learning rate not available, defaulting to the learning rate in corresponding config file."
-        )
-    learning_rate = (
-        args.lr
-        if args.lr is not None and args.lr != config.optimizer_config["lr"]
-        else config.optimizer_config["lr"]
-    )
-    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
-    writer = None
-    save_path = None
-    if global_rank == 0:
-        dt_now = (
-            str(datetime.datetime.now())
-            .replace(" ", "_")
-            .replace(":", "-")
-            .replace(".", "-")
-        )
-        writer_name = f"_dataset_{args.dataset}_loss_{args.loss_type}_on_{dt_now}"
-        save_path = f"./logs/{args.dataset}_{args.loss_type}_{dt_now}/"
-        if args.model_path is not None:
-            save_path = "/".join(args.model_path.split("/")[:-1])
-        writer = SummaryWriter(log_dir=save_path, comment=writer_name)
-
-    # Trainer Loop
-    trainer = Trainer(
-        model=model,
-        args=args,
-        optimizer=optimizer,
-        loss_fn=loss_fn,
-        writer=writer,
-        save_path=save_path,
-        std=config.dataset_config["std"],
-        mean=config.dataset_config["mean"],
-        input_shape=config.model_config["input_size"],
-    )
-    trainer.train(
-        max_epochs=args.epochs, dataloader=train_loader, sampler=train_sampler
-    )
-    if global_rank == 0:
-        writer.close()
-
-    dist.destroy_process_group()
-
-
-if __name__ == "__main__":
-    args = _parse_args()
-    print(args)
-    main()
diff --git a/src/dataloader.py b/src/dataloader.py
deleted file mode 100644
index 2dda0f7..0000000
--- a/src/dataloader.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import json
-import os
-from typing import Dict, Tuple
-
-import torch
-from PIL import Image
-from torch.utils.data import DataLoader, Dataset
-from torch.utils.data.distributed import DistributedSampler
-from torchvision import datasets, transforms
-
-
-class CelebAHQDataset(Dataset):
-    """CelebAHQ dataset."""
-
-    def __init__(self, data_path: str, transform: transforms = None) -> None:
-        """Initialize the CelebAHQ dataset.
-
-        Args:
-            data_path (str): Data path
-            transform (transforms, optional): Data transforms. Defaults to None.
-        """
-        super().__init__()
-        self.data_path = data_path
-        self.transform = transform
-        self.labels = self.get_labels("./data/CelebAHQ/identity_CelebAHQ.txt")
-        self.img_names = os.listdir(data_path)
-
-    def get_labels(self, path: str) -> Dict[str, int]:
-        """Ĺoad image labels.
-
-        Args:
-            path (str): Labels path
-
-        Returns:
-            Dict[str, int]: Dictionary containing image name and label
-        """
-        hq_labels = json.load(open(path, "r"))
-        # for key, value in hq_labels.items():
-        #     value = int(value.replace("\n", ""))
-        #     hq_labels[key] = value
-        return hq_labels
-
-    def __len__(self) -> int:
-        """Total number of images.
-
-        Returns:
-            int: _Length of images
-        """
-        return len(self.img_names)
-
-    def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]:
-        """Load each image and its correspondng label.
-
-        Args:
-            index (int): Index of the image
-
-        Returns:
-            Tuple[torch.Tensor, int]: A tuple containing image and its label
-        """
-        img_path = os.path.join(self.data_path, self.img_names[index])
-        # img = io.read_image(img_path)
-        img = Image.open(img_path)
-        if self.transform:
-            img = self.transform(img)
-        label = self.labels[self.img_names[index]]
-        return img, label
-
-
-class CelabADataset(Dataset):
-    """CelebA dataset."""
-
-    def __init__(self) -> None:
-        super().__init__()
-        raise NotImplementedError
-
-
-def get_dataloaders(
-    dataset_name: str,
-    batch_size: int,
-    val_size: int,
-    data_path: str = None,
-    only_datasets: bool = True,
-) -> Tuple[DataLoader, DataLoader]:
-    """Get the dataloaders based on the dataset.
-
-    Args:
-        dataset_name (str): Name of the dataset
-        batch_size (int): Batch size
-        data_path (str): Path to dataset. Defaults to None.
-        only_datasets (bool): Return only datasets. Defaults to True.
-
-    Returns:
-        Tuple[DataLoader, DataLoader]: A tuple containing train and validation dataloaders/datasets
-    """
-    train_set, val_set = None, None
-    if "mnist" in dataset_name.lower():
-        train_set = datasets.MNIST(
-            "../mnist_data",
-            download=True,
-            train=True,
-            transform=transforms.Compose(
-                [
-                    transforms.ToTensor(),
-                ]
-            ),
-        )
-        val_set = datasets.MNIST(
-            "../mnist_data",
-            download=True,
-            train=False,
-            transform=transforms.Compose(
-                [
-                    transforms.ToTensor(),
-                ]
-            ),
-        )
-    elif "celebahq" in dataset_name.lower():
-        normalize = transforms.Normalize(
-            mean=[0.5169, 0.4167, 0.36327], std=[0.29934, 0.27131, 0.26649]
-        )
-        train_set = CelebAHQDataset(
-            data_path,
-            transform=transforms.Compose(
-                [transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize]
-            ),
-        )
-        val_set = CelebAHQDataset(
-            data_path,
-            transform=transforms.Compose([transforms.ToTensor()]),
-        )
-    elif "cifar10" in dataset_name.lower():
-        # TODO: Get these normalize values from the config file later
-        normalize = transforms.Normalize(
-            mean=[0.5071, 0.4867, 0.4408], std=[0.2675, 0.2565, 0.2761]
-        )
-
-        train_set = datasets.CIFAR10(
-            "../cifar_data",
-            download=True,
-            train=True,
-            transform=transforms.Compose(
-                [transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize]
-            ),
-        )
-        val_set = datasets.CIFAR10(
-            "../cifar_data",
-            download=True,
-            train=False,
-            transform=transforms.Compose([transforms.ToTensor()]),
-        )
-    elif "celeba" in dataset_name.lower():
-        raise NotImplementedError
-
-    if only_datasets:
-        return train_set, val_set
-
-    trainloader = DataLoader(
-        train_set, batch_size=batch_size, shuffle=True, num_workers=48
-    )
-    valloader = DataLoader(val_set, batch_size=val_size, shuffle=False, num_workers=48)
-    return trainloader, valloader
-
-
-def get_distributed_dataloader(
-    dataset: Dataset,
-    world_size: int,
-    global_seed: int,
-    batch_size: int,
-    num_workers: int,
-) -> Tuple[DataLoader, torch.utils.data.Sampler]:
-    """Return the distributed dataloader.
-
-    Args:
-        dataset (Dataset): Dataset object
-        world_size (int): Total number of available GPUs
-        global_seed (int): Overall seed
-        batch_size (int): Batch size
-        num_workers (int): Total number of workers in each node
-
-    Returns:
-        Tuple[DataLoader, torch.utils.data.Sampler]: Tuple containing dataloader and datasampler
-    """
-    sampler = DistributedSampler(
-        dataset, num_replicas=world_size, shuffle=True, seed=global_seed
-    )
-    return (
-        DataLoader(
-            dataset,
-            batch_size=batch_size // world_size,
-            shuffle=False,
-            sampler=sampler,
-            num_workers=num_workers,
-            pin_memory=True,
-            drop_last=True,
-        ),
-        sampler,
-    )
diff --git a/src/freq_math.py b/src/freq_math.py
index 79f31b1..fbfe39b 100644
--- a/src/freq_math.py
+++ b/src/freq_math.py
@@ -1,16 +1,13 @@
-from copy import deepcopy
-from functools import partial
+"""Wavelet utils."""
+
 from itertools import product
-from typing import Tuple
+from typing import Optional
 
-import matplotlib.pyplot as plt
 import numpy as np
 import ptwt
 import pywt
 import torch
-import torch.nn.functional as F
 from scipy import linalg
-from tqdm import tqdm
 
 
 def get_freq_order(level: int):
@@ -68,29 +65,23 @@ def fold_channels(input_tensor: torch.Tensor) -> torch.Tensor:
     return torch.reshape(input_tensor, (-1, shape[-2], shape[-1]))
 
 
-def unfold_channels(
-    input_tensor: torch.Tensor, original_shape: Tuple[int, int, int, int]
-) -> torch.Tensor:
-    """Restore channels from the leading batch-dimension.
-
-    Args:
-        array (torch.Tensor): An [B*C, packets, H, W] input array.
-
-    Returns:
-        torch.Tensor: Output of shape [B, packets, H, W, C]
-    """
-
-    _, packets, _, _ = input_tensor.shape
-    b, c, h, w = original_shape
-    return torch.reshape(input_tensor, (b, packets, c, h, w))
-
-
 def forward_wavelet_packet_transform(
     tensor: torch.Tensor,
     wavelet: str,
     max_level: int,
     log_scale: bool,
 ) -> torch.Tensor:
+    """Compute wavelet packet transform.
+
+    Args:
+        tensor (torch.Tensor): Input torch tensor
+        wavelet (str): Choice of wavelet
+        max_level (int): Level of decomposition
+        log_scale (bool): Log scale boolean
+
+    Returns:
+        torch.Tensor: Packets
+    """
     # ideally the output dtype should depend in the input.
     # tensor = tensor.type(torch.FloatTensor)
     packets = ptwt.WaveletPacket2D(tensor, pywt.Wavelet(wavelet), maxlevel=max_level)
@@ -99,43 +90,16 @@ def forward_wavelet_packet_transform(
     # for node in packets.get_natural_order(max_level):
     # packet = torch.squeeze(packets[node], dim=1)
     # packet_list.append(packets[node])
-    wp_pt_rs = torch.stack(packet_list, axis=1)
+    wp_pt_rs = torch.stack(packet_list, dim=1)
     if log_scale:
         wp_pt_rs = torch.log(torch.abs(wp_pt_rs) + 1e-6)
 
     return wp_pt_rs
 
 
-def batched_packet_transform(
-    tensor: torch.Tensor, wavelet: str, max_level: int, log_scale: bool, batch_size: int
-) -> torch.Tensor:
-    """Compute wavelet packet transform over batches.
-
-    Args:
-        tensor (torch.Tensor): Input tensor of shape [BS, CHANNELS, HEIGHT, WIDTH]
-        wavelet (str): Choice of wavelet
-        max_level (int): Maximum decomposition level
-        log_scale (bool): Whether to apply log scale
-        batch_size (int): Batch size for tensor split
-
-    Returns:
-        torch.Tensor: Tensor containing packets of shape [BS, PACKETS, CHANNELS, HEIGHT, WIDTH]
-
-    """
-    assert (
-        len(tensor.shape) == 4
-    ), "Input tensor for packet transforms must have 4 dimensions"
-    batched_tensor = tensor.split(split_size=batch_size, dim=0)
-    packets = []
-    for image_batch in batched_tensor:
-        packets.append(
-            forward_wavelet_packet_transform(image_batch, wavelet, max_level, log_scale)
-        )
-    return torch.cat(packets, dim=0)
-
-
 def generate_frequency_packet_image(packet_array: np.ndarray, degree: int):
     """Create a ready-to-polt image with frequency-order packages.
+
        Given a packet array in natural order, creat an image which is
        ready to plot in frequency order.
     Args:
@@ -159,42 +123,10 @@ def generate_frequency_packet_image(packet_array: np.ndarray, degree: int):
     return np.concatenate(image, 2)
 
 
-def inverse_wavelet_packet_transform(
-    packet_tensor: torch.Tensor, wavelet: str, max_level: int
-):
-    batch, _, channels, _, _ = packet_tensor.shape
-
-    def get_node_order(level):
-        wp_natural_path = list(product(["a", "h", "v", "d"], repeat=level))
-        return ["".join(p) for p in wp_natural_path]
-
-    wp_dict = {}
-    for pos, path in enumerate(get_node_order(max_level)):
-        wp_dict[path] = packet_tensor[:, pos, :, :, :]
-
-    for level in reversed(range(max_level)):
-        for node in get_node_order(level):
-            data_a = fold_channels(wp_dict[node + "a"])
-            data_h = fold_channels(wp_dict[node + "h"])
-            data_v = fold_channels(wp_dict[node + "v"])
-            data_d = fold_channels(wp_dict[node + "d"])
-            rec = ptwt.waverec2(
-                [data_a, (data_h, data_v, data_d)], pywt.Wavelet(wavelet)
-            )
-            height = rec.shape[1]
-            width = rec.shape[2]
-            rec = unfold_channels(
-                torch.unsqueeze(rec, 1), [batch, channels, height, width]
-            )
-            rec = torch.squeeze(rec, 1)
-            wp_dict[node] = rec
-    return rec
-
-
 def compute_kl_divergence(
-    output: torch.Tensor, target: torch.Tensor, eps: float = 1e-30
+    output: torch.Tensor, target: torch.Tensor, eps: Optional[float] = 1e-30
 ) -> torch.Tensor:
-    """Compute KL Divergence
+    """Compute KL Divergence.
 
     Args:
         output (torch.Tensor): Output Images
@@ -208,52 +140,13 @@ def compute_kl_divergence(
     return target * torch.log((target / (output + eps)) + eps)
 
 
-def fourier_power_divergence(
-    output: torch.Tensor, target: torch.Tensor
-) -> torch.Tensor:
-    """Power spectrum entropy metric as presented in:
-    https://openaccess.thecvf.com/content_ICCV_2019/papers/Hernandez_Human_Motion_Prediction_via_Spatio-Temporal_Inpainting_ICCV_2019_paper.pdf
-
-    Args:
-        output (torch.Tensor): The network output.
-        target (torch.Tensor): The target image.
-
-    Returns:
-        (torch.Tensor): A scalar metric.
-
-    TODO:REMOVE
-    """
-    assert (
-        output.shape == target.shape
-    ), "Sampled and reference images should have same shape."
-
-    output_fft = torch.abs(torch.fft.fft2(output)) ** 2
-    target_fft = torch.abs(torch.fft.fft2(target)) ** 2
-
-    b, c, _, _ = output_fft.shape
-
-    output_fft = output_fft.swapaxes(0, 1)
-    target_fft = target_fft.swapaxes(0, 1)
-    output_power = torch.reshape(output_fft, (c, -1))
-    target_power = torch.reshape(target_fft, (c, -1))
-    output_power = output_power / torch.sum(output_power, dim=-1, keepdim=True)
-    target_power = target_power / torch.sum(target_power, dim=-1, keepdim=True)
-
-    kld_AB = compute_kl_divergence(output_power, target_power)
-    kld_BA = compute_kl_divergence(target_power, output_power)
-
-    kld_AB = torch.sum(kld_AB, dim=-1)
-    kld_BA = torch.sum(kld_BA, dim=-1)
-    return torch.mean(kld_AB), torch.mean(kld_BA)
-
-
 def wavelet_packet_power_divergence(
     output: torch.Tensor,
     target: torch.Tensor,
     level: int,
     wavelet: str,
     log_scale: bool,
-) -> torch.Tensor:
+) -> float:
     """Compute the wavelet packet power divergence.
 
     Daubechies wavelets are orthogonal, see Ripples in Mathematics page 129:
@@ -273,17 +166,17 @@ def wavelet_packet_power_divergence(
         wavelet(str): Type of wavelet to use. Defaults to sym5
 
     Returns:
-        torch.Tensor: Wavelet power divergence metric
+        float: Wavelet power divergence metric
     """
     assert (
         output.shape == target.shape
     ), "Sampled and reference images should have same shape."
 
-    output_packets = batched_packet_transform(
-        output, max_level=level, wavelet=wavelet, log_scale=log_scale, batch_size=2500
+    output_packets = forward_wavelet_packet_transform(
+        output, max_level=level, wavelet=wavelet, log_scale=log_scale
     )
-    target_packets = batched_packet_transform(
-        target, max_level=level, wavelet=wavelet, log_scale=log_scale, batch_size=2500
+    target_packets = forward_wavelet_packet_transform(
+        target, max_level=level, wavelet=wavelet, log_scale=log_scale
     )
 
     B, P, C, H, W = output_packets.shape
@@ -304,7 +197,7 @@ def wavelet_packet_power_divergence(
             max_val = torch.max(
                 torch.max(torch.abs(op_pack)), torch.max(torch.abs(tg_pack))
             )
-            max_val = 1e-12 if max_val == 0 else max_val
+            max_val = torch.Tensor(1e-12) if max_val == 0 else max_val
             op_pack = op_pack / max_val
             tg_pack = tg_pack / max_val
             output_hist, _ = torch.histogram(
@@ -329,11 +222,13 @@ def wavelet_packet_power_divergence(
     kld_ab = compute_kl_divergence(output_hist, target_hist)
     kld_ba = compute_kl_divergence(target_hist, output_hist)
     kld = 0.5 * (kld_ab + kld_ba)
-    return torch.mean(kld).item()  # Average kldivergence across packets and channels
+    return float(
+        torch.mean(kld).item()
+    )  # Average kldivergence across packets and channels
 
 
 def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
-    """Implementation from https://github.com/mseitzer/pytorch-fid.
+    """Frechet Distance Implementation from https://github.com/bioinf-jku/TTUR/blob/master/fid.py.
 
     Numpy implementation of the Frechet Distance.
     The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
@@ -352,10 +247,12 @@ def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
     -- sigma2: The covariance matrix over activations, precalculated on an
                representative data set.
 
+    Raises:
+        ValueError: Value error if imaginary component has large value.
+
     Returns:
     --   : The Frechet Distance.
     """
-
     mu1 = np.atleast_1d(mu1)
     mu2 = np.atleast_1d(mu2)
 
@@ -392,87 +289,3 @@ def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
     tr_covmean = np.trace(covmean)
 
     return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
-
-
-def wavelet_packet_frechet_distance(
-    output: torch.Tensor,
-    target: torch.Tensor,
-    level: int,
-    wavelet: str,
-    log_scale: bool,
-) -> float:
-    """Compute wavelet packet Frechet distance.
-
-    Args:
-        output (torch.Tensor): Generated images of shape [B, C, H, W].
-        target (torch.Tensor): Ground truth images. Same shape as output.
-        level (int, optional): Wavelet decomposition level. Defaults to 4.
-        wavelet (str, optional): Wavelet type to use. Defaults to "sym5".
-
-    Returns:
-        float: Wavelet packet Frechet distance.
-    """
-    assert output.shape == target.shape, "Output and target must be of same shape."
-    # Compute wavelet packet transform.
-    output_packets = batched_packet_transform(
-        tensor=output,
-        wavelet=wavelet,
-        max_level=level,
-        log_scale=log_scale,
-        batch_size=2500,
-    )
-    target_packets = batched_packet_transform(
-        tensor=target,
-        wavelet=wavelet,
-        max_level=level,
-        log_scale=log_scale,
-        batch_size=2500,
-    )
-    assert (
-        output_packets.shape == target_packets.shape
-    ), "Output & target packets are not of same shape."
-
-    # Permute patches and batch dimensions
-    output_packets = torch.permute(output_packets, (1, 0, 2, 3, 4))
-    target_packets = torch.permute(target_packets, (1, 0, 2, 3, 4))
-    PACKETS, BATCH, CHANNELS, HEIGHT, WIDTH = output_packets.shape
-
-    # Flatten each packet in batch into vector
-    output_reshaped = torch.reshape(
-        output_packets, (PACKETS, BATCH, CHANNELS * HEIGHT * WIDTH)
-    )
-    target_reshaped = torch.reshape(
-        target_packets, (PACKETS, BATCH, CHANNELS * HEIGHT * WIDTH)
-    )
-
-    output_array = output_reshaped.detach().cpu().numpy()
-    target_array = target_reshaped.detach().cpu().numpy()
-
-    output_means = [
-        np.mean(output_array[packet_no, :, :], axis=0) for packet_no in range(PACKETS)
-    ]
-    target_means = [
-        np.mean(target_array[packet_no, :, :], axis=0) for packet_no in range(PACKETS)
-    ]
-
-    output_covs = [
-        np.cov(output_array[packet_no, :, :], rowvar=False)
-        for packet_no in range(PACKETS)
-    ]
-    target_covs = [
-        np.cov(target_array[packet_no, :, :], rowvar=False)
-        for packet_no in range(PACKETS)
-    ]
-
-    # print("Computing per packet FID...")
-    frechet_distances = []
-    # for packet_no in tqdm(range(PACKETS)):
-    for packet_no in range(PACKETS):
-        frechet_distance = calculate_frechet_distance(
-            mu1=output_means[packet_no],
-            mu2=target_means[packet_no],
-            sigma1=output_covs[packet_no],
-            sigma2=target_covs[packet_no],
-        )
-        frechet_distances.append(frechet_distance)
-    return np.mean(frechet_distances)
diff --git a/src/fwd.py b/src/fwd.py
new file mode 100644
index 0000000..00d9620
--- /dev/null
+++ b/src/fwd.py
@@ -0,0 +1,263 @@
+"""Frechet Wavelet Distance computation."""
+
+import os
+import pathlib
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+from typing import List, Tuple
+
+import numpy as np
+import torch as th
+import torchvision.transforms as tv
+from PIL import Image
+from tqdm import tqdm
+
+from .freq_math import calculate_frechet_distance, forward_wavelet_packet_transform
+
+th.set_default_dtype(th.float64)
+
+
+parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+parser.add_argument(
+    "--batch-size",
+    type=int,
+    default=128,
+    help="Batch size for wavelet packet transform.",
+)
+parser.add_argument(
+    "--num-processes", type=int, default=None, help="Number of multiprocess."
+)
+parser.add_argument(
+    "--save-packets", action="store_true", help="Save the packets as npz file."
+)
+parser.add_argument("--wavelet", type=str, default="sym5", help="Choice of wavelet.")
+parser.add_argument(
+    "--max_level", type=int, default=4, help="wavelet decomposition level"
+)
+parser.add_argument(
+    "--log_scale", action="store_true", help="Use log scaling for wavelets."
+)
+parser.add_argument(
+    "path",
+    type=str,
+    nargs=2,
+    help="Path to the generated images or path to .npz statistics file.",
+)
+
+IMAGE_EXTS = {"jpg", "jpeg", "png"}
+NUM_PROCESSES = None
+
+
+class ImagePathDataset(th.utils.data.Dataset):
+    """Image dataset."""
+
+    def __init__(self, files, transforms=None):
+        """File initialization."""
+        self.files = files
+        self.transforms = transforms
+
+    def __len__(self):
+        """Length of dataset."""
+        return len(self.files)
+
+    def __getitem__(self, i):
+        """Load the image."""
+        path = self.files[i]
+        img = Image.open(path).convert("RGB")
+        if self.transforms is not None:
+            img = self.transforms(img)
+        return img
+
+
+def compute_packet_statistics(
+    dataloader: th.utils.data.DataLoader, wavelet: str, max_level: int, log_scale: bool
+) -> Tuple[np.ndarray, ...]:
+    """Compute wavelet packet transform across batches.
+
+    Args:
+        dataloader (th.utils.data.DataLoader): Torch dataloader.
+        wavelet (str): Choice of wavelet.
+        max_level (int): Wavelet decomposition level.
+        log_scale (bool): Apply log scale.
+
+    Returns:
+        Tuple[np.ndarray, ...]: Mean and sigma for each packet.
+    """
+    packets = []
+    device = th.device("cuda:0") if th.cuda.is_available() else th.device("cpu")
+    for img_batch in tqdm(dataloader):
+        if isinstance(img_batch, list):
+            img_batch = img_batch[0]
+        img_batch = img_batch.to(device)
+        packets.append(
+            forward_wavelet_packet_transform(
+                img_batch, wavelet, max_level, log_scale
+            ).cpu()
+        )
+    packet_tensor = th.cat(packets, dim=0)
+    packet_tensor = th.permute(packet_tensor, (1, 0, 2, 3, 4))
+    P, BS, C, H, W = packet_tensor.shape
+    packet_tensor = th.reshape(packet_tensor, (P, BS, C * H * W))
+    print("Computing mean and std for each packet.")
+    mu = th.mean(packet_tensor, dim=1).numpy()
+    sigma = th.stack(
+        [th.cov(packet_tensor[p, :, :].T) for p in range(P)], dim=0
+    ).numpy()
+    return mu, sigma
+
+
+def calculate_path_statistics(
+    path: str, wavelet: str, max_level: int, log_scale: bool, batch_size: int
+) -> Tuple[np.ndarray, ...]:
+    """Compute mean and sigma for given path.
+
+    Args:
+        path (str): npz path or image directory.
+        wavelet (str): Choice of wavelet.
+        max_level (int): Decomposition level.
+        log_scale (bool): Apply log scale.
+        batch_size (int): Batch size for packet decomposition.
+
+    Raises:
+        ValueError: Error if mu and sigma cannot be calculated.
+
+    Returns:
+        Tuple[np.ndarray, ...]: Tuple containing mean and sigma for each packet.
+    """
+    mu, sigma = None, None
+    if path.endswith(".npz"):
+        with np.load(path) as fp:
+            mu = fp["mu"][:]
+            sigma = fp["sigma"][:]
+    else:
+        posfix_path = pathlib.Path(path)
+        img_names = sorted(
+            [name for ext in IMAGE_EXTS for name in posfix_path.glob(f"*.{ext}")]
+        )
+        dataloader = th.utils.data.DataLoader(
+            ImagePathDataset(img_names, transforms=tv.ToTensor()),
+            batch_size=batch_size,
+            shuffle=False,
+            drop_last=False,
+            num_workers=NUM_PROCESSES,
+        )
+        mu, sigma = compute_packet_statistics(
+            dataloader=dataloader,
+            wavelet=wavelet,
+            max_level=max_level,
+            log_scale=log_scale,
+        )
+
+    if (mu is None) or (sigma is None):
+        raise ValueError(f"The file path: {path} is empty/doesn't have statistics.")
+    return mu, sigma
+
+
+def compute_avg_frechet_distance(mu1, mu2, sigma1, sigma2):
+    """Compute avg frechet distance over packets."""
+    frechet_distances = []
+    for packet_no in tqdm(range(len(mu1))):
+        fd = calculate_frechet_distance(
+            mu1=mu1[packet_no, :],
+            mu2=mu2[packet_no, :],
+            sigma1=sigma1[packet_no, :, :],
+            sigma2=sigma2[packet_no, :, :],
+        )
+        frechet_distances.append(fd)
+    return np.mean(frechet_distances)
+
+
+def compute_fwd(
+    paths: List[str], wavelet: str, max_level: int, log_scale: bool, batch_size: int
+) -> float:
+    """Compute Frechet Wavelet Distance.
+
+    Args:
+        paths (List[str]): List containing path of source and generated images.
+        wavelet (str): Choice of wavelet.
+        max_level (int): Decomposition level.
+        log_scale (bool): Apply log scale.
+        batch_size (int): Batch size for packet decomposition.
+
+    Raises:
+        RuntimeError: Error if path doesn't exist.
+
+    Returns:
+        float: Frechet Wavelet Distance.
+    """
+    for path in paths:
+        if not os.path.exists(path):
+            raise RuntimeError(f"Invalid path: {path}")
+
+    print(f"Computing stats for path: {paths[0]}")
+    mu_1, sigma_1 = calculate_path_statistics(
+        paths[0], wavelet, max_level, log_scale, batch_size
+    )
+    print(f"Computing stats for path: {paths[1]}")
+    mu_2, sigma_2 = calculate_path_statistics(
+        paths[1], wavelet, max_level, log_scale, batch_size
+    )
+
+    print("Computing Frechet distances for each packet.")
+    return compute_avg_frechet_distance(mu_1, mu_2, sigma_1, sigma_2)
+
+
+def save_packets(
+    paths: List[str], wavelet: str, max_level: int, log_scale: bool, batch_size: int
+) -> None:
+    """Save packets.
+
+    Args:
+        paths (List[str]): List of paths containing input and output files.
+        wavelet (str): Choice of wavelet.
+        max_level (int): Decomposition level.
+        log_scale (bool): Apply log scale.
+        batch_size (int): Batch size for packet decomposition.
+
+    Raises:
+        RuntimeError: Error if input path is invalid.
+        RuntimeError: Error if the output file already exists.
+    """
+    if not os.path.exists(paths[0]):
+        raise RuntimeError(f"Invalid path: {paths[0]}")
+
+    if os.path.exists(paths[1]):
+        raise RuntimeError(f"Stats file already exists at the given path: {paths[1]}")
+
+    print(f"Computing stats for path: {paths[0]}")
+    mu_1, sigma_1 = calculate_path_statistics(
+        paths[0], wavelet, max_level, log_scale, batch_size
+    )
+    np.savez_compressed(paths[1], mu=mu_1, sigma=sigma_1)
+
+
+def main():
+    """Compute FWD given paths."""
+    global NUM_PROCESSES, IMAGE_EXTS
+
+    th.manual_seed(0)
+    th.use_deterministic_algorithms(True)
+    args = parser.parse_args()
+    print(args)
+    if args.num_processes is None:
+        try:
+            num_cpus = len(os.sched_getaffinity(0))
+        except AttributeError:
+            num_cpus = os.cpu_count()
+        NUM_PROCESSES = min(num_cpus, 16) if num_cpus is not None else 0
+    else:
+        NUM_PROCESSES = args.num_processes
+    print(f"Num work: {NUM_PROCESSES}")
+    if args.save_packets:
+        save_packets(
+            args.path, args.wavelet, args.max_level, args.log_scale, args.batch_size
+        )
+        return
+
+    fwd = compute_fwd(
+        args.path, args.wavelet, args.max_level, args.log_scale, args.batch_size
+    )
+    print(f"FWD: {fwd}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/improved_UNet.py b/src/improved_UNet.py
deleted file mode 100644
index 351572a..0000000
--- a/src/improved_UNet.py
+++ /dev/null
@@ -1,436 +0,0 @@
-"""UNet as implemented in https://arxiv.org/abs/2102.09672."""
-
-import math
-from abc import abstractmethod
-from typing import Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .nn import (
-    SiLU,
-    avg_pool_nd,
-    checkpoint,
-    conv_nd,
-    linear,
-    normalization,
-    timestep_embedding,
-    zero_module,
-)
-
-
-class TimeStepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x: torch.Tensor, emb: torch.Tensor):
-        """Forward pass for the embedding block.
-
-        Args:
-            x (torch.Tensor): Input tensor
-            emb (torch.Tensor): Time and class embedding
-        """
-
-
-class TimeStepEmbedSequential(nn.Sequential):
-    def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:
-        """Forward pass with embedding.
-
-        Args:
-            x (torch.Tensor): Input tensor
-            emb (torch.Tensor): Time and class embedding
-
-        Returns:
-            torch.Tensor: Output tensor
-        """
-        for layer in self:
-            if isinstance(layer, TimeStepBlock):
-                x = layer(x, emb)
-            else:
-                x = layer(x)
-        return x
-
-
-class UpSample(nn.Module):
-    """Upsampling layer with an optional convolution."""
-
-    def __init__(self, channels: int, use_conv: bool, dims: int = 2) -> None:
-        """Initialize upsample layer.
-
-        Args:
-            channels (int): Output channels
-            use_conv (bool): Boolean specifying to use convolution.
-            dims (int, optional): Dimensions for convolution. Defaults to 2.
-        """
-        super().__init__()
-        self.channels = channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, channels, channels, 3, padding=1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """Forward pass.
-
-        Args:
-            x (torch.Tensor): Input tensor of shape [N, C, spatial_dims]
-
-        Returns:
-            torch.Tensor: Upsampled tesnor of shape [N, C, 2*spatial_dims]
-        """
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
-class DownSample(nn.Module):
-    """Downsampling layer"""
-
-    def __init__(self, channels: int, use_conv: bool, dims: int = 2):
-        """Initialize downsample layer.
-
-        Args:
-            channels (int): Output channels
-            use_conv (bool): Boolean specifying to use convolution
-            dims (int, optional): Dimensions for convolution. Defaults to 2.
-        """
-        super().__init__()
-        self.channels = channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.multiplication = conv_nd(
-                dims, channels, channels, 3, stride=stride, padding=1
-            )
-        else:
-            self.multiplication = avg_pool_nd(stride)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """Forward pass.
-
-        Args:
-            x (torch.Tensor): Input tensor of shape [N, C, spatial_dims]
-
-        Returns:
-            torch.Tensor: Downsampled tesnor of shape [N, C, spatial_dims/2]
-        """
-        assert x.shape[1] == self.channels
-        return self.multiplication(x)
-
-
-class ResBlock(TimeStepBlock):
-    """Residual Block with dynamic channels."""
-
-    def __init__(
-        self,
-        channels: int,
-        emb_channels: int,
-        dropout: float,
-        out_channels: Union[int, None] = None,
-        use_conv: bool = False,
-        use_scale_shift_norm: bool = False,
-        dims: int = 2,
-        use_checkpoint: bool = False,
-    ) -> None:
-        """Residual block for each UNet step.
-
-        Args:
-            channels (int): Input channels
-            emb_channels (int): Embedded channels
-            dropout (float): Dropout probability
-            out_channels (Union, optional): Output channels. Defaults to None.
-            use_conv (bool, optional): Boolean for using convolution. Defaults to False.
-            use_scale_shift_norm (bool, optional): Boolean for scale shift norm. Defaults to False.
-            dims (int, optional): Dimensions for convolution. Defaults to 2.
-            use_checkpoint (bool, optional): Use checkpoint. Defaults to False.
-        """
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.droput = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-        self.emb_layers = nn.Sequential(
-            SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:
-        return checkpoint(self.call, (x, emb), self.parameters(), self.use_checkpoint)
-
-    def call(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:
-        h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1]
-            scale, shift = torch.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class Attention(nn.Module):
-    """Attention block."""
-
-    def __init__(
-        self, channels: int, num_heads: int = 1, use_checkpoint: bool = False
-    ) -> None:
-        super().__init__()
-        self.channels = channels
-        self.num_heads = num_heads
-        self.use_checkpoint = use_checkpoint
-
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return checkpoint(self.call, (x,), self.parameters(), self.use_checkpoint)
-
-    def call(self, x: torch.Tensor) -> torch.Tensor:
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        qkv = qkv.reshape(b * self.num_heads, -1, qkv.shape[2])
-        h = self.scaled_dot_product(qkv)
-        h = h.reshape(b, -1, h.shape[-1])
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-    def scaled_dot_product(self, x: torch.Tensor) -> torch.Tensor:
-        ch = x.shape[1] // 3
-        q, k, v = torch.split(x, ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = torch.einsum("bct, bcs -> bts", q * scale, k * scale)
-        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
-        return torch.einsum("bts, bcs -> bct", weight, v)
-
-
-class Improv_UNet(nn.Module):
-    """UNet model with attention and timestep embedding."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        model_channels: int,
-        out_channels: int,
-        num_res_blocks: int,
-        attention_resolutions: Tuple[int],
-        dropout: float = 0,
-        channel_mult: Tuple[int] = (1, 2, 4, 8),
-        conv_resample: bool = True,
-        dims: int = 2,
-        num_classes: Union[int, None] = None,
-        use_checkpoint: bool = False,
-        num_heads: int = 1,
-        num_heads_upsample: int = -1,
-        use_scale_shift_norm: bool = False,
-    ) -> None:
-        super().__init__()
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.num_heads = num_heads
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimeStepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1),
-                )
-            ]
-        )
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    layers.append(
-                        Attention(
-                            ch, use_checkpoint=use_checkpoint, num_heads=num_heads
-                        )
-                    )
-                self.input_blocks.append(TimeStepEmbedSequential(*layers))
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                self.input_blocks.append(
-                    TimeStepEmbedSequential(DownSample(ch, conv_resample, dims=dims))
-                )
-                input_block_chans.append(ch)
-                ds *= 2
-
-        self.middle_blocks = TimeStepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            Attention(ch, use_checkpoint=use_checkpoint, num_heads=num_heads),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                layers = [
-                    ResBlock(
-                        ch + input_block_chans.pop(),
-                        time_embed_dim,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                if ds in attention_resolutions:
-                    layers.append(
-                        Attention(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    layers.append(UpSample(ch, conv_resample, dims=dims))
-                    ds //= 2
-                self.output_blocks.append(TimeStepEmbedSequential(*layers))
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-
-    def forward(
-        self, x: torch.Tensor, timesteps: torch.Tensor, y: torch.Tensor = None
-    ) -> torch.Tensor:
-        """Forward pass."""
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-
-        hs = []
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        h = x
-        for module in self.input_blocks:
-            h = module(h, emb)
-            hs.append(h)
-        h = self.middle_blocks(h, emb)
-        for module in self.output_blocks:
-            cat_in = torch.cat([h, hs.pop()], dim=1)
-            h = module(cat_in, emb)
-        h = h.type(x.dtype)
-        return self.out(h)
-
-
-class SuperResModel(Improv_UNet):
-    """UNet Model to perform super resolution."""
-
-    def __init__(self, in_channels: int, *args, **kwargs) -> None:
-        super().__init__(in_channels * 2, *args, **kwargs)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        timesteps: torch.Tensor,
-        low_res: torch.Tensor = None,
-        **kwargs
-    ) -> torch.Tensor:
-        _, _, new_height, new_width = x.shape
-        upsampled_res = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
-        x = torch.cat((x, upsampled_res), dim=1)
-        return super().forward(x, timesteps, **kwargs)
diff --git a/src/networks.py b/src/networks.py
deleted file mode 100644
index ffeb20d..0000000
--- a/src/networks.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# TODO: remove?
-
-from typing import Tuple
-
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-import numpy as np
-
-from src.freq_math import (
-    forward_wavelet_packet_transform,
-    inverse_wavelet_packet_transform,
-)
-
-
-@jax.jit
-def pad_odd(input_x: jnp.ndarray) -> jnp.ndarray:
-    # dont pad the batch axis.
-    pad_list = [(0, 0, 0)]
-    for axis_shape in input_x.shape[1:-1]:
-        if axis_shape % 2 != 0:
-            pad_list.append((0, 1, 0))
-        else:
-            pad_list.append((0, 0, 0))
-    # dont pad the features
-    pad_list.append((0, 0, 0))
-    return jax.lax.pad(input_x, 0.0, pad_list)
-
-
-class UNet(nn.Module):
-    output_channels: int
-    transpose_conv = False
-    wavelet_packets: int = False
-    base_feat_no = 64  # TODO!
-
-    @nn.compact
-    def __call__(self, x_in: Tuple[jnp.ndarray]):
-        # Downscaling block
-        def down_block(x_bin, feats, time_lbl):
-            y = nn.relu(
-                nn.Conv(features=feats, kernel_size=(3, 3), padding="SAME")(x_bin)
-            )
-            time, label = time_lbl
-            time_emb = nn.Dense(np.prod(y.shape[1:]))(time)
-            lbl_emb = nn.Dense(np.prod(y.shape[1:]))(label)
-            time_lbl_emb = time_emb + lbl_emb
-            time_lbl_emb = jnp.reshape(time_lbl_emb, y.shape)
-            y = y + time_lbl_emb
-            y = nn.relu(
-                nn.Conv(
-                    features=feats, kernel_size=(3, 3), strides=(2, 2), padding="SAME"
-                )(y)
-            )
-            y = nn.GroupNorm()(y)
-            return pad_odd(y)
-
-        # Upscaling block
-        def up_block(x_bin, x_cat, feats, time_lbl):
-            B, H, W, C = x_bin.shape
-            if self.transpose_conv:
-                y = nn.ConvTranspose(
-                    features=feats, kernel_size=(3, 3), strides=(2, 2)
-                )(x_bin)
-            else:
-                y = jax.image.resize(x_bin, (B, H * 2, W * 2, C), "nearest")
-            y = y[:, : x_cat.shape[1], : x_cat.shape[2], :]
-            # y_cat = jnp.concatenate([x_cat, y], axis=-1)
-            y = nn.relu(nn.Conv(features=feats, kernel_size=(3, 3), padding="SAME")(y))
-            time, label = time_lbl
-            time_emb = nn.Dense(np.prod(y.shape[1:]))(time)
-            lbl_emb = nn.Dense(np.prod(y.shape[1:]))(label)
-            time_lbl_emb = time_emb + lbl_emb
-            time_lbl_emb = jnp.reshape(time_lbl_emb, y.shape)
-            y = y + time_lbl_emb
-            y = nn.relu(nn.Conv(features=feats, kernel_size=(3, 3), padding="SAME")(y))
-            y = nn.GroupNorm()(y)
-            return y
-
-        x, time, label = x_in
-        # x_in = jnp.expand_dims(x, -1)
-        x_in = x
-        init_feat = self.base_feat_no
-        time_lbl = time, label
-
-        # Encoder
-        x1 = nn.relu(
-            nn.Conv(features=init_feat, kernel_size=(3, 3), padding="SAME")(x_in)
-        )
-        x2 = down_block(x1, init_feat, time_lbl)
-        x3 = down_block(x2, init_feat * 2, time_lbl)
-        x4 = down_block(x3, init_feat * 4, time_lbl)
-        x5 = down_block(x4, init_feat * 8, time_lbl)
-        x6 = nn.relu(
-            nn.Conv(features=init_feat * 8, kernel_size=(3, 3), padding="SAME")(x5)
-        )
-        x7 = down_block(x6, init_feat * 16, time_lbl)
-        x8 = x7 + nn.relu(
-            nn.Conv(features=init_feat * 16, kernel_size=(3, 3), padding="SAME")(x7)
-        )
-        # Decoder
-        x9 = up_block(x8, x6, init_feat * 8, time_lbl)
-        x9 = x9 + x6
-        x10 = up_block(x9, x5, init_feat * 8, time_lbl)
-        x10 = x10 + x5
-        x11 = up_block(x10, x4, init_feat * 4, time_lbl)
-        x11 = x11 + x4
-        x12 = up_block(x11, x3, init_feat * 2, time_lbl)
-        x12 = x12 + x3
-        x13 = up_block(x12, x2, init_feat, time_lbl)
-        x13 = x13 + x2
-        x14 = up_block(x13, x1, init_feat, time_lbl)
-        y = nn.Conv(features=self.output_channels, kernel_size=(1, 1), padding="SAME")(
-            x14
-        )
-        return y
-
-
-# class Packetformer(nn.module):
-#    pass
diff --git a/src/nn.py b/src/nn.py
deleted file mode 100644
index c49ac06..0000000
--- a/src/nn.py
+++ /dev/null
@@ -1,303 +0,0 @@
-"""
-Various utilities for neural networks as in https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/nn.py.
-"""
-
-import argparse
-import math
-from functools import partial
-from typing import List
-
-import torch
-import torch.nn as nn
-
-from src.freq_math import forward_wavelet_packet_transform
-
-
-class SiLU(nn.Module):
-    def forward(self, x):
-        return nn.functional.silu(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def update_ema(target_params, source_params, rate=0.99):
-    """
-    Update target parameters to be closer to those of source parameters using
-    an exponential moving average.
-
-    :param target_params: the target parameter sequence.
-    :param source_params: the source parameter sequence.
-    :param rate: the EMA rate (closer to 1 means slower).
-    """
-    for targ, src in zip(target_params, source_params):
-        targ.detach().mul_(rate).add_(src, alpha=1 - rate)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-
-
-def timestep_embedding(timesteps, dim, max_period=10000):
-    """
-    Create sinusoidal timestep embeddings.
-
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    half = dim // 2
-    freqs = torch.exp(
-        -math.log(max_period)
-        * torch.arange(start=0, end=half, dtype=torch.float32)
-        / half
-    ).to(device=timesteps.device)
-    args = timesteps[:, None].float() * freqs[None]
-    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-    if dim % 2:
-        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    return embedding
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad():
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def get_loss_fn(args: argparse.Namespace, weights=None):
-    if args.loss_type.lower() == "mse":
-        return nn.MSELoss()
-    elif args.loss_type.lower() == "packet":
-        return partial(
-            PacketLoss,
-            wavelet=args.wavelet,
-            level=args.max_level,
-            norm_fn=args.packet_norm_type,
-            norm_weights=weights,
-        )
-    elif args.loss_type.lower() == "mixed":
-        return partial(
-            MixedLoss,
-            wavelet=args.wavelet,
-            level=args.max_level,
-            norm_fn=args.packet_norm_type,
-            sigma=args.loss_sigma,
-            norm_weight=weights,
-        )
-
-
-def PacketLoss(
-    output: torch.Tensor,
-    target: torch.Tensor,
-    wavelet: str,
-    level: int,
-    norm_fn: str = None,
-    norm_weights: List[float] = None,
-) -> torch.Tensor:
-    """Computes packet loss with various normalization techniques.
-
-    Args:
-        output (torch.Tensor): Network predictions
-        target (torch.Tensor): Target values
-        wavelet (str): Input wavelet
-        level (int): Level of wavelet decomposition
-        norm_fn (str, optional): norm function to use. Defaults to None.
-        norm_weights (List[float], optional): norm weights to use. Defaults to None.
-
-    Returns:
-        torch.Tensor: packet loss value
-    """
-
-    def weighted_packets(
-        packets: torch.Tensor, norm_weights: List[float]
-    ) -> torch.Tensor:
-        """Weigh the packets based on wavelet norm.
-
-        Args:
-            packets (torch.Tensor): Input packets of shape (bs, packets, c, h, w)
-            norm_weights (List[float]): norm weights to use
-
-        Raises:
-            ValueError: if weights are not provided
-
-        Returns:
-            torch.Tensor: Weighted packets
-        """
-        if norm_weights is None:
-            raise ValueError(
-                "Norm weights must be provided as a list, else use log scale norm."
-            )
-        norm_weights = torch.reshape(torch.Tensor(norm_weights), (1, -1, 1, 1, 1))
-        return norm_weights * packets
-
-    def log_scale_packets(
-        packets: torch.Tensor,
-    ) -> torch.Tensor:  # TODO: Rename this to max scaling instead of log scaling
-        """Normalize packets by log scaling.
-
-        Args:
-            packets (torch.Tensor): Packets of shape (bs, packets, c, h, w)
-
-        Returns:
-            torch.Tensor: log scale normalized packets
-        """
-        # return torch.sign(packets) * torch.log(torch.abs(packets) + 1e-8)
-        packets_max = torch.amax(torch.abs(packets), dim=(0, 2, 3, 4), keepdim=True)
-        return packets / (packets_max + 1e-12)
-
-    output_packets = forward_wavelet_packet_transform(
-        output, wavelet, level, log_scale=False
-    )
-    target_packets = forward_wavelet_packet_transform(
-        target, wavelet, level, log_scale=False
-    )
-    if norm_fn is not None:
-        packet_norm = (
-            log_scale_packets
-            if "log" in norm_fn
-            else partial(weighted_packets, norm_weights=norm_weights)
-        )
-        output_packets = packet_norm(output_packets)
-        target_packets = packet_norm(target_packets)
-    packet_mse = torch.mean(0.5 * (output_packets - target_packets) ** 2)
-    return packet_mse
-
-
-def MixedLoss(
-    output: torch.Tensor,
-    target: torch.Tensor,
-    sigma: float,
-    wavelet: str,
-    level: int,
-    norm_fn: str = None,
-    norm_weights: List[float] = None,
-) -> torch.Tensor:
-    """Compute MSE with Packet loss.
-
-    Args:
-        output (torch.Tensor): Network predictions
-        target (torch.Tensor): Target values
-        sigma (float): weighting factor
-        wavelet (str): Wavelet to use for packet loss
-        level (int): Level of decomposition of wavelet tree
-        norm_fn (str, optional): Normalization function for packet loss. Defaults to None.
-        norm_weights (List[float], optional): normalized weights for packet loss. Defaults to None.
-
-    Returns:
-        torch.Tensor: Combination loss of MSE and Packet
-    """
-    mse_loss = nn.functional.mse_loss(output, target)
-    packet_loss = PacketLoss(output, target, wavelet, level, norm_fn, norm_weights)
-    return mse_loss + (sigma * packet_loss)
diff --git a/src/sample_util.py b/src/sample_util.py
deleted file mode 100644
index 7ec6675..0000000
--- a/src/sample_util.py
+++ /dev/null
@@ -1,187 +0,0 @@
-from typing import List, Tuple
-
-import torch
-import torch.nn as nn
-from tqdm import tqdm
-
-
-@torch.jit.script
-def linear_noise_scheduler(
-    current_time_step: int, max_steps: int
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """Sample linear noise scheduler.
-
-    Args:
-        current_time_step (int): Current time
-        max_steps (int): Maximum number of steps
-
-    Returns:
-        Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Tuple containing alpha_bar, alpha and betas at current time
-    """
-    betas = torch.linspace(0.0001, 0.02, max_steps)
-    alphas = 1 - betas
-    alpha_ts = torch.cumprod(alphas, dim=0)
-    return (
-        alpha_ts[current_time_step],
-        alphas[current_time_step],
-        betas[current_time_step],
-    )
-
-
-def sample_noise(
-    img: torch.Tensor,
-    alpha_t: float,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """Diffusion forward step.
-
-    Args:
-        img (torch.Tensor): Input image
-        alpha_t (float): Alpha value for the corresponding timestep
-
-    Returns:
-        Tuple[torch.Tensor, torch.Tensor]: Tuple containing the noised image and noise
-    """
-    noise = torch.randn_like(img)
-    a = (alpha_t**0.5) * img
-    b = ((1 - alpha_t) ** 0.5) * noise
-    x = a + b
-    return x, noise
-
-
-# def linear_alpha(current_step: int, max_steps: int) -> float:
-#     return current_step / max_steps
-
-
-# def sample_noise_simple(
-#     img: jnp.ndarray,
-#     current_time_step: int,
-#     key: jnp.ndarray,
-#     max_steps: int,
-#     alpha_function: callable = linear_alpha,
-# ):
-#     alpha = alpha_function(current_time_step, max_steps)
-#     noise = jax.random.normal(key, shape=img.shape)
-#     x = (1 - alpha) * img + noise * alpha
-#     return x, img - x
-
-
-# def sample_net_noise(
-#     net_state: FrozenDict,
-#     model: nn.Module,
-#     key: int,
-#     input_shape: List[int],
-#     max_steps: int,
-#     label: int = 3338,
-#     sampling_function: callable = sample_noise,
-# ):
-#     prng_key = jax.random.PRNGKey(key)
-#     process_array = jax.random.normal(prng_key, shape=[1] + input_shape)
-
-#     for time in reversed(range(max_steps)):
-#         de_noise = model.apply(
-#             net_state,
-#             (
-#                 process_array,
-#                 jnp.expand_dims(jnp.array(time), -1),
-#                 jnp.expand_dims(jnp.array([label]), 0),
-#             ),
-#         )
-#         if sampling_function == sample_noise_simple:
-#             process_array += de_noise
-#         else:
-#             process_array -= de_noise
-
-#         prng_key = jax.random.split(prng_key, 1)[0]
-#         process_array = sampling_function(process_array, time, prng_key, max_steps)[0]
-#     return process_array[0]
-
-
-def sample_DDPM(
-    class_labels: torch.Tensor,
-    model: nn.Module,
-    max_steps: int,
-    input_shape: List[int],
-    device: torch.device,
-) -> torch.Tensor:
-    """DDPM Sampling from https://arxiv.org/pdf/2006.11239.pdf.
-
-    Args:
-        class_labels (torch.Tensor): Labels for class conditioning
-        model (nn.Module): Model instance
-        max_steps (int): Maximum steps
-        input_shape (List[int]): Input images shape
-        device (torch.device): torch device to use
-
-    Returns:
-        torch.Tensor: Returned sampled image
-    """
-    # device = torch.device('cpu')
-    model = model.to(device)
-    x_t = torch.randn(input_shape, device=device)
-    x_t_1 = x_t
-    class_labels = class_labels.to(device)
-
-    for time in tqdm(reversed(range(max_steps)), total=max_steps):
-        alpha_t, alpha, _ = linear_noise_scheduler(time, max_steps)
-        z = torch.randn_like(x_t_1, device=device)
-        time = torch.unsqueeze(torch.tensor(time, device=device), dim=-1)
-        denoise = model(x_t_1, time, return_dict=False)[0]
-        x_mean = (x_t_1 - (denoise * ((1 - alpha) / ((1 - alpha_t) ** 0.5)))) / (
-            alpha**0.5
-        )
-        x_t_1 = x_mean + ((1 - alpha) ** 0.5) * z
-    return x_t_1
-
-
-def sample_DDIM(
-    class_labels: torch.Tensor,
-    model: nn.Module,
-    max_steps: int,
-    input_shape: List[int],
-    device: torch.device,
-    eta: float = 0.0,
-    tau_steps: int = 1,
-) -> torch.Tensor:
-    """DDIM Sampling from https://arxiv.org/pdf/2010.02502.pdf.
-
-    Args:
-        class_labels (torch.Tensor): Labels for class conditioning
-        model (nn.Module): Model instance
-        max_steps (int): Maximum steps
-        input_shape (List[int]): Input images shape
-        device (torch.device): torch device to use
-        eta (float, optional): Eta for sigmal calculation, Defaults to 0.0.
-        tau_steps (int, optional): Tau steps for DDIM, Defaults to 1.
-
-    Returns:
-        torch.Tensor: Sampled image
-    """
-    model = model.to(device)
-    x_t = torch.rand(input_shape, device=device)
-    x_t_1 = x_t
-    class_labels = class_labels.to(device)
-
-    for time in reversed(range(max_steps)):
-        alpha_t, _, _ = linear_noise_scheduler(time, max_steps)
-        alpha_t_1 = 1.0
-        if time != 0:
-            alpha_t_1, _, _ = linear_noise_scheduler(time - 1, max_steps)
-
-        sigma_t = eta * (
-            (torch.sqrt((1 - alpha_t_1) / (1 - alpha_t)))
-            * (torch.sqrt((1 - alpha_t) / alpha_t_1))
-        )
-
-        z = torch.rand_like(x_t_1, device=device)
-        time = torch.unsqueeze(torch.tensor(time, device=device), dim=-1)
-        denoise = model(x_t_1, time, class_labels)
-        # First term
-        pred_x_0 = torch.sqrt(alpha_t_1 / alpha_t) * (
-            x_t_1 - (torch.sqrt(1 - alpha_t)) * denoise
-        )
-        # Second term
-        point_x_t = torch.sqrt(1 - alpha_t_1 - (sigma_t**2)) * denoise
-        # Final term
-        x_mean = pred_x_0 + point_x_t
-        x_t_1 = x_mean + sigma_t * z
-    return x_t_1
diff --git a/src/sampler.py b/src/sampler.py
deleted file mode 100644
index 613ce61..0000000
--- a/src/sampler.py
+++ /dev/null
@@ -1,100 +0,0 @@
-"""Sample the data in distributed manner."""
-
-import argparse
-import os
-import time
-from typing import List
-
-import numpy as np
-import torch
-import torch.nn as nn
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-
-from src.sample_util import sample_DDIM, sample_DDPM
-from src.util import _get_global_rank, _get_local_rank
-
-
-class Sampler:
-    def __init__(
-        self,
-        model: nn.Module,
-        args: argparse.Namespace,
-        save_path: str,
-        std: List[float] = [],
-        mean: List[float] = [],
-    ) -> None:
-        """DDP Sample class.
-
-        Args:
-            model (nn.Module): Model Instance
-            args (argparse.Namespace): User defined arguments
-            save_path (str): Sample save path
-            std (List[float]): Standard deviation if any. Defaults to empty list.
-            mean (List[float]): Mean of dataset if any. Defaults to empty list.
-        """
-        self.__local_rank = _get_local_rank()
-        self.__global_rank = (
-            _get_global_rank() if args.distribute else self.__local_rank
-        )
-
-        self.model = model.to(self.__local_rank)
-        if os.path.exists(args.ckpt_path):
-            if self.__global_rank == 0:
-                self.__load_checkpoint(args.ckpt_path)
-        else:
-            raise ValueError(
-                "Needs a trained model path. Please provide or check your path."
-            )
-        self.model = DDP(self.model, device_ids=[self.__local_rank])
-
-        self.__save_path = save_path
-        self.__input_shape = (args.batch_size, 3, args.input_shape, args.input_shape)
-        self.__sampler = sample_DDPM if args.sampler == "DDPM" else sample_DDIM
-        self.__std, self.__mean = torch.empty((1, 3, 1, 1)), torch.empty((1, 3, 1, 1))
-        if len(std) != 0 and len(mean) != 0:
-            self.__std = torch.reshape(torch.tensor(std), (1, 3, 1, 1)).to(
-                self.__local_rank
-            )
-            self.__mean = torch.reshape(torch.tensor(mean), (1, 3, 1, 1)).to(
-                self.__local_rank
-            )
-        self.__timesteps = args.diff_steps
-
-    def __load_checkpoint(self, path: str) -> None:
-        """Load the sampling checkpoint.
-
-        Args:
-            path (str): Model path
-        """
-        checkpoint = torch.load(path)
-        self.model.load_state_dict(checkpoint["model_state"])
-        print("Model loaded successfully...")
-
-    def conditional_sample(self, batched_labels: DataLoader) -> None:
-        """Sample batch of images sampled conditionally.
-
-        Args:
-            batched_labels (DataLoader): Batches of class labels.
-        """
-        with torch.no_grad():
-            self.model.eval()
-            for _, labels in tqdm(batched_labels):
-                x_0 = self.__sampler(
-                    class_labels=labels,
-                    model=self.model,
-                    max_steps=self.__timesteps,
-                    input_shape=self.__input_shape,
-                    device=self.__local_rank,
-                )
-
-                if self.__std.nelement() != 0 and self.__mean.nelement() != 0:
-                    x_0 = (x_0 * self.__std) + self.__mean
-
-                np_imgs = x_0.cpu().permute(0, 2, 3, 1).numpy()
-                str_time = str(time.time()).replace(".", "_")
-                fname = f"batch_{str_time}.npz"
-                fpath = os.path.join(self.__save_path, fname)
-                np.savez(fpath, x=np_imgs)
-                print(f"Saved batch at {fname}..", flush=True)
diff --git a/src/trainer.py b/src/trainer.py
deleted file mode 100644
index 41c5eb9..0000000
--- a/src/trainer.py
+++ /dev/null
@@ -1,257 +0,0 @@
-"""Torch DDP training script for diffusion."""
-
-import argparse
-import os.path
-from typing import List, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.data import DataLoader, Sampler
-from torch.utils.tensorboard import SummaryWriter
-
-from src.freq_math import fourier_power_divergence, wavelet_packet_power_divergence
-from src.sample_util import linear_noise_scheduler, sample_DDPM, sample_noise
-from src.util import _get_global_rank, _get_local_rank
-
-
-class Trainer:
-    def __init__(
-        self,
-        model: nn.Module,
-        args: argparse.Namespace,
-        optimizer: optim.Optimizer,
-        loss_fn: nn.Module,
-        writer: Union[SummaryWriter, None],
-        save_path: str,
-        std: List[float] = [],
-        mean: List[float] = [],
-        input_shape: int = 32,
-    ) -> None:
-        """DDP Train class.
-
-        Args:
-            model (nn.Module): Model Instance
-            args (argparse.Namespace): User defined arguments
-            optimizer (optim.Optimizer): Optimizer
-            loss_fn (nn.Module): Loss function
-            writer (Union[SummaryWriter, None]): tensorboard writer object for process 0 else None
-            save_path (str): Path to save the model
-            std (List[float]): Standard deviation of the dataset if any. Defaults to empty list
-            mean (List[float]): Mean of the dataset if any. Defaults to empty list
-            input_shape (int): Input dimension shape
-        """
-        # Initialize ranks of device.
-        self.__local_rank = _get_local_rank()
-        self.__global_rank = self.__local_rank
-        if args.distribute:
-            self.__global_rank = _get_global_rank()
-
-        self.__elapsed_epochs = 0
-        # Initialize the model.
-        self.model = model.to(self.__local_rank)
-        if args.model_path is not None:
-            if os.path.exists(args.model_path):
-                if self.__global_rank == 0:
-                    self.__load_checkpoint(args.model_path)
-        self.model = DDP(
-            self.model, device_ids=[self.__local_rank]  # , find_unused_parameters=True
-        )
-
-        # Initialize training variables
-        self.__optimizer = optimizer
-        self.__loss_fn = loss_fn
-        self.__tensorboard = writer
-        self.__clip_grad_norm = args.clip_grad_norm
-        self.__time_steps = args.time_steps
-        self.__save_every = args.save_every
-        self.__save_path = save_path
-        self.__print_every = args.print_every
-        self.__std, self.__mean = torch.empty((1, 3, 1, 1)), torch.empty((1, 3, 1, 1))
-        if len(std) != 0 and len(mean) != 0:
-            self.__std = torch.reshape(torch.tensor(std), (1, 3, 1, 1)).to(
-                self.__local_rank
-            )
-            self.__mean = torch.reshape(torch.tensor(mean), (1, 3, 1, 1)).to(
-                self.__local_rank
-            )
-        self.__input_dim = input_shape
-
-    def __load_checkpoint(self, path: str) -> None:
-        """Load the existing checkpoint.
-
-        Args:
-            path (str): Existing model path
-        """
-        checkpoint = torch.load(path)
-        self.model.load_state_dict(checkpoint["model_state"])
-        self.__elapsed_epochs = checkpoint["elapsed_epochs"] + 1
-        print(
-            f"[GPU{self.__global_rank}] Resuming training from epoch: {self.__elapsed_epochs}"
-        )
-
-    def __save_checkpoint(self, epoch: int) -> None:
-        """Save checkpoint.
-
-        Args:
-            epoch (int): Current epoch
-        """
-        checkpoint = {}
-        checkpoint["model_state"] = self.model.module.state_dict()
-        checkpoint["elapsed_epochs"] = epoch
-        file_path = os.path.join(self.__save_path, f"model_ckpt_epoch_{epoch}.pt")
-        torch.save(checkpoint, file_path)
-        print(f"===> Checkpoint saved at epoch {epoch} to {file_path}")
-
-    def __train_step(self, dataloader: DataLoader) -> float:
-        """Train for one epoch.
-
-        Args:
-            dataloader (DataLoader): Training dataloader
-
-        Returns:
-            float: Epoch loss
-        """
-        self.model.train()
-        per_step_loss = 0.0
-        total_steps = 0
-        self.model.train()
-        for input, class_label in dataloader:
-            # Preprocess and load input to corresponding device.
-            x, y, current_steps = self.__preprocess_input(input)
-            x, y = x.to(self.__local_rank), y.to(self.__local_rank)
-            class_label, current_steps = class_label.to(
-                self.__local_rank
-            ), current_steps.to(self.__local_rank)
-
-            # Forward pass and loss calculation
-            self.__optimizer.zero_grad()
-            pred_noise = self.model(
-                x, current_steps, return_dict=False
-            )  # , class_label)
-            loss_val = self.__loss_fn(pred_noise[0], y)
-
-            torch.cuda.synchronize()
-            # Backward pass
-            loss_val.backward()
-            if self.__clip_grad_norm > 0:
-                torch.nn.utils.clip_grad_norm_(
-                    self.model.parameters(), self.__clip_grad_norm
-                )
-            self.__optimizer.step()
-            per_step_loss += loss_val.item()
-            total_steps += 1
-            if total_steps % self.__print_every == 0 and self.__global_rank == 0:
-                print(f"Step: {total_steps}, Loss: {per_step_loss / total_steps}")
-        avg_loss = per_step_loss / total_steps
-        return avg_loss
-
-    def train(self, max_epochs: int, dataloader: DataLoader, sampler: Sampler) -> None:
-        """Training looooooop.
-
-        Args:
-            max_epochs (int): Total epochs
-            dataloader (DataLoader): Training dataloader
-            sampler (Sampler): Datasampler
-        """
-        for epoch in range(self.__elapsed_epochs, max_epochs):
-            if self.__global_rank == 0:
-                print(f"Epoch: {epoch}, total_batches: {len(dataloader)}")
-
-            sampler.set_epoch(epoch)
-            epoch_loss = self.__train_step(dataloader)
-
-            if self.__global_rank == 0:
-                print(f"Training loss: {epoch_loss}", flush=True)
-                self.__tensorboard.add_scalar("Train Loss", epoch_loss, epoch)
-                last_epoch = epoch == max_epochs - 1
-                # if (epoch % 5 == 0) or last_epoch:
-                if (epoch >= 0) or last_epoch:
-                    # Sample validation set
-                    sample_imgs, original_imgs = self.__validation_sample(
-                        16, dataloader
-                    )
-                    self.__tensorboard.add_images(
-                        "Original images", original_imgs[:8, :, :, :], epoch
-                    )
-                    self.__tensorboard.add_images(
-                        "Sampled images", sample_imgs[:8, :, :, :], epoch
-                    )
-                    # Compute fourier and wavelet power spectrum loss
-                    fft_ab, fft_ba = fourier_power_divergence(
-                        sample_imgs, original_imgs
-                    )
-                    packet_ab, packet_ba = wavelet_packet_power_divergence(
-                        sample_imgs, original_imgs
-                    )
-                    fft_mean = 0.5 * (fft_ab + fft_ba)
-                    packet_mean = 0.5 * (packet_ab + packet_ba)
-                    self.__tensorboard.add_scalar("PS_fft_KLD", fft_mean.item(), epoch)
-                    self.__tensorboard.add_scalar(
-                        "PS_packet_KLD", packet_mean.item(), epoch
-                    )
-                    self.model.train()
-                if (epoch % self.__save_every == 0) or last_epoch:
-                    self.__save_checkpoint(epoch)
-
-    def __validation_sample(
-        self, bs: int, dataloader: DataLoader
-    ) -> Tuple[torch.Tensor]:
-        """Generate samples for validation purpose.
-
-        Args:
-            bs (int): Batch size for validation
-            dataloader (DataLoader): Training datalaoder for labels
-
-        Returns:
-            Tuple[torch.Tensor]: Tuple containing sampled and original images
-        """
-        with torch.no_grad():
-            self.model.eval()
-            imgs, labels = next(iter(dataloader))
-            if len(imgs) > bs:
-                imgs, labels = imgs[:bs, :, :, :], labels[:bs]
-            else:
-                bs = len(imgs)
-            imgs = imgs.to(self.__local_rank)
-
-            x_0 = sample_DDPM(
-                class_labels=labels,
-                model=self.model,
-                max_steps=self.__time_steps,
-                input_shape=(bs, 3, self.__input_dim, self.__input_dim),
-                device=self.__local_rank,
-            )
-
-            if self.__std.nelement() != 0 and self.__mean.nelement() != 0:
-                x_0 = (x_0 * self.__std) + self.__mean
-                imgs = (imgs * self.__std) + self.__mean
-            print(x_0.shape)
-            print(imgs.shape)
-            assert (
-                x_0.shape == imgs.shape
-            ), "generated images must be same shape as input."
-            return x_0, imgs
-
-    def __preprocess_input(self, input_imgs: torch.Tensor) -> Tuple[torch.Tensor]:
-        """Apply noising to the input images.
-
-        Args:
-            input_imgs (torch.Tensor): Input images of shape [BSxCxHxW]
-
-        Returns:
-            Tuple[torch.Tensor]: Tuple containing noised input, noise and step values.
-        """
-        current_steps = torch.randint(
-            high=self.__time_steps, size=[input_imgs.shape[0]]
-        )
-        alphas_t = torch.tensor(
-            [
-                linear_noise_scheduler(time, self.__time_steps)[0]
-                for time in current_steps
-            ]
-        ).reshape(len(current_steps), 1)
-        batch_map = torch.vmap(sample_noise, randomness="different")
-        x, y = batch_map(input_imgs, alphas_t)
-        return x, y, current_steps
diff --git a/src/util.py b/src/util.py
deleted file mode 100644
index cae2834..0000000
--- a/src/util.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import argparse
-import os
-from typing import List, Optional
-
-import matplotlib.animation as manimation
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def _get_local_rank():
-    """Return local rank."""
-    return int(os.environ["LOCAL_RANK"])
-
-
-def _get_global_rank():
-    """Retrun global rank."""
-    return int(os.environ["RANK"])
-
-
-def _parse_args():
-    """Parse cmd line args for training an image classifier."""
-    parser = argparse.ArgumentParser(description="Run a diffusion model")
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=50,
-        help="input batch size for testing (default: 50)",
-    )
-    parser.add_argument(
-        "--lr",
-        type=float,
-        default=None,
-        help="User defined learning rate (default: lr specified in config file)",
-    )
-    parser.add_argument(
-        "--epochs", type=int, default=51, help="number of epochs (default: 21)"
-    )
-    parser.add_argument(
-        "--seed", type=int, default=42, help="initial seed value (default:42)"
-    )
-    parser.add_argument(
-        "--time-steps", type=int, default=40, help="steps per diffusion"
-    )
-    parser.add_argument(
-        "--save-every",
-        type=int,
-        default=50,
-        help="Save the model for every specified epochs",
-    )
-    parser.add_argument(
-        "--print-every", type=int, default=100, help="Print every specified step"
-    )
-    parser.add_argument(
-        "--model-path",
-        type=str,
-        default=None,
-        help="Saved model path in case of resuming the training",
-    )
-    parser.add_argument(
-        "--allow-tf32", help="Use tensorflot32 operations.", action="store_true"
-    )
-    parser.add_argument(
-        "--loss-type",
-        default="MSE",
-        choices=["MSE", "PACKET", "MIXED"],
-        help="Choice of loss function",
-    )
-    parser.add_argument(
-        "--packet-norm-type",
-        type=str,
-        default=None,
-        choices=[None, "log", "weighted"],
-        help="Provide packet norm type only when using packet or mixed loss.",
-    )
-    parser.add_argument(
-        "--wavelet",
-        type=str,
-        default="sym5",
-        choices=["Haar", "sym5"],
-        help="Type of wavelet for packet loss computation.",
-    )
-    parser.add_argument(
-        "--max-level", type=int, default=2, help="Depth of wavelet decomposition."
-    )
-    parser.add_argument(
-        "--loss-sigma", type=float, default=0.3, help="Weigting factor for mixed loss."
-    )
-    parser.add_argument("--logdir", type=str, default="./log", help="logdir name.")
-    parser.add_argument(
-        "--clip-grad-norm", type=float, default=0.0, help="Gradient clipping value."
-    )
-    parser.add_argument(
-        "--distribute", help="Use for multinode training.", action="store_true"
-    )
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        default="CIFAR10",
-        choices=["CIFAR10", "CELEBA64", "CELEBAHQ64", "CELEBAHQ128", "CELEBAHQ256"],
-        help="Select the dataset to diffuse",
-    )
-    return parser.parse_args()
-
-
-def _sampler_args():
-    parser = argparse.ArgumentParser(
-        prog="Image sampler", description="Sample Images from the diffusion model"
-    )
-    parser.add_argument("--ckpt-path", required=True, help="Checkpoint path")
-    parser.add_argument(
-        "--input-shape", type=int, required=True, help="Sampled input shape"
-    )
-    parser.add_argument("--seed", type=int, default=42, help="Seed value")
-    parser.add_argument(
-        "--diff-steps", type=int, default=1000, help="Number of diffusion steps"
-    )
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        default="CIFAR10",
-        choices=["CIFAR10", "CELEBA64", "CELEBAHQ64", "CELEBAHQ128", "CELEBAHQ256"],
-        help="Select the dataset to diffuse",
-    )
-    parser.add_argument(
-        "--allow-tf32", help="Use tensorflot32 operations.", action="store_true"
-    )
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=50,
-        help="input batch size for testing (default: 50)",
-    )
-    parser.add_argument(
-        "--distribute", help="Use for multinode training.", action="store_true"
-    )
-    parser.add_argument(
-        "--sampler",
-        default="DDPM",
-        choices=["DDPM", "DDIM"],
-        help="Use DDIM Sampling else DDPM is used by default",
-    )
-    return parser.parse_args()
-
-
-def write_movie(
-    images: List[np.ndarray],
-    name: Optional[str] = "diff_movie",
-    xlim: Optional[int] = 3,
-    ylim: Optional[int] = 3,
-):
-    """Write the optimization steps into a mp4-movie file.
-
-    Args:
-        images (list): A list with diffusion steps.
-        name (str, optional): The name of the movie file. Defaults to "grad_movie".
-        xlim (int, optional): Largest x value in the data. Defaults to 3.
-        ylim (int, optional): Largest y value in the data. Defaults to 3.
-
-    Raises:
-        RuntimeError: If conda ffmpeg package is not installed.
-    """
-    try:
-        ffmpeg_writer = manimation.writers["ffmpeg"]
-    except RuntimeError:
-        raise RuntimeError(
-            "RuntimeError: If you are using anaconda or miniconda there might "
-            "be a missing package named ffmpeg. Try installing it with "
-            "'conda install -c conda-forge ffmpeg' in your terminal."
-        )
-
-    metadata = dict(title="Diffusion", artist="Matplotlib", comment="Diffusion movie!")
-    writer = ffmpeg_writer(fps=60, metadata=metadata)
-
-    fig = plt.figure()
-    l = plt.imshow(images[0] / np.max(np.abs(images[0])))
-    plt.colorbar()
-
-    # plt.xlim(-xlim, xlim)
-    # plt.ylim(-ylim, ylim)
-
-    with writer.saving(fig, f"{name}.gif", 100):
-        for img in images:
-            l.set_data(img / np.max(np.abs(img)))
-            writer.grab_frame()
-
-
-# def _save_model(
-#     checkpoint_dir: str,
-#     time: datetime.datetime,
-#     epochs: int,
-#     model_data: Tuple[FrozenDict],
-# ) -> None:
-#     """Save the model parameters
-
-#     Args:
-#         checkpoint_dir (str): Checkpoint directory
-#         time (datetime.datetime): Time of saving
-#         epochs (int): Epoch of saving
-#         model_data (Tuple[FrozenDict]): Tuple containing the model data
-#     """
-#     save_dir = os.path.join(checkpoint_dir, f"e_{epochs}_time_{time}.pkl")
-#     with open(save_dir, "wb") as fp:
-#         pickle.dump(model_data, fp)


Test patch:
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 661ca6a..5d4338f 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -21,3 +21,35 @@ jobs:
       - name: Test with pytest
         run:
           nox -s fast-test
+  lint:
+    name: lint
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.11]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: pip install nox
+      - name: Run flake8
+        run: nox -s lint
+  typing:
+    name: typing
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.11]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: pip install nox
+      - name: Run mypy
+        run: nox -s typing
\ No newline at end of file
diff --git a/tests/test_fid.py b/tests/test_fid.py
index 2ee1b1d..b2eea30 100644
--- a/tests/test_fid.py
+++ b/tests/test_fid.py
@@ -1,7 +1,7 @@
 """Test Frechet Inception distance."""
 
 import pytest
-import torch
+import torch as th
 import numpy as np
 from typing import Tuple
 
@@ -11,11 +11,12 @@
 from copy import deepcopy
 from torch.nn.functional import adaptive_avg_pool2d
 
+
 @pytest.mark.slow
-def forward_pass(images: torch.Tensor) -> Tuple:
+def forward_pass(images: th.Tensor) -> Tuple:
     block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]
     model = InceptionV3([block_idx])
-    with torch.no_grad():
+    with th.no_grad():
         pred = model(images)[0]
     if pred.size(2) != 1 or pred.size(3) != 1:
         pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
@@ -40,9 +41,9 @@ def test_same_input():
 def test_shuffle_input():
     tensor_images = get_images(img_size=256)
     output_images = deepcopy(tensor_images)
-    permutation = torch.randperm(len(output_images))
+    permutation = th.randperm(len(output_images))
     shuffled_images = output_images[permutation, :, :, :]
-    assert not torch.allclose(shuffled_images, output_images)
+    assert not th.allclose(shuffled_images, output_images)
     mu_orig, sigma_orig = forward_pass(tensor_images)
     mu_shuff, sigma_shuff = forward_pass(shuffled_images)
     mu_op, sigma_op = forward_pass(output_images)
diff --git a/tests/test_freq.py b/tests/test_freq.py
index 644f397..f0072d4 100644
--- a/tests/test_freq.py
+++ b/tests/test_freq.py
@@ -1,52 +1,108 @@
+from typing import Tuple
+
 import pytest
 import scipy
-import torch
+import torch as th
+import ptwt
+import pywt
 
+from itertools import product
 from src.freq_math import (
     generate_frequency_packet_image,
-    forward_wavelet_packet_transform,
-    inverse_wavelet_packet_transform,
-    batched_packet_transform,
+    forward_wavelet_packet_transform
 )
 from .test_wavelet_frechet_distance import get_images
 
+
 @pytest.mark.slow
 def test_loss():
     import scipy.datasets
     import matplotlib.pyplot as plt
 
-    face = torch.Tensor(scipy.datasets.face())
-    face = torch.stack([face, face, face, face], axis=0)
-    face = face.type(torch.FloatTensor) / 255.0
+    face = th.Tensor(scipy.datasets.face())
+    face = th.stack([face, face, face, face], axis=0)
+    face = face.type(th.FloatTensor) / 255.0
     b, h, w, c = face.shape
-    face = torch.reshape(face, (b, c, h, w))
+    face = th.reshape(face, (b, c, h, w))
 
-    packets = forward_wavelet_packet_transform(face, wavelet="Haar", max_level=3)
+    packets = forward_wavelet_packet_transform(face, wavelet="Haar", max_level=3, log_scale=False)
     p_image = generate_frequency_packet_image(packets, 3)
     assert p_image.shape == (4, 3, 768, 1024)
 
 
 def test_inverse_wp():
-    face = torch.Tensor(scipy.datasets.face())
-    face = torch.stack([face, face, face, face], axis=0)
-    face = face.type(torch.FloatTensor) / 255.0
+    face = th.Tensor(scipy.datasets.face())
+    face = th.stack([face, face, face, face], axis=0)
+    face = face.type(th.FloatTensor) / 255.0
     b, h, w, c = face.shape
-    face = torch.reshape(face, (b, c, h, w))
+    face = th.reshape(face, (b, c, h, w))
 
-    packets = forward_wavelet_packet_transform(face, max_level=3, wavelet="db3", 
-                                               log_scale=False)
+    packets = forward_wavelet_packet_transform(face, max_level=3, wavelet="db3", log_scale=False)
 
     reconstruction = inverse_wavelet_packet_transform(
         packets, max_level=3, wavelet="db3"
     )
-    assert torch.max(torch.abs(reconstruction[:, :, :768, :1024] - face)) < 1e-5
+    assert th.max(th.abs(reconstruction[:, :, :768, :1024] - face)) < 1e-5
 
 
-@pytest.mark.slow
-def test_batched_packet_transform():
-    images = get_images(256)
-    permutation = torch.randperm(len(images))
-    images = images[permutation, :, :, :]
-    bacthed_packets = batched_packet_transform(tensor=images, wavelet="sym5", max_level=4, batch_size=2)
-    packets = forward_wavelet_packet_transform(tensor=images, wavelet="sym5", max_level=4)
-    assert torch.allclose(bacthed_packets, packets)
+
+def fold_channels(input_tensor: th.Tensor) -> th.Tensor:
+    """Fold a trailing (color-) channel into the batch dimension.
+
+    Args:
+        input_tensor (th.Tensor): An array of shape [B, C, H, W]
+
+    Returns:
+        th.Tensor: The folded [B*C, H, W] image.
+    """
+    shape = input_tensor.shape
+    return th.reshape(input_tensor, (-1, shape[-2], shape[-1]))
+
+
+def unfold_channels(
+    input_tensor: th.Tensor, original_shape: Tuple[int, int, int, int]
+) -> th.Tensor:
+    """Restore channels from the leading batch-dimension.
+
+    Args:
+        array (th.Tensor): An [B*C, packets, H, W] input array.
+
+    Returns:
+        th.Tensor: Output of shape [B, packets, H, W, C]
+    """
+
+    _, packets, _, _ = input_tensor.shape
+    b, c, h, w = original_shape
+    return th.reshape(input_tensor, (b, packets, c, h, w))
+
+
+def inverse_wavelet_packet_transform(
+    packet_tensor: th.Tensor, wavelet: str, max_level: int
+):
+    batch, _, channels, _, _ = packet_tensor.shape
+
+    def get_node_order(level):
+        wp_natural_path = list(product(["a", "h", "v", "d"], repeat=level))
+        return ["".join(p) for p in wp_natural_path]
+
+    wp_dict = {}
+    for pos, path in enumerate(get_node_order(max_level)):
+        wp_dict[path] = packet_tensor[:, pos, :, :, :]
+
+    for level in reversed(range(max_level)):
+        for node in get_node_order(level):
+            data_a = fold_channels(wp_dict[node + "a"])
+            data_h = fold_channels(wp_dict[node + "h"])
+            data_v = fold_channels(wp_dict[node + "v"])
+            data_d = fold_channels(wp_dict[node + "d"])
+            rec = ptwt.waverec2(
+                [data_a, (data_h, data_v, data_d)], pywt.Wavelet(wavelet)
+            )
+            height = rec.shape[1]
+            width = rec.shape[2]
+            rec = unfold_channels(
+                th.unsqueeze(rec, 1), [batch, channels, height, width]
+            )
+            rec = th.squeeze(rec, 1)
+            wp_dict[node] = rec
+    return rec
diff --git a/tests/test_packet_loss.py b/tests/test_packet_loss.py
deleted file mode 100644
index 9d4d79a..0000000
--- a/tests/test_packet_loss.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import torch
-import numpy as np
-from scipy import datasets
-
-from src.nn import PacketLoss, MixedLoss
-
-def prepare_input():
-    face = torch.Tensor(datasets.face())
-    face = torch.stack([face, face, face, face], dim=0)
-    face = face / 255.0
-    face = face.permute(0, 3, 1, 2)
-    return face
-
-
-def test_packet_loss_unit_weights():
-    face = prepare_input()
-    packet_loss = PacketLoss(face, face, wavelet='Haar', level=3, norm_fn='weighted', norm_weights=[1.]*64)
-    assert np.allclose(packet_loss.item(), 0.0)
-
-
-def test_packet_loss_without_norm():
-    face = prepare_input()
-    packet_loss = PacketLoss(face, face, wavelet='Haar', level=3, norm_fn=None, norm_weights=None)
-    assert np.allclose(packet_loss.item(), 0.0)
-
-
-def test_packet_loss_log_scale():
-    face = prepare_input()
-    packet_loss = PacketLoss(face, face, wavelet='Haar', level=3, norm_fn='log', norm_weights=None)
-    assert np.allclose(packet_loss.item(), 0.0)
-
-
-def test_mixed_loss_all_combinations():
-    face = prepare_input()
-    mixed_loss_1 = MixedLoss(face, face, sigma=0.5, wavelet='Haar', level=3, norm_fn=None, norm_weights=None)
-    mixed_loss_2 = MixedLoss(face, face, sigma=0.6, wavelet='db3', level=3, norm_fn='log', norm_weights=None)
-    mixed_loss_3 = MixedLoss(face, face, sigma=0.8, wavelet='Haar', level=3, norm_fn="weighted", norm_weights=[1.]*64)
-    assert np.allclose(mixed_loss_1.item(), 0.0)
-    assert np.allclose(mixed_loss_2.item(), 0.0)
-    assert np.allclose(mixed_loss_3.item(), 0.0)
diff --git a/tests/test_wavelet_frechet_distance.py b/tests/test_wavelet_frechet_distance.py
index 94b1911..29f6267 100644
--- a/tests/test_wavelet_frechet_distance.py
+++ b/tests/test_wavelet_frechet_distance.py
@@ -3,14 +3,24 @@
 import torch
 import pytest
 import numpy as np
-import numpy.matlib
 
 from itertools import pairwise
-from src.freq_math import wavelet_packet_frechet_distance
+from src.fwd import compute_packet_statistics, compute_avg_frechet_distance
 from sklearn.datasets import load_sample_images
 from torchvision import transforms
 from copy import deepcopy
 
+torch.set_default_dtype(torch.float64)
+torch.use_deterministic_algorithms(True)
+
+make_dataloader = lambda x: torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x), batch_size=1, shuffle=False, drop_last=False)
+default_params = {
+    'dataloader': None,
+    'wavelet': 'sym5',
+    'max_level': 2,
+    'log_scale': False
+}
+
 @pytest.mark.slow
 def get_images(img_size: int = 64) -> torch.Tensor:
     """Generate images of given size.
@@ -26,23 +36,29 @@ def get_images(img_size: int = 64) -> torch.Tensor:
     tower = torch.Tensor(dataset.images[0])
     flower = torch.Tensor(dataset.images[1])
     images = torch.stack([tower, tower, tower, tower, flower, flower, flower, flower], axis=0)
-    images = images.type(torch.FloatTensor) / 255.0
+    images = images/255.0
     images = torch.permute(images, [0, 3, 1, 2])
     images = transforms.functional.resize(images, (img_size, img_size))
     assert images.shape == (8, 3, img_size, img_size)
     return images
 
 
+def compute_fwd(target_images, output_images):
+    default_params['dataloader'] = make_dataloader(target_images)
+    mu1, sigma1 = compute_packet_statistics(**default_params)
+    default_params['dataloader'] = make_dataloader(output_images)
+    mu2, sigma2 = compute_packet_statistics(**default_params)
+    
+    distance = compute_avg_frechet_distance(mu1, mu2, sigma1, sigma2)
+    return distance
+
+
 @pytest.mark.slow
 def test_same_input():
     target_images = get_images()
     output_images = deepcopy(target_images)
-    distance = wavelet_packet_frechet_distance(output=output_images,
-                                               target=target_images,
-                                               level=2,
-                                               wavelet="sym5",
-                                               log_scale=False)
-    assert np.allclose(distance, 0.0, atol=1e-3)
+    fwd = compute_fwd(target_images, output_images)
+    assert np.allclose(fwd, 0.0, atol=1e-3)
 
 
 @pytest.mark.slow
@@ -53,46 +69,39 @@ def test_shuffle_input():
     permutation = torch.randperm(len(target_images))
     shuffled_images = output_images[permutation, :, :, :]
     assert not torch.allclose(shuffled_images, output_images)
-    shuffled_distance = wavelet_packet_frechet_distance(output=shuffled_images,
-                                                        target=target_images,
-                                                        level=2,
-                                                        wavelet="sym5",
-                                                        log_scale=False)
-    unshuffled_distance = wavelet_packet_frechet_distance(output=output_images,
-                                                          target=target_images,
-                                                          level=2,
-                                                          wavelet="sym5",
-                                                          log_scale=False)
-    assert np.allclose(shuffled_distance, unshuffled_distance)
+
+    shuffled_fwd = compute_fwd(target_images, shuffled_images)
+    unshuffled_fwd = compute_fwd(target_images, output_images)
+    assert np.allclose(shuffled_fwd, unshuffled_fwd, atol=1e-5)
 
 
 @pytest.mark.slow
 @pytest.mark.parametrize("wavelet", ["sym5", "db5", "Haar"])
 @pytest.mark.parametrize("level", [2, 3, 4])
 def test_various_wavelets(wavelet, level):
-    target_images = get_images().type(torch.float64)
+    target_images = get_images()
     output_images = deepcopy(target_images)
-    distance = wavelet_packet_frechet_distance(output=output_images,
-                                               target=target_images,
-                                               level=level,
-                                               wavelet=wavelet,
-                                               log_scale=False)
-    assert np.allclose(distance, 0.0, atol=1e-3)
+
+    default_params['wavelet'] = wavelet
+    default_params['max_level'] = level
+
+    fwd = compute_fwd(target_images, output_images)
+    assert np.allclose(fwd, 0.0, atol=1e-3)
 
 
 @pytest.mark.slow
 @pytest.mark.parametrize("img_size_level", [(32, 1), (64, 2), (128, 3)]) #  (256, 4)
 def test_various_image_sizes(img_size_level):
     size, level = img_size_level
+
+    default_params['max_level'] = level
+
     target_images = get_images(size)
     output_images = deepcopy(target_images)
     assert output_images.shape == (8, 3, size, size)
-    distance = wavelet_packet_frechet_distance(output=output_images,
-                                               target=target_images,
-                                               level=level,
-                                               wavelet="sym5",
-                                               log_scale=False)
-    assert np.allclose(distance, 0.0, atol=1e-3)
+    
+    fwd = compute_fwd(target_images, output_images)
+    assert np.allclose(fwd, 0.0, atol=1e-3)
 
 @pytest.mark.slow
 def test_checkerboard_FWD():
@@ -119,10 +128,10 @@ def test_checkerboard_FWD():
         # generate 10 images via a random fft-shift
         img_list = []
         for _ in range(1000):
-            input_ = numpy.fft.fft2(grid)
+            input_ = np.fft.fft2(grid)
             shift = np.random.uniform(-1, 1)*tile_size//2
             image = scipy.ndimage.fourier_shift(input_, shift=shift)
-            image = numpy.fft.ifft2(image)
+            image = np.fft.ifft2(image)
             img_list.append(image.real)
         # B, H, W
         img_array = np.stack(img_list, axis=0)
@@ -132,13 +141,11 @@ def test_checkerboard_FWD():
         images.append(img_array)
 
     # a list for the wavelet frecet distances.
-    wfd_list = []
+    fwd_list = []
     reference = images[0]
+    default_params['max_level'] = 3
     for cimgs in images[1:]:
-        wfd_list.append(wavelet_packet_frechet_distance(output=torch.from_numpy(cimgs),
-                                                        target=torch.from_numpy(reference),
-                                                        level=3,
-                                                        wavelet="sym5",
-                                                        log_scale=False))
+        fwd = compute_fwd(target_images=torch.from_numpy(reference),
+                          output_images=torch.from_numpy(cimgs))
 
-    assert all(a < b for a, b in pairwise(wfd_list))
+    assert all(a < b for a, b in pairwise(fwd_list))