egs: update test dataset configuration

notebooks/check_metric.ipynb

@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Apply profit to the metrics\n",
+    "def get_polyfit_val(self, sig, bak, ovr, is_personalized_MOS=False):\n",
+    "    if is_personalized_MOS:\n",
+    "        p_ovr = np.poly1d([-0.00533021, 0.005101, 1.18058466, -0.11236046])\n",
+    "        p_sig = np.poly1d([-0.01019296, 0.02751166, 1.19576786, -0.24348726])\n",
+    "        p_bak = np.poly1d([-0.04976499, 0.44276479, -0.1644611, 0.96883132])\n",
+    "    else:\n",
+    "        p_ovr = np.poly1d([-0.06766283, 1.11546468, 0.04602535])\n",
+    "        p_sig = np.poly1d([-0.08397278, 1.22083953, 0.0052439])\n",
+    "        p_bak = np.poly1d([-0.13166888, 1.60915514, -0.39604546])\n",
+    "\n",
+    "    sig_poly = p_sig(sig)\n",
+    "    bak_poly = p_bak(bak)\n",
+    "    ovr_poly = p_ovr(ovr)\n",
+    "\n",
+    "    return sig_poly, bak_poly, ovr_poly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3.163024522199478, 3.6073455928302955, 2.750688625478262)\n"
+     ]
+    }
+   ],
+   "source": [
+    "a_lif_metrics = (3.36576, 3.47725, 2.95402)\n",
+    "a_lif_poly = get_polyfit_val(None, *a_lif_metrics)\n",
+    "print(a_lif_poly)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "synops = 419556 * 2\n",
+    "neuronops = 2762\n",
+    "buffer_latency = 0.032\n",
+    "enc_dec_latency = 0.030 / 1000\n",
+    "dns_latency = 0\n",
+    "dt = 0.008"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "866732\n",
+      "Solution Latency                 :  32.030 ms\n",
+      "Power proxy (Effective SynOPS)   : 108341500.000 ops/s\n",
+      "PDP proxy (SynOPS-delay product) :  1790.797 ops\n"
+     ]
+    }
+   ],
+   "source": [
+    "latency = buffer_latency + enc_dec_latency + dns_latency\n",
+    "effective_synops_rate = (synops + 10 * neuronops) / dt\n",
+    "synops_delay_product = 55.91 * 10**3 * latency\n",
+    "\n",
+    "print(synops + 10 * neuronops)\n",
+    "print(f'Solution Latency                 : {latency * 1000: .3f} ms')\n",
+    "print(f'Power proxy (Effective SynOPS)   : {effective_synops_rate:.3f} ops/s')\n",
+    "print(f'PDP proxy (SynOPS-delay product) : {synops_delay_product: .3f} ops')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solution Latency                 :  32.030 ms\n",
+      "Power proxy (Effective SynOPS)   : 108341500.000 ops/s\n",
+      "PDP proxy (SynOPS-delay product) :  3470178.245 ops\n"
+     ]
+    }
+   ],
+   "source": [
+    "latency = buffer_latency + enc_dec_latency + dns_latency\n",
+    "effective_synops_rate = (synops + 10 * neuronops) / dt\n",
+    "synops_delay_product = effective_synops_rate * latency\n",
+    "\n",
+    "print(f'Solution Latency                 : {latency * 1000: .3f} ms')\n",
+    "print(f'Power proxy (Effective SynOPS)   : {effective_synops_rate:.3f} ops/s')\n",
+    "print(f'PDP proxy (SynOPS-delay product) : {synops_delay_product: .3f} ops')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "audiozen",
+   "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": "3.10.11"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

recipes/intel_ndns/spiking_fullsubnet/baseline_m.toml

@@ -86,8 +86,8 @@ num_workers = 8
 [test_dataset]
 path = "dataloader.DNSAudio"
 [test_dataset.args]
-root = "/datasets/IntelNeuromorphicDNSChallenge-latest/data/MicrosoftDNS_4_ICASSP/test_set_1/"
+root = "/nfs/xhao/data/intel_ndns/test_set/"
 train = false
 [test_dataset.dataloader]
-batch_size = 24
-num_workers = 8
+batch_size = 1
+num_workers = 0

recipes/intel_ndns/spiking_fullsubnet/dataloader.py

@@ -27,6 +27,8 @@ def __init__(self, root="./", limit=None, offset=0, sublen=6, train=True) -> Non
         if limit:
             self.noisy_files = self.noisy_files[:limit]
 
+        print(f"Found {len(self.noisy_files)} files.")
+
         self.file_id_from_name = re.compile(r"fileid_(\d+)")
         self.snr_from_name = re.compile(r"snr(-?\d+)")
         self.target_level_from_name = re.compile(r"tl(-?\d+)")

recipes/intel_ndns/spiking_fullsubnet/trainer.py

@@ -99,3 +99,9 @@ def validation_epoch_end(self, outputs, log_to_tensorboard=True):
                     self.writer.add_scalar(f"metrics_{dataloader_idx}/{metric}", value, self.state.epochs_trained)
 
         return score
+
+    def test_step(self, batch, batch_idx, dataloader_idx=0):
+        return self.validation_step(batch, batch_idx, dataloader_idx)
+
+    def test_epoch_end(self, outputs, log_to_tensorboard=True):
+        return self.test_epoch_end(outputs, log_to_tensorboard=False)