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Last active February 4, 2021 14:06
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High Gamma Decoding and Gradient Visualization

First call:

python run_exp_and_store_amp_grads_corr.py subject_id

e.g.

python run_exp_and_store_amp_grads_corr.py 2

for subject id 2, check inside the run_exp_and_store_amp_grads_corr.py to see where results will be stored, hyperparameters etc And do this for all subjects (1-14).

Afterwards, run notebook below to produce visualizations.

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import math\n",
"\n",
"\n",
"CHANNEL_10_20_APPROX = (\n",
" \"angle\",\n",
" (\"Fpz\", (0.000, 4.000)),\n",
" (\"Fp1\", (-3.500, 3.500)),\n",
" (\"Fp2\", (3.500, 3.500)),\n",
" (\"AFp3h\", (-1.000, 3.500)),\n",
" (\"AFp4h\", (1.000, 3.500)),\n",
" (\"AF7\", (-4.000, 3.000)),\n",
" (\"AF3\", (-2.000, 3.000)),\n",
" (\"AFz\", (0.000, 3.000)),\n",
" (\"AF4\", (2.000, 3.000)),\n",
" (\"AF8\", (4.000, 3.000)),\n",
" (\"AFF5h\", (-2.500, 2.500)),\n",
" (\"AFF1\", (-0.500, 2.500)),\n",
" (\"AFF2\", (0.500, 2.500)),\n",
" (\"AFF6h\", (2.500, 2.500)),\n",
" (\"F7\", (-4.000, 2.000)),\n",
" (\"F5\", (-3.000, 2.000)),\n",
" (\"F3\", (-2.000, 2.000)),\n",
" (\"F1\", (-1.000, 2.000)),\n",
" (\"Fz\", (0.000, 2.000)),\n",
" (\"F2\", (1.000, 2.000)),\n",
" (\"F4\", (2.000, 2.000)),\n",
" (\"F6\", (3.000, 2.000)),\n",
" (\"F8\", (4.000, 2.000)),\n",
" (\"FFT7h\", (-3.500, 1.500)),\n",
" (\"FFC5h\", (-2.500, 1.500)),\n",
" (\"FFC3h\", (-1.500, 1.500)),\n",
" (\"FFC1h\", (-0.500, 1.500)),\n",
" (\"FFC2h\", (0.500, 1.500)),\n",
" (\"FFC4h\", (1.500, 1.500)),\n",
" (\"FFC6h\", (2.500, 1.500)),\n",
" (\"FFT8h\", (3.500, 1.500)),\n",
" (\"FT9\", (-5.000, 1.000)),\n",
" (\"FT7\", (-4.000, 1.000)),\n",
" (\"FC5\", (-3.000, 1.000)),\n",
" (\"FC3\", (-2.000, 1.000)),\n",
" (\"FC1\", (-1.000, 1.000)),\n",
" (\"FCz\", (0.000, 1.000)),\n",
" (\"FC2\", (1.000, 1.000)),\n",
" (\"FC4\", (2.000, 1.000)),\n",
" (\"FC6\", (3.000, 1.000)),\n",
" (\"FT8\", (4.000, 1.000)),\n",
" (\"FT10\", (5.000, 1.000)),\n",
" (\"FTT9h\", (-4.500, 0.500)),\n",
" (\"FTT7h\", (-3.500, 0.500)),\n",
" (\"FCC5h\", (-2.500, 0.500)),\n",
" (\"FCC3h\", (-1.500, 0.500)),\n",
" (\"FCC1h\", (-0.500, 0.500)),\n",
" (\"FCC2h\", (0.500, 0.500)),\n",
" (\"FCC4h\", (1.500, 0.500)),\n",
" (\"FCC6h\", (2.500, 0.500)),\n",
" (\"FTT8h\", (3.500, 0.500)),\n",
" (\"FTT10h\", (4.500, 0.500)),\n",
" (\"M1\", (-5.000, 0.000)),\n",
" # notsure if correct:\n",
" (\"T9\", (-4.500, 0.000)),\n",
" (\"T7\", (-4.000, 0.000)),\n",
" (\"C5\", (-3.000, 0.000)),\n",
" (\"C3\", (-2.000, 0.000)),\n",
" (\"C1\", (-1.000, 0.000)),\n",
" (\"Cz\", (0.000, 0.000)),\n",
" (\"C2\", (1.000, 0.000)),\n",
" (\"C4\", (2.000, 0.000)),\n",
" (\"C6\", (3.000, 0.000)),\n",
" (\"T8\", (4.000, 0.000)),\n",
" (\"T10\", (4.500, 0.000)),\n",
" (\"M2\", (5.000, 0.000)),\n",
" (\"TTP7h\", (-3.500, -0.500)),\n",
" (\"CCP5h\", (-2.500, -0.500)),\n",
" (\"CCP3h\", (-1.500, -0.500)),\n",
" (\"CCP1h\", (-0.500, -0.500)),\n",
" (\"CCP2h\", (0.500, -0.500)),\n",
" (\"CCP4h\", (1.500, -0.500)),\n",
" (\"CCP6h\", (2.500, -0.500)),\n",
" (\"TTP8h\", (3.500, -0.500)),\n",
" (\"TP7\", (-4.000, -1.000)),\n",
" (\"CP5\", (-3.000, -1.000)),\n",
" (\"CP3\", (-2.000, -1.000)),\n",
" (\"CP1\", (-1.000, -1.000)),\n",
" (\"CPz\", (0.000, -1.000)),\n",
" (\"CP2\", (1.000, -1.000)),\n",
" (\"CP4\", (2.000, -1.000)),\n",
" (\"CP6\", (3.000, -1.000)),\n",
" (\"TP8\", (4.000, -1.000)),\n",
" (\"TPP9h\", (-4.500, -1.500)),\n",
" (\"TPP7h\", (-3.500, -1.500)),\n",
" (\"CPP5h\", (-2.500, -1.500)),\n",
" (\"CPP3h\", (-1.500, -1.500)),\n",
" (\"CPP1h\", (-0.500, -1.500)),\n",
" (\"CPP2h\", (0.500, -1.500)),\n",
" (\"CPP4h\", (1.500, -1.500)),\n",
" (\"CPP6h\", (2.500, -1.500)),\n",
" (\"TPP8h\", (3.500, -1.500)),\n",
" (\"TPP10h\", (4.500, -1.500)),\n",
" (\"P9\", (-5.000, -2.000)),\n",
" (\"P7\", (-4.000, -2.000)),\n",
" (\"P5\", (-3.000, -2.000)),\n",
" (\"P3\", (-2.000, -2.000)),\n",
" (\"P1\", (-1.000, -2.000)),\n",
" (\"Pz\", (0.000, -2.000)),\n",
" (\"P2\", (1.000, -2.000)),\n",
" (\"P4\", (2.000, -2.000)),\n",
" (\"P6\", (3.000, -2.000)),\n",
" (\"P8\", (4.000, -2.000)),\n",
" (\"P10\", (5.000, -2.000)),\n",
" (\"PPO9h\", (-4.500, -2.500)),\n",
" (\"PPO5h\", (-3.000, -2.500)),\n",
" (\"PPO1\", (-0.650, -2.500)),\n",
" (\"PPO2\", (0.650, -2.500)),\n",
" (\"PPO6h\", (3.000, -2.500)),\n",
" (\"PPO10h\", (4.500, -2.500)),\n",
" (\"PO9\", (-5.000, -3.000)),\n",
" (\"PO7\", (-4.000, -3.000)),\n",
" (\"PO5\", (-3.000, -3.000)),\n",
" (\"PO3\", (-2.000, -3.000)),\n",
" (\"PO1\", (-1.000, -3.000)),\n",
" (\"POz\", (0.000, -3.000)),\n",
" (\"PO2\", (1.000, -3.000)),\n",
" (\"PO4\", (2.000, -3.000)),\n",
" (\"PO6\", (3.000, -3.000)),\n",
" (\"PO8\", (4.000, -3.000)),\n",
" (\"PO10\", (5.000, -3.000)),\n",
" (\"POO9h\", (-4.500, -3.250)),\n",
" (\"POO3h\", (-2.000, -3.250)),\n",
" (\"POO4h\", (2.000, -3.250)),\n",
" (\"POO10h\", (4.500, -3.250)),\n",
" (\"O1\", (-2.500, -3.750)),\n",
" (\"Oz\", (0.000, -3.750)),\n",
" (\"O2\", (2.500, -3.750)),\n",
" (\"OI1h\", (1.500, -4.250)),\n",
" (\"OI2h\", (-1.500, -4.250)),\n",
" (\"I1\", (1.000, -4.500)),\n",
" (\"Iz\", (0.000, -4.500)),\n",
" (\"I2\", (-1.000, -4.500)),\n",
")\n",
"\n",
"\n",
"def get_channelpos(channame, chan_pos_list):\n",
" if chan_pos_list[0] == \"angle\":\n",
" return get_channelpos_from_angle(channame, chan_pos_list[1:])\n",
" elif chan_pos_list[0] == \"cartesian\":\n",
" channame = channame.lower()\n",
" for name, coords in chan_pos_list[1:]:\n",
" if name.lower() == channame:\n",
" return coords[0], coords[1]\n",
" return None\n",
" else:\n",
" raise ValueError(\n",
" \"Unknown first element \"\n",
" \"{:s} (should be type of positions)\".format(chan_pos_list[0])\n",
" )\n",
"\n",
"\n",
"def get_channelpos_from_angle(channame, chan_pos_list=CHANNEL_10_20_APPROX):\n",
" \"\"\"Return the x/y position of a channel.\n",
"\n",
" This method calculates the stereographic projection of a channel\n",
" from ``CHANNEL_10_20``, suitable for a scalp plot.\n",
"\n",
" Parameters\n",
" ----------\n",
" channame : str\n",
" Name of the channel, the search is case insensitive.\n",
"\n",
" chan_pos_list=CHANNEL_10_20_APPROX,\n",
" interpolation='bilinear'\n",
"\n",
" Returns\n",
" -------\n",
" x, y : float or None\n",
" The projected point on the plane if the point is known,\n",
" otherwise ``None``\n",
"\n",
" Examples\n",
" --------\n",
"\n",
" >>> plot.get_channelpos_from_angle('C2')\n",
" (0.1720792096741632, 0.0)\n",
" >>> # the channels are case insensitive\n",
" >>> plot.get_channelpos_from_angle('c2')\n",
" (0.1720792096741632, 0.0)\n",
" >>> # lookup for an invalid channel\n",
" >>> plot.get_channelpos_from_angle('foo')\n",
" None\n",
"\n",
" \"\"\"\n",
" channame = channame.lower()\n",
" for i in chan_pos_list:\n",
" if i[0].lower() == channame:\n",
" # convert the 90/4th angular position into x, y, z\n",
" p = i[1]\n",
" x, y = _convert_2d_angle_to_2d_coord(*p)\n",
" return x, y\n",
" return None\n",
"\n",
"\n",
"def _convert_2d_angle_to_2d_coord(a, b):\n",
" # convert the 90/4th angular position into x, y, z\n",
" ea, eb = a * (90 / 4), b * (90 / 4)\n",
" ea = ea * math.pi / 180\n",
" eb = eb * math.pi / 180\n",
" x = math.sin(ea) * math.cos(eb)\n",
" y = math.sin(eb)\n",
" z = math.cos(ea) * math.cos(eb)\n",
" # Calculate the stereographic projection.\n",
" # Given a unit sphere with radius ``r = 1`` and center at\n",
" # the origin. Project the point ``p = (x, y, z)`` from the\n",
" # sphere's South pole (0, 0, -1) on a plane on the sphere's\n",
" # North pole (0, 0, 1).\n",
" #\n",
" # The formula is:\n",
" #\n",
" # P' = P * (2r / (r + z))\n",
" #\n",
" # We changed the values to move the point of projection\n",
" # further below the south pole\n",
" mu = 1 / (1.3 + z)\n",
" x *= mu\n",
" y *= mu\n",
" return x, y\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"C_sensors = [\n",
" 'FC5', 'FC1', 'FC2', 'FC6', 'C3', 'Cz', 'C4', 'CP5',\n",
" 'CP1', 'CP2', 'CP6', 'FC3', 'FCz', 'FC4', 'C5', 'C1', 'C2', 'C6',\n",
" 'CP3', 'CPz', 'CP4', 'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h', 'FCC5h',\n",
" 'FCC3h', 'FCC4h', 'FCC6h', 'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h', 'CPP5h',\n",
" 'CPP3h', 'CPP4h', 'CPP6h', 'FFC1h', 'FFC2h', 'FCC1h', 'FCC2h', 'CCP1h',\n",
" 'CCP2h', 'CPP1h', 'CPP2h']\n",
"positions = [get_channelpos(name, CHANNEL_10_20_APPROX) for name in C_sensors]\n",
"positions = np.array(positions)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import logging\n",
"logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',\n",
" level=logging.INFO)\n",
"log = logging.getLogger()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-02-04 15:04:47,376 INFO : Load ./results/1_avg_amp_grads.npy...\n",
"2021-02-04 15:04:47,379 INFO : Load ./results/2_avg_amp_grads.npy...\n",
"2021-02-04 15:04:47,381 INFO : Load ./results/3_avg_amp_grads.npy...\n",
"2021-02-04 15:04:47,386 INFO : Load ./results/4_avg_amp_grads.npy...\n",
"2021-02-04 15:04:47,389 INFO : Load ./results/5_avg_amp_grads.npy...\n",
"2021-02-04 15:04:47,392 INFO : Load ./results/6_avg_amp_grads.npy...\n",
"2021-02-04 15:04:47,395 INFO : Load ./results/7_avg_amp_grads.npy...\n",
"2021-02-04 15:04:47,398 INFO : Load ./results/8_avg_amp_grads.npy...\n",
"2021-02-04 15:04:47,401 WARNING : ./results/9_avg_amp_grads.npy missing...\n",
"2021-02-04 15:04:47,401 WARNING : ./results/10_avg_amp_grads.npy missing...\n",
"2021-02-04 15:04:47,402 WARNING : ./results/11_avg_amp_grads.npy missing...\n",
"2021-02-04 15:04:47,403 WARNING : ./results/12_avg_amp_grads.npy missing...\n",
"2021-02-04 15:04:47,403 WARNING : ./results/13_avg_amp_grads.npy missing...\n",
"2021-02-04 15:04:47,404 WARNING : ./results/14_avg_amp_grads.npy missing...\n"
]
}
],
"source": [
"import os.path\n",
"\n",
"output_dir = './results'\n",
"amp_grads_per_subject = []\n",
"for subject_id in range(1,15):\n",
" filename = os.path.join(output_dir, f\"{subject_id}_avg_amp_grads.npy\")\n",
" if os.path.isfile(filename):\n",
" log.info(f\"Load {filename}...\")\n",
" avg_amp_grads = np.load(filename)\n",
" amp_grads_per_subject.append(avg_amp_grads)\n",
" else:\n",
" log.warning(f\"{filename} missing...\")\n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Average over subjects\n",
"overall_amp_grads = np.mean(amp_grads_per_subject, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x288 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x288 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x288 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import mne\n",
"from IPython.display import display\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import cm\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'png'\n",
"sfreq = 250\n",
"\n",
"for start_freq, end_freq in ((7,13), (13,31), (71,91)):\n",
" input_window_samples = 1000\n",
" freqs = np.fft.rfftfreq(input_window_samples, d=1.0/sfreq)\n",
" i_start = np.searchsorted(freqs, start_freq)\n",
" i_stop = np.searchsorted(freqs, end_freq) + 1\n",
"\n",
" freq_corr = np.mean(overall_amp_grads[:,:,i_start:i_stop], axis=2)\n",
"\n",
" max_abs_val = np.max(np.abs(freq_corr))\n",
"\n",
" class_to_id = {\"Right Hand\": 0, \"Left Hand\": 1, \"Rest\": 2, \"Feet\": 3}\n",
"\n",
" fig, axes = plt.subplots(1, len(class_to_id), figsize=(14,4))\n",
" for class_name, i_class in class_to_id.items():\n",
" ax = axes[i_class]\n",
" mne.viz.plot_topomap(freq_corr[i_class], positions/8, # division by 8 just looked ok... no logic :)\n",
" vmin=-max_abs_val, vmax=max_abs_val, contours=0,\n",
" cmap=cm.coolwarm, show=False, extrapolate='local',\n",
" axes=ax);\n",
" ax.set_title(class_name.replace('_', ' '))\n",
" fig.suptitle(f\"{start_freq}-{end_freq} Hz\")\n",
" display(fig)\n",
" plt.close(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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",
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# Authors: Robin Schirrmeister <[email protected]>
#
# License: BSD (3-clause)
import argparse
import logging
import os.path
import sys
import numpy as np
import torch
from braindecode import EEGClassifier
from braindecode.datasets.moabb import MOABBDataset
from braindecode.datautil.preprocess import MNEPreproc, NumpyPreproc, preprocess
from braindecode.datautil.preprocess import exponential_moving_standardize
from braindecode.datautil.windowers import create_windows_from_events
from braindecode.models import Deep4Net
from braindecode.models import ShallowFBCSPNet
from braindecode.models.util import to_dense_prediction_model, get_output_shape
from braindecode.training.losses import CroppedLoss
from braindecode.util import set_random_seeds
from braindecode.visualization.gradients import compute_amplitude_gradients
from skorch.callbacks import LRScheduler
from skorch.helper import predefined_split
from torch.utils.data import Subset
log = logging.getLogger(__name__)
def load_preprocessed_data(subject_id, low_cut_hz, high_cut_hz):
assert model_name in ['deep', 'shallow']
log.info("Load dataset...")
dataset = MOABBDataset(dataset_name="Schirrmeister2017", subject_ids=[subject_id])
C_sensors = [
'FC5', 'FC1', 'FC2', 'FC6', 'C3', 'Cz', 'C4', 'CP5',
'CP1', 'CP2', 'CP6', 'FC3', 'FCz', 'FC4', 'C5', 'C1', 'C2', 'C6',
'CP3', 'CPz', 'CP4', 'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h', 'FCC5h',
'FCC3h', 'FCC4h', 'FCC6h', 'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h', 'CPP5h',
'CPP3h', 'CPP4h', 'CPP6h', 'FFC1h', 'FFC2h', 'FCC1h', 'FCC2h', 'CCP1h',
'CCP2h', 'CPP1h', 'CPP2h']
# Parameters for exponential moving standardization
factor_new = 1e-3
init_block_size = 1000
log.info("Preprocess dataset...")
preprocessors = [
# keep only C sensors
# MNEPreproc(fn='pick_types', eeg=True, meg=False, stim=False),
MNEPreproc(fn='pick_channels', ch_names=C_sensors, ordered=True),
# convert from volt to microvolt, directly modifying the numpy array
NumpyPreproc(fn=lambda x: x * 1e6),
NumpyPreproc(fn=lambda x: np.clip(x, -800, 800)),
MNEPreproc(fn='resample', sfreq=250),
# bandpass filter
MNEPreproc(fn='filter', l_freq=low_cut_hz, h_freq=high_cut_hz),
# exponential moving standardization
NumpyPreproc(fn=exponential_moving_standardize, factor_new=factor_new,
init_block_size=init_block_size)
]
# Transform the data
preprocess(dataset, preprocessors)
return dataset
def create_cropped_model(model_name, n_chans):
######################################################################
# Now we create the model. To enable it to be used in cropped decoding
# efficiently, we manually set the length of the final convolution layer
# to some length that makes the receptive field of the ConvNet smaller
# than ``input_window_samples`` (see ``final_conv_length=30`` in the model
# definition).
#
cuda = torch.cuda.is_available() # check if GPU is available, if True chooses to use it
device = 'cuda' if cuda else 'cpu'
if cuda:
torch.backends.cudnn.benchmark = True
seed = 20200220 # random seed to make results reproducible
# Set random seed to be able to reproduce results
set_random_seeds(seed=seed, cuda=cuda)
n_classes = 4
if model_name == 'shallow':
model = ShallowFBCSPNet(
n_chans,
n_classes,
input_window_samples=None, # no need to provide if final_conv_length given
final_conv_length=30,
)
else:
assert model_name == 'deep'
model = Deep4Net(
n_chans,
n_classes,
input_window_samples=None, # no need to provide if final_conv_length given
final_conv_length=2,
)
# Send model to GPU
if cuda:
model.cuda()
######################################################################
# And now we transform model with strides to a model that outputs dense
# prediction, so we can use it to obtain predictions for all
# crops.
#
to_dense_prediction_model(model)
return model
def cut_windows(dataset, input_window_samples, window_stride_samples):
######################################################################
# Cut the data into windows
# -------------------------
#
######################################################################
# In contrast to trialwise decoding, we have to supply an explicit window size and window stride to the
# ``create_windows_from_events`` function.
#
trial_start_offset_seconds = -0.5
# Extract sampling frequency, check that they are same in all datasets
sfreq = dataset.datasets[0].raw.info['sfreq']
assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
# Calculate the trial start offset in samples.
trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
# Create windows using braindecode function for this. It needs parameters to define how
# trials should be used.
windows_dataset = create_windows_from_events(
dataset,
trial_start_offset_samples=trial_start_offset_samples,
trial_stop_offset_samples=0,
window_size_samples=input_window_samples,
window_stride_samples=window_stride_samples,
drop_last_window=False,
preload=True,
)
return windows_dataset
def split_into_train_valid(windows_dataset):
######################################################################
# Split the dataset
# -----------------
#
# This code is the same as in trialwise decoding.
#
splitted = windows_dataset.split('run')
full_train_set = splitted['train']
n_split = int(np.round(0.8 * len(full_train_set)))
# ensure this is mutiple of 2 (number of windows per trial)
n_windows_per_trial = 2 # here set by hand
n_split = n_split - (n_split % n_windows_per_trial)
valid_set = Subset(full_train_set, range(n_split, len(full_train_set)))
train_set = Subset(full_train_set, range(0, n_split))
return train_set, valid_set
def run_training(model, model_name, train_set, valid_set, device, n_epochs):
if model_name == 'shallow':
# These values we found good for shallow network:
lr = 0.0625 * 0.01
weight_decay = 0
else:
assert model_name == 'deep'
# For deep4 they should be:
lr = 1 * 0.01
weight_decay = 0.5 * 0.001
batch_size = 64
clf = EEGClassifier(
model,
cropped=True,
criterion=CroppedLoss,
criterion__loss_function=torch.nn.functional.nll_loss,
optimizer=torch.optim.AdamW,
train_split=predefined_split(valid_set),
optimizer__lr=lr,
optimizer__weight_decay=weight_decay,
iterator_train__shuffle=True,
batch_size=batch_size,
callbacks=[
"accuracy", ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
],
device=device,
)
# Model training for a specified number of epochs. `y` is None as it is already supplied
# in the dataset.
clf.fit(train_set, y=None, epochs=n_epochs)
return clf
def compute_and_store_amp_grads(model, train_set, filename):
amp_grads_per_filter = compute_amplitude_gradients(model, train_set, batch_size=64)
# average across compute windows
avg_amp_grads_per_filter = np.mean(amp_grads_per_filter, axis=1)
np.save(filename, avg_amp_grads_per_filter)
def run_exp(
subject_id, low_cut_hz, high_cut_hz, n_epochs,
model_name, output_dir):
log.info(f"Load and preprocess data for subject {subject_id}...")
dataset = load_preprocessed_data(subject_id, low_cut_hz, high_cut_hz)
# Extract number of chans from dataset to create model
n_chans = dataset[0][0].shape[0]
log.info("Create cropped model...")
model = create_cropped_model(model_name, n_chans)
# Cut windows from the preprocessed data, using number of predictions
# per compute window to cut non-overlapping fully covering windows
# (except for overlap of last window to stay within trial bounds)
log.info("Cut windows from dataset ...")
input_window_samples = 1000
# To know the models’ receptive field, we calculate the shape of model
# output for a dummy input.
n_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]
windows_dataset = cut_windows(
dataset, input_window_samples, window_stride_samples=n_preds_per_input)
# Split into train and valid, ignoring final evaluation for now
log.info("Split into train and valid...")
train_set, valid_set = split_into_train_valid(windows_dataset)
# Run actual training
log.info("Run training...")
run_training(model, model_name, train_set, valid_set, 'cuda', n_epochs)
log.info("Compute and store amplitude gradients ...")
amp_grads_filename = os.path.join(output_dir, f"{subject_id}_avg_amp_grads.npy")
compute_and_store_amp_grads(model, train_set, filename=amp_grads_filename)
log.info("... Done.")
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="""Launch an experiment from a YAML experiment file.
Example: ./train_experiments.py configs/config.py """
)
parser.add_argument('subject_id', type=int,
help='''Run for subject id....''')
args = parser.parse_args()
subject_id = args.subject_id
low_cut_hz = None # low cut frequency for filtering
high_cut_hz = None # high cut frequency for filtering
n_epochs = 20
model_name = 'deep'
output_dir = './results/'
logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',
level=logging.DEBUG, stream=sys.stdout)
run_exp(
subject_id, low_cut_hz, high_cut_hz, n_epochs,
model_name, output_dir)
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