From a172f9c0573acca46bfdb0f875804e8aac23b3ca Mon Sep 17 00:00:00 2001 From: PierreGtch Date: Thu, 1 Feb 2024 09:13:14 +0000 Subject: [PATCH] deploy: NeuroTechX/moabb@fcfae76ddc053288434ae93d7f3ef3808545d2ef --- .../auto_examples_python.zip | Bin 126013 -> 125063 bytes .../plot_braindecode.ipynb | 6 +- .../auto_examples_jupyter.zip | Bin 180415 -> 179421 bytes .../load_model.ipynb | 4 +- .../load_model.py | 17 +- .../auto_tutorials_jupyter.zip | Bin 41030 -> 41030 bytes .../plot_braindecode.py | 19 +- .../auto_tutorials_python.zip | Bin 27844 -> 27844 bytes docs/_images/sphx_glr_plot_benchmark_001.png | Bin 26570 -> 26558 bytes .../sphx_glr_plot_benchmark_DL_001.png | Bin 38944 -> 38954 bytes .../sphx_glr_plot_benchmark_DL_thumb.png | Bin 12807 -> 12812 bytes ...phx_glr_plot_benchmark_braindecode_001.png | Bin 35661 -> 35883 bytes ...x_glr_plot_benchmark_braindecode_thumb.png | Bin 12162 -> 12282 bytes .../_images/sphx_glr_plot_benchmark_thumb.png | Bin 9350 -> 9340 bytes 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zttg?M9#jZ7n+{iJa2%tYBxUWDn%*>>9_|7?VP=mlzH%oui{lt!9mZF<%~nsB z8z7s0j4=?L46fn>3l@BelZMfhxFE-c+eA?-K0l4V-3$6k938GNukQSOu!!Rrq2Blk zw>|X|#|8be>h}1Q7u=#CHc9s2`nnoOG4Zh>h7*U*?t>iOv!A0ieK3pQGh9J@4&n`VQwEqr&))%)meoz1;()1bdz9(l5vMj7LWqO;3{IexfDa z_>#E%AKGVIfb22q4Mfi7ALQ|QgzkV{t$B0RAgX!`)(>DP?hWp6`)-v;mw>w~`8 zk9U17BX^VQAlH`oURXG;#}B3k_hGP%>|;J+T8eg1c>u~;QPEpWQRe$mMe_`aI\n# Bruno Aristimunha \n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\nimport mne\nimport seaborn as sns\nimport torch\nfrom braindecode import EEGClassifier\nfrom braindecode.models import EEGNetv4\nfrom sklearn.pipeline import Pipeline\nfrom skorch.callbacks import EarlyStopping, EpochScoring\nfrom skorch.dataset import ValidSplit\n\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.evaluations import CrossSessionEvaluation\nfrom moabb.paradigms import MotorImagery\nfrom moabb.pipelines.utils_pytorch import BraindecodeDatasetLoader, InputShapeSetterEEG\nfrom moabb.utils import setup_seed\n\n\nmne.set_log_level(False)\n\n# Print Information PyTorch\nprint(f\"Torch Version: {torch.__version__}\")\n\n# Set up GPU if it is there\ncuda = torch.cuda.is_available()\ndevice = \"cuda\" if cuda else \"cpu\"\nprint(\"GPU is\", \"AVAILABLE\" if cuda else \"NOT AVAILABLE\")" + "# Authors: Igor Carrara \n# Bruno Aristimunha \n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\nimport mne\nimport seaborn as sns\nimport torch\nfrom braindecode import EEGClassifier\nfrom braindecode.models import EEGNetv4\nfrom sklearn.pipeline import make_pipeline\nfrom skorch.callbacks import EarlyStopping, EpochScoring\nfrom skorch.dataset import ValidSplit\n\nfrom moabb.datasets import BNCI2014_001\nfrom moabb.evaluations import CrossSessionEvaluation\nfrom moabb.paradigms import MotorImagery\nfrom moabb.utils import setup_seed\n\n\nmne.set_log_level(False)\n\n# Print Information PyTorch\nprint(f\"Torch Version: {torch.__version__}\")\n\n# Set up GPU if it is there\ncuda = torch.cuda.is_available()\ndevice = \"cuda\" if cuda else \"cpu\"\nprint(\"GPU is\", \"AVAILABLE\" if cuda else \"NOT AVAILABLE\")" ] }, { @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "# Set random seed to be able to reproduce results\nseed = 42\nsetup_seed(seed)\n\n# Ensure that all operations are deterministic on GPU (if used) for reproducibility\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n\n# Hyperparameter\nLEARNING_RATE = 0.0625 * 0.01 # parameter taken from Braindecode\nWEIGHT_DECAY = 0 # parameter taken from Braindecode\nBATCH_SIZE = 64 # parameter taken from BrainDecode\nEPOCH = 10\nPATIENCE = 3\nfmin = 4\nfmax = 100\ntmin = 0\ntmax = None\n\n# Load the dataset\ndataset = BNCI2014_001()\nevents = [\"right_hand\", \"left_hand\"]\nparadigm = MotorImagery(\n events=events, n_classes=len(events), fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax\n)\nsubjects = [1]\nX, _, _ = paradigm.get_data(dataset=dataset, subjects=subjects)\n# Define Transformer of Dataset compatible with Brain Decode\ncreate_dataset = BraindecodeDatasetLoader()" + "# Set random seed to be able to reproduce results\nseed = 42\nsetup_seed(seed)\n\n# Ensure that all operations are deterministic on GPU (if used) for reproducibility\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n\n# Hyperparameter\nLEARNING_RATE = 0.0625 * 0.01 # parameter taken from Braindecode\nWEIGHT_DECAY = 0 # parameter taken from Braindecode\nBATCH_SIZE = 64 # parameter taken from BrainDecode\nEPOCH = 10\nPATIENCE = 3\nfmin = 4\nfmax = 100\ntmin = 0\ntmax = None\n\n# Load the dataset\ndataset = BNCI2014_001()\nevents = [\"right_hand\", \"left_hand\"]\nparadigm = MotorImagery(\n events=events, n_classes=len(events), fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax\n)\nsubjects = [1]\nX, _, _ = paradigm.get_data(dataset=dataset, subjects=subjects)" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "model = EEGNetv4(in_chans=1, n_classes=1, input_window_samples=100)\n\n# Send model to GPU\nif cuda:\n model.cuda()\n\n# Define a Skorch classifier\nclf = EEGClassifier(\n module=model,\n criterion=torch.nn.CrossEntropyLoss,\n optimizer=torch.optim.Adam,\n optimizer__lr=LEARNING_RATE,\n batch_size=BATCH_SIZE,\n max_epochs=EPOCH,\n train_split=ValidSplit(0.2, random_state=seed),\n device=device,\n callbacks=[\n EarlyStopping(monitor=\"valid_loss\", patience=PATIENCE),\n EpochScoring(\n scoring=\"accuracy\", on_train=True, name=\"train_acc\", lower_is_better=False\n ),\n EpochScoring(\n scoring=\"accuracy\", on_train=False, name=\"valid_acc\", lower_is_better=False\n ),\n InputShapeSetterEEG(\n params_list=[\"in_chans\", \"input_window_samples\", \"n_classes\"],\n ),\n ],\n verbose=1, # Not printing the results for each epoch\n)\n\n# Create the pipelines\npipes = {}\npipes[\"EEGNetV4\"] = Pipeline([(\"Braindecode_dataset\", create_dataset), (\"Net\", clf)])" + "# Define a Skorch classifier\nclf = EEGClassifier(\n module=EEGNetv4,\n optimizer=torch.optim.Adam,\n optimizer__lr=LEARNING_RATE,\n batch_size=BATCH_SIZE,\n max_epochs=EPOCH,\n train_split=ValidSplit(0.2, random_state=seed),\n device=device,\n callbacks=[\n EarlyStopping(monitor=\"valid_loss\", patience=PATIENCE),\n EpochScoring(\n scoring=\"accuracy\", on_train=True, name=\"train_acc\", lower_is_better=False\n ),\n EpochScoring(\n scoring=\"accuracy\", on_train=False, name=\"valid_acc\", lower_is_better=False\n ),\n ],\n verbose=1, # Not printing the results for each epoch\n)\n\n# Create the pipelines\npipes = {}\npipes[\"EEGNetV4\"] = make_pipeline(clf)" ] }, { diff --git a/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip index cb4626e6c5db41248b8e41e2c11775aa01a20891..4e2878ea765c80e25f37db8e059570735f6a30a8 100644 GIT binary patch delta 1452 zcmZ8gZA@Eb6n;Mt7_^jRP};TKZmHcI7B;rV88?&$YhesRm?k%KtV1Z{qm+(-37QzH 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a/docs/_downloads/744775bda3ec8d62d828dcc3cff768f5/load_model.ipynb b/docs/_downloads/744775bda3ec8d62d828dcc3cff768f5/load_model.ipynb index 7f02cb5b..79d95539 100644 --- a/docs/_downloads/744775bda3ec8d62d828dcc3cff768f5/load_model.ipynb +++ b/docs/_downloads/744775bda3ec8d62d828dcc3cff768f5/load_model.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "# Authors: Igor Carrara \n#\n# License: BSD (3-clause)\n\nfrom pickle import load\n\nimport keras\nimport torch\nfrom braindecode import EEGClassifier\nfrom braindecode.models import EEGInception\nfrom scikeras.wrappers import KerasClassifier\nfrom sklearn.pipeline import Pipeline\nfrom skorch.callbacks import EarlyStopping, EpochScoring\nfrom skorch.dataset import ValidSplit\n\nfrom moabb import set_log_level\nfrom moabb.pipelines.features import StandardScaler_Epoch\nfrom moabb.pipelines.utils_pytorch import BraindecodeDatasetLoader, InputShapeSetterEEG\nfrom moabb.utils import setup_seed\n\n\nset_log_level(\"info\")" + "# Authors: Igor Carrara \n#\n# License: BSD (3-clause)\n\nfrom pickle import load\n\nimport keras\nimport torch\nfrom braindecode import EEGClassifier\nfrom braindecode.models import EEGInception\nfrom scikeras.wrappers import KerasClassifier\nfrom sklearn.pipeline import Pipeline, make_pipeline\nfrom skorch.callbacks import EarlyStopping, EpochScoring\nfrom skorch.dataset import ValidSplit\n\nfrom moabb import set_log_level\nfrom moabb.pipelines.features import StandardScaler_Epoch\nfrom moabb.utils import setup_seed\n\n\nset_log_level(\"info\")" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "# Set EEG Inception model\nmodel = EEGInception(in_channels=22, n_classes=2)\n\n# Hyperparameter\nLEARNING_RATE = 0.0001\nWEIGHT_DECAY = 0\nBATCH_SIZE = 64\nSEED = 42\nVERBOSE = 1\nEPOCH = 2\nPATIENCE = 3\n\n# Define a Skorch classifier\nclf = EEGClassifier(\n module=model,\n criterion=torch.nn.CrossEntropyLoss,\n optimizer=torch.optim.Adam,\n optimizer__lr=LEARNING_RATE,\n batch_size=BATCH_SIZE,\n max_epochs=EPOCH,\n train_split=ValidSplit(0.2, random_state=SEED),\n callbacks=[\n EarlyStopping(monitor=\"valid_loss\", patience=PATIENCE),\n EpochScoring(\n scoring=\"accuracy\", on_train=True, name=\"train_acc\", lower_is_better=False\n ),\n EpochScoring(\n scoring=\"accuracy\", on_train=False, name=\"valid_acc\", lower_is_better=False\n ),\n InputShapeSetterEEG(\n params_list=[\"in_channels\", \"input_window_samples\", \"n_classes\"],\n ),\n ],\n verbose=VERBOSE, # Not printing the results for each epoch\n)\n\nclf.initialize()\n\nf_params = \"./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_model.pkl\"\nf_optimizer = \"./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_optim.pkl\"\nf_history = \"./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_history.json\"\n\nclf.load_params(f_params=f_params, f_optimizer=f_optimizer, f_history=f_history)\n\n# Create the dataset\ncreate_dataset = BraindecodeDatasetLoader(drop_last_window=False)\n\n# Create the pipelines\npipes_pytorch = Pipeline([(\"Braindecode_dataset\", create_dataset), (\"EEGInception\", clf)])" + "# Hyperparameter\nLEARNING_RATE = 0.0001\nWEIGHT_DECAY = 0\nBATCH_SIZE = 64\nSEED = 42\nVERBOSE = 1\nEPOCH = 2\nPATIENCE = 3\n\n# Define a Skorch classifier\nclf = EEGClassifier(\n module=EEGInception,\n optimizer=torch.optim.Adam,\n optimizer__lr=LEARNING_RATE,\n batch_size=BATCH_SIZE,\n max_epochs=EPOCH,\n train_split=ValidSplit(0.2, random_state=SEED),\n callbacks=[\n EarlyStopping(monitor=\"valid_loss\", patience=PATIENCE),\n EpochScoring(\n scoring=\"accuracy\", on_train=True, name=\"train_acc\", lower_is_better=False\n ),\n EpochScoring(\n scoring=\"accuracy\", on_train=False, name=\"valid_acc\", lower_is_better=False\n ),\n ],\n verbose=VERBOSE, # Not printing the results for each epoch\n)\n\nclf.initialize()\n\nf_params = \"./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_model.pkl\"\nf_optimizer = \"./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_optim.pkl\"\nf_history = \"./results/Models_CrossSession/BNCI2014-001/1/braindecode_EEGInception/EEGInception_fitted_best_history.json\"\n\nclf.load_params(f_params=f_params, f_optimizer=f_optimizer, f_history=f_history)\n\n# Create the pipelines\npipes_pytorch = make_pipeline(clf)" ] } ], diff --git a/docs/_downloads/8fed52c2a57f02454c33ebb464a390de/load_model.py b/docs/_downloads/8fed52c2a57f02454c33ebb464a390de/load_model.py index a35bb211..4d96ae51 100644 --- a/docs/_downloads/8fed52c2a57f02454c33ebb464a390de/load_model.py +++ b/docs/_downloads/8fed52c2a57f02454c33ebb464a390de/load_model.py @@ -16,13 +16,12 @@ from braindecode import EEGClassifier from braindecode.models import EEGInception from scikeras.wrappers import KerasClassifier -from sklearn.pipeline import Pipeline +from sklearn.pipeline import Pipeline, make_pipeline from skorch.callbacks import EarlyStopping, EpochScoring from skorch.dataset import ValidSplit from moabb import set_log_level from moabb.pipelines.features import StandardScaler_Epoch -from moabb.pipelines.utils_pytorch import BraindecodeDatasetLoader, InputShapeSetterEEG from moabb.utils import setup_seed @@ -70,9 +69,6 @@ ############################################################################### # Loading the PyTorch model -# Set EEG Inception model -model = EEGInception(in_channels=22, n_classes=2) - # Hyperparameter LEARNING_RATE = 0.0001 WEIGHT_DECAY = 0 @@ -84,8 +80,7 @@ # Define a Skorch classifier clf = EEGClassifier( - module=model, - criterion=torch.nn.CrossEntropyLoss, + module=EEGInception, optimizer=torch.optim.Adam, optimizer__lr=LEARNING_RATE, batch_size=BATCH_SIZE, @@ -99,9 +94,6 @@ EpochScoring( scoring="accuracy", on_train=False, name="valid_acc", lower_is_better=False ), - InputShapeSetterEEG( - params_list=["in_channels", "input_window_samples", "n_classes"], - ), ], verbose=VERBOSE, # Not printing the results for each epoch ) @@ -114,8 +106,5 @@ clf.load_params(f_params=f_params, f_optimizer=f_optimizer, f_history=f_history) -# Create the dataset -create_dataset = BraindecodeDatasetLoader(drop_last_window=False) - # Create the pipelines -pipes_pytorch = Pipeline([("Braindecode_dataset", create_dataset), ("EEGInception", clf)]) +pipes_pytorch = make_pipeline(clf) diff --git a/docs/_downloads/97a1de59bce682890841bb846e3dd09c/auto_tutorials_jupyter.zip b/docs/_downloads/97a1de59bce682890841bb846e3dd09c/auto_tutorials_jupyter.zip index ffe00046557a04583e24155ad5e29508dd2bba13..83bbc3fd5ecc897c267e4fe2acc93457feb357ad 100644 GIT binary patch delta 273 zcmX?hfa%x)Cf)#VW)=|!5J>cN+??Haj(>7O9|yBLkaIO}vOKeor{hLm0a@m7pg^aa zrv`H^kf9)}FAP=^WS}F)%g7|c3^!u(%lXob43jJSv~>c!5i&sYA|^YWU`dc?U;tqW zu$2&EN#jla$p_}EDFE$?5S^F$?6wR80|-l@D9YELC?hkuejbkk&@B;X4zxUF)L>u$ zVO&qHVzB`Wx=j$hcjiFU%YRWbk(Ah)upw!oi#e&IwQiH@5|TMq6`cmtcRk()nL*BBVL4RpgSjP z&6j4Xahp8PN1q?fpviOQNi&_uo4j+LngY=M5!3q0Qz!EST?WJ&DCWlXO_b4^Y_LEK E0Iz~a>Hq)$ diff --git a/docs/_downloads/c9b0437022c98a63476dd3cad6cb69c7/plot_braindecode.py b/docs/_downloads/c9b0437022c98a63476dd3cad6cb69c7/plot_braindecode.py index 06ff0693..e845dbfc 100644 --- a/docs/_downloads/c9b0437022c98a63476dd3cad6cb69c7/plot_braindecode.py +++ b/docs/_downloads/c9b0437022c98a63476dd3cad6cb69c7/plot_braindecode.py @@ -16,14 +16,13 @@ import torch from braindecode import EEGClassifier from braindecode.models import EEGNetv4 -from sklearn.pipeline import Pipeline +from sklearn.pipeline import make_pipeline from skorch.callbacks import EarlyStopping, EpochScoring from skorch.dataset import ValidSplit from moabb.datasets import BNCI2014_001 from moabb.evaluations import CrossSessionEvaluation from moabb.paradigms import MotorImagery -from moabb.pipelines.utils_pytorch import BraindecodeDatasetLoader, InputShapeSetterEEG from moabb.utils import setup_seed @@ -81,8 +80,6 @@ ) subjects = [1] X, _, _ = paradigm.get_data(dataset=dataset, subjects=subjects) -# Define Transformer of Dataset compatible with Brain Decode -create_dataset = BraindecodeDatasetLoader() ############################################################################## # Create Pipelines @@ -94,16 +91,9 @@ # callbacks InputShapeSetterEEG, where we have to specify the correct name of the parameter. # Here, we will use the EEGNet v4 model [1]_ . -model = EEGNetv4(in_chans=1, n_classes=1, input_window_samples=100) - -# Send model to GPU -if cuda: - model.cuda() - # Define a Skorch classifier clf = EEGClassifier( - module=model, - criterion=torch.nn.CrossEntropyLoss, + module=EEGNetv4, optimizer=torch.optim.Adam, optimizer__lr=LEARNING_RATE, batch_size=BATCH_SIZE, @@ -118,16 +108,13 @@ EpochScoring( scoring="accuracy", on_train=False, name="valid_acc", lower_is_better=False ), - InputShapeSetterEEG( - params_list=["in_chans", "input_window_samples", "n_classes"], - ), ], verbose=1, # Not printing the results for each epoch ) # Create the pipelines pipes = {} -pipes["EEGNetV4"] = Pipeline([("Braindecode_dataset", create_dataset), ("Net", clf)]) +pipes["EEGNetV4"] = make_pipeline(clf) ############################################################################## # Evaluation diff --git a/docs/_downloads/cab7a090c4183ca69dc0cd84d3b04413/auto_tutorials_python.zip b/docs/_downloads/cab7a090c4183ca69dc0cd84d3b04413/auto_tutorials_python.zip index db2375f5b8ef3766bde7bb9b3d657d33ea9bb399..d51f8f35bd41d0e26fd3e5e59a952d0db8428dd1 100644 GIT binary patch delta 265 zcmX?dlkvz+M&1B#W)=|!5D52l+$@;5gOeL3;0_d+>`^Vj3}kHNZ4+en0SXA|)k!fY z0vXc;zwm%$HuuU^@$)h=i7>;BnVgj)&B!oWK2bv_z#Ab0R2;FBWl@&51Oo#Ii-N6% z5K9^lb53^2QC0vcTId-zH$;$u0ffa-6getQl#!gwlg$Hi%jEOf(oCm}CoAgdOqR&u r0l8|jG*HOfZ?Z+UHQ1!H9~r!C_!$^LSR2J8r|8LZI*}-P5RiJJ zQl*1{bW)UZC`vn01S1@ft{my*e7}7%cV^aI@4LQl=6%;)-@SKcP1N8i|NrxUp1t?4 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zm6`g~+qC2%#-&Vvz^=Qg=zt^pqqLAGo<0a9K4`E2Jr7P&W=ajV5sl{Orf&&i(@sF- z4quo8OnHFhvy>?;D=SlPZOOqlauYPF1Iaqq$S1)x&ts;RTu@MOFTbk%UX>jf&*Yw; z{AiOQDB)9>WZQNDev<63&qRQsid{`CIJ=)}0u8&&(s=IKuF;P{c!eE~Ow-v}^wYop zkV0TF-Ey>~U1(bFhLqTr$nbJ7JBQ0etcRh`bYT(t%*G_Z8RFN46vJ?IFWt9bFR|ZqL^+Je<||< rG}@wR@tQ-VAkQAiX~xFBPd;ZDo%B1pk}~ZE0k4}kZXqkRY@hrW>@Y3` diff --git a/docs/auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html b/docs/auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html index f424c9fe..64e67e34 100644 --- a/docs/auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html +++ b/docs/auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html @@ -781,7 +781,7 @@

    Evaluationplt.show() -plot filterbank csp vs csp

    Total running time of the script: ( 0 minutes 13.040 seconds)

    +plot filterbank csp vs csp

    Total running time of the script: ( 0 minutes 13.249 seconds)

    Estimated memory usage: 282 MB

    @@ -837,8 +837,8 @@

    Load Best Model ParameterTotal running time of the script: ( 0 minutes 13.269 seconds)

    -

    Estimated memory usage: 181 MB

    +

    Total running time of the script: ( 0 minutes 12.873 seconds)

    +

    Estimated memory usage: 143 MB

    @@ -860,7 +858,7 @@

    Advanced MNE PipelineAdvanced MNE PipelineAdvanced MNE Pipeline @@ -924,7 +922,7 @@

    Numpy-based PipelineNumpy-based PipelineNumpy-based Pipeline @@ -978,8 +976,8 @@

    Combining Resultsplt.show() -Algorithm comparison

    Total running time of the script: ( 0 minutes 46.379 seconds)

    -

    Estimated memory usage: 237 MB

    +Algorithm comparison

    Total running time of the script: ( 0 minutes 47.225 seconds)

    +

    Estimated memory usage: 169 MB

    -plot select electrodes resample

    Total running time of the script: ( 0 minutes 39.840 seconds)

    -

    Estimated memory usage: 306 MB

    +plot select electrodes resample

    Total running time of the script: ( 0 minutes 40.930 seconds)

    +

    Estimated memory usage: 331 MB

    -Algorithm comparison

    Total running time of the script: ( 0 minutes 39.538 seconds)

    -

    Estimated memory usage: 230 MB

    +Algorithm comparison

    Total running time of the script: ( 0 minutes 39.162 seconds)

    +

    Estimated memory usage: 183 MB

    -Errorbar shows Mean-CI across permutations

    Total running time of the script: ( 1 minutes 55.046 seconds)

    +Errorbar shows Mean-CI across permutations

    Total running time of the script: ( 1 minutes 58.916 seconds)

    Estimated memory usage: 12 MB

    -Errorbar shows Mean-CI across permutations

    Total running time of the script: ( 0 minutes 35.660 seconds)

    -

    Estimated memory usage: 181 MB

    +Errorbar shows Mean-CI across permutations

    Total running time of the script: ( 0 minutes 35.388 seconds)

    +

    Estimated memory usage: 143 MB

    -Errorbar shows Mean-CI across permutations

    Total running time of the script: ( 1 minutes 5.138 seconds)

    -

    Estimated memory usage: 12 MB

    +Errorbar shows Mean-CI across permutations

    Total running time of the script: ( 1 minutes 7.312 seconds)

    +

    Estimated memory usage: 11 MB

    Loading the PyTorch model

    -
    # Set EEG Inception model
    -model = EEGInception(in_channels=22, n_classes=2)
    -
    -# Hyperparameter
    +
    # Hyperparameter
     LEARNING_RATE = 0.0001
     WEIGHT_DECAY = 0
     BATCH_SIZE = 64
    @@ -675,8 +671,7 @@
     
     # Define a Skorch classifier
     clf = EEGClassifier(
    -    module=model,
    -    criterion=torch.nn.CrossEntropyLoss,
    +    module=EEGInception,
         optimizer=torch.optim.Adam,
         optimizer__lr=LEARNING_RATE,
         batch_size=BATCH_SIZE,
    @@ -690,9 +685,6 @@
             EpochScoring(
                 scoring="accuracy", on_train=False, name="valid_acc", lower_is_better=False
             ),
    -        InputShapeSetterEEG(
    -            params_list=["in_channels", "input_window_samples", "n_classes"],
    -        ),
         ],
         verbose=VERBOSE,  # Not printing the results for each epoch
     )
    @@ -705,11 +697,8 @@
     
     clf.load_params(f_params=f_params, f_optimizer=f_optimizer, f_history=f_history)
     
    -# Create the dataset
    -create_dataset = BraindecodeDatasetLoader(drop_last_window=False)
    -
     # Create the pipelines
    -pipes_pytorch = Pipeline([("Braindecode_dataset", create_dataset), ("EEGInception", clf)])
    +pipes_pytorch = make_pipeline(clf)
     

    Total running time of the script: ( 0 minutes 0.000 seconds)

    diff --git a/docs/auto_examples/plot_benchmark.html b/docs/auto_examples/plot_benchmark.html index d9b40b4c..9e52efe2 100644 --- a/docs/auto_examples/plot_benchmark.html +++ b/docs/auto_examples/plot_benchmark.html @@ -725,7 +725,7 @@

    Running the benchmark)

    -
    2024-01-05 10:25:08,362 INFO MainThread moabb.evaluations.base Processing dataset: Zhou2016
    +
    -Scores per dataset and algorithm
    2024-01-05 10:25:36,685 WARNING MainThread moabb.analysis.plotting Dataset names are too similar, turning off name shortening
    +Scores per dataset and algorithm
    2024-02-01 08:47:11,099 WARNING MainThread moabb.analysis.plotting Dataset names are too similar, turning off name shortening
     /home/runner/work/moabb/moabb/moabb/analysis/plotting.py:70: UserWarning: The palette list has more values (6) than needed (2), which may not be intended.
       sea.stripplot(
     
    -

    Total running time of the script: ( 0 minutes 29.284 seconds)

    -

    Estimated memory usage: 325 MB

    +

    Total running time of the script: ( 0 minutes 28.211 seconds)

    +

    Estimated memory usage: 322 MB

    The deep learning architectures implemented in MOABB are: @@ -1058,8 +1048,8 @@

    ReferencesTotal running time of the script: ( 8 minutes 57.061 seconds)

    -

    Estimated memory usage: 815 MB

    +

    Total running time of the script: ( 9 minutes 7.285 seconds)

    +

    Estimated memory usage: 852 MB

    The deep learning architectures implemented in MOABB using Braindecode are:

    @@ -1483,8 +1450,8 @@

    ReferencesTotal running time of the script: ( 5 minutes 30.697 seconds)

    -

    Estimated memory usage: 1116 MB

    +

    Total running time of the script: ( 3 minutes 8.219 seconds)

    +

    Estimated memory usage: 621 MB

    -

    Total running time of the script: ( 1 minutes 32.458 seconds)

    -

    Estimated memory usage: 61 MB

    +

    Total running time of the script: ( 1 minutes 30.933 seconds)

    +

    Estimated memory usage: 87 MB

    /home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
    @@ -741,16 +738,9 @@ 

    Create Pipelines[1] .

    -
    model = EEGNetv4(in_chans=1, n_classes=1, input_window_samples=100)
    -
    -# Send model to GPU
    -if cuda:
    -    model.cuda()
    -
    -# Define a Skorch classifier
    +
    # Define a Skorch classifier
     clf = EEGClassifier(
    -    module=model,
    -    criterion=torch.nn.CrossEntropyLoss,
    +    module=EEGNetv4,
         optimizer=torch.optim.Adam,
         optimizer__lr=LEARNING_RATE,
         batch_size=BATCH_SIZE,
    @@ -765,16 +755,13 @@ 

    Create PipelinesEpochScoring( scoring="accuracy", on_train=False, name="valid_acc", lower_is_better=False ), - InputShapeSetterEEG( - params_list=["in_chans", "input_window_samples", "n_classes"], - ), ], verbose=1, # Not printing the results for each epoch ) # Create the pipelines pipes = {} -pipes["EEGNetV4"] = Pipeline([("Braindecode_dataset", create_dataset), ("Net", clf)]) +pipes["EEGNetV4"] = make_pipeline(clf)

    @@ -849,26 +836,32 @@

    EvaluationTotal running time of the script: ( 0 minutes 18.636 seconds)

    -

    Estimated memory usage: 215 MB

    +

    Total running time of the script: ( 0 minutes 14.381 seconds)

    +

    Estimated memory usage: 168 MB

    -dataset = Zhou2016, dataset = BNCI2014-001

    Total running time of the script: ( 0 minutes 14.982 seconds)

    -

    Estimated memory usage: 233 MB

    +dataset = Zhou2016, dataset = BNCI2014-001

    Total running time of the script: ( 0 minutes 15.434 seconds)

    +

    Estimated memory usage: 190 MB

    -plot cross subject ssvep

    Total running time of the script: ( 0 minutes 15.632 seconds)

    -

    Estimated memory usage: 21 MB

    +plot cross subject ssvep

    Total running time of the script: ( 0 minutes 16.055 seconds)

    +

    Estimated memory usage: 19 MB

    -
    Using array cache: 0.34 seconds
    -Using epochs cache: 0.46 seconds
    +
    Using array cache: 0.35 seconds
    +Using epochs cache: 0.52 seconds
     Using raw cache: 0.74 seconds
    -Without cache: 2.38 seconds
    +Without cache: 2.43 seconds
     

    As you can see, using a raw cache is more than 5 times faster than @@ -934,8 +934,8 @@

    Cleanup

    -

    Total running time of the script: ( 0 minutes 26.424 seconds)

    -

    Estimated memory usage: 233 MB

    +

    Total running time of the script: ( 0 minutes 26.985 seconds)

    +

    Estimated memory usage: 249 MB

    -

    Total running time of the script: ( 0 minutes 22.949 seconds)

    -

    Estimated memory usage: 560 MB

    +

    Total running time of the script: ( 0 minutes 23.135 seconds)

    +

    Estimated memory usage: 688 MB