New processing code in python
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941DF56C7242C3F1
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__pycache__
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*_DATA
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*_proc
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from glob import glob
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import numpy as np
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from aux_functions import load_pickle, save_pickle
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EXP_NO = 2
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MODEL_SIZE = 1
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CRITERIA = ""
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data_type = {"cifar": "CIFAR", "mnist": "MNIST"}
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experiment_criterias = {
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"Early": "/Early_Stopping",
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"Efficiency": "/Efficiency_Stopping",
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"100": "/100_epoch",
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"Accuracy": "/Accuracy_Bound",
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"Energy": "/Energy_Bound",
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}
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model_type = {"bayes": "bayes_exp_*.pkl", "freq": "freq_exp_data_*.pkl"}
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EXPERIMENT_PATH = f"./EXPERIMENT_{EXP_NO}_DATA"
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models_exp = {
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"cifar": {
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"bayes": {
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1: None,
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2: None,
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3: None,
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4: None,
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5: None,
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6: None,
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7: None,
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},
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"freq": {
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1: None,
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2: None,
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3: None,
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4: None,
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5: None,
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6: None,
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7: None,
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},
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},
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"mnist": {
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"bayes": {
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1: None,
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2: None,
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3: None,
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4: None,
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5: None,
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6: None,
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7: None,
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},
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"freq": {
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1: None,
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2: None,
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3: None,
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4: None,
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5: None,
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6: None,
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7: None,
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},
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},
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}
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path = (
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f"{EXPERIMENT_PATH}"
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f"{experiment_criterias['Efficiency']}"
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f"/{data_type['cifar']}"
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f"/{model_type['bayes']}"
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)
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path = glob(path)
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for t in zip(range(1, 8), path):
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models_exp["cifar"]["bayes"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
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path = (
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f"{EXPERIMENT_PATH}"
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f"{experiment_criterias['Efficiency']}"
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f"/{data_type['mnist']}"
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f"/{model_type['bayes']}"
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)
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path = glob(path)
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for t in zip(range(1, 8), path):
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models_exp["mnist"]["bayes"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
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path = (
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f"{EXPERIMENT_PATH}"
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f"{experiment_criterias['Efficiency']}"
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f"/{data_type['cifar']}"
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f"/{model_type['freq']}"
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)
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path = glob(path)
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for t in zip(range(1, 8), path):
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models_exp["cifar"]["freq"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
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path = (
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f"{EXPERIMENT_PATH}"
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f"{experiment_criterias['Efficiency']}"
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f"/{data_type['mnist']}"
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f"/{model_type['freq']}"
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)
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path = glob(path)
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for t in zip(range(1, 8), path):
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models_exp["mnist"]["freq"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
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# print(models_exp)
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file_name = f"Experiment_{EXP_NO}_accuracy_data.pkl"
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save_pickle(file_name, models_exp)
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import pickle
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def load_pickle(fpath):
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gpu_data = []
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with open(fpath, "rb") as openfile:
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while True:
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try:
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gpu_data = pickle.load(openfile)
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except EOFError:
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break
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return gpu_data
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def save_pickle(fpath, data):
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with open(fpath, "wb") as f:
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pickle.dump(data, f)
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from glob import glob
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import numpy as np
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from aux_functions import load_pickle, save_pickle
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EXP_NO = 2
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MODEL_SIZE = 1
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CRITERIA = ""
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data_type = {"cifar": "CIFAR", "mnist": "MNIST"}
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experiment_criterias = {
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"Early": "/Early_Stopping",
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"Efficiency": "/Efficiency_Stopping",
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"100": "/100_epoch",
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"Accuracy": "/Accuracy_Bound",
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"Energy": "/Energy_Bound",
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}
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model_type = {"bayes": "bayes_exp_*.pkl", "freq": "freq_exp_data_*.pkl"}
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EXPERIMENT_PATH = f"./EXPERIMENT_{EXP_NO}_DATA"
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models_exp = {
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"cifar": {
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"bayes": {
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1: None,
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2: None,
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3: None,
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4: None,
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5: None,
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6: None,
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7: None,
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},
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"freq": {
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1: None,
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2: None,
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3: None,
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4: None,
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5: None,
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6: None,
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7: None,
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},
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},
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"mnist": {
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"bayes": {
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1: None,
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2: None,
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3: None,
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4: None,
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5: None,
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6: None,
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7: None,
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},
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"freq": {
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1: None,
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2: None,
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3: None,
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4: None,
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5: None,
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6: None,
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7: None,
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},
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},
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}
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path = (
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f"{EXPERIMENT_PATH}"
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f"{experiment_criterias['Efficiency']}"
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f"/{data_type['cifar']}"
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f"/{model_type['bayes']}"
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)
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path = glob(path)
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for t in zip(range(1, 8), path):
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models_exp["cifar"]["bayes"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
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path = (
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f"{EXPERIMENT_PATH}"
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f"{experiment_criterias['Efficiency']}"
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f"/{data_type['mnist']}"
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f"/{model_type['bayes']}"
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)
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path = glob(path)
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for t in zip(range(1, 8), path):
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models_exp["mnist"]["bayes"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
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path = (
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f"{EXPERIMENT_PATH}"
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f"{experiment_criterias['Efficiency']}"
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f"/{data_type['cifar']}"
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f"/{model_type['freq']}"
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)
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path = glob(path)
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for t in zip(range(1, 8), path):
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models_exp["cifar"]["freq"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
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path = (
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f"{EXPERIMENT_PATH}"
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f"{experiment_criterias['Efficiency']}"
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f"/{data_type['mnist']}"
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f"/{model_type['freq']}"
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)
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path = glob(path)
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for t in zip(range(1, 8), path):
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models_exp["mnist"]["freq"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
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# print(models_exp)
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file_name = f"Experiment_{EXP_NO}_accuracy_data.pkl"
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save_pickle(file_name, models_exp)
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from glob import glob
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# can be 1,2 or 3
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exp_no = 2
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# can be 1 to 7
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model_size = 1
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crit = "Early"
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experiment_criterias = {
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"Early": "/Early_Stopping",
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"Efficiency": "/Efficiency_Stopping",
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"100": "/100_epoch",
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"Accuracy": "/Accuracy_Bound",
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"Energy": "/Energy_Bound",
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}
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bcnn_path = {
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"prefix_path_mnist": "/checkpoints/MNIST/bayesian",
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"mid_path_mnist": "/model_lenet_lrt_softplus_",
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"prefix_path_cifar": "/checkpoints/CIFAR10/bayesian",
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"mid_path_cifar": "/model_lenet_lrt_softplus_",
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"end_path": "_epoch_",
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"noise_path": "_noise_",
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"suffix_path": ".pt",
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}
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lenet_path = {
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"prefix_path_mnist": "/checkpoints/MNIST/frequentist",
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"mid_path_mnist": "/model_lenet_",
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"prefix_path_cifar": "/checkpoints/CIFAR10/frequentist",
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"mid_path_cifar": "/model_lenet_",
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"end_path": "_epoch_",
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"noise_path": "_noise_",
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"suffix_path": ".pt",
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}
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# print(len(glob(experiment_2_path)))
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def get_experiment_epochs(
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experiment_number=2, size=7, model_type="bcnn", data_type="cifar", stopping_criteria="Efficiency"
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):
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models_epochs = {
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1: None,
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2: None,
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3: None,
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4: None,
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5: None,
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6: None,
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7: None,
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}
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EXPERIMENT_PATH = f"./EXPERIMENT_{experiment_number}_DATA"
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if (model_type == "bcnn") and (data_type == "cifar"):
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for s in range(size):
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experiment_path = (
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f"{EXPERIMENT_PATH}{experiment_criterias[stopping_criteria]}"
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f"{bcnn_path['prefix_path_cifar']}{bcnn_path['mid_path_cifar']}"
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f"{s+1}*.pt"
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)
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models_epochs[s + 1] = len(glob(experiment_path))
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return models_epochs
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if (model_type == "bcnn") and (data_type == "mnist"):
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for s in range(size):
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experiment_path = (
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f"{EXPERIMENT_PATH}{experiment_criterias[stopping_criteria]}"
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f"{bcnn_path['prefix_path_mnist']}{bcnn_path['mid_path_mnist']}"
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f"{s+1}*.pt"
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)
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models_epochs[s + 1] = len(glob(experiment_path))
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return models_epochs
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if (model_type == "lenet") and (data_type == "cifar"):
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for s in range(size):
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experiment_path = (
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f"{EXPERIMENT_PATH}{experiment_criterias[stopping_criteria]}"
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f"{lenet_path['prefix_path_cifar']}{lenet_path['mid_path_cifar']}"
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f"{s+1}*.pt"
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)
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models_epochs[s + 1] = len(glob(experiment_path))
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return models_epochs
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if (model_type == "lenet") and (data_type == "mnist"):
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for s in range(size):
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experiment_path = (
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f"{EXPERIMENT_PATH}{experiment_criterias[stopping_criteria]}"
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f"{lenet_path['prefix_path_mnist']}{lenet_path['mid_path_mnist']}"
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f"{s+1}*.pt"
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)
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models_epochs[s + 1] = len(glob(experiment_path))
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return models_epochs
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# ./EXPERIMENT_2_DATA/Efficiency_Stopping/checkpoints/MNIST/frequentist/size_/model_lenet_7*.pt
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# print(get_experiment_epochs(experiment_number=2, size=7, model_type="bcnn", data_type="mnist"))
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import pickle
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import torch
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import torch.linalg as alg
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def square_matrix(tensor):
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tensor_size = tensor.size()
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if len(tensor_size) == 0:
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return tensor
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if len(tensor_size) == 1:
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temp = torch.zeros([tensor_size[0], tensor_size[0] - 1])
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return torch.cat((temp.T, tensor.reshape(1, tensor_size[0])))
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if len(tensor_size) == 2:
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if tensor_size[0] > tensor_size[1]:
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temp = torch.zeros([tensor_size[0], tensor_size[0] - tensor_size[1]])
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return torch.cat((temp.T, tensor))
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if tensor_size[0] < tensor_size[1]:
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temp = torch.zeros([tensor_size[1], tensor_size[1] - tensor_size[0]])
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return torch.cat((temp.T, tensor))
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return tensor
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if len(tensor_size) > 2:
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temp_tensor = tensor.detach().clone()
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for i, x in enumerate(tensor):
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# print("i: {}".format(i))
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for j, t in enumerate(x):
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# print("j: {}".format(j))
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t_size = t.size()
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if t_size[0] > t_size[1]:
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temp = torch.zeros([t_size[0], t_size[0] - t_size[1]])
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temp_tensor[i][j] = torch.cat((temp.T, t))
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elif t_size[0] < t_size[1]:
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temp = torch.zeros([t_size[1], t_size[1] - t_size[0]])
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temp_tensor[i][j] = torch.cat((temp.T, t))
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else:
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temp_tensor[i][j] = t
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return temp_tensor
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def neumann_entropy(tensor):
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tensor_size = tensor.size()
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if len(tensor_size) == 0:
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return tensor
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if len(tensor_size) == 1:
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return 0
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if len(tensor_size) == 2:
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e = alg.eigvals(tensor) # .real
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# se = sum(e)
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# e = e / se
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temp_abs = torch.abs(e)
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# temp_abs = e
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temp = torch.log(temp_abs).real
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temp = torch.nan_to_num(temp, nan=0.0, posinf=0.0, neginf=0.0)
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return -1 * torch.sum(temp_abs * temp)
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if len(tensor_size) > 2:
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for i, x in enumerate(tensor):
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for j, t in enumerate(x):
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e = alg.eigvals(t) # .real
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# se = sum(e)
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# e = e / se
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temp_abs = torch.abs(e)
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# temp_abs = e
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temp = torch.log(temp_abs).real
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temp = torch.nan_to_num(temp, nan=0.0, posinf=0.0, neginf=0.0)
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return -1 * torch.sum(temp_abs * temp)
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def load_pickle(fpath):
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with open(fpath, "rb") as f:
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data = pickle.load(f)
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return data
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def save_pickle(pickle_name, data_dump):
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with open(pickle_name, "wb") as f:
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pickle.dump(data_dump, f)
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def chunks(lst, n):
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"""Yield successive n-sized chunks from lst."""
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for i in range(0, len(lst), n):
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yield lst[i : i + n]
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def split(lst, n):
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k, m = divmod(len(lst), n)
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return (lst[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))
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import pickle
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import torch
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from find_epochs import get_experiment_epochs
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# import warnings
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# import numpy as np
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# model_lenet_1_epoch_4.pt
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# can be 1,2 or 3
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exp_no = 2
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# can be 1 to 7
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model_size = 1
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EXPERIMENT_2_PATH = "./EXPERIMENT_2_DATA"
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EXPERIMENT_3_PATH = "./EXPERIMENT_3_DATA"
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crit = "Efficiency"
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experiment_criterias = {
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"Early": "/Early_Stopping",
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"Efficiency": "/Efficiency_Stopping",
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"100": "/100_epoch",
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"Accuracy": "/Accuracy_Bound",
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"Energy": "/Energy_Bound",
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}
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bcnn_path = {
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"prefix_path_mnist": "/checkpoints/MNIST/bayesian",
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"mid_path_mnist": "/model_lenet_lrt_softplus_",
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"prefix_path_cifar": "/checkpoints/CIFAR10/bayesian",
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"mid_path_cifar": "/model_lenet_lrt_softplus_",
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"end_path": "_epoch_",
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"noise_path": "_noise_",
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"suffix_path": ".pt",
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}
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lenet_path = {
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"prefix_path_mnist": "/checkpoints/MNIST/frequentist",
|
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"mid_path_mnist": "/model_lenet_",
|
||||
"prefix_path_cifar": "/checkpoints/CIFAR10/frequentist",
|
||||
"mid_path_cifar": "/model_lenet_",
|
||||
"end_path": "_epoch_",
|
||||
"noise_path": "_noise_",
|
||||
"suffix_path": ".pt",
|
||||
}
|
||||
|
||||
experiment_2_criterias = {
|
||||
"Early": "/Early_Stopping",
|
||||
"Efficiency": "/Efficiency_Stopping",
|
||||
}
|
||||
|
||||
model_noise = None
|
||||
|
||||
datasets = ["MNIST", "CIFAR10"]
|
||||
|
||||
bcnn_layers = [
|
||||
"conv1.W_mu",
|
||||
"conv1.W_rho",
|
||||
"conv1.bias_mu",
|
||||
"conv1.bias_rho",
|
||||
"conv2.W_mu",
|
||||
"conv2.W_rho",
|
||||
"conv2.bias_mu",
|
||||
"conv2.bias_rho",
|
||||
"fc1.W_mu",
|
||||
"fc1.W_rho",
|
||||
"fc1.bias_mu",
|
||||
"fc1.bias_rho",
|
||||
"fc2.W_mu",
|
||||
"fc2.W_rho",
|
||||
"fc2.bias_mu",
|
||||
"fc2.bias_rho",
|
||||
"fc3.W_mu",
|
||||
"fc3.W_rho",
|
||||
"fc3.bias_mu",
|
||||
"fc3.bias_rho",
|
||||
]
|
||||
|
||||
lenet_layers = [
|
||||
"conv1.weight",
|
||||
"conv1.bias",
|
||||
"conv2.weight",
|
||||
"conv2.bias",
|
||||
"fc1.weight",
|
||||
"fc1.bias",
|
||||
"fc2.weight",
|
||||
"fc2.bias",
|
||||
"fc3.weight",
|
||||
"fc3.bias",
|
||||
]
|
||||
|
||||
|
||||
bcnn_base = {
|
||||
"conv1.W_mu": None,
|
||||
"conv1.W_rho": None,
|
||||
"conv1.bias_mu": None,
|
||||
"conv1.bias_rho": None,
|
||||
"conv2.W_mu": None,
|
||||
"conv2.W_rho": None,
|
||||
"conv2.bias_mu": None,
|
||||
"conv2.bias_rho": None,
|
||||
"fc1.W_mu": None,
|
||||
"fc1.W_rho": None,
|
||||
"fc1.bias_mu": None,
|
||||
"fc1.bias_rho": None,
|
||||
"fc2.W_mu": None,
|
||||
"fc2.W_rho": None,
|
||||
"fc2.bias_mu": None,
|
||||
"fc2.bias_rho": None,
|
||||
"fc3.W_mu": None,
|
||||
"fc3.W_rho": None,
|
||||
"fc3.bias_mu": None,
|
||||
"fc3.bias_rho": None,
|
||||
}
|
||||
|
||||
|
||||
lenet_base = {
|
||||
"conv1.weight": None,
|
||||
"conv1.bias": None,
|
||||
"conv2.weight": None,
|
||||
"conv2.bias": None,
|
||||
"fc1.weight": None,
|
||||
"fc1.bias": None,
|
||||
"fc2.weight": None,
|
||||
"fc2.bias": None,
|
||||
"fc3.weight": None,
|
||||
"fc3.bias": None,
|
||||
}
|
||||
|
||||
bcnn_cifar_size_data = {}
|
||||
bcnn_mnist_size_data = {}
|
||||
|
||||
lenet_cifar_size_data = {}
|
||||
lenet_mnist_size_data = {}
|
||||
|
||||
# CUDA settings
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
def load_model(save_path, gpu):
|
||||
gpu = str(gpu)
|
||||
if gpu == "cuda:0":
|
||||
# warnings.warn("I'm in gpu")
|
||||
model = torch.load(save_path, weights_only=True)
|
||||
return model
|
||||
elif gpu == "cpu":
|
||||
# warnings.warn("I'm in cpu")
|
||||
model = torch.load(save_path, map_location=torch.device("cpu"), weights_only=True)
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
for model_size in range(1, 8):
|
||||
bcnn_cifar = []
|
||||
for model_epoch in range(
|
||||
0,
|
||||
get_experiment_epochs(experiment_number=2, size=7, model_type="bcnn", data_type="cifar")[
|
||||
model_size
|
||||
],
|
||||
):
|
||||
# LeNet CIFAR
|
||||
bcnn_cifar.append(
|
||||
load_model(
|
||||
(
|
||||
f"{EXPERIMENT_2_PATH}{experiment_2_criterias['Efficiency']}"
|
||||
f"{bcnn_path['prefix_path_cifar']}{bcnn_path['mid_path_cifar']}"
|
||||
f"{model_size}{bcnn_path['end_path']}{model_epoch}{bcnn_path['noise_path']}"
|
||||
f"{model_noise}{bcnn_path['suffix_path']}"
|
||||
),
|
||||
"cpu",
|
||||
)
|
||||
)
|
||||
|
||||
bcnn_cifar_size_data[model_size] = {k: v for k, v in enumerate(bcnn_cifar)}
|
||||
|
||||
del bcnn_cifar
|
||||
|
||||
lenet_cifar = []
|
||||
for model_epoch in range(
|
||||
0,
|
||||
get_experiment_epochs(experiment_number=2, size=7, model_type="lenet", data_type="cifar")[
|
||||
model_size
|
||||
],
|
||||
):
|
||||
# LeNet CIFAR
|
||||
lenet_cifar.append(
|
||||
load_model(
|
||||
f"{EXPERIMENT_2_PATH}{experiment_2_criterias['Efficiency']}"
|
||||
f"{lenet_path['prefix_path_cifar']}{lenet_path['mid_path_cifar']}"
|
||||
f"{model_size}{lenet_path['end_path']}{model_epoch+1}{lenet_path['noise_path']}"
|
||||
f"{model_noise}{lenet_path['suffix_path']}",
|
||||
"cpu",
|
||||
)
|
||||
)
|
||||
|
||||
lenet_cifar_size_data[model_size] = {k: v for k, v in enumerate(lenet_cifar)}
|
||||
|
||||
del lenet_cifar
|
||||
|
||||
bcnn_mnist = []
|
||||
for model_epoch in range(
|
||||
0,
|
||||
get_experiment_epochs(experiment_number=2, size=7, model_type="bcnn", data_type="mnist")[
|
||||
model_size
|
||||
],
|
||||
):
|
||||
# LeNet MNIST
|
||||
bcnn_mnist.append(
|
||||
load_model(
|
||||
f"{EXPERIMENT_2_PATH}{experiment_2_criterias['Efficiency']}"
|
||||
f"{bcnn_path['prefix_path_mnist']}{bcnn_path['mid_path_mnist']}"
|
||||
f"{model_size}{bcnn_path['end_path']}{model_epoch}{bcnn_path['noise_path']}"
|
||||
f"{model_noise}{bcnn_path['suffix_path']}",
|
||||
"cpu",
|
||||
)
|
||||
)
|
||||
|
||||
bcnn_mnist_size_data[model_size] = {k: v for k, v in enumerate(bcnn_mnist)}
|
||||
|
||||
del bcnn_mnist
|
||||
|
||||
lenet_mnist = []
|
||||
for model_epoch in range(
|
||||
0,
|
||||
get_experiment_epochs(experiment_number=2, size=7, model_type="lenet", data_type="mnist")[
|
||||
model_size
|
||||
],
|
||||
):
|
||||
# LeNet MNIST
|
||||
lenet_mnist.append(
|
||||
load_model(
|
||||
f"{EXPERIMENT_2_PATH}{experiment_2_criterias['Efficiency']}"
|
||||
f"{lenet_path['prefix_path_mnist']}{lenet_path['mid_path_mnist']}"
|
||||
f"{model_size}{lenet_path['end_path']}{model_epoch+1}{lenet_path['noise_path']}"
|
||||
f"{model_noise}{lenet_path['suffix_path']}",
|
||||
"cpu",
|
||||
)
|
||||
)
|
||||
|
||||
lenet_mnist_size_data[model_size] = {k: v for k, v in enumerate(lenet_mnist)}
|
||||
|
||||
del lenet_mnist
|
||||
|
||||
# Saving all here
|
||||
with open("bayes_data_cifar.pkl", "wb") as f:
|
||||
pickle.dump(bcnn_cifar_size_data, f)
|
||||
|
||||
with open("lenet_data_cifar.pkl", "wb") as f:
|
||||
pickle.dump(lenet_cifar_size_data, f)
|
||||
|
||||
with open("bayes_data_mnist.pkl", "wb") as f:
|
||||
pickle.dump(bcnn_mnist_size_data, f)
|
||||
|
||||
with open("lenet_data_mnist.pkl", "wb") as f:
|
||||
pickle.dump(lenet_mnist_size_data, f)
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
[tool.pylint.'FORMAT']
|
||||
max-line-length = 110
|
||||
|
||||
[tool.pylint.'MESSAGES CONTROL']
|
||||
disable = ["missing-module-docstring", "missing-function-docstring", "import-error"]
|
||||
|
||||
[tool.black]
|
||||
line-length = 110
|
||||
|
||||
Loading…
Reference in New Issue