smart_augmentation/higher/smart_aug/old/utils_old.py

import numpy as np
import json, math, time, os
import matplotlib.pyplot as plt
import copy
import gc

from torchviz import make_dot 

import torch
import torch.nn.functional as F

import time

class timer():
    def __init__(self):
        self._start_time=time.time()
    def exec_time(self):
        end = time.time()
        res = end-self._start_time
        self._start_time=end
        return res

def plot_res(log, fig_name='res', param_names=None):

    epochs = [x["epoch"] for x in log]

    fig, ax = plt.subplots(ncols=3, figsize=(15, 3))

    ax[0].set_title('Loss')
    ax[0].plot(epochs,[x["train_loss"] for x in log], label='Train')
    ax[0].plot(epochs,[x["val_loss"] for x in log], label='Val')
    ax[0].legend()
        
    ax[1].set_title('Acc')
    ax[1].plot(epochs,[x["acc"] for x in log]) 

    if log[0]["param"]!= None:
        if isinstance(log[0]["param"],float):
            ax[2].set_title('Mag')
            ax[2].plot(epochs,[x["param"] for x in log], label='Mag')
            ax[2].legend()
        else :
            ax[2].set_title('Prob')
            #for idx, _ in enumerate(log[0]["param"]):
                #ax[2].plot(epochs,[x["param"][idx] for x in log], label='P'+str(idx))
            if not param_names : param_names = ['P'+str(idx) for idx, _ in enumerate(log[0]["param"])]
            proba=[[x["param"][idx] for x in log] for idx, _ in enumerate(log[0]["param"])]
            ax[2].stackplot(epochs, proba, labels=param_names)
            ax[2].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5)) 
            

    fig_name = fig_name.replace('.',',')
    plt.savefig(fig_name)
    plt.close()

def plot_res_compare(filenames, fig_name='res'):

    all_data=[]
    #legend=""
    for idx, file in enumerate(filenames):
        #legend+=str(idx)+'-'+file+'\n'
        with open(file) as json_file:
            data = json.load(json_file)
            all_data.append(data)

    n_tf = [len(x["Param_names"]) for x in all_data]
    acc = [x["Accuracy"] for x in all_data]
    time = [x["Time"][0] for x in all_data]

    fig, ax = plt.subplots(ncols=3, figsize=(30, 8))

    ax[0].plot(n_tf, acc)
    ax[1].plot(n_tf, time)

    ax[0].set_title('Acc')
    ax[1].set_title('Time')
    #for a in ax: a.legend()

    fig_name = fig_name.replace('.',',')
    plt.savefig(fig_name, bbox_inches='tight')
    plt.close()

def plot_TF_res(log, tf_names, fig_name='res'):

    mean = np.mean([x["param"] for x in log], axis=0)
    std = np.std([x["param"] for x in log], axis=0)

    fig, ax = plt.subplots(1, 1, figsize=(30, 8), sharey=True)
    ax.bar(tf_names, mean, yerr=std)
    #ax.bar(tf_names, log[-1]["param"])

    fig_name = fig_name.replace('.',',')
    plt.savefig(fig_name, bbox_inches='tight')
    plt.close()

def model_copy(src,dst, patch_copy=True, copy_grad=True):
    #model=copy.deepcopy(fmodel) #Pas approprie, on ne souhaite que les poids/grad (pas tout fmodel et ses etats)

    dst.load_state_dict(src.state_dict()) #Do not copy gradient ! 

    if patch_copy:
        dst['model'].load_state_dict(src['model'].state_dict()) #Copie donnee manquante ?
        dst['data_aug'].load_state_dict(src['data_aug'].state_dict())

    #Copie des gradients
    if copy_grad:
        for paramName, paramValue, in src.named_parameters():
          for netCopyName, netCopyValue, in dst.named_parameters():
            if paramName == netCopyName:
              netCopyValue.grad = paramValue.grad
              #netCopyValue=copy.deepcopy(paramValue)

    try: #Data_augV4
        dst['data_aug']._input_info = src['data_aug']._input_info 
        dst['data_aug']._TF_matrix = src['data_aug']._TF_matrix
    except:
        pass

def optim_copy(dopt, opt):

    #inner_opt.load_state_dict(diffopt.state_dict()) #Besoin sauver etat otpim (momentum, etc.) => Ne copie pas le state...
    #opt_param=higher.optim.get_trainable_opt_params(diffopt)

    for group_idx, group in enumerate(opt.param_groups):
       # print('gp idx',group_idx)
        for p_idx, p in enumerate(group['params']):
            opt.state[p]=dopt.state[group_idx][p_idx]

class loss_monitor(): #Voir https://github.com/pytorch/ignite
    def __init__(self, patience, end_train=1):
        self.patience = patience
        self.end_train = end_train
        self.counter = 0
        self.best_score = None
        self.reached_limit = 0

    def register(self, loss):
        if self.best_score is None:
            self.best_score = loss
        elif loss > self.best_score:
            self.counter += 1
            #if not self.reached_limit: 
            print("loss no improve counter", self.counter, self.reached_limit)
        else:
            self.best_score = loss
            self.counter = 0
    def limit_reached(self):
        if self.counter >= self.patience:
            self.counter = 0
            self.reached_limit +=1
            self.best_score = None
        return self.reached_limit

    def end_training(self):
        if self.limit_reached() >= self.end_train:
            return True
        else:
            return False

    def reset(self):
        self.__init__(self.patience, self.end_train)