How to use base_resize method in toolium

Best Python code snippet using toolium_python

optimization_l2.py

Source:optimization_l2.py

1import json2import time3import torch4from distutils.dir_util import copy_tree5from DC.common import vgg_normalize, vgg_renormalize, d2_distance_matrix, select_distance_matrix_idx, \6    sort_by_z, custom_vgg, remove_invisible_z7from DC.compositing import composite_layers8from DC.constants import *9from DC.loss import loss_fn_l210from DC.utils import load_element, load_base_pattern, init_element_pos, render_constants, make_cuda, init_optimizer, \11    save_tensor, get_ss_l2, select_op_variables, save_tensor_z12# -------------------------  -------------------------13if not resume:14    copy_tree(cwd+ '/DC', folder+ '/DC')15    with open(folder+'hyperparameters.txt', 'w') as f:16        json.dump(args.__dict__, f, indent=2)17    if not os.path.exists(folder):18        os.makedirs(folder)19    else:20        print('FOLDER ALREADY EXISTS')21# -------------------------  -------------------------22w = 123w1 = 124w2 = 0.525w3 = 0.526w_o = 027tau =1.528remove_things = False29num_seeds = 130def main():31    global soft, w, w1, w2, w3, w_o, init_iter, base_resize, expand_size, n_soft_elements, tau, remove_things, num_seeds32    if args.complex_background:33        base_pattern, background, background_img = load_base_pattern(pattern_filename, args.tiled, base_resize, blur=False, complex_background=True)34        background_img = background_img.cuda()35    else:36        base_pattern, background = load_base_pattern(pattern_filename, args.tiled, base_resize, blur=False)37        background_img = None38    elements = load_element(element_filename, size=base_resize)39    parameters = init_element_pos(number)40    constants = render_constants(number[0]*number[1], scale_x_single, scale_y_single, base_resize, n_soft_elements, expand_size, color=args.color)41    base_pattern, background, mean_c, std_c = make_cuda([base_pattern, background, mean, std])42    base_pattern = vgg_normalize(base_pattern, mean_c[:,:3], std_c[:,:3])43    c_variables = make_cuda(parameters+constants)44    init_c_variables = []45    for i in c_variables:46        init_c_variables.append(i.clone().detach())47    op_variables = select_op_variables(n_elements, args, c_variables)48    if args.tiled:49        base_resize = [512, 512]50    background.requires_grad = True51    if non_white_background:52        op_variables = op_variables+[background, ]53    optimizer, scheduler = init_optimizer(op_variables, args.lr)54    # ----------------- remove unnecessary ------------------------------55    def remove_unecessary(c_variables):56        if not args.color:57            c_variables = remove_invisible_z(c_variables, 2000)58        c_variables = sort_by_z(c_variables)59        element_classes = torch.argmax(c_variables[7], dim=1)60        distance_matrix = d2_distance_matrix(torch.cat([c_variables[0][:, None]/10, c_variables[1][:, None]/10], dim=1))61        idx = select_distance_matrix_idx(distance_matrix, element_classes, c_variables[5], [10/256, 20/256, 10/256, 10/256, 20/256, 15/256, 15/256, 30/256, 30/256], min_dist=10/256)62        new_c_variables = []63        for i in c_variables:64            new_c_variables.append(i[idx])65        c_variables = make_cuda(new_c_variables)66        op_variables = select_op_variables(n_elements, args, c_variables)67        if non_white_background:68            op_variables = op_variables + [background, ]69        optimizer, scheduler = init_optimizer(op_variables, args.lr*0.1)70        return c_variables, optimizer, scheduler71    # ----------------- init ------------------------------72    c_variables[6].requires_grad = False73    if resume:74        load_iter = args.resume_int75        PATH = folder + 'checkpoint_' + str(load_iter)76        checkpoint = torch.load(PATH)77        c_variables = checkpoint['c_variables']78        background = checkpoint['background']79        if args.seed:80            c_variables_1 = c_variables.copy()81            c_variables_1[6] = c_variables_1[6] / 282            c_variables_2 = []83            for c in init_c_variables:84                c_variables_2.append(c.clone().detach())85            c_variables = []86            for idx in range(len(c_variables_1)):87                c_variables.append(torch.cat([c_variables_1[idx], c_variables_2[idx], ]))88        c_variables = make_cuda(c_variables)89        torch.cuda.empty_cache()90        op_variables = select_op_variables(n_elements, args, c_variables)91        background.requires_grad = True92        if non_white_background:93            op_variables = op_variables + [background, ]94        optimizer, scheduler = init_optimizer(op_variables, args.lr)95        # optimizer.load_state_dict(checkpoint['optimizer_state_dict'])96        # scheduler = CyclicLR(optimizer, base_lr=lr, max_lr=lr * 4, cycle_momentum=False, mode='exp_range',97        #                      step_size_up=1500)98        # ----------------------declare variables -------------------99        init_iter = load_iter-1100        w = 1101        w1 = 1102        w2 = 1103        w3 = 1.0104        w_o = 0105        # tau = tau + 10106        if args.layers:107            c_variables[6].requires_grad = True108    current_soft = False109    base_pattern = vgg_renormalize(base_pattern, mean_c[:, :3], std_c[:, :3])110    save_tensor(folder, 'BASE', base_pattern)111    ss = get_ss_l2(base_pattern)112    start_time = time.time()113    for i in range(init_iter+1, init_iter + iter):114        # tau = 0.1115        render, overlap = composite_layers(elements, c_variables, background, n_elements, expand_size, current_soft, tau, color=args.color, background_img=background_img)116        ws = [w, w1, w2, w3, w_o]117        loss = loss_fn_l2(render, overlap, ss, ws) + overlap*w_o118        if i%10==0:119            save_tensor(folder, str(i), render)120            print('iteration {} '.format(i), (loss).to('cpu'), (overlap*(w_o)))121        optimizer.zero_grad()122        loss.backward()123        optimizer.step()124        ## Everything below this is to improve convergence125        if (i- 100000)==500 :126            w_o = 0#(10**-7)127            if args.layers:128                c_variables[6].requires_grad = True129        if i%3000==0:130            # tau = tau +1131            print(torch.exp(c_variables[6]/25))132            if args.layers:133                c_variables[6].requires_grad = True134            save_tensor_z(folder, str(i)+'_z', render, c_variables[0]/10, c_variables[1]/10, c_variables[6], size=128)135            save_tensor_z(folder, str(i)+'_idx', render, c_variables[0]/10, c_variables[1]/10, torch.arange(c_variables[6].shape[0]), size=128)136            torch.save({137                'c_variables': c_variables,138                'optimizer_state_dict': optimizer.state_dict(),139                'base_size':base_resize,140                'xy_scale_factor':10,141                'soft':current_soft,142                'background':background}, folder+'checkpoint_'+str(i))143            w1 = min(w1 + 0.05, 1.5)144            w1 = min(w1 + 0.05, 1.5)145            w2 = max(w2 - 0.05, 0.2)146            w3 = max(w3 - 0.05, 0.2)147        if ((i- 100000)%8000 - 4000 == 500 or  (i- 100000)%8000== 500)and remove_things:148            w_o = 0149            for g in optimizer.param_groups:150                g['lr'] = args.lr151        if (i- 100000)%8000==4000 and remove_things:152            w_o = 0153            elapsed_time = time.time() - start_time154            print(time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))155            print(elapsed_time)156            c_variables, optimizer, scheduler = remove_unecessary(c_variables)157            print(torch.nn.functional.sigmoid(c_variables[5]/10))158            render, overlap = composite_layers(elements, c_variables, background, n_elements, expand_size, current_soft,159                                               tau, color=args.color, background_img=background_img)160            save_tensor(folder, 'postremoval_'+str(i), render)161        if args.seed and num_seeds>0:162            if (i- 100000)%8000==0:163                num_seeds = num_seeds - 1164                torch.save({165                    'c_variables': c_variables,166                    'optimizer_state_dict': optimizer.state_dict(),167                    'base_size': base_resize,168                    'xy_scale_factor': 10,169                    'soft': current_soft,170                    'background': background}, folder + 'checkpoint_' + str(i))171                if args.rotation:172                    c_variables_1 = c_variables.copy()173                    c_variables_1[6] = c_variables_1[6]*2/3174                    # c_variables_1[7] = c_variables_1[7]175                    c_variables_2 = c_variables.copy()176                    c_variables_2[2] = c_variables_2[2] + (90/(1.5*3.14159*57.2958))177                    c_variables_2[6] = c_variables_2[6]*2/3178                    # c_variables_2[7] = torch.ones_like(c_variables_2[7])#/4179                    c_variables_3 = c_variables.copy()180                    c_variables_3[2] = c_variables_3[2] + (180/(1.5*3.14159*57.2958))181                    c_variables_3[6] = c_variables_3[6]*2/3182                    # c_variables_3[7] = torch.ones_like(c_variables_3[7])#/4183                    c_variables_4 = c_variables.copy()184                    c_variables_4[2] = c_variables_4[2] + (270/(1.5*3.14159*57.2958))185                    c_variables_4[6] = c_variables_4[6]*2/3186                    c_variables = []187                    for idx in range(len(c_variables_1)):188                        c_variables.append(torch.cat([c_variables_1[idx], c_variables_2[idx], c_variables_3[idx], c_variables_4[idx]]))189                    c_variables = make_cuda(c_variables)190                    op_variables = select_op_variables(n_elements, args, c_variables)191                    render, overlap = composite_layers(elements, c_variables, background, n_elements, expand_size, current_soft,192                                                       tau, color=args.color, background_img=background_img)193                    save_tensor(folder, 'postseeding_'+str(i), render)194                    optimizer, scheduler = init_optimizer(op_variables+[background, ], args.lr*0.1)195                    remove_things = True196                else:197                    c_variables_1 = c_variables.copy()198                    if args.color:199                        c_variables_1[7] = torch.ones_like(c_variables_1[7])200                        c_variables_2 = []201                        for c in init_c_variables:202                            c_variables_2.append(c.clone().detach())203                        c_variables = []204                        for idx in range(len(c_variables_1)):205                            c_variables.append(torch.cat([c_variables_1[idx], c_variables_2[idx],]))206                    else:207                        c_variables_1[6] = c_variables_1[6] / 2208                        c_variables_2 = []209                        for c in init_c_variables:210                            c_variables_2.append(c.clone().detach())211                        c_variables = []212                        for idx in range(len(c_variables_1)):213                            c_variables.append(torch.cat([c_variables_1[idx], c_variables_2[idx],]))214                    c_variables = make_cuda(c_variables)215                    op_variables = select_op_variables(n_elements, args, c_variables)216                    render, overlap = composite_layers(elements, c_variables, background, n_elements, expand_size, current_soft,217                                                       tau, color=args.color)218                    save_tensor(folder, 'postseeding_'+str(i), render)219                    if non_white_background:220                        op_variables = op_variables + [background, ]221                    optimizer, scheduler = init_optimizer(op_variables, args.lr*0.1)222                    remove_things = True223                w_o = 0224if __name__ == '__main__':...

constants.py

Source:constants.py

1import math2import os3from DC.common import make_tensor4from DC.config import args5from DC.scales import all_scales6# matplotlib.use('Agg')7# ------------------------- variables -------------------------8cwd = os.getcwd()9pattern = args.pattern10n_elements = 111n_soft_elements = int(len(all_scales[pattern])/2)12folder_root = args.folder13if folder_root == '':14    folder_root = cwd#os.path.dirname(cwd)15element_filename = []16for i in range(n_soft_elements):17    element_filename.append('{}/data/{}/elements/{}.png'.format(folder_root, pattern, i+1))18pattern_filename = '{}/data/{}/pattern.png'.format(folder_root, pattern)19folder = "{}/logs/expansion/{}_{}/".format(folder_root, pattern, args.version)20non_white_background = args.non_white21# number = [4, 4]22scale_x_single = all_scales[pattern][0::2]23scale_y_single = all_scales[pattern][1::2]24# ------------------------- calculated constants -------------------------25base_resize = [args.base_size, args.base_size]26expand_size = [1, 3, base_resize[0], base_resize[1]]27if args.expand:28    expand_size = [1, 3, base_resize[0]*2, base_resize[0]*2]29number = [int(1/(scale_y_single[0]* base_resize[1]/expand_size[3])), int(1/(scale_x_single[0]* base_resize[0]/expand_size[2]))]30number[0] = args.sample#(int(number[0] / n_elements)+1) * n_elements31number[1] = args.sample#(int(number[1] / n_elements)+1) * n_elements32repeats = int(number[0] * number[1] / n_elements)33print('Number of elements:', number)34expand_size[0] = repeats35# ------------------------- initializations and constants -------------------------36lr = args.lr37iter = args.num_iter38LBFGS = False39multi = True40soft = args.soft41smooth = False42init_iter = 10000043resume = args.resume44d_threshold = (math.sqrt((max(scale_x_single) * base_resize[0]) ** 2 + (max(scale_y_single) * base_resize[1]) ** 2) -20) / 245mean = make_tensor([0.485, 0.456, 0.406, 0])[None,:,None, None]46std = make_tensor([0.229, 0.224, 0.225, 1])[None,:,None, None]47content_layers = ['pool_4', 'relu4_2',]# 'relu5_2']48content_weights = {'pool_4': 1.0, 'relu4_2': 1.0,}# 'relu5_2': 1.0}49style_layers = ['conv_1', 'conv_2', 'conv_3', 'conv_4']#, 'conv_5']50style_weights = {'conv_1': 0.2, 'conv_2': 0.2, 'conv_3': 0.2, 'conv_4': 0.2}#, 'conv_5': 200}51hist_layers = ['relu4_2', 'conv_1']...

hyperopt.py

Source:hyperopt.py

1from pytorch_ML.hyperopt import Hyperopt2import numpy as np3from numpy import random4from filet.mask_discriminator.transformations import PreProcessor_Crop_n_Resize_Box5class Mask_Hyperopt(Hyperopt):6    def __init__(self,base_lr,**kwargs):7        super().__init__(**kwargs)8        self.base_lr = base_lr9    def suggest_hyper_dict(self):10        '''11        Overloaded from base12        '''13        base_resize = (400,620)14        resize_scales = [0.33,0.5,0.75,1]15        chosen_resize_scale_index = np.random.randint(0,len(resize_scales))16        chosen_scale = resize_scales[chosen_resize_scale_index]17        resize_tup = [int(chosen_scale*base_resize[0]),int(chosen_scale * base_resize[1])]18        is_resize_sq = np.random.randint(0, 2)19        if is_resize_sq:20            resize_tup[0] = ( resize_tup[0] + resize_tup[1] ) // 221            resize_tup[1] = resize_tup[0]22        pad_choices = [5*i+25 for i in range(6)]23        pad = pad_choices[np.random.randint(0,len(pad_choices))]24        generated_values = {25            "optimizer" : { "lr": self.base_lr * random.uniform(1,100), "momentum": random.uniform(0.1, 0.6)},26            "scheduler" : {'gamma' : None},27            "loss" : {},28            "net" : {'two_layer_head' : random.random()>0.5,},29            'resize' : resize_tup,30            'pad' : pad,31        }32        lr_half_time = random.uniform(1000, 7500)33        generated_values['scheduler']["gamma"] = np.exp(-np.log(2)/lr_half_time)34        return generated_values35    def construct_trainer_objs(self,hyper):36        '''37        builds model, optimizer,scheduler and other objects needed for training. Must be deterministic given hyper_dict38        '''39        hyper_objs = super().construct_trainer_objs(hyper)40        dt = self.dt41        prep = PreProcessor_Crop_n_Resize_Box(resize_dims=hyper['resize'],pad=hyper['pad'],mean=[0.2010, 0.1944, 0.2488, 0.0000],std=[0.224, 0.224, 0.224, 1])42        dt.dataset.set_preprocessor(prep)43        hyper_objs['dt'] = dt...

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