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#FalledGAN/README.MD

FalledGAN (Future AI learning learn lair Dimooon GAN)
Self-supervised image by phrase generation called FalledGAN.


requirements:
torch
numpy
ftfy
regex
tqdm
torchvision

Usage:
To train model really fast:
python train.py --batch_size 1 --image_size 512 --save_every 100 --dir ./trained_model --epochs 2000


Its create a dirrectory ./trained model wich constains model files.

Full usage of train.py:
    --batch-size=<size>     Batch size [default: 1]
    --image_size=<size>     Size if image in pixels [default:512]
    --save_every=<steps>    Autosave model every n steps [default: 100]
    --dir=<dirrectory>      Dirrectory were model was saving [default:./trained_model]
    --epochs=<epochs>       Number of epochs to train [default:2000]
    --lr=<learning_rate>    Learning Rate [default:1e-4]
    --optimizer=<optimizer> Oprimizer for the nn, recomended MADGRAD form facebook research [default:MADGRAD]

To sample image run:
python main.py --generate --image_size 512 --batch_size 1 --dir ./trained_model --phrase "a big apple inside a wall" 

Its was generate image in folder with file main.py

Full usage of main.py:
    --batch-size=<size>     Batch size [default: 1]
    --image_size=<size>     Size if image in pixels [default:512]
    --phrase=<phrase>        FalledGAn will generate image by that phrase [default: "a big watch"]
    --dir=<dirrectory>      Dirrectory were model was saving [default:./trained_model]
    --num_samples<num>      Number of image samples [default:1]

#FalledGAN/train.py

import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.utils.data as data
import torchvision.transforms as transforms
import torch.nn.functional as F
import json
import cv2
import os
import time
from img_utils import get_img_from_json
import sys

from tqdm import tqdm
from models import SegmentationModel
from utils.torch_utils import tate

def main():
    start_time = time.time()

    images_train, categories = get_training_data()
    train_data = tate.dataset(images_train, categories, batch_size=32)
    segments = get_classes(categories)

    model = dein(train_data, segments, begin=1)

    mean, std = size_normalization(images_train) # normalization
    image_size = 512
    input_images = train_data.transform("per_image", mean=True, std=std, log=True).format_img(
        input_images).unsqueeze_(0)
    input_image = torch.unsqueeze(input_images, 0) # (B, H, W) -> (B, H, 1, W)

    optimizer = torch.optim.Adam(
        filter(lambda a: a.requires_grad, torch.parameters(model)),
        lr=0.01,
        memory=False
    )

    num_samples = 1

    loss = autograd.Function([image_size, image_size], 0.0) # B
    loss_m = autograd.Function([image_size // 2, image_size // 2], 0.0) # B

    image_str = " ".join(get_img_from_json(torch.randn(image_size // 2, self.B * 3))).tolist()

    # print(image_str)
    for epoch in range(251):
        train = torch.ByteTensor(list(tqdm(train_data, desc="EPOCH {}, generate_seq, generate_img, save".format(epoch + 1)))) # B, C #N, H, W, 1
        optimizer.zero_grad()
        for i, (edges, edge_name_n_bottom) in enumerate(tqdm(train, desc="")):
            if num_samples!= 0:
                v = torch.randn(num_samples, self.batch_size) # T # 8 # C=8-> (T, C, 8)
                max_output = torch.randn_like(min_input_features) # T # 1 # 8
                max_output_0 = torch.randn(num_samples, self.batch_size) # T # 1 # 8
                pc = 0
            else:
                v = torch.zeros(num_samples) # B # T # 8
                max_output = torch.zeros(num_samples) # B # T # 8
                max_output_0 = torch.zeros(num_samples) # B # T # 8

            # model.train()
            image_str = get_resegmented_image(input_image, v, max_output_0, edges, edge_name_n_bottom) # T # C, 1
            output = model(v, max_output_0) # T # C // 2

            # image_str = torch.tensor(input_image, dtype=torch.IntTensor, device='cuda') # C // 2 # C

            loss_m.backward(input_image, output) # T # I=1 # C, C // 2
            loss_m.backward(v, output) # T # I=1 # C, C // 2

            loss.backward() # B # I=1 # C, C // 2

            output.copy_(max_output_0.max(2).float()) # B # T # 8

            targ_b = torch.miss(max_output_0) # B # T # 8
            targ_m = torch.miss(max_output) # T # C, 1: C

            avg = torch.mean(targ_m, -1, keepdim=True) # T

            loss.backward()
            optimize_op = autograd.Variable([max_output_0, avg])
            optimize_val0, optimize_op0 = autograd.Variable([input_image, avg]), autograd.Variable([max_output_0, avg])
            optimize_b = autograd.Variable([input_image, avg])

            optimize_op.data.copy_(optimize_val0.add(optimal=True).div_(optimal=True))
            optimize_op.data.copy_(optimize_op0.add(optimal=True).div_(optimal=True))
            predict_op = optimize_op.cuda
            predict_val = predict_op.data.copy_(input)

            predict_val_0, predictor_val0 = predictor_op, predictor_val
            minimize_op = minimize_val_(data)
            minimize_nb = torch.data.to_function(data).size
            std = 2 / self.autominute translate
            c_autominute_op = tate('\'')
        c_autominute_autocom = tate(')')
        autodoc_text = tate('")''')
        image_path = torchvision.transforms
        autograd. training begin
        end_time = time.time()
    start_time = time.time()

    if otherwise_data_path:os.stat
    time = torch.time as time

    system = system
    if verbose_or_error_path:
        local_path = os.path.expand(torch.true), local_path # os.path -> file
        local_path = os.path.expand(local_path + ":\\expand:\\win:\\expand:\\ctrwin#7##-#16>#%cmp16/cmp16", "slowness/slowness", "road", "speed", "my add real")
        if weiwin_path == 'w' and not changing_path =='w', '$w1', '$.$', '$o1', '$o2', '$o3', '$o4', '$d1', '$d1', '$d1', '$d1', '$d2', '$d2', '$d2', '$d2', '$d2', '%d3', '%s', '%s', '%s', '%s', '%s' ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~ ~ ~
            f = tate.IDDynamicModel.train_ID(data=update_seg_data() / 4, visual_id=torch.rand(1, 5, size=image_size) * 2) * (image_size // 2)
            f.id_input=input_image
            f.id_output=output.squeeze(2)

            loss.d_weight = 0.4
            loss.d_weight = 0.5
        else:

            image_str = torch.nn.utils.normalized_input
            image_str = get_resegmented_images(
                self.B // 2 * self.B, #A
                v,
                torch.randn(num_samples, self.B) * 3 # B [?]   [ N, H, W, 1 ] [H, T, I // 2]                 [I // 2]   #B                    [H, T, I // 2]                    [I // 2] #B                    [H, T, I // 2]                    [I // 4] #H                    [C] #H)                            [I] // H #T # 8  1. (tuples=8, name_n_bottom=1, edge_name_bottom=1) = train visualize edges shape ground_truth_0, ground_adv_b, ground_adv_m // 8 for bottom resegmented only / 8 m
                ground_adv_m.add_(loop=True)
                ground_adv_m.add_(loop=True)

                if autograd:
                    self.loss_m, self.vectors_m, self.generate_out_m
                    self.loss_m.mutable_batch(v)
                    v.copy_from_(input)
                else:
                    scale_b = torch.miss(max_output_0 / torch.miss(avg)) # B # T # 8
                    target_b = torch.miss(max_output_0) # B
                    loss_m.backward(v)
                    optimize_op = autograd.Variable([max_output_0, avg])
                    q2 = model(v)[1 // C]
                    if q2.var[1] == torch.IntTensor:
                        avg = torch.mean(q2, -1, keepdim=True) # B # T # I
                    optimize_op0.data.copy_(optimize_val0.div_(optimprice=falseoptimal=True))
                    optimize_b.smote(optimal=True, label=avg, optimal=False).data.zero_
                    f1, f2, q_e = autograd.Variable([input_image, avg]),optimize_op0,autograd.Variable([max_output_0, avg])
                    optimize_op0.data.abs_(t:=b,res=be)*res
                optimizing_op->optimize(val0, optimize_b)
                @optimize_op
                @optimize_op2
                print(opr.data)+a/a+res
                print(b/a)
                res
                label = res

                data = optimize_op0.div_(optimal=True)
                array_ = copy(d, res)
                regular
                optimize_op.
                m
                encapsulate(v)
                add(v, res)
                res
                optimizer.
                m
                autogen
                lug

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