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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.
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