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deletion_batch_v4_gpu1.py
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deletion_batch_v4_gpu1.py
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import argparse
import os
import random
import shutil
import time
import sys
import warnings
from srblib import abs_path
from PIL import ImageFilter, Image
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm, trange
import skimage
# Fixing for deterministic results
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# bibliotecas inpainter
sys.path.insert(0, './generativeimptorch')
from utils.tools import get_config, get_model_list
from model.networks import Generator
imagenet_val_xml_path = './val_bb'
imagenet_val_path = './val/'
base_img_dir = abs_path(imagenet_val_path)
input_dir_path = 'images_list.txt'
text_file = abs_path(input_dir_path)
imagenet_class_mappings = './imagenet_class_mappings'
torch.manual_seed(0)
learning_rate = 0.1 * 0.8 # orig (0.3) 0.1 (preservation sparser) 0.3 (preservation dense)
max_iterations = 228 # 130 *2
l1_coeff = 0.01e-5 * 2 # *2 *4 *0.5 (robusto)
size = 224
tv_beta = 3
tv_coeff = 1e-2
factorTV = 1 * 0.5 * 0.005 # 1(dense) o 0.5 (sparser/sharp) #0.5 (preservation)
def inpainter(img, mask):
config = get_config('./generativeimptorch/configs/config.yaml')
checkpoint_path = os.path.join('./generativeimptorch/checkpoints',
config['dataset_name'],
config['mask_type'] + '_' + config['expname'])
cuda = config['cuda']
device_ids = config['gpu_ids']
with torch.no_grad(): # enter no grad context
# Test a single masked image with a given mask
x = img
# denormaliza imagenet y se normaliza a inpainter [-1,1] mean=0.5, std=0.5
x = transforms.Normalize(mean=[0.015 / 0.229, 0.044 / 0.224, 0.094 / 0.225],
std=[0.5 / 0.229, 0.5 / 0.224, 0.5 / 0.225])(x)
x = x * (mask)
# Define the trainer
netG = Generator(config['netG'], cuda, device_ids)
# Resume weight
last_model_name = get_model_list(checkpoint_path, "gen", iteration=0)
netG.load_state_dict(torch.load(last_model_name))
# netG = torch.nn.parallel.DataParallel(netG, device_ids=[0, 1])
netG.cuda()
# Inference
x1, x2, offset_flow = netG(x, (1. - mask))
return x2
def tv_norm(input, tv_beta):
img = input[:, 0, :]
row_grad = torch.abs((img[:, :-1, :] - img[:, 1:, :])).pow(tv_beta).sum(dim=(1, 2))
col_grad = torch.abs((img[:, :, :-1] - img[:, :, 1:])).pow(tv_beta).sum(dim=(1, 2))
return row_grad + col_grad
torch.cuda.set_device(1) # especificar cual gpu 0 o 1
# model = models.googlenet(pretrained=True)
# model = models.resnet50(pretrained=True)
# model = models.vgg16(pretrained=True)
model = models.alexnet(pretrained=True)
model.cuda()
model.eval()
print('GPU 1 explicacion ver 4')
img_name_list = []
with open(text_file, 'r') as f:
for line in f:
img_name_list.append(line.split('\n')[0])
def imagenet_label_mappings():
fileName = os.path.join(imagenet_class_mappings, 'imagenet_label_mapping')
with open(fileName, 'r') as f:
image_label_mapping = {int(x.split(":")[0]): x.split(":")[1].strip()
for x in f.readlines() if len(x.strip()) > 0}
return image_label_mapping
im_label_map = imagenet_label_mappings()
class DataProcessing:
def __init__(self, data_path, transform, img_idxs=[0, 1], if_noise=0, noise_var=0.0):
self.data_path = data_path
self.transform = transform
self.if_noise = if_noise
self.noise_mean = 0
self.noise_var = noise_var
img_list = img_name_list[img_idxs[0]:img_idxs[1]]
self.img_filenames = [os.path.join(data_path, f'{i}.JPEG') for i in img_list]
# self.img_filenames.sort()
def __getitem__(self, index):
img = Image.open(os.path.join(self.data_path, self.img_filenames[index])).convert('RGB')
target = self.get_image_class(os.path.join(self.data_path, self.img_filenames[index]))
if self.if_noise == 1:
img = skimage.util.random_noise(np.asarray(img), mode='gaussian',
mean=self.noise_mean, var=self.noise_var,
) # numpy, dtype=float64,range (0, 1)
img = Image.fromarray(np.uint8(img * 255))
img = self.transform(img)
return img, target, os.path.join(self.data_path, self.img_filenames[index])
# return img, target
def __len__(self):
return len(self.img_filenames)
def get_image_class(self, filepath):
# ImageNet 2012 validation set images?
with open(os.path.join(imagenet_class_mappings, "ground_truth_val2012")) as f:
ground_truth_val2012 = {x.split()[0]: int(x.split()[1])
for x in f.readlines() if len(x.strip()) > 0}
def get_class(f):
ret = ground_truth_val2012.get(f, None)
return ret
image_class = get_class(filepath.split('/')[-1])
return image_class
transform_val = transforms.Compose([
transforms.Resize((256, 256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
# Plots image from tensor
def tensor_imshow(inp, title=None, **kwargs):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
# Mean and std for ImageNet
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp, **kwargs)
if title is not None:
plt.title(title)
plt.show()
# list_of_layers = ['conv1',
# 'conv2',
# 'conv3',
# 'inception3a',
# 'inception3b',
# 'inception4a',
# 'inception4b',
# 'inception4c',
# 'inception4d',
# 'inception4e',
# 'inception5a',
# 'inception5b',
# 'fc'
# ]
# capas para resnet50
# list_of_layers = ['relu',
# 'layer1.0',
# 'layer1.1',
# 'layer1.2',
# 'layer2.0',
# 'layer2.1',
# 'layer2.2',
# 'layer2.3',
# 'layer3.0',
# 'layer3.1',
# 'layer3.2',
# 'layer3.3',
# 'layer3.4',
# 'layer3.5',
# 'layer4.0',
# 'layer4.1',
# 'layer4.2',
# ]
# capas para vgg16
# list_of_layers = ['features.1',
# 'features.3',
# 'features.6',
# 'features.8',
# 'features.11',
# 'features.13',
# 'features.15',
# 'features.18',
# 'features.20',
# 'features.22',
# 'features.25',
# 'features.27',
# 'features.29'
# ]
# capas para alexnet
list_of_layers = ['features.1',
'features.4',
'features.7',
'features.9',
'features.11',
'classifier.2',
'classifier.5'
]
activation_orig = {}
def get_activation_orig(name):
def hook(model, input, output):
activation_orig[name] = output
return hook
def get_activation_mask(name):
def hook(model, input, output):
act_mask = output
# print(act_mask.shape). #debug
# print(activation_orig[name].shape) #debug
limite_sup = (act_mask <= torch.fmax(torch.tensor(0), activation_orig[name]))
limite_inf = (act_mask >= torch.fmin(torch.tensor(0), activation_orig[name]))
oper = limite_sup * limite_inf
# print('oper shape=',oper.shape). #debug
act_mask.requires_grad_(True)
act_mask.retain_grad()
h = act_mask.register_hook(lambda grad: grad * oper)
# x.register_hook(update_gradients(2))
# activation[name]=act_mask
# h.remove()
return hook
def my_explanation(img_batch, max_iterations, gt_category):
F_hook = []
exp_hook = []
for module_name, module in model.named_modules():
if module_name in list_of_layers:
F_hook.append(module.register_forward_hook(get_activation_orig(module_name)))
# for name, layer in model.named_children():
# if name in list_of_layers:
# F_hook.append(layer.register_forward_hook(get_activation_orig(name)))
# se calculan las activaciones para el batch de imágenes y se almacenan en la lista activation_orig
# la funcion "feed forward" registra los hook
org_softmax = torch.nn.Softmax(dim=1)(model(img_batch))
# se borran los hook registrados en Feed Forward
for fh in F_hook:
fh.remove()
for module_name, module in model.named_modules():
if module_name in list_of_layers:
exp_hook.append(module.register_forward_hook(get_activation_mask(module_name)))
# for name, layer in model.named_children():
# if name in list_of_layers:
# exp_hook.append(layer.register_forward_hook(get_activation_mask(name)))
for param in model.parameters():
param.requires_grad = False
np.random.seed(seed=0)
mask = torch.from_numpy(np.float32(np.random.uniform(0, 0.01, size=(1, 1, 224, 224))))
mask = mask.expand(img_batch.size(0), 1, 224, 224)
mask = mask.cuda()
mask.requires_grad = True
# null_img = torch.zeros(img_batch.size(0), 3, 224, 224).cuda()
# null_img_blur = transforms.GaussianBlur(kernel_size=223, sigma=10)(img_batch)
# null_img_blur.requires_grad = False
# null_img = null_img_blur.cuda()
optimizer = torch.optim.Adam([mask], lr=learning_rate)
for i in trange(max_iterations):
extended_mask = mask.expand(img_batch.size(0), 3, 224, 224)
img_inpainted = inpainter(img_batch, mask)
img_inpainted = transforms.Normalize(mean=-1, std=2)(img_inpainted)
img_inpainted = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])(img_inpainted)
perturbated_input = img_batch.mul(extended_mask) + img_inpainted.mul(1 - extended_mask)
# perturbated_input = perturbated_input.to(torch.float32)
optimizer.zero_grad()
outputs = torch.nn.Softmax(dim=1)(model(perturbated_input)) # (3,1000)
preds = outputs[torch.arange(0, img_batch.size(0)).tolist(), gt_category.tolist()]
loss = l1_coeff * torch.sum(torch.abs(1 - mask), dim=(1, 2, 3)) + preds + \
factorTV * tv_coeff * tv_norm(mask, tv_beta)
loss.backward(gradient=torch.ones_like(loss).cuda())
optimizer.step()
mask.data.clamp_(0, 1)
for eh in exp_hook:
eh.remove()
# Para visualizar las máscaras
# mask_np = (mask.cpu().detach().numpy())
#
# for i in range(mask_np.shape[0]):
# plt.imshow(1 - mask_np[i, 0, :, :])
# plt.show()
return mask
batch_size = 25
# batch_size = 1
val_dataset = DataProcessing(base_img_dir, transform_val, img_idxs=[100, 200], if_noise=1, noise_var=0.1)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=10,
pin_memory=True)
init_time = time.time()
iterator = tqdm(enumerate(val_loader), total=len(val_loader), desc='batch')
# save_path = './resnet50_v4'
# save_path = './resnet50_v4_tv'
# save_path = './resnet50_v4_tv_0.05'
# save_path = './resnet50_v4_tv_0.1'
# save_path = './vgg16_v4'
# save_path = './vgg16_v4_tv'
# save_path = './vgg16_v4_tv_0.05'
# save_path = './vgg16_v4_tv_0.1'
# save_path = './alexnet_v4'
# save_path = './alexnet_v4_tv'
# save_path = './alexnet_v4_tv_0.05'
save_path = './alexnet_v4_tv_0.1'
for i, (images, target, file_names) in iterator:
images.requires_grad = False
images = images.cuda()
mask = my_explanation(images, max_iterations, target)
mask_np = (mask.cpu().detach().numpy())
for idx, file_name in enumerate(file_names):
mask_file = ('{}_mask.npy'.format(file_name.split('/')[-1].split('.JPEG')[0]))
np.save(os.path.abspath(os.path.join(save_path, mask_file)), 1 - mask_np[idx, 0, :, :])
print('Time taken: {:.3f}'.format(time.time() - init_time))