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test_transform.py
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import torch
import cv2
import numpy as np
from augmentations import generate_hist_augs, opts, mean_domains, std_domains
from histaugan.model import MD_multi
import imageio
from torchvision import transforms
import time
from utils import PPlot
def main():
trnsfrms_val = transforms.Compose(
[
transforms.ToTensor()
]
)
model = MD_multi(opts)
model.resume(opts.resume, train=False)
model.to('cuda:0')
model.eval()
# img = imageio.v2.imread(r'D:\jassorRepository\OCELOT_Dataset\what.jpg')
img = cv2.imread(r'D:\jassorRepository\OCELOT_Dataset\how.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (512, 512))
pplt = PPlot()
# pplt.add(img)
img = trnsfrms_val(img).cuda() # torch.Size([3, 512, 512])
print(img.shape)
img1 = img.cpu().numpy()
print(np.max(img1), np.min(img1))
z_content = model.enc_c(img.sub(0.5).mul(2).unsqueeze(0))
# 实验
for d in range(5):
for seed in [
torch.tensor([[10, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.float32),
torch.tensor([[0, 1, 0, 0, 0, 0, 0, 0]], dtype=torch.float32),
torch.tensor([[0, 0, 0, 4, 0, 0, 0, 0]], dtype=torch.float32),
torch.tensor([[0, 0, 0, 0, 3, 0, 3, 0]], dtype=torch.float32),
torch.tensor([[0, 0, 0, 0, 0, 2, 0, 0]], dtype=torch.float32),
torch.tensor([[0, 0, 0, 0, 0, 0, 0, 10]], dtype=torch.float32),
]:
print(seed)
z_attribute = (seed * std_domains[0] + mean_domains[0]).to('cuda:0')
domain = torch.eye(5)[d].unsqueeze(0).to('cuda:0')
out = model.gen(z_content, z_attribute, domain).detach().squeeze(0)
out = out.add(1).div(2).permute(1, 2, 0).cpu().numpy()
# pplt.title(int(seed.sum()))
pplt.add(out)
pplt.show()
# # 实验 -- 确定参数如下所示
# d = 4
# for i, j in [(i, j) for i in range(10) for j in range(10)]:
# if i >= 6:
# pplt.add(np.zeros((10, 10), dtype=np.uint8))
# continue
# seed = torch.zeros(1, 8, dtype=torch.float32)
# if i == 0:
# seed[0, 0] = 1 * j
# elif i == 1:
# seed[0, 1] = 0.1 * j
# elif i == 2:
# seed[0, 3] = 0.4 * j
# elif i == 3:
# seed[0, (4, 6)] = 0.3 * j
# elif i == 4:
# seed[0, 5] = 0.2 * j
# elif i == 5:
# seed[0, 7] = 1 * j
# print(i, j, seed)
# z_attribute = (seed * std_domains[0] + mean_domains[0]).to('cuda:0')
# domain = torch.eye(5)[d].unsqueeze(0).to('cuda:0')
# out = model.gen(z_content, z_attribute, domain).detach().squeeze(0)
# out = out.add(1).div(2).permute(1, 2, 0).cpu().numpy()
# # pplt.title(int(seed.sum()))
# pplt.add(out)
# pplt.show()
# 实验
# d = 4
# for i, j in [(i, j) for i in range(10) for j in range(10)]:
# if i >= 3:
# pplt.add(np.zeros((10, 10), dtype=np.uint8))
# continue
# seed = torch.zeros(1, 8, dtype=torch.float32)
# if i == 0:
# seed[0, 7] = 1 * j
# elif i == 1:
# seed[0, 0] = 1 * j
# elif i == 2:
# seed[0, (7, 0)] = 1 * j
# print(i, j, seed)
# z_attribute = (seed * std_domains[0] + mean_domains[0]).to('cuda:0')
# domain = torch.eye(5)[d].unsqueeze(0).to('cuda:0')
# out = model.gen(z_content, z_attribute, domain).detach().squeeze(0)
# out = out.add(1).div(2).permute(1, 2, 0).cpu().numpy()
# # pplt.title(int(seed.sum()))
# pplt.add(out)
# pplt.show()
# # 实验
# d = 3
# for i, j in [(i, j) for i in range(8) for j in range(8)]:
# seed = torch.zeros(1, 8, dtype=torch.float32)
# seed[0, i] = 1 * j
# print(i, j, seed)
# z_attribute = (seed * std_domains[0] + mean_domains[0]).to('cuda:0')
# domain = torch.eye(5)[d].unsqueeze(0).to('cuda:0')
# out = model.gen(z_content, z_attribute, domain).detach().squeeze(0)
# out = out.add(1).div(2).permute(1, 2, 0).cpu().numpy()
# # pplt.title(int(seed.sum()))
# pplt.add(out)
# pplt.show()
# # 控制变量法观察：固定 domain，改变 z_attribute
# # github code
# for d in range(5):
# for _ in range(5):
# seed = torch.randn((1, 8,))
# z_attribute = (seed * std_domains[d] + mean_domains[d]).to('cuda:0')
# domain = torch.eye(5)[d].unsqueeze(0).to('cuda:0')
# out = model.gen(z_content, z_attribute, domain).detach().squeeze(0)
# out = out.add(1).div(2).permute(1, 2, 0).cpu().numpy()
# print(out.shape, out.dtype, out.mean())
# pplt.add(out)
# pplt.show()
# # 控制变量法观察：固定 z_attribute，改变 domain
# seed = torch.randn((1, 8,))
# # github code
# for _ in range(5):
# for d in range(5):
# z_attribute = (seed * std_domains[d] + mean_domains[d]).to('cuda:0')
# domain = torch.eye(5)[d].unsqueeze(0).to('cuda:0')
# out = model.gen(z_content, z_attribute, domain).detach().squeeze(0)
# out = out.add(1).div(2).permute(1, 2, 0).cpu().numpy()
# print(out.shape, out.dtype, out.mean())
# pplt.add(out)
# pplt.show()
# 炳豆方法
# for iiii in range(1):
# aa1 = time.time()
# for i in range(5):
# out = generate_hist_augs(
# img,
# domain,
# model,
# z_content,
# same_attribute=False,
# new_domain=i,
# stats=(mean_domains, std_domains),
# device='cuda:0'
# )
# out_img = out.add(1).div(2).permute(1, 2, 0).cpu().numpy()
# # print(np.max(out_img),np.min(out_img))
# # imageio.imwrite(rf'D:\jassorRepository\OCELOT_Dataset\{i}.jpg', out_img)
# aa2 = time.time()
#
# print(7777, aa2 - aa1)
biao = [
[],
]
main()