outputRs = outputR.permute(0,2,3,1)

#由于代碼中的中間結(jié)果是帶有梯度的要進(jìn)行detach()操作
k = outputRs.cpu().detach().numpy()

		for i in range(10):
			res = k[i] #得到batch中其中一步的圖片
			image = Image.fromarray(np.uint8(res)).convert('RGB')
			#image.show()
			#通過(guò)時(shí)間命名存儲(chǔ)結(jié)果
			timestamp = datetime.datetime.now().strftime("%M-%S")
			savepath = timestamp + '_r.jpg'
			image.save(savepath)

import torch
import numpy as np
 
var = 2
 
# reference: https://pytorch.apachecn.org/#/docs/1.7/03
if var == 1: # 張量初始化
    # 1.直接生成張量, 注意: torch.tensor與torch.Tensor的區(qū)別: torch.Tensor是torch.FloatTensor的別名;而torch.tensor則根據(jù)輸入數(shù)據(jù)推斷數(shù)據(jù)類型
    data = [[1, 2], [3, 4]]
    x_data = torch.tensor(data); print(f"x_data: {x_data}, type: {x_data.type()}") # type: torch.LongTensor
    y_data = torch.Tensor(data); print(f"y_data: {y_data}, type: {y_data.type()}") # type: torch.FloatTensor
    z_data = torch.IntTensor(data); print(f"z_data: {z_data}, type: {z_data.type()}") # type: torch.IntTensor
 
    # 2.通過(guò)Numpy數(shù)組來(lái)生成張量,反過(guò)來(lái)也可以由張量生成Numpy數(shù)組
    np_array = np.array(data)
    x_np = torch.from_numpy(np_array); print("x_np:\n", x_np)
    y_np = torch.tensor(np_array); print("y_np:\n", y_np) # torch.tensor總是拷貝數(shù)據(jù)
    z_np = torch.as_tensor(np_array); print("z_np:\n", z_np) # 使用torch.as_tensor可避免拷貝數(shù)據(jù)
 
    # 3.通過(guò)已有的張量來(lái)生成新的張量: 新的張量將繼承已有張量的屬性(結(jié)構(gòu)、類型),也可以重新指定新的數(shù)據(jù)類型
    x_ones = torch.ones_like(x_data); print(f"x_ones: {x_ones}, type: {x_ones.type()}") # 保留x_data的屬性
    x_rand = torch.rand_like(x_data, dtype=torch.float); print(f"x_rand: {x_rand}, type: {x_rand.type()}") # 重寫(xiě)x_data的數(shù)據(jù)類型: long -> float
 
    tensor = torch.tensor((), dtype=torch.int32); print(f"shape of tensor: {tensor.shape}, type: {tensor.type()}")
    new_tensor = tensor.new_ones((2, 3)); print(f"shape of new_tensor: {new_tensor.shape}, type: {new_tensor.type()}")
 
    # 4.通過(guò)指定數(shù)據(jù)維度來(lái)生成張量
    shape = (2, 3) # shape是元組類型,用來(lái)描述張量的維數(shù)
    rand_tensor = torch.rand(shape); print(f"rand_tensor: {rand_tensor}, type: {rand_tensor.type()}")
    ones_tensor = torch.ones(shape, dtype=torch.int); print(f"ones_tensor: {ones_tensor}, type: {ones_tensor.type()}")
    zeros_tensor = torch.zeros(shape, device=torch.device("cpu")); print("zeros_tensor:", zeros_tensor)
 
    # 5.可以使用requires_grad=True創(chuàng)建張量,以便torch.autograd記錄對(duì)它們的操作以進(jìn)行自動(dòng)微分
    x = torch.tensor([[1., -1.], [1., 1.]], requires_grad=True)
    out = x.pow(2).sum(); print(f"out: {out}")
    # out.backward(); print(f"x: {x}\nx.grad: {x.grad}")
elif var == 2: # 張量屬性: 從張量屬性我們可以得到張量的維數(shù)、數(shù)據(jù)類型以及它們所存儲(chǔ)的設(shè)備(CPU或GPU)
    tensor = torch.rand(3, 4)
    print(f"shape of tensor: {tensor.shape}")
    print(f"datatype of tensor: {tensor.dtype}") # torch.float32
    print(f"device tensor is stored on: {tensor.device}") # cpu或cuda
    print(f"tensor layout: {tensor.layout}") # tensor如何在內(nèi)存中存儲(chǔ)
    print(f"tensor dim: {tensor.ndim}") # tensor維度
elif var == 3: # 張量運(yùn)算: 有超過(guò)100種張量相關(guān)的運(yùn)算操作,例如轉(zhuǎn)置、索引、切片、數(shù)學(xué)運(yùn)算、線性代數(shù)、隨機(jī)采樣等
    # 所有這些運(yùn)算都可以在GPU上運(yùn)行(相對(duì)于CPU來(lái)說(shuō)可以達(dá)到更高的運(yùn)算速度)
    tensor = torch.rand((4, 4), dtype=torch.float); print(f"src: {tensor}")
 
    # 判斷當(dāng)前環(huán)境GPU是否可用,然后將tensor導(dǎo)入GPU內(nèi)運(yùn)行
    if torch.cuda.is_available():
        tensor = tensor.to("cuda")
 
    # 1.張量的索引和切片
    tensor[:, 1] = 0; print(f"index: {tensor}") # 將第1列(從0開(kāi)始)的數(shù)據(jù)全部賦值為0
 
    # 2.張量的拼接: 可以通過(guò)torch.cat方法將一組張量按照指定的維度進(jìn)行拼接,也可以參考torch.stack方法,但與torch.cat稍微有點(diǎn)不同
    cat = torch.cat([tensor, tensor], dim=1); print(f"cat:\n {cat}")
 
    # 3.張量的乘積和矩陣乘法
    print(f"tensor.mul(tensor):\n {tensor.mul(tensor)}") # 逐個(gè)元素相乘結(jié)果
    print(f"tensor * tensor:\n {tensor * tensor}") # 等價(jià)寫(xiě)法
 
    print(f"tensor.matmul(tensor.T):\n {tensor.matmul(tensor.T)}") # 張量與張量的矩陣乘法
    print(f"tensor @ tensor.T:\n {tensor @ tensor.T}") # 等價(jià)寫(xiě)法
 
    # 4.自動(dòng)賦值運(yùn)算: 通常在方法后有"_"作為后綴,例如:x.copy_(y), x.t_()操作會(huì)改變x的取值(in-place)
    print(f"tensor:\n {tensor}")
    print(f"tensor:\n {tensor.add_(5)}")
elif var == 4: # Tensor與Numpy的轉(zhuǎn)化: 張量和Numpy array數(shù)組在CPU上可以共用一塊內(nèi)存區(qū)域,改變其中一個(gè)另一個(gè)也會(huì)隨之改變
    # 1.由張量變換為Numpy array數(shù)組
    t = torch.ones(5); print(f"t: {t}")
    n = t.numpy(); print(f"n: {n}")
 
    t.add_(1) # 修改張量的值,則Numpy array數(shù)組值也會(huì)隨之改變
    print(f"t: {t}")
    print(f"n: {n}")
 
    # 2.由Numpy array數(shù)組轉(zhuǎn)為張量
    n = np.ones(5); print(f"n: {n}")
    t = torch.from_numpy(n); print(f"t: {t}")
 
    np.add(n, 1, out=n) # 修改Numpy array數(shù)組的值,則張量值也會(huì)隨之改變
    print(f"n: {n}")
    print(f"t: {t}")
 
print("test finish")