物联201王彦皓paddlepaddle

1.

import inline as inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import paddle
from paddle.nn import Conv2D
from paddle.nn.initializer import Assign

w = np.array([1, 0, -1], dtype='float32')
w = w.reshape([1, 1, 1, 3])
conv = Conv2D(in_channels=1, out_channels=1, kernel_size=[1, 3],
              weight_attr=paddle.ParamAttr(
                  initializer=Assign(value=w)))

img = np.ones([50, 50], dtype='float32')
img[:, 30:] = 0.
x = img.reshape([1, 1, 50, 50])
x = paddle.to_tensor(x)
y = conv(x)
out = y.numpy()
f = plt.subplot(121)
f.set_title('input image', fontsize=15)
plt.imshow(img, cmap='gray')
f = plt.subplot(122)
f.set_title('output featuremap', fontsize=15)
plt.imshow(out.squeeze(), cmap='gray')
plt.show()

print(conv.weight)
print(conv.bias)

 2.

import matplotlib.pyplot as plt

from PIL import Image

import numpy as np

import paddle

from paddle.nn import Conv2D

from paddle.nn.initializer import Assign

 

img = Image.open('E:\Workspace\python\study\image\OIP-C.jpg')

 

w = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype='float32') / 8

w = w.reshape([1, 1, 3, 3])

w = np.repeat(w, 3, axis=1)

 

conv = Conv2D(in_channels=3, out_channels=1, kernel_size=[3, 3],

              weight_attr=paddle.ParamAttr(

                  initializer=Assign(value=w)))

 

x = np.array(img).astype('float32')

x = np.transpose(x, (2, 0, 1))

x = x.reshape(1, 3, img.height, img.width)

x = paddle.to_tensor(x)

y = conv(x)

out = y.numpy()

plt.figure(figsize=(20, 10))

f = plt.subplot(121)

f.set_title('input image', fontsize=15)

plt.imshow(img)

f = plt.subplot(122)

f.set_title('output feature map', fontsize=15)

plt.imshow(out.squeeze(), cmap='gray')

plt.show()

 

3.

import paddle
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
from paddle.nn import Conv2D
from paddle.nn.initializer import Assign

img = Image.open('E:\Workspace\python\study\image\OIP-C.jpg').convert('L')
img = np.array(img)

w = np.ones([1, 1, 5, 5], dtype='float32') / 25
conv = Conv2D(in_channels=1, out_channels=1, kernel_size=[5, 5],
              weight_attr=paddle.ParamAttr(
                  initializer=Assign(value=w)))
x = img.astype('float32')
x = x.reshape(1, 1, img.shape[0], img.shape[1])
x = paddle.to_tensor(x)
y = conv(x)
out = y.numpy()

plt.figure(figsize=(20, 12))
f = plt.subplot(121)
f.set_title('input image')
plt.imshow(img, cmap='gray')
import paddle
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
from paddle.nn import Conv2D
from paddle.nn.initializer import Assign

img = Image.open('E:\Workspace\python\study\image\OIP-C.jpg').convert('L')
img = np.array(img)

w = np.ones([1, 1, 5, 5], dtype='float32') / 25
conv = Conv2D(in_channels=1, out_channels=1, kernel_size=[5, 5],
              weight_attr=paddle.ParamAttr(
                  initializer=Assign(value=w)))
x = img.astype('float32')
x = x.reshape(1, 1, img.shape[0], img.shape[1])
x = paddle.to_tensor(x)
y = conv(x)
out = y.numpy()

plt.figure(figsize=(20, 12))
f = plt.subplot(121)
f.set_title('input image')
plt.imshow(img, cmap='gray')

plt.show()