To render an image if shape 27x35 I use :
random_image = []
for x in range(1 , 946):
random_image.append(random.randint(0 , 255))
random_image_arr = np.array(random_image)
matplotlib.pyplot.imshow(random_image_arr.reshape(27 , 35))
This generates :
I then try to apply a convolution to the image using the torch.nn.Conv2d
:
conv2 = torch.nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)
image_d = np.asarray(random_image_arr.reshape(27 , 35))
conv2(torch.from_numpy(image_d))
But this displays error :
~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py in forward(self, input)
299 def forward(self, input):
300 return F.conv2d(input, self.weight, self.bias, self.stride,
--> 301 self.padding, self.dilation, self.groups)
302
303
RuntimeError: input has less dimensions than expected
The shape of the input image_d
is (27, 35)
Should I change the parameters of Conv2d
in order to apply the convolution to the image ?
Update. From @McLawrence answer I have :
random_image = []
for x in range(1 , 946):
random_image.append(random.randint(0 , 255))
random_image_arr = np.array(random_image)
matplotlib.pyplot.imshow(random_image_arr.reshape(27 , 35))
This renders image :
Applying the convolution operation :
conv2 = torch.nn.Conv2d(1, 18, kernel_size=3, stride=1, padding=1)
image_d = torch.FloatTensor(np.asarray(random_image_arr.reshape(1, 1, 27 , 35))).numpy()
fc = conv2(torch.from_numpy(image_d))
matplotlib.pyplot.imshow(fc[0][0].data.numpy())
renders image :
There are two problems with your code:
First, 2d convolutions in pytorch
are defined only for 4d tensors.
This is convenient for use in neural networks. The first dimension is the batch size while the second dimension are the channels (a RGB image for example has three channels). So you have to reshape your tensor like
image_d = torch.FloatTensor(np.asarray(random_image_arr.reshape(1, 1, 27 , 35)))
The FloatTensor
is important here, since convolutions are not defined on the LongTensor
which will be created automatically if your numpy
array only includes int
s.
Secondly, You have created a convolution with three input channels, while your image has just one channel (it is greyscale). So you have to adjust the convolution to:
conv2 = torch.nn.Conv2d(1, 18, kernel_size=3, stride=1, padding=1)