def transform_screen_data(self, screen):
# Convert to float, rescale, convert to tensor
screen = np.ascontigousarray(screen, dtype=np.float32) / 255
screen = torch.from_numpy(screen)
# Use torchvision package to compose image transforms
resize = T.compose([T.ToPILImage(), T.Resize((40, 90)), T.Totensor()])
return resize(screen).unsqueeze(0).to(
self.device) # add a batch dimension
convh = conv2d_out(conv2d_out(conv2d_out(h)))
linear_input_size = convw * convh * 32
self.head = nn.Linear(linear_input_size, outputs)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
return self.head(x.view(x.size(0), -1))
# Input Extraction
# Applying transforms to the image
resize = T.compose(
[T.ToPILImage(),
T.Resize(40, interpolation=Image.CUBIC),
T.ToTensor()])
def get_cart_location(screen_width):
world_width = env.x_threshold * 2
scale = screen_width / world_width
return int(env.state[0] * scale + screen_width / 2.0) # MIDDLE OF CART
def get_screen():
# Returned screen requested by gym is 400x600x3, but is sometimes larger
# such as 800x1200x3. Transpose it into torch order (CHW).
screen = env.render(mode='rgb_array').transpose((2, 0, 1))
# Cart is in the lower half, so strip off the top and bottom of the screen
_, screen_height, screen_width = screen.shape
import torch
import torchvision
import torchvision.transforms as T
#------------------------------------------------------------------------------------------------------------------------------------------
#STEP1: Loading dataset with suitable transforms
#general transformation to be applied to all images
transform=T.compose([T.toTensor(),T.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
#Get the training dataset apply the transforms and apply load it in a variable with appt. batch size shuffling etc.
trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)
trainloader=torch.utils.data.DataLoader(trainset,batch_size = 4,shuffle=True, num_workers=2)
#Get the training dataset apply the transforms and apply load it in a variable with appt. batch size shuffling etc.
testset=torchvision.datasets.CIFAR10(root='./data',train=False,download=True,transofrm=transform)
testloader=torch.utils.data.Dataloader(testset,shuffle=True,batch_size=4,num_workers=2)
classes=('plane' , 'car', 'bird' , 'cat', 'deer','dog', 'frog', 'horse', 'ship' , 'truck')
#------------------------------------------------------------------------------------------------------------------------------------------
#STEP2:Displaying Training Images