def __init__(self, d):
super(decoder5, self).__init__()
# decoder
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv15.weight = torch.nn.Parameter(
torch.Tensor(d.modules[1].weight))
self.conv15.bias = torch.nn.Parameter(torch.Tensor(d.modules[1].bias))
self.relu15 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
# 28 x 28
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv16.weight = torch.nn.Parameter(
torch.Tensor(d.modules[5].weight))
self.conv16.bias = torch.nn.Parameter(torch.Tensor(d.modules[5].bias))
self.relu16 = nn.ReLU(inplace=True)
# 28 x 28
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv17.weight = torch.nn.Parameter(
torch.Tensor(d.modules[8].weight))
self.conv17.bias = torch.nn.Parameter(torch.Tensor(d.modules[8].bias))
self.relu17 = nn.ReLU(inplace=True)
# 28 x 28
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv18.weight = torch.nn.Parameter(
torch.Tensor(d.modules[11].weight))
self.conv18.bias = torch.nn.Parameter(torch.Tensor(d.modules[11].bias))
self.relu18 = nn.ReLU(inplace=True)
# 28 x 28
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(512, 256, 3, 1, 0)
self.conv19.weight = torch.nn.Parameter(
torch.Tensor(d.modules[14].weight))
self.conv19.bias = torch.nn.Parameter(torch.Tensor(d.modules[14].bias))
self.relu19 = nn.ReLU(inplace=True)
# 28 x 28
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
# 56 x 56
self.reflecPad20 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv20 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv20.weight = torch.nn.Parameter(
torch.Tensor(d.modules[18].weight))
self.conv20.bias = torch.nn.Parameter(torch.Tensor(d.modules[18].bias))
self.relu20 = nn.ReLU(inplace=True)
# 56 x 56
self.reflecPad21 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv21 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv21.weight = torch.nn.Parameter(
torch.Tensor(d.modules[21].weight))
self.conv21.bias = torch.nn.Parameter(torch.Tensor(d.modules[21].bias))
self.relu21 = nn.ReLU(inplace=True)
self.reflecPad22 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv22 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv22.weight = torch.nn.Parameter(
torch.Tensor(d.modules[24].weight))
self.conv22.bias = torch.nn.Parameter(torch.Tensor(d.modules[24].bias))
self.relu22 = nn.ReLU(inplace=True)
self.reflecPad23 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv23 = nn.Conv2d(256, 128, 3, 1, 0)
self.conv23.weight = torch.nn.Parameter(
torch.Tensor(d.modules[27].weight))
self.conv23.bias = torch.nn.Parameter(torch.Tensor(d.modules[27].bias))
self.relu23 = nn.ReLU(inplace=True)
self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
# 112 X 112
self.reflecPad24 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv24 = nn.Conv2d(128, 128, 3, 1, 0)
self.conv24.weight = torch.nn.Parameter(
torch.Tensor(d.modules[31].weight))
self.conv24.bias = torch.nn.Parameter(torch.Tensor(d.modules[31].bias))
self.relu24 = nn.ReLU(inplace=True)
self.reflecPad25 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv25 = nn.Conv2d(128, 64, 3, 1, 0)
self.conv25.weight = torch.nn.Parameter(
torch.Tensor(d.modules[34].weight))
self.conv25.bias = torch.nn.Parameter(torch.Tensor(d.modules[34].bias))
self.relu25 = nn.ReLU(inplace=True)
self.unpool4 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad26 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv26 = nn.Conv2d(64, 64, 3, 1, 0)
self.conv26.weight = torch.nn.Parameter(
torch.Tensor(d.modules[38].weight))
self.conv26.bias = torch.nn.Parameter(torch.Tensor(d.modules[38].bias))
self.relu26 = nn.ReLU(inplace=True)
self.reflecPad27 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv27 = nn.Conv2d(64, 3, 3, 1, 0)
self.conv27.weight = torch.nn.Parameter(
torch.Tensor(d.modules[41].weight))
self.conv27.bias = torch.nn.Parameter(torch.Tensor(d.modules[41].bias))
def build_layers(
img_sz,
img_fm,
init_fm,
max_fm,
n_layers,
n_attr,
n_skip,
deconv_method,
instance_norm,
enc_dropout,
dec_dropout,
):
"""
Build auto-encoder layers.
"""
assert init_fm <= max_fm
assert n_skip <= n_layers - 1
assert np.log2(img_sz).is_integer()
assert n_layers <= int(np.log2(img_sz))
assert type(instance_norm) is bool
assert 0 <= enc_dropout < 1
assert 0 <= dec_dropout < 1
norm_fn = nn.InstanceNorm2d if instance_norm else nn.BatchNorm2d
enc_layers = []
dec_layers = []
n_in = img_fm
n_out = init_fm
for i in range(n_layers):
enc_layer = []
dec_layer = []
skip_connection = n_layers - (n_skip + 1) <= i < n_layers - 1
n_dec_in = n_out + n_attr + (n_out if skip_connection else 0)
n_dec_out = n_in
# encoder layer
enc_layer.append(nn.Conv2d(n_in, n_out, 4, 2, 1))
if i > 0:
enc_layer.append(norm_fn(n_out, affine=True))
enc_layer.append(nn.LeakyReLU(0.2, inplace=True))
if enc_dropout > 0:
enc_layer.append(nn.Dropout(enc_dropout))
# decoder layer
if deconv_method == "upsampling":
dec_layer.append(nn.UpsamplingNearest2d(scale_factor=2))
dec_layer.append(nn.Conv2d(n_dec_in, n_dec_out, 3, 1, 1))
elif deconv_method == "convtranspose":
dec_layer.append(
nn.ConvTranspose2d(n_dec_in, n_dec_out, 4, 2, 1, bias=False))
else:
assert deconv_method == "pixelshuffle"
dec_layer.append(nn.Conv2d(n_dec_in, n_dec_out * 4, 3, 1, 1))
dec_layer.append(nn.PixelShuffle(2))
if i > 0:
dec_layer.append(norm_fn(n_dec_out, affine=True))
if dec_dropout > 0 and i >= n_layers - 3:
dec_layer.append(nn.Dropout(dec_dropout))
dec_layer.append(nn.ReLU(inplace=True))
else:
pass
# dec_layer.append(nn.Tanh())
# update
n_in = n_out
n_out = min(2 * n_out, max_fm)
enc_layers.append(nn.Sequential(*enc_layer))
dec_layers.insert(0, nn.Sequential(*dec_layer))
return enc_layers, dec_layers
def __init__(self):
super(decoder5, self).__init__()
# decoder
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
# 28 x 28
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu16 = nn.ReLU(inplace=True)
# 28 x 28
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu17 = nn.ReLU(inplace=True)
# 28 x 28
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu18 = nn.ReLU(inplace=True)
# 28 x 28
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu19 = nn.ReLU(inplace=True)
# 28 x 28
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
# 56 x 56
self.reflecPad20 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv20 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu20 = nn.ReLU(inplace=True)
# 56 x 56
self.reflecPad21 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv21 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu21 = nn.ReLU(inplace=True)
self.reflecPad22 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv22 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu22 = nn.ReLU(inplace=True)
self.reflecPad23 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv23 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu23 = nn.ReLU(inplace=True)
self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
# 112 X 112
self.reflecPad24 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv24 = nn.Conv2d(128, 128, 3, 1, 0)
self.relu24 = nn.ReLU(inplace=True)
self.reflecPad25 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv25 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu25 = nn.ReLU(inplace=True)
self.unpool4 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad26 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv26 = nn.Conv2d(64, 64, 3, 1, 0)
self.relu26 = nn.ReLU(inplace=True)
self.reflecPad27 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv27 = nn.Conv2d(64, 3, 3, 1, 0)
def deconv_block(in_dim, out_dim):
return nn.Sequential(
nn.Conv2d(in_dim, out_dim, kernel_size=3, stride=1, padding=1),
nn.ELU(True),
nn.Conv2d(out_dim, out_dim, kernel_size=3, stride=1, padding=1),
nn.ELU(True), nn.UpsamplingNearest2d(scale_factor=2))
def __init__(self,d):
super(decoder,self).__init__()
# decoder
self.reflecPad15 = nn.ReflectionPad2d((1,1,1,1))
self.conv15 = nn.Conv2d(512,512,3,1,0)
self.conv15.weight = torch.nn.Parameter(d.get(1).weight.float())
self.conv15.bias = torch.nn.Parameter(d.get(1).bias.float())
self.relu15 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
# 28 x 28
self.reflecPad16 = nn.ReflectionPad2d((1,1,1,1))
self.conv16 = nn.Conv2d(512,512,3,1,0)
self.conv16.weight = torch.nn.Parameter(d.get(5).weight.float())
self.conv16.bias = torch.nn.Parameter(d.get(5).bias.float())
self.relu16 = nn.ReLU(inplace=True)
# 28 x 28
self.reflecPad17 = nn.ReflectionPad2d((1,1,1,1))
self.conv17 = nn.Conv2d(512,512,3,1,0)
self.conv17.weight = torch.nn.Parameter(d.get(8).weight.float())
self.conv17.bias = torch.nn.Parameter(d.get(8).bias.float())
self.relu17 = nn.ReLU(inplace=True)
# 28 x 28
self.reflecPad18 = nn.ReflectionPad2d((1,1,1,1))
self.conv18 = nn.Conv2d(512,512,3,1,0)
self.conv18.weight = torch.nn.Parameter(d.get(11).weight.float())
self.conv18.bias = torch.nn.Parameter(d.get(11).bias.float())
self.relu18 = nn.ReLU(inplace=True)
# 28 x 28
self.reflecPad19 = nn.ReflectionPad2d((1,1,1,1))
self.conv19 = nn.Conv2d(512,256,3,1,0)
self.conv19.weight = torch.nn.Parameter(d.get(14).weight.float())
self.conv19.bias = torch.nn.Parameter(d.get(14).bias.float())
self.relu19 = nn.ReLU(inplace=True)
# 28 x 28
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
# 56 x 56
self.reflecPad20 = nn.ReflectionPad2d((1,1,1,1))
self.conv20 = nn.Conv2d(256,256,3,1,0)
self.conv20.weight = torch.nn.Parameter(d.get(18).weight.float())
self.conv20.bias = torch.nn.Parameter(d.get(18).bias.float())
self.relu20 = nn.ReLU(inplace=True)
# 56 x 56
self.reflecPad21 = nn.ReflectionPad2d((1,1,1,1))
self.conv21 = nn.Conv2d(256,256,3,1,0)
self.conv21.weight = torch.nn.Parameter(d.get(21).weight.float())
self.conv21.bias = torch.nn.Parameter(d.get(21).bias.float())
self.relu21 = nn.ReLU(inplace=True)
self.reflecPad22 = nn.ReflectionPad2d((1,1,1,1))
self.conv22 = nn.Conv2d(256,256,3,1,0)
self.conv22.weight = torch.nn.Parameter(d.get(24).weight.float())
self.conv22.bias = torch.nn.Parameter(d.get(24).bias.float())
self.relu22 = nn.ReLU(inplace=True)
self.reflecPad23 = nn.ReflectionPad2d((1,1,1,1))
self.conv23 = nn.Conv2d(256,128,3,1,0)
self.conv23.weight = torch.nn.Parameter(d.get(27).weight.float())
self.conv23.bias = torch.nn.Parameter(d.get(27).bias.float())
self.relu23 = nn.ReLU(inplace=True)
self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
# 112 X 112
self.reflecPad24 = nn.ReflectionPad2d((1,1,1,1))
self.conv24 = nn.Conv2d(128,128,3,1,0)
self.conv24.weight = torch.nn.Parameter(d.get(31).weight.float())
self.conv24.bias = torch.nn.Parameter(d.get(31).bias.float())
self.relu24 = nn.ReLU(inplace=True)
self.reflecPad25 = nn.ReflectionPad2d((1,1,1,1))
self.conv25 = nn.Conv2d(128,64,3,1,0)
self.conv25.weight = torch.nn.Parameter(d.get(34).weight.float())
self.conv25.bias = torch.nn.Parameter(d.get(34).bias.float())
self.relu25 = nn.ReLU(inplace=True)
self.unpool4 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad26 = nn.ReflectionPad2d((1,1,1,1))
self.conv26 = nn.Conv2d(64,64,3,1,0)
self.conv26.weight = torch.nn.Parameter(d.get(38).weight.float())
self.conv26.bias = torch.nn.Parameter(d.get(38).bias.float())
self.relu26 = nn.ReLU(inplace=True)
self.reflecPad27 = nn.ReflectionPad2d((1,1,1,1))
self.conv27 = nn.Conv2d(64,3,3,1,0)
self.conv27.weight = torch.nn.Parameter(d.get(41).weight.float())
self.conv27.bias = torch.nn.Parameter(d.get(41).bias.float())
def detect_object_in_image(net_model,
pnp_solver,
in_img,
config,
grid_belief_debug=False,
norm_belief=True,
run_sampling=False,
network='dope'):
'''Detect objects in a image using a specific trained network model'''
if in_img is None:
return []
# Run network inference
image_tensor = transform(in_img)
image_torch = Variable(image_tensor).cuda().unsqueeze(0)
with torch.cuda.amp.autocast():
out, seg = net_model(image_torch)
vertex2 = out[-1][0].to(torch.float32)
aff = seg[-1][0].to(torch.float32)
# Find objects from network output
detected_objects = ObjectDetector.find_object_poses(
vertex2, aff, pnp_solver, config
# run_sampling=run_sampling,
# scale_factor = scale_factor,
# OFFSET_DUE_TO_UPSAMPLING = OFFSET_DUE_TO_UPSAMPLING
)
if not grid_belief_debug:
return detected_objects
else:
# Run the belief maps debug display on the beliefmaps
upsampling = nn.UpsamplingNearest2d(scale_factor=1)
tensor = vertex2 # shape [9, 50, 50]
belief_imgs = []
in_img = (torch.tensor(in_img).float() / 255.0)
in_img *= 0.7
for j in range(tensor.size()[0]):
belief = tensor[j].clone()
if norm_belief:
belief -= float(torch.min(belief).item())
belief /= float(torch.max(belief).item())
# print (image_torch.size())
# raise()
# belief *= 0.5
# print(in_img.size())
belief = upsampling(
belief.unsqueeze(0).unsqueeze(0)).squeeze().squeeze().data
belief = torch.clamp(belief, 0, 1).cpu()
belief = torch.cat([
# belief.unsqueeze(0) + in_img[:,:,0],
# belief.unsqueeze(0) + in_img[:,:,1],
# belief.unsqueeze(0) + in_img[:,:,2]
belief.unsqueeze(0),
belief.unsqueeze(0),
belief.unsqueeze(0)
]).unsqueeze(0)
belief = torch.clamp(belief, 0, 1)
# belief_imgs.append(belief.data.squeeze().cpu().numpy().transpose(1,2,0))
belief_imgs.append(belief.data.squeeze().numpy())
# Create the image grid
belief_imgs = torch.tensor(np.array(belief_imgs))
im_belief = ObjectDetector.get_image_grid(belief_imgs,
None,
mean=0,
std=1)
return detected_objects, im_belief
return self.lambda_func(self.forward_prepare(input))
class LambdaMap(LambdaBase):
def forward(self, input):
return list(map(self.lambda_func, self.forward_prepare(input)))
class LambdaReduce(LambdaBase):
def forward(self, input):
return reduce(self.lambda_func, self.forward_prepare(input))
feature_invertor_conv3_1 = nn.Sequential( # Sequential,
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 128, (3, 3)),
nn.ReLU(),
nn.UpsamplingNearest2d(scale_factor=2),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 128, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 64, (3, 3)),
nn.ReLU(),
nn.UpsamplingNearest2d(scale_factor=2),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 64, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 3, (3, 3)),
)
def UpLayer(type, scale_factor=2):
if type == 'nearest':
return nn.UpsamplingNearest2d(scale_factor=scale_factor)
elif type == 'bilinear':
return nn.UpsamplingBilinear2d(scale_factor=2)
def __init__(self, d):
super(decoder4, self).__init__()
# decoder
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.conv11.weight = torch.nn.Parameter(d.get(1).weight.float())
self.conv11.bias = torch.nn.Parameter(d.get(1).bias.float())
self.relu11 = nn.ReLU(inplace=True)
# 28 x 28
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
# 56 x 56
self.reflecPad12 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv12 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv12.weight = torch.nn.Parameter(d.get(5).weight.float())
self.conv12.bias = torch.nn.Parameter(d.get(5).bias.float())
self.relu12 = nn.ReLU(inplace=True)
# 56 x 56
self.reflecPad13 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv13 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv13.weight = torch.nn.Parameter(d.get(8).weight.float())
self.conv13.bias = torch.nn.Parameter(d.get(8).bias.float())
self.relu13 = nn.ReLU(inplace=True)
# 56 x 56
self.reflecPad14 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv14 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv14.weight = torch.nn.Parameter(d.get(11).weight.float())
self.conv14.bias = torch.nn.Parameter(d.get(11).bias.float())
self.relu14 = nn.ReLU(inplace=True)
# 56 x 56
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(256, 128, 3, 1, 0)
self.conv15.weight = torch.nn.Parameter(d.get(14).weight.float())
self.conv15.bias = torch.nn.Parameter(d.get(14).bias.float())
self.relu15 = nn.ReLU(inplace=True)
# 56 x 56
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
# 112 x 112
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(128, 128, 3, 1, 0)
self.conv16.weight = torch.nn.Parameter(d.get(18).weight.float())
self.conv16.bias = torch.nn.Parameter(d.get(18).bias.float())
self.relu16 = nn.ReLU(inplace=True)
# 112 x 112
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(128, 64, 3, 1, 0)
self.conv17.weight = torch.nn.Parameter(d.get(21).weight.float())
self.conv17.bias = torch.nn.Parameter(d.get(21).bias.float())
self.relu17 = nn.ReLU(inplace=True)
# 112 x 112
self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
# 224 x 224
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(64, 64, 3, 1, 0)
self.conv18.weight = torch.nn.Parameter(d.get(25).weight.float())
self.conv18.bias = torch.nn.Parameter(d.get(25).bias.float())
self.relu18 = nn.ReLU(inplace=True)
# 224 x 224
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(64, 3, 3, 1, 0)
self.conv19.weight = torch.nn.Parameter(d.get(28).weight.float())
self.conv19.bias = torch.nn.Parameter(d.get(28).bias.float())
def Upsample(x):
Upsample_m = nn.UpsamplingNearest2d(scale_factor=2) #size=(120,360)
x = Upsample_m(x)
return x
def __init__(self, img_size, channels, kernel_size, dropout, final_activation=nn.Tanh()):
super().__init__()
# TODO: use nn.ReflectionPad2d to allow even numbered kernel sizes
# Padding to ensure that input and output dims are the same
pad = (int)((kernel_size - 1)/2)
self.kernel_size = kernel_size
self.img_size = img_size
self.channels = channels
self.pad = pad
fc_nodes = (int)(8*8*channels)
# Output of encoder is a
self.encoder = nn.Sequential(
nn.Conv2d(
3, # input channels
channels, # output channels
kernel_size, # kernel size
stride=1,
padding=pad,
),
nn.ELU(),
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels, channels, kernel_size, stride=2, padding=pad),
nn.ELU(),
nn.Dropout2d(dropout),
# Image size halved
nn.Conv2d(channels, channels*2, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels*2, channels*2, kernel_size, stride=2, padding=pad),
nn.ELU(),
# Image size quartered
nn.Conv2d(channels*2, channels*3, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels*3, channels*3, kernel_size, stride=2, padding=pad),
nn.ELU(),
# Image size one eigth
nn.Conv2d(channels*3, channels*4, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels*4, channels*4, kernel_size, stride=2, padding=pad),
nn.ELU(),
# Image size on sixteenth
nn.Conv2d(channels*4, channels*5, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels*5, channels*5, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Dropout2d(dropout),
# nn.Conv2d(channels, channels, kernel_size, stride=2, padding=pad),
# nn.ELU(),
)
self.fc1 = nn.Linear(8*8*channels*5, 64)
self.fc2 = nn.Linear(64, 8*8*channels)
self.decoder = nn.Sequential(
# Image size one sixteenth
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Dropout2d(dropout),
nn.UpsamplingNearest2d(scale_factor=2),
# Image size one eigth
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.UpsamplingNearest2d(scale_factor=2),
# Image size one quarter
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.UpsamplingNearest2d(scale_factor=2),
# Image size halved
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.UpsamplingNearest2d(scale_factor=2),
# Image size normal
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Conv2d(channels, channels, kernel_size, stride=1, padding=pad),
nn.ELU(),
nn.Dropout2d(dropout),
nn.Conv2d(channels, 3, kernel_size, stride=1, padding=pad),
final_activation,
)
def __init__(self, base_n_channels, neck_n_channels):
super(RIN, self).__init__()
assert base_n_channels >= 8, "Base num channels should be at least 8"
assert neck_n_channels >= 32, "Neck num channels should be at least 32"
self.pc1 = PCBlock(channels_in=3,
channels_out=base_n_channels,
kernel_size=5,
stride=1,
padding=2)
self.pc2 = PCBlock(channels_in=base_n_channels,
channels_out=base_n_channels * 2,
kernel_size=3,
stride=2,
padding=1)
self.pc3 = PCBlock(channels_in=base_n_channels * 2,
channels_out=base_n_channels * 2,
kernel_size=3,
stride=1,
padding=1)
self.pc4 = PCBlock(channels_in=base_n_channels * 2,
channels_out=base_n_channels * 4,
kernel_size=3,
stride=2,
padding=1)
self.pc5 = PCBlock(channels_in=base_n_channels * 4,
channels_out=base_n_channels * 4,
kernel_size=3,
stride=1,
padding=1)
self.pc6 = PCBlock(channels_in=base_n_channels * 4,
channels_out=base_n_channels * 4,
kernel_size=3,
stride=1,
padding=2,
dilation=2)
self.pc7 = PCBlock(channels_in=base_n_channels * 4,
channels_out=base_n_channels * 4,
kernel_size=3,
stride=1,
padding=2,
dilation=2)
self.pc8 = PCBlock(channels_in=base_n_channels * 4,
channels_out=base_n_channels * 4,
kernel_size=3,
stride=1,
padding=4,
dilation=4)
self.pc9 = PCBlock(channels_in=base_n_channels * 4,
channels_out=base_n_channels * 4,
kernel_size=3,
stride=1,
padding=4,
dilation=4)
self.pc10 = PCBlock(channels_in=base_n_channels * 4,
channels_out=base_n_channels * 4,
kernel_size=3,
stride=1,
padding=1)
self.upsample = nn.UpsamplingNearest2d(scale_factor=2.0)
self.pc11 = PCBlock(channels_in=base_n_channels * 4 + neck_n_channels,
channels_out=base_n_channels * 2,
kernel_size=3,
stride=1,
padding=1)
self.pc12 = PCBlock(channels_in=base_n_channels * 2,
channels_out=base_n_channels * 2,
kernel_size=3,
stride=1,
padding=1)
self.pc13 = PCBlock(channels_in=base_n_channels * 2,
channels_out=base_n_channels,
kernel_size=3,
stride=1,
padding=1)
self.pc14 = PCBlock(channels_in=base_n_channels,
channels_out=base_n_channels,
kernel_size=3,
stride=1,
padding=1)
self.conv1 = nn.Conv2d(base_n_channels,
3,
kernel_size=3,
stride=1,
padding=1)
self.init_weights(init_type="normal", gain=0.02)
def __init__(self,
in_channels,
out_channels,
post_conv=True,
use_dropout=False,
dropout_prob=0.1,
norm=nn.BatchNorm2d,
upsampling_mode='transpose'):
'''
:param in_channels: Number of input channels
:param out_channels: Number of output channels
:param post_conv: Whether to have another convolutional layer after the upsampling layer.
:param use_dropout: bool. Whether to use dropout or not.
:param dropout_prob: Float. The dropout probability (if use_dropout is True)
:param norm: Which norm to use. If None, no norm is used. Default is Batchnorm with affinity.
:param upsampling_mode: Which upsampling mode:
transpose: Upsampling with stride-2, kernel size 4 transpose convolutions.
bilinear: Feature map is upsampled with bilinear upsampling, then a conv layer.
nearest: Feature map is upsampled with nearest neighbor upsampling, then a conv layer.
shuffle: Feature map is upsampled with pixel shuffling, then a conv layer.
'''
super().__init__()
net = list()
if upsampling_mode == 'transpose':
net += [
nn.ConvTranspose2d(in_channels,
out_channels,
kernel_size=4,
stride=2,
padding=1,
bias=True if norm is None else False)
]
elif upsampling_mode == 'bilinear':
net += [nn.UpsamplingBilinear2d(scale_factor=2)]
net += [
Conv2dSame(in_channels,
out_channels,
kernel_size=3,
bias=True if norm is None else False)
]
elif upsampling_mode == 'nearest':
net += [nn.UpsamplingNearest2d(scale_factor=2)]
net += [
Conv2dSame(in_channels,
out_channels,
kernel_size=3,
bias=True if norm is None else False)
]
elif upsampling_mode == 'shuffle':
net += [nn.PixelShuffle(upscale_factor=2)]
net += [
Conv2dSame(in_channels // 4,
out_channels,
kernel_size=3,
bias=True if norm is None else False)
]
else:
raise ValueError("Unknown upsampling mode!")
if norm is not None:
net += [norm(out_channels, affine=True)]
net += [nn.ReLU(True)]
if use_dropout:
net += [nn.Dropout2d(dropout_prob, False)]
if post_conv:
net += [
Conv2dSame(out_channels,
out_channels,
kernel_size=3,
bias=True if norm is None else False)
]
if norm is not None:
net += [norm(out_channels, affine=True)]
net += [nn.ReLU(True)]
if use_dropout:
net += [nn.Dropout2d(0.1, False)]
self.net = nn.Sequential(*net)
def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
super(TestUpsampleNearest2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, kernel_size=kernel_size, bias=bias)
self.up = nn.UpsamplingNearest2d(scale_factor=2)
def lua_recursive_model(module, seq):
for m in module.modules:
name = type(m).__name__
real = m
if name == 'TorchObject':
name = m._typename.replace('cudnn.', '')
m = m._obj
if name == 'SpatialConvolution':
if not hasattr(m, 'groups'): m.groups = 1
n = nn.Conv2d(m.nInputPlane,
m.nOutputPlane, (m.kW, m.kH), (m.dW, m.dH),
(m.padW, m.padH),
1,
m.groups,
bias=(m.bias is not None))
copy_param(m, n)
add_submodule(seq, n)
elif name == 'SpatialBatchNormalization':
n = nn.BatchNorm2d(m.running_mean.size(0), m.eps, m.momentum,
m.affine)
copy_param(m, n)
add_submodule(seq, n)
elif name == 'ReLU':
n = nn.ReLU()
add_submodule(seq, n)
elif name == 'SpatialMaxPooling':
n = nn.MaxPool2d((m.kW, m.kH), (m.dW, m.dH), (m.padW, m.padH),
ceil_mode=m.ceil_mode)
add_submodule(seq, n)
elif name == 'SpatialAveragePooling':
n = nn.AvgPool2d((m.kW, m.kH), (m.dW, m.dH), (m.padW, m.padH),
ceil_mode=m.ceil_mode)
add_submodule(seq, n)
elif name == 'SpatialUpSamplingNearest':
n = nn.UpsamplingNearest2d(scale_factor=m.scale_factor)
add_submodule(seq, n)
elif name == 'View':
n = Lambda(lambda x: x.view(x.size(0), -1))
add_submodule(seq, n)
elif name == 'Linear':
# Linear in pytorch only accept 2D input
n1 = Lambda(lambda x: x.view(1, -1) if 1 == len(x.size()) else x)
n2 = nn.Linear(m.weight.size(1),
m.weight.size(0),
bias=(m.bias is not None))
copy_param(m, n2)
n = nn.Sequential(n1, n2)
add_submodule(seq, n)
elif name == 'Dropout':
m.inplace = False
n = nn.Dropout(m.p)
add_submodule(seq, n)
elif name == 'SoftMax':
n = nn.Softmax()
add_submodule(seq, n)
elif name == 'Identity':
n = Lambda(lambda x: x) # do nothing
add_submodule(seq, n)
elif name == 'SpatialFullConvolution':
n = nn.ConvTranspose2d(m.nInputPlane, m.nOutputPlane, (m.kW, m.kH),
(m.dW, m.dH), (m.padW, m.padH))
add_submodule(seq, n)
elif name == 'SpatialReplicationPadding':
n = nn.ReplicationPad2d((m.pad_l, m.pad_r, m.pad_t, m.pad_b))
add_submodule(seq, n)
elif name == 'SpatialReflectionPadding':
n = nn.ReflectionPad2d((m.pad_l, m.pad_r, m.pad_t, m.pad_b))
add_submodule(seq, n)
elif name == 'Copy':
n = Lambda(lambda x: x) # do nothing
add_submodule(seq, n)
elif name == 'Narrow':
n = Lambda(lambda x, a=
(m.dimension, m.index, m.length): x.narrow(*a))
add_submodule(seq, n)
elif name == 'SpatialCrossMapLRN':
lrn = torch.legacy.nn.SpatialCrossMapLRN(m.size, m.alpha, m.beta,
m.k)
n = Lambda(lambda x, lrn=lrn: Variable(lrn.forward(x.data)))
add_submodule(seq, n)
elif name == 'Sequential':
n = nn.Sequential()
lua_recursive_model(m, n)
add_submodule(seq, n)
elif name == 'ConcatTable': # output is list
n = LambdaMap(lambda x: x)
lua_recursive_model(m, n)
add_submodule(seq, n)
elif name == 'CAddTable': # input is list
n = LambdaReduce(lambda x, y: x + y)
add_submodule(seq, n)
elif name == 'Concat':
dim = m.dimension
n = LambdaReduce(lambda x, y, dim=dim: torch.cat((x, y), dim))
lua_recursive_model(m, n)
add_submodule(seq, n)
elif name == 'TorchObject':
print('Not Implement', name, real._typename)
else:
print('Not Implement', name)
def __init__(self, image_size, num_blocks):
super(Generator, self).__init__()
self.in_channels = 3
self.dim = 64
self.out_channels = 3
self.num_blocks = num_blocks
self.image_size = image_size
# Down-Sampling #
down_sampling = []
down_sampling += [
nn.ReflectionPad2d(padding=3),
nn.Conv2d(self.in_channels,
self.dim,
kernel_size=7,
stride=1,
padding=0,
bias=False),
nn.InstanceNorm2d(self.dim),
nn.ReLU(inplace=True)
]
num_down_sampling = 2
for i in range(num_down_sampling):
factor = 2**i
down_sampling += [
nn.ReflectionPad2d(padding=1),
nn.Conv2d(self.dim * factor,
self.dim * factor * 2,
kernel_size=3,
stride=2,
padding=0,
bias=False),
nn.InstanceNorm2d(self.dim * factor * 2),
nn.ReLU(inplace=True),
]
factor = 2**num_down_sampling
for i in range(num_blocks):
down_sampling += [ResNetBlock(self.dim * factor)]
self.down_sampling = nn.Sequential(*down_sampling)
# Class Activation Map (CAM) #
self.gap_fc = nn.Linear(self.dim * factor, 1, bias=False)
self.gmp_fc = nn.Linear(self.dim * factor, 1, bias=False)
self.conv = nn.Sequential(
nn.Conv2d(self.dim * factor * 2,
self.dim * factor,
kernel_size=1,
stride=1,
padding=0,
bias=True), nn.ReLU(inplace=True))
# Block for Gamma and Beta #
self.fc = nn.Sequential(
nn.Linear(image_size // factor * image_size // factor * self.dim *
factor,
self.dim * factor,
bias=False), nn.ReLU(inplace=True),
nn.Linear(self.dim * factor, self.dim * factor, bias=False),
nn.ReLU(inplace=True))
self.gamma = nn.Linear(self.dim * factor,
self.dim * factor,
bias=False)
self.beta = nn.Linear(self.dim * factor, self.dim * factor, bias=False)
# Up-Sampling #
for i in range(num_blocks):
setattr(self, "UpBlock" + str(i + 1),
ResNetAdaLINBlock(self.dim * factor))
up_sampling = []
num_up_sampling = 2
for i in range(num_up_sampling):
factor = 2**(num_up_sampling - i)
up_sampling += [
nn.UpsamplingNearest2d(scale_factor=2),
nn.ReflectionPad2d(padding=1),
nn.Conv2d(self.dim * factor,
int(self.dim * factor / 2),
kernel_size=3,
stride=1,
padding=0,
bias=False),
ILN(int(self.dim * factor / 2)),
nn.ReLU(inplace=True)
]
up_sampling += [
nn.ReflectionPad2d(padding=3),
nn.Conv2d(self.dim,
self.out_channels,
kernel_size=7,
stride=1,
padding=0,
bias=False),
nn.Tanh()
]
self.up_sampling = nn.Sequential(*up_sampling)
def __init__(self, h, n, input_dim=(64, 64, 3)):
super(D, self).__init__()
self.n = n
self.h = h
channel, width, height = input_dim
self.blocks = int(np.log2(width) - 2)
print("[!] {} blocks in D ".format(self.blocks))
encoder_layers = []
encoder_layers.append(
nn.Conv2d(3, n, kernel_size=3, stride=1, padding=1))
prev_channel_size = n
for i in range(self.blocks):
channel_size = (i + 1) * n
encoder_layers.append(
nn.Conv2d(prev_channel_size,
channel_size,
kernel_size=3,
stride=1,
padding=1))
encoder_layers.append(nn.ELU())
encoder_layers.append(
nn.Conv2d(channel_size,
channel_size,
kernel_size=3,
stride=1,
padding=1))
encoder_layers.append(nn.ELU())
if i < self.blocks - 1:
# Downsampling
encoder_layers.append(
nn.Conv2d(channel_size,
channel_size,
kernel_size=3,
stride=2,
padding=1))
encoder_layers.append(nn.ELU())
prev_channel_size = channel_size
self.encoder = nn.Sequential(*encoder_layers)
self.fc_encode = nn.Linear(8 * 8 * self.blocks * n, h)
self.fc_decode = nn.Linear(h, 8 * 8 * n)
decoder_layers = []
for i in range(self.blocks):
decoder_layers.append(
nn.Conv2d(n, n, kernel_size=3, stride=1, padding=1))
decoder_layers.append(nn.ELU())
decoder_layers.append(
nn.Conv2d(n, n, kernel_size=3, stride=1, padding=1))
decoder_layers.append(nn.ELU())
if i < self.blocks - 1:
decoder_layers.append(nn.UpsamplingNearest2d(scale_factor=2))
decoder_layers.append(
nn.Conv2d(n, channel, kernel_size=3, stride=1, padding=1))
self.decoder = nn.Sequential(*decoder_layers)
def create_up(in_c, out_c, stride=2, dilation=1):
model = nn.Sequential(nn.UpsamplingNearest2d(stride, stride),
nn.Conv2d(in_c, out_c, 3, 1, (dilation - 1) + 1),
nn.BatchNorm2d(out_c), nn.PReLU())
return model
def forward(self, x):
# Encoder part with 6 Resblock-a (D6)
x_conv_1 = self.conv1(x)
# print(x_conv_1.size())
x_resblock_1 = self.rest_block_1(x_conv_1)
x_conv_2 = self.conv2(x_resblock_1)
# print(f'conv2 size: {x_conv_2.size()}')
x_resblock_2 = self.rest_block_2(x_conv_2)
x_conv_3 = self.conv3(x_resblock_2)
# print(f'conv3 size: {x_conv_3.size()}')
x_resblock_3 = self.rest_block_3(x_conv_3)
x_conv_4 = self.conv4(x_resblock_3)
# print(f'conv4 size: {x_conv_4.size()}')
x_resblock_4 = self.rest_block_4(x_conv_4)
x_conv_5 = self.conv5(x_resblock_4)
# print(f'conv5 size: {x_conv_4.size()}')
x_resblock_5 = self.rest_block_5(x_conv_5)
x_conv_6 = self.conv6(x_resblock_5)
# print(f'conv6 size: {x_conv_5.size()}')
x_resblock_6 = self.rest_block_6(x_conv_6)
x_pooling_1 = self.PSPPooling(x_resblock_6)
#Decoder part upsampling with combine
#up5
# print(f'pooling 1 size : {x_pooling_1.size()}')
x_convup_1 = self.convup1(x_pooling_1)
# x_upsampling1 = nn.UpsamplingNearest2d(scale_factor=2)(x_convup_1)
x_upsampling1 = nn.UpsamplingNearest2d(scale_factor=2)(x_convup_1)
# print(f'upsampling 1 size: {x_upsampling1.size()} x_restblock_5 size: {x_resblock_5.size()}')
x_combine_1 = self.combine1(x_upsampling1, x_resblock_5)
# print(f'combine 1 output {x_combine_1.size()}')
x_resblockup_5 = self.rest_block_up_5(x_combine_1)
#up4
# print(f'resblockup 5 size : {x_resblockup_5.size()}')
x_convup_2 = self.convup2(x_resblockup_5)
# print(f'convup2 size : {x_convup_2.size()}')
x_upsampling2 = nn.UpsamplingNearest2d(scale_factor=2)(x_convup_2)
# print(f'x_upsampling2 size : {x_upsampling2.size()} x_resblock_4 size: {x_resblock_4.size()}')
x_combine_2 = self.combine2(x_upsampling2, x_resblock_4)
x_resblockup_4 = self.rest_block_up_4(x_combine_2)
#up3
x_convup_3 = self.convup3(x_resblockup_4)
x_upsampling3 = nn.UpsamplingNearest2d(scale_factor=2)(x_convup_3)
x_combine_3 = self.combine3(x_upsampling3, x_resblock_3)
x_resblockup_3 = self.rest_block_up_3(x_combine_3)
#up2
x_convup_4 = self.convup4(x_resblockup_3)
x_upsampling4 = nn.UpsamplingNearest2d(scale_factor=2)(x_convup_4)
x_combine_4 = self.combine4(x_upsampling4, x_resblock_2)
x_resblockup_2 = self.rest_block_up_2(x_combine_4)
#up1
x_convup_5 = self.convup5(x_resblockup_2)
x_upsampling5 = nn.UpsamplingNearest2d(scale_factor=2)(x_convup_5)
x_combine_5 = self.combine5(x_upsampling5, x_resblock_1)
x_resblockup_1 = self.rest_block_up_1(x_combine_5)
x_combine_6 = self.combine5(x_resblockup_1, x_conv_1)
# print(f'x_combine6 size: {x_combine_6.size()}')
x_pooling_2 = self.PSPPoolingResult(x_combine_6)
x_conv_result = self.conv_final(x_pooling_2)
return x_conv_result
def __init__(self,
c3,
c4,
c5,
inner_channels,
weight_inputs=True,
first=False):
super(BiFPNLayer, self).__init__()
self.first = first
if self.first:
self.c3_latent = nn.Sequential(
Conv2dDynamicSamePadding(c3, inner_channels, 1),
nn.BatchNorm2d(inner_channels, momentum=0.01, eps=1e-3))
self.c4_latent = nn.Sequential(
Conv2dDynamicSamePadding(c4, inner_channels, 1),
nn.BatchNorm2d(inner_channels, momentum=0.01, eps=1e-3))
self.c5_latent = nn.Sequential(
Conv2dDynamicSamePadding(c5, inner_channels, 1),
nn.BatchNorm2d(inner_channels, momentum=0.01, eps=1e-3))
self.c5_to_p6 = nn.Sequential(
Conv2dDynamicSamePadding(c5, inner_channels, 1),
nn.BatchNorm2d(inner_channels, momentum=0.01, eps=1e-3),
MaxPool2dDynamicSamePadding(3, 2))
self.p6_to_p7 = nn.Sequential(MaxPool2dDynamicSamePadding(3, 2))
self.c4_latent_re = nn.Sequential(
Conv2dDynamicSamePadding(c4, inner_channels, 1),
nn.BatchNorm2d(inner_channels, momentum=0.01, eps=1e-3))
self.c5_latent_re = nn.Sequential(
Conv2dDynamicSamePadding(c5, inner_channels, 1),
nn.BatchNorm2d(inner_channels, momentum=0.01, eps=1e-3))
self.p6_0 = DWSConv2d(inner_channels, inner_channels, act=False)
self.p6_0_scale = ScaleWeight(2, requires_grad=weight_inputs)
self.p5_0 = DWSConv2d(inner_channels, inner_channels, act=False)
self.p5_0_scale = ScaleWeight(2, requires_grad=weight_inputs)
self.p4_0 = DWSConv2d(inner_channels, inner_channels, act=False)
self.p4_0_scale = ScaleWeight(2, requires_grad=weight_inputs)
self.p3_1 = DWSConv2d(inner_channels, inner_channels, act=False)
self.p3_1_scale = ScaleWeight(2, requires_grad=weight_inputs)
self.p4_1 = DWSConv2d(inner_channels, inner_channels, act=False)
self.p4_1_scale = ScaleWeight(3, requires_grad=weight_inputs)
self.p5_1 = DWSConv2d(inner_channels, inner_channels, act=False)
self.p5_1_scale = ScaleWeight(3, requires_grad=weight_inputs)
self.p6_1 = DWSConv2d(inner_channels, inner_channels, act=False)
self.p6_1_scale = ScaleWeight(3, requires_grad=weight_inputs)
self.p7_1 = DWSConv2d(inner_channels, inner_channels, act=False)
self.p7_1_scale = ScaleWeight(2, requires_grad=weight_inputs)
self.up_sample = nn.UpsamplingNearest2d(scale_factor=2)
self.down_sample = MaxPool2dDynamicSamePadding(3, 2)
self.act = MemoryEfficientSwish()
def upsample_conv(self, x, conv):
return conv(nn.UpsamplingNearest2d(scale_factor=2)(x))
def __init__(self, nc, ngf, ndf, latent_variable_size):
super(VAE, self).__init__()
#self.cuda = True
self.nc = nc
self.ngf = ngf
self.ndf = ndf
self.latent_variable_size = latent_variable_size
# encoder
self.e1 = nn.Conv2d(nc, ndf, 4, 2, 1)
self.bn1 = nn.BatchNorm2d(ndf)
self.e2 = nn.Conv2d(ndf, ndf * 2, 4, 2, 1)
self.bn2 = nn.BatchNorm2d(ndf * 2)
self.e3 = nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1)
self.bn3 = nn.BatchNorm2d(ndf * 4)
self.e4 = nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1)
self.bn4 = nn.BatchNorm2d(ndf * 8)
self.e5 = nn.Conv2d(ndf * 8, ndf * 16, 4, 2, 1)
self.bn5 = nn.BatchNorm2d(ndf * 16)
self.e6 = nn.Conv2d(ndf * 16, ndf * 32, 4, 2, 1)
self.bn6 = nn.BatchNorm2d(ndf * 32)
self.e7 = nn.Conv2d(ndf * 32, ndf * 64, 4, 2, 1)
self.bn7 = nn.BatchNorm2d(ndf * 64)
self.fc1 = nn.Linear(ndf * 64 * 4 * 4, latent_variable_size)
self.fc2 = nn.Linear(ndf * 64 * 4 * 4, latent_variable_size)
# decoder
self.d1 = nn.Linear(latent_variable_size, ngf * 64 * 4 * 4)
self.up1 = nn.UpsamplingNearest2d(scale_factor=2)
self.pd1 = nn.ReplicationPad2d(1)
self.d2 = nn.Conv2d(ngf * 64, ngf * 32, 3, 1)
self.bn8 = nn.BatchNorm2d(ngf * 32, 1.e-3)
self.up2 = nn.UpsamplingNearest2d(scale_factor=2)
self.pd2 = nn.ReplicationPad2d(1)
self.d3 = nn.Conv2d(ngf * 32, ngf * 16, 3, 1)
self.bn9 = nn.BatchNorm2d(ngf * 16, 1.e-3)
self.up3 = nn.UpsamplingNearest2d(scale_factor=2)
self.pd3 = nn.ReplicationPad2d(1)
self.d4 = nn.Conv2d(ngf * 16, ngf * 8, 3, 1)
self.bn10 = nn.BatchNorm2d(ngf * 8, 1.e-3)
self.up4 = nn.UpsamplingNearest2d(scale_factor=2)
self.pd4 = nn.ReplicationPad2d(1)
self.d5 = nn.Conv2d(ngf * 8, ngf * 4, 3, 1)
self.bn11 = nn.BatchNorm2d(ngf * 4, 1.e-3)
self.up5 = nn.UpsamplingNearest2d(scale_factor=2)
self.pd5 = nn.ReplicationPad2d(1)
self.d6 = nn.Conv2d(ngf * 4, ngf * 2, 3, 1)
self.bn12 = nn.BatchNorm2d(ngf * 2, 1.e-3)
self.up6 = nn.UpsamplingNearest2d(scale_factor=2)
self.pd6 = nn.ReplicationPad2d(1)
self.d7 = nn.Conv2d(ngf * 2, ngf, 3, 1)
self.bn13 = nn.BatchNorm2d(ngf, 1.e-3)
self.up7 = nn.UpsamplingNearest2d(scale_factor=2)
self.pd7 = nn.ReplicationPad2d(1)
self.d8 = nn.Conv2d(ngf, nc, 3, 1)
self.leakyrelu = nn.LeakyReLU(0.2)
self.relu = nn.ReLU()
#self.sigmoid = nn.Sigmoid()
self.maxpool = nn.MaxPool2d((2, 2), (2, 2))
def __init__(self, in_, out, scale):
super().__init__()
self.up_conv = nn.Conv2d(in_, out, 1)
self.upsample = nn.UpsamplingNearest2d(scale_factor=scale)
def __init__(self, previous_in_channels, out_channels, kernel_size):
super(Decoder,
self).__init__(previous_in_channels,
out_channels,
kernel_size,
pre_output=nn.UpsamplingNearest2d(scale_factor=2))
def __init__(self, opt, test=False, input_=None, target=None):
super(Resnet_ae, self).__init__()
self.__name__ = "resnet_ae"
# define variables
bsz = 1 if test else opt.bsz
if input_ is not None:
self.input = input_
else:
self.input = torch.FloatTensor(bsz * opt.input_len, opt.nc_in, 64,
64)
self.input = Variable(self.input)
if target is not None:
self.target = target
else:
self.target = torch.FloatTensor(bsz * opt.target_len, opt.nc_out,
64, 64)
self.target = Variable(self.target)
self.criterion = nn.MSELoss()
if opt.instanceNorm:
Norm = nn.InstanceNorm2d
else:
Norm = nn.BatchNorm2d
# define model
self.nc_out = opt.nc_out
self.latentDim = opt.latentDim
self.input_len, self.target_len = opt.input_len, opt.target_len
self.frame_height, self.frame_width = opt.frame_width, opt.frame_height
resnet = torchvision.models.resnet18(True)
self.resnet_features = nn.Sequential(*list(resnet.children())[:6])
middleNL = nn.Sigmoid() if opt.middleNL == "sigmoid" else nn.Tanh()
self.encoder = nn.Sequential(nn.Linear(128 * 8 * 8, opt.latentDim),
middleNL)
self.decoder = nn.Linear(opt.input_len * opt.latentDim,
opt.target_len * 128 * 8 * 8)
self.deconv = nn.Sequential(
nn.Conv2d(128, opt.nf * 4, 3, 1, 1),
Norm(opt.nf * 4),
nn.ReLU(),
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(opt.nf * 4, opt.nf * 2, 3, 1, 1),
Norm(opt.nf * 2),
nn.ReLU(),
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(opt.nf * 2, opt.nf, 3, 1, 1),
Norm(opt.nf),
nn.ReLU(),
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(opt.nf, opt.target_len * opt.nc_out, 3, 1, 1),
nn.Sigmoid(),
)
# define maskPredictor
if opt.maskPredictor:
optmp = {
"frame_width": opt.frame_width,
"frame_height": opt.frame_height,
"input_len": 1,
"target_len": 1,
"nc_in": opt.nc_in,
"nc_out": opt.nc_out,
"nf": opt.nf,
"latentDim": 128,
"instanceNorm": False,
"middleNL": opt.middleNL,
"bsz": None,
"maskPredictor": None,
"lr": None,
"beta1": None,
}
optmp = utils.to_namespace(optmp)
self.maskPredictor = Resnet_ae(optmp, False, self.target,
self.target).eval()
self.maskPredictor.load(opt.maskPredictor)
else:
self.maskPredictor = None
# does this have to be done at the end of __init__ ?
if opt.lr is not None:
self.optimizer = optim.Adam(self.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
def __init__(self,
input_nc,
output_nc,
ngf=64,
norm_layer=nn.BatchNorm2d,
use_dropout=False,
n_blocks=6,
gpu_ids=[],
padding_type='reflect',
upsample=False):
assert (n_blocks >= 0)
super(ResnetGenerator, self).__init__()
self.input_nc = input_nc
self.output_nc = output_nc
self.ngf = ngf
self.gpu_ids = gpu_ids
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
model = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
norm_layer(ngf),
nn.ReLU(True)
]
n_downsampling = 2
for i in range(n_downsampling - 1):
mult = 2**i
model += [
nn.Conv2d(ngf * mult,
ngf * mult * 2,
kernel_size=3,
stride=2,
padding=1,
bias=use_bias),
norm_layer(ngf * mult * 2),
nn.ReLU(True)
]
mult = 2**(n_downsampling - 1)
model += [
nn.Conv2d(ngf * mult,
ngf * mult * 2,
kernel_size=3,
stride=2,
padding=1,
bias=use_bias)
]
mult = 2**n_downsampling
for i in range(n_blocks):
model += [
ResnetBlock(ngf * mult,
padding_type=padding_type,
norm_layer=norm_layer,
use_dropout=use_dropout,
use_bias=use_bias)
]
model += [norm_layer(ngf * mult), nn.ReLU(True)]
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
if upsample:
model += [
nn.UpsamplingNearest2d(scale_factor=2),
nn.ReflectionPad2d(1),
nn.Conv2d(ngf * mult,
int(ngf * mult / 2),
kernel_size=3,
bias=use_bias)
]
else:
model += [
nn.ConvTranspose2d(ngf * mult,
int(ngf * mult / 2),
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
bias=use_bias)
]
model += [norm_layer(int(ngf * mult / 2)), nn.ReLU(True)]
model += [nn.ReflectionPad2d(3)]
model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
model += [nn.Tanh()]
self.model = nn.Sequential(*model)
def __init__(self,
imsize=(1, 28, 28),
outsize=None,
s=32,
mean=None,
std=None):
super(ConvNet, self).__init__()
print("Version 0.6")
pow_pad = (2**(int(np.ceil(np.log2(imsize[-2])))) - imsize[-2],
2**(int(np.ceil(np.log2(imsize[-1])))) - imsize[-1])
kern_size = 4 * ((imsize[1] + pow_pad[0]) // 16) * (
(imsize[2] + pow_pad[1]) // 16) * s
print("Additional padding to fit 2 exp:", pow_pad)
print("Kern size:", kern_size)
self.imsize = imsize
if outsize is None:
self.outsize = imsize
else:
self.outsize = outsize
if mean is None:
self.register_buffer('mean', torch.zeros(imsize))
else:
self.register_buffer('mean', torch.Tensor(mean))
if std is None:
self.register_buffer('std', torch.ones(imsize))
else:
self.register_buffer('std', torch.Tensor(std))
self.layers = nn.Sequential(
nn.Conv2d(imsize[0], imsize[0], kernel_size=1,
padding=pow_pad), #32x32x1 = 1024
nn.BatchNorm2d(imsize[0]),
nn.ReLU(),
nn.Conv2d(imsize[0], 1 * s, kernel_size=5,
padding=2), #32x32x32 = 32768
nn.BatchNorm2d(1 * s),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2), #16x16x32 = 8192
nn.Conv2d(1 * s, 2 * s, kernel_size=3, padding=1),
nn.BatchNorm2d(2 * s),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2), # 8x8x64 = 4096
nn.Conv2d(2 * s, 4 * s, kernel_size=3, padding=1),
nn.BatchNorm2d(4 * s),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2), # 4x4x128 = 2048
nn.Conv2d(4 * s, 4 * s, kernel_size=3, padding=1),
nn.BatchNorm2d(4 * s),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2), # 2x2x128 = 512
KernModule(h=kern_size),
nn.UpsamplingNearest2d(scale_factor=2),
nn.ConvTranspose2d(4 * s, 4 * s, kernel_size=3, padding=1),
nn.BatchNorm2d(4 * s),
nn.ReLU(),
nn.UpsamplingNearest2d(scale_factor=2),
nn.ConvTranspose2d(4 * s, 2 * s, kernel_size=3, padding=1),
nn.BatchNorm2d(2 * s),
nn.ReLU(),
nn.UpsamplingNearest2d(scale_factor=2),
nn.ConvTranspose2d(2 * s, 1 * s, kernel_size=3, padding=1),
nn.BatchNorm2d(1 * s),
nn.ReLU(),
nn.UpsamplingNearest2d(scale_factor=2),
nn.ConvTranspose2d(1 * s,
self.outsize[0],
kernel_size=5,
padding=2),
nn.Sigmoid())
def __init__(self,
input_nc,
output_nc,
ngf=64,
norm_layer=nn.BatchNorm2d,
use_dropout=False,
n_blocks=6,
gpu_ids=[],
padding_type='reflect',
multisa=False,
upsample=False):
assert (n_blocks >= 0)
super(ResnetDecoder, self).__init__()
self.input_nc = input_nc
self.output_nc = output_nc
self.ngf = ngf
self.gpu_ids = gpu_ids
self.saliency = multisa
usesa = 1 if multisa else 0
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
model = []
n_downsampling = 2
mult = 2**n_downsampling
for i in range(n_blocks):
model += [
ResnetBlock(ngf * mult,
padding_type=padding_type,
norm_layer=norm_layer,
use_dropout=use_dropout,
use_bias=use_bias)
]
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
if upsample:
model += [
nn.UpsamplingNearest2d(scale_factor=2),
nn.ReflectionPad2d(1),
nn.Conv2d(ngf * mult + usesa,
int(ngf * mult / 2),
kernel_size=3,
bias=use_bias)
]
else:
model += [
nn.ConvTranspose2d(ngf * mult + usesa,
int(ngf * mult / 2),
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
bias=use_bias)
]
model += [norm_layer(int(ngf * mult / 2)), nn.ReLU(True)]
model += [nn.ReflectionPad2d(3)]
model += [nn.Conv2d(ngf + usesa, output_nc, kernel_size=7, padding=0)]
model += [nn.Tanh()]
self.model = nn.ModuleList(model)
def __init__(self, d):
super(decoder4, self).__init__()
# decoder
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.conv11.weight = torch.nn.Parameter(
torch.from_numpy(d.modules[1].weight).float())
self.conv11.bias = torch.nn.Parameter(
torch.from_numpy(d.modules[1].bias).float())
self.relu11 = nn.ReLU(inplace=True)
# 28 x 28
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
# 56 x 56
self.reflecPad12 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv12 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv12.weight = torch.nn.Parameter(
torch.from_numpy(d.modules[5].weight).float())
self.conv12.bias = torch.nn.Parameter(
torch.from_numpy(d.modules[5].bias).float())
self.relu12 = nn.ReLU(inplace=True)
# 56 x 56
self.reflecPad13 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv13 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv13.weight = torch.nn.Parameter(
torch.from_numpy(d.modules[8].weight).float())
self.conv13.bias = torch.nn.Parameter(
torch.from_numpy(d.modules[8].bias).float())
self.relu13 = nn.ReLU(inplace=True)
# 56 x 56
self.reflecPad14 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv14 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv14.weight = torch.nn.Parameter(
torch.from_numpy(d.modules[11].weight).float())
self.conv14.bias = torch.nn.Parameter(
torch.from_numpy(d.modules[11].bias).float())
self.relu14 = nn.ReLU(inplace=True)
# 56 x 56
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(256, 128, 3, 1, 0)
self.conv15.weight = torch.nn.Parameter(
torch.from_numpy(d.modules[14].weight).float())
self.conv15.bias = torch.nn.Parameter(
torch.from_numpy(d.modules[14].bias).float())
self.relu15 = nn.ReLU(inplace=True)
# 56 x 56
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
# 112 x 112
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(128, 128, 3, 1, 0)
self.conv16.weight = torch.nn.Parameter(
torch.from_numpy(d.modules[18].weight).float())
self.conv16.bias = torch.nn.Parameter(
torch.from_numpy(d.modules[18].bias).float())
self.relu16 = nn.ReLU(inplace=True)
# 112 x 112
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(128, 64, 3, 1, 0)
self.conv17.weight = torch.nn.Parameter(
torch.from_numpy(d.modules[21].weight).float())
self.conv17.bias = torch.nn.Parameter(
torch.from_numpy(d.modules[21].bias).float())
self.relu17 = nn.ReLU(inplace=True)
# 112 x 112
self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
# 224 x 224
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(64, 64, 3, 1, 0)
self.conv18.weight = torch.nn.Parameter(
torch.from_numpy(d.modules[25].weight).float())
self.conv18.bias = torch.nn.Parameter(
torch.from_numpy(d.modules[25].bias).float())
self.relu18 = nn.ReLU(inplace=True)
# 224 x 224
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(64, 3, 3, 1, 0)
self.conv19.weight = torch.nn.Parameter(
torch.from_numpy(d.modules[28].weight).float())
self.conv19.bias = torch.nn.Parameter(
torch.from_numpy(d.modules[28].bias).float())
def create_network(self, blocks):
models = nn.ModuleList()
prev_filters = 3
out_filters = []
conv_id = 0
for block in blocks:
if block['type'] == 'net':
prev_filters = int(block['channels'])
continue
elif block['type'] == 'convolutional':
conv_id = conv_id + 1
batch_normalize = int(block['batch_normalize'])
filters = int(block['filters'])
kernel_size = int(block['size'])
stride = int(block['stride'])
is_pad = int(block['pad'])
pad = (kernel_size - 1) // 2 if is_pad else 0
activation = block['activation']
model = nn.Sequential()
if batch_normalize:
model.add_module(
'conv{0}'.format(conv_id),
nn.Conv2d(prev_filters,
filters,
kernel_size,
stride,
pad,
bias=False))
model.add_module('bn{0}'.format(conv_id),
nn.BatchNorm2d(filters, eps=1e-4))
#model.add_module('bn{0}'.format(conv_id), BN2d(filters))
else:
model.add_module(
'conv{0}'.format(conv_id),
nn.Conv2d(prev_filters, filters, kernel_size, stride,
pad))
if activation == 'leaky':
model.add_module('leaky{0}'.format(conv_id),
nn.LeakyReLU(0.1, inplace=True))
elif activation == 'relu':
model.add_module('relu{0}'.format(conv_id),
nn.ReLU(inplace=True))
prev_filters = filters
out_filters.append(prev_filters)
models.append(model)
elif block['type'] == 'maxpool':
pool_size = int(block['size'])
stride = int(block['stride'])
if stride > 1:
model = nn.MaxPool2d(pool_size, stride)
else:
model = MaxPoolStride1()
out_filters.append(prev_filters)
models.append(model)
elif block['type'] == 'avgpool':
model = GlobalAvgPool2d()
out_filters.append(prev_filters)
models.append(model)
elif block['type'] == 'softmax':
model = nn.Softmax()
out_filters.append(prev_filters)
models.append(model)
elif block['type'] == 'cost':
if block['_type'] == 'sse':
model = nn.MSELoss(size_average=True)
elif block['_type'] == 'L1':
model = nn.L1Loss(size_average=True)
elif block['_type'] == 'smooth':
model = nn.SmoothL1Loss(size_average=True)
out_filters.append(1)
models.append(model)
elif block['type'] == 'reorg':
stride = int(block['stride'])
prev_filters = stride * stride * prev_filters
out_filters.append(prev_filters)
models.append(Reorg(stride))
elif block['type'] == 'route':
layers = block['layers'].split(',')
ind = len(models)
layers = [
int(i) if int(i) > 0 else int(i) + ind for i in layers
]
if len(layers) == 1:
prev_filters = out_filters[layers[0]]
elif len(layers) == 2:
assert (layers[0] == ind - 1)
prev_filters = out_filters[layers[0]] + out_filters[
layers[1]]
out_filters.append(prev_filters)
models.append(EmptyModule())
elif block['type'] == 'shortcut':
ind = len(models)
prev_filters = out_filters[ind - 1]
out_filters.append(prev_filters)
models.append(EmptyModule())
elif block['type'] == 'connected':
filters = int(block['output'])
if block['activation'] == 'linear':
model = nn.Linear(prev_filters, filters)
elif block['activation'] == 'leaky':
model = nn.Sequential(nn.Linear(prev_filters, filters),
nn.LeakyReLU(0.1, inplace=True))
elif block['activation'] == 'relu':
model = nn.Sequential(nn.Linear(prev_filters, filters),
nn.ReLU(inplace=True))
prev_filters = filters
out_filters.append(prev_filters)
models.append(model)
elif block['type'] == 'region':
if self.distiling:
loss = DistiledRegionLoss()
else:
loss = RegionLoss()
anchors = block['anchors'].split(',')
if anchors == ['']:
loss.anchors = []
else:
loss.anchors = [float(i) for i in anchors]
loss.num_classes = int(block['classes'])
loss.num_anchors = int(block['num'])
loss.anchor_step = len(loss.anchors) // loss.num_anchors
loss.object_scale = float(block['object_scale'])
loss.noobject_scale = float(block['noobject_scale'])
loss.class_scale = float(block['class_scale'])
loss.coord_scale = float(block['coord_scale'])
out_filters.append(prev_filters)
models.append(loss)
elif block['type'] == 'upsample':
model = nn.UpsamplingNearest2d(
scale_factor=int(block['stride']))
out_filters.append(prev_filters)
models.append(model)
else:
print('unknown type %s' % (block['type']))
return models
