diff --git a/codes/models/SRGAN_model.py b/codes/models/SRGAN_model.py
index c729624f..f95bbfdb 100644
--- a/codes/models/SRGAN_model.py
+++ b/codes/models/SRGAN_model.py
@@ -347,20 +347,24 @@ class SRGANModel(BaseModel):
SWAP_MAX_DIM = w // 4
SWAP_MIN_DIM = 16
assert SWAP_MAX_DIM > 0
- random_swap_count = random.randint(0, 4)
- for i in range(random_swap_count):
- # Make the swap across fake_H and var_ref
- swap_x, swap_y = random.randint(0, w - SWAP_MIN_DIM), random.randint(0, h - SWAP_MIN_DIM)
- swap_w, swap_h = random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM), random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM)
- if swap_x + swap_w > w:
- swap_w = w - swap_x
- if swap_y + swap_h > h:
- swap_h = h - swap_y
- t = fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)].clone()
- fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = var_ref[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)]
- var_ref[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = t
- real[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 0.0
- fake[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 1.0
+ if random.random() > .5: # Make this only happen half the time. Earlier experiments had it happen
+ # more often and the model was "cheating" by using the presence of
+ # easily discriminated fake swaps to count the entire generated image
+ # as fake.
+ random_swap_count = random.randint(0, 4)
+ for i in range(random_swap_count):
+ # Make the swap across fake_H and var_ref
+ swap_x, swap_y = random.randint(0, w - SWAP_MIN_DIM), random.randint(0, h - SWAP_MIN_DIM)
+ swap_w, swap_h = random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM), random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM)
+ if swap_x + swap_w > w:
+ swap_w = w - swap_x
+ if swap_y + swap_h > h:
+ swap_h = h - swap_y
+ t = fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)].clone()
+ fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = var_ref[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)]
+ var_ref[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = t
+ real[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 0.0
+ fake[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 1.0
# Interpolate down to the dimensionality that the discriminator uses.
real = F.interpolate(real, size=disc_output_shape[2:], mode="bilinear")
diff --git a/codes/models/archs/SwitchedResidualGenerator_arch.py b/codes/models/archs/SwitchedResidualGenerator_arch.py
index d3253d3f..ef63ab60 100644
--- a/codes/models/archs/SwitchedResidualGenerator_arch.py
+++ b/codes/models/archs/SwitchedResidualGenerator_arch.py
@@ -11,12 +11,12 @@ from switched_conv_util import save_attention_to_image
class MultiConvBlock(nn.Module):
- def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1, bn=False, weight_init_factor=1):
+ def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1, norm=False, weight_init_factor=1):
assert depth >= 2
super(MultiConvBlock, self).__init__()
self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01))
- self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, norm=bn, bias=False, weight_init_factor=weight_init_factor)] +
- [ConvBnLelu(filters_mid, filters_mid, kernel_size, norm=bn, bias=False, weight_init_factor=weight_init_factor) for i in range(depth - 2)] +
+ self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, norm=norm, bias=False, weight_init_factor=weight_init_factor)] +
+ [ConvBnLelu(filters_mid, filters_mid, kernel_size, norm=norm, bias=False, weight_init_factor=weight_init_factor) for i in range(depth - 2)] +
[ConvBnLelu(filters_mid, filters_out, kernel_size, activation=False, norm=False, bias=False, weight_init_factor=weight_init_factor)])
self.scale = nn.Parameter(torch.full((1,), fill_value=scale_init))
self.bias = nn.Parameter(torch.zeros(1))
@@ -167,7 +167,7 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module):
self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
for _ in range(switch_depth):
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
- pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, bn=False, bias=False, weight_init_factor=.1)
+ pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers, weight_init_factor=.1)
switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
diff --git a/codes/utils/numeric_stability.py b/codes/utils/numeric_stability.py
index 4aa40ba9..bfcedc70 100644
--- a/codes/utils/numeric_stability.py
+++ b/codes/utils/numeric_stability.py
@@ -78,11 +78,7 @@ def test_numeric_stability(mod: nn.Module, format, iterations=50, device='cuda')
stds.append(torch.std(measure).detach())
return torch.stack(means), torch.stack(stds)
-'''
- def __init__(self, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
- trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
- heightened_final_step=50000, upsample_factor=1, add_scalable_noise_to_transforms=False):
- '''
+
if __name__ == "__main__":
'''
test_stability(functools.partial(nsg.NestedSwitchedGenerator,
@@ -107,8 +103,8 @@ if __name__ == "__main__":
trans_layers=4,
transformation_filters=64,
upsample_factor=4),
- torch.randn(1, 3, 64, 64),
- device='cuda')
+ torch.randn(1, 3, 32, 32),
+ device='cpu')
'''
test_stability(functools.partial(srg1.ConfigurableSwitchedResidualGenerator,