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import gc
import torch
def value_map(inputs, min_in, max_in, min_out, max_out):
return (inputs - min_in) * (max_out - min_out) / (max_in - min_in) + min_out
def flush(garbage_collect=True):
torch.cuda.empty_cache()
if garbage_collect:
gc.collect()
def get_mean_std(tensor):
if len(tensor.shape) == 3:
tensor = tensor.unsqueeze(0)
elif len(tensor.shape) != 4:
raise Exception("Expected tensor of shape (batch_size, channels, width, height)")
mean, variance = torch.mean(
tensor, dim=[2, 3], keepdim=True
), torch.var(
tensor, dim=[2, 3],
keepdim=True
)
std = torch.sqrt(variance + 1e-5)
return mean, std
def adain(content_features, style_features):
# Assumes that the content and style features are of shape (batch_size, channels, width, height)
dims = [2, 3]
if len(content_features.shape) == 3:
# content_features = content_features.unsqueeze(0)
# style_features = style_features.unsqueeze(0)
dims = [1]
# Step 1: Calculate mean and variance of content features
content_mean, content_var = torch.mean(content_features, dim=dims, keepdim=True), torch.var(content_features,
dim=dims,
keepdim=True)
# Step 2: Calculate mean and variance of style features
style_mean, style_var = torch.mean(style_features, dim=dims, keepdim=True), torch.var(style_features, dim=dims,
keepdim=True)
# Step 3: Normalize content features
content_std = torch.sqrt(content_var + 1e-5)
normalized_content = (content_features - content_mean) / content_std
# Step 4: Scale and shift normalized content with style's statistics
style_std = torch.sqrt(style_var + 1e-5)
stylized_content = normalized_content * style_std + style_mean
return stylized_content
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