def __init__(self, pretrained_model, latent_size): self.flow_shape = pretrained_model.base_dist.shape self.latent_size = latent_size # initialize transforms with first scale of pretrained models transforms = pretrained_model.transforms[0:28] # Replace slice layer with a compression flow mencoder = ConditionalNormal(ConvEncoderNet( in_channels=48, out_channels=768, mid_channels=[64, 128, 256], max_pool=True, batch_norm=True), split_dim=1) mdecoder = ConditionalNormal(ConvDecoderNet( in_channels=768, out_shape=(48 * 2, 8, 8), mid_channels=[256, 128, 64], batch_norm=True, in_lambda=lambda x: x.view(x.shape[0], x.shape[1], 1, 1)), split_dim=1) mid_vae = VAE(encoder=mencoder, decoder=mdecoder) reshape = Reshape(input_shape=(768, ), output_shape=(12, 8, 8)) transforms.extend([mid_vae, reshape]) # Non-dimension preserving flows current_shape = pretrained_model.base_dist.shape flat_dim = current_shape[0] * current_shape[1] * current_shape[2] fencoder = ConditionalNormal( MLP(flat_dim, 2 * latent_size, hidden_units=[512, 256], activation='relu', in_lambda=lambda x: x.view(x.shape[0], flat_dim))) fdecoder = ConditionalNormal(MLP( latent_size, 2 * flat_dim, hidden_units=[256, 512], activation='relu', out_lambda=lambda x: x.view(x.shape[0], current_shape[0] * 2, current_shape[1], current_shape[2])), split_dim=1) # append last scale of pretrained model and extend with the compressive VAE transforms.extend(pretrained_model.transforms[29:]) final_vae = VAE(encoder=fencoder, decoder=fdecoder) transforms.append(final_vae) # Base distribution for non-dimension preserving portion of flow base1 = StandardNormal((latent_size, )) super(CompressPretrained, self).__init__(base_dist=[None, base1], transforms=transforms)
def test_stochastic_transform_is_well_behaved(self): batch_size = 8 data_size = 10 latent_size = 2 x = torch.randn(batch_size, data_size) encoder = ConditionalNormal(nn.Linear(data_size,2*latent_size)) decoder = ConditionalNormal(nn.Linear(latent_size,2*data_size)) transform = VAE(decoder=decoder, encoder=encoder) self.assert_stochastic_transform_is_well_behaved(transform, x, z_shape=(batch_size, latent_size), z_dtype=torch.float)
#model = NDPFlow(base_dist=[StandardNormal((16,7,7)), StandardNormal((latent_size,))], model = NDPFlow(base_dist=[None, StandardNormal((latent_size,))], transforms=[ UniformDequantization(num_bits=8), ActNormBijection2d(1), ScalarAffineBijection(scale=2.0), ScalarAffineBijection(shift=-0.5), Squeeze2d(), ActNormBijection2d(4), Conv1x1(4), AffineCouplingBijection(net(4)), ActNormBijection2d(4), Conv1x1(4), AffineCouplingBijection(net(4)), Squeeze2d(), ActNormBijection2d(16), Conv1x1(16), AffineCouplingBijection(net(16)), ActNormBijection2d(16), Conv1x1(16), AffineCouplingBijection(net(16)), VAE(encoder=encoder, decoder=decoder) ]).to(device) print(model) ########### ## Optim ## ########### optimizer = Adam(model.parameters(), lr=1e-3) ########### ## Train ## ########### print('Training...')
def __init__(self, data_shape, num_bits, base_distribution, num_scales, num_steps, actnorm, vae_hidden_units, coupling_network, dequant, dequant_steps, dequant_context, coupling_blocks, coupling_channels, coupling_dropout, coupling_gated_conv=None, coupling_depth=None, coupling_mixtures=None): assert len(base_distribution) == 1, "Only a single base distribution is supported" transforms = [] current_shape = data_shape if num_steps == 0: num_scales = 0 if dequant == 'uniform' or num_steps == 0 or num_scales == 0: # no bijective flows defaults to only using uniform dequantization transforms.append(UniformDequantization(num_bits=num_bits)) elif dequant == 'flow': dequantize_flow = DequantizationFlow(data_shape=data_shape, num_bits=num_bits, num_steps=dequant_steps, coupling_network=coupling_network, num_context=dequant_context, num_blocks=coupling_blocks, mid_channels=coupling_channels, depth=coupling_depth, dropout=coupling_dropout, gated_conv=coupling_gated_conv, num_mixtures=coupling_mixtures) transforms.append(VariationalDequantization(encoder=dequantize_flow, num_bits=num_bits)) # Change range from [0,1]^D to [-0.5, 0.5]^D transforms.append(ScalarAffineBijection(shift=-0.5)) for scale in range(num_scales): # squeeze to exchange height and width for more channels transforms.append(Squeeze2d()) current_shape = (current_shape[0] * 4, current_shape[1] // 2, current_shape[2] // 2) # Dimension preserving components for step in range(num_steps): if actnorm: transforms.append(ActNormBijection2d(current_shape[0])) transforms.append(Conv1x1(current_shape[0])) if coupling_network == "conv": transforms.append( Coupling(in_channels=current_shape[0], num_blocks=coupling_blocks, mid_channels=coupling_channels, depth=coupling_depth, dropout=coupling_dropout, gated_conv=coupling_gated_conv, coupling_network=coupling_network)) else: transforms.append( MixtureCoupling(in_channels=current_shape[0], mid_channels=coupling_channels, num_mixtures=coupling_mixtures, num_blocks=coupling_blocks, dropout=coupling_dropout)) # Non-dimension preserving flows: reduce the dimensionality of data by 2 (channel-wise) if actnorm: transforms.append(ActNormBijection2d(current_shape[0])) assert current_shape[0] % 2 == 0, f"Current shape {current_shape[1]}x{current_shape[2]} must be divisible by two" latent_size = (current_shape[0] * current_shape[1] * current_shape[2]) // 2 encoder = ConditionalNormal( ConvEncoderNet(in_channels=current_shape[0], out_channels=latent_size, mid_channels=vae_hidden_units, max_pool=True, batch_norm=True), split_dim=1) decoder = ConditionalNormal( ConvDecoderNet(in_channels=latent_size, out_shape=(current_shape[0] * 2, current_shape[1], current_shape[2]), mid_channels=list(reversed(vae_hidden_units)), batch_norm=True, in_lambda=lambda x: x.view(x.shape[0], x.shape[1], 1, 1)), split_dim=1) transforms.append(VAE(encoder=encoder, decoder=decoder)) current_shape = (current_shape[0] // 2, current_shape[1], current_shape[2]) if scale < num_scales - 1: # reshape latent sample to have height and width transforms.append(Reshape(input_shape=(latent_size,), output_shape=current_shape)) # Base distribution for dimension preserving portion of flow if base_distribution == "n": base_dist = StandardNormal((latent_size,)) elif base_distribution == "c": base_dist = ConvNormal2d((latent_size,)) elif base_distribution == "u": base_dist = StandardUniform((latent_size,)) else: raise ValueError("Base distribution must be one of n=Normal, u=Uniform, or c=ConvNormal") # for reference save the shape output by the bijective flow self.latent_size = latent_size self.flow_shape = current_shape super(MultilevelCompressiveFlow, self).__init__(base_dist=[None, base_dist], transforms=transforms)
def __init__(self, data_shape, cond_shape, num_bits, num_scales, num_steps, actnorm, conditional_channels, lowres_encoder_channels, lowres_encoder_blocks, lowres_encoder_depth, lowres_upsampler_channels, pooling, compression_ratio, coupling_network, coupling_blocks, coupling_channels, coupling_dropout=0.0, coupling_gated_conv=None, coupling_depth=None, coupling_mixtures=None, dequant="flow", dequant_steps=4, dequant_context=32, dequant_blocks=2, augment_steps=4, augment_context=32, augment_blocks=2, augment_size=None, checkerboard_scales=[], tuple_flip=True): if len(compression_ratio) == 1 and num_scales > 1: compression_ratio = [compression_ratio[0]] * (num_scales - 1) assert all([ compression_ratio[s] >= 0.0 and compression_ratio[s] < 1.0 for s in range(num_scales - 1) ]) # initialize context. Only upsample context in ContextInit if latent shape doesn't change during the flow. context_init = ContextInit(num_bits=num_bits, in_channels=cond_shape[0], out_channels=lowres_encoder_channels, mid_channels=lowres_encoder_channels, num_blocks=lowres_encoder_blocks, depth=lowres_encoder_depth, dropout=coupling_dropout) transforms = [] current_shape = data_shape if dequant == 'uniform': transforms.append(UniformDequantization(num_bits=num_bits)) elif dequant == 'flow': dequantize_flow = DequantizationFlow( data_shape=data_shape, num_bits=num_bits, num_steps=dequant_steps, coupling_network=coupling_network, num_context=dequant_context, num_blocks=dequant_blocks, mid_channels=coupling_channels, depth=coupling_depth, dropout=0.0, gated_conv=False, num_mixtures=coupling_mixtures, checkerboard=True, tuple_flip=tuple_flip) transforms.append( VariationalDequantization(encoder=dequantize_flow, num_bits=num_bits)) # Change range from [0,1]^D to [-0.5, 0.5]^D transforms.append(ScalarAffineBijection(shift=-0.5)) # Initial squeezing if current_shape[1] >= 128 and current_shape[2] >= 128: # H x W -> 64 x 64 transforms.append(Squeeze2d()) current_shape = (current_shape[0] * 4, current_shape[1] // 2, current_shape[2] // 2) if current_shape[1] >= 64 and current_shape[2] >= 64: # H x W -> 32 x 32 transforms.append(Squeeze2d()) current_shape = (current_shape[0] * 4, current_shape[1] // 2, current_shape[2] // 2) if 0 not in checkerboard_scales or (current_shape[1] > 32 and current_shape[2] > 32): # Only go to 16 x 16 if not doing checkerboard splits first transforms.append(Squeeze2d()) current_shape = (current_shape[0] * 4, current_shape[1] // 2, current_shape[2] // 2) # add in augmentation channels if desired if augment_size is not None and augment_size > 0: #transforms.append(Augment(StandardUniform((augment_size, current_shape[1], current_shape[2])), x_size=current_shape[0])) #transforms.append(Augment(StandardNormal((augment_size, current_shape[1], current_shape[2])), x_size=current_shape[0])) augment_flow = AugmentFlow(data_shape=current_shape, augment_size=augment_size, num_steps=augment_steps, coupling_network=coupling_network, mid_channels=coupling_channels, num_context=augment_context, num_mixtures=coupling_mixtures, num_blocks=augment_blocks, dropout=0.0, checkerboard=True, tuple_flip=tuple_flip) transforms.append( Augment(encoder=augment_flow, x_size=current_shape[0])) current_shape = (current_shape[0] + augment_size, current_shape[1], current_shape[2]) for scale in range(num_scales): # First and Third scales use checkerboard split pattern checkerboard = scale in checkerboard_scales context_out_channels = min(current_shape[0], coupling_channels) context_out_shape = (context_out_channels, current_shape[1], current_shape[2] // 2) if checkerboard else (context_out_channels, current_shape[1], current_shape[2]) # reshape the context to the current size for all flow steps at this scale context_upsampler_net = UpsamplerNet( in_channels=lowres_encoder_channels, out_shape=context_out_shape, mid_channels=lowres_upsampler_channels) transforms.append( ContextUpsampler(context_net=context_upsampler_net, direction='forward')) for step in range(num_steps): flip = (step % 2 == 0) if tuple_flip else False if len(conditional_channels) == 0: if actnorm: transforms.append(ActNormBijection2d(current_shape[0])) transforms.append(Conv1x1(current_shape[0])) else: if actnorm: transforms.append( ConditionalActNormBijection2d( cond_shape=current_shape, out_channels=current_shape[0], mid_channels=conditional_channels)) transforms.append( ConditionalConv1x1(cond_shape=current_shape, out_channels=current_shape[0], mid_channels=conditional_channels, slogdet_cpu=True)) if coupling_network in ["conv", "densenet"]: transforms.append( SRCoupling(x_size=context_out_shape, y_size=current_shape, mid_channels=coupling_channels, depth=coupling_depth, num_blocks=coupling_blocks, dropout=coupling_dropout, gated_conv=coupling_gated_conv, coupling_network=coupling_network, checkerboard=checkerboard, flip=flip)) elif coupling_network == "transformer": transforms.append( SRMixtureCoupling(x_size=context_out_shape, y_size=current_shape, mid_channels=coupling_channels, dropout=coupling_dropout, num_blocks=coupling_blocks, num_mixtures=coupling_mixtures, checkerboard=checkerboard, flip=flip)) # Upsample context (for the previous flows, only if moving in the inverse direction) transforms.append( ContextUpsampler(context_net=context_upsampler_net, direction='inverse')) if scale < num_scales - 1: if pooling == 'none' or compression_ratio[scale] == 0.0: # fully bijective flow with multi-scale architecture transforms.append(Squeeze2d()) current_shape = (current_shape[0] * 4, current_shape[1] // 2, current_shape[2] // 2) elif pooling == 'slice': # slice some of the dimensions (channel-wise) out from further flow steps unsliced_channels = int( max( 1, 4 * current_shape[0] * (1.0 - compression_ratio[scale]))) sliced_channels = int(4 * current_shape[0] - unsliced_channels) noise_shape = (sliced_channels, current_shape[1] // 2, current_shape[2] // 2) transforms.append(Squeeze2d()) transforms.append( Slice(StandardNormal(noise_shape), num_keep=unsliced_channels, dim=1)) current_shape = (unsliced_channels, current_shape[1] // 2, current_shape[2] // 2) elif pooling == 'max': # max pooling to compress dimensions spatially, h//2 and w//2 noise_shape = (current_shape[0] * 3, current_shape[1] // 2, current_shape[2] // 2) decoder = StandardHalfNormal(noise_shape) transforms.append( SimpleMaxPoolSurjection2d(decoder=decoder)) current_shape = (current_shape[0], current_shape[1] // 2, current_shape[2] // 2) elif pooling == "mvae": # Compressive flow: reduce the dimensionality of data by 2 (channel-wise) compressed_channels = max( 1, int(current_shape[0] * (1.0 - compression_ratio[scale]))) latent_size = compressed_channels * current_shape[ 1] * current_shape[2] vae_channels = [ current_shape[0] * 2, current_shape[0] * 4, current_shape[0] * 8 ] encoder = ConditionalNormal(ConvEncoderNet( in_channels=current_shape[0], out_channels=latent_size, mid_channels=vae_channels, max_pool=True, batch_norm=True), split_dim=1) decoder = ConditionalNormal(ConvDecoderNet( in_channels=latent_size, out_shape=(current_shape[0] * 2, current_shape[1], current_shape[2]), mid_channels=list(reversed(vae_channels)), batch_norm=True, in_lambda=lambda x: x.view(x.shape[0], x.shape[1], 1, 1 )), split_dim=1) transforms.append(VAE(encoder=encoder, decoder=decoder)) transforms.append( Reshape(input_shape=(latent_size, ), output_shape=(compressed_channels, current_shape[1], current_shape[2]))) # after reducing channels with mvae, squeeze to reshape latent space before another sequence of flows transforms.append(Squeeze2d()) current_shape = ( compressed_channels * 4, # current_shape[0] * 4 current_shape[1] // 2, current_shape[2] // 2) else: raise ValueError( "pooling argument must be either mvae, slice, max, or none" ) else: if actnorm: transforms.append(ActNormBijection2d(current_shape[0])) # for reference save the shape output by the bijective flow self.latent_size = current_shape[0] * current_shape[1] * current_shape[ 2] self.flow_shape = current_shape super(SRPoolFlow, self).__init__(base_dist=ConvNormal2d(current_shape), transforms=transforms, context_init=context_init)
def __init__(self, data_shape, num_bits, base_distributions, num_scales, num_steps, actnorm, vae_hidden_units, latent_size, vae_activation, coupling_network, dequant, dequant_steps, dequant_context, coupling_blocks, coupling_channels, coupling_dropout, coupling_growth=None, coupling_gated_conv=None, coupling_depth=None, coupling_mixtures=None): transforms = [] current_shape = data_shape if num_steps == 0: num_scales = 0 if dequant == 'uniform' or num_steps == 0 or num_scales == 0: # no bijective flows defaults to only using uniform dequantization transforms.append(UniformDequantization(num_bits=num_bits)) elif dequant == 'flow': dequantize_flow = DequantizationFlow( data_shape=data_shape, num_bits=num_bits, num_steps=dequant_steps, coupling_network=coupling_network, num_context=dequant_context, num_blocks=coupling_blocks, mid_channels=coupling_channels, depth=coupling_depth, growth=coupling_growth, dropout=coupling_dropout, gated_conv=coupling_gated_conv, num_mixtures=coupling_mixtures) transforms.append( VariationalDequantization(encoder=dequantize_flow, num_bits=num_bits)) # Change range from [0,1]^D to [-0.5, 0.5]^D transforms.append(ScalarAffineBijection(shift=-0.5)) # Initial squeeze transforms.append(Squeeze2d()) current_shape = (current_shape[0] * 4, current_shape[1] // 2, current_shape[2] // 2) # Dimension preserving flows for scale in range(num_scales): for step in range(num_steps): if actnorm: transforms.append(ActNormBijection2d(current_shape[0])) transforms.append(Conv1x1(current_shape[0])) if coupling_network in ["conv", "densenet"]: transforms.append( Coupling(in_channels=current_shape[0], num_blocks=coupling_blocks, mid_channels=coupling_channels, depth=coupling_depth, growth=coupling_growth, dropout=coupling_dropout, gated_conv=coupling_gated_conv, coupling_network=coupling_network)) else: transforms.append( MixtureCoupling(in_channels=current_shape[0], mid_channels=coupling_channels, num_mixtures=coupling_mixtures, num_blocks=coupling_blocks, dropout=coupling_dropout)) if scale < num_scales - 1: transforms.append(Squeeze2d()) current_shape = (current_shape[0] * 4, current_shape[1] // 2, current_shape[2] // 2) else: if actnorm: transforms.append(ActNormBijection2d(current_shape[0])) # Base distribution for dimension preserving portion of flow if len(base_distributions) > 1: if base_distributions[0] == "n": base0 = StandardNormal(current_shape) elif base_distributions[0] == "c": base0 = ConvNormal2d(current_shape) elif base_distributions[0] == "u": base0 = StandardUniform(current_shape) else: raise ValueError( "Base distribution must be one of n=Noraml, u=Uniform, or c=ConvNormal" ) else: base0 = None # for reference save the shape output by the bijective flow self.flow_shape = current_shape # Non-dimension preserving flows flat_dim = current_shape[0] * current_shape[1] * current_shape[2] encoder = ConditionalNormal( MLP(flat_dim, 2 * latent_size, hidden_units=vae_hidden_units, activation=vae_activation, in_lambda=lambda x: x.view(x.shape[0], flat_dim))) decoder = ConditionalNormal(MLP( latent_size, 2 * flat_dim, hidden_units=list(reversed(vae_hidden_units)), activation=vae_activation, out_lambda=lambda x: x.view(x.shape[0], current_shape[0] * 2, current_shape[1], current_shape[2])), split_dim=1) transforms.append(VAE(encoder=encoder, decoder=decoder)) # Base distribution for non-dimension preserving portion of flow #self.latent_size = latent_size if base_distributions[-1] == "n": base1 = StandardNormal((latent_size, )) elif base_distributions[-1] == "c": base1 = ConvNormal2d((latent_size, )) elif base_distributions[-1] == "u": base1 = StandardUniform((latent_size, )) else: raise ValueError( "Base distribution must be one of n=Noraml, u=Uniform, or c=ConvNormal" ) super(VAECompressiveFlow, self).__init__(base_dist=[base0, base1], transforms=transforms)
in_lambda=lambda x: 2 * x.view(x.shape[0], 784).float() - 1)) decoder = ConditionalBernoulli(MLP(latent_sizes[0], 784, hidden_units=[512,256], activation='relu', out_lambda=lambda x: x.view(x.shape[0], 1, 28, 28))) encoder2 = ConditionalNormal(MLP(latent_sizes[0], 2*latent_sizes[1], hidden_units=[256,128], activation='relu')) decoder2 = ConditionalNormal(MLP(latent_sizes[1], 2*latent_sizes[0], hidden_units=[256,128], activation='relu')) model = Flow(base_dist=StandardNormal((latent_sizes[-1],)), transforms=[ VAE(encoder=encoder, decoder=decoder), VAE(encoder=encoder2, decoder=decoder2), ]).to(device) ########### ## Optim ## ########### optimizer = Adam(model.parameters(), lr=1e-3) ########### ## Train ## ########### print('Training...') for epoch in range(20):
encoder = ConditionalNormal( MLP(784, 2 * latent_size, hidden_units=[512, 256], activation='relu', in_lambda=lambda x: 2 * x.view(x.shape[0], 784).float() - 1)) decoder = ConditionalBernoulli( MLP(latent_size, 784, hidden_units=[512, 256], activation='relu', out_lambda=lambda x: x.view(x.shape[0], 1, 28, 28))) model = Flow(base_dist=StandardNormal((latent_size, )), transforms=[VAE(encoder=encoder, decoder=decoder)]).to(device) ########### ## Optim ## ########### optimizer = Adam(model.parameters(), lr=1e-3) ########### ## Train ## ########### print('Training...') for epoch in range(15): l = 0.0 for i, x in enumerate(train_loader):