Add support for a gaussian-diffusion-based wave tacotron

codes/data/audio/nv_tacotron_dataset.py

@@ -23,6 +23,8 @@ class TextMelLoader(torch.utils.data.Dataset):
         self.max_wav_value = hparams.max_wav_value
         self.sampling_rate = hparams.sampling_rate
         self.load_mel_from_disk = hparams.load_mel_from_disk
+        self.return_wavs = hparams.return_wavs
+        assert not (self.load_mel_from_disk and self.return_wavs)
         self.stft = layers.TacotronSTFT(
             hparams.filter_length, hparams.hop_length, hparams.win_length,
             hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
@@ -47,6 +49,9 @@ class TextMelLoader(torch.utils.data.Dataset):
             audio_norm = audio / self.max_wav_value
             audio_norm = audio_norm.unsqueeze(0)
             audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
+            if self.return_wavs:
+                melspec = audio_norm
+            else:
                 melspec = self.stft.mel_spectrogram(audio_norm)
                 melspec = torch.squeeze(melspec, 0)
         else:
@@ -124,13 +129,18 @@ if __name__ == '__main__':
     params = {
         'mode': 'nv_tacotron',
         'path': 'E:\\audio\\LJSpeech-1.1\\ljs_audio_text_train_filelist.txt',
-
+        'phase': 'train',
+        'n_workers': 0,
+        'batch_size': 2,
+        'return_wavs': True,
     }
-    from data import create_dataset
-    ds = create_dataset(params)
+    from data import create_dataset, create_dataloader
+
+    ds, c = create_dataset(params, return_collate=True)
+    dl = create_dataloader(ds, params, collate_fn=c)
     i = 0
     m = []
-    for b in ds:
+    for b in dl:
         m.append(b)
         i += 1
         if i > 9999:

codes/models/diffusion/gaussian_diffusion.py

@@ -817,6 +817,81 @@ class GaussianDiffusion:
 
         return terms
 
+    def autoregressive_training_losses(self, model, x_start, t, model_output_keys, gd_out_key, model_kwargs=None, noise=None):
+        """
+        Compute training losses for a single timestep.
+
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param t: a batch of timestep indices.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param noise: if specified, the specific Gaussian noise to try to remove.
+        :return: a dict with the key "loss" containing a tensor of shape [N].
+                 Some mean or variance settings may also have other keys.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+        if noise is None:
+            noise = th.randn_like(x_start)
+        x_t = self.q_sample(x_start, t, noise=noise)
+        x_tn1 = self.q_sample(x_start, t-1, noise=noise)
+
+        terms = {}
+
+        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
+            assert False  # not currently supported for this type of diffusion.
+        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
+            model_outputs = model(x_t, x_tn1, self._scale_timesteps(t), **model_kwargs)
+            terms.update({k: o for k, o in zip(model_output_keys, model_outputs)})
+            model_output = terms[gd_out_key]
+            if self.model_var_type in [
+                ModelVarType.LEARNED,
+                ModelVarType.LEARNED_RANGE,
+            ]:
+                B, C = x_t.shape[:2]
+                assert model_output.shape == (B, C, 2, *x_t.shape[2:])
+                model_output, model_var_values = model_output[:, :, 0], model_output[:, :, 1]
+                # Learn the variance using the variational bound, but don't let
+                # it affect our mean prediction.
+                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
+                terms["vb"] = self._vb_terms_bpd(
+                    model=lambda *args, r=frozen_out: r,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t,
+                    clip_denoised=False,
+                )["output"]
+                if self.loss_type == LossType.RESCALED_MSE:
+                    # Divide by 1000 for equivalence with initial implementation.
+                    # Without a factor of 1/1000, the VB term hurts the MSE term.
+                    terms["vb"] *= self.num_timesteps / 1000.0
+
+            if self.model_mean_type == ModelMeanType.PREVIOUS_X:
+                target = self.q_posterior_mean_variance(
+                    x_start=x_start, x_t=x_t, t=t
+                )[0]
+                x_start_pred = torch.zeros(x_start)  # Not supported.
+            elif self.model_mean_type == ModelMeanType.START_X:
+                target = x_start
+                x_start_pred = model_output
+            elif self.model_mean_type == ModelMeanType.EPSILON:
+                target = noise
+                x_start_pred = self._predict_xstart_from_eps(x_t, t, model_output)
+            else:
+                raise NotImplementedError(self.model_mean_type)
+            assert model_output.shape == target.shape == x_start.shape
+            terms["mse"] = mean_flat((target - model_output) ** 2)
+            terms["x_start_predicted"] = x_start_pred
+            if "vb" in terms:
+                terms["loss"] = terms["mse"] + terms["vb"]
+            else:
+                terms["loss"] = terms["mse"]
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        return terms
+
     def _prior_bpd(self, x_start):
         """
         Get the prior KL term for the variational lower-bound, measured in

codes/models/diffusion/respace.py

@@ -95,16 +95,22 @@ class SpacedDiffusion(GaussianDiffusion):
     ):  # pylint: disable=signature-differs
         return super().training_losses(self._wrap_model(model), *args, **kwargs)
 
+    def autoregressive_training_losses(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().autoregressive_training_losses(self._wrap_model(model, True), *args, **kwargs)
+
     def condition_mean(self, cond_fn, *args, **kwargs):
         return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
 
     def condition_score(self, cond_fn, *args, **kwargs):
         return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
 
-    def _wrap_model(self, model):
-        if isinstance(model, _WrappedModel):
+    def _wrap_model(self, model, autoregressive=False):
+        if isinstance(model, _WrappedModel) or isinstance(model, _WrappedAutoregressiveModel):
             return model
-        return _WrappedModel(
+        mod = _WrappedAutoregressiveModel if autoregressive else _WrappedModel
+        return mod(
             model, self.timestep_map, self.rescale_timesteps, self.original_num_steps
         )
 
@@ -126,3 +132,18 @@ class _WrappedModel:
         if self.rescale_timesteps:
             new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
         return self.model(x, new_ts, **kwargs)
+
+
+class _WrappedAutoregressiveModel:
+    def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps):
+        self.model = model
+        self.timestep_map = timestep_map
+        self.rescale_timesteps = rescale_timesteps
+        self.original_num_steps = original_num_steps
+
+    def __call__(self, x, x0, ts, **kwargs):
+        map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
+        new_ts = map_tensor[ts]
+        if self.rescale_timesteps:
+            new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
+        return self.model(x, x0, new_ts, **kwargs)

codes/models/diffusion/unet_diffusion.py

@@ -441,6 +441,7 @@ class UNetModel(nn.Module):
         use_scale_shift_norm=False,
         resblock_updown=False,
         use_new_attention_order=False,
+        use_raw_y_as_embedding=False,
     ):
         super().__init__()
 
@@ -471,6 +472,8 @@ class UNetModel(nn.Module):
 
         if self.num_classes is not None:
             self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+        self.use_raw_y_as_embedding = use_raw_y_as_embedding
+        assert (self.num_classes is not None) != use_raw_y_as_embedding  # These are mutually-exclusive.
 
         self.input_blocks = nn.ModuleList(
             [
@@ -630,16 +633,14 @@ class UNetModel(nn.Module):
         :param y: an [N] Tensor of labels, if class-conditional.
         :return: an [N x C x ...] Tensor of outputs.
         """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-
         hs = []
         emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
 
         if self.num_classes is not None:
             assert y.shape == (x.shape[0],)
             emb = emb + self.label_emb(y)
+        if self.use_raw_y_as_embedding:
+            emb = emb + y
 
         h = x.type(self.dtype)
         for module in self.input_blocks:

codes/models/tacotron2/tacotron2.py

@@ -32,7 +32,7 @@ class LocationLayer(nn.Module):
 
 class Attention(nn.Module):
     def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
-                 attention_location_n_filters, attention_location_kernel_size):
+                 attention_location_n_filters=32, attention_location_kernel_size=31):
         super(Attention, self).__init__()
         self.query_layer = LinearNorm(attention_rnn_dim, attention_dim,
                                       bias=False, w_init_gain='tanh')
@@ -528,6 +528,9 @@ def register_nv_tacotron2(opt_net, opt):
 
 if __name__ == '__main__':
     tron = register_nv_tacotron2({}, {})
-    inputs = torch.randint(high=24, size=(1,12)), torch.tensor([12]), torch.randn((1,80,749)), 800, torch.tensor([749])
-    out = tron(inputs)
+    inputs = torch.randint(high=24, size=(1,12)), \
+             torch.tensor([12]), \
+             torch.randn((1,80,749)), \
+             torch.tensor([749])
+    out = tron(*inputs)
     print(out)

codes/models/tacotron2/wave_tacotron.py

@@ -0,0 +1,230 @@
+from math import sqrt
+import torch
+from munch import munchify
+from torch.autograd import Variable
+from torch import nn
+from torch.nn import functional as F
+
+from models.diffusion.unet_diffusion import UNetModel
+from models.tacotron2.layers import ConvNorm, LinearNorm
+from models.tacotron2.hparams import create_hparams
+from models.tacotron2.tacotron2 import Prenet, Attention, Encoder
+from trainer.networks import register_model
+from models.tacotron2.taco_utils import get_mask_from_lengths
+from utils.util import opt_get, checkpoint
+
+
+class WavDecoder(nn.Module):
+    def __init__(self, dec_channels, K_ms=40, sample_rate=24000, dropout_probability=.1):
+        super().__init__()
+        self.dec_channels = dec_channels
+        self.K = int(sample_rate * (K_ms/1000))  # 960 with the defaults
+        self.clarifier = UNetModel(image_size=self.K,
+                                   in_channels=1,
+                                   model_channels=dec_channels // 4,  # This is a requirement to enable to load the embedding produced by the decoder into the unet model.
+                                   out_channels=2,  # 2 channels: eps_pred and variance_pred
+                                   num_res_blocks=2,
+                                   attention_resolutions=(16,32),
+                                   dims=1,
+                                   dropout=.1,
+                                   channel_mult=(1,1,1,2,4,8),
+                                   use_raw_y_as_embedding=True)
+        assert self.K % 64 == 0  # Otherwise the UNetModel breaks.
+        self.pre_rnn = Prenet(self.K, [dec_channels, dec_channels])
+        self.attention_rnn = nn.LSTMCell(dec_channels*2, dec_channels)
+        self.attention_layer = Attention(dec_channels, dec_channels, dec_channels)
+        self.decoder_rnn = nn.LSTMCell(dec_channels*2, dec_channels, 1)
+        self.linear_projection = LinearNorm(dec_channels*2, self.dec_channels)
+        self.gate_layer = LinearNorm(self.dec_channels*2, 1, bias=True, w_init_gain='sigmoid')
+        self.dropout_probability = dropout_probability
+
+    def chunk_wav(self, wav):
+        wavs = list(torch.split(wav, self.K, dim=-1))
+        # Pad the last chunk as needed.
+        padding_needed = self.K - wavs[-1].shape[-1]
+        if padding_needed > 0:
+            wavs[-1] = F.pad(wavs[-1], (0,padding_needed))
+
+        wavs = torch.stack(wavs, dim=1)  # wavs.shape = (b,s,K) where s=decoder sequence length
+        return wavs, padding_needed
+
+    def prepare_decoder_inputs(self, inp):
+        # inp.shape = (b,s,K) chunked waveform.
+        b,s,K = inp.shape
+        first_frame = torch.zeros(b,1,K).to(inp.device)
+        x = torch.cat([first_frame, inp[:,:-1]], dim=1)  # It is now aligned for teacher forcing.
+        return x
+
+    def initialize_decoder_states(self, memory, mask):
+        """ Initializes attention rnn states, decoder rnn states, attention
+        weights, attention cumulative weights, attention context, stores memory
+        and stores processed memory
+        PARAMS
+        ------
+        memory: Encoder outputs
+        mask: Mask for padded data if training, expects None for inference
+        """
+        B = memory.size(0)
+        MAX_TIME = memory.size(1)
+
+        self.attention_hidden = Variable(memory.data.new(B, self.dec_channels).zero_())
+        self.attention_cell = Variable(memory.data.new(B, self.dec_channels).zero_())
+
+        self.decoder_hidden = Variable(memory.data.new(B, self.dec_channels).zero_())
+        self.decoder_cell = Variable(memory.data.new(B, self.dec_channels).zero_())
+
+        self.attention_weights = Variable(memory.data.new(B, MAX_TIME).zero_())
+        self.attention_weights_cum = Variable(memory.data.new(B, MAX_TIME).zero_())
+        self.attention_context = Variable(memory.data.new(B, self.dec_channels).zero_())
+
+        self.memory = memory
+        self.processed_memory = checkpoint(self.attention_layer.memory_layer, memory)
+        self.mask = mask
+
+    def teardown_states(self):
+        self.attention_hidden = None
+        self.attention_cell = None
+        self.decoder_hidden = None
+        self.decoder_cell = None
+        self.attention_weights = None
+        self.attention_weights_cum = None
+        self.attention_context = None
+        self.memory = None
+        self.processed_memory = None
+
+    def produce_context(self, decoder_input):
+        """ Produces a context and a stop token prediction using the built-in RNN.
+        PARAMS
+        ------
+        decoder_input: prior diffusion step that has been resolved.
+
+        RETURNS
+        -------
+        mel_output:
+        gate_output: gate output energies
+        attention_weights:
+        """
+        cell_input = torch.cat((decoder_input, self.attention_context), -1)
+        self.attention_hidden, self.attention_cell = self.attention_rnn(cell_input, (self.attention_hidden, self.attention_cell))
+        self.attention_hidden = F.dropout(self.attention_hidden, self.dropout_probability, self.training)
+
+        attention_weights_cat = torch.cat((self.attention_weights.unsqueeze(1), self.attention_weights_cum.unsqueeze(1)), dim=1)
+        self.attention_context, self.attention_weights = checkpoint(self.attention_layer, self.attention_hidden, self.memory,
+                                                                    self.processed_memory, attention_weights_cat, self.mask)
+
+        self.attention_weights_cum += self.attention_weights
+        decoder_input = torch.cat((self.attention_hidden, self.attention_context), -1)
+        self.decoder_hidden, self.decoder_cell = self.decoder_rnn(decoder_input, (self.decoder_hidden, self.decoder_cell))
+        self.decoder_hidden = F.dropout(self.decoder_hidden, self.dropout_probability, self.training)
+
+        decoder_hidden_attention_context = torch.cat((self.decoder_hidden, self.attention_context), dim=1)
+        decoder_output = checkpoint(self.linear_projection, decoder_hidden_attention_context)
+
+        gate_prediction = self.gate_layer(decoder_hidden_attention_context)
+        return decoder_output, gate_prediction, self.attention_weights
+
+    def recombine(self, diffusion_eps, gate_outputs, alignments, padding_added):
+        # (T_out, B) -> (B, T_out)
+        alignments = torch.stack(alignments, dim=1).repeat(1, self.K, 1)
+        # (T_out, B) -> (B, T_out)
+        gate_outputs = torch.stack(gate_outputs, dim=1).repeat(1, self.K)
+
+        b,s,_,K = diffusion_eps.shape
+        # (B, S, 2, K) -> (B, 2, S*K)
+        diffusion_eps = diffusion_eps.permute(0,2,1,3).reshape(b, 2, s*K)
+
+        return diffusion_eps[:,:,:-padding_added], gate_outputs[:,:-padding_added], alignments[:,:-padding_added]
+
+    def forward(self, wav, wav_corrected, timesteps, text_enc, memory_lengths):
+        '''
+        Performs a training forward pass with the given data.
+        :param wav: (b,n) diffused waveform tensor on the interval [-1,1]
+        :param wav_corrected: (b,n) waveform tensor that has had one step of diffusion correction over <wav>
+        :param text_enc: (b,e) embedding post-encoder with e=self.dec_channels
+        '''
+
+        # Start by splitting up the provided waveforms into discrete segments.
+        wavs, padding_added = self.chunk_wav(wav)
+        wavs_corrected, _ = self.chunk_wav(wav_corrected)
+        wavs_corrected = self.prepare_decoder_inputs(wavs_corrected)
+        wavs_corrected = checkpoint(self.pre_rnn, wavs_corrected)
+
+        self.initialize_decoder_states(text_enc, mask=~get_mask_from_lengths(memory_lengths))
+        decoder_contexts, gate_outputs, alignments = [], [], []
+        while len(decoder_contexts) < wavs_corrected.size(1):
+            decoder_input = wavs_corrected[:, len(decoder_contexts)]
+            dec_context, gate_output, attention_weights = self.produce_context(decoder_input)
+            decoder_contexts += [dec_context.squeeze(1)]
+            gate_outputs += [gate_output.squeeze(1)]
+            alignments += [attention_weights]
+        self.teardown_states()
+
+        # diffusion_inputs and wavs needs to have the sequence and batch dimensions combined, and needs a channel dimension
+        diffusion_emb = torch.stack(decoder_contexts, dim=1)
+        b,s,c = diffusion_emb.shape
+        diffusion_emb = diffusion_emb.reshape(b*s,c)
+        wavs = wavs.reshape(b*s,1,self.K)
+        diffusion_eps = self.clarifier(wavs, timesteps.repeat(s), diffusion_emb).reshape(b,s,2,self.K)
+        # Recombine diffusion outputs across the sequence into a single prediction.
+        diffusion_eps, gate_outputs, alignments = self.recombine(diffusion_eps, gate_outputs, alignments, padding_added)
+        return diffusion_eps, gate_outputs, alignments
+
+
+class WaveTacotron2(nn.Module):
+    def __init__(self, hparams):
+        super().__init__()
+        self.mask_padding = hparams.mask_padding
+        self.fp16_run = hparams.fp16_run
+        self.n_mel_channels = hparams.n_mel_channels
+        self.n_frames_per_step = hparams.n_frames_per_step
+        self.embedding = nn.Embedding(
+            hparams.n_symbols, hparams.symbols_embedding_dim)
+        std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim))
+        val = sqrt(3.0) * std  # uniform bounds for std
+        self.embedding.weight.data.uniform_(-val, val)
+        self.encoder = Encoder(hparams)
+        self.decoder = WavDecoder(hparams.encoder_embedding_dim)
+
+    def parse_output(self, outputs, output_lengths=None):
+        if self.mask_padding and output_lengths is not None:
+            mask_fill = outputs[0].shape[-1]
+            mask = ~get_mask_from_lengths(output_lengths, mask_fill)
+            mask = mask.expand(mask.size(0), 2, mask.size(1))
+
+            outputs[0].data.masked_fill_(mask, 0.0)
+            outputs[0] = outputs[0].unsqueeze(1)  # Re-add channel dimension.
+            outputs[1].data.masked_fill_(mask[:,0], 1e3)  # gate energies
+
+        return outputs
+
+    def forward(self, wavs_diffused, wavs_corrected, timesteps, text_inputs, text_lengths, output_lengths):
+        # Squeeze the channel dimension out of the input wavs - we only handle single-channel audio here.
+        wavs_diffused = wavs_diffused.squeeze(dim=1)
+        wavs_corrected = wavs_corrected.squeeze(dim=1)
+
+        text_lengths, output_lengths = text_lengths.data, output_lengths.data
+        embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
+        encoder_outputs = checkpoint(self.encoder, embedded_inputs, text_lengths)
+        eps_pred, gate_outputs, alignments = self.decoder(
+            wavs_diffused, wavs_corrected, timesteps, encoder_outputs, memory_lengths=text_lengths)
+
+        return self.parse_output([eps_pred, gate_outputs, alignments], output_lengths)
+
+
+@register_model
+def register_diffusion_wavetron(opt_net, opt):
+    hparams = create_hparams()
+    hparams.update(opt_net)
+    hparams = munchify(hparams)
+    return WaveTacotron2(hparams)
+
+
+if __name__ == '__main__':
+    tron = register_diffusion_wavetron({}, {})
+    out = tron(wavs_diffused=torch.randn(2, 1, 22000),
+               wavs_corrected=torch.randn(2, 1, 22000),
+               timesteps=torch.LongTensor([555, 543]),
+               text_inputs=torch.randint(high=24, size=(2,12)),
+               text_lengths=torch.tensor([12, 12]),
+               output_lengths=torch.tensor([21995]))
+    print([o.shape for o in out])

codes/train.py

@@ -300,7 +300,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vqvae_xform_audio_lj.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_wave_tacotron_diffusion_lj.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/trainer/injectors/base_injectors.py

@@ -10,6 +10,15 @@ from utils.util import opt_get
 from utils.weight_scheduler import get_scheduler_for_opt
 
 
+class SqueezeInjector(Injector):
+    def __init__(self, opt, env):
+        super().__init__(opt, env)
+        self.dim = opt['dim']
+
+    def forward(self, state):
+        return {self.output: state[self.input].squeeze(dim=self.dim)}
+
+
 # Uses a generator to synthesize an image from [in] and injects the results into [out]
 # Note that results are *not* detached.
 class GeneratorInjector(Injector):

codes/trainer/injectors/gaussian_diffusion_injector.py

@@ -35,6 +35,39 @@ class GaussianDiffusionInjector(Injector):
                 self.output_x_start_key: diffusion_outputs['x_start_predicted']}
 
 
+class AutoregressiveGaussianDiffusionInjector(Injector):
+    def __init__(self, opt, env):
+        super().__init__(opt, env)
+        self.generator = opt['generator']
+        self.output_variational_bounds_key = opt['out_key_vb_loss']
+        self.output_x_start_key = opt['out_key_x_start']
+        opt['diffusion_args']['betas'] = get_named_beta_schedule(**opt['beta_schedule'])
+        opt['diffusion_args']['use_timesteps'] = space_timesteps(opt['beta_schedule']['num_diffusion_timesteps'],
+                                                                 [opt['beta_schedule']['num_diffusion_timesteps']])
+        self.diffusion = SpacedDiffusion(**opt['diffusion_args'])
+        self.schedule_sampler = create_named_schedule_sampler(opt['sampler_type'], self.diffusion)
+        self.model_input_keys = opt_get(opt, ['model_input_keys'], [])
+        self.model_output_keys = opt['model_output_keys']
+        self.model_eps_pred_key = opt['prediction_key']
+
+    def forward(self, state):
+        gen = self.env['generators'][self.opt['generator']]
+        hq = state[self.input]
+        model_inputs = {k: state[v] for k, v in self.model_input_keys.items()}
+        t, weights = self.schedule_sampler.sample(hq.shape[0], hq.device)
+        diffusion_outputs = self.diffusion.autoregressive_training_losses(gen, hq, t, self.model_output_keys,
+                                                                          self.model_eps_pred_key,
+                                                                          model_kwargs=model_inputs)
+        if isinstance(self.schedule_sampler, LossAwareSampler):
+            self.schedule_sampler.update_with_local_losses(t, diffusion_outputs['losses'])
+        outputs = {k: diffusion_outputs[k] for k in self.model_output_keys}
+        outputs.update({self.output: diffusion_outputs['mse'],
+                self.output_variational_bounds_key: diffusion_outputs['vb'],
+                self.output_x_start_key: diffusion_outputs['x_start_predicted']})
+        return outputs
+
+
+
 # Performs inference using a network trained to predict a reverse diffusion process, which nets a image.
 class GaussianDiffusionInferenceInjector(Injector):
     def __init__(self, opt, env):

codes/trainer/losses.py

@@ -121,7 +121,13 @@ class CrossEntropy(ConfigurableLoss):
     def __init__(self, opt, env):
         super().__init__(opt, env)
         self.opt = opt
+        self.subtype = opt_get(opt, ['subtype'], 'ce')
+        if self.subtype == 'ce':
             self.ce = nn.CrossEntropyLoss()
+        elif self.subtype == 'bce':
+            self.ce = nn.BCEWithLogitsLoss()
+        else:
+            assert False
 
     def forward(self, _, state):
         logits = state[self.opt['logits']]
@@ -135,8 +141,14 @@ class CrossEntropy(ConfigurableLoss):
             logits = logits * mask
         if self.opt['swap_channels']:
             logits = logits.permute(0,2,3,1).contiguous()
+        if self.subtype == 'bce':
+            logits = logits.reshape(-1, 1)
+            labels = labels.reshape(-1, 1)
+        else:
+            logits = logits.view(-1, logits.size(-1))
+            labels = labels.view(-1)
             assert labels.max()+1 <= logits.shape[-1]
-        return self.ce(logits.view(-1, logits.size(-1)), labels.view(-1))
+        return self.ce(logits, labels)
 
 
 class PixLoss(ConfigurableLoss):