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| # coding=utf-8 | |
| # Copyright 2023 The Google Research Authors. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Video module library.""" | |
| import functools | |
| from typing import Any, Callable, Dict, Iterable, Mapping, NamedTuple, Optional, Tuple, Union | |
| from flax import linen as nn | |
| import jax.numpy as jnp | |
| from invariant_slot_attention.lib import utils | |
| from invariant_slot_attention.modules import misc | |
| Shape = Tuple[int] | |
| DType = Any | |
| Array = Any # jnp.ndarray | |
| ArrayTree = Union[Array, Iterable["ArrayTree"], Mapping[str, "ArrayTree"]] # pytype: disable=not-supported-yet | |
| ProcessorState = ArrayTree | |
| PRNGKey = Array | |
| NestedDict = Dict[str, Any] | |
| class CorrectorPredictorTuple(NamedTuple): | |
| corrected: ProcessorState | |
| predicted: ProcessorState | |
| class Processor(nn.Module): | |
| """Recurrent processor module. | |
| This module is scanned (applied recurrently) over the sequence dimension of | |
| the input and applies a corrector and a predictor module. The corrector is | |
| only applied if new inputs (such as a new image/frame) are received and uses | |
| the new input to correct its internal state. | |
| The predictor is equivalent to a latent transition model and produces a | |
| prediction for the state at the next time step, given the current (corrected) | |
| state. | |
| """ | |
| corrector: Callable[[ProcessorState, Array], ProcessorState] | |
| predictor: Callable[[ProcessorState], ProcessorState] | |
| def __call__(self, state, inputs, | |
| padding_mask, | |
| train): | |
| # Only apply corrector if we receive new inputs. | |
| if inputs is not None: | |
| corrected_state = self.corrector(state, inputs, padding_mask, train=train) | |
| # Otherwise simply use previous state as input for predictor. | |
| else: | |
| corrected_state = state | |
| # Always apply predictor (i.e. transition model). | |
| predicted_state = self.predictor(corrected_state, train=train) | |
| # Prepare outputs in a format compatible with nn.scan. | |
| new_state = predicted_state | |
| outputs = CorrectorPredictorTuple( | |
| corrected=corrected_state, predicted=predicted_state) | |
| return new_state, outputs | |
| class SAVi(nn.Module): | |
| """Video model consisting of encoder, recurrent processor, and decoder.""" | |
| encoder: Callable[[], nn.Module] | |
| decoder: Callable[[], nn.Module] | |
| corrector: Callable[[], nn.Module] | |
| predictor: Callable[[], nn.Module] | |
| initializer: Callable[[], nn.Module] | |
| decode_corrected: bool = True | |
| decode_predicted: bool = True | |
| def __call__(self, video, conditioning = None, | |
| continue_from_previous_state = False, | |
| padding_mask = None, | |
| train = False): | |
| """Performs a forward pass on a video. | |
| Args: | |
| video: Video of shape `[batch_size, n_frames, height, width, n_channels]`. | |
| conditioning: Optional jnp.ndarray used for conditioning the initial state | |
| of the recurrent processor. | |
| continue_from_previous_state: Boolean, whether to continue from a previous | |
| state or not. If True, the conditioning variable is used directly as | |
| initial state. | |
| padding_mask: Binary mask for padding video inputs (e.g. for videos of | |
| different sizes/lengths). Zero corresponds to padding. | |
| train: Indicating whether we're training or evaluating. | |
| Returns: | |
| A dictionary of model predictions. | |
| """ | |
| processor = Processor( | |
| corrector=self.corrector(), predictor=self.predictor()) # pytype: disable=wrong-arg-types | |
| if padding_mask is None: | |
| padding_mask = jnp.ones(video.shape[:-1], jnp.int32) | |
| # video.shape = (batch_size, n_frames, height, width, n_channels) | |
| # Vmapped over sequence dim. | |
| encoded_inputs = self.encoder()(video, padding_mask, train) # pytype: disable=not-callable | |
| if continue_from_previous_state: | |
| assert conditioning is not None, ( | |
| "When continuing from a previous state, the state has to be passed " | |
| "via the `conditioning` variable, which cannot be `None`.") | |
| init_state = conditioning[:, -1] # We currently only use last state. | |
| else: | |
| # Same as above but without encoded inputs. | |
| init_state = self.initializer()( | |
| conditioning, batch_size=video.shape[0], train=train) # pytype: disable=not-callable | |
| # Scan recurrent processor over encoded inputs along sequence dimension. | |
| _, states = processor(init_state, encoded_inputs, padding_mask, train) | |
| # type(states) = CorrectorPredictorTuple. | |
| # states.corrected.shape = (batch_size, n_frames, ..., n_features). | |
| # states.predicted.shape = (batch_size, n_frames, ..., n_features). | |
| # Decode latent states. | |
| decoder = self.decoder() # Vmapped over sequence dim. | |
| outputs = decoder(states.corrected, | |
| train) if self.decode_corrected else None # pytype: disable=not-callable | |
| outputs_pred = decoder(states.predicted, | |
| train) if self.decode_predicted else None # pytype: disable=not-callable | |
| return { | |
| "states": states.corrected, | |
| "states_pred": states.predicted, | |
| "outputs": outputs, | |
| "outputs_pred": outputs_pred, | |
| } | |
| class FrameEncoder(nn.Module): | |
| """Encoder for single video frame, vmapped over time axis.""" | |
| backbone: Callable[[], nn.Module] | |
| pos_emb: Callable[[], nn.Module] = misc.Identity | |
| reduction: Optional[str] = None | |
| output_transform: Callable[[], nn.Module] = misc.Identity | |
| # Vmapped application of module, consumes time axis (axis=1). | |
| def __call__(self, inputs, padding_mask = None, | |
| train = False): | |
| del padding_mask # Unused. | |
| # inputs.shape = (batch_size, height, width, n_channels) | |
| x = self.backbone()(inputs, train=train) | |
| x = self.pos_emb()(x) | |
| if self.reduction == "spatial_flatten": | |
| batch_size, height, width, n_features = x.shape | |
| x = jnp.reshape(x, (batch_size, height * width, n_features)) | |
| elif self.reduction == "spatial_average": | |
| x = jnp.mean(x, axis=(1, 2)) | |
| elif self.reduction == "all_flatten": | |
| batch_size, height, width, n_features = x.shape | |
| x = jnp.reshape(x, (batch_size, height * width * n_features)) | |
| elif self.reduction is not None: | |
| raise ValueError("Unknown reduction type: {}.".format(self.reduction)) | |
| output_block = self.output_transform() | |
| if hasattr(output_block, "qkv_size"): | |
| # Project to qkv_size if used transformer. | |
| x = nn.relu(nn.Dense(output_block.qkv_size)(x)) | |
| x = output_block(x, train=train) | |
| return x | |