744 lines
29 KiB
Python
744 lines
29 KiB
Python
# coding=utf-8
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# Copyright 2023 Google AI and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch ViViT model."""
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import math
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from typing import Optional, Set, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
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from .configuration_vivit import VivitConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "google/vivit-b-16x2-kinetics400"
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_CONFIG_FOR_DOC = "VivitConfig"
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from ..deprecated._archive_maps import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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class VivitTubeletEmbeddings(nn.Module):
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"""
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Construct Vivit Tubelet embeddings.
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This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of
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shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder.
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The seq_len (the number of patches) equals (number of frames // tubelet_size[0]) * (height // tubelet_size[1]) *
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(width // tubelet_size[2]).
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"""
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def __init__(self, config):
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super().__init__()
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self.num_frames = config.num_frames
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self.image_size = config.image_size
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self.patch_size = config.tubelet_size
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self.num_patches = (
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(self.image_size // self.patch_size[2])
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* (self.image_size // self.patch_size[1])
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* (self.num_frames // self.patch_size[0])
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)
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self.embed_dim = config.hidden_size
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self.projection = nn.Conv3d(
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config.num_channels, config.hidden_size, kernel_size=config.tubelet_size, stride=config.tubelet_size
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)
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def forward(self, pixel_values):
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batch_size, num_frames, num_channels, height, width = pixel_values.shape
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if height != self.image_size or width != self.image_size:
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raise ValueError(
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f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size})."
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)
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# permute to (batch_size, num_channels, num_frames, height, width)
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pixel_values = pixel_values.permute(0, 2, 1, 3, 4)
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x = self.projection(pixel_values)
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# out_batch_size, out_num_channels, out_num_frames, out_height, out_width = x.shape
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x = self.projection(pixel_values).flatten(2).transpose(1, 2)
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return x
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class VivitEmbeddings(nn.Module):
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"""
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Vivit Embeddings.
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Creates embeddings from a video using VivitTubeletEmbeddings, adds CLS token and positional embeddings.
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"""
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def __init__(self, config):
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super().__init__()
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self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.patch_embeddings = VivitTubeletEmbeddings(config)
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self.position_embeddings = nn.Parameter(
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torch.zeros(1, self.patch_embeddings.num_patches + 1, config.hidden_size)
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)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.config = config
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def forward(self, pixel_values):
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batch_size = pixel_values.shape[0]
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embeddings = self.patch_embeddings(pixel_values)
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cls_tokens = self.cls_token.tile([batch_size, 1, 1])
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embeddings = torch.cat((cls_tokens, embeddings), dim=1)
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# add positional encoding to each token
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embeddings = embeddings + self.position_embeddings
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embeddings = self.dropout(embeddings)
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return embeddings
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# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Vivit
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class VivitSelfAttention(nn.Module):
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def __init__(self, config: VivitConfig) -> None:
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
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f"heads {config.num_attention_heads}."
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
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) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
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mixed_query_layer = self.query(hidden_states)
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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return outputs
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# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Vivit
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class VivitSelfOutput(nn.Module):
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"""
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The residual connection is defined in VivitLayer instead of here (as is the case with other models), due to the
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layernorm applied before each block.
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"""
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def __init__(self, config: VivitConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Vivit
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class VivitAttention(nn.Module):
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def __init__(self, config: VivitConfig) -> None:
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super().__init__()
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self.attention = VivitSelfAttention(config)
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self.output = VivitSelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads: Set[int]) -> None:
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.attention.query = prune_linear_layer(self.attention.query, index)
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self.attention.key = prune_linear_layer(self.attention.key, index)
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self.attention.value = prune_linear_layer(self.attention.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
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self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states: torch.Tensor,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
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self_outputs = self.attention(hidden_states, head_mask, output_attentions)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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class VivitIntermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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class VivitOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states + input_tensor
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return hidden_states
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class VivitLayer(nn.Module):
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"""This corresponds to the EncoderBlock class in the scenic/vivit implementation."""
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def __init__(self, config):
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = VivitAttention(config)
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self.intermediate = VivitIntermediate(config)
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self.output = VivitOutput(config)
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self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states, head_mask=None, output_attentions=False):
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self_attention_outputs = self.attention(
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# in Vivit, layernorm is applied before self-attention
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self.layernorm_before(hidden_states),
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head_mask,
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output_attentions=output_attentions,
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)
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attention_output = self_attention_outputs[0]
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# add self attentions if we output attention weights
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outputs = self_attention_outputs[1:]
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# first residual connection
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hidden_states = attention_output + hidden_states
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# in Vivit, layernorm is also applied after self-attention
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layer_output = self.layernorm_after(hidden_states)
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layer_output = self.intermediate(layer_output)
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# second residual connection is done here
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layer_output = self.output(layer_output, hidden_states)
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outputs = (layer_output,) + outputs
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return outputs
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class VivitEncoder(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([VivitLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states,
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head_mask=None,
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output_attentions=False,
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output_hidden_states=False,
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return_dict=True,
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):
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all_hidden_states = () if output_hidden_states else None
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all_self_attentions = () if output_attentions else None
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for i, layer_module in enumerate(self.layer):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer_head_mask = head_mask[i] if head_mask is not None else None
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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layer_module.__call__,
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hidden_states,
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layer_head_mask,
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output_attentions,
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)
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else:
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layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_self_attentions = all_self_attentions + (layer_outputs[1],)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if not return_dict:
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return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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)
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class VivitPooler(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class VivitPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = VivitConfig
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base_model_prefix = "vivit"
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main_input_name = "pixel_values"
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supports_gradient_checkpointing = True
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def _init_weights(self, module):
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"""Initialize the weights"""
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if isinstance(module, (nn.Linear, nn.Conv3d)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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elif isinstance(module, nn.Parameter):
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module.data.normal_(mean=0.0, std=self.config.initializer_range)
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VIVIT_START_DOCSTRING = r"""
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This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
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as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
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behavior.
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Parameters:
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config ([`VivitConfig`]): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the
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configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
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"""
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VIVIT_INPUTS_DOCSTRING = r"""
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Args:
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pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
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Pixel values. Pixel values can be obtained using [`VivitImageProcessor`]. See
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[`VivitImageProcessor.preprocess`] for details.
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head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
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Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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@add_start_docstrings(
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"The bare ViViT Transformer model outputting raw hidden-states without any specific head on top.",
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VIVIT_START_DOCSTRING,
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)
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class VivitModel(VivitPreTrainedModel):
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def __init__(self, config, add_pooling_layer=True):
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super().__init__(config)
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self.config = config
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self.embeddings = VivitEmbeddings(config)
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self.encoder = VivitEncoder(config)
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self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.pooler = VivitPooler(config) if add_pooling_layer else None
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.embeddings.patch_embeddings
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def _prune_heads(self, heads_to_prune):
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"""
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|
Prunes heads of the model.
|
|
|
|
Args:
|
|
heads_to_prune:
|
|
dict of {layer_num: list of heads to prune in this layer}
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@add_start_docstrings_to_model_forward(VIVIT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithPooling]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> import av
|
|
>>> import numpy as np
|
|
|
|
>>> from transformers import VivitImageProcessor, VivitModel
|
|
>>> from huggingface_hub import hf_hub_download
|
|
|
|
>>> np.random.seed(0)
|
|
|
|
|
|
>>> def read_video_pyav(container, indices):
|
|
... '''
|
|
... Decode the video with PyAV decoder.
|
|
... Args:
|
|
... container (`av.container.input.InputContainer`): PyAV container.
|
|
... indices (`List[int]`): List of frame indices to decode.
|
|
... Returns:
|
|
... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
|
|
... '''
|
|
... frames = []
|
|
... container.seek(0)
|
|
... start_index = indices[0]
|
|
... end_index = indices[-1]
|
|
... for i, frame in enumerate(container.decode(video=0)):
|
|
... if i > end_index:
|
|
... break
|
|
... if i >= start_index and i in indices:
|
|
... frames.append(frame)
|
|
... return np.stack([x.to_ndarray(format="rgb24") for x in frames])
|
|
|
|
|
|
>>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
|
|
... '''
|
|
... Sample a given number of frame indices from the video.
|
|
... Args:
|
|
... clip_len (`int`): Total number of frames to sample.
|
|
... frame_sample_rate (`int`): Sample every n-th frame.
|
|
... seg_len (`int`): Maximum allowed index of sample's last frame.
|
|
... Returns:
|
|
... indices (`List[int]`): List of sampled frame indices
|
|
... '''
|
|
... converted_len = int(clip_len * frame_sample_rate)
|
|
... end_idx = np.random.randint(converted_len, seg_len)
|
|
... start_idx = end_idx - converted_len
|
|
... indices = np.linspace(start_idx, end_idx, num=clip_len)
|
|
... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
|
|
... return indices
|
|
|
|
|
|
>>> # video clip consists of 300 frames (10 seconds at 30 FPS)
|
|
>>> file_path = hf_hub_download(
|
|
... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
|
|
... )
|
|
>>> container = av.open(file_path)
|
|
|
|
>>> # sample 32 frames
|
|
>>> indices = sample_frame_indices(clip_len=32, frame_sample_rate=1, seg_len=container.streams.video[0].frames)
|
|
>>> video = read_video_pyav(container=container, indices=indices)
|
|
|
|
>>> image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400")
|
|
>>> model = VivitModel.from_pretrained("google/vivit-b-16x2-kinetics400")
|
|
|
|
>>> # prepare video for the model
|
|
>>> inputs = image_processor(list(video), return_tensors="pt")
|
|
|
|
>>> # forward pass
|
|
>>> outputs = model(**inputs)
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
>>> list(last_hidden_states.shape)
|
|
[1, 3137, 768]
|
|
```"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(pixel_values)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
sequence_output = self.layernorm(sequence_output)
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""ViViT Transformer model with a video classification head on top (a linear layer on top of the final hidden state of the
|
|
[CLS] token) e.g. for Kinetics-400.""",
|
|
VIVIT_START_DOCSTRING,
|
|
)
|
|
class VivitForVideoClassification(VivitPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.vivit = VivitModel(config, add_pooling_layer=False)
|
|
|
|
# Classifier head
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(VIVIT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.FloatTensor], ImageClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> import av
|
|
>>> import numpy as np
|
|
>>> import torch
|
|
|
|
>>> from transformers import VivitImageProcessor, VivitForVideoClassification
|
|
>>> from huggingface_hub import hf_hub_download
|
|
|
|
>>> np.random.seed(0)
|
|
|
|
|
|
>>> def read_video_pyav(container, indices):
|
|
... '''
|
|
... Decode the video with PyAV decoder.
|
|
... Args:
|
|
... container (`av.container.input.InputContainer`): PyAV container.
|
|
... indices (`List[int]`): List of frame indices to decode.
|
|
... Returns:
|
|
... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
|
|
... '''
|
|
... frames = []
|
|
... container.seek(0)
|
|
... start_index = indices[0]
|
|
... end_index = indices[-1]
|
|
... for i, frame in enumerate(container.decode(video=0)):
|
|
... if i > end_index:
|
|
... break
|
|
... if i >= start_index and i in indices:
|
|
... frames.append(frame)
|
|
... return np.stack([x.to_ndarray(format="rgb24") for x in frames])
|
|
|
|
|
|
>>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
|
|
... '''
|
|
... Sample a given number of frame indices from the video.
|
|
... Args:
|
|
... clip_len (`int`): Total number of frames to sample.
|
|
... frame_sample_rate (`int`): Sample every n-th frame.
|
|
... seg_len (`int`): Maximum allowed index of sample's last frame.
|
|
... Returns:
|
|
... indices (`List[int]`): List of sampled frame indices
|
|
... '''
|
|
... converted_len = int(clip_len * frame_sample_rate)
|
|
... end_idx = np.random.randint(converted_len, seg_len)
|
|
... start_idx = end_idx - converted_len
|
|
... indices = np.linspace(start_idx, end_idx, num=clip_len)
|
|
... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
|
|
... return indices
|
|
|
|
|
|
>>> # video clip consists of 300 frames (10 seconds at 30 FPS)
|
|
>>> file_path = hf_hub_download(
|
|
... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
|
|
... )
|
|
>>> container = av.open(file_path)
|
|
|
|
>>> # sample 32 frames
|
|
>>> indices = sample_frame_indices(clip_len=32, frame_sample_rate=4, seg_len=container.streams.video[0].frames)
|
|
>>> video = read_video_pyav(container=container, indices=indices)
|
|
|
|
>>> image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400")
|
|
>>> model = VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400")
|
|
|
|
>>> inputs = image_processor(list(video), return_tensors="pt")
|
|
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**inputs)
|
|
... logits = outputs.logits
|
|
|
|
>>> # model predicts one of the 400 Kinetics-400 classes
|
|
>>> predicted_label = logits.argmax(-1).item()
|
|
>>> print(model.config.id2label[predicted_label])
|
|
LABEL_116
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.vivit(
|
|
pixel_values,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.classifier(sequence_output[:, 0, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.num_labels == 1:
|
|
# We are doing regression
|
|
loss_fct = MSELoss()
|
|
loss = loss_fct(logits.view(-1), labels.view(-1))
|
|
else:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return ImageClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|