app.py

import cv2
import gradio as gr
import imutils
import numpy as np
import torch
from pytorchvideo.transforms import (
    ApplyTransformToKey,
    Normalize,
    RandomShortSideScale,
    RemoveKey,
    ShortSideScale,
    UniformTemporalSubsample,
)
from torchvision.transforms import (
    Compose,
    Lambda,
    RandomCrop,
    RandomHorizontalFlip,
    Resize,
)
# my code below
# import transformers.models.timesformer.modeling_timesformer
from transformers.models.timesformer.modeling_timesformer import TimeSformerDropPath, TimeSformerAttention, TimesformerIntermediate, TimesformerOutput, TimesformerLayer, TimesformerEncoder, TimesformerModel, TIMESFORMER_INPUTS_DOCSTRING, _CONFIG_FOR_DOC, TimesformerEmbeddings, TimesformerForVideoClassification
from transformers import TimesformerConfig
configuration = TimesformerConfig()
import collections
from typing import Optional, Tuple, Union

import torch
import torch.nn.functional
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput, ImageClassifierOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from transformers.models.timesformer.configuration_timesformer import TimesformerConfig
class MyTimesformerLayer(TimesformerLayer):
    def __init__(self, config: configuration, layer_index: int) -> None:
        super().__init__()

        attention_type = config.attention_type

        drop_path_rates = [
            x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)
        ]  # stochastic depth decay rule
        drop_path_rate = drop_path_rates[layer_index]

        self.drop_path = TimeSformerDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
        self.attention = TimeSformerAttention(config)
        self.intermediate = TimesformerIntermediate(config)
        self.output = TimesformerOutput(config)
        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        self.config = config
        self.attention_type = attention_type
        if attention_type not in ["divided_space_time", "space_only", "joint_space_time"]:
            raise ValueError("Unknown attention type: {}".format(attention_type))

        # Temporal Attention Parameters
        if self.attention_type == "divided_space_time":
            self.temporal_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
            self.temporal_attention = TimeSformerAttention(config)
            self.temporal_dense = nn.Linear(config.hidden_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor, output_attentions: bool = False):
        num_frames = self.config.num_frames
        num_patch_width = self.config.image_size // self.config.patch_size
        batch_size = hidden_states.shape[0]
        num_spatial_tokens = (hidden_states.size(1) - 1) // num_frames
        num_patch_height = num_spatial_tokens // num_patch_width

        if self.attention_type in ["space_only", "joint_space_time"]:
            self_attention_outputs = self.attention(
                self.layernorm_before(hidden_states), output_attentions=output_attentions
            )
            attention_output = self_attention_outputs[0]
            outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

            hidden_states = hidden_states + self.drop_path(attention_output)

            layer_output = self.layernorm_after(hidden_states)
            layer_output = self.intermediate(layer_output)
            layer_output = self.output(layer_output)
            layer_output = hidden_states + self.drop_path(layer_output)

            outputs = (layer_output,) + outputs

            return outputs

        elif self.attention_type == "divided_space_time":
            # Spatial
            init_cls_token = hidden_states[:, 0, :].unsqueeze(1)
            cls_token = init_cls_token.repeat(1, num_frames, 1)
            cls_token = cls_token.reshape(batch_size * num_frames, 1, cls_token.shape[2])
            spatial_embedding = hidden_states[:, 1:, :]
            spatial_embedding = (
                spatial_embedding.reshape(
                    batch_size, num_patch_height, num_patch_width, num_frames, spatial_embedding.shape[2]
                )
                .permute(0, 3, 1, 2, 4)
                .reshape(batch_size * num_frames, num_patch_height * num_patch_width, spatial_embedding.shape[2])
            )
            spatial_embedding = torch.cat((cls_token, spatial_embedding), 1)

            spatial_attention_outputs = self.attention(
                self.layernorm_before(spatial_embedding), output_attentions=output_attentions
            )
            attention_output = spatial_attention_outputs[0]
            outputs = spatial_attention_outputs[1:]  # add self attentions if we output attention weights

            residual_spatial = self.drop_path(attention_output)

            # Taking care of CLS token
            cls_token = residual_spatial[:, 0, :]
            cls_token = cls_token.reshape(batch_size, num_frames, cls_token.shape[1])
            cls_token = torch.mean(cls_token, 1, True)  # averaging for every frame
            residual_spatial = residual_spatial[:, 1:, :]
            residual_spatial = (
                residual_spatial.reshape(
                    batch_size, num_frames, num_patch_height, num_patch_width, residual_spatial.shape[2]
                )
                .permute(0, 2, 3, 1, 4)
                .reshape(batch_size, num_patch_height * num_patch_width * num_frames, residual_spatial.shape[2])
            )
            residual = residual_spatial
            hidden_states = hidden_states[:, 1:, :] + residual_spatial

            # Temporal
            temporal_embedding = hidden_states
            temporal_embedding = temporal_embedding.reshape(
                batch_size, num_patch_height, num_patch_width, num_frames, temporal_embedding.shape[2]
            ).reshape(batch_size * num_patch_height * num_patch_width, num_frames, temporal_embedding.shape[2])

            temporal_attention_outputs = self.temporal_attention(
                self.temporal_layernorm(temporal_embedding),
            )
            attention_output = temporal_attention_outputs[0]

            residual_temporal = self.drop_path(attention_output)

            residual_temporal = residual_temporal.reshape(
                batch_size, num_patch_height, num_patch_width, num_frames, residual_temporal.shape[2]
            ).reshape(batch_size, num_patch_height * num_patch_width * num_frames, residual_temporal.shape[2])
            residual_temporal = self.temporal_dense(residual_temporal)
            hidden_states = hidden_states + residual_temporal

            # Mlp
            hidden_states = torch.cat((init_cls_token, hidden_states), 1) + torch.cat((cls_token, residual_temporal), 1)
            layer_output = self.layernorm_after(hidden_states)
            layer_output = self.intermediate(layer_output)
            layer_output = self.output(layer_output)
            layer_output = hidden_states + self.drop_path(layer_output)

            outputs = (layer_output,) + outputs

            return outputs
import transformers.models.timesformer.modeling_timesformer
class MyTimesformerEncoder(TimesformerEncoder):
    def __init__(self, config: configuration) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([MyTimesformerLayer(config, ind) for ind in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ) -> Union[tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    layer_module.__call__,
                    hidden_states,
                    output_attentions,
                )
            else:
                layer_outputs = layer_module(hidden_states, output_attentions)

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class MyTimesformerModel(TimesformerModel):
    def __init__(self, config: configuration):
        super().__init__(config)
        self.config = config

        self.embeddings = TimesformerEmbeddings(config)
        self.encoder = TimesformerEncoder(config)

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.patch_embeddings

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @add_start_docstrings_to_model_forward(TIMESFORMER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]:
        r"""
        Returns:

        Examples:

        ```python
        >>> import av
        >>> import numpy as np

        >>> from transformers import AutoImageProcessor, TimesformerModel
        >>> 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 8 frames
        >>> indices = sample_frame_indices(clip_len=8, frame_sample_rate=4, seg_len=container.streams.video[0].frames)
        >>> video = read_video_pyav(container, indices)

        >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base")
        >>> model = TimesformerModel.from_pretrained("facebook/timesformer-base-finetuned-k400")

        >>> # 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, 1569, 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

        embedding_output = self.embeddings(pixel_values)

        encoder_outputs = self.encoder(
            embedding_output,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        if self.layernorm is not None:
            sequence_output = self.layernorm(sequence_output)

        if not return_dict:
            return (sequence_output,) + encoder_outputs[1:]

        return BaseModelOutput(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )

class MyTimesformerForVideoClassification(TimesformerForVideoClassification):
    def __init__(self, config):
        super().__init__(config)

        self.num_labels = config.num_labels
        self.timesformer = MyTimesformerModel(config)

        # 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(TIMESFORMER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, 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 torch
        >>> import numpy as np

        >>> from transformers import AutoImageProcessor, TimesformerForVideoClassification
        >>> 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 8 frames
        >>> indices = sample_frame_indices(clip_len=8, frame_sample_rate=1, seg_len=container.streams.video[0].frames)
        >>> video = read_video_pyav(container, indices)

        >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics")
        >>> model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400")

        >>> 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])
        eating spaghetti
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.timesformer(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0][:, 0]

        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

        
from transformers import AutoImageProcessor

MODEL_CKPT = "JackWong0911/timesformer-base-finetuned-k400-kinetic400-subset-epoch6real-num_frame_10_myViT2_more_data"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

MODEL = MyTimesformerForVideoClassification.from_pretrained(MODEL_CKPT).to(DEVICE)
PROCESSOR = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics")

RESIZE_TO = PROCESSOR.size["shortest_edge"]
NUM_FRAMES_TO_SAMPLE = MODEL.config.num_frames
IMAGE_STATS = {"image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225]}
VAL_TRANSFORMS = Compose(
    [
        UniformTemporalSubsample(NUM_FRAMES_TO_SAMPLE),
        Lambda(lambda x: x / 255.0),
        Normalize(IMAGE_STATS["image_mean"], IMAGE_STATS["image_std"]),
        Resize((RESIZE_TO, RESIZE_TO)),
    ]
)
LABELS = list(MODEL.config.label2id.keys())


def parse_video(video_file):
    """A utility to parse the input videos.

    Reference: https://pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/
    """
    vs = cv2.VideoCapture(video_file)

    # try to determine the total number of frames in the video file
    try:
        prop = (
            cv2.cv.CV_CAP_PROP_FRAME_COUNT
            if imutils.is_cv2()
            else cv2.CAP_PROP_FRAME_COUNT
        )
        total = int(vs.get(prop))
        print("[INFO] {} total frames in video".format(total))

    # an error occurred while trying to determine the total
    # number of frames in the video file
    except:
        print("[INFO] could not determine # of frames in video")
        print("[INFO] no approx. completion time can be provided")
        total = -1

    frames = []

    # loop over frames from the video file stream
    while True:
        # read the next frame from the file
        (grabbed, frame) = vs.read()
        if frame is not None:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            frames.append(frame)
        # if the frame was not grabbed, then we have reached the end
        # of the stream
        if not grabbed:
            break

    return frames


def preprocess_video(frames: list):
    """Utility to apply preprocessing transformations to a video tensor."""
    # Each frame in the `frames` list has the shape: (height, width, num_channels).
    # Collated together the `frames` has the the shape: (num_frames, height, width, num_channels).
    # So, after converting the `frames` list to a torch tensor, we permute the shape
    # such that it becomes (num_channels, num_frames, height, width) to make
    # the shape compatible with the preprocessing transformations. After applying the
    # preprocessing chain, we permute the shape to (num_frames, num_channels, height, width)
    # to make it compatible with the model. Finally, we add a batch dimension so that our video
    # classification model can operate on it.
    video_tensor = torch.tensor(np.array(frames).astype(frames[0].dtype))
    video_tensor = video_tensor.permute(
        3, 0, 1, 2
    )  # (num_channels, num_frames, height, width)
    video_tensor_pp = VAL_TRANSFORMS(video_tensor)
    video_tensor_pp = video_tensor_pp.permute(
        1, 0, 2, 3
    )  # (num_frames, num_channels, height, width)
    video_tensor_pp = video_tensor_pp.unsqueeze(0)
    return video_tensor_pp.to(DEVICE)


def infer(video_file):
    frames = parse_video(video_file)
    video_tensor = preprocess_video(frames)
    inputs = {"pixel_values": video_tensor}

    # forward pass
    with torch.no_grad():
        outputs = MODEL(**inputs)
        logits = outputs.logits
    softmax_scores = torch.nn.functional.softmax(logits, dim=-1).squeeze(0)
    confidences = {LABELS[i]: float(softmax_scores[i]) for i in range(len(LABELS))}
    return confidences


gr.Interface(
    fn=infer,
    inputs=gr.Video(type="file"),
    outputs=gr.Label(num_top_classes=3),
    examples=[
        ["examples/archery.mp4"],
        ["examples/bowling.mp4"],
        ["examples/flying_kite.mp4"],
        ["examples/high_jump.mp4"],
        ["examples/marching.mp4"],
    ],
    title="MyViT fine-tuned on a subset of Kinetics400",
    description=(
        "Gradio demo for MyViT for video classification. To use it, simply upload your video or click one of the"
        " examples to load them. Read more at the links below."
    ),
    article=(
        "<div style='text-align: center;'><a href='https://huggingface.co/docs/transformers/model_doc/videomae' target='_blank'>MyViT</a>"
        " <center><a href='https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset' target='_blank'>Fine-tuned Model</a></center></div>"
    ),
    allow_flagging=False,
    allow_screenshot=False,
).launch()