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"""Contains a PyTorch definition for Gated Separable 3D network (S3D-G) |
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with a text module for computing joint text-video embedding from raw text |
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and video input. The following code will enable you to load the HowTo100M |
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pretrained S3D Text-Video model from: |
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A. Miech, J.-B. Alayrac, L. Smaira, I. Laptev, J. Sivic and A. Zisserman, |
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End-to-End Learning of Visual Representations from Uncurated Instructional Videos. |
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https://arxiv.org/abs/1912.06430. |
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S3D-G was proposed by: |
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S. Xie, C. Sun, J. Huang, Z. Tu and K. Murphy, |
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Rethinking Spatiotemporal Feature Learning For Video Understanding. |
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https://arxiv.org/abs/1712.04851. |
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Tensorflow code: https://github.com/tensorflow/models/blob/master/research/slim/nets/s3dg.py |
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The S3D architecture was slightly modified with a space to depth trick for TPU |
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optimization. |
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""" |
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import torch as th |
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import torch.nn.functional as F |
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import torch.nn as nn |
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import os |
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import numpy as np |
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import re |
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class InceptionBlock(nn.Module): |
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def __init__( |
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self, |
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input_dim, |
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num_outputs_0_0a, |
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num_outputs_1_0a, |
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num_outputs_1_0b, |
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num_outputs_2_0a, |
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num_outputs_2_0b, |
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num_outputs_3_0b, |
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gating=True, |
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): |
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super(InceptionBlock, self).__init__() |
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self.conv_b0 = STConv3D(input_dim, num_outputs_0_0a, [1, 1, 1]) |
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self.conv_b1_a = STConv3D(input_dim, num_outputs_1_0a, [1, 1, 1]) |
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self.conv_b1_b = STConv3D( |
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num_outputs_1_0a, num_outputs_1_0b, [3, 3, 3], padding=1, separable=True |
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) |
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self.conv_b2_a = STConv3D(input_dim, num_outputs_2_0a, [1, 1, 1]) |
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self.conv_b2_b = STConv3D( |
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num_outputs_2_0a, num_outputs_2_0b, [3, 3, 3], padding=1, separable=True |
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) |
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self.maxpool_b3 = th.nn.MaxPool3d((3, 3, 3), stride=1, padding=1) |
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self.conv_b3_b = STConv3D(input_dim, num_outputs_3_0b, [1, 1, 1]) |
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self.gating = gating |
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self.output_dim = ( |
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num_outputs_0_0a + num_outputs_1_0b + num_outputs_2_0b + num_outputs_3_0b |
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) |
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if gating: |
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self.gating_b0 = SelfGating(num_outputs_0_0a) |
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self.gating_b1 = SelfGating(num_outputs_1_0b) |
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self.gating_b2 = SelfGating(num_outputs_2_0b) |
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self.gating_b3 = SelfGating(num_outputs_3_0b) |
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def forward(self, input): |
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"""Inception block |
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""" |
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b0 = self.conv_b0(input) |
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b1 = self.conv_b1_a(input) |
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b1 = self.conv_b1_b(b1) |
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b2 = self.conv_b2_a(input) |
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b2 = self.conv_b2_b(b2) |
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b3 = self.maxpool_b3(input) |
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b3 = self.conv_b3_b(b3) |
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if self.gating: |
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b0 = self.gating_b0(b0) |
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b1 = self.gating_b1(b1) |
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b2 = self.gating_b2(b2) |
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b3 = self.gating_b3(b3) |
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return th.cat((b0, b1, b2, b3), dim=1) |
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class SelfGating(nn.Module): |
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def __init__(self, input_dim): |
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super(SelfGating, self).__init__() |
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self.fc = nn.Linear(input_dim, input_dim) |
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|
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def forward(self, input_tensor): |
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"""Feature gating as used in S3D-G. |
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""" |
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spatiotemporal_average = th.mean(input_tensor, dim=[2, 3, 4]) |
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weights = self.fc(spatiotemporal_average) |
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weights = th.sigmoid(weights) |
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return weights[:, :, None, None, None] * input_tensor |
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class STConv3D(nn.Module): |
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def __init__( |
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self, input_dim, output_dim, kernel_size, stride=1, padding=0, separable=False |
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): |
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super(STConv3D, self).__init__() |
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self.separable = separable |
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self.relu = nn.ReLU(inplace=True) |
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assert len(kernel_size) == 3 |
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if separable and kernel_size[0] != 1: |
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spatial_kernel_size = [1, kernel_size[1], kernel_size[2]] |
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temporal_kernel_size = [kernel_size[0], 1, 1] |
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if isinstance(stride, list) and len(stride) == 3: |
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spatial_stride = [1, stride[1], stride[2]] |
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temporal_stride = [stride[0], 1, 1] |
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else: |
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spatial_stride = [1, stride, stride] |
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temporal_stride = [stride, 1, 1] |
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if isinstance(padding, list) and len(padding) == 3: |
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spatial_padding = [0, padding[1], padding[2]] |
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temporal_padding = [padding[0], 0, 0] |
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else: |
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spatial_padding = [0, padding, padding] |
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temporal_padding = [padding, 0, 0] |
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if separable: |
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self.conv1 = nn.Conv3d( |
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input_dim, |
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output_dim, |
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kernel_size=spatial_kernel_size, |
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stride=spatial_stride, |
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padding=spatial_padding, |
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bias=False, |
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) |
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self.bn1 = nn.BatchNorm3d(output_dim) |
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self.conv2 = nn.Conv3d( |
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output_dim, |
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output_dim, |
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kernel_size=temporal_kernel_size, |
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stride=temporal_stride, |
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padding=temporal_padding, |
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bias=False, |
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) |
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self.bn2 = nn.BatchNorm3d(output_dim) |
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else: |
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self.conv1 = nn.Conv3d( |
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input_dim, |
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output_dim, |
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kernel_size=kernel_size, |
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stride=stride, |
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padding=padding, |
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bias=False, |
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) |
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self.bn1 = nn.BatchNorm3d(output_dim) |
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def forward(self, input): |
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out = self.relu(self.bn1(self.conv1(input))) |
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if self.separable: |
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out = self.relu(self.bn2(self.conv2(out))) |
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return out |
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class MaxPool3dTFPadding(th.nn.Module): |
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def __init__(self, kernel_size, stride=None, padding="SAME"): |
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super(MaxPool3dTFPadding, self).__init__() |
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if padding == "SAME": |
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padding_shape = self._get_padding_shape(kernel_size, stride) |
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self.padding_shape = padding_shape |
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self.pad = th.nn.ConstantPad3d(padding_shape, 0) |
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self.pool = th.nn.MaxPool3d(kernel_size, stride, ceil_mode=True) |
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def _get_padding_shape(self, filter_shape, stride): |
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def _pad_top_bottom(filter_dim, stride_val): |
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pad_along = max(filter_dim - stride_val, 0) |
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pad_top = pad_along // 2 |
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pad_bottom = pad_along - pad_top |
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return pad_top, pad_bottom |
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padding_shape = [] |
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for filter_dim, stride_val in zip(filter_shape, stride): |
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pad_top, pad_bottom = _pad_top_bottom(filter_dim, stride_val) |
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padding_shape.append(pad_top) |
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padding_shape.append(pad_bottom) |
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depth_top = padding_shape.pop(0) |
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depth_bottom = padding_shape.pop(0) |
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padding_shape.append(depth_top) |
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padding_shape.append(depth_bottom) |
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return tuple(padding_shape) |
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def forward(self, inp): |
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inp = self.pad(inp) |
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out = self.pool(inp) |
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return out |
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class Sentence_Embedding(nn.Module): |
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def __init__( |
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self, |
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embd_dim, |
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num_embeddings=66250, |
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word_embedding_dim=300, |
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token_to_word_path="dict.npy", |
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max_words=16, |
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output_dim=2048, |
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): |
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super(Sentence_Embedding, self).__init__() |
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self.word_embd = nn.Embedding(num_embeddings, word_embedding_dim) |
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self.fc1 = nn.Linear(word_embedding_dim, output_dim) |
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self.fc2 = nn.Linear(output_dim, embd_dim) |
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self.word_to_token = {} |
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self.max_words = max_words |
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token_to_word = np.load(token_to_word_path) |
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for i, t in enumerate(token_to_word): |
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self.word_to_token[t] = i + 1 |
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def _zero_pad_tensor_token(self, tensor, size): |
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if len(tensor) >= size: |
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return tensor[:size] |
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else: |
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zero = th.zeros(size - len(tensor)).long() |
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return th.cat((tensor, zero), dim=0) |
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def _split_text(self, sentence): |
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w = re.findall(r"[\w']+", str(sentence)) |
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return w |
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def _words_to_token(self, words): |
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words = [ |
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self.word_to_token[word] for word in words if word in self.word_to_token |
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] |
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if words: |
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we = self._zero_pad_tensor_token(th.LongTensor(words), self.max_words) |
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return we |
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else: |
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return th.zeros(self.max_words).long() |
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def _words_to_ids(self, x): |
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split_x = [self._words_to_token(self._split_text(sent.lower())) for sent in x] |
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return th.stack(split_x, dim=0) |
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def forward(self, x): |
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x = self._words_to_ids(x) |
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x = self.word_embd(x) |
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x = F.relu(self.fc1(x)) |
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x = th.max(x, dim=1)[0] |
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x = self.fc2(x) |
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return {'text_embedding': x} |
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class S3D(nn.Module): |
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def __init__(self, dict_path, num_classes=512, gating=True, space_to_depth=True): |
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super(S3D, self).__init__() |
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self.num_classes = num_classes |
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self.gating = gating |
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self.space_to_depth = space_to_depth |
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if space_to_depth: |
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self.conv1 = STConv3D( |
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24, 64, [2, 4, 4], stride=1, padding=(1, 2, 2), separable=False |
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) |
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else: |
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self.conv1 = STConv3D( |
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3, 64, [3, 7, 7], stride=2, padding=(1, 3, 3), separable=False |
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) |
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self.conv_2b = STConv3D(64, 64, [1, 1, 1], separable=False) |
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self.conv_2c = STConv3D(64, 192, [3, 3, 3], padding=1, separable=True) |
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self.gating = SelfGating(192) |
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self.maxpool_2a = MaxPool3dTFPadding( |
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kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME" |
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) |
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self.maxpool_3a = MaxPool3dTFPadding( |
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kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME" |
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) |
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self.mixed_3b = InceptionBlock(192, 64, 96, 128, 16, 32, 32) |
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self.mixed_3c = InceptionBlock( |
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self.mixed_3b.output_dim, 128, 128, 192, 32, 96, 64 |
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) |
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self.maxpool_4a = MaxPool3dTFPadding( |
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kernel_size=(3, 3, 3), stride=(2, 2, 2), padding="SAME" |
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) |
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self.mixed_4b = InceptionBlock( |
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self.mixed_3c.output_dim, 192, 96, 208, 16, 48, 64 |
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) |
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self.mixed_4c = InceptionBlock( |
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self.mixed_4b.output_dim, 160, 112, 224, 24, 64, 64 |
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) |
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self.mixed_4d = InceptionBlock( |
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self.mixed_4c.output_dim, 128, 128, 256, 24, 64, 64 |
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) |
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self.mixed_4e = InceptionBlock( |
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self.mixed_4d.output_dim, 112, 144, 288, 32, 64, 64 |
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) |
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self.mixed_4f = InceptionBlock( |
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self.mixed_4e.output_dim, 256, 160, 320, 32, 128, 128 |
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) |
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self.maxpool_5a = self.maxPool3d_5a_2x2 = MaxPool3dTFPadding( |
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kernel_size=(2, 2, 2), stride=(2, 2, 2), padding="SAME" |
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) |
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self.mixed_5b = InceptionBlock( |
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self.mixed_4f.output_dim, 256, 160, 320, 32, 128, 128 |
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) |
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self.mixed_5c = InceptionBlock( |
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self.mixed_5b.output_dim, 384, 192, 384, 48, 128, 128 |
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) |
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self.fc = nn.Linear(self.mixed_5c.output_dim, num_classes) |
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self.text_module = Sentence_Embedding(num_classes, |
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token_to_word_path=dict_path) |
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def _space_to_depth(self, input): |
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"""3D space to depth trick for TPU optimization. |
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""" |
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B, C, T, H, W = input.shape |
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input = input.view(B, C, T // 2, 2, H // 2, 2, W // 2, 2) |
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input = input.permute(0, 3, 5, 7, 1, 2, 4, 6) |
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input = input.contiguous().view(B, 8 * C, T // 2, H // 2, W // 2) |
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return input |
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def forward(self, inputs): |
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"""Defines the S3DG base architecture.""" |
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if self.space_to_depth: |
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inputs = self._space_to_depth(inputs) |
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net = self.conv1(inputs) |
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if self.space_to_depth: |
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net = net[:, :, 1:, 1:, 1:] |
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net = self.maxpool_2a(net) |
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net = self.conv_2b(net) |
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net = self.conv_2c(net) |
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if self.gating: |
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net = self.gating(net) |
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net = self.maxpool_3a(net) |
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net = self.mixed_3b(net) |
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net = self.mixed_3c(net) |
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net = self.maxpool_4a(net) |
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net = self.mixed_4b(net) |
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net = self.mixed_4c(net) |
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net = self.mixed_4d(net) |
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net = self.mixed_4e(net) |
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net = self.mixed_4f(net) |
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net = self.maxpool_5a(net) |
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net = self.mixed_5b(net) |
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net = self.mixed_5c(net) |
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net = th.mean(net, dim=[2, 3, 4]) |
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return {'video_embedding': self.fc(net), 'mixed_5c': net} |
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