322 lines
13 KiB
Python
322 lines
13 KiB
Python
# Original code from https://github.com/piergiaj/pytorch-i3d
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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class MaxPool3dSamePadding(nn.MaxPool3d):
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def compute_pad(self, dim, s):
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if s % self.stride[dim] == 0:
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return max(self.kernel_size[dim] - self.stride[dim], 0)
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else:
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return max(self.kernel_size[dim] - (s % self.stride[dim]), 0)
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def forward(self, x):
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# compute 'same' padding
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(batch, channel, t, h, w) = x.size()
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out_t = np.ceil(float(t) / float(self.stride[0]))
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out_h = np.ceil(float(h) / float(self.stride[1]))
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out_w = np.ceil(float(w) / float(self.stride[2]))
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pad_t = self.compute_pad(0, t)
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pad_h = self.compute_pad(1, h)
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pad_w = self.compute_pad(2, w)
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pad_t_f = pad_t // 2
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pad_t_b = pad_t - pad_t_f
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pad_h_f = pad_h // 2
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pad_h_b = pad_h - pad_h_f
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pad_w_f = pad_w // 2
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pad_w_b = pad_w - pad_w_f
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pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b)
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x = F.pad(x, pad)
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return super(MaxPool3dSamePadding, self).forward(x)
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class Unit3D(nn.Module):
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def __init__(self, in_channels,
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output_channels,
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kernel_shape=(1, 1, 1),
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stride=(1, 1, 1),
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padding=0,
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activation_fn=F.relu,
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use_batch_norm=True,
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use_bias=False,
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name='unit_3d'):
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"""Initializes Unit3D module."""
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super(Unit3D, self).__init__()
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self._output_channels = output_channels
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self._kernel_shape = kernel_shape
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self._stride = stride
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self._use_batch_norm = use_batch_norm
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self._activation_fn = activation_fn
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self._use_bias = use_bias
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self.name = name
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self.padding = padding
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self.conv3d = nn.Conv3d(in_channels=in_channels,
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out_channels=self._output_channels,
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kernel_size=self._kernel_shape,
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stride=self._stride,
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padding=0, # we always want padding to be 0 here. We will dynamically pad based on input size in forward function
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bias=self._use_bias)
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if self._use_batch_norm:
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self.bn = nn.BatchNorm3d(self._output_channels, eps=1e-5, momentum=0.001)
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def compute_pad(self, dim, s):
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if s % self._stride[dim] == 0:
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return max(self._kernel_shape[dim] - self._stride[dim], 0)
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else:
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return max(self._kernel_shape[dim] - (s % self._stride[dim]), 0)
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def forward(self, x):
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# compute 'same' padding
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(batch, channel, t, h, w) = x.size()
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out_t = np.ceil(float(t) / float(self._stride[0]))
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out_h = np.ceil(float(h) / float(self._stride[1]))
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out_w = np.ceil(float(w) / float(self._stride[2]))
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pad_t = self.compute_pad(0, t)
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pad_h = self.compute_pad(1, h)
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pad_w = self.compute_pad(2, w)
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pad_t_f = pad_t // 2
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pad_t_b = pad_t - pad_t_f
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pad_h_f = pad_h // 2
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pad_h_b = pad_h - pad_h_f
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pad_w_f = pad_w // 2
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pad_w_b = pad_w - pad_w_f
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pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b)
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x = F.pad(x, pad)
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x = self.conv3d(x)
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if self._use_batch_norm:
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x = self.bn(x)
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if self._activation_fn is not None:
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x = self._activation_fn(x)
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return x
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class InceptionModule(nn.Module):
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def __init__(self, in_channels, out_channels, name):
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super(InceptionModule, self).__init__()
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self.b0 = Unit3D(in_channels=in_channels, output_channels=out_channels[0], kernel_shape=[1, 1, 1], padding=0,
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name=name+'/Branch_0/Conv3d_0a_1x1')
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self.b1a = Unit3D(in_channels=in_channels, output_channels=out_channels[1], kernel_shape=[1, 1, 1], padding=0,
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name=name+'/Branch_1/Conv3d_0a_1x1')
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self.b1b = Unit3D(in_channels=out_channels[1], output_channels=out_channels[2], kernel_shape=[3, 3, 3],
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name=name+'/Branch_1/Conv3d_0b_3x3')
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self.b2a = Unit3D(in_channels=in_channels, output_channels=out_channels[3], kernel_shape=[1, 1, 1], padding=0,
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name=name+'/Branch_2/Conv3d_0a_1x1')
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self.b2b = Unit3D(in_channels=out_channels[3], output_channels=out_channels[4], kernel_shape=[3, 3, 3],
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name=name+'/Branch_2/Conv3d_0b_3x3')
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self.b3a = MaxPool3dSamePadding(kernel_size=[3, 3, 3],
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stride=(1, 1, 1), padding=0)
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self.b3b = Unit3D(in_channels=in_channels, output_channels=out_channels[5], kernel_shape=[1, 1, 1], padding=0,
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name=name+'/Branch_3/Conv3d_0b_1x1')
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self.name = name
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def forward(self, x):
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b0 = self.b0(x)
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b1 = self.b1b(self.b1a(x))
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b2 = self.b2b(self.b2a(x))
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b3 = self.b3b(self.b3a(x))
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return torch.cat([b0,b1,b2,b3], dim=1)
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class InceptionI3d(nn.Module):
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"""Inception-v1 I3D architecture.
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The model is introduced in:
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Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
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Joao Carreira, Andrew Zisserman
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https://arxiv.org/pdf/1705.07750v1.pdf.
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See also the Inception architecture, introduced in:
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Going deeper with convolutions
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Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
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Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
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http://arxiv.org/pdf/1409.4842v1.pdf.
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"""
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# Endpoints of the model in order. During construction, all the endpoints up
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# to a designated `final_endpoint` are returned in a dictionary as the
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# second return value.
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VALID_ENDPOINTS = (
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'Conv3d_1a_7x7',
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'MaxPool3d_2a_3x3',
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'Conv3d_2b_1x1',
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'Conv3d_2c_3x3',
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'MaxPool3d_3a_3x3',
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'Mixed_3b',
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'Mixed_3c',
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'MaxPool3d_4a_3x3',
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'Mixed_4b',
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'Mixed_4c',
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'Mixed_4d',
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'Mixed_4e',
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'Mixed_4f',
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'MaxPool3d_5a_2x2',
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'Mixed_5b',
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'Mixed_5c',
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'Logits',
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'Predictions',
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)
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def __init__(self, num_classes=400, spatial_squeeze=True,
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final_endpoint='Logits', name='inception_i3d', in_channels=3, dropout_keep_prob=0.5):
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"""Initializes I3D model instance.
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Args:
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num_classes: The number of outputs in the logit layer (default 400, which
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matches the Kinetics dataset).
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spatial_squeeze: Whether to squeeze the spatial dimensions for the logits
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before returning (default True).
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final_endpoint: The model contains many possible endpoints.
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`final_endpoint` specifies the last endpoint for the model to be built
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up to. In addition to the output at `final_endpoint`, all the outputs
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at endpoints up to `final_endpoint` will also be returned, in a
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dictionary. `final_endpoint` must be one of
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InceptionI3d.VALID_ENDPOINTS (default 'Logits').
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name: A string (optional). The name of this module.
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Raises:
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ValueError: if `final_endpoint` is not recognized.
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"""
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if final_endpoint not in self.VALID_ENDPOINTS:
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raise ValueError('Unknown final endpoint %s' % final_endpoint)
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super(InceptionI3d, self).__init__()
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self._num_classes = num_classes
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self._spatial_squeeze = spatial_squeeze
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self._final_endpoint = final_endpoint
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self.logits = None
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if self._final_endpoint not in self.VALID_ENDPOINTS:
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raise ValueError('Unknown final endpoint %s' % self._final_endpoint)
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self.end_points = {}
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end_point = 'Conv3d_1a_7x7'
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self.end_points[end_point] = Unit3D(in_channels=in_channels, output_channels=64, kernel_shape=[7, 7, 7],
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stride=(2, 2, 2), padding=(3,3,3), name=name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'MaxPool3d_2a_3x3'
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self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[1, 3, 3], stride=(1, 2, 2),
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padding=0)
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if self._final_endpoint == end_point: return
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end_point = 'Conv3d_2b_1x1'
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self.end_points[end_point] = Unit3D(in_channels=64, output_channels=64, kernel_shape=[1, 1, 1], padding=0,
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name=name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'Conv3d_2c_3x3'
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self.end_points[end_point] = Unit3D(in_channels=64, output_channels=192, kernel_shape=[3, 3, 3], padding=1,
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name=name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'MaxPool3d_3a_3x3'
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self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[1, 3, 3], stride=(1, 2, 2),
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padding=0)
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if self._final_endpoint == end_point: return
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end_point = 'Mixed_3b'
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self.end_points[end_point] = InceptionModule(192, [64,96,128,16,32,32], name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'Mixed_3c'
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self.end_points[end_point] = InceptionModule(256, [128,128,192,32,96,64], name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'MaxPool3d_4a_3x3'
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self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[3, 3, 3], stride=(2, 2, 2),
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padding=0)
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if self._final_endpoint == end_point: return
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end_point = 'Mixed_4b'
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self.end_points[end_point] = InceptionModule(128+192+96+64, [192,96,208,16,48,64], name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'Mixed_4c'
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self.end_points[end_point] = InceptionModule(192+208+48+64, [160,112,224,24,64,64], name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'Mixed_4d'
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self.end_points[end_point] = InceptionModule(160+224+64+64, [128,128,256,24,64,64], name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'Mixed_4e'
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self.end_points[end_point] = InceptionModule(128+256+64+64, [112,144,288,32,64,64], name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'Mixed_4f'
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self.end_points[end_point] = InceptionModule(112+288+64+64, [256,160,320,32,128,128], name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'MaxPool3d_5a_2x2'
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self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[2, 2, 2], stride=(2, 2, 2),
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padding=0)
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if self._final_endpoint == end_point: return
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end_point = 'Mixed_5b'
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self.end_points[end_point] = InceptionModule(256+320+128+128, [256,160,320,32,128,128], name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'Mixed_5c'
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self.end_points[end_point] = InceptionModule(256+320+128+128, [384,192,384,48,128,128], name+end_point)
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if self._final_endpoint == end_point: return
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end_point = 'Logits'
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self.avg_pool = nn.AvgPool3d(kernel_size=[2, 7, 7],
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stride=(1, 1, 1))
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self.dropout = nn.Dropout(dropout_keep_prob)
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self.logits = Unit3D(in_channels=384+384+128+128, output_channels=self._num_classes,
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kernel_shape=[1, 1, 1],
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padding=0,
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activation_fn=None,
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use_batch_norm=False,
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use_bias=True,
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name='logits')
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self.build()
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def replace_logits(self, num_classes):
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self._num_classes = num_classes
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self.logits = Unit3D(in_channels=384+384+128+128, output_channels=self._num_classes,
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kernel_shape=[1, 1, 1],
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padding=0,
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activation_fn=None,
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use_batch_norm=False,
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use_bias=True,
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name='logits')
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def build(self):
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for k in self.end_points.keys():
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self.add_module(k, self.end_points[k])
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def forward(self, x):
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for end_point in self.VALID_ENDPOINTS:
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if end_point in self.end_points:
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x = self._modules[end_point](x) # use _modules to work with dataparallel
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x = self.logits(self.dropout(self.avg_pool(x)))
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if self._spatial_squeeze:
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logits = x.squeeze(3).squeeze(3)
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logits = logits.mean(dim=2)
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# logits is batch X time X classes, which is what we want to work with
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return logits
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def extract_features(self, x):
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for end_point in self.VALID_ENDPOINTS:
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if end_point in self.end_points:
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x = self._modules[end_point](x)
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return self.avg_pool(x) |