add i3d model

model/i3d.py

@@ -0,0 +1,335 @@
+# This code is from https://github.com/piergiaj/pytorch-i3d/blob/master/pytorch_i3d.py
+# I only fix flake8 errors and do some cleaning here
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class MaxPool3dSamePadding(nn.MaxPool3d):
+
+    def compute_pad(self, dim, s):
+        if s % self.stride[dim] == 0:
+            return max(self.kernel_size[dim] - self.stride[dim], 0)
+        else:
+            return max(self.kernel_size[dim] - (s % self.stride[dim]), 0)
+
+    def forward(self, x):
+        # compute 'same' padding
+        (batch, channel, t, h, w) = x.size()
+        pad_t = self.compute_pad(0, t)
+        pad_h = self.compute_pad(1, h)
+        pad_w = self.compute_pad(2, w)
+
+        pad_t_f = pad_t // 2
+        pad_t_b = pad_t - pad_t_f
+        pad_h_f = pad_h // 2
+        pad_h_b = pad_h - pad_h_f
+        pad_w_f = pad_w // 2
+        pad_w_b = pad_w - pad_w_f
+
+        pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b)
+        x = F.pad(x, pad)
+        return super(MaxPool3dSamePadding, self).forward(x)
+
+
+class Unit3D(nn.Module):
+
+    def __init__(self, in_channels,
+                 output_channels,
+                 kernel_shape=(1, 1, 1),
+                 stride=(1, 1, 1),
+                 padding=0,
+                 activation_fn=F.relu,
+                 use_batch_norm=True,
+                 use_bias=False,
+                 name='unit_3d'):
+        """Initializes Unit3D module."""
+        super(Unit3D, self).__init__()
+
+        self._output_channels = output_channels
+        self._kernel_shape = kernel_shape
+        self._stride = stride
+        self._use_batch_norm = use_batch_norm
+        self._activation_fn = activation_fn
+        self._use_bias = use_bias
+        self.name = name
+        self.padding = padding
+
+        self.conv3d = nn.Conv3d(in_channels=in_channels,
+                                out_channels=self._output_channels,
+                                kernel_size=self._kernel_shape,
+                                stride=self._stride,
+                                padding=0,  # we always want padding to be 0 here. We will
+                                            # dynamically pad based on input size in forward function
+                                bias=self._use_bias)
+
+        if self._use_batch_norm:
+            self.bn = nn.BatchNorm3d(self._output_channels, eps=0.001, momentum=0.01)
+
+    def compute_pad(self, dim, s):
+        if s % self._stride[dim] == 0:
+            return max(self._kernel_shape[dim] - self._stride[dim], 0)
+        else:
+            return max(self._kernel_shape[dim] - (s % self._stride[dim]), 0)
+
+    def forward(self, x):
+        # compute 'same' padding
+        (batch, channel, t, h, w) = x.size()
+        pad_t = self.compute_pad(0, t)
+        pad_h = self.compute_pad(1, h)
+        pad_w = self.compute_pad(2, w)
+
+        pad_t_f = pad_t // 2
+        pad_t_b = pad_t - pad_t_f
+        pad_h_f = pad_h // 2
+        pad_h_b = pad_h - pad_h_f
+        pad_w_f = pad_w // 2
+        pad_w_b = pad_w - pad_w_f
+
+        pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b)
+        x = F.pad(x, pad)
+
+        x = self.conv3d(x)
+        if self._use_batch_norm:
+            x = self.bn(x)
+        if self._activation_fn is not None:
+            x = self._activation_fn(x)
+        return x
+
+
+class InceptionModule(nn.Module):
+    def __init__(self, in_channels, out_channels, name):
+        super(InceptionModule, self).__init__()
+
+        self.b0 = Unit3D(in_channels=in_channels, output_channels=out_channels[0], kernel_shape=[1, 1, 1], padding=0,
+                         name=name + '/Branch_0/Conv3d_0a_1x1')
+        self.b1a = Unit3D(in_channels=in_channels, output_channels=out_channels[1], kernel_shape=[1, 1, 1], padding=0,
+                          name=name + '/Branch_1/Conv3d_0a_1x1')
+        self.b1b = Unit3D(in_channels=out_channels[1], output_channels=out_channels[2], kernel_shape=[3, 3, 3],
+                          name=name + '/Branch_1/Conv3d_0b_3x3')
+        self.b2a = Unit3D(in_channels=in_channels, output_channels=out_channels[3], kernel_shape=[1, 1, 1], padding=0,
+                          name=name + '/Branch_2/Conv3d_0a_1x1')
+        self.b2b = Unit3D(in_channels=out_channels[3], output_channels=out_channels[4], kernel_shape=[3, 3, 3],
+                          name=name + '/Branch_2/Conv3d_0b_3x3')
+        self.b3a = MaxPool3dSamePadding(kernel_size=[3, 3, 3],
+                                        stride=(1, 1, 1), padding=0)
+        self.b3b = Unit3D(in_channels=in_channels, output_channels=out_channels[5], kernel_shape=[1, 1, 1], padding=0,
+                          name=name + '/Branch_3/Conv3d_0b_1x1')
+        self.name = name
+
+    def forward(self, x):
+        b0 = self.b0(x)
+        b1 = self.b1b(self.b1a(x))
+        b2 = self.b2b(self.b2a(x))
+        b3 = self.b3b(self.b3a(x))
+        return torch.cat([b0, b1, b2, b3], dim=1)
+
+
+class InceptionI3d(nn.Module):
+    """Inception-v1 I3D architecture.
+    The model is introduced in:
+        Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
+        Joao Carreira, Andrew Zisserman
+        https://arxiv.org/pdf/1705.07750v1.pdf.
+    See also the Inception architecture, introduced in:
+        Going deeper with convolutions
+        Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
+        Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
+        http://arxiv.org/pdf/1409.4842v1.pdf.
+    """
+
+    # Endpoints of the model in order. During construction, all the endpoints up
+    # to a designated `final_endpoint` are returned in a dictionary as the
+    # second return value.
+    VALID_ENDPOINTS = (
+        'Conv3d_1a_7x7',
+        'MaxPool3d_2a_3x3',
+        'Conv3d_2b_1x1',
+        'Conv3d_2c_3x3',
+        'MaxPool3d_3a_3x3',
+        'Mixed_3b',
+        'Mixed_3c',
+        'MaxPool3d_4a_3x3',
+        'Mixed_4b',
+        'Mixed_4c',
+        'Mixed_4d',
+        'Mixed_4e',
+        'Mixed_4f',
+        'MaxPool3d_5a_2x2',
+        'Mixed_5b',
+        'Mixed_5c',
+        'Logits',
+        'Predictions',
+    )
+
+    def __init__(self, num_classes=400, spatial_squeeze=True,
+                 final_endpoint='Logits', name='inception_i3d', in_channels=3, dropout_keep_prob=0.5):
+        """Initializes I3D model instance.
+        Args:
+          num_classes: The number of outputs in the logit layer (default 400, which
+              matches the Kinetics dataset).
+          spatial_squeeze: Whether to squeeze the spatial dimensions for the logits
+              before returning (default True).
+          final_endpoint: The model contains many possible endpoints.
+              `final_endpoint` specifies the last endpoint for the model to be built
+              up to. In addition to the output at `final_endpoint`, all the outputs
+              at endpoints up to `final_endpoint` will also be returned, in a
+              dictionary. `final_endpoint` must be one of
+              InceptionI3d.VALID_ENDPOINTS (default 'Logits').
+          name: A string (optional). The name of this module.
+        Raises:
+          ValueError: if `final_endpoint` is not recognized.
+        """
+
+        if final_endpoint not in self.VALID_ENDPOINTS:
+            raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+        super(InceptionI3d, self).__init__()
+        self._num_classes = num_classes
+        self._spatial_squeeze = spatial_squeeze
+        self._final_endpoint = final_endpoint
+        self.logits = None
+
+        if self._final_endpoint not in self.VALID_ENDPOINTS:
+            raise ValueError('Unknown final endpoint %s' % self._final_endpoint)
+
+        self.end_points = {}
+        end_point = 'Conv3d_1a_7x7'
+        self.end_points[end_point] = Unit3D(in_channels=in_channels, output_channels=64, kernel_shape=[7, 7, 7],
+                                            stride=(2, 2, 2), padding=(3, 3, 3), name=name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'MaxPool3d_2a_3x3'
+        self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[1, 3, 3], stride=(1, 2, 2),
+                                                          padding=0)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Conv3d_2b_1x1'
+        self.end_points[end_point] = Unit3D(in_channels=64, output_channels=64, kernel_shape=[1, 1, 1], padding=0,
+                                            name=name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Conv3d_2c_3x3'
+        self.end_points[end_point] = Unit3D(in_channels=64, output_channels=192, kernel_shape=[3, 3, 3], padding=1,
+                                            name=name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'MaxPool3d_3a_3x3'
+        self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[1, 3, 3], stride=(1, 2, 2),
+                                                          padding=0)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Mixed_3b'
+        self.end_points[end_point] = InceptionModule(192, [64, 96, 128, 16, 32, 32], name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Mixed_3c'
+        self.end_points[end_point] = InceptionModule(256, [128, 128, 192, 32, 96, 64], name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'MaxPool3d_4a_3x3'
+        self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[3, 3, 3], stride=(2, 2, 2),
+                                                          padding=0)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Mixed_4b'
+        self.end_points[end_point] = InceptionModule(128 + 192 + 96 + 64, [192, 96, 208, 16, 48, 64], name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Mixed_4c'
+        self.end_points[end_point] = InceptionModule(192 + 208 + 48 + 64, [160, 112, 224, 24, 64, 64], name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Mixed_4d'
+        self.end_points[end_point] = InceptionModule(160 + 224 + 64 + 64, [128, 128, 256, 24, 64, 64], name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Mixed_4e'
+        self.end_points[end_point] = InceptionModule(128 + 256 + 64 + 64, [112, 144, 288, 32, 64, 64], name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Mixed_4f'
+        self.end_points[end_point] = InceptionModule(
+            112 + 288 + 64 + 64, [256, 160, 320, 32, 128, 128], name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'MaxPool3d_5a_2x2'
+        self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[2, 2, 2], stride=(2, 2, 2),
+                                                          padding=0)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Mixed_5b'
+        self.end_points[end_point] = InceptionModule(
+            256 + 320 + 128 + 128, [256, 160, 320, 32, 128, 128], name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Mixed_5c'
+        self.end_points[end_point] = InceptionModule(
+            256 + 320 + 128 + 128, [384, 192, 384, 48, 128, 128], name + end_point)
+        if self._final_endpoint == end_point:
+            return
+
+        end_point = 'Logits'
+        self.avg_pool = nn.AvgPool3d(kernel_size=[2, 7, 7],
+                                     stride=(1, 1, 1))
+        self.dropout = nn.Dropout(dropout_keep_prob)
+        self.logits = Unit3D(in_channels=384 + 384 + 128 + 128, output_channels=self._num_classes,
+                             kernel_shape=[1, 1, 1],
+                             padding=0,
+                             activation_fn=None,
+                             use_batch_norm=False,
+                             use_bias=True,
+                             name='logits')
+
+        self.build()
+
+    def replace_logits(self, num_classes):
+        self._num_classes = num_classes
+        self.logits = Unit3D(in_channels=384 + 384 + 128 + 128, output_channels=self._num_classes,
+                             kernel_shape=[1, 1, 1],
+                             padding=0,
+                             activation_fn=None,
+                             use_batch_norm=False,
+                             use_bias=True,
+                             name='logits')
+
+    def build(self):
+        for k in self.end_points.keys():
+            self.add_module(k, self.end_points[k])
+
+    def forward(self, x):
+        for end_point in self.VALID_ENDPOINTS:
+            if end_point in self.end_points:
+                x = self._modules[end_point](x)  # use _modules to work with dataparallel
+
+        x = self.logits(self.dropout(self.avg_pool(x)))
+        if self._spatial_squeeze:
+            logits = x.squeeze(3).squeeze(3)
+        # logits is batch X time X classes, which is what we want to work with
+        return logits
+
+    def extract_features(self, x, target_endpoint='Logits'):
+        for end_point in self.VALID_ENDPOINTS:
+            if end_point in self.end_points:
+                x = self._modules[end_point](x)
+                if end_point == target_endpoint:
+                    break
+        if target_endpoint == 'Logits':
+            return x.mean(4).mean(3).mean(2)
+        else:
+            return x