phenaki-cvivit / frozen_models /pytorch_i3d /pytorch_i3d.py

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	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch.autograd import Variable

	import numpy as np

	import os
	import sys
	from collections import OrderedDict


	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()
	#print t,h,w
	out_t = np.ceil(float(t) / float(self.stride[0]))
	out_h = np.ceil(float(h) / float(self.stride[1]))
	out_w = np.ceil(float(w) / float(self.stride[2]))
	#print out_t, out_h, out_w
	pad_t = self.compute_pad(0, t)
	pad_h = self.compute_pad(1, h)
	pad_w = self.compute_pad(2, w)
	#print pad_t, pad_h, pad_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)
	#print x.size()
	#print pad
	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()
	#print t,h,w
	out_t = np.ceil(float(t) / float(self._stride[0]))
	out_h = np.ceil(float(h) / float(self._stride[1]))
	out_w = np.ceil(float(w) / float(self._stride[2]))
	#print out_t, out_h, out_w
	pad_t = self.compute_pad(0, t)
	pad_h = self.compute_pad(1, h)
	pad_w = self.compute_pad(2, w)
	#print pad_t, pad_h, pad_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)
	#print x.size()
	#print pad
	x = F.pad(x, pad)
	#print x.size()

	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):
	for end_point in self.VALID_ENDPOINTS:
	if end_point in self.end_points:
	x = self._modules[end_point](x)
	return self.avg_pool(x)