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import torch
import torch.nn as nn
import torch.nn.functional as functional
import random
class TransNetV2(nn.Module):
def __init__(self,
F=16, L=3, S=2, D=1024,
use_many_hot_targets=True,
use_frame_similarity=True,
use_color_histograms=True,
use_mean_pooling=False,
dropout_rate=0.5,
use_convex_comb_reg=False, # not supported
use_resnet_features=False, # not supported
use_resnet_like_top=False, # not supported
frame_similarity_on_last_layer=False): # not supported
super(TransNetV2, self).__init__()
if use_resnet_features or use_resnet_like_top or use_convex_comb_reg or frame_similarity_on_last_layer:
raise NotImplemented("Some options not implemented in Pytorch version of Transnet!")
self.SDDCNN = nn.ModuleList(
[StackedDDCNNV2(in_filters=3, n_blocks=S, filters=F, stochastic_depth_drop_prob=0.)] +
[StackedDDCNNV2(in_filters=(F * 2 ** (i - 1)) * 4, n_blocks=S, filters=F * 2 ** i) for i in range(1, L)]
)
self.frame_sim_layer = FrameSimilarity(
sum([(F * 2 ** i) * 4 for i in range(L)]), lookup_window=101, output_dim=128, similarity_dim=128, use_bias=True
) if use_frame_similarity else None
self.color_hist_layer = ColorHistograms(
lookup_window=101, output_dim=128
) if use_color_histograms else None
self.dropout = nn.Dropout(dropout_rate) if dropout_rate is not None else None
output_dim = ((F * 2 ** (L - 1)) * 4) * 3 * 6 # 3x6 for spatial dimensions
if use_frame_similarity: output_dim += 128
if use_color_histograms: output_dim += 128
self.fc1 = nn.Linear(output_dim, D)
self.cls_layer1 = nn.Linear(D, 1)
self.cls_layer2 = nn.Linear(D, 1) if use_many_hot_targets else None
self.use_mean_pooling = use_mean_pooling
self.eval()
def forward(self, inputs):
assert isinstance(inputs, torch.Tensor) and list(inputs.shape[2:]) == [27, 48, 3] and inputs.dtype == torch.uint8, \
"incorrect input type and/or shape"
# uint8 of shape [B, T, H, W, 3] to float of shape [B, 3, T, H, W]
x = inputs.permute([0, 4, 1, 2, 3]).float()
x = x.div_(255.)
block_features = []
for block in self.SDDCNN:
x = block(x)
block_features.append(x)
if self.use_mean_pooling:
x = torch.mean(x, dim=[3, 4])
x = x.permute(0, 2, 1)
else:
x = x.permute(0, 2, 3, 4, 1)
x = x.reshape(x.shape[0], x.shape[1], -1)
if self.frame_sim_layer is not None:
x = torch.cat([self.frame_sim_layer(block_features), x], 2)
if self.color_hist_layer is not None:
x = torch.cat([self.color_hist_layer(inputs), x], 2)
x = self.fc1(x)
x = functional.relu(x)
if self.dropout is not None:
x = self.dropout(x)
one_hot = self.cls_layer1(x)
if self.cls_layer2 is not None:
return one_hot, {"many_hot": self.cls_layer2(x)}
return one_hot
class StackedDDCNNV2(nn.Module):
def __init__(self,
in_filters,
n_blocks,
filters,
shortcut=True,
use_octave_conv=False, # not supported
pool_type="avg",
stochastic_depth_drop_prob=0.0):
super(StackedDDCNNV2, self).__init__()
if use_octave_conv:
raise NotImplemented("Octave convolution not implemented in Pytorch version of Transnet!")
assert pool_type == "max" or pool_type == "avg"
if use_octave_conv and pool_type == "max":
print("WARN: Octave convolution was designed with average pooling, not max pooling.")
self.shortcut = shortcut
self.DDCNN = nn.ModuleList([
DilatedDCNNV2(in_filters if i == 1 else filters * 4, filters, octave_conv=use_octave_conv,
activation=functional.relu if i != n_blocks else None) for i in range(1, n_blocks + 1)
])
self.pool = nn.MaxPool3d(kernel_size=(1, 2, 2)) if pool_type == "max" else nn.AvgPool3d(kernel_size=(1, 2, 2))
self.stochastic_depth_drop_prob = stochastic_depth_drop_prob
def forward(self, inputs):
x = inputs
shortcut = None
for block in self.DDCNN:
x = block(x)
if shortcut is None:
shortcut = x
x = functional.relu(x)
if self.shortcut is not None:
if self.stochastic_depth_drop_prob != 0.:
if self.training:
if random.random() < self.stochastic_depth_drop_prob:
x = shortcut
else:
x = x + shortcut
else:
x = (1 - self.stochastic_depth_drop_prob) * x + shortcut
else:
x += shortcut
x = self.pool(x)
return x
class DilatedDCNNV2(nn.Module):
def __init__(self,
in_filters,
filters,
batch_norm=True,
activation=None,
octave_conv=False): # not supported
super(DilatedDCNNV2, self).__init__()
if octave_conv:
raise NotImplemented("Octave convolution not implemented in Pytorch version of Transnet!")
assert not (octave_conv and batch_norm)
self.Conv3D_1 = Conv3DConfigurable(in_filters, filters, 1, use_bias=not batch_norm)
self.Conv3D_2 = Conv3DConfigurable(in_filters, filters, 2, use_bias=not batch_norm)
self.Conv3D_4 = Conv3DConfigurable(in_filters, filters, 4, use_bias=not batch_norm)
self.Conv3D_8 = Conv3DConfigurable(in_filters, filters, 8, use_bias=not batch_norm)
self.bn = nn.BatchNorm3d(filters * 4, eps=1e-3) if batch_norm else None
self.activation = activation
def forward(self, inputs):
conv1 = self.Conv3D_1(inputs)
conv2 = self.Conv3D_2(inputs)
conv3 = self.Conv3D_4(inputs)
conv4 = self.Conv3D_8(inputs)
x = torch.cat([conv1, conv2, conv3, conv4], dim=1)
if self.bn is not None:
x = self.bn(x)
if self.activation is not None:
x = self.activation(x)
return x
class Conv3DConfigurable(nn.Module):
def __init__(self,
in_filters,
filters,
dilation_rate,
separable=True,
octave=False, # not supported
use_bias=True,
kernel_initializer=None): # not supported
super(Conv3DConfigurable, self).__init__()
if octave:
raise NotImplemented("Octave convolution not implemented in Pytorch version of Transnet!")
if kernel_initializer is not None:
raise NotImplemented("Kernel initializers are not implemented in Pytorch version of Transnet!")
assert not (separable and octave)
if separable:
# (2+1)D convolution https://arxiv.org/pdf/1711.11248.pdf
conv1 = nn.Conv3d(in_filters, 2 * filters, kernel_size=(1, 3, 3),
dilation=(1, 1, 1), padding=(0, 1, 1), bias=False)
conv2 = nn.Conv3d(2 * filters, filters, kernel_size=(3, 1, 1),
dilation=(dilation_rate, 1, 1), padding=(dilation_rate, 0, 0), bias=use_bias)
self.layers = nn.ModuleList([conv1, conv2])
else:
conv = nn.Conv3d(in_filters, filters, kernel_size=3,
dilation=(dilation_rate, 1, 1), padding=(dilation_rate, 1, 1), bias=use_bias)
self.layers = nn.ModuleList([conv])
def forward(self, inputs):
x = inputs
for layer in self.layers:
x = layer(x)
return x
class FrameSimilarity(nn.Module):
def __init__(self,
in_filters,
similarity_dim=128,
lookup_window=101,
output_dim=128,
stop_gradient=False, # not supported
use_bias=False):
super(FrameSimilarity, self).__init__()
if stop_gradient:
raise NotImplemented("Stop gradient not implemented in Pytorch version of Transnet!")
self.projection = nn.Linear(in_filters, similarity_dim, bias=use_bias)
self.fc = nn.Linear(lookup_window, output_dim)
self.lookup_window = lookup_window
assert lookup_window % 2 == 1, "`lookup_window` must be odd integer"
def forward(self, inputs):
x = torch.cat([torch.mean(x, dim=[3, 4]) for x in inputs], dim=1)
x = torch.transpose(x, 1, 2)
x = self.projection(x)
x = functional.normalize(x, p=2, dim=2)
batch_size, time_window = x.shape[0], x.shape[1]
similarities = torch.bmm(x, x.transpose(1, 2)) # [batch_size, time_window, time_window]
similarities_padded = functional.pad(similarities, [(self.lookup_window - 1) // 2, (self.lookup_window - 1) // 2])
batch_indices = torch.arange(0, batch_size, device=x.device).view([batch_size, 1, 1]).repeat(
[1, time_window, self.lookup_window])
time_indices = torch.arange(0, time_window, device=x.device).view([1, time_window, 1]).repeat(
[batch_size, 1, self.lookup_window])
lookup_indices = torch.arange(0, self.lookup_window, device=x.device).view([1, 1, self.lookup_window]).repeat(
[batch_size, time_window, 1]) + time_indices
similarities = similarities_padded[batch_indices, time_indices, lookup_indices]
return functional.relu(self.fc(similarities))
class ColorHistograms(nn.Module):
def __init__(self,
lookup_window=101,
output_dim=None):
super(ColorHistograms, self).__init__()
self.fc = nn.Linear(lookup_window, output_dim) if output_dim is not None else None
self.lookup_window = lookup_window
assert lookup_window % 2 == 1, "`lookup_window` must be odd integer"
@staticmethod
def compute_color_histograms(frames):
frames = frames.int()
def get_bin(frames):
# returns 0 .. 511
R, G, B = frames[:, :, 0], frames[:, :, 1], frames[:, :, 2]
R, G, B = R >> 5, G >> 5, B >> 5
return (R << 6) + (G << 3) + B
batch_size, time_window, height, width, no_channels = frames.shape
assert no_channels == 3
frames_flatten = frames.view(batch_size * time_window, height * width, 3)
binned_values = get_bin(frames_flatten)
frame_bin_prefix = (torch.arange(0, batch_size * time_window, device=frames.device) << 9).view(-1, 1)
binned_values = (binned_values + frame_bin_prefix).view(-1)
histograms = torch.zeros(batch_size * time_window * 512, dtype=torch.int32, device=frames.device)
histograms.scatter_add_(0, binned_values, torch.ones(len(binned_values), dtype=torch.int32, device=frames.device))
histograms = histograms.view(batch_size, time_window, 512).float()
histograms_normalized = functional.normalize(histograms, p=2, dim=2)
return histograms_normalized
def forward(self, inputs):
x = self.compute_color_histograms(inputs)
batch_size, time_window = x.shape[0], x.shape[1]
similarities = torch.bmm(x, x.transpose(1, 2)) # [batch_size, time_window, time_window]
similarities_padded = functional.pad(similarities, [(self.lookup_window - 1) // 2, (self.lookup_window - 1) // 2])
batch_indices = torch.arange(0, batch_size, device=x.device).view([batch_size, 1, 1]).repeat(
[1, time_window, self.lookup_window])
time_indices = torch.arange(0, time_window, device=x.device).view([1, time_window, 1]).repeat(
[batch_size, 1, self.lookup_window])
lookup_indices = torch.arange(0, self.lookup_window, device=x.device).view([1, 1, self.lookup_window]).repeat(
[batch_size, time_window, 1]) + time_indices
similarities = similarities_padded[batch_indices, time_indices, lookup_indices]
if self.fc is not None:
return functional.relu(self.fc(similarities))
return similarities
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