From 392f91b2f3837cb1b7df25d7b0499dff62320900 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Mon, 13 Mar 2023 13:19:13 +0900
Subject: [PATCH] from local

---
 egs/librispeech/ASR/.prepare.sh.swp           | Bin 0 -> 4096 bytes
 .../conformer.py                              | 234 ++++++++++++++++++
 2 files changed, 234 insertions(+)
 create mode 100644 egs/librispeech/ASR/.prepare.sh.swp

diff --git a/egs/librispeech/ASR/.prepare.sh.swp b/egs/librispeech/ASR/.prepare.sh.swp
new file mode 100644
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diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_gpat/conformer.py b/egs/librispeech/ASR/pruned_transducer_stateless_gpat/conformer.py
index a591964cc..6937bf5ee 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless_gpat/conformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_gpat/conformer.py
@@ -1595,6 +1595,240 @@ class Conv2dSubsampling(nn.Module):
         return x
 
 
+ACTIVATIONS = {
+    'relu': torch.nn.ReLU,
+    'leaky_relu': torch.nn.LeakyReLU,
+    'gelu': torch.nn.GELU,
+}
+
+
+class FCLayer(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, activation='gelu'):
+        super(FCLayer, self).__init__()
+        in_channels = int(in_channels)
+        out_channels = int(out_channels)
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.linear = torch.nn.Linear(in_channels, out_channels)
+        self.ins = torch.nn.InstanceNorm1d(out_channels, affine=True)
+        self.act = ACTIVATIONS[activation]()
+
+    def forward(self, x):
+        out = self.linear(x)
+        out = out.transpose(1,2)
+        out = self.ins(out)
+        out = self.act(out)
+        out = out.transpose(1,2)
+
+        return out
+
+
+class ConvLayer2(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, groups=1):
+        super(ConvLayer2, self).__init__()
+        reflection_padding = kernel_size // 2
+        self.reflection_pad = torch.nn.ReflectionPad1d(reflection_padding)
+        self.conv1d = ScaledConv1d(
+            in_channels, out_channels, kernel_size, stride, groups=groups)
+
+    def forward(self, x, pad=False):
+        if pad:
+            out = self.reflection_pad(x)
+        else:
+            out = x
+        out = self.conv1d(out)
+        return out
+
+
+class ResidualBlock2(torch.nn.Module):
+    """ResidualBlock
+    introduced in: https://arxiv.org/abs/1512.03385
+    recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
+    """
+
+    def __init__(self, channels, activation='gelu'):
+        super(ResidualBlock2, self).__init__()
+        self.conv1 = nn.Conv1d(channels, channels, kernel_size=2)
+        self.in1 = nn.InstanceNorm1d(channels, affine=True)
+        self.conv2 = nn.Conv1d(channels, channels, kernel_size=2)
+        self.in2 = nn.InstanceNorm1d(channels, affine=True)
+        self.reflection_pad = torch.nn.ReflectionPad1d(1)
+        self.act = ACTIVATIONS[activation]()
+
+    def forward(self, x):
+        residual = x
+        out = self.act(self.in1(self.conv1(self.reflection_pad(x))))
+        out = self.in2(self.conv2(out))
+        out = out + residual
+        return out
+
+
+class ViewMaker1(BaseFairseqModel):
+    '''Viewmaker network that stochastically maps a multichannel 2D input to an output of the same size.'''
+    def __init__(self, num_channels=512, distortion_budget=0.02, activation='gelu',
+                clamp=True, frequency_domain=False, downsample_to=False, num_res_blocks=0, num_noise=0):
+        '''Initialize the Viewmaker network.
+
+        Args:
+            num_channels: Number of channels in the input (e.g. 1 for speech, 3 for images)
+                Input will have shape [batch_size, num_channels, height, width]
+            distortion_budget: Distortion budget of the viewmaker (epsilon, in the paper).
+                Controls how strong the perturbations can be.
+            activation: The activation function used in the network ('relu' and 'leaky_relu' currently supported)
+            clamp: Whether to clamp the outputs to [0, 1] (useful to ensure output is, e.g., a valid image)
+            frequency_domain: Whether to apply perturbation (and distortion budget) in the frequency domain.
+                This is useful for shifting the inductive bias of the viewmaker towards more global / textural views.
+            downsample_to: Downsamples the image, applies viewmaker, then upsamples. Possibly useful for
+                higher-resolution inputs, but not evaluaed in the paper.
+            num_res_blocks: Number of residual blocks to use in the network.
+        '''
+        super().__init__()
+
+        self.num_channels = num_channels
+        self.num_res_blocks = num_res_blocks
+        self.activation = activation
+        self.clamp = clamp
+        self.frequency_domain = frequency_domain
+        self.downsample_to = downsample_to
+        self.distortion_budget = distortion_budget
+        self.num_noise = num_noise
+        self.act = ACTIVATIONS[activation]()
+
+        # Initial convolution layers (+ 1 for noise filter)
+        self.conv1 = ConvLayer2(self.num_channels + self.num_noise, \
+                self.num_channels, kernel_size=2, stride=1)
+        self.in1 = torch.nn.InstanceNorm1d(self.num_channels, affine=True)
+        self.conv2 = ConvLayer2(self.num_channels, self.num_channels, kernel_size=2, stride=1)
+        self.in2 = torch.nn.InstanceNorm1d(self.num_channels, affine=True)
+        self.conv3 = ConvLayer2(self.num_channels, self.num_channels, kernel_size=2, stride=1)
+        self.in3 = torch.nn.InstanceNorm1d(self.num_channels, affine=True)
+        self.conv4 = ConvLayer2(self.num_channels, self.num_channels, kernel_size=2, stride=1)
+        self.in4 = torch.nn.InstanceNorm1d(self.num_channels, affine=True)
+
+        # Residual layers have +N for added random channels
+        if not self.num_noise:
+            self.res1 = ResidualBlock2(self.num_channels + 1)
+            self.res2 = ResidualBlock2(self.num_channels + 2)
+            self.res3 = ResidualBlock2(self.num_channels + 3)
+            self.res4 = ResidualBlock2(self.num_channels + 4)
+            self.res5 = ResidualBlock2(self.num_channels + 5)
+            self.conv5 = ConvLayer2(self.num_channels+self.num_res_blocks, \
+                self.num_channels, kernel_size=2, stride=1)
+        else:
+            self.res1 = ResidualBlock2(self.num_channels)
+            self.res2 = ResidualBlock2(self.num_channels)
+            self.res3 = ResidualBlock2(self.num_channels)
+            self.res4 = ResidualBlock2(self.num_channels)
+            self.res5 = ResidualBlock2(self.num_channels)
+            self.conv5 = ConvLayer2(self.num_channels, \
+                self.num_channels, kernel_size=2, stride=1)
+
+        self.ins5 = torch.nn.InstanceNorm1d(self.num_channels, affine=True)
+        self.conv6 = ConvLayer2(self.num_channels, self.num_channels, kernel_size=2, stride=1)
+
+    @staticmethod
+    def zero_init(m):
+        if isinstance(m, (nn.Linear, nn.Conv2d)):
+            # actual 0 has symmetry problems
+            init.normal_(m.weight.data, mean=0, std=1e-4)
+            # init.constant_(m.weight.data, 0)
+            init.constant_(m.bias.data, 0)
+        elif isinstance(m, nn.BatchNorm1d):
+            pass
+
+    def add_noise_channel(self, x, num=1, bound_multiplier=1):
+        # bound_multiplier is a scalar or a 1D tensor of length batch_size
+        batch_size = x.size(0)
+        filter_size = x.size(-1)
+        shp = (batch_size, num, filter_size)
+        bound_multiplier = torch.tensor(bound_multiplier, device=x.device)
+        noise = torch.rand(shp, device=x.device) * bound_multiplier.view(-1, 1, 1)
+        #if x.dtype == 'torch.cuda.float16':
+        #    print('fuck'*100)
+        #noise.type(torch.cuda.float16)
+        noise = noise.half()
+        return torch.cat((x, noise), dim=1)
+
+    def basic_net(self, y, num_res_blocks=3, bound_multiplier=1):
+        if self.num_noise:
+            y = self.add_noise_channel(y, num=self.num_noise, bound_multiplier=bound_multiplier)
+        y = self.act(self.in1(self.conv1(y)))
+        y = self.act(self.in2(self.conv2(y, pad=True)))
+        y = self.act(self.in3(self.conv3(y)))
+        y = self.act(self.in4(self.conv4(y, pad=True)))
+
+        # Features that could be useful for other auxilary layers / losses.
+        # [batch_size, 128]
+        features = y.clone().mean([-1, -2])
+
+        for i, res in enumerate([self.res1, self.res2, self.res3, self.res4, self.res5]):
+            if i < num_res_blocks:
+                if not self.num_noise:
+                    y = res(self.add_noise_channel(y, bound_multiplier=bound_multiplier))
+                else:
+                    y = res(y)
+
+        y = self.act(self.ins5(self.conv5(y, pad=True)))
+        y = self.conv6(y)
+
+        return y, features
+    
+    def get_delta(self, y_pixels, eps=1e-4):
+        '''Constrains the input perturbation by projecting it onto an L1 sphere'''
+        distortion_budget = self.distortion_budget
+        delta = torch.tanh(y_pixels) # Project to [-1, 1]
+        avg_magnitude = delta.abs().mean([1,2], keepdim=True)
+        max_magnitude = distortion_budget
+        delta = delta * max_magnitude / (avg_magnitude + eps)
+        return delta
+
+    def get_delta2(self, y_pixels, padding_mask, eps=1e-4):
+        '''Constrains the input perturbation by projecting it onto an L1 sphere'''
+        if padding_mask is not None:
+            padding_mask_ = torch.logical_not(padding_mask)
+            padding_mask_ = padding_mask_.long().unsqueeze(2)
+            y_pixels = y_pixels.transpose(1,2)
+            y_pixels *= padding_mask_
+            y_pixels = y_pixels.transpose(1,2)
+
+        distortion_budget = self.distortion_budget
+        delta = torch.tanh(y_pixels) # Project to [-1, 1]
+        avg_magnitude = delta.abs().mean([1,2], keepdim=True)
+        max_magnitude = distortion_budget
+        delta = delta * max_magnitude / (avg_magnitude + eps)
+        return delta
+
+    def forward(self, x, padding_mask):
+        x = x.transpose(1,2)
+        if self.downsample_to:
+            # Downsample.
+            x_orig = x
+            x = torch.nn.functional.interpolate(
+                x, size=(self.downsample_to, self.downsample_to), mode='bilinear')
+        y = x
+
+        if self.frequency_domain and 0:
+            # Input to viewmaker is in frequency domain, outputs frequency domain perturbation.
+            # Uses the Discrete Cosine Transform.
+            # shape still [batch_size, C, W, H]
+            y = dct.dct_2d(y)
+
+        y_pixels, features = self.basic_net(y, self.num_res_blocks, bound_multiplier=1)
+        #delta = self.get_delta(y_pixels.clone())
+        delta = self.get_delta2(y_pixels.clone(), padding_mask)
+
+        # Additive perturbation
+        #result = x + delta
+        result = y_pixels
+
+        delta = delta.transpose(1,2)
+        result = result.transpose(1,2)
+
+        return result, delta
+
+
 if __name__ == "__main__":
     torch.set_num_threads(1)
     torch.set_num_interop_threads(1)