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ventral_models.py
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import numpy as np
import math
import torch
from torch import nn
from scipy.stats import special_ortho_group
from skimage.transform import warp_polar
from utils import Timer
# from modules import RNN, MultRNN, MultiplicativeLayer
class LogPolarBasicMLP(nn.Module):
def __init__(self, input_size, layer_width, penult_size, output_size, device, drop=0.5):
super().__init__()
self.device = device
self.imsize = (48, 42)
self.layer0 = nn.Linear(input_size, layer_width)
self.layer1 = nn.Linear(layer_width, layer_width)
self.layer2 = nn.Linear(layer_width, layer_width)
self.drop_layer = nn.Dropout(p=drop)
self.layer3 = nn.Linear(layer_width, 100)
self.layer4 = nn.Linear(100, penult_size)
self.out = nn.Linear(penult_size, output_size)
self.LReLU = nn.LeakyReLU(0.1)
def warp(self, batch_of_img, xx, yy):
nex, h, w = batch_of_img.shape
warped_batch = np.zeros((nex, h*w))
for i, (img, x, y) in enumerate(zip(batch_of_img, xx, yy)):
warped = warp_polar(torch.squeeze(img), scaling='log', output_shape=self.imsize, center=(y.numpy(), x.numpy()), mode='edge')
warped_batch[i] = warped.flatten()
warped_batch = torch.from_numpy(warped_batch).float().to(self.device)
return warped_batch
def forward(self, im, xx, yy):
# warped = warp_polar(torch.squeeze(im), scaling='log', output_shape=self.imsize, center=(yy.numpy(), xx.numpy()), mode='edge')
# warped = torch.from_numpy(warped.flatten()).unsqueeze(0).to(self.device)
# timer = Timer()
warped = self.warp(im, xx, yy)
# timer.stop_timer()
x = self.LReLU(self.layer0(warped))
x = self.LReLU(self.layer1(x))
x = self.LReLU(self.layer2(x))
x = self.drop_layer(x)
x = self.LReLU(self.layer3(x))
x = self.LReLU(self.layer4(x))
pred = self.out(x)
return pred, x
class BasicMLP(nn.Module):
def __init__(self, input_size, layer_width, penult_size, output_size, drop=0.5):
super().__init__()
self.penult_size = penult_size
self.layer0 = nn.Linear(input_size, layer_width)
self.layer1 = nn.Linear(layer_width, layer_width)
self.layer2 = nn.Linear(layer_width, layer_width)
self.drop_layer = nn.Dropout(p=drop)
self.layer3 = nn.Linear(layer_width, 100)
self.layer4 = nn.Linear(100, penult_size)
self.out = nn.Linear(penult_size, output_size)
self.LReLU = nn.LeakyReLU(0.1)
def forward(self, x):
x = self.LReLU(self.layer0(x))
x = self.LReLU(self.layer1(x))
x = self.LReLU(self.layer2(x))
x = self.drop_layer(x)
x = self.LReLU(self.layer3(x))
x = self.LReLU(self.layer4(x))
pred = self.out(x)
return pred, x
class MLP(nn.Module):
def __init__(self, input_size, layer_width, penult_size, output_size, drop=0.5):
super().__init__()
self.layer0 = nn.Linear(input_size, layer_width)
self.BN0 = torch.nn.BatchNorm1d(layer_width)
self.layer1 = nn.Linear(layer_width, layer_width)
self.BN1 = torch.nn.BatchNorm1d(layer_width)
self.layer2 = nn.Linear(layer_width, layer_width)
self.BN2 = torch.nn.BatchNorm1d(layer_width)
self.drop_layer = nn.Dropout(p=drop)
self.layer3 = nn.Linear(layer_width, 100)
self.BN3 = torch.nn.BatchNorm1d(100)
self.layer4 = nn.Linear(100, penult_size)
self.BN4 = torch.nn.BatchNorm1d(penult_size)
# self.layers = [self.layer1, self.layer2, self.layer3]
self.out = nn.Linear(penult_size, output_size)
self.LReLU = nn.LeakyReLU(0.1)
def forward(self, x):
x = self.LReLU(self.BN0(self.layer0(x)))
x = self.LReLU(self.BN1(self.layer1(x)))
x = self.LReLU(self.BN2(self.layer2(x)))
x = self.drop_layer(x)
x = self.LReLU(self.BN3(self.layer3(x)))
x = self.LReLU(self.BN4(self.layer4(x)))
# for layerid in range(self.n_layers):
# x = self.LReLU(self.layers[layerid](x))
pred = self.out(x)
# pred = torch.clamp(pred, -1e6, 1e6)
return pred, x
class old_MLP(nn.Module):
def __init__(self, input_size, layer_width, n_layers, output_size, drop=0.5):
super().__init__()
self.n_layers = n_layers
self.layer0 = nn.Linear(input_size, layer_width)
self.BN0 = torch.nn.BatchNorm1d(layer_width)
self.layer1 = nn.Linear(layer_width, layer_width)
self.BN1 = torch.nn.BatchNorm1d(layer_width)
self.layer2 = nn.Linear(layer_width, layer_width)
self.BN2 = torch.nn.BatchNorm1d(layer_width)
self.drop_layer = nn.Dropout(p=drop)
self.layer3 = nn.Linear(layer_width, 100)
self.BN3 = torch.nn.BatchNorm1d(100)
# self.layers = [self.layer1, self.layer2, self.layer3]
self.out = nn.Linear(100, output_size)
self.LReLU = nn.LeakyReLU(0.1)
def forward(self, x):
x = self.LReLU(self.BN0(self.layer0(x)))
x = self.LReLU(self.BN1(self.layer1(x)))
x = self.LReLU(self.BN2(self.layer2(x)))
x = self.drop_layer(x)
x = self.LReLU(self.BN3(self.layer3(x)))
# x = self.LReLU(self.BN4(self.layer4(x)))
pred = self.out(x)
# pred = torch.clamp(pred, -1e6, 1e6)
return pred, x
class ConvNet(nn.Module):
"""Main Ventral Stream module.
Batch-norm seems to affect OOD generalization so shouldn't be included in any models, probably.
"""
def __init__(self, width, height, penult_size, output_size, **kwargs):
super().__init__()
grid = kwargs['grid'] if 'grid' in kwargs.keys() else 9
big = kwargs['big'] if 'big' in kwargs.keys() else False
dropout = kwargs['dropout'] if 'dropout' in kwargs.keys() else 0.0
# Larger version
# if big:
# self.kernel1_size = 2
# self.cnn1_nchannels_out = 56
# self.poolsize = 2
# self.kernel2_size = 2
# self.cnn2_nchannels_out = 56
# self.kernel3_size = 2
# self.cnn3_nchannels_out = 56
# self.kernel1_size = 3
# self.cnn1_nchannels_out = 128
# self.poolsize = 2
# self.kernel2_size = 2
# self.cnn2_nchannels_out = 256
# self.kernel3_size = 2
# self.cnn3_nchannels_out = 32
# elif grid==3: # Smaller version
self.kernel1_size = (3, 3) # height, width?
self.cnn1_nchannels_out = 10
# self.poolsize = 2
self.kernel2_size = (3, 3)
self.cnn2_nchannels_out = 10
self.kernel3_size = (3, 3)
self.cnn3_nchannels_out = 10
# elif grid==6: # Smaller version
# self.kernel1_size = 3
# self.cnn1_nchannels_out = 33
# self.poolsize = 2
# self.kernel2_size = 2
# self.cnn2_nchannels_out = 30
# self.kernel3_size = 2
# self.cnn3_nchannels_out = 20
# elif grid==9:
# self.kernel1_size = 3
# self.cnn1_nchannels_out = 33
# self.poolsize = 2
# self.kernel2_size = 2
# self.cnn2_nchannels_out = 20
# self.kernel3_size = 2
# self.cnn3_nchannels_out = 20
self.output_size = output_size
self.LReLU = nn.LeakyReLU(0.1)
# Default initialization is init.kaiming_uniform_(self.weight, a=math.sqrt(5))
# (Also assumes leaky Relu for gain)
# which is appropriate for all layers here
self.conv1 = nn.Conv2d(1, self.cnn1_nchannels_out, self.kernel1_size) # (NChannels_in, NChannels_out, kernelsize)
# self.BN0 = torch.nn.BatchNorm2d(self.cnn1_nchannels_out)
# torch.nn.init.kaiming_uniform_(self.conv1.weight, nonlinearity='relu')
# self.pool = nn.MaxPool2d(self.poolsize, self.poolsize) # kernel height, kernel width
self.conv2 = nn.Conv2d(self.cnn1_nchannels_out, self.cnn2_nchannels_out, self.kernel2_size) # (NChannels_in, NChannels_out, kernelsize)
# self.BN1 = torch.nn.BatchNorm2d(self.cnn2_nchannels_out)
self.conv3 = nn.Conv2d(self.cnn2_nchannels_out, self.cnn3_nchannels_out, self.kernel3_size)
# torch.nn.init.kaiming_uniform_(self.conv2.weight, nonlinearity='relu')
# track the size of the cnn transformations
# self.cnn2_width_out = ((width - self.kernel1_size+1) - self.kernel2_size + 1) // self.poolsize
# self.cnn2_height_out = ((height - self.kernel1_size+1) - self.kernel2_size + 1) // self.poolsize
# self.cnn3_width_out = (((width - self.kernel1_size+1) - self.kernel2_size + 1) // self.poolsize) - self.kernel3_size+1
# self.cnn3_height_out = (((height - self.kernel1_size+1) - self.kernel2_size + 1) // self.poolsize) - self.kernel3_size+1
self.cnn2_width_out = ((width - self.kernel1_size[1]+1) - self.kernel2_size[1] + 1)
self.cnn2_height_out = ((height - self.kernel1_size[0]+1) - self.kernel2_size[0] + 1)
self.cnn3_width_out = self.cnn2_width_out - self.kernel3_size[1] + 1
self.cnn3_height_out = self.cnn2_height_out - self.kernel3_size[0] + 1
# FC layers
self.fc1_size = 50
self.fc2_size = penult_size
# self.fc1 = nn.Linear(int(self.cnn2_nchannels_out * self.cnn2_width_out * self.cnn2_height_out), self.fc1_size) # size input, size output
# self.fc1_size = 120
self.fc1 = nn.Linear(int(self.cnn3_nchannels_out * self.cnn3_width_out * self.cnn3_height_out), self.fc1_size) # size input, size output
self.fc2 = nn.Linear(self.fc1_size, self.fc2_size)
self.fc3 = nn.Linear(self.fc2_size, output_size)
# Dropout
self.drop_layer = nn.Dropout(p=dropout)
def forward(self, x):
x = self.LReLU(self.conv1(x))
# x = self.BN0(self.LReLU(self.conv1(x)))
# x = self.pool(self.LReLU(self.conv2(x)))
x = self.LReLU(self.conv2(x))
# x = self.BN1(self.LReLU(self.conv2(x)))
x = self.LReLU(self.conv3(x))
x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3]) # this reshapes the tensor to be a 1D vector, from whatever the final convolutional layer output
# add dropout before fully connected layers, widest part of network
x = self.drop_layer(x)
x = self.LReLU(self.fc1(x))
fc2 = self.LReLU(self.fc2(x))
out = self.fc3(fc2)
return out, fc2