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GDAS : Searching for A Robust Neural Architecture in Four GPU Hours
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gdas.py
# https://github.com/D-X-Y/AutoDL-Projects/issues/99
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import tensorflow as tf
# import numpy as np
VISUALIZER = 1
DEBUG = 1
logdir = 'runs/gdas_experiment_1'
if VISUALIZER:
# https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html
from torch.utils.tensorboard import SummaryWriter
# from tensorboardX import SummaryWriter
# default `log_dir` is "runs" - we'll be more specific here
writer = SummaryWriter(logdir)
# https://github.com/szagoruyko/pytorchviz
from torchviz import make_dot
if DEBUG:
torch.autograd.set_detect_anomaly(True)
tf.debugging.experimental.enable_dump_debug_info(logdir, tensor_debug_mode="FULL_HEALTH", circular_buffer_size=-1)
USE_CUDA = torch.cuda.is_available()
# https://arxiv.org/pdf/1806.09055.pdf#page=12
TEST_DATASET_RATIO = 0.5 # 50 percent of the dataset is dedicated for testing purpose
BATCH_SIZE = 4
NUM_OF_IMAGE_CHANNELS = 3 # RGB
IMAGE_HEIGHT = 32
IMAGE_WIDTH = 32
NUM_OF_IMAGE_CLASSES = 10
SIZE_OF_HIDDEN_LAYERS = 64
NUM_EPOCHS = 1
LEARNING_RATE = 0.025
MOMENTUM = 0.9
NUM_OF_CELLS = 8
NUM_OF_MIXED_OPS = 4
NUM_OF_PREVIOUS_CELLS_OUTPUTS = 2 # last_cell_output , second_last_cell_output
NUM_OF_NODES_IN_EACH_CELL = 5 # including the last node that combines the output from all 4 previous nodes
MAX_NUM_OF_CONNECTIONS_PER_NODE = NUM_OF_NODES_IN_EACH_CELL
NUM_OF_CHANNELS = 16
INTERVAL_BETWEEN_REDUCTION_CELLS = 3
PREVIOUS_PREVIOUS = 2 # (n-2)
REDUCTION_STRIDE = 2
NORMAL_STRIDE = 1
TAU_GUMBEL = 0.5
EDGE_WEIGHTS_NETWORK_IN_SIZE = 5
EDGE_WEIGHTS_NETWORK_OUT_SIZE = 2
# https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,
shuffle=True, num_workers=2)
valset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
valloader = torch.utils.data.DataLoader(valset, batch_size=BATCH_SIZE,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
TRAIN_BATCH_SIZE = int(len(trainset) * (1 - TEST_DATASET_RATIO))
# https://discordapp.com/channels/687504710118146232/703298739732873296/853270183649083433
# for training for edge weights as well as internal NN function weights
class Edge(nn.Module):
def __init__(self):
super(Edge, self).__init__()
# https://stackoverflow.com/a/51027227/8776167
# self.linear = nn.Linear(EDGE_WEIGHTS_NETWORK_IN_SIZE, EDGE_WEIGHTS_NETWORK_OUT_SIZE)
# https://pytorch.org/docs/stable/generated/torch.nn.parameter.Parameter.html
self.weights = nn.Parameter(torch.zeros(1),
requires_grad=True) # for edge weights, not for internal NN function weights
def __freeze_w(self):
self.weights.requires_grad = False
def __unfreeze_w(self):
self.weights.requires_grad = True
def __freeze_f(self):
for param in self.f.parameters():
param.requires_grad = False
def __unfreeze_f(self):
for param in self.f.parameters():
param.requires_grad = True
# for NN functions internal weights training
def forward_f(self, x):
self.__unfreeze_f()
self.__freeze_w()
# inheritance in python classes and SOLID principles
# https://en.wikipedia.org/wiki/SOLID
# https://blog.cleancoder.com/uncle-bob/2020/10/18/Solid-Relevance.html
return self.f(x)
# self-defined initial NAS architecture, for supernet architecture edge weight training
def forward_edge(self, x):
self.__freeze_f()
self.__unfreeze_w()
return x * self.weights
def forward(self, x, types):
y_hat = None
if types == "f":
y_hat = self.forward_f(x)
elif types == "edge":
y_hat = self.forward_edge(x)
return y_hat
class ConvEdge(Edge):
def __init__(self, stride):
super().__init__()
self.f = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=(3, 3), stride=(stride, stride), padding=1)
# class LinearEdge(Edge):
# def __init__(self):
# super().__init__()
# self.f = nn.Linear(84, 10)
class MaxPoolEdge(Edge):
def __init__(self, stride):
super().__init__()
self.f = nn.MaxPool2d(kernel_size=3, stride=stride, padding=1, ceil_mode=True)
class AvgPoolEdge(Edge):
def __init__(self, stride):
super().__init__()
self.f = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1, ceil_mode=True)
class Skip(nn.Module):
def forward(self, x):
return x
class SkipEdge(Edge):
def __init__(self):
super().__init__()
self.f = Skip()
# to collect and manage different edges between 2 nodes
class Connection(nn.Module):
def __init__(self, stride):
super(Connection, self).__init__()
if USE_CUDA:
# creates distinct edges and references each of them in a list (self.edges)
# self.linear_edge = LinearEdge().cuda()
self.conv2d_edge = ConvEdge(stride).cuda()
self.maxpool_edge = MaxPoolEdge(stride).cuda()
self.avgpool_edge = AvgPoolEdge(stride).cuda()
self.skip_edge = SkipEdge().cuda()
else:
# creates distinct edges and references each of them in a list (self.edges)
# self.linear_edge = LinearEdge()
self.conv2d_edge = ConvEdge(stride)
self.maxpool_edge = MaxPoolEdge(stride)
self.avgpool_edge = AvgPoolEdge(stride)
self.skip_edge = SkipEdge()
# self.edges = [self.conv2d_edge, self.maxpool_edge, self.avgpool_edge, self.skip_edge]
# python list will break the computation graph, need to use nn.ModuleList as a differentiable python list
self.edges = nn.ModuleList([self.conv2d_edge, self.maxpool_edge, self.avgpool_edge, self.skip_edge])
self.edge_weights = torch.zeros(NUM_OF_MIXED_OPS, requires_grad=True)
self.edges_results = torch.zeros([BATCH_SIZE, NUM_OF_IMAGE_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH],
requires_grad=False)
# for approximate architecture gradient
self.f_weights = torch.zeros(NUM_OF_MIXED_OPS, requires_grad=True)
self.f_weights_backup = torch.zeros(NUM_OF_MIXED_OPS, requires_grad=True)
self.weight_plus = torch.zeros(NUM_OF_MIXED_OPS, requires_grad=True)
self.weight_minus = torch.zeros(NUM_OF_MIXED_OPS, requires_grad=True)
# use linear transformation (weighted summation) to combine results from different edges
self.combined_feature_map = torch.zeros([BATCH_SIZE, NUM_OF_IMAGE_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH],
requires_grad=False)
if USE_CUDA:
self.combined_feature_map = self.combined_feature_map.cuda()
for e in range(NUM_OF_MIXED_OPS):
with torch.no_grad():
self.edge_weights[e] = self.edges[e].weights
# https://stackoverflow.com/a/45024500/8776167 extracts the weights learned through NN functions
# self.f_weights[e] = list(self.edges[e].parameters())
# Refer to GDAS equations (5) and (6)
# if one_hot is already there, would summation be required given that all other entries are forced to 0 ?
# It's not required, but you don't know, which index is one hot encoded 1.
# https://pytorch.org/docs/stable/nn.functional.html#gumbel-softmax
# See also https://github.com/D-X-Y/AutoDL-Projects/issues/10#issuecomment-916619163
gumbel = F.gumbel_softmax(self.edge_weights, tau=TAU_GUMBEL, hard=True)
self.chosen_edge = torch.argmax(gumbel, dim=0) # converts one-hot encoding into integer
def reinit(self):
self.combined_feature_map = torch.zeros([BATCH_SIZE, NUM_OF_IMAGE_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH],
requires_grad=False)
if USE_CUDA:
self.combined_feature_map = self.combined_feature_map.cuda()
# See https://www.reddit.com/r/pytorch/comments/rtlvtk/tensorboard_issue_with_selfdefined_forward/
# Tensorboard visualization requires a generic forward() function
def forward(self, x, types=None):
if USE_CUDA:
self.edges_results = self.edges_results.cuda()
for e in range(NUM_OF_MIXED_OPS):
self.edges_results = self.edges_results + self.edges[e].forward(x, types)
return self.edges_results
# to collect and manage multiple different connections between a particular node and its neighbouring nodes
class Node(nn.Module):
def __init__(self, stride):
super(Node, self).__init__()
# two types of output connections
# Type 1: (multiple edges) output connects to the input of the other intermediate nodes
# Type 2: (single edge) output connects directly to the final output node
# Type 1
self.connections = nn.ModuleList([Connection(stride) for i in range(MAX_NUM_OF_CONNECTIONS_PER_NODE)])
# Type 2
# depends on PREVIOUS node's Type 1 output
self.output = torch.zeros([BATCH_SIZE, NUM_OF_IMAGE_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH],
requires_grad=False) # for initialization
if USE_CUDA:
self.output = self.output.cuda()
def reinit(self):
self.output = torch.zeros([BATCH_SIZE, NUM_OF_IMAGE_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH],
requires_grad=False)
if USE_CUDA:
self.output = self.output.cuda()
# See https://www.reddit.com/r/pytorch/comments/rtlvtk/tensorboard_issue_with_selfdefined_forward/
# Tensorboard visualization requires a generic forward() function
def forward(self, x, node_num=0, types=None):
# not all nodes have same number of Type-1 output connection
for cc in range(MAX_NUM_OF_CONNECTIONS_PER_NODE - node_num - 1):
y = self.connections[cc].forward(x, types)
# combines all the feature maps from different mixed ops edges
self.connections[cc].combined_feature_map = \
self.connections[cc].combined_feature_map + y # Ltrain(w±, alpha)
return self.connections[0].combined_feature_map # why 0 ? all cc indices have the same variable value
# to manage all nodes within a cell
class Cell(nn.Module):
def __init__(self, stride):
super(Cell, self).__init__()
# all the coloured edges inside
# https://user-images.githubusercontent.com/3324659/117573177-20ea9a80-b109-11eb-9418-16e22e684164.png
# A single cell contains 'NUM_OF_NODES_IN_EACH_CELL' distinct nodes
# for the k-th node, we have (k+1) preceding nodes.
# Each intermediate state, 0->3 ('NUM_OF_NODES_IN_EACH_CELL-1'),
# is connected to each previous intermediate state
# as well as the output of the previous two cells, c_{k-2} and c_{k-1} (after a preprocessing layer).
# previous_previous_cell_output = c_{k-2}
# previous_cell_output = c{k-1}
self.nodes = nn.ModuleList([Node(stride) for i in range(NUM_OF_NODES_IN_EACH_CELL)])
# just for variables initialization
self.previous_cell = 0
self.previous_previous_cell = 0
self.output = torch.zeros([BATCH_SIZE, NUM_OF_IMAGE_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH],
requires_grad=False)
if USE_CUDA:
self.output = self.output.cuda()
for n in range(NUM_OF_NODES_IN_EACH_CELL):
# 'add' then 'concat' feature maps from different nodes
# needs to take care of tensor dimension mismatch
# See https://github.com/D-X-Y/AutoDL-Projects/issues/99#issuecomment-869100416
self.output = self.output + self.nodes[n].output
def reinit(self):
self.output = torch.zeros([BATCH_SIZE, NUM_OF_IMAGE_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH],
requires_grad=False)
if USE_CUDA:
self.output = self.output.cuda()
# See https://www.reddit.com/r/pytorch/comments/rtlvtk/tensorboard_issue_with_selfdefined_forward/
# Tensorboard visualization requires a generic forward() function
def forward(self, x, x1, x2, c=0, types=None):
for n in range(NUM_OF_NODES_IN_EACH_CELL):
if c <= 1:
if n == 0:
# Uses datasets as input
# x = train_inputs
if USE_CUDA:
x = x.cuda()
# combines all the feature maps from different mixed ops edges
self.nodes[n].output = \
self.nodes[n].forward(x, node_num=n, types=types) # Ltrain(w±, alpha)
else:
# Uses feature map output from previous neighbour nodes for further processing
for ni in range(n):
# nodes[ni] for previous nodes only
# connections[n-1] for neighbour nodes only
x = self.nodes[ni].connections[n-1].combined_feature_map
y = self.nodes[ni].forward(x, node_num=n, types=types) # Ltrain(w±, alpha)
# combines all the feature maps from different mixed ops edges
self.nodes[n].output = \
self.nodes[n].forward(y, node_num=n, types=types) # Ltrain(w±, alpha)
# Uses feature map output from previous neighbour cells for further processing
y1 = self.nodes[NUM_OF_NODES_IN_EACH_CELL - 1].forward(x1, node_num=n, types=types)
y2 = self.nodes[NUM_OF_NODES_IN_EACH_CELL - 1].forward(x2, node_num=n, types=types)
# combines all the feature maps from different mixed ops edges
self.nodes[n].output = \
self.nodes[n].forward(y1 + y2, node_num=n, types=types) # Ltrain(w±, alpha)
else:
if n == 0:
# Uses feature map output from previous neighbour cells for further processing
y1 = self.nodes[NUM_OF_NODES_IN_EACH_CELL - 1].forward(x1, node_num=n, types=types)
y2 = self.nodes[NUM_OF_NODES_IN_EACH_CELL - 1].forward(x2, node_num=n, types=types)
# combines all the feature maps from different mixed ops edges
self.nodes[n].output = \
self.nodes[n].forward(y1 + y2, node_num=n, types=types) # Ltrain(w±, alpha)
else:
# Uses feature map output from previous neighbour nodes for further processing
for ni in range(n):
# nodes[ni] for previous nodes only
# connections[n-1] for neighbour nodes only
x = self.nodes[ni].connections[n-1].combined_feature_map
y = self.nodes[ni].forward(x, node_num=n, types=types) # Ltrain(w±, alpha)
# combines all the feature maps from different mixed ops edges
self.nodes[n].output = \
self.nodes[n].forward(y, node_num=n, types=types) # Ltrain(w±, alpha)
# Uses feature map output from previous neighbour cells for further processing
y1 = self.nodes[NUM_OF_NODES_IN_EACH_CELL - 1].forward(x1, node_num=n, types=types)
y2 = self.nodes[NUM_OF_NODES_IN_EACH_CELL - 1].forward(x2, node_num=n, types=types)
# combines all the feature maps from different mixed ops edges
self.nodes[n].output = \
self.nodes[n].forward(y1 + y2, node_num=n, types=types) # Ltrain(w±, alpha)
# to manage all nodes
class Graph(nn.Module):
def __init__(self):
super(Graph, self).__init__()
stride = 0 # just to initialize a variable
for i in range(NUM_OF_CELLS):
if i % INTERVAL_BETWEEN_REDUCTION_CELLS == 0:
stride = REDUCTION_STRIDE # to emulate reduction cell by using normal cell with stride=2
else:
stride = NORMAL_STRIDE # normal cell
self.cells = nn.ModuleList([Cell(stride) for i in range(NUM_OF_CELLS)])
self.linears = nn.Linear(NUM_OF_IMAGE_CHANNELS * IMAGE_HEIGHT * IMAGE_WIDTH, NUM_OF_IMAGE_CLASSES)
def reinit(self):
# See https://discuss.pytorch.org/t/tensorboard-issue-with-self-defined-forward-function/140628/20?u=promach
for c in range(NUM_OF_CELLS):
self.cells[c].reinit()
for n in range(NUM_OF_NODES_IN_EACH_CELL):
self.cells[c].nodes[n].reinit()
# not all nodes have same number of Type-1 output connection
for cc in range(MAX_NUM_OF_CONNECTIONS_PER_NODE - n - 1):
self.cells[c].nodes[n].connections[cc].reinit()
def print_debug(self):
for c in range(NUM_OF_CELLS):
for n in range(NUM_OF_NODES_IN_EACH_CELL):
# not all nodes have same number of Type-1 output connection
for cc in range(MAX_NUM_OF_CONNECTIONS_PER_NODE - n - 1):
for e in range(NUM_OF_MIXED_OPS):
if DEBUG:
print("c = ", c, " , n = ", n, " , cc = ", cc, " , e = ", e)
print("graph.cells[", c, "].nodes[", n, "].connections[", cc,
"].combined_feature_map.grad_fn = ",
self.cells[c].nodes[n].connections[cc].combined_feature_map.grad_fn)
print("graph.cells[", c, "].output.grad_fn = ",
self.cells[c].output.grad_fn)
print("graph.cells[", c, "].nodes[", n, "].output.grad_fn = ",
self.cells[c].nodes[n].output.grad_fn)
if VISUALIZER == 0:
self.cells[c].nodes[n].output.retain_grad()
print("gradwalk(graph.cells[", c, "].nodes[", n, "].output.grad_fn)")
# gradwalk(graph.cells[c].nodes[n].output.grad_fn)
# 'add' then 'concat' feature maps from different nodes
# needs to take care of tensor dimension mismatch
# See https://github.com/D-X-Y/AutoDL-Projects/issues/99#issuecomment-869100416
self.cells[c].output = self.cells[c].output + self.cells[c].nodes[n].output
if DEBUG:
print("graph.cells[", c, "].output.grad_fn = ",
self.cells[c].output.grad_fn)
if VISUALIZER == 0:
self.cells[c].output.retain_grad()
print("gradwalk(graph.cells[", c, "].output.grad_fn)")
# gradwalk(graph.cells[c].output.grad_fn)
# See https://www.reddit.com/r/pytorch/comments/rtlvtk/tensorboard_issue_with_selfdefined_forward/
# Tensorboard visualization requires a generic forward() function
def forward(self, x, types=None):
# train_inputs = x
# https://www.reddit.com/r/learnpython/comments/no7btk/how_to_carry_extra_information_across_dag/
# https://docs.python.org/3/tutorial/datastructures.html
# generates a supernet consisting of 'NUM_OF_CELLS' cells
# each cell contains of 'NUM_OF_NODES_IN_EACH_CELL' nodes
# refer to PNASNet https://arxiv.org/pdf/1712.00559.pdf#page=5 for the cell arrangement
# https://pytorch.org/tutorials/beginner/examples_autograd/two_layer_net_custom_function.html
# encodes the cells and nodes arrangement in the multigraph
outputs1 = 0 # just for initialization, no special meaning
for c in range(NUM_OF_CELLS):
x1 = self.cells[c - 1].output
x2 = self.cells[c - PREVIOUS_PREVIOUS].output
self.cells[c].forward(x, x1, x2, c, types=types)
output_tensor = self.cells[NUM_OF_CELLS - 1].output
output_tensor = output_tensor.view(output_tensor.shape[0], -1)
if USE_CUDA:
output_tensor = output_tensor.cuda()
if DEBUG and VISUALIZER == 0:
output_tensor.retain_grad()
print("gradwalk(output_tensor.grad_fn)")
# gradwalk(output_tensor.grad_fn)
if USE_CUDA:
outputs1 = self.linears(output_tensor).cuda()
else:
outputs1 = self.linears(output_tensor)
if USE_CUDA:
outputs1 = outputs1.cuda()
return outputs1
total_grad_out = []
total_grad_in = []
def hook_fn_backward(module, grad_input, grad_output):
print(module) # for distinguishing module
# In order to comply with the order back-propagation, let's print grad_output
print('grad_output', grad_output)
# Reprint grad_input
print('grad_input', grad_input)
# Save to global variables
total_grad_in.append(grad_input)
total_grad_out.append(grad_output)
# for tracking the gradient back-propagation operation
def gradwalk(x, _depth=0):
if hasattr(x, 'grad'):
x = x.grad
if hasattr(x, 'next_functions'):
for fn in x.next_functions:
print(' ' * _depth + str(fn))
gradwalk(fn[0], _depth + 1)
# https://translate.google.com/translate?sl=auto&tl=en&u=http://khanrc.github.io/nas-4-darts-tutorial.html
def train_NN(forward_pass_only):
if DEBUG:
print("Entering train_NN(), forward_pass_only = ", forward_pass_only)
graph = Graph()
if USE_CUDA:
graph = graph.cuda()
if DEBUG:
modules = graph.named_children()
print("modules = ", modules)
if VISUALIZER == 0:
# Tensorboard does not like backward hook
for name, module in graph.named_modules():
module.register_full_backward_hook(hook_fn_backward)
criterion = nn.CrossEntropyLoss()
# criterion = nn.BCELoss()
optimizer1 = optim.SGD(graph.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)
# just for initialization, no special meaning
Ltrain = 0
NN_input = 0
NN_output = torch.tensor(0)
NN_train_labels = 0
for train_data, val_data in (zip(trainloader, valloader)):
NN_input, NN_train_labels = train_data
# val_inputs, val_labels = val_data
if USE_CUDA:
NN_input = NN_input.cuda()
NN_train_labels = NN_train_labels.cuda()
if forward_pass_only == 0:
# zero the parameter gradients
optimizer1.zero_grad()
# do train thing for internal NN function weights
NN_output = graph.forward(NN_input, types="f")
if VISUALIZER:
writer.add_graph(graph, NN_input)
writer.close()
make_dot(NN_output.mean(), params=dict(graph.named_parameters())).render("gdas_torchviz", format="svg")
if DEBUG:
print("outputs1.size() = ", NN_output.size())
print("train_labels.size() = ", NN_train_labels.size())
Ltrain = criterion(NN_output, NN_train_labels)
if forward_pass_only == 0:
# backward pass
if DEBUG:
Ltrain = Ltrain.requires_grad_()
Ltrain.retain_grad()
Ltrain.register_hook(lambda x: print(x))
Ltrain.backward()
if DEBUG:
print("starts to print graph.named_parameters()")
for name, param in graph.named_parameters():
print(name, param.grad)
print("finished printing graph.named_parameters()")
print("starts gradwalk()")
# gradwalk(Ltrain.grad_fn)
print("finished gradwalk()")
optimizer1.step()
graph.reinit()
else:
graph.reinit()
# no need to save model parameters for next epoch
return Ltrain
# DARTS's approximate architecture gradient. Refer to equation (8)
# needs to save intermediate trained model for Ltrain
path = './model.pth'
torch.save(graph, path)
if DEBUG:
print("after multiple for-loops")
return Ltrain
def train_architecture(forward_pass_only, train_or_val='val'):
if DEBUG:
print("Entering train_architecture(), forward_pass_only = ", forward_pass_only, " , train_or_val = ",
train_or_val)
graph = Graph()
if USE_CUDA:
graph = graph.cuda()
criterion = nn.CrossEntropyLoss()
optimizer2 = optim.SGD(graph.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)
# just for initialization, no special meaning
Lval = 0
train_inputs = 0
train_labels = 0
val_inputs = 0
val_labels = 0
if forward_pass_only == 0:
# do train thing for architecture edge weights
graph.train()
# zero the parameter gradients
optimizer2.zero_grad()
if DEBUG:
print("before multiple for-loops")
for train_data, val_data in (zip(trainloader, valloader)):
train_inputs, train_labels = train_data
val_inputs, val_labels = val_data
if USE_CUDA:
train_inputs = train_inputs.cuda()
train_labels = train_labels.cuda()
val_inputs = val_inputs.cuda()
val_labels = val_labels.cuda()
# forward pass
# use linear transformation ('weighted sum then concat') to combine results from different nodes
# into an output feature map to be fed into the next neighbour node for further processing
for c in range(NUM_OF_CELLS):
for n in range(NUM_OF_NODES_IN_EACH_CELL):
# not all nodes have same number of Type-1 output connection
for cc in range(MAX_NUM_OF_CONNECTIONS_PER_NODE - n - 1):
for e in range(NUM_OF_MIXED_OPS):
x = 0 # depends on the input tensor dimension requirement
if c == 0:
if train_or_val == 'val':
x = val_inputs
else:
x = train_inputs
else:
# Uses feature map output from previous neighbour node for further processing
x = graph.cells[c].nodes[n - 1].connections[cc].combined_feature_map
# need to take care of tensors dimension mismatch
graph.cells[c].nodes[n].connections[cc].combined_feature_map = \
graph.cells[c].nodes[n].connections[cc].combined_feature_map + \
graph.cells[c].nodes[n].connections[cc].edges[e].forward(x, "edge") # Lval(w*, alpha)
output2_tensor = graph.cells[NUM_OF_CELLS - 1].output
output2_tensor = output2_tensor.view(output2_tensor.shape[0], -1)
if USE_CUDA:
output2_tensor = output2_tensor.cuda()
if USE_CUDA:
m_linear = nn.Linear(NUM_OF_IMAGE_CHANNELS * IMAGE_HEIGHT * IMAGE_WIDTH, NUM_OF_IMAGE_CLASSES).cuda()
else:
m_linear = nn.Linear(NUM_OF_IMAGE_CHANNELS * IMAGE_HEIGHT * IMAGE_WIDTH, NUM_OF_IMAGE_CLASSES)
outputs2 = m_linear(output2_tensor)
if USE_CUDA:
outputs2 = outputs2.cuda()
if DEBUG:
print("outputs2.size() = ", outputs2.size())
print("val_labels.size() = ", val_labels.size())
print("train_labels.size() = ", train_labels.size())
if train_or_val == 'val':
loss = criterion(outputs2, val_labels)
else:
loss = criterion(outputs2, train_labels)
if forward_pass_only == 0:
# backward pass
Lval = loss
Lval = Lval.requires_grad_()
Lval.backward()
if DEBUG:
for name, param in graph.named_parameters():
print(name, param.grad)
optimizer2.step()
else:
# no need to save model parameters for next epoch
return loss
# needs to save intermediate trained model for Lval
path = './model.pth'
torch.save(graph, path)
# DARTS's approximate architecture gradient. Refer to equation (8) and https://i.imgur.com/81JFaWc.png
sigma = LEARNING_RATE
epsilon = 0.01 / torch.norm(Lval)
for c in range(NUM_OF_CELLS):
for n in range(NUM_OF_NODES_IN_EACH_CELL):
# not all nodes have same number of Type-1 output connection
for cc in range(MAX_NUM_OF_CONNECTIONS_PER_NODE - n - 1):
CC = graph.cells[c].nodes[n].connections[cc]
for e in range(NUM_OF_MIXED_OPS):
for w in graph.cells[c].nodes[n].connections[cc].edges[e].f.parameters():
# https://mythrex.github.io/math_behind_darts/
# Finite Difference Method
CC.weight_plus = w + epsilon * Lval
CC.weight_minus = w - epsilon * Lval
# Backups original f_weights
CC.f_weights_backup = w
# replaces f_weights with weight_plus before NN training
for c in range(NUM_OF_CELLS):
for n in range(NUM_OF_NODES_IN_EACH_CELL):
# not all nodes have same number of Type-1 output connection
for cc in range(MAX_NUM_OF_CONNECTIONS_PER_NODE - n - 1):
CC = graph.cells[c].nodes[n].connections[cc]
for e in range(NUM_OF_MIXED_OPS):
for w in graph.cells[c].nodes[n].connections[cc].edges[e].f.parameters():
w = CC.weight_plus
# test NN to obtain loss
Ltrain_plus = train_architecture(forward_pass_only=1, train_or_val='train')
# replaces f_weights with weight_minus before NN training
for c in range(NUM_OF_CELLS):
for n in range(NUM_OF_NODES_IN_EACH_CELL):
# not all nodes have same number of Type-1 output connection
for cc in range(MAX_NUM_OF_CONNECTIONS_PER_NODE - n - 1):
CC = graph.cells[c].nodes[n].connections[cc]
for e in range(NUM_OF_MIXED_OPS):
for w in graph.cells[c].nodes[n].connections[cc].edges[e].f.parameters():
w = CC.weight_minus
# test NN to obtain loss
Ltrain_minus = train_architecture(forward_pass_only=1, train_or_val='train')
# Restores original f_weights
for c in range(NUM_OF_CELLS):
for n in range(NUM_OF_NODES_IN_EACH_CELL):
# not all nodes have same number of Type-1 output connection
for cc in range(MAX_NUM_OF_CONNECTIONS_PER_NODE - n - 1):
CC = graph.cells[c].nodes[n].connections[cc]
for e in range(NUM_OF_MIXED_OPS):
for w in graph.cells[c].nodes[n].connections[cc].edges[e].f.parameters():
w = CC.f_weights_backup
if DEBUG:
print("after multiple for-loops")
L2train_Lval = (Ltrain_plus - Ltrain_minus) / (2 * epsilon)
return Lval - L2train_Lval
if __name__ == "__main__":
run_num = 0
not_converged = 1
while not_converged:
print("run_num = ", run_num)
ltrain = train_NN(forward_pass_only=0)
print("Finished train_NN()")
if VISUALIZER or DEBUG:
break # visualizer does not need more than a single run
# 'train_or_val' to differentiate between using training dataset and validation dataset
lval = train_architecture(forward_pass_only=0, train_or_val='val')
print("Finished train_architecture()")
print("lval = ", lval, " , ltrain = ", ltrain)
not_converged = (lval > 0.01) or (ltrain > 0.01)
run_num = run_num + 1
# do test thing
Raw
runtime.log
/home/phung/PycharmProjects/venv/py39/bin/python /home/phung/PycharmProjects/beginner_tutorial/gdas.py
Files already downloaded and verified
Files already downloaded and verified
run_num = 0
Entering train_NN(), forward_pass_only = 0
modules = <generator object Module.named_children at 0x7f16a2e85040>
Cannot insert a Tensor that requires grad as a constant. Consider making it a parameter or input, or detaching the gradient
Tensor:
(1,1,.,.) =
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-15.5072 -0.1251 25.6830 50.1746 67.5196 65.7014 57.2054
-8.0197 7.9882 35.2084 59.5613 69.2746 66.9879 62.9265
2.2895 23.7756 44.3000 62.2853 63.0237 59.9734 55.9629
24.2114 40.1442 55.3663 60.4716 61.7552 60.8929 59.2389
43.1664 60.0977 64.2011 68.1877 77.6621 80.5319 74.6192
63.7718 70.6980 65.5206 80.1913 93.8257 95.8802 92.4984
74.9608 66.2626 74.3060 93.3508 103.6712 106.2128 103.6031
73.0946 70.9612 86.8544 102.6991 109.8337 111.3051 106.6731
82.0438 89.5448 101.6418 109.2087 112.8337 112.7208 106.3688
98.0690 104.9863 109.9367 112.6901 113.7038 112.7851 103.7102
106.1938 109.7079 112.1405 113.3127 113.5862 111.6052 98.7197
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104.7703 109.9119 111.8132 112.1192 111.5788 108.0187 97.6402
102.9105 108.4494 110.4828 111.4786 111.0212 107.2795 98.6680
99.7810 105.1828 108.0264 109.0349 107.7430 103.5381 94.6493
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(2,1,.,.) =
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31.6665 33.2062 34.1797 33.5643 33.0451 32.0430 29.5934
53.2214 53.8921 54.6502 54.0594 52.3514 48.5310 43.1047
64.0985 63.7211 62.0312 59.7567 55.3783 48.0647 41.1412
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44.6622 50.0062 52.1760 51.2065 46.8913 37.0603 26.8445
45.4744 57.2953 66.4915 67.1011 61.9276 51.9440 42.1920
29.8086 50.8067 68.9147 73.3563 68.5454 58.7928 48.1395
3.8228 36.7201 61.9829 71.1861 67.2917 57.9040 47.8330
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41.9714 38.3040 35.1730 27.1094
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36.5701 31.6286 27.4363 19.5451
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(3,1,.,.) =
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41.0091 29.2440 16.4830 8.5236 19.2669 37.7880 50.2695 55.0822
31.4383 12.8749 2.0876 5.8144 22.9612 43.9660 59.9225 66.1410
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39.7414 41.2686 40.2048 38.6709 44.4708 47.2302 40.0115 6.7897
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0.5308 -1.8852 -5.7138 -12.2113 -23.2285 -32.1508 -36.2027 -28.1582
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3.3365 -20.3681 -42.0747 -52.4387 -53.6473 -54.9111 -50.4793 -38.9011
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11.3844 -6.4661 -23.2990 -33.7418 -34.8826 -44.1631 -51.5754 -42.8689
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(4,1,.,.) =
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-0.4997 -7.3814 -14.6524 -27.3246 -38.6323 -39.9897 -41.1621 -39.7500
17.4949 14.7794 8.5426 -0.1765 -8.2402 -18.9440 -34.2101 -37.1241
33.8320 32.8923 29.1507 21.8206 10.4240 -7.7641 -25.7279 -30.6681
41.6547 41.2552 38.2248 30.9168 18.0800 -0.2845 -13.0538 -17.7733
41.2929 39.6815 37.8901 32.2757 21.1444 7.3203 -0.4132 -4.0312
34.7549 32.3873 30.1524 27.4916 21.1539 12.2947 7.0722 5.7422
24.2261 20.3686 17.3926 16.3115 15.1272 12.2374 9.4364 9.4966
11.7373 6.9031 3.3242 3.3504 4.7198 6.1904 7.8377 9.4380
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-0.5726 2.0897 2.6787 1.9242 0.3533 -1.1568 -2.0064 -3.8450
2.3057 7.5944 9.6578 9.0424 8.0037 7.2558 5.8772 4.4459
1.6254 6.9093 9.7227 10.5007 11.3214 12.3841 12.7296 12.6205
-1.0719 3.0746 6.2807 7.7588 9.5911 11.5871 13.4586 15.0337
0.2032 3.6347 5.6839 6.2339 6.5291 8.0145 10.4235 12.9319
1.2758 4.9740 7.6429 8.7477 8.9124 9.0049 9.9955 11.2444
1.4090 5.0470 7.9584 9.9977 11.2953 12.5200 14.2179 15.8372
0.4413 3.7285 6.8168 9.2509 10.9562 12.7307 14.8018 16.7852
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1.6574 -5.6720 -12.9969 -20.2496 -21.4816 -17.3700 -2.6105 16.0629
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8.4730 4.1297 -5.9234 -15.1509 -14.4387 -5.8514 7.3161 19.5893
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11.6348 9.9550 5.0653 -6.1261 -20.1398 -11.7380 -2.8815 -1.6826
15.8538 15.2739 10.6302 -1.8965 -6.8910 5.0302 15.7464 17.6629
14.8765 15.2844 10.9749 3.5385 7.7628 18.8145 26.8985 27.4738
12.1569 12.6067 9.9786 10.6280 18.3714 26.3064 31.8359 31.6601
16.8164 16.1508 13.8119 13.7522 20.4957 26.8828 30.6047 31.9355
18.0281 16.9551 12.9467 10.3290 14.4588 18.6793 23.4191 27.7789
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15.7802 18.9600 10.3996 -6.4548 -25.1547 -40.0032 -53.4817 -58.2096
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14.5291 19.5056 15.8161 3.5432 -8.4941 -7.8532 -4.4420 -1.4709
10.9514 14.9745 12.1889 4.6085 -3.6877 -0.2756 5.6616 10.3240
5.5383 9.1877 8.5740 5.3287 0.3655 3.1480 10.5674 16.3960
10.0587 16.3942 17.3696 11.5949 4.9113 9.9988 15.6754 20.4869
18.7075 25.9188 24.8651 17.6307 14.1352 19.2264 24.5668 28.5080
25.9089 30.8979 28.5545 23.4645 23.8106 27.6098 32.9116 36.4689
28.4220 31.0696 29.5162 28.0787 28.3568 32.4961 36.4677 39.0231
30.1761 32.7854 32.0082 32.3325 34.0219 33.3658 32.6372 36.1462
30.4029 37.4166 38.3451 40.7506 40.4746 35.6568 34.5121 38.1547
26.4924 39.4002 46.6737 50.3975 52.0161 51.5776 53.2307 52.2313
17.4311 33.5892 49.1446 59.7653 64.7360 69.2286 68.0634 57.1204
0.7989 23.8250 44.7584 60.9574 71.6791 74.6651 68.5801 54.6928
-12.4482 12.0960 35.4040 54.3467 65.5561 65.0399 59.3666 47.6005
-1.9537 2.0553 21.4031 39.2740 43.3262 43.0576 39.4694 32.2570
12.1160 4.4037 10.0495 16.0363 18.5173 19.6116 18.9364 17.2400
21.6422 14.8027 8.0503 9.5093 13.7360 16.3087 17.0154 16.1899
28.3065 22.4957 15.7052 14.4422 19.0456 21.5157 22.1897 21.5077
30.9080 26.4061 19.3978 18.1560 22.2577 24.3836 24.8029 25.0163
28.1302 25.0901 19.4685 18.2630 21.4950 22.6092 23.0914 23.9870
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4.7226 23.8146 48.6709 63.2851 58.1670 34.0621 -3.0943 -22.9131
18.0400 32.3782 52.3811 60.7836 49.9739 24.3779 -10.9069 -26.9281
23.4939 37.4508 50.8279 49.7889 36.5651 11.4041 -18.0863 -29.9137
25.1252 36.3920 37.7682 32.1838 20.8846 -0.6679 -23.6825 -31.2201
31.4813 31.0843 23.3562 17.8675 6.7307 -9.2443 -27.9881 -30.4644
37.2571 31.8128 21.7354 11.4661 1.7996 -13.8783 -28.9617 -25.1606
38.4249 32.8042 24.8068 17.2702 4.1986 -10.9767 -20.5480 -15.5674
37.4346 35.5149 31.3607 22.3962 10.0077 -1.2839 -8.2870 -5.3875
40.5336 38.6148 33.8083 25.9409 16.6026 9.4106 4.1844 4.5309
44.7833 38.3321 34.4452 29.0618 24.2814 19.7197 16.1993 14.6814
44.4568 37.8928 34.1529 32.6594 30.4886 28.5548 26.9906 24.1288
42.3052 35.0554 33.8870 33.9280 34.0226 34.2197 34.3795 29.5772
35.8703 30.5838 30.9973 32.2651 33.6645 35.4771 36.4926 30.4031
25.4665 23.0815 25.1877 27.7196 30.9676 33.6129 34.9067 28.4846
14.5169 15.7410 18.2721 22.0206 25.7014 28.2699 29.6254 23.9478
14.8495 13.9001 14.5507 17.0051 19.3801 21.1651 22.0012 17.7288
20.5904 20.2611 19.9363 19.2860 19.1210 18.2797 16.4403 12.8477
25.4396 25.2981 24.7856 24.2895 23.3566 21.5447 18.4828 13.2261
24.5828 24.8150 24.7476 24.3106 23.4126 21.3807 17.8836 12.1203
(1,2,.,.) =
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2.3846 24.0464 44.0169 55.2098 48.7748 32.2938 20.2723 14.9363
7.8805 29.3894 49.7229 54.5624 42.9570 28.0573 21.6361 19.0767
19.0807 34.1538 49.4806 46.6434 38.1921 20.4647 14.3257 18.3333
30.8715 39.3421 43.5604 40.3116 36.6609 30.6807 21.2802 19.4073
44.5811 44.6357 51.3203 56.3806 56.7171 49.5790 39.2134 30.8989
54.2390 43.5180 59.1057 72.4067 71.9955 65.6746 57.6730 36.5285
47.8852 49.6065 71.2657 80.9022 79.4452 77.3391 66.3628 34.0915
45.5943 61.0374 79.1409 83.5144 82.5996 81.4395 66.5010 31.4329
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4.4189 8.8628 12.1128 13.3165 14.0081 17.5825 19.7590 19.6126
14.2388 18.5778 22.0581 24.1094 23.8343 25.5345 28.2938 25.6222
(3,2,.,.) =
Columns 1 to 8 -16.6847 -14.2567 -12.9117 -11.1636 -11.4787 -9.8439 -5.0495 -2.6906
-23.1240 -19.2701 -18.2903 -14.4586 -13.4610 -12.5919 -9.7019 -7.7059
-22.2848 -17.8199 -16.9520 -13.5897 -11.6041 -11.3418 -11.1012 -6.6488
-19.3825 -14.1840 -13.1480 -11.7426 -10.4918 -9.2679 -6.5937 -0.4914
-18.4476 -12.3455 -11.5347 -10.7400 -8.9124 -5.2724 0.7125 9.2694
-19.5089 -12.7173 -12.7314 -11.4721 -5.3307 1.7921 11.6655 17.2891
-21.8778 -16.0911 -16.9857 -9.6546 -0.4148 10.4329 19.8243 24.3773
-25.8397 -21.8713 -22.7477 -8.4991 9.8578 16.4218 22.7695 25.7695
-29.7575 -27.4443 -25.1332 -8.1446 12.2722 20.7549 20.9833 19.1757
-34.0664 -32.1454 -27.8084 -10.5051 9.3286 16.9306 12.2088 6.0856
-38.1942 -37.1463 -32.7822 -17.9578 -3.9322 0.9048 0.8862 -7.2272
-35.9751 -35.4527 -36.6853 -34.0330 -28.1170 -22.0145 -18.4904 -16.7991
-27.1699 -26.0607 -30.5713 -38.8938 -42.5401 -32.1682 -24.5364 -17.6292
-23.3853 -9.5552 -10.2710 -17.9384 -27.9822 -25.1839 -16.4922 -9.1453
-25.1122 -6.4151 8.2096 4.4224 -2.4789 -4.8550 -9.9410 -2.3859
-26.6111 -9.0332 8.3974 19.5375 18.3595 10.6508 -2.8183 -0.3270
-32.7373 -18.2351 0.5238 17.5322 24.3593 18.2473 5.7649 1.1054
-37.6081 -31.0766 -12.6271 7.7601 18.8568 22.0941 14.7136 10.8588
-40.9511 -37.3317 -28.8511 -9.8070 6.4414 16.4989 20.0379 16.2723
-44.8669 -42.5832 -41.6314 -28.5649 -8.7960 8.1027 17.9613 22.0842
-52.4430 -49.2314 -51.6163 -43.2519 -24.7991 -4.0330 10.9727 24.4998
-59.5748 -56.5681 -57.6603 -55.0686 -38.9170 -19.3836 2.2766 20.0061
-60.4400 -58.8482 -60.8549 -62.2472 -51.2162 -33.4535 -11.0841 12.3363
-53.9881 -51.9920 -54.9429 -62.8052 -60.5576 -46.5103 -24.3478 -2.4062
-48.3247 -46.3965 -52.3850 -60.7480 -66.0186 -55.9002 -39.2253 -21.6569
-44.1767 -44.1500 -50.0271 -55.1246 -63.0608 -63.1886 -53.1725 -34.0563
-40.1173 -41.1975 -43.5939 -49.7206 -59.4169 -66.8192 -61.1785 -49.0242
-36.3638 -35.6097 -39.1017 -47.0838 -56.6684 -60.5542 -65.3097 -61.5385
-34.9158 -32.7738 -38.1514 -44.8127 -49.1072 -52.5055 -61.3779 -67.0447
-36.0317 -33.0336 -35.9804 -40.8889 -41.3166 -46.1402 -54.5979 -61.0688
-38.7300 -36.2911 -36.7009 -36.0911 -35.2419 -38.8271 -44.9025 -53.3268
-37.5928 -39.1074 -39.7245 -35.2338 -32.7076 -32.8481 -37.9690 -47.1033
Columns 9 to 16 -0.8753 0.9136 1.5592 2.2190 0.5762 -5.6205 -9.4605 -5.4704
-5.5388 -0.4900 2.7473 3.1859 -1.0869 -11.1779 -16.8965 -13.3474
0.3624 5.5018 8.2283 7.5465 1.3055 -3.8070 -7.7514 -10.7409
8.5633 12.5415 12.4775 5.1431 5.4556 6.6288 2.3133 -4.6416
15.1705 17.2689 10.7214 -1.0350 5.5281 11.0065 7.3885 2.7684
20.1638 18.2379 9.0001 -4.0221 4.7053 11.9161 14.6597 13.7625
21.1127 14.3989 8.4889 -2.5203 6.4375 15.9263 21.6487 23.4730
17.2787 5.8337 3.4298 -2.6196 5.9458 20.8331 31.1034 33.6729
13.9806 -3.2795 -7.0557 -3.6170 7.7490 23.6565 36.5527 40.4446
3.6763 -8.2497 -11.9312 0.3152 14.1591 28.0420 38.9924 42.6141
-9.3415 -13.1122 -10.7666 2.2957 16.1778 30.6668 40.0091 41.8727
-17.1353 -17.2574 -11.3749 1.0832 12.4173 27.4001 33.5574 36.1418
-12.3459 -11.2294 -14.2401 -10.6718 0.7900 16.5385 21.7188 27.7743
-1.9568 2.3263 -5.2255 -17.2850 -6.0496 5.8931 7.9434 16.7876
9.0986 13.9434 5.2930 -13.2140 -13.3044 -0.4068 8.8654 15.1033
14.6347 21.1185 13.7429 0.9466 -8.1888 -1.5338 13.0271 20.1389
16.3460 27.2023 25.0505 17.1844 4.3325 -1.4001 13.8488 19.6472
15.0208 28.7443 32.3071 26.7657 12.3694 -3.1555 7.5590 11.5298
14.1317 26.0894 35.0698 31.8318 18.1106 -6.3895 -9.9088 -5.5883
19.1625 21.2276 33.5339 35.5574 22.7385 -4.2336 -27.8397 -25.7149
26.6966 19.7567 31.4701 36.6587 27.0290 -0.5340 -29.2609 -39.6072
28.5564 25.5332 31.2839 33.8139 29.6706 6.6118 -17.4304 -43.4761
23.9543 30.0175 30.6115 27.6184 28.9303 19.0297 -4.5272 -36.8701
13.8914 27.2199 30.6471 26.4301 31.4332 27.4326 5.8384 -22.7360
3.4564 18.9100 25.8192 27.6355 31.9695 30.9195 15.0384 -7.2551
-14.3397 1.8268 12.9585 23.6599 29.0893 30.1531 21.7884 0.8872
-35.1839 -18.5294 -0.7711 13.0061 22.6436 22.9020 19.8854 0.7610
-51.3427 -35.8326 -17.7397 -1.8952 4.7111 6.3895 4.9092 -8.1179
-60.8091 -49.5582 -36.1021 -24.9005 -17.9737 -11.0098 -12.1832 -21.1752
-63.5858 -59.2643 -52.9643 -44.4404 -29.8651 -23.2326 -24.8709 -29.8501
-55.7515 -56.3631 -59.2220 -51.0695 -38.4502 -35.6076 -36.4972 -38.0441
-49.2616 -50.5818 -55.1163 -50.7889 -43.8405 -43.4757 -43.6005 -42.3832
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-10.7520 -13.5045 -19.4148 -24.7806 -30.3007 -38.6603 -30.8180 -15.0728
-12.8728 -14.9941 -15.5868 -16.8683 -22.9901 -33.7505 -24.4526 -3.3431
-10.2589 -10.8767 -7.7200 -8.0039 -14.9989 -20.4812 -12.4780 3.7502
-0.6248 -1.2838 1.8456 1.8507 -1.1599 -1.1896 1.7719 9.7305
12.5274 11.8870 11.5316 11.0498 13.6648 14.4981 14.8674 13.6248
23.0028 20.3839 16.3928 15.2624 21.1744 23.2232 21.6888 17.8628
31.4198 24.9950 23.5110 22.9851 25.4362 27.3882 25.3447 22.3473
36.7778 30.2412 31.8042 32.1874 32.0669 28.4470 24.4058 20.9506
39.6125 35.2607 35.6141 34.7132 31.2567 23.5841 15.3395 10.2671
41.1286 37.8571 33.6702 29.4961 25.6398 17.6497 3.2625 -5.1700
39.9708 37.4602 28.3478 23.2955 23.7953 18.6931 2.6227 -8.6527
34.5887 33.7051 23.8007 20.8377 26.4481 19.1615 3.3288 -6.3140
25.4412 25.9838 20.1247 23.2905 26.9465 17.8697 3.6471 -5.6211
20.9688 21.6076 22.7948 27.2471 24.6315 17.6957 5.6459 -3.6171
22.5400 22.1183 24.4460 30.2402 30.6054 24.0040 19.4320 6.9724
20.4584 20.0013 20.7330 26.2979 43.7708 44.5979 37.8754 9.8727
12.0809 12.8709 12.4194 18.4192 46.8261 57.4274 43.8504 4.4357
-3.5281 -0.1517 1.0457 13.1652 47.1490 60.9419 41.4905 0.5962
-18.1624 -12.9969 -11.5918 4.7443 45.5204 61.1635 37.3923 -2.1674
-32.0009 -30.9267 -31.2089 -2.8275 42.2351 59.3660 35.6966 -5.4342
-50.6065 -50.3745 -45.6069 -9.1160 36.8697 55.1951 36.5677 -0.6932
-52.5834 -56.7491 -49.4477 -15.9534 29.4090 49.0509 37.4496 7.1316
-38.2855 -43.3069 -47.3859 -21.7614 22.3123 42.2240 39.0849 15.1489
-22.9768 -30.2709 -37.0886 -24.8236 14.5702 34.2475 39.7943 21.2524
-16.9501 -25.3560 -32.3998 -28.0855 1.4774 26.4009 37.7805 26.1694
-19.7235 -29.9872 -33.4882 -34.1339 -12.6969 15.4210 28.9720 27.5866
-27.6764 -38.0743 -42.8465 -43.2524 -24.6041 0.7750 16.0522 24.2644
-37.1065 -45.1407 -49.9815 -50.0677 -36.4176 -13.7163 6.0843 18.0137
-40.1906 -45.4894 -46.9282 -49.0770 -43.2223 -28.8217 -7.9653 5.3058
-40.7609 -44.1596 -42.6654 -43.9889 -46.6168 -41.6409 -26.5981 -7.3938
-41.3554 -42.2585 -40.4611 -39.8682 -42.6222 -46.0191 -35.5928 -22.6478
Columns 25 to 32 -17.7102 -11.7133 -10.5582 -18.3062 -31.9989 -38.0428 -32.8695 -33.7046
-5.7325 -2.3313 -5.0385 -21.4017 -40.1452 -43.1709 -29.0334 -25.8997
11.7873 14.9024 6.5456 -15.3745 -39.4144 -43.1428 -22.0049 -14.4510
18.8273 21.7022 12.1521 -10.3384 -28.6005 -35.7965 -21.3776 -11.3639
18.9200 20.0528 13.4088 -2.5917 -10.7269 -16.5793 -19.4416 -17.2047
12.5144 13.4214 11.6535 5.0196 3.4730 -2.6138 -15.7001 -29.0908
11.0730 4.8496 5.7108 7.7574 10.8354 6.2457 -6.5360 -23.4883
11.0314 -5.5074 -6.5104 6.8564 16.9678 15.4995 5.2341 -16.5113
10.4982 -11.0168 -10.0738 9.6822 19.7805 18.8932 8.6786 -13.0271
5.5531 -5.7943 -1.8043 13.0496 18.4970 13.5545 4.7281 -12.9985
-5.9800 -3.3178 5.3613 12.9348 9.6571 3.0528 -4.1053 -16.3035
-5.2408 2.0652 6.6506 3.2060 -2.9587 -9.2395 -13.7280 -18.2999
-1.4023 0.1440 -3.3336 -7.1102 -13.8859 -19.2296 -19.9327 -19.9023
-8.4584 -11.2823 -13.5312 -21.5946 -26.1107 -27.0060 -21.6036 -20.0315
-18.9409 -25.0683 -24.8087 -29.8228 -37.4737 -32.2368 -21.3857 -19.1890
-22.3850 -37.9765 -32.9430 -32.9070 -36.4850 -30.0478 -19.2833 -17.2182
-25.3086 -40.6407 -33.2221 -30.8244 -32.2820 -27.1348 -17.9638 -16.0205
-28.2732 -37.4090 -31.5000 -30.8959 -31.3289 -25.5984 -18.0032 -16.1567
-32.2784 -34.1908 -30.9536 -32.5431 -29.9772 -25.2121 -18.7247 -17.9492
-31.2238 -32.5084 -31.3665 -31.0290 -29.0452 -23.0915 -19.2756 -20.8319
-28.2050 -30.0898 -30.0914 -30.0744 -27.4323 -23.2163 -19.3051 -19.7520
-22.6997 -27.4595 -28.2800 -29.5057 -27.7630 -24.4074 -20.4812 -21.2048
-17.5108 -24.9231 -25.7398 -27.3833 -28.4262 -25.6743 -21.1138 -22.1387
-11.8394 -22.8183 -24.7774 -26.1203 -27.6460 -26.1298 -20.8186 -20.6618
-7.0732 -23.4963 -26.1257 -25.3894 -27.0077 -27.6561 -22.8214 -22.2121
-1.8258 -21.5258 -28.4165 -25.1279 -26.5202 -30.2019 -26.0439 -25.9763
6.8353 -11.8887 -24.9348 -25.7196 -26.0318 -30.3860 -29.0958 -29.3533
15.3580 -2.8438 -20.2515 -23.0414 -22.9091 -27.0434 -30.4191 -30.3334
19.5947 3.3431 -10.5987 -12.5770 -12.9121 -21.1134 -30.6151 -32.8639
13.7528 10.5772 -1.1020 -2.2552 -9.3930 -20.4602 -32.8258 -37.2465
2.2221 7.2001 5.3531 -0.9299 -12.9746 -22.8065 -34.5771 -40.8682
-9.9798 -4.8022 -1.6029 -5.1386 -16.0906 -26.2150 -34.3696 -38.9833
(4,2,.,.) =
Columns 1 to 8 -30.6153 -31.5315 -35.9364 -33.5531 -29.5402 -28.0125 -28.3486 -31.8471
-41.6369 -40.8240 -44.4168 -41.9367 -37.9680 -36.8730 -37.1831 -40.3878
-50.5277 -49.5091 -50.6954 -49.8157 -48.4048 -48.4682 -48.9924 -50.3864
-55.1051 -53.0492 -52.8623 -52.8312 -53.3521 -54.6479 -56.0726 -57.5768
-56.1247 -52.8604 -50.9063 -50.9585 -52.1713 -53.6710 -55.0726 -56.2318
-55.8308 -49.5845 -46.6393 -47.1124 -47.9920 -48.4222 -47.6867 -47.7357
-55.1366 -46.4976 -38.9733 -38.0729 -38.9174 -40.6309 -39.5086 -36.9138
-53.7022 -44.5077 -32.8306 -29.6742 -30.5990 -37.6030 -37.1673 -30.9524
-46.8341 -42.3837 -31.8356 -27.1381 -28.4399 -36.9902 -37.2164 -29.7941
-38.5462 -36.7422 -32.7555 -32.2931 -31.3354 -34.3287 -37.7579 -33.5223
-31.2208 -30.9797 -37.8656 -44.2166 -39.8523 -37.4249 -42.5674 -41.7619
-26.3180 -28.2440 -38.8552 -51.8692 -46.8632 -43.2318 -44.6530 -44.3582
-23.8910 -26.7341 -36.9088 -48.9439 -54.5456 -47.8041 -41.1131 -35.8995
-21.3706 -24.2789 -32.5861 -42.9992 -50.4329 -43.1181 -36.8065 -31.2138
-18.6928 -22.5301 -29.3497 -38.1549 -42.8266 -38.8212 -35.4199 -29.6844
-18.1186 -23.3593 -28.1869 -32.3939 -33.1261 -31.8508 -35.4631 -30.1791
-20.5593 -26.3754 -26.4329 -22.8577 -21.7892 -24.3240 -29.1004 -23.7547
-17.1345 -18.5078 -13.6278 -9.3325 -7.6211 -9.7766 -11.5678 -9.2354
-6.6280 -4.2008 1.8334 5.6975 6.9327 6.0711 6.3585 6.6316
-1.3704 2.6701 9.4038 13.2593 14.7993 15.5177 16.3217 16.1512
0.0970 5.3340 11.0177 14.5344 16.5280 18.0148 19.2959 19.5091
-0.2857 4.9168 9.2705 12.5957 15.0365 16.8112 18.5324 19.4064
-0.0973 2.0948 4.9496 8.0090 10.0525 12.9062 15.8613 16.7680
7.8176 8.3492 7.1991 5.6737 5.5986 8.5760 11.5694 11.8109
13.4536 14.7504 13.7397 12.8730 12.3858 12.7155 12.3930 10.3477
14.4073 16.9449 17.5090 18.7133 19.5788 19.5014 18.7330 17.1752
11.8456 14.4334 16.1778 18.8077 21.2613 22.4770 22.6900 22.2473
7.7253 9.7808 12.9192 16.4692 18.7090 20.4585 22.1209 23.0410
8.0509 11.6421 13.7404 14.6823 15.0304 16.3760 18.4509 20.2941
8.7339 12.5233 15.0296 16.0675 15.9750 16.1060 17.2168 18.4881
8.4242 12.3004 15.0071 16.4427 17.1994 18.1522 19.6437 21.0827
6.5881 9.5190 12.1037 13.9888 15.1717 16.6134 18.1868 19.5390
Columns 9 to 16 -38.0811 -39.9544 -36.9486 -35.6058 -35.8118 -37.4913 -37.5723 -35.6547
-46.2711 -46.8337 -42.8420 -41.1878 -41.7534 -43.5957 -42.9676 -40.5183
-54.0772 -52.4082 -47.7480 -45.7233 -45.9983 -47.5657 -46.2316 -43.5088
-58.9538 -55.3673 -50.8915 -49.1533 -48.9115 -49.8471 -48.0854 -44.8768
-57.4753 -56.2371 -52.0142 -49.0248 -48.3264 -48.8977 -50.0060 -47.3359
-47.9753 -47.5701 -45.4179 -41.6902 -40.6285 -42.0417 -46.1088 -44.1917
-36.7853 -37.3488 -35.4925 -32.0020 -31.6397 -35.0910 -38.2052 -30.4802
-30.0633 -31.9519 -29.1607 -24.7075 -25.0774 -33.5689 -31.2682 -16.3054
-28.4070 -30.3981 -25.8847 -20.9041 -24.5715 -33.4626 -25.9569 -6.0888
-32.1203 -29.8617 -21.9176 -19.4145 -26.0866 -31.5214 -21.1660 -1.4339
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-33.3107 -26.7889 -19.6522 -21.3212 -23.4054 -19.0945 -13.3946 -0.4700
-26.3762 -23.0773 -21.5681 -24.4160 -23.5044 -18.7628 -15.0028 -4.0486
-24.1142 -22.2193 -26.0422 -29.0740 -23.4927 -19.9592 -16.5295 -5.4700
-23.3463 -24.7353 -31.1671 -33.4890 -23.9184 -22.0667 -16.9638 -5.6861
-20.5616 -24.9008 -34.8697 -33.3976 -26.2540 -26.2198 -16.2435 -5.5438
-8.8647 -15.4178 -28.1090 -30.8400 -28.9848 -29.3745 -14.5313 2.2606
4.7955 -4.7961 -18.3015 -21.4037 -21.2331 -23.1345 -12.0198 7.0888
13.5786 5.0428 -6.1023 -13.0165 -14.6078 -14.4111 -5.4419 11.2181
17.8789 12.6849 2.7100 -10.7129 -12.9034 -7.9816 1.1639 14.8384
18.8032 16.3388 6.3605 -7.7332 -12.6709 -6.0509 2.5707 13.6878
16.5780 15.8547 9.1088 -3.7698 -13.6470 -8.4687 -2.7966 8.2197
12.1646 12.6428 9.9648 -2.0682 -15.4361 -17.5134 -16.6985 -3.0778
8.9261 8.8975 6.9322 -2.5165 -18.7000 -29.3924 -29.4891 -17.1131
15.8422 14.4545 11.1838 -0.7945 -20.0732 -20.0218 -16.9053 -16.5119
21.6853 20.8663 17.5396 5.3883 -15.0742 -6.5718 2.1315 0.6106
23.6274 23.9117 21.2038 8.1018 -8.9490 2.6084 12.6932 12.9589
21.5174 22.6296 20.8973 10.4194 3.2673 10.3453 17.5939 18.1441
18.7438 18.8575 16.4485 11.8153 12.5986 15.6801 18.4455 19.6023
21.6811 21.5828 17.7837 10.4548 13.5617 15.3799 16.9035 19.1448
20.5645 20.8563 17.6900 8.3712 7.9684 8.1011 10.0445 15.1424
Columns 17 to 24 -31.7360 -26.5720 -23.9534 -23.7792 -25.5086 -30.7933 -39.2514 -45.3171
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-43.4209 -39.7853 -37.8069 -37.7187 -41.7846 -48.3883 -47.8378 -51.0368
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-28.8813 -30.2602 -31.0518 -26.6858 -26.0815 -29.7400 -42.2723 -49.6952
-11.6637 -15.2258 -21.5641 -21.3648 -21.0660 -28.6377 -44.6418 -53.7004
2.4298 -2.7238 -11.1603 -16.4811 -21.2197 -30.8447 -45.4205 -56.4419
12.8252 10.4150 -0.9171 -15.0965 -23.6171 -32.9991 -46.4601 -53.8671
16.8056 18.9191 4.8973 -13.0957 -23.9776 -32.0424 -40.1102 -43.9702
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(4,3,.,.) =
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51.8846 51.0501 53.7646 57.0308 60.3700 63.0481 63.7865 58.0206
48.2052 47.5515 50.5871 55.0772 59.5585 63.7399 65.8463 60.7248
38.0658 38.5105 43.0329 48.7062 54.6693 59.1620 61.5058 57.2390
27.8526 29.1017 33.8353 40.2292 45.7627 49.8615 51.7400 48.2200
31.8622 29.5878 29.8241 33.7864 37.5757 39.8845 40.5458 37.2494
41.8055 40.3313 39.1189 38.7566 38.6835 37.4826 34.5553 29.4285
47.1542 46.9981 46.4659 45.6105 44.3029 41.5119 36.6253 28.7814
39.3012 40.1436 40.1276 39.6747 38.6660 36.2682 31.9187 24.5210
[ torch.cuda.FloatTensor{4,3,32,32} ]
Error occurs, No graph saved
Traceback (most recent call last):
File "/home/phung/PycharmProjects/beginner_tutorial/gdas.py", line 829, in <module>
ltrain = train_NN(forward_pass_only=0)
File "/home/phung/PycharmProjects/beginner_tutorial/gdas.py", line 585, in train_NN
writer.add_graph(graph, NN_input)
File "/home/phung/PycharmProjects/venv/py39/lib/python3.9/site-packages/torch/utils/tensorboard/writer.py", line 736, in add_graph
self._get_file_writer().add_graph(graph(model, input_to_model, verbose, use_strict_trace))
File "/home/phung/PycharmProjects/venv/py39/lib/python3.9/site-packages/torch/utils/tensorboard/_pytorch_graph.py", line 295, in graph
raise e
File "/home/phung/PycharmProjects/venv/py39/lib/python3.9/site-packages/torch/utils/tensorboard/_pytorch_graph.py", line 289, in graph
trace = torch.jit.trace(model, args, strict=use_strict_trace)
File "/home/phung/PycharmProjects/venv/py39/lib/python3.9/site-packages/torch/jit/_trace.py", line 741, in trace
return trace_module(
File "/home/phung/PycharmProjects/venv/py39/lib/python3.9/site-packages/torch/jit/_trace.py", line 958, in trace_module
module._c._create_method_from_trace(
File "/home/phung/PycharmProjects/venv/py39/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/phung/PycharmProjects/venv/py39/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1090, in _slow_forward
result = self.forward(*input, **kwargs)
File "/home/phung/PycharmProjects/beginner_tutorial/gdas.py", line 485, in forward
self.cells[c].forward(x, x1, x2, c, types=types)
File "/home/phung/PycharmProjects/beginner_tutorial/gdas.py", line 342, in forward
self.nodes[n].forward(x, node_num=n, types=types) # Ltrain(w±, alpha)
File "/home/phung/PycharmProjects/beginner_tutorial/gdas.py", line 282, in forward
y = self.connections[cc].forward(x, types)
File "/home/phung/PycharmProjects/beginner_tutorial/gdas.py", line 243, in forward
self.edges_results = self.edges_results.cuda()
RuntimeError: Cannot insert a Tensor that requires grad as a constant. Consider making it a parameter or input, or detaching the gradient
Tensor:
(1,1,.,.) =
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45.4744 57.2953 66.4915 67.1011 61.9276 51.9440 42.1920
29.8086 50.8067 68.9147 73.3563 68.5454 58.7928 48.1395
3.8228 36.7201 61.9829 71.1861 67.2917 57.9040 47.8330
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2.0579 1.9520 2.5118 4.9358
24.8037 24.2842 22.9820 19.3707
37.8047 36.0490 32.2246 26.4156
41.9714 38.3040 35.1730 27.1094
41.4784 36.8420 33.1855 24.9387
36.5701 31.6286 27.4363 19.5451
28.5064 25.5267 22.8936 15.9561
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(3,1,.,.) =
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21.2345 32.0695 35.4018 23.5252 9.5203 3.0126 -2.5618 -12.6389
36.8958 43.7896 35.9984 20.7996 11.2288 11.3390 7.7195 0.8444
47.1140 43.9667 31.5659 15.7755 10.3370 18.5994 21.3531 19.2095
47.8077 38.9941 26.0983 11.6099 15.5626 27.7637 35.7602 38.4320
41.0091 29.2440 16.4830 8.5236 19.2669 37.7880 50.2695 55.0822
31.4383 12.8749 2.0876 5.8144 22.9612 43.9660 59.9225 66.1410
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15.3472 19.2055 11.4622 2.0331 -7.2527 1.8828 21.2105 36.5080
24.6798 29.3967 27.9584 19.5088 9.0675 5.8710 22.9056 35.1619
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16.6743 15.4024 13.2283 8.6342 10.1928 11.8265 15.3938 20.3199
38.0998 34.6472 26.7740 24.9103 27.9606 30.4587 31.2294 30.0674
54.2621 46.8235 41.0603 41.5864 43.2727 45.4664 42.0369 35.8028
63.8699 57.6396 55.1278 56.6642 56.7747 51.1641 44.6283 33.1900
69.1437 66.4653 64.7672 62.1038 56.2629 46.7944 36.5404 23.6858
71.1903 69.8048 64.0029 55.1107 47.8880 38.3457 22.9859 8.5589
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60.1386 59.7200 50.8410 42.3459 41.8001 32.3993 14.7765 0.9237
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39.7414 41.2686 40.2048 38.6709 44.4708 47.2302 40.0115 6.7897
25.8904 26.5735 25.7328 30.1118 48.9973 59.3321 45.9271 3.4092
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(4,1,.,.) =
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(2,2,.,.) =
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-56.7540 -60.6660 -61.5896 -60.9815 -58.5577 -54.0022 -50.3022 -49.4867
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-29.3046 -26.9749 -23.6069 -21.2677 -21.5662 -22.9889 -23.3359 -23.2357
-46.8647 -45.4923 -41.5444 -38.3155 -39.0899 -40.8567 -42.0471 -41.8723
-55.5318 -56.0146 -54.3983 -52.2740 -53.5687 -56.4609 -58.7213 -58.5797
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-35.8153 -42.3852 -46.2096 -49.2117 -55.7347 -61.8063 -65.6007 -67.5955
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-51.4761 -47.1232 -43.3747 -44.2231 -40.9117 -36.5947 -35.1354 -38.1328
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17.4057 6.0774 -3.0758 -25.8782 -45.2219 -45.2809 -41.4539 -24.0618
51.9883 44.8601 19.7473 -20.5055 -36.1605 -31.5135 -26.7057 -22.0119
68.0783 56.1361 21.6914 -20.0179 -23.8750 -11.6420 -5.6209 -0.8588
57.6059 44.8051 16.8294 -16.3485 -17.2830 -4.0948 4.0322 9.2745
Columns 25 to 32 -8.3330 -7.3120 -2.2215 1.1732 2.8090 3.4500 3.2266 2.0362
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-43.5903 -29.5422 -2.7697 19.9533 29.5380 31.3083 31.0009 27.4029
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-45.6070 -27.0430 1.3605 21.2182 27.7957 28.7699 26.9162 23.2519
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2.6024 23.3474 26.1478 17.1930 -10.0077 -28.0542 -18.6660 -9.4954
12.6262 46.5880 57.7550 42.3683 13.5878 -16.5442 -19.0435 -14.6973
14.8518 53.5541 65.9920 54.7558 23.4540 -13.8407 -27.9556 -28.0204
13.7931 43.8025 50.6854 45.2300 20.0783 -15.1421 -36.9484 -39.0569
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4.4189 8.8628 12.1128 13.3165 14.0081 17.5825 19.7590 19.6126
14.2388 18.5778 22.0581 24.1094 23.8343 25.5345 28.2938 25.6222
(3,2,.,.) =
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-23.1240 -19.2701 -18.2903 -14.4586 -13.4610 -12.5919 -9.7019 -7.7059
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-25.1122 -6.4151 8.2096 4.4224 -2.4789 -4.8550 -9.9410 -2.3859
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12.5274 11.8870 11.5316 11.0498 13.6648 14.4981 14.8674 13.6248
23.0028 20.3839 16.3928 15.2624 21.1744 23.2232 21.6888 17.8628
31.4198 24.9950 23.5110 22.9851 25.4362 27.3882 25.3447 22.3473
36.7778 30.2412 31.8042 32.1874 32.0669 28.4470 24.4058 20.9506
39.6125 35.2607 35.6141 34.7132 31.2567 23.5841 15.3395 10.2671
41.1286 37.8571 33.6702 29.4961 25.6398 17.6497 3.2625 -5.1700
39.9708 37.4602 28.3478 23.2955 23.7953 18.6931 2.6227 -8.6527
34.5887 33.7051 23.8007 20.8377 26.4481 19.1615 3.3288 -6.3140
25.4412 25.9838 20.1247 23.2905 26.9465 17.8697 3.6471 -5.6211
20.9688 21.6076 22.7948 27.2471 24.6315 17.6957 5.6459 -3.6171
22.5400 22.1183 24.4460 30.2402 30.6054 24.0040 19.4320 6.9724
20.4584 20.0013 20.7330 26.2979 43.7708 44.5979 37.8754 9.8727
12.0809 12.8709 12.4194 18.4192 46.8261 57.4274 43.8504 4.4357
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12.5144 13.4214 11.6535 5.0196 3.4730 -2.6138 -15.7001 -29.0908
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10.4982 -11.0168 -10.0738 9.6822 19.7805 18.8932 8.6786 -13.0271
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(4,2,.,.) =
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-1.3704 2.6701 9.4038 13.2593 14.7993 15.5177 16.3217 16.1512
0.0970 5.3340 11.0177 14.5344 16.5280 18.0148 19.2959 19.5091
-0.2857 4.9168 9.2705 12.5957 15.0365 16.8112 18.5324 19.4064
-0.0973 2.0948 4.9496 8.0090 10.0525 12.9062 15.8613 16.7680
7.8176 8.3492 7.1991 5.6737 5.5986 8.5760 11.5694 11.8109
13.4536 14.7504 13.7397 12.8730 12.3858 12.7155 12.3930 10.3477
14.4073 16.9449 17.5090 18.7133 19.5788 19.5014 18.7330 17.1752
11.8456 14.4334 16.1778 18.8077 21.2613 22.4770 22.6900 22.2473
7.7253 9.7808 12.9192 16.4692 18.7090 20.4585 22.1209 23.0410
8.0509 11.6421 13.7404 14.6823 15.0304 16.3760 18.4509 20.2941
8.7339 12.5233 15.0296 16.0675 15.9750 16.1060 17.2168 18.4881
8.4242 12.3004 15.0071 16.4427 17.1994 18.1522 19.6437 21.0827
6.5881 9.5190 12.1037 13.9888 15.1717 16.6134 18.1868 19.5390
Columns 9 to 16 -38.0811 -39.9544 -36.9486 -35.6058 -35.8118 -37.4913 -37.5723 -35.6547
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4.7955 -4.7961 -18.3015 -21.4037 -21.2331 -23.1345 -12.0198 7.0888
13.5786 5.0428 -6.1023 -13.0165 -14.6078 -14.4111 -5.4419 11.2181
17.8789 12.6849 2.7100 -10.7129 -12.9034 -7.9816 1.1639 14.8384
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15.8422 14.4545 11.1838 -0.7945 -20.0732 -20.0218 -16.9053 -16.5119
21.6853 20.8663 17.5396 5.3883 -15.0742 -6.5718 2.1315 0.6106
23.6274 23.9117 21.2038 8.1018 -8.9490 2.6084 12.6932 12.9589
21.5174 22.6296 20.8973 10.4194 3.2673 10.3453 17.5939 18.1441
18.7438 18.8575 16.4485 11.8153 12.5986 15.6801 18.4455 19.6023
21.6811 21.5828 17.7837 10.4548 13.5617 15.3799 16.9035 19.1448
20.5645 20.8563 17.6900 8.3712 7.9684 8.1011 10.0445 15.1424
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12.8252 10.4150 -0.9171 -15.0965 -23.6171 -32.9991 -46.4601 -53.8671
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16.2487 21.1052 9.1056 -9.5822 -20.4989 -24.7554 -28.7186 -30.5546
14.1429 19.9900 11.7705 -6.4527 -16.3747 -14.2919 -15.0035 -14.7220
10.6424 15.7690 11.4487 -3.5166 -13.2508 -6.6962 -3.8412 -1.8041
7.0300 9.9291 7.6416 -1.2254 -10.5902 -3.5999 1.9548 5.0092
2.9152 4.5570 2.8532 -1.9032 -7.7965 -2.1482 3.4229 7.2830
4.6588 8.8961 8.3489 2.7474 -4.3353 1.8975 6.3595 9.2517
10.3459 14.7953 13.8801 6.2124 1.7392 8.0334 11.9929 15.3362
15.1058 17.0020 15.6148 9.3982 7.7180 12.9636 16.4764 19.9896
19.1546 17.7756 15.3808 11.0398 9.6396 13.3426 17.3960 21.4909
23.5716 21.9994 18.8161 12.7486 11.3063 10.5889 13.6643 18.8452
25.4141 28.5031 24.7646 19.7116 17.3460 11.2266 14.3764 20.3971
21.7060 32.5306 33.2884 29.4318 30.0691 27.9322 29.4521 30.7364
14.1925 28.4076 38.5276 41.0791 43.3783 46.4566 45.0340 36.4746
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-15.0692 10.2222 32.2647 42.1480 49.1446 49.3612 44.0733 34.5273
-6.9316 2.8685 23.2276 35.5735 37.8311 37.0967 34.6839 28.6455
5.4988 1.5131 17.6955 23.7457 24.2827 25.4463 24.6326 22.0244
12.6958 9.1525 15.2361 20.3217 23.8527 25.9830 26.0583 24.8078
18.7460 17.2891 19.2580 23.0133 27.3435 29.0768 29.1091 28.5561
21.3871 22.1330 22.8295 24.9325 28.7967 30.3067 30.1635 30.1423
19.2865 21.2255 21.6209 23.2002 25.9752 26.8870 26.8504 27.3673
Columns 25 to 32 -46.2929 -45.8514 -43.8133 -42.0758 -37.2443 -28.1002 -22.5110 -22.4611
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8.1526 16.8650 36.7063 45.0007 38.6178 18.0170 -12.9313 -27.0494
11.6091 20.2892 33.7044 35.6224 27.9035 8.0284 -18.3743 -29.7858
11.3965 19.5592 22.5863 20.2763 14.4020 -1.8836 -21.7937 -30.7546
16.3651 15.2092 9.1531 8.1118 2.9091 -9.3774 -24.1398 -30.1404
21.1623 16.3668 6.3052 2.0496 -1.7600 -12.5608 -23.3100 -23.8790
22.2473 17.7449 10.1178 7.9337 2.4403 -7.7597 -12.8632 -12.1756
21.1939 20.0816 17.8756 15.1619 8.9420 2.6407 0.5657 0.7166
22.9229 23.8982 23.3829 20.4815 16.7441 13.7642 12.8930 12.1950
28.0445 27.3273 27.6899 26.4823 25.2613 24.2942 23.8800 22.5411
28.9310 29.1998 31.3952 32.2238 32.5202 32.7017 32.8972 31.4875
29.5986 30.5773 32.7135 34.2784 35.6347 36.9571 38.0529 36.7636
29.1209 30.0166 32.2120 33.9855 35.8697 37.7760 39.2828 37.9701
25.4318 27.2111 29.4819 31.8337 34.5026 36.5293 37.9826 36.5371
20.8510 22.7705 25.2048 28.3100 31.0736 32.6094 33.8156 32.3447
23.1712 22.3006 22.5951 24.6402 26.4801 27.1895 27.9190 26.6536
27.9899 27.5725 27.3290 27.2686 27.1475 26.0548 24.2875 22.0305
30.7982 30.8289 30.4758 30.2773 29.9263 28.3289 25.7655 21.9456
28.2622 28.4333 28.4101 28.3268 28.0044 26.6870 23.9720 20.5055
(1,3,.,.) =
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3.2119 7.3782 12.7543 17.6353 20.3025 21.7642 23.5680 25.9008
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34.9482 39.3551 42.1820 42.2396 40.0202 37.8015 35.7557 31.3026
47.9373 48.6581 51.0663 53.3519 54.3788 54.5934 53.7148 48.1448
51.8846 51.0501 53.7646 57.0308 60.3700 63.0481 63.7865 58.0206
48.2052 47.5515 50.5871 55.0772 59.5585 63.7399 65.8463 60.7248
38.0658 38.5105 43.0329 48.7062 54.6693 59.1620 61.5058 57.2390
27.8526 29.1017 33.8353 40.2292 45.7627 49.8615 51.7400 48.2200
31.8622 29.5878 29.8241 33.7864 37.5757 39.8845 40.5458 37.2494
41.8055 40.3313 39.1189 38.7566 38.6835 37.4826 34.5553 29.4285
47.1542 46.9981 46.4659 45.6105 44.3029 41.5119 36.6253 28.7814
39.3012 40.1436 40.1276 39.6747 38.6660 36.2682 31.9187 24.5210
[ torch.cuda.FloatTensor{4,3,32,32} ]
Process finished with exit code 1
@yongen9696
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missing forward in your Graph()?

@buttercutter
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@yongen9696 No, search for functions definitions of def forward_f(self, x): and def forward_edge(self, x):

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