diff --git a/checkpoints.lua b/checkpoints.lua index 97fe54d98..60fb143b4 100644 --- a/checkpoints.lua +++ b/checkpoints.lua @@ -25,7 +25,7 @@ function checkpoint.latest(opt) return latest, optimState end -function checkpoint.save(epoch, model, optimState, bestModel) +function checkpoint.save(epoch, model, optimState, bestModel, opt) -- Don't save the DataParallelTable for easier loading on other machines if torch.type(model) == 'nn.DataParallelTable' then model = model:get(1) @@ -34,16 +34,16 @@ function checkpoint.save(epoch, model, optimState, bestModel) local modelFile = 'model_' .. epoch .. '.t7' local optimFile = 'optimState_' .. epoch .. '.t7' - torch.save(modelFile, model) - torch.save(optimFile, optimState) - torch.save('latest.t7', { + torch.save(paths.concat(opt.resume, modelFile), model) + torch.save(paths.concat(opt.resume, optimFile), optimState) + torch.save(paths.concat(opt.resume, 'latest.t7'), { epoch = epoch, modelFile = modelFile, optimFile = optimFile, }) if bestModel then - torch.save('model_best.t7', model) + torch.save(paths.concat(opt.resume, 'model_best.t7'), model) end end diff --git a/dataloader.lua b/dataloader.lua index a995bffad..223b42e25 100644 --- a/dataloader.lua +++ b/dataloader.lua @@ -39,7 +39,11 @@ function DataLoader:__init(dataset, opt, split) end torch.setnumthreads(1) _G.dataset = dataset - _G.preprocess = dataset:preprocess() + if opt.netType == 'inception-resnet-v2' or opt.netType == 'inception-resnet-v2-aux' then + _G.preprocess = dataset:preprocess(328, 299) + else + _G.preprocess = dataset:preprocess(256, 224) + end return dataset:size() end diff --git a/datasets/create-imagenet-lmdb.lua b/datasets/create-imagenet-lmdb.lua new file mode 100644 index 000000000..a6f6c6ea2 --- /dev/null +++ b/datasets/create-imagenet-lmdb.lua @@ -0,0 +1,189 @@ +--[[ + A script to conver images to lmdb dataset + + References + 1. https://github.com/facebook/fb.resnet.torch/blob/master/datasets/init.lua + 2. https://github.com/eladhoffer/ImageNet-Training/blob/master/CreateLMDBs.lua +]]-- + +local ffi = require 'ffi' +local image = require 'image' + +-- Define local functions +local function isvalid(opt, cachePath) + local imageInfo = torch.load(cachePath) + if imageInfo.basedir and imageInfo.basedir ~= opt.data then + return false + end + return true +end + +local function split(dataset) + local tokens = {} + for word in string.gmatch(dataset, '([^-]+)') do + table.insert(tokens, word) + end + assert(tokens[2] == 'lmdb', string.format('opt.dataset should be -lmdb form; opt.dataset = %s', dataset)) + return tokens[1] +end + +local function _loadImage(path) + local ok, input = pcall(function() + return image.load(path, 3, 'float') + end) + + -- Sometimes image.load fails because the file extension does not match the + -- image format. In that case, use image.decompress on a ByteTensor. + if not ok then + local f = io.open(path, 'r') + assert(f, 'Error reading: ' .. tostring(path)) + local data = f:read('*a') + f:close() + + local b = torch.ByteTensor(string.len(data)) + ffi.copy(b:data(), data, b:size(1)) + + input = image.decompress(b, 3, 'float') + end + + return input +end + +local function getItem(basedir, imageClass, imagePath, i) + local path = ffi.string(imagePath[i]:data()) + local image = _loadImage(paths.concat(basedir, path)) + local class = imageClass[i] + + return { + input = image, + target = class, + } +end + + + +-- Init opt +local opt = {} +opt.gen = 'gen' +opt.dataset = 'imagenet-lmdb' +opt.data = '/media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC' + +opt.data_lmdb = '/media/data1/image' +opt.shuffle = true +--print(opt) + +-- Load imageInfo +local cachePath = paths.concat(opt.gen, split(opt.dataset) .. '.t7') +if not paths.filep(cachePath) or not isvalid(opt, cachePath) then + paths.mkdir('gen') + + local script = paths.dofile(split(opt.dataset) .. '-gen.lua') + script.exec(opt, cachePath) +end +local imageInfo = torch.load(cachePath) +--print(imageInfo) + +-- Create LMDB +local lmdb = require 'lmdb' + +local train_env = lmdb.env{ + Path = paths.concat(opt.data_lmdb, 'train_lmdb'), + Name = 'train_lmdb' +} + +local val_env= lmdb.env{ + Path = paths.concat(opt.data_lmdb, 'val_lmdb'), + Name = 'val_lmdb' +} + +local path = ffi.string(imageInfo.train.imagePath[1]:data()) +--local image = self:_loadImage(paths.concat(self.dir, path)) +--local class = self.imageInfo.imageClass[i] +print(path) + +local n_images = (#imageInfo.train.imagePath)[1] +print(string.format("n_image: %d", n_images)) + +local idxs +if opt.shuffle then + idxs = torch.randperm(n_images) +else + idxs = torch.range(1, n_images) +end +print(string.format("opt.shuffle: %s, idxs[1]: %d", opt.shuffle, idxs[1])) + +local basedir = paths.concat(imageInfo.basedir, 'train') +--local item = getItem(basedir, imageInfo.train.imageClass, imageInfo.train.imagePath, idxs[1]) +--print(item.target) +----print(item.input) +--print(#item.input) +--print(item.input[1][1][1]) + +train_env:open() +local txn = train_env:txn() +local cursor = txn:cursor() +for i = 1, 1000 do --n_images do + local item = getItem(basedir, imageInfo.train.imageClass, imageInfo.train.imagePath, idxs[i]) + + cursor:put(string.format("%07d", i), item, lmdb.C.MDB_NODUPDATA) + if i % 100 == 0 then + txn:commit() + print(train_env:stat()) + collectgarbage() + txn = train_env:txn() + cursor = txn:cursor() + end + xlua.progress(i, n_images) +end +txn:commit() +train_env:close() + +--[[ +local sys = require 'sys' +local n_test = 5000 +sys.tic() +-------Read------- +train_env:open() +print(train_env:stat()) -- Current status +local reader = train_env:txn(true) --Read-only transaction +--local y = torch.Tensor(10,3,256,256) +local y = {} + +local idxs = torch.randperm(n_test) +for i=1,n_test do + local item = reader:get(string.format("%07d", idxs[i])) + if i % 1000 == 0 then + print(string.format('%d: %d', i, idxs[i])) + print(#item.input) + end + --print(item) + --print(#item.input) + --print(item.input[1][1][1]) +end +reader:abort() +train_env:close() +print(sys.toc()) +collectgarbage() + +sys.tic() +-------Read------- +train_env:open() +print(train_env:stat()) -- Current status +local reader = train_env:txn(true) --Read-only transaction +--local y = torch.Tensor(10,3,256,256) +local y = {} + +for i=1,n_test do + local item = reader:get(string.format("%07d", i)) + if i % 1000 == 0 then + print(string.format('%d: %d', i, i)) + print(#item.input) + end + --print(item) + --print(#item.input) + --print(item.input[1][1][1]) +end +reader:abort() +train_env:close() +print(sys.toc()) +]]-- diff --git a/datasets/imagenet-lmdb.lua b/datasets/imagenet-lmdb.lua new file mode 100644 index 000000000..70811bc99 --- /dev/null +++ b/datasets/imagenet-lmdb.lua @@ -0,0 +1,87 @@ +-- +-- Copyright (c) 2016, Facebook, Inc. +-- All rights reserved. +-- +-- This source code is licensed under the BSD-style license found in the +-- LICENSE file in the root directory of this source tree. An additional grant +-- of patent rights can be found in the PATENTS file in the same directory. +-- +-- ImageNet dataset loader +-- + +local image = require 'image' +local paths = require 'paths' +local t = require 'datasets/transforms' +local lmdb = require 'lmdb' + +local M = {} +local ImagenetLMDBDataset = torch.class('resnet.ImagenetLMDBDataset', M) + +function ImagenetLMDBDataset:__init(imageInfo, opt, split) + self.imageInfo = imageInfo[split] + self.opt = opt + self.split = split + --self.dir = paths.concat(opt.data, split) + --assert(paths.dirp(self.dir), 'directory does not exist: ' .. self.dir) + + self.env = lmdb.env{ + Path = paths.concat(opt.data, string.format('%s_lmdb', split)), + Name = string.format('%s_lmdb', split) + } + assert(env:open(), 'directory does not exist: ' .. string.format('%s_lmdb', split)) + self.stat = env:stat() -- Current status + + self.reader = env:txn(true) --Read-only transaction + self.idxs = torch.randperm(n_images) + assert(self.imageInfo.imageClass:size(1) == #self.idxs, string.format('Something wrong with lmdb. The lmdb db should have %d number of items, but it has %d', self.imageInfo.imageClass:size(1), #self.idxs)) +end + +function ImagenetLMDBDataset:get(i) + local item = reader:get(string.format("%07d", self.idxs[i])) + return item +end + +function ImagenetLMDBDataset:size() + return self.imageInfo.imageClass:size(1) +end + +-- Computed from random subset of ImageNet training images +local meanstd = { + mean = { 0.485, 0.456, 0.406 }, + std = { 0.229, 0.224, 0.225 }, +} +local pca = { + eigval = torch.Tensor{ 0.2175, 0.0188, 0.0045 }, + eigvec = torch.Tensor{ + { -0.5675, 0.7192, 0.4009 }, + { -0.5808, -0.0045, -0.8140 }, + { -0.5836, -0.6948, 0.4203 }, + }, +} + +function ImagenetLMDBDataset:preprocess() + if self.split == 'train' then + return t.Compose{ + t.RandomSizedCrop(224), + t.ColorJitter({ + brightness = 0.4, + contrast = 0.4, + saturation = 0.4, + }), + t.Lighting(0.1, pca.eigval, pca.eigvec), + t.ColorNormalize(meanstd), + t.HorizontalFlip(0.5), + } + elseif self.split == 'val' then + local Crop = self.opt.tenCrop and t.TenCrop or t.CenterCrop + return t.Compose{ + t.Scale(256), + t.ColorNormalize(meanstd), + Crop(224), + } + else + error('invalid split: ' .. self.split) + end +end + +return M.ImagenetLMDBDataset diff --git a/datasets/imagenet.lua b/datasets/imagenet.lua index a28294ee3..a26f745a4 100644 --- a/datasets/imagenet.lua +++ b/datasets/imagenet.lua @@ -77,10 +77,13 @@ local pca = { }, } -function ImagenetDataset:preprocess() +function ImagenetDataset:preprocess(minSize, cropSize) + -- minSize : 256, cropSize : 224 for resnet + -- minSize : 328, cropSize : 299 for inception-resnet-v2 + if self.split == 'train' then return t.Compose{ - t.RandomSizedCrop(224), + t.RandomSizedCrop(cropSize), t.ColorJitter({ brightness = 0.4, contrast = 0.4, @@ -93,9 +96,9 @@ function ImagenetDataset:preprocess() elseif self.split == 'val' then local Crop = self.opt.tenCrop and t.TenCrop or t.CenterCrop return t.Compose{ - t.Scale(256), + t.Scale(minSize), t.ColorNormalize(meanstd), - Crop(224), + Crop(cropSize), } else error('invalid split: ' .. self.split) diff --git a/drawnet.lua b/drawnet.lua new file mode 100644 index 000000000..dc30cc1ac --- /dev/null +++ b/drawnet.lua @@ -0,0 +1,104 @@ +local generateGraph = require 'optnet.graphgen' + +-- visual properties of the generated graph +-- follows graphviz attributes +local graphOpts = { + displayProps = {shape='ellipse',fontsize=14, style='solid'}, + nodeData = function(oldData, tensor) + --return oldData .. '\n' .. 'Size: '.. tensor:numel() + local text_sz = '' + for i = 1,tensor:dim() do + if i == 1 then + text_sz = text_sz .. '' .. tensor:size(i) + else + text_sz = text_sz .. ', ' .. tensor:size(i) + end + end + return oldData .. '\n' .. 'Size: {'.. text_sz .. '}\n' .. 'Mem size: ' .. tensor:numel() + end +} + +local function copyInputs(sample) + -- Copies the input to a CUDA tensor, if using 1 GPU, or to pinned memory, + -- if using DataParallelTable. The target is always copied to a CUDA tensor + local input = torch.CudaTensor() + --print('type of input: ' .. torch.type(input)) + --print(sample:size()) + input:resize(sample:size()):copy(sample) + + return input +end + +local function drawModel(model, input, name) + -- model: A network architecture + -- input: The input for the given network architecture + -- name: The model name (string). + -- The files, '.dot' and '.svg' will be generated. + + local input_ + if torch.type(input) == 'table' then + input_ = {} + --print('table: ', #input) + for i = 1,#input do + input_[i] = copyInputs(input[i]) + --print(torch.type(input_[i])) + end + else + input_ = copyInputs(input) + --print(torch.type(input_)) + end + + g = generateGraph(model, input_, graphOpts) + graph.dot(g, name, name) + + --print(torch.type(g)) + --print(g) + --print(#g.nodes) + --print(g.nodes[#g.nodes]:label()) + --print(g:leaves()) + + return g +end + +--------------------------------------------------------------------------- +-- Sample input +local eps = 1e-12 +local batch_size = 1 +local n_rois = 10 +local height = 299 +local width = 299 + +local input_image = torch.floor(torch.rand(batch_size, 3, height, width) * 256) +local input_rois = torch.cat(torch.floor(torch.rand(n_rois, 1) * height/2), + torch.floor(torch.rand(n_rois, 1) * width/2), 2) +input_rois = torch.cat(input_rois, + torch.add(input_rois, + torch.cat(torch.floor(torch.rand(n_rois, 1) * height/2), + torch.floor(torch.rand(n_rois, 1) * width/2), 2))) +input_rois = torch.cat(torch.floor(torch.rand(n_rois,1) * batch_size) + 1, input_rois, 2) +--print(#input_rois) +--print(input_rois[1]) +--print(#input_image) + +--------------------------------------------------------------------------- +-- Create net w/ options +print('Create network architectures') + +local createModel = paths.dofile('models/inception-resnet-v2.lua') +--local createModel = paths.dofile('models/inception-resnet-v2-aux.lua') + +local opt = {}; +opt.cudnn = 'fastest' + +local model = createModel(opt) + +local g = drawModel(model, input_image, 'inception-resnet-v2') +--local g = drawModel(model, input_image, 'inception-resnet-v2-aux') + +--local output = model:forward(input_image:cuda()) +--print(output) + +print('Done.') + + + diff --git a/inception-resnet-v2.dot b/inception-resnet-v2.dot new file mode 100644 index 000000000..3cac08206 --- /dev/null +++ b/inception-resnet-v2.dot @@ -0,0 +1,4311 @@ +digraph G { + graph [bb="0,0,5300,46824"]; + node [label="\N", + shape=oval + ]; + n1 [color=cyan3, + fontsize=14, + height=1.3356, + label="Input\nStorage id: 1\nSize: {1, 3, 299, 299}\nMem size: 268203", + pos="666,46776", + shape=ellipse, + style=solid, + width=2.6788]; + n2 [color=burlywood1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(3 -> 32, 3x3, 2,2) without bias\nStorage id: 2\nSize: {1, 32, 149, 149}\nMem size: 710432", + pos="666,46644", + shape=ellipse, + style=solid, + width=6.5007]; + n1 -> n2 [pos="e,666,46692 666,46728 666,46720 666,46711 666,46703"]; + n3 [color=antiquewhite, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 3\nSize: {1, 32, 149, 149}\nMem size: 710432", + pos="666,46512", + shape=ellipse, + style=solid, + width=3.5652]; + n2 -> n3 [pos="e,666,46560 666,46596 666,46588 666,46579 666,46571"]; + n4 [color=antiquewhite, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 3\nSize: {1, 32, 149, 149}\nMem size: 710432", + pos="666,46380", + shape=ellipse, + style=solid, + width=2.817]; + n3 -> n4 [pos="e,666,46428 666,46464 666,46456 666,46447 666,46439"]; + n5 [color=darkgoldenrod2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3) without bias\nStorage id: 4\nSize: {1, 32, 147, 147}\nMem size: 691488", + pos="666,46248", + shape=ellipse, + style=solid, + width=6.1434]; + n4 -> n5 [pos="e,666,46296 666,46332 666,46324 666,46315 666,46307"]; + n6 [color=darkgoldenrod2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 5\nSize: {1, 32, 147, 147}\nMem size: 691488", + pos="666,46116", + shape=ellipse, + style=solid, + width=3.5652]; + n5 -> n6 [pos="e,666,46164 666,46200 666,46192 666,46183 666,46175"]; + n7 [color=darkgoldenrod2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 5\nSize: {1, 32, 147, 147}\nMem size: 691488", + pos="666,45984", + shape=ellipse, + style=solid, + width=2.817]; + n6 -> n7 [pos="e,666,46032 666,46068 666,46060 666,46051 666,46043"]; + n8 [color=blue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 6\nSize: {1, 64, 147, 147}\nMem size: 1382976", + pos="666,45852", + shape=ellipse, + style=solid, + width=7.0968]; + n7 -> n8 [pos="e,666,45900 666,45936 666,45928 666,45919 666,45911"]; + n9 [color=deepskyblue3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 7\nSize: {1, 64, 147, 147}\nMem size: 1382976", + pos="666,45720", + shape=ellipse, + style=solid, + width=3.5652]; + n8 -> n9 [pos="e,666,45768 666,45804 666,45796 666,45787 666,45779"]; + n10 [color=deepskyblue3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 7\nSize: {1, 64, 147, 147}\nMem size: 1382976", + pos="666,45588", + shape=ellipse, + style=solid, + width=2.817]; + n9 -> n10 [pos="e,666,45636 666,45672 666,45664 666,45655 666,45647"]; + n11 [color=cornsilk, + fontsize=14, + height=1.3356, + label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 8\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="525,45324", + shape=ellipse, + style=solid, + width=3.7843]; + n10 -> n11 [pos="e,523.91,45372 601.38,45551 584.14,45538 567.22,45522 556,45504 533.77,45468 526.38,45420 524.35,45383"]; + n12 [color=darkturquoise, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(64 -> 96, 3x3, 2,2) without bias\nStorage id: 9\nSize: {1, 96, 73, 73}\nMem size: 511584", + pos="804,45456", + shape=ellipse, + style=solid, + width=6.6389]; + n10 -> n12 [pos="e,754.65,45503 710.8,45545 722.41,45534 735.05,45522 747.18,45511"]; + n15 [color=cornsilk1, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 11\nSize: {1, 160, 73, 73}\nMem size: 852640", + pos="686,45060", + shape=ellipse, + style=solid, + width=2.6788]; + n11 -> n15 [pos="e,658.14,45106 553.41,45277 581.2,45232 623.51,45163 652.82,45115"]; + n13 [color=gainsboro, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 10\nSize: {1, 96, 73, 73}\nMem size: 511584", + pos="808,45324", + shape=ellipse, + style=solid, + width=3.5652]; + n12 -> n13 [pos="e,806.54,45372 805.46,45408 805.71,45400 805.97,45391 806.23,45383"]; + n14 [color=gainsboro, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 10\nSize: {1, 96, 73, 73}\nMem size: 511584", + pos="767,45192", + shape=ellipse, + style=solid, + width=2.5643]; + n13 -> n14 [pos="e,781.78,45240 793.2,45276 790.5,45268 787.66,45258 784.88,45250"]; + n14 -> n15 [pos="e,714.05,45106 738.9,45146 732.61,45136 725.89,45125 719.42,45115"]; + n16 [color=beige, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 64, 1x1) without bias\nStorage id: 12\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="451,44928", + shape=ellipse, + style=solid, + width=6.2816]; + n15 -> n16 [pos="e,530.62,44973 622.41,45024 596.89,45010 567.05,44993 539.48,44978"]; + n22 [color=chocolate3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 64, 1x1) without bias\nStorage id: 16\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="934,44928", + shape=ellipse, + style=solid, + width=6.2816]; + n15 -> n22 [pos="e,850.32,44973 751.48,45025 778.92,45010 811.33,44993 841.16,44978"]; + n17 [color=chocolate1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 13\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="445,44796", + shape=ellipse, + style=solid, + width=3.5652]; + n16 -> n17 [pos="e,447.19,44844 448.82,44880 448.44,44872 448.05,44863 447.66,44855"]; + n18 [color=chocolate1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 13\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="444,44664", + shape=ellipse, + style=solid, + width=2.5643]; + n17 -> n18 [pos="e,444.37,44712 444.64,44748 444.57,44740 444.51,44731 444.44,44723"]; + n19 [color=bisque, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(64 -> 96, 3x3) without bias\nStorage id: 14\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="444,44532", + shape=ellipse, + style=solid, + width=6.1434]; + n18 -> n19 [pos="e,444,44580 444,44616 444,44608 444,44599 444,44591"]; + n20 [color=chocolate2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 15\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="508,44400", + shape=ellipse, + style=solid, + width=3.5652]; + n19 -> n20 [pos="e,485.14,44447 467.11,44484 471.51,44475 476.16,44466 480.68,44456"]; + n21 [color=chocolate2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 15\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="544,44136", + shape=ellipse, + style=solid, + width=2.5643]; + n20 -> n21 [pos="e,537.51,44184 514.48,44352 520.55,44308 529.64,44241 536.13,44194"]; + n34 [color=darkgoldenrod, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 24\nSize: {1, 192, 71, 71}\nMem size: 967872", + pos="685,43344", + shape=ellipse, + style=solid, + width=2.6788]; + n21 -> n34 [pos="e,661.57,43391 561.35,44088 579.07,44037 604,43950 604,43873 604,43873 604,43873 604,43607 604,43533 633.82,43452 657.27,43400"]; + n23 [color=cornsilk3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 17\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="937,44796", + shape=ellipse, + style=solid, + width=3.5652]; + n22 -> n23 [pos="e,935.9,44844 935.09,44880 935.28,44872 935.48,44863 935.67,44855"]; + n24 [color=cornsilk3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 17\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="938,44664", + shape=ellipse, + style=solid, + width=2.5643]; + n23 -> n24 [pos="e,937.63,44712 937.36,44748 937.43,44740 937.49,44731 937.56,44723"]; + n25 [color=aquamarine3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(64 -> 64, 7x1, 1,1, 3,0) without bias\nStorage id: 18\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="939,44532", + shape=ellipse, + style=solid, + width=7.0968]; + n24 -> n25 [pos="e,938.63,44580 938.36,44616 938.43,44608 938.49,44599 938.56,44591"]; + n26 [color=aliceblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 19\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="937,44400", + shape=ellipse, + style=solid, + width=3.5652]; + n25 -> n26 [pos="e,937.73,44448 938.27,44484 938.15,44476 938.02,44467 937.89,44459"]; + n27 [color=aliceblue, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 19\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="921,44268", + shape=ellipse, + style=solid, + width=2.5643]; + n26 -> n27 [pos="e,926.79,44316 931.18,44352 930.15,44343 929.07,44335 928.02,44326"]; + n28 [color=gold4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(64 -> 64, 1x7, 1,1, 0,3) without bias\nStorage id: 20\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="913,44136", + shape=ellipse, + style=solid, + width=7.0968]; + n27 -> n28 [pos="e,915.92,44184 918.09,44220 917.59,44212 917.06,44203 916.54,44195"]; + n29 [color=beige, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 21\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="905,44004", + shape=ellipse, + style=solid, + width=3.5652]; + n28 -> n29 [pos="e,907.92,44052 910.09,44088 909.59,44080 909.06,44071 908.54,44063"]; + n30 [color=beige, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 21\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="875,43872", + shape=ellipse, + style=solid, + width=2.5643]; + n29 -> n30 [pos="e,885.81,43920 894.17,43956 892.19,43948 890.11,43938 888.08,43930"]; + n31 [color=deepskyblue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(64 -> 96, 3x3) without bias\nStorage id: 22\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="860,43740", + shape=ellipse, + style=solid, + width=6.1434]; + n30 -> n31 [pos="e,865.48,43788 869.54,43824 868.6,43816 867.61,43807 866.65,43799"]; + n32 [color=deepskyblue3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 23\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="810,43608", + shape=ellipse, + style=solid, + width=3.5652]; + n31 -> n32 [pos="e,828.02,43656 841.95,43692 838.62,43683 835.11,43674 831.69,43665"]; + n33 [color=deepskyblue3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 23\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="767,43476", + shape=ellipse, + style=solid, + width=2.5643]; + n32 -> n33 [pos="e,782.36,43523 794.48,43560 791.58,43551 788.52,43542 785.55,43533"]; + n33 -> n34 [pos="e,713.4,43390 738.55,43430 732.19,43420 725.38,43409 718.83,43399"]; + n35 [color=cornsilk, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 192, 3x3, 2,2) without bias\nStorage id: 25\nSize: {1, 192, 35, 35}\nMem size: 235200", + pos="547,43212", + shape=ellipse, + style=solid, + width=6.8916]; + n34 -> n35 [pos="e,596.12,43259 640.58,43301 628.83,43290 615.99,43278 603.69,43266"]; + n38 [color=cyan3, + fontsize=14, + height=1.3356, + label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 27\nSize: {1, 192, 35, 35}\nMem size: 235200", + pos="828,43080", + shape=ellipse, + style=solid, + width=3.7843]; + n34 -> n38 [pos="e,831.68,43128 752.84,43310 772.36,43297 791.67,43280 804,43260 826.16,43224 831.56,43176 831.76,43138"]; + n36 [color=darkorange4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 26\nSize: {1, 192, 35, 35}\nMem size: 235200", + pos="545,43080", + shape=ellipse, + style=solid, + width=3.5652]; + n35 -> n36 [pos="e,545.73,43128 546.27,43164 546.15,43156 546.02,43147 545.89,43139"]; + n37 [color=darkorange4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 26\nSize: {1, 192, 35, 35}\nMem size: 235200", + pos="624,42948", + shape=ellipse, + style=solid, + width=2.6788]; + n36 -> n37 [pos="e,596.55,42994 573.07,43033 578.97,43023 585.25,43013 591.3,43003"]; + n39 [color=firebrick3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="706,42816", + shape=ellipse, + style=solid, + width=2.6788]; + n37 -> n39 [pos="e,677.6,42862 652.45,42902 658.81,42892 665.62,42881 672.17,42871"]; + n38 -> n39 [pos="e,727.47,42863 806.33,43032 785.47,42988 753.9,42920 731.77,42872"]; + n40 [color=firebrick2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 29\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="226,42684", + shape=ellipse, + style=solid, + width=6.2816]; + n39 -> n40 [pos="e,363.74,42722 621.84,42792 553.37,42774 455.09,42747 373.59,42725"]; + n43 [color=blanchedalmond, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 31\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="526,42552", + shape=ellipse, + style=solid, + width=6.2816]; + n39 -> n43 [pos="e,553.2,42600 668.58,42771 658.33,42759 647.45,42745 638,42732 609.32,42692 579.95,42645 558.48,42609"]; + n49 [color=antiquewhite, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 35\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="873,42684", + shape=ellipse, + style=solid, + width=6.2816]; + n39 -> n49 [pos="e,814.4,42731 757.47,42775 772.95,42763 790.17,42749 806.48,42737"]; + n62 [color=firebrick3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1050,40968", + shape=ellipse, + style=solid, + width=2.6788]; + n39 -> n62 [pos="e,1080.1,41014 800.96,42807 905.56,42796 1064.7,42773 1108,42732 1167.5,42675 1154,42635 1154,42553 1154,42553 1154,42553 1154,41231 \ +1154,41155 1115.4,41074 1085.2,41023"]; + n41 [color=chocolate, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 30\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="153,42552", + shape=ellipse, + style=solid, + width=3.5652]; + n40 -> n41 [pos="e,179.07,42599 199.65,42636 194.57,42627 189.21,42617 184,42608"]; + n42 [color=chocolate, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 30\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="293,41892", + shape=ellipse, + style=solid, + width=2.5643]; + n41 -> n42 [pos="e,265.49,41938 165.73,42504 178.72,42452 197,42365 197,42289 197,42289 197,42289 197,42155 197,42079 232.71,41999 260.57,41947"]; + n58 [color=deepskyblue3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 41\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="653,41496", + shape=ellipse, + style=solid, + width=2.6788]; + n42 -> n58 [pos="e,607.29,41539 325.41,41847 353.07,41810 394.52,41756 434,41712 487.74,41652 554.33,41588 599.88,41546"]; + n44 [color=darkolivegreen4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 32\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="507,42420", + shape=ellipse, + style=solid, + width=3.5652]; + n43 -> n44 [pos="e,513.88,42468 519.09,42504 517.87,42495 516.58,42487 515.33,42478"]; + n45 [color=darkolivegreen4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 32\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="490,42288", + shape=ellipse, + style=solid, + width=2.5643]; + n44 -> n45 [pos="e,496.15,42336 500.81,42372 499.72,42363 498.58,42355 497.45,42346"]; + n46 [color=darkolivegreen4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 33\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="484,42156", + shape=ellipse, + style=solid, + width=7.0968]; + n45 -> n46 [pos="e,486.19,42204 487.82,42240 487.44,42232 487.05,42223 486.66,42215"]; + n47 [color=blueviolet, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 34\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="514,42024", + shape=ellipse, + style=solid, + width=3.5652]; + n46 -> n47 [pos="e,503.14,42072 494.92,42108 496.84,42099 498.87,42091 500.85,42082"]; + n48 [color=blueviolet, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 34\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="536,41760", + shape=ellipse, + style=solid, + width=2.5643]; + n47 -> n48 [pos="e,532.04,41808 517.96,41976 521.67,41932 527.23,41865 531.19,41818"]; + n48 -> n58 [pos="e,632.31,41543 556.51,41713 576.48,41668 606.85,41600 628.18,41553"]; + n50 [color=darkorange, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="899,42552", + shape=ellipse, + style=solid, + width=3.5652]; + n49 -> n50 [pos="e,889.59,42600 882.46,42636 884.13,42627 885.88,42619 887.6,42610"]; + n51 [color=darkorange, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="895,42420", + shape=ellipse, + style=solid, + width=2.5643]; + n50 -> n51 [pos="e,896.46,42468 897.54,42504 897.29,42496 897.03,42487 896.77,42479"]; + n52 [color=black, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 37\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="870,42288", + shape=ellipse, + style=solid, + width=7.0968]; + n51 -> n52 [pos="e,879.1,42336 885.97,42372 884.37,42364 882.67,42355 881.02,42346"]; + n53 [color=cyan2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="887,42156", + shape=ellipse, + style=solid, + width=3.5652]; + n52 -> n53 [pos="e,880.85,42204 876.19,42240 877.28,42231 878.42,42223 879.55,42214"]; + n54 [color=cyan2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="879,42024", + shape=ellipse, + style=solid, + width=2.5643]; + n53 -> n54 [pos="e,881.92,42072 884.09,42108 883.59,42100 883.06,42091 882.54,42083"]; + n55 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 39\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="820,41892", + shape=ellipse, + style=solid, + width=7.0968]; + n54 -> n55 [pos="e,841.3,41940 858.03,41977 853.96,41968 849.65,41958 845.45,41949"]; + n56 [color=cadetblue2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="790,41760", + shape=ellipse, + style=solid, + width=3.5652]; + n55 -> n56 [pos="e,800.86,41808 809.08,41844 807.16,41835 805.13,41827 803.15,41818"]; + n57 [color=cadetblue2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="757,41628", + shape=ellipse, + style=solid, + width=2.5643]; + n56 -> n57 [pos="e,768.9,41676 778.09,41712 775.91,41704 773.62,41694 771.39,41686"]; + n57 -> n58 [pos="e,688.36,41541 722.08,41583 713.34,41572 703.88,41561 694.85,41549"]; + n59 [color=cadetblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 42\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="729,41364", + shape=ellipse, + style=solid, + width=6.3961]; + n58 -> n59 [pos="e,701.53,41412 679.58,41450 685.02,41440 690.81,41430 696.43,41421"]; + n60 [color=gold4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 43\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="863,41232", + shape=ellipse, + style=solid, + width=3.5652]; + n59 -> n60 [pos="e,817.33,41277 776.62,41317 787.48,41306 799.09,41295 810.15,41284"]; + n61 [color=darkseagreen3, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 44\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="946,41100", + shape=ellipse, + style=solid, + width=2.6788]; + n60 -> n61 [pos="e,917.28,41146 892.26,41185 898.61,41175 905.37,41165 911.88,41154"]; + n61 -> n62 [pos="e,1014.8,41013 981.21,41055 989.93,41044 999.37,41032 1008.4,41021"]; + n63 [color=firebrick3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1050,40836", + shape=ellipse, + style=solid, + width=2.6788]; + n62 -> n63 [pos="e,1050,40884 1050,40920 1050,40912 1050,40903 1050,40895"]; + n64 [color=gold4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 45\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="570,40704", + shape=ellipse, + style=solid, + width=6.2816]; + n63 -> n64 [pos="e,707.74,40742 965.84,40812 897.37,40794 799.09,40767 717.59,40745"]; + n67 [color=aquamarine2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 47\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="833,40572", + shape=ellipse, + style=solid, + width=6.2816]; + n63 -> n67 [pos="e,860.69,40620 999.56,40794 984.81,40782 969.13,40767 956,40752 921.87,40713 889.15,40665 866.05,40628"]; + n73 [color=cyan3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 51\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1191,40704", + shape=ellipse, + style=solid, + width=6.2816]; + n63 -> n73 [pos="e,1140.8,40751 1095.4,40793 1107.5,40782 1120.8,40770 1133.4,40758"]; + n86 [color=firebrick3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1365,38988", + shape=ellipse, + style=solid, + width=2.6788]; + n63 -> n86 [pos="e,1391.9,39035 1144.7,40826 1243.1,40815 1387.9,40792 1426,40752 1482,40694 1458,40654 1458,40573 1458,40573 1458,40573 1458,39251 \ +1458,39176 1423.6,39095 1396.7,39044"]; + n65 [color=gold4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 46\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="460,40572", + shape=ellipse, + style=solid, + width=3.5652]; + n64 -> n65 [pos="e,498.02,40618 530.6,40656 522.15,40646 513.16,40636 504.54,40626"]; + n66 [color=gold4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 46\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="616,39912", + shape=ellipse, + style=solid, + width=2.5643]; + n65 -> n66 [pos="e,582.47,39957 471.28,40524 482.8,40472 499,40385 499,40309 499,40309 499,40309 499,40175 499,40097 542.7,40017 576.68,39966"]; + n82 [color=darkgoldenrod3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 57\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="1045,39516", + shape=ellipse, + style=solid, + width=2.6788]; + n66 -> n82 [pos="e,986.5,39555 649.8,39867 679.23,39830 723.83,39775 767,39732 830.83,39668 849.94,39655 922,39600 939.79,39586 959.64,39573 978.12,\ +39560"]; + n68 [color=cyan1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 48\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="812,40440", + shape=ellipse, + style=solid, + width=3.5652]; + n67 -> n68 [pos="e,819.6,40488 825.36,40524 824.01,40515 822.59,40507 821.21,40498"]; + n69 [color=cyan1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 48\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="793,40308", + shape=ellipse, + style=solid, + width=2.5643]; + n68 -> n69 [pos="e,799.88,40356 805.09,40392 803.87,40383 802.58,40375 801.33,40366"]; + n70 [color=gainsboro, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 49\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="786,40176", + shape=ellipse, + style=solid, + width=7.0968]; + n69 -> n70 [pos="e,788.56,40224 790.45,40260 790.01,40252 789.55,40243 789.1,40235"]; + n71 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 50\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="838,40044", + shape=ellipse, + style=solid, + width=3.5652]; + n70 -> n71 [pos="e,819.35,40092 804.92,40128 808.38,40119 812.01,40110 815.56,40101"]; + n72 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 50\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="869,39780", + shape=ellipse, + style=solid, + width=2.5643]; + n71 -> n72 [pos="e,863.41,39828 843.58,39996 848.81,39952 856.64,39885 862.22,39838"]; + n72 -> n82 [pos="e,1014.8,39562 899.06,39734 929.53,39689 976.68,39619 1009.1,39570"]; + n74 [color=darkorange, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 52\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1206,40572", + shape=ellipse, + style=solid, + width=3.5652]; + n73 -> n74 [pos="e,1200.6,40620 1196.5,40656 1197.4,40647 1198.4,40639 1199.4,40630"]; + n75 [color=darkorange, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 52\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1202,40440", + shape=ellipse, + style=solid, + width=2.5643]; + n74 -> n75 [pos="e,1203.5,40488 1204.5,40524 1204.3,40516 1204,40507 1203.8,40499"]; + n76 [color=darkolivegreen1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 53\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1174,40308", + shape=ellipse, + style=solid, + width=7.0968]; + n75 -> n76 [pos="e,1184.2,40356 1191.9,40392 1190.1,40384 1188.2,40375 1186.3,40366"]; + n77 [color=bisque2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 54\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1189,40176", + shape=ellipse, + style=solid, + width=3.5652]; + n76 -> n77 [pos="e,1183.6,40224 1179.5,40260 1180.4,40251 1181.4,40243 1182.4,40234"]; + n78 [color=bisque2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 54\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1168,40044", + shape=ellipse, + style=solid, + width=2.5643]; + n77 -> n78 [pos="e,1175.6,40092 1181.4,40128 1180,40119 1178.6,40111 1177.2,40102"]; + n79 [color=darkkhaki, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 55\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1161,39912", + shape=ellipse, + style=solid, + width=7.0968]; + n78 -> n79 [pos="e,1163.6,39960 1165.5,39996 1165,39988 1164.6,39979 1164.1,39971"]; + n80 [color=gainsboro, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 56\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1143,39780", + shape=ellipse, + style=solid, + width=3.5652]; + n79 -> n80 [pos="e,1149.5,39828 1154.5,39864 1153.3,39855 1152.1,39847 1150.9,39838"]; + n81 [color=gainsboro, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 56\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1105,39648", + shape=ellipse, + style=solid, + width=2.5643]; + n80 -> n81 [pos="e,1118.7,39696 1129.3,39732 1126.8,39724 1124.1,39714 1121.6,39706"]; + n81 -> n82 [pos="e,1066.4,39563 1083.8,39601 1079.6,39592 1075,39582 1070.6,39572"]; + n83 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 58\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1122,39384", + shape=ellipse, + style=solid, + width=6.3961]; + n82 -> n83 [pos="e,1094.2,39432 1071.9,39470 1077.4,39460 1083.3,39450 1089,39441"]; + n84 [color=brown1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 59\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1212,39252", + shape=ellipse, + style=solid, + width=3.5652]; + n83 -> n84 [pos="e,1180.2,39299 1154.5,39336 1161,39327 1167.8,39317 1174.4,39307"]; + n85 [color=cornsilk2, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 60\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1272,39120", + shape=ellipse, + style=solid, + width=2.6788]; + n84 -> n85 [pos="e,1250.7,39167 1233.5,39204 1237.7,39195 1242.1,39186 1246.4,39176"]; + n85 -> n86 [pos="e,1332.9,39034 1304,39074 1311.4,39064 1319.3,39053 1326.9,39042"]; + n87 [color=firebrick3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1365,38856", + shape=ellipse, + style=solid, + width=2.6788]; + n86 -> n87 [pos="e,1365,38904 1365,38940 1365,38932 1365,38923 1365,38915"]; + n88 [color=brown4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 61\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="894,38724", + shape=ellipse, + style=solid, + width=6.2816]; + n87 -> n88 [pos="e,1029.9,38763 1281.3,38832 1214.5,38813 1119.3,38787 1040,38765"]; + n91 [color=cyan3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 63\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1194,38592", + shape=ellipse, + style=solid, + width=6.2816]; + n87 -> n91 [pos="e,1215,38640 1322.2,38812 1310.7,38800 1298.8,38786 1289,38772 1261.7,38733 1236.9,38686 1219.5,38650"]; + n97 [color=gainsboro, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 67\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1524,38724", + shape=ellipse, + style=solid, + width=6.2816]; + n87 -> n97 [pos="e,1468.2,38771 1414.4,38815 1429,38803 1445,38790 1460.3,38777"]; + n110 [color=firebrick3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1708,37008", + shape=ellipse, + style=solid, + width=2.6788]; + n87 -> n110 [pos="e,1736.1,37054 1459.9,38846 1562.5,38835 1716.9,38812 1759,38772 1818.3,38715 1805,38675 1805,38593 1805,38593 1805,38593 1805,37271 \ +1805,37196 1769.1,37115 1741,37063"]; + n89 [color=darkslategray1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 62\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="821,38592", + shape=ellipse, + style=solid, + width=3.5652]; + n88 -> n89 [pos="e,847.07,38639 867.65,38676 862.57,38667 857.21,38657 852,38648"]; + n90 [color=darkslategray1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 62\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1075,37932", + shape=ellipse, + style=solid, + width=2.5643]; + n89 -> n90 [pos="e,1009.8,37966 823.02,38544 825.09,38491 828,38404 828,38329 828,38329 828,38329 828,38195 828,38093 928.28,38015 1001.1,37971"]; + n106 [color=bisque, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 73\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="1306,37536", + shape=ellipse, + style=solid, + width=2.6788]; + n90 -> n106 [pos="e,1259.6,37579 1081.1,37884 1087.3,37846 1099.3,37794 1121,37752 1154.5,37688 1210.6,37626 1252.3,37586"]; + n92 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 64\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1180,38460", + shape=ellipse, + style=solid, + width=3.5652]; + n91 -> n92 [pos="e,1185.1,38508 1188.9,38544 1188,38535 1187.1,38527 1186.1,38518"]; + n93 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 64\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1136,38328", + shape=ellipse, + style=solid, + width=2.5643]; + n92 -> n93 [pos="e,1151.7,38375 1164.1,38412 1161.2,38403 1158,38394 1155,38385"]; + n94 [color=bisque4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 65\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1127,38196", + shape=ellipse, + style=solid, + width=7.0968]; + n93 -> n94 [pos="e,1130.3,38244 1132.7,38280 1132.2,38272 1131.6,38263 1131,38255"]; + n95 [color=crimson, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 66\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1182,38064", + shape=ellipse, + style=solid, + width=3.5652]; + n94 -> n95 [pos="e,1162.3,38112 1147,38148 1150.7,38139 1154.6,38130 1158.4,38121"]; + n96 [color=crimson, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 66\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1223,37800", + shape=ellipse, + style=solid, + width=2.5643]; + n95 -> n96 [pos="e,1215.6,37848 1189.4,38016 1196.3,37972 1206.6,37905 1214,37858"]; + n96 -> n106 [pos="e,1291.3,37584 1237.7,37752 1251.8,37708 1273.1,37641 1288.1,37593"]; + n98 [color=bisque1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 68\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1567,38592", + shape=ellipse, + style=solid, + width=3.5652]; + n97 -> n98 [pos="e,1551.4,38640 1539.6,38676 1542.4,38667 1545.4,38658 1548.2,38650"]; + n99 [color=bisque1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 68\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1559,38460", + shape=ellipse, + style=solid, + width=2.5643]; + n98 -> n99 [pos="e,1561.9,38508 1564.1,38544 1563.6,38536 1563.1,38527 1562.5,38519"]; + n100 [color=beige, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 69\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1521,38328", + shape=ellipse, + style=solid, + width=7.0968]; + n99 -> n100 [pos="e,1534.8,38376 1545.4,38412 1542.9,38404 1540.2,38395 1537.7,38386"]; + n101 [color=dodgerblue3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 70\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1530,38196", + shape=ellipse, + style=solid, + width=3.5652]; + n100 -> n101 [pos="e,1526.7,38244 1524.3,38280 1524.8,38272 1525.4,38263 1526,38255"]; + n102 [color=dodgerblue3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 70\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1511,38064", + shape=ellipse, + style=solid, + width=2.5643]; + n101 -> n102 [pos="e,1517.9,38112 1523.1,38148 1521.9,38139 1520.6,38131 1519.3,38122"]; + n103 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 71\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1507,37932", + shape=ellipse, + style=solid, + width=7.0968]; + n102 -> n103 [pos="e,1508.5,37980 1509.5,38016 1509.3,38008 1509,37999 1508.8,37991"]; + n104 [color=darkgoldenrod3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 72\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1471,37800", + shape=ellipse, + style=solid, + width=3.5652]; + n103 -> n104 [pos="e,1484,37848 1493.9,37884 1491.6,37875 1489.1,37866 1486.7,37858"]; + n105 [color=darkgoldenrod3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 72\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1443,37668", + shape=ellipse, + style=solid, + width=2.5643]; + n104 -> n105 [pos="e,1453.1,37716 1460.8,37752 1459,37743 1457.1,37735 1455.3,37726"]; + n105 -> n106 [pos="e,1350.2,37579 1399.3,37626 1386.1,37613 1371.4,37599 1357.7,37586"]; + n107 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 74\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1440,37404", + shape=ellipse, + style=solid, + width=6.3961]; + n106 -> n107 [pos="e,1392.1,37451 1349.5,37493 1360.8,37482 1373.1,37470 1384.8,37459"]; + n108 [color=azure1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 75\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1544,37272", + shape=ellipse, + style=solid, + width=3.5652]; + n107 -> n108 [pos="e,1507.7,37318 1477.3,37356 1485.1,37347 1493.4,37336 1501.4,37326"]; + n109 [color=blueviolet, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 76\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1612,37140", + shape=ellipse, + style=solid, + width=2.6788]; + n108 -> n109 [pos="e,1588,37187 1568.4,37224 1573.2,37215 1578.3,37205 1583.3,37196"]; + n109 -> n110 [pos="e,1675.1,37054 1644.8,37095 1652.6,37084 1660.9,37073 1669,37062"]; + n111 [color=firebrick3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1708,36876", + shape=ellipse, + style=solid, + width=2.6788]; + n110 -> n111 [pos="e,1708,36924 1708,36960 1708,36952 1708,36943 1708,36935"]; + n112 [color=cornsilk4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 77\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1228,36744", + shape=ellipse, + style=solid, + width=6.2816]; + n111 -> n112 [pos="e,1365.7,36782 1623.8,36852 1555.4,36834 1457.1,36807 1375.6,36785"]; + n115 [color=deepskyblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 79\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1512,36612", + shape=ellipse, + style=solid, + width=6.2816]; + n111 -> n115 [pos="e,1533.5,36660 1656.8,36835 1642,36822 1626.5,36807 1614,36792 1583.3,36754 1556.5,36706 1538,36669"]; + n121 [color=cornsilk2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 83\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1849,36744", + shape=ellipse, + style=solid, + width=6.2816]; + n111 -> n121 [pos="e,1798.8,36791 1753.4,36833 1765.5,36822 1778.8,36810 1791.4,36798"]; + n134 [color=firebrick3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2023,35028", + shape=ellipse, + style=solid, + width=2.6788]; + n111 -> n134 [pos="e,2049.9,35075 1802.7,36866 1901.1,36855 2045.9,36832 2084,36792 2140,36734 2116,36694 2116,36613 2116,36613 2116,36613 2116,35291 \ +2116,35216 2081.6,35135 2054.7,35084"]; + n113 [color=gold1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 78\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1139,36612", + shape=ellipse, + style=solid, + width=3.5652]; + n112 -> n113 [pos="e,1170.5,36659 1195.9,36696 1189.5,36687 1182.7,36677 1176.2,36667"]; + n114 [color=gold1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 78\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1265,35952", + shape=ellipse, + style=solid, + width=2.5643]; + n113 -> n114 [pos="e,1230.3,35997 1140.4,36564 1141.9,36511 1144,36424 1144,36349 1144,36349 1144,36349 1144,36215 1144,36136 1189.4,36056 1224.6,36005"]; + n130 [color=azure2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 89\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="1702,35556", + shape=ellipse, + style=solid, + width=2.6788]; + n114 -> n130 [pos="e,1643.3,35594 1300.4,35908 1331.4,35870 1378.4,35816 1423,35772 1487.5,35709 1505.8,35695 1578,35640 1596,35626 1616.1,35612 1634.8,\ +35600"]; + n116 [color=chartreuse, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 80\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1468,36480", + shape=ellipse, + style=solid, + width=3.5652]; + n115 -> n116 [pos="e,1483.9,36528 1496,36564 1493.1,36555 1490.1,36546 1487.2,36538"]; + n117 [color=chartreuse, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 80\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1447,36348", + shape=ellipse, + style=solid, + width=2.5643]; + n116 -> n117 [pos="e,1454.6,36396 1460.4,36432 1459,36423 1457.6,36415 1456.2,36406"]; + n118 [color=chocolate1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 81\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1438,36216", + shape=ellipse, + style=solid, + width=7.0968]; + n117 -> n118 [pos="e,1441.3,36264 1443.7,36300 1443.2,36292 1442.6,36283 1442,36275"]; + n119 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 82\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1493,36084", + shape=ellipse, + style=solid, + width=3.5652]; + n118 -> n119 [pos="e,1473.3,36132 1458,36168 1461.7,36159 1465.6,36150 1469.4,36141"]; + n120 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 82\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1525,35820", + shape=ellipse, + style=solid, + width=2.5643]; + n119 -> n120 [pos="e,1519.2,35868 1498.8,36036 1504.2,35992 1512.2,35925 1518,35878"]; + n120 -> n130 [pos="e,1671.6,35602 1555.2,35774 1585.9,35729 1633.3,35659 1665.9,35610"]; + n122 [color=deeppink2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 84\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1885,36612", + shape=ellipse, + style=solid, + width=3.5652]; + n121 -> n122 [pos="e,1872,36660 1862.1,36696 1864.4,36687 1866.9,36678 1869.3,36670"]; + n123 [color=deeppink2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 84\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1879,36480", + shape=ellipse, + style=solid, + width=2.5643]; + n122 -> n123 [pos="e,1881.2,36528 1882.8,36564 1882.4,36556 1882,36547 1881.7,36539"]; + n124 [color=bisque1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 85\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1832,36348", + shape=ellipse, + style=solid, + width=7.0968]; + n123 -> n124 [pos="e,1849,36396 1862.2,36432 1859,36424 1855.7,36415 1852.5,36406"]; + n125 [color=deepskyblue4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 86\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1841,36216", + shape=ellipse, + style=solid, + width=3.5652]; + n124 -> n125 [pos="e,1837.7,36264 1835.3,36300 1835.8,36292 1836.4,36283 1837,36275"]; + n126 [color=deepskyblue4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 86\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1822,36084", + shape=ellipse, + style=solid, + width=2.5643]; + n125 -> n126 [pos="e,1828.9,36132 1834.1,36168 1832.9,36159 1831.6,36151 1830.3,36142"]; + n127 [color=chartreuse2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 87\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1818,35952", + shape=ellipse, + style=solid, + width=7.0968]; + n126 -> n127 [pos="e,1819.5,36000 1820.5,36036 1820.3,36028 1820,36019 1819.8,36011"]; + n128 [color=aquamarine1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 88\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1800,35820", + shape=ellipse, + style=solid, + width=3.5652]; + n127 -> n128 [pos="e,1806.5,35868 1811.5,35904 1810.3,35895 1809.1,35887 1807.9,35878"]; + n129 [color=aquamarine1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 88\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1762,35688", + shape=ellipse, + style=solid, + width=2.5643]; + n128 -> n129 [pos="e,1775.7,35736 1786.3,35772 1783.8,35764 1781.1,35754 1778.6,35746"]; + n129 -> n130 [pos="e,1723.4,35603 1740.8,35641 1736.6,35632 1732,35622 1727.6,35612"]; + n131 [color=aquamarine2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 90\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1780,35424", + shape=ellipse, + style=solid, + width=6.3961]; + n130 -> n131 [pos="e,1751.8,35472 1729.3,35510 1734.9,35500 1740.8,35490 1746.6,35481"]; + n132 [color=gold, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 91\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1870,35292", + shape=ellipse, + style=solid, + width=3.5652]; + n131 -> n132 [pos="e,1838.2,35339 1812.5,35376 1819,35367 1825.8,35357 1832.4,35347"]; + n133 [color=azure1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 92\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1931,35160", + shape=ellipse, + style=solid, + width=2.6788]; + n132 -> n133 [pos="e,1909.3,35207 1891.9,35244 1896.1,35235 1900.6,35226 1905,35216"]; + n133 -> n134 [pos="e,1991.3,35074 1962.7,35114 1970,35104 1977.8,35093 1985.3,35082"]; + n135 [color=firebrick3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2023,34896", + shape=ellipse, + style=solid, + width=2.6788]; + n134 -> n135 [pos="e,2023,34944 2023,34980 2023,34972 2023,34963 2023,34955"]; + n136 [color=deepskyblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 93\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1543,34764", + shape=ellipse, + style=solid, + width=6.2816]; + n135 -> n136 [pos="e,1680.7,34802 1938.8,34872 1870.4,34854 1772.1,34827 1690.6,34805"]; + n139 [color=goldenrod2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 95\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1843,34632", + shape=ellipse, + style=solid, + width=6.2816]; + n135 -> n139 [pos="e,1870.2,34680 1985.6,34851 1975.3,34839 1964.5,34825 1955,34812 1926.3,34772 1896.9,34725 1875.5,34689"]; + n145 [color=black, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 99\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="2190,34764", + shape=ellipse, + style=solid, + width=6.2816]; + n135 -> n145 [pos="e,2131.4,34811 2074.5,34855 2090,34843 2107.2,34829 2123.5,34817"]; + n158 [color=firebrick3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2379,33048", + shape=ellipse, + style=solid, + width=2.6788]; + n135 -> n158 [pos="e,2405.6,33095 2118,34887 2222.6,34876 2381.7,34853 2425,34812 2484.5,34755 2471,34715 2471,34633 2471,34633 2471,34633 2471,33311 \ +2471,33236 2437,33155 2410.3,33104"]; + n137 [color=bisque1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 94\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1470,34632", + shape=ellipse, + style=solid, + width=3.5652]; + n136 -> n137 [pos="e,1496.1,34679 1516.6,34716 1511.6,34707 1506.2,34697 1501,34688"]; + n138 [color=bisque1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 94\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1646,33972", + shape=ellipse, + style=solid, + width=2.5643]; + n137 -> n138 [pos="e,1608.5,34016 1482.7,34584 1495.7,34532 1514,34445 1514,34369 1514,34369 1514,34369 1514,34235 1514,34155 1563.8,34074 1602.2,34024"]; + n154 [color=gold2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 105\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="1995,33576", + shape=ellipse, + style=solid, + width=2.6788]; + n138 -> n154 [pos="e,1950.2,33619 1676.9,33927 1703.2,33890 1742.9,33836 1781,33792 1833,33732 1898,33668 1942.6,33626"]; + n140 [color=goldenrod4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 96\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1824,34500", + shape=ellipse, + style=solid, + width=3.5652]; + n139 -> n140 [pos="e,1830.9,34548 1836.1,34584 1834.9,34575 1833.6,34567 1832.3,34558"]; + n141 [color=goldenrod4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 96\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1807,34368", + shape=ellipse, + style=solid, + width=2.5643]; + n140 -> n141 [pos="e,1813.2,34416 1817.8,34452 1816.7,34443 1815.6,34435 1814.5,34426"]; + n142 [color=cornsilk3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 97\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1801,34236", + shape=ellipse, + style=solid, + width=7.0968]; + n141 -> n142 [pos="e,1803.2,34284 1804.8,34320 1804.4,34312 1804,34303 1803.7,34295"]; + n143 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 98\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1852,34104", + shape=ellipse, + style=solid, + width=3.5652]; + n142 -> n143 [pos="e,1833.7,34152 1819.6,34188 1822.9,34179 1826.5,34170 1830,34161"]; + n144 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 98\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1883,33840", + shape=ellipse, + style=solid, + width=2.5643]; + n143 -> n144 [pos="e,1877.4,33888 1857.6,34056 1862.8,34012 1870.6,33945 1876.2,33898"]; + n144 -> n154 [pos="e,1975.2,33623 1902.8,33793 1921.9,33748 1950.9,33680 1971.3,33632"]; + n146 [color=cyan1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 100\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="2216,34632", + shape=ellipse, + style=solid, + width=3.5652]; + n145 -> n146 [pos="e,2206.6,34680 2199.5,34716 2201.1,34707 2202.9,34699 2204.6,34690"]; + n147 [color=cyan1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 100\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="2212,34500", + shape=ellipse, + style=solid, + width=2.5643]; + n146 -> n147 [pos="e,2213.5,34548 2214.5,34584 2214.3,34576 2214,34567 2213.8,34559"]; + n148 [color=aliceblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 101\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="2187,34368", + shape=ellipse, + style=solid, + width=7.0968]; + n147 -> n148 [pos="e,2196.1,34416 2203,34452 2201.4,34444 2199.7,34435 2198,34426"]; + n149 [color=coral2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 102\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="2204,34236", + shape=ellipse, + style=solid, + width=3.5652]; + n148 -> n149 [pos="e,2197.8,34284 2193.2,34320 2194.3,34311 2195.4,34303 2196.5,34294"]; + n150 [color=coral2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 102\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="2182,34104", + shape=ellipse, + style=solid, + width=2.5643]; + n149 -> n150 [pos="e,2190,34152 2196,34188 2194.6,34179 2193.1,34171 2191.6,34162"]; + n151 [color=gold2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 103\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="2174,33972", + shape=ellipse, + style=solid, + width=7.0968]; + n150 -> n151 [pos="e,2176.9,34020 2179.1,34056 2178.6,34048 2178.1,34039 2177.5,34031"]; + n152 [color=dodgerblue4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 104\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="2139,33840", + shape=ellipse, + style=solid, + width=3.5652]; + n151 -> n152 [pos="e,2151.7,33888 2161.3,33924 2159,33915 2156.6,33906 2154.3,33898"]; + n153 [color=dodgerblue4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 104\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="2072,33708", + shape=ellipse, + style=solid, + width=2.5643]; + n152 -> n153 [pos="e,2095.6,33755 2115,33792 2110.2,33783 2105.2,33773 2100.3,33764"]; + n153 -> n154 [pos="e,2021.9,33622 2045.3,33662 2039.4,33652 2033.2,33641 2027.2,33631"]; + n155 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 106\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2142,33444", + shape=ellipse, + style=solid, + width=6.3961]; + n154 -> n155 [pos="e,2089.9,33491 2041.5,33534 2054.5,33522 2068.8,33510 2082.4,33498"]; + n156 [color=cadetblue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 107\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2228,33312", + shape=ellipse, + style=solid, + width=3.5652]; + n155 -> n156 [pos="e,2197.6,33359 2173,33396 2179.2,33387 2185.6,33377 2191.9,33368"]; + n157 [color=blue1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 108\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2287,33180", + shape=ellipse, + style=solid, + width=2.6788]; + n156 -> n157 [pos="e,2266,33227 2249.1,33264 2253.3,33255 2257.6,33246 2261.8,33236"]; + n157 -> n158 [pos="e,2347.3,33094 2318.7,33134 2326,33124 2333.8,33113 2341.3,33102"]; + n159 [color=firebrick3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2379,32916", + shape=ellipse, + style=solid, + width=2.6788]; + n158 -> n159 [pos="e,2379,32964 2379,33000 2379,32992 2379,32983 2379,32975"]; + n160 [color=darkseagreen3, + fontsize=14, + height=1.3356, + label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 109\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2159,32388", + shape=ellipse, + style=solid, + width=3.7843]; + n159 -> n160 [pos="e,2138.6,32436 2312,32881 2249.7,32846 2160.9,32783 2122,32700 2083.5,32618 2111.2,32510 2134.9,32445"]; + n161 [color=darkorange4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 384, 3x3, 2,2) without bias\nStorage id: 110\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2379,32652", + shape=ellipse, + style=solid, + width=6.8916]; + n159 -> n161 [pos="e,2379,32700 2379,32868 2379,32824 2379,32757 2379,32710"]; + n164 [color=darkkhaki, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 256, 1x1) without bias\nStorage id: 112\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2706,32784", + shape=ellipse, + style=solid, + width=6.3961]; + n159 -> n164 [pos="e,2600.7,32827 2453.6,32885 2494.3,32869 2545.7,32849 2591.4,32831"]; + n173 [color=coral, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2379,31596", + shape=ellipse, + style=solid, + width=2.817]; + n160 -> n173 [pos="e,2329.7,31638 2172.9,32340 2187.1,32288 2207,32201 2207,32125 2207,32125 2207,32125 2207,31859 2207,31773 2272.3,31694 2322.4,31645"]; + n162 [color=cornsilk2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 111\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2379,32520", + shape=ellipse, + style=solid, + width=3.5652]; + n161 -> n162 [pos="e,2379,32568 2379,32604 2379,32596 2379,32587 2379,32579"]; + n163 [color=cornsilk2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 111\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2379,32256", + shape=ellipse, + style=solid, + width=2.6788]; + n162 -> n163 [pos="e,2379,32304 2379,32472 2379,32428 2379,32361 2379,32314"]; + n163 -> n173 [pos="e,2379,31644 2379,32208 2379,32155 2379,32068 2379,31993 2379,31993 2379,31993 2379,31859 2379,31789 2379,31708 2379,31655"]; + n165 [color=gold2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 113\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2774,32652", + shape=ellipse, + style=solid, + width=3.5652]; + n164 -> n165 [pos="e,2749.7,32699 2730.5,32736 2735.2,32727 2740.2,32718 2745,32708"]; + n166 [color=gold2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 113\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2748,32520", + shape=ellipse, + style=solid, + width=2.6788]; + n165 -> n166 [pos="e,2757.4,32568 2764.5,32604 2762.9,32595 2761.1,32587 2759.4,32578"]; + n167 [color=firebrick, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 114\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2698,32388", + shape=ellipse, + style=solid, + width=7.3732]; + n166 -> n167 [pos="e,2716.1,32436 2730.1,32472 2726.8,32464 2723.2,32455 2719.8,32446"]; + n168 [color=blue2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 115\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2692,32256", + shape=ellipse, + style=solid, + width=3.5652]; + n167 -> n168 [pos="e,2694.2,32304 2695.8,32340 2695.4,32332 2695,32323 2694.7,32315"]; + n169 [color=blue2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 115\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2687,32124", + shape=ellipse, + style=solid, + width=2.6788]; + n168 -> n169 [pos="e,2688.8,32172 2690.2,32208 2689.9,32200 2689.5,32191 2689.2,32183"]; + n170 [color=darkgreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 116\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2666,31992", + shape=ellipse, + style=solid, + width=6.8916]; + n169 -> n170 [pos="e,2673.7,32040 2679.4,32076 2678,32068 2676.7,32059 2675.3,32051"]; + n171 [color=aquamarine, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 117\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2601,31860", + shape=ellipse, + style=solid, + width=3.5652]; + n170 -> n171 [pos="e,2624.2,31907 2642.5,31944 2638.1,31935 2633.3,31926 2628.7,31916"]; + n172 [color=aquamarine, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 117\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2552,31728", + shape=ellipse, + style=solid, + width=2.6788]; + n171 -> n172 [pos="e,2569.5,31775 2583.3,31812 2580,31803 2576.5,31794 2573.1,31785"]; + n172 -> n173 [pos="e,2432.4,31637 2499.2,31687 2480.6,31673 2459.7,31658 2440.5,31643"]; + n174 [color=bisque1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 119\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="1890,31464", + shape=ellipse, + style=solid, + width=6.5343]; + n173 -> n174 [pos="e,2031.1,31503 2291.3,31572 2221.8,31553 2123,31527 2040.8,31505"]; + n177 [color=burlywood, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 121\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2379,31464", + shape=ellipse, + style=solid, + width=6.5343]; + n173 -> n177 [pos="e,2379,31512 2379,31548 2379,31540 2379,31531 2379,31523"]; + n190 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2555,29748", + shape=ellipse, + style=solid, + width=2.817]; + n173 -> n190 [pos="e,2580.2,29795 2478.5,31586 2529.6,31576 2588.2,31556 2623,31512 2673,31450 2642,31413 2642,31333 2642,31333 2642,31333 2642,30011 \ +2642,29936 2609.8,29856 2584.6,29804"]; + n175 [color=blanchedalmond, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 120\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="1900,31332", + shape=ellipse, + style=solid, + width=3.5652]; + n174 -> n175 [pos="e,1896.3,31380 1893.6,31416 1894.3,31408 1894.9,31399 1895.6,31391"]; + n176 [color=blanchedalmond, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 120\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="1957,31068", + shape=ellipse, + style=solid, + width=2.6788]; + n175 -> n176 [pos="e,1946.7,31116 1910.3,31284 1919.9,31240 1934.3,31173 1944.6,31126"]; + n186 [color=floralwhite, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 127\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2348,30276", + shape=ellipse, + style=solid, + width=2.6788]; + n176 -> n186 [pos="e,2264.8,30301 1971.2,31020 1985.6,30968 2006,30881 2006,30805 2006,30805 2006,30805 2006,30539 2006,30413 2153.8,30339 2255.1,30304"]; + n178 [color=gold1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 122\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2371,31332", + shape=ellipse, + style=solid, + width=3.5652]; + n177 -> n178 [pos="e,2373.9,31380 2376.1,31416 2375.6,31408 2375.1,31399 2374.5,31391"]; + n179 [color=gold1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 122\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2356,31200", + shape=ellipse, + style=solid, + width=2.6788]; + n178 -> n179 [pos="e,2361.4,31248 2365.5,31284 2364.6,31275 2363.6,31267 2362.6,31258"]; + n180 [color=darkolivegreen1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 123\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2348,31068", + shape=ellipse, + style=solid, + width=7.3732]; + n179 -> n180 [pos="e,2350.9,31116 2353.1,31152 2352.6,31144 2352.1,31135 2351.5,31127"]; + n181 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 124\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2348,30936", + shape=ellipse, + style=solid, + width=3.5652]; + n180 -> n181 [pos="e,2348,30984 2348,31020 2348,31012 2348,31003 2348,30995"]; + n182 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 124\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2348,30804", + shape=ellipse, + style=solid, + width=2.6788]; + n181 -> n182 [pos="e,2348,30852 2348,30888 2348,30880 2348,30871 2348,30863"]; + n183 [color=dodgerblue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 125\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2348,30672", + shape=ellipse, + style=solid, + width=7.3732]; + n182 -> n183 [pos="e,2348,30720 2348,30756 2348,30748 2348,30739 2348,30731"]; + n184 [color=azure, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 126\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2348,30540", + shape=ellipse, + style=solid, + width=3.5652]; + n183 -> n184 [pos="e,2348,30588 2348,30624 2348,30616 2348,30607 2348,30599"]; + n185 [color=azure, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 126\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2348,30408", + shape=ellipse, + style=solid, + width=2.6788]; + n184 -> n185 [pos="e,2348,30456 2348,30492 2348,30484 2348,30475 2348,30467"]; + n185 -> n186 [pos="e,2348,30324 2348,30360 2348,30352 2348,30343 2348,30335"]; + n187 [color=firebrick2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 128\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2364,30144", + shape=ellipse, + style=solid, + width=6.5343]; + n186 -> n187 [pos="e,2358.2,30192 2353.8,30228 2354.8,30220 2355.9,30211 2356.9,30203"]; + n188 [color=darkolivegreen4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 129\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2425,30012", + shape=ellipse, + style=solid, + width=3.5652]; + n187 -> n188 [pos="e,2403.2,30059 2386,30096 2390.2,30087 2394.7,30078 2399,30068"]; + n189 [color=forestgreen, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 130\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2469,29880", + shape=ellipse, + style=solid, + width=2.817]; + n188 -> n189 [pos="e,2453.1,29928 2440.9,29964 2443.8,29955 2446.9,29946 2449.9,29937"]; + n189 -> n190 [pos="e,2525.2,29794 2498.8,29834 2505.5,29824 2512.6,29813 2519.5,29803"]; + n191 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2555,29616", + shape=ellipse, + style=solid, + width=2.817]; + n190 -> n191 [pos="e,2555,29664 2555,29700 2555,29692 2555,29683 2555,29675"]; + n192 [color=darkseagreen3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 131\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2066,29484", + shape=ellipse, + style=solid, + width=6.5343]; + n191 -> n192 [pos="e,2207.1,29523 2467.3,29592 2397.8,29573 2299,29547 2216.8,29525"]; + n195 [color=aliceblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 133\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2555,29484", + shape=ellipse, + style=solid, + width=6.5343]; + n191 -> n195 [pos="e,2555,29532 2555,29568 2555,29560 2555,29551 2555,29543"]; + n208 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2731,27768", + shape=ellipse, + style=solid, + width=2.817]; + n191 -> n208 [pos="e,2756.2,27815 2654.5,29606 2705.6,29596 2764.2,29576 2799,29532 2849,29470 2818,29433 2818,29353 2818,29353 2818,29353 2818,28031 \ +2818,27956 2785.8,27876 2760.6,27824"]; + n193 [color=azure, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 132\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2076,29352", + shape=ellipse, + style=solid, + width=3.5652]; + n192 -> n193 [pos="e,2072.3,29400 2069.6,29436 2070.3,29428 2070.9,29419 2071.6,29411"]; + n194 [color=azure, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 132\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2133,29088", + shape=ellipse, + style=solid, + width=2.6788]; + n193 -> n194 [pos="e,2122.7,29136 2086.3,29304 2095.9,29260 2110.3,29193 2120.6,29146"]; + n204 [color=burlywood4, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 139\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2524,28296", + shape=ellipse, + style=solid, + width=2.6788]; + n194 -> n204 [pos="e,2440.8,28321 2147.2,29040 2161.6,28988 2182,28901 2182,28825 2182,28825 2182,28825 2182,28559 2182,28433 2329.8,28359 2431.1,28324"]; + n196 [color=beige, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 134\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2547,29352", + shape=ellipse, + style=solid, + width=3.5652]; + n195 -> n196 [pos="e,2549.9,29400 2552.1,29436 2551.6,29428 2551.1,29419 2550.5,29411"]; + n197 [color=beige, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 134\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2532,29220", + shape=ellipse, + style=solid, + width=2.6788]; + n196 -> n197 [pos="e,2537.4,29268 2541.5,29304 2540.6,29295 2539.6,29287 2538.6,29278"]; + n198 [color=dodgerblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 135\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2524,29088", + shape=ellipse, + style=solid, + width=7.3732]; + n197 -> n198 [pos="e,2526.9,29136 2529.1,29172 2528.6,29164 2528.1,29155 2527.5,29147"]; + n199 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 136\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2524,28956", + shape=ellipse, + style=solid, + width=3.5652]; + n198 -> n199 [pos="e,2524,29004 2524,29040 2524,29032 2524,29023 2524,29015"]; + n200 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 136\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2524,28824", + shape=ellipse, + style=solid, + width=2.6788]; + n199 -> n200 [pos="e,2524,28872 2524,28908 2524,28900 2524,28891 2524,28883"]; + n201 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 137\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2524,28692", + shape=ellipse, + style=solid, + width=7.3732]; + n200 -> n201 [pos="e,2524,28740 2524,28776 2524,28768 2524,28759 2524,28751"]; + n202 [color=blue4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 138\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2524,28560", + shape=ellipse, + style=solid, + width=3.5652]; + n201 -> n202 [pos="e,2524,28608 2524,28644 2524,28636 2524,28627 2524,28619"]; + n203 [color=blue4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 138\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2524,28428", + shape=ellipse, + style=solid, + width=2.6788]; + n202 -> n203 [pos="e,2524,28476 2524,28512 2524,28504 2524,28495 2524,28487"]; + n203 -> n204 [pos="e,2524,28344 2524,28380 2524,28372 2524,28363 2524,28355"]; + n205 [color=darkorange2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 140\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2540,28164", + shape=ellipse, + style=solid, + width=6.5343]; + n204 -> n205 [pos="e,2534.2,28212 2529.8,28248 2530.8,28240 2531.9,28231 2532.9,28223"]; + n206 [color=aquamarine, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 141\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2601,28032", + shape=ellipse, + style=solid, + width=3.5652]; + n205 -> n206 [pos="e,2579.2,28079 2562,28116 2566.2,28107 2570.7,28098 2575,28088"]; + n207 [color=dodgerblue1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 142\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2645,27900", + shape=ellipse, + style=solid, + width=2.817]; + n206 -> n207 [pos="e,2629.1,27948 2616.9,27984 2619.8,27975 2622.9,27966 2625.9,27957"]; + n207 -> n208 [pos="e,2701.2,27814 2674.8,27854 2681.5,27844 2688.6,27833 2695.5,27823"]; + n209 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2731,27636", + shape=ellipse, + style=solid, + width=2.817]; + n208 -> n209 [pos="e,2731,27684 2731,27720 2731,27712 2731,27703 2731,27695"]; + n210 [color=gold, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 143\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2242,27504", + shape=ellipse, + style=solid, + width=6.5343]; + n209 -> n210 [pos="e,2383.1,27543 2643.3,27612 2573.8,27593 2475,27567 2392.8,27545"]; + n213 [color=antiquewhite3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 145\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2731,27504", + shape=ellipse, + style=solid, + width=6.5343]; + n209 -> n213 [pos="e,2731,27552 2731,27588 2731,27580 2731,27571 2731,27563"]; + n226 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2907,25788", + shape=ellipse, + style=solid, + width=2.817]; + n209 -> n226 [pos="e,2932.2,25835 2830.5,27626 2881.6,27616 2940.2,27596 2975,27552 3025,27490 2994,27453 2994,27373 2994,27373 2994,27373 2994,26051 \ +2994,25976 2961.8,25896 2936.6,25844"]; + n211 [color=cadetblue4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 144\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2280,27372", + shape=ellipse, + style=solid, + width=3.5652]; + n210 -> n211 [pos="e,2266.2,27420 2255.8,27456 2258.3,27447 2260.9,27438 2263.4,27430"]; + n212 [color=cadetblue4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 144\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2309,27108", + shape=ellipse, + style=solid, + width=2.6788]; + n211 -> n212 [pos="e,2303.8,27156 2285.2,27324 2290.1,27280 2297.4,27213 2302.7,27166"]; + n222 [color=darkgoldenrod, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 151\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2700,26316", + shape=ellipse, + style=solid, + width=2.6788]; + n212 -> n222 [pos="e,2616.8,26341 2323.2,27060 2337.6,27008 2358,26921 2358,26845 2358,26845 2358,26845 2358,26579 2358,26453 2505.8,26379 2607.1,26344"]; + n214 [color=burlywood4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 146\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2716,27372", + shape=ellipse, + style=solid, + width=3.5652]; + n213 -> n214 [pos="e,2721.4,27420 2725.5,27456 2724.6,27447 2723.6,27439 2722.6,27430"]; + n215 [color=burlywood4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 146\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2708,27240", + shape=ellipse, + style=solid, + width=2.6788]; + n214 -> n215 [pos="e,2710.9,27288 2713.1,27324 2712.6,27316 2712.1,27307 2711.5,27299"]; + n216 [color=burlywood1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 147\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2700,27108", + shape=ellipse, + style=solid, + width=7.3732]; + n215 -> n216 [pos="e,2702.9,27156 2705.1,27192 2704.6,27184 2704.1,27175 2703.5,27167"]; + n217 [color=brown, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 148\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2700,26976", + shape=ellipse, + style=solid, + width=3.5652]; + n216 -> n217 [pos="e,2700,27024 2700,27060 2700,27052 2700,27043 2700,27035"]; + n218 [color=brown, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 148\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2700,26844", + shape=ellipse, + style=solid, + width=2.6788]; + n217 -> n218 [pos="e,2700,26892 2700,26928 2700,26920 2700,26911 2700,26903"]; + n219 [color=firebrick, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 149\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2700,26712", + shape=ellipse, + style=solid, + width=7.3732]; + n218 -> n219 [pos="e,2700,26760 2700,26796 2700,26788 2700,26779 2700,26771"]; + n220 [color=deeppink4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 150\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2700,26580", + shape=ellipse, + style=solid, + width=3.5652]; + n219 -> n220 [pos="e,2700,26628 2700,26664 2700,26656 2700,26647 2700,26639"]; + n221 [color=deeppink4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 150\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2700,26448", + shape=ellipse, + style=solid, + width=2.6788]; + n220 -> n221 [pos="e,2700,26496 2700,26532 2700,26524 2700,26515 2700,26507"]; + n221 -> n222 [pos="e,2700,26364 2700,26400 2700,26392 2700,26383 2700,26375"]; + n223 [color=brown, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 152\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2716,26184", + shape=ellipse, + style=solid, + width=6.5343]; + n222 -> n223 [pos="e,2710.2,26232 2705.8,26268 2706.8,26260 2707.9,26251 2708.9,26243"]; + n224 [color=cadetblue2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 153\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2777,26052", + shape=ellipse, + style=solid, + width=3.5652]; + n223 -> n224 [pos="e,2755.2,26099 2738,26136 2742.2,26127 2746.7,26118 2751,26108"]; + n225 [color=deeppink3, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 154\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2821,25920", + shape=ellipse, + style=solid, + width=2.817]; + n224 -> n225 [pos="e,2805.1,25968 2792.9,26004 2795.8,25995 2798.9,25986 2801.9,25977"]; + n225 -> n226 [pos="e,2877.2,25834 2850.8,25874 2857.5,25864 2864.6,25853 2871.5,25843"]; + n227 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2907,25656", + shape=ellipse, + style=solid, + width=2.817]; + n226 -> n227 [pos="e,2907,25704 2907,25740 2907,25732 2907,25723 2907,25715"]; + n228 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 155\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2418,25524", + shape=ellipse, + style=solid, + width=6.5343]; + n227 -> n228 [pos="e,2559.1,25563 2819.3,25632 2749.8,25613 2651,25587 2568.8,25565"]; + n231 [color=firebrick4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 157\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2907,25524", + shape=ellipse, + style=solid, + width=6.5343]; + n227 -> n231 [pos="e,2907,25572 2907,25608 2907,25600 2907,25591 2907,25583"]; + n244 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3083,23808", + shape=ellipse, + style=solid, + width=2.817]; + n227 -> n244 [pos="e,3108.2,23855 3006.5,25646 3057.6,25636 3116.2,25616 3151,25572 3201,25510 3170,25473 3170,25393 3170,25393 3170,25393 3170,24071 \ +3170,23996 3137.8,23916 3112.6,23864"]; + n229 [color=darkkhaki, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 156\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2428,25392", + shape=ellipse, + style=solid, + width=3.5652]; + n228 -> n229 [pos="e,2424.3,25440 2421.6,25476 2422.3,25468 2422.9,25459 2423.6,25451"]; + n230 [color=darkkhaki, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 156\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2485,25128", + shape=ellipse, + style=solid, + width=2.6788]; + n229 -> n230 [pos="e,2474.7,25176 2438.3,25344 2447.9,25300 2462.3,25233 2472.6,25186"]; + n240 [color=bisque2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 163\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2876,24336", + shape=ellipse, + style=solid, + width=2.6788]; + n230 -> n240 [pos="e,2792.8,24361 2499.2,25080 2513.6,25028 2534,24941 2534,24865 2534,24865 2534,24865 2534,24599 2534,24473 2681.8,24399 2783.1,24364"]; + n232 [color=azure3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 158\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2899,25392", + shape=ellipse, + style=solid, + width=3.5652]; + n231 -> n232 [pos="e,2901.9,25440 2904.1,25476 2903.6,25468 2903.1,25459 2902.5,25451"]; + n233 [color=azure3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 158\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2884,25260", + shape=ellipse, + style=solid, + width=2.6788]; + n232 -> n233 [pos="e,2889.4,25308 2893.5,25344 2892.6,25335 2891.6,25327 2890.6,25318"]; + n234 [color=dodgerblue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 159\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2876,25128", + shape=ellipse, + style=solid, + width=7.3732]; + n233 -> n234 [pos="e,2878.9,25176 2881.1,25212 2880.6,25204 2880.1,25195 2879.5,25187"]; + n235 [color=black, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 160\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2876,24996", + shape=ellipse, + style=solid, + width=3.5652]; + n234 -> n235 [pos="e,2876,25044 2876,25080 2876,25072 2876,25063 2876,25055"]; + n236 [color=black, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 160\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2876,24864", + shape=ellipse, + style=solid, + width=2.6788]; + n235 -> n236 [pos="e,2876,24912 2876,24948 2876,24940 2876,24931 2876,24923"]; + n237 [color=darkorange3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 161\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2876,24732", + shape=ellipse, + style=solid, + width=7.3732]; + n236 -> n237 [pos="e,2876,24780 2876,24816 2876,24808 2876,24799 2876,24791"]; + n238 [color=coral3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 162\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2876,24600", + shape=ellipse, + style=solid, + width=3.5652]; + n237 -> n238 [pos="e,2876,24648 2876,24684 2876,24676 2876,24667 2876,24659"]; + n239 [color=coral3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 162\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2876,24468", + shape=ellipse, + style=solid, + width=2.6788]; + n238 -> n239 [pos="e,2876,24516 2876,24552 2876,24544 2876,24535 2876,24527"]; + n239 -> n240 [pos="e,2876,24384 2876,24420 2876,24412 2876,24403 2876,24395"]; + n241 [color=blue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 164\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2892,24204", + shape=ellipse, + style=solid, + width=6.5343]; + n240 -> n241 [pos="e,2886.2,24252 2881.8,24288 2882.8,24280 2883.9,24271 2884.9,24263"]; + n242 [color=darksalmon, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 165\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2953,24072", + shape=ellipse, + style=solid, + width=3.5652]; + n241 -> n242 [pos="e,2931.2,24119 2914,24156 2918.2,24147 2922.7,24138 2927,24128"]; + n243 [color=gainsboro, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 166\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2997,23940", + shape=ellipse, + style=solid, + width=2.817]; + n242 -> n243 [pos="e,2981.1,23988 2968.9,24024 2971.8,24015 2974.9,24006 2977.9,23997"]; + n243 -> n244 [pos="e,3053.2,23854 3026.8,23894 3033.5,23884 3040.6,23873 3047.5,23863"]; + n245 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3083,23676", + shape=ellipse, + style=solid, + width=2.817]; + n244 -> n245 [pos="e,3083,23724 3083,23760 3083,23752 3083,23743 3083,23735"]; + n246 [color=blueviolet, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 167\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2594,23544", + shape=ellipse, + style=solid, + width=6.5343]; + n245 -> n246 [pos="e,2735.1,23583 2995.3,23652 2925.8,23633 2827,23607 2744.8,23585"]; + n249 [color=chocolate4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 169\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3083,23544", + shape=ellipse, + style=solid, + width=6.5343]; + n245 -> n249 [pos="e,3083,23592 3083,23628 3083,23620 3083,23611 3083,23603"]; + n262 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3259,21828", + shape=ellipse, + style=solid, + width=2.817]; + n245 -> n262 [pos="e,3284.2,21875 3182.5,23666 3233.6,23656 3292.2,23636 3327,23592 3377,23530 3346,23493 3346,23413 3346,23413 3346,23413 3346,22091 \ +3346,22016 3313.8,21936 3288.6,21884"]; + n247 [color=darkseagreen2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 168\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2604,23412", + shape=ellipse, + style=solid, + width=3.5652]; + n246 -> n247 [pos="e,2600.3,23460 2597.6,23496 2598.3,23488 2598.9,23479 2599.6,23471"]; + n248 [color=darkseagreen2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 168\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2661,23148", + shape=ellipse, + style=solid, + width=2.6788]; + n247 -> n248 [pos="e,2650.7,23196 2614.3,23364 2623.9,23320 2638.3,23253 2648.6,23206"]; + n258 [color=dodgerblue2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 175\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3052,22356", + shape=ellipse, + style=solid, + width=2.6788]; + n248 -> n258 [pos="e,2968.8,22381 2675.2,23100 2689.6,23048 2710,22961 2710,22885 2710,22885 2710,22885 2710,22619 2710,22493 2857.8,22419 2959.1,22384"]; + n250 [color=dodgerblue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 170\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3075,23412", + shape=ellipse, + style=solid, + width=3.5652]; + n249 -> n250 [pos="e,3077.9,23460 3080.1,23496 3079.6,23488 3079.1,23479 3078.5,23471"]; + n251 [color=dodgerblue1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 170\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3060,23280", + shape=ellipse, + style=solid, + width=2.6788]; + n250 -> n251 [pos="e,3065.4,23328 3069.5,23364 3068.6,23355 3067.6,23347 3066.6,23338"]; + n252 [color=brown3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 171\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3052,23148", + shape=ellipse, + style=solid, + width=7.3732]; + n251 -> n252 [pos="e,3054.9,23196 3057.1,23232 3056.6,23224 3056.1,23215 3055.5,23207"]; + n253 [color=brown2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 172\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3052,23016", + shape=ellipse, + style=solid, + width=3.5652]; + n252 -> n253 [pos="e,3052,23064 3052,23100 3052,23092 3052,23083 3052,23075"]; + n254 [color=brown2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 172\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3052,22884", + shape=ellipse, + style=solid, + width=2.6788]; + n253 -> n254 [pos="e,3052,22932 3052,22968 3052,22960 3052,22951 3052,22943"]; + n255 [color=darkgoldenrod4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 173\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3052,22752", + shape=ellipse, + style=solid, + width=7.3732]; + n254 -> n255 [pos="e,3052,22800 3052,22836 3052,22828 3052,22819 3052,22811"]; + n256 [color=antiquewhite, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 174\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3052,22620", + shape=ellipse, + style=solid, + width=3.5652]; + n255 -> n256 [pos="e,3052,22668 3052,22704 3052,22696 3052,22687 3052,22679"]; + n257 [color=antiquewhite, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 174\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3052,22488", + shape=ellipse, + style=solid, + width=2.6788]; + n256 -> n257 [pos="e,3052,22536 3052,22572 3052,22564 3052,22555 3052,22547"]; + n257 -> n258 [pos="e,3052,22404 3052,22440 3052,22432 3052,22423 3052,22415"]; + n259 [color=deeppink4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 176\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3068,22224", + shape=ellipse, + style=solid, + width=6.5343]; + n258 -> n259 [pos="e,3062.2,22272 3057.8,22308 3058.8,22300 3059.9,22291 3060.9,22283"]; + n260 [color=brown, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 177\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3129,22092", + shape=ellipse, + style=solid, + width=3.5652]; + n259 -> n260 [pos="e,3107.2,22139 3090,22176 3094.2,22167 3098.7,22158 3103,22148"]; + n261 [color=bisque2, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 178\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3173,21960", + shape=ellipse, + style=solid, + width=2.817]; + n260 -> n261 [pos="e,3157.1,22008 3144.9,22044 3147.8,22035 3150.9,22026 3153.9,22017"]; + n261 -> n262 [pos="e,3229.2,21874 3202.8,21914 3209.5,21904 3216.6,21893 3223.5,21883"]; + n263 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3259,21696", + shape=ellipse, + style=solid, + width=2.817]; + n262 -> n263 [pos="e,3259,21744 3259,21780 3259,21772 3259,21763 3259,21755"]; + n264 [color=coral2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 179\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2770,21564", + shape=ellipse, + style=solid, + width=6.5343]; + n263 -> n264 [pos="e,2911.1,21603 3171.3,21672 3101.8,21653 3003,21627 2920.8,21605"]; + n267 [color=antiquewhite1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 181\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3259,21564", + shape=ellipse, + style=solid, + width=6.5343]; + n263 -> n267 [pos="e,3259,21612 3259,21648 3259,21640 3259,21631 3259,21623"]; + n280 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3435,19848", + shape=ellipse, + style=solid, + width=2.817]; + n263 -> n280 [pos="e,3460.2,19895 3358.5,21686 3409.6,21676 3468.2,21656 3503,21612 3553,21550 3522,21513 3522,21433 3522,21433 3522,21433 3522,20111 \ +3522,20036 3489.8,19956 3464.6,19904"]; + n265 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 180\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2780,21432", + shape=ellipse, + style=solid, + width=3.5652]; + n264 -> n265 [pos="e,2776.3,21480 2773.6,21516 2774.3,21508 2774.9,21499 2775.6,21491"]; + n266 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 180\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2837,21168", + shape=ellipse, + style=solid, + width=2.6788]; + n265 -> n266 [pos="e,2826.7,21216 2790.3,21384 2799.9,21340 2814.3,21273 2824.6,21226"]; + n276 [color=deeppink3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 187\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3228,20376", + shape=ellipse, + style=solid, + width=2.6788]; + n266 -> n276 [pos="e,3144.8,20401 2851.2,21120 2865.6,21068 2886,20981 2886,20905 2886,20905 2886,20905 2886,20639 2886,20513 3033.8,20439 3135.1,20404"]; + n268 [color=gold, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 182\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3244,21432", + shape=ellipse, + style=solid, + width=3.5652]; + n267 -> n268 [pos="e,3249.4,21480 3253.5,21516 3252.6,21507 3251.6,21499 3250.6,21490"]; + n269 [color=gold, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 182\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3236,21300", + shape=ellipse, + style=solid, + width=2.6788]; + n268 -> n269 [pos="e,3238.9,21348 3241.1,21384 3240.6,21376 3240.1,21367 3239.5,21359"]; + n270 [color=cornsilk3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 183\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3228,21168", + shape=ellipse, + style=solid, + width=7.3732]; + n269 -> n270 [pos="e,3230.9,21216 3233.1,21252 3232.6,21244 3232.1,21235 3231.5,21227"]; + n271 [color=deepskyblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 184\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3228,21036", + shape=ellipse, + style=solid, + width=3.5652]; + n270 -> n271 [pos="e,3228,21084 3228,21120 3228,21112 3228,21103 3228,21095"]; + n272 [color=deepskyblue, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 184\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3228,20904", + shape=ellipse, + style=solid, + width=2.6788]; + n271 -> n272 [pos="e,3228,20952 3228,20988 3228,20980 3228,20971 3228,20963"]; + n273 [color=firebrick4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 185\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3228,20772", + shape=ellipse, + style=solid, + width=7.3732]; + n272 -> n273 [pos="e,3228,20820 3228,20856 3228,20848 3228,20839 3228,20831"]; + n274 [color=deeppink1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 186\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3228,20640", + shape=ellipse, + style=solid, + width=3.5652]; + n273 -> n274 [pos="e,3228,20688 3228,20724 3228,20716 3228,20707 3228,20699"]; + n275 [color=deeppink1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 186\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3228,20508", + shape=ellipse, + style=solid, + width=2.6788]; + n274 -> n275 [pos="e,3228,20556 3228,20592 3228,20584 3228,20575 3228,20567"]; + n275 -> n276 [pos="e,3228,20424 3228,20460 3228,20452 3228,20443 3228,20435"]; + n277 [color=gold3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 188\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3244,20244", + shape=ellipse, + style=solid, + width=6.5343]; + n276 -> n277 [pos="e,3238.2,20292 3233.8,20328 3234.8,20320 3235.9,20311 3236.9,20303"]; + n278 [color=chocolate, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 189\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3305,20112", + shape=ellipse, + style=solid, + width=3.5652]; + n277 -> n278 [pos="e,3283.2,20159 3266,20196 3270.2,20187 3274.7,20178 3279,20168"]; + n279 [color=darkturquoise, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 190\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3348,19980", + shape=ellipse, + style=solid, + width=2.817]; + n278 -> n279 [pos="e,3332.5,20028 3320.5,20064 3323.4,20055 3326.4,20046 3329.3,20037"]; + n279 -> n280 [pos="e,3404.9,19894 3378.2,19934 3384.9,19924 3392.2,19913 3399.1,19903"]; + n281 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3435,19716", + shape=ellipse, + style=solid, + width=2.817]; + n280 -> n281 [pos="e,3435,19764 3435,19800 3435,19792 3435,19783 3435,19775"]; + n282 [color=chartreuse2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 191\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2946,19584", + shape=ellipse, + style=solid, + width=6.5343]; + n281 -> n282 [pos="e,3087.1,19623 3347.3,19692 3277.8,19673 3179,19647 3096.8,19625"]; + n285 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 193\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3435,19584", + shape=ellipse, + style=solid, + width=6.5343]; + n281 -> n285 [pos="e,3435,19632 3435,19668 3435,19660 3435,19651 3435,19643"]; + n298 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3611,17868", + shape=ellipse, + style=solid, + width=2.817]; + n281 -> n298 [pos="e,3636.2,17915 3534.5,19706 3585.6,19696 3644.2,19676 3679,19632 3729,19570 3698,19533 3698,19453 3698,19453 3698,19453 3698,18131 \ +3698,18056 3665.8,17976 3640.6,17924"]; + n283 [color=dodgerblue2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 192\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2956,19452", + shape=ellipse, + style=solid, + width=3.5652]; + n282 -> n283 [pos="e,2952.3,19500 2949.6,19536 2950.3,19528 2950.9,19519 2951.6,19511"]; + n284 [color=dodgerblue2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 192\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3013,19188", + shape=ellipse, + style=solid, + width=2.6788]; + n283 -> n284 [pos="e,3002.7,19236 2966.3,19404 2975.9,19360 2990.3,19293 3000.6,19246"]; + n294 [color=azure1, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 199\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3404,18396", + shape=ellipse, + style=solid, + width=2.6788]; + n284 -> n294 [pos="e,3320.8,18421 3027.2,19140 3041.6,19088 3062,19001 3062,18925 3062,18925 3062,18925 3062,18659 3062,18533 3209.8,18459 3311.1,18424"]; + n286 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 194\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3427,19452", + shape=ellipse, + style=solid, + width=3.5652]; + n285 -> n286 [pos="e,3429.9,19500 3432.1,19536 3431.6,19528 3431.1,19519 3430.5,19511"]; + n287 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 194\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3412,19320", + shape=ellipse, + style=solid, + width=2.6788]; + n286 -> n287 [pos="e,3417.4,19368 3421.5,19404 3420.6,19395 3419.6,19387 3418.6,19378"]; + n288 [color=coral1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 195\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3404,19188", + shape=ellipse, + style=solid, + width=7.3732]; + n287 -> n288 [pos="e,3406.9,19236 3409.1,19272 3408.6,19264 3408.1,19255 3407.5,19247"]; + n289 [color=bisque2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 196\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3404,19056", + shape=ellipse, + style=solid, + width=3.5652]; + n288 -> n289 [pos="e,3404,19104 3404,19140 3404,19132 3404,19123 3404,19115"]; + n290 [color=bisque2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 196\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3404,18924", + shape=ellipse, + style=solid, + width=2.6788]; + n289 -> n290 [pos="e,3404,18972 3404,19008 3404,19000 3404,18991 3404,18983"]; + n291 [color=blue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 197\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3404,18792", + shape=ellipse, + style=solid, + width=7.3732]; + n290 -> n291 [pos="e,3404,18840 3404,18876 3404,18868 3404,18859 3404,18851"]; + n292 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 198\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3404,18660", + shape=ellipse, + style=solid, + width=3.5652]; + n291 -> n292 [pos="e,3404,18708 3404,18744 3404,18736 3404,18727 3404,18719"]; + n293 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 198\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3404,18528", + shape=ellipse, + style=solid, + width=2.6788]; + n292 -> n293 [pos="e,3404,18576 3404,18612 3404,18604 3404,18595 3404,18587"]; + n293 -> n294 [pos="e,3404,18444 3404,18480 3404,18472 3404,18463 3404,18455"]; + n295 [color=ghostwhite, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 200\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3420,18264", + shape=ellipse, + style=solid, + width=6.5343]; + n294 -> n295 [pos="e,3414.2,18312 3409.8,18348 3410.8,18340 3411.9,18331 3412.9,18323"]; + n296 [color=dodgerblue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 201\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3481,18132", + shape=ellipse, + style=solid, + width=3.5652]; + n295 -> n296 [pos="e,3459.2,18179 3442,18216 3446.2,18207 3450.7,18198 3455,18188"]; + n297 [color=forestgreen, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 202\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3524,18000", + shape=ellipse, + style=solid, + width=2.817]; + n296 -> n297 [pos="e,3508.5,18048 3496.5,18084 3499.4,18075 3502.4,18066 3505.3,18057"]; + n297 -> n298 [pos="e,3580.9,17914 3554.2,17954 3560.9,17944 3568.2,17933 3575.1,17923"]; + n299 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3611,17736", + shape=ellipse, + style=solid, + width=2.817]; + n298 -> n299 [pos="e,3611,17784 3611,17820 3611,17812 3611,17803 3611,17795"]; + n300 [color=ghostwhite, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 203\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3122,17604", + shape=ellipse, + style=solid, + width=6.5343]; + n299 -> n300 [pos="e,3263.1,17643 3523.3,17712 3453.8,17693 3355,17667 3272.8,17645"]; + n303 [color=darkgreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 205\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3611,17604", + shape=ellipse, + style=solid, + width=6.5343]; + n299 -> n303 [pos="e,3611,17652 3611,17688 3611,17680 3611,17671 3611,17663"]; + n316 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3787,15888", + shape=ellipse, + style=solid, + width=2.817]; + n299 -> n316 [pos="e,3812.2,15935 3710.5,17726 3761.6,17716 3820.2,17696 3855,17652 3905,17590 3874,17553 3874,17473 3874,17473 3874,17473 3874,16151 \ +3874,16076 3841.8,15996 3816.6,15944"]; + n301 [color=darkorange4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 204\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3132,17472", + shape=ellipse, + style=solid, + width=3.5652]; + n300 -> n301 [pos="e,3128.3,17520 3125.6,17556 3126.3,17548 3126.9,17539 3127.6,17531"]; + n302 [color=darkorange4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 204\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3189,17208", + shape=ellipse, + style=solid, + width=2.6788]; + n301 -> n302 [pos="e,3178.7,17256 3142.3,17424 3151.9,17380 3166.3,17313 3176.6,17266"]; + n312 [color=cornsilk1, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 211\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3580,16416", + shape=ellipse, + style=solid, + width=2.6788]; + n302 -> n312 [pos="e,3496.5,16440 3202.9,17160 3217.1,17108 3237,17021 3237,16945 3237,16945 3237,16945 3237,16679 3237,16553 3385.3,16479 3486.9,16444"]; + n304 [color=dodgerblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 206\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3603,17472", + shape=ellipse, + style=solid, + width=3.5652]; + n303 -> n304 [pos="e,3605.9,17520 3608.1,17556 3607.6,17548 3607.1,17539 3606.5,17531"]; + n305 [color=dodgerblue, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 206\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3588,17340", + shape=ellipse, + style=solid, + width=2.6788]; + n304 -> n305 [pos="e,3593.4,17388 3597.5,17424 3596.6,17415 3595.6,17407 3594.6,17398"]; + n306 [color=gold, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 207\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3580,17208", + shape=ellipse, + style=solid, + width=7.3732]; + n305 -> n306 [pos="e,3582.9,17256 3585.1,17292 3584.6,17284 3584.1,17275 3583.5,17267"]; + n307 [color=darkgoldenrod1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 208\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3580,17076", + shape=ellipse, + style=solid, + width=3.5652]; + n306 -> n307 [pos="e,3580,17124 3580,17160 3580,17152 3580,17143 3580,17135"]; + n308 [color=darkgoldenrod1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 208\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3580,16944", + shape=ellipse, + style=solid, + width=2.6788]; + n307 -> n308 [pos="e,3580,16992 3580,17028 3580,17020 3580,17011 3580,17003"]; + n309 [color=chartreuse, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 209\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3580,16812", + shape=ellipse, + style=solid, + width=7.3732]; + n308 -> n309 [pos="e,3580,16860 3580,16896 3580,16888 3580,16879 3580,16871"]; + n310 [color=darkgoldenrod2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 210\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3580,16680", + shape=ellipse, + style=solid, + width=3.5652]; + n309 -> n310 [pos="e,3580,16728 3580,16764 3580,16756 3580,16747 3580,16739"]; + n311 [color=darkgoldenrod2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 210\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3580,16548", + shape=ellipse, + style=solid, + width=2.6788]; + n310 -> n311 [pos="e,3580,16596 3580,16632 3580,16624 3580,16615 3580,16607"]; + n311 -> n312 [pos="e,3580,16464 3580,16500 3580,16492 3580,16483 3580,16475"]; + n313 [color=dodgerblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 212\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3596,16284", + shape=ellipse, + style=solid, + width=6.5343]; + n312 -> n313 [pos="e,3590.2,16332 3585.8,16368 3586.8,16360 3587.9,16351 3588.9,16343"]; + n314 [color=brown4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 213\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3657,16152", + shape=ellipse, + style=solid, + width=3.5652]; + n313 -> n314 [pos="e,3635.2,16199 3618,16236 3622.2,16227 3626.7,16218 3631,16208"]; + n315 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 214\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3701,16020", + shape=ellipse, + style=solid, + width=2.817]; + n314 -> n315 [pos="e,3685.1,16068 3672.9,16104 3675.8,16095 3678.9,16086 3681.9,16077"]; + n315 -> n316 [pos="e,3757.2,15934 3730.8,15974 3737.5,15964 3744.6,15953 3751.5,15943"]; + n317 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3787,15756", + shape=ellipse, + style=solid, + width=2.817]; + n316 -> n317 [pos="e,3787,15804 3787,15840 3787,15832 3787,15823 3787,15815"]; + n318 [color=cyan3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 215\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3298,15624", + shape=ellipse, + style=solid, + width=6.5343]; + n317 -> n318 [pos="e,3439.1,15663 3699.3,15732 3629.8,15713 3531,15687 3448.8,15665"]; + n321 [color=azure4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 217\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3787,15624", + shape=ellipse, + style=solid, + width=6.5343]; + n317 -> n321 [pos="e,3787,15672 3787,15708 3787,15700 3787,15691 3787,15683"]; + n334 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3963,13908", + shape=ellipse, + style=solid, + width=2.817]; + n317 -> n334 [pos="e,3988.2,13955 3886.5,15746 3937.6,15736 3996.2,15716 4031,15672 4081,15610 4050,15573 4050,15493 4050,15493 4050,15493 4050,14171 \ +4050,14096 4017.8,14016 3992.6,13964"]; + n319 [color=darkkhaki, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 216\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3308,15492", + shape=ellipse, + style=solid, + width=3.5652]; + n318 -> n319 [pos="e,3304.3,15540 3301.6,15576 3302.3,15568 3302.9,15559 3303.6,15551"]; + n320 [color=darkkhaki, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 216\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3365,15228", + shape=ellipse, + style=solid, + width=2.6788]; + n319 -> n320 [pos="e,3354.7,15276 3318.3,15444 3327.9,15400 3342.3,15333 3352.6,15286"]; + n330 [color=darkseagreen1, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 223\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3756,14436", + shape=ellipse, + style=solid, + width=2.6788]; + n320 -> n330 [pos="e,3672.8,14461 3379.2,15180 3393.6,15128 3414,15041 3414,14965 3414,14965 3414,14965 3414,14699 3414,14573 3561.8,14499 3663.1,14464"]; + n322 [color=darkseagreen, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 218\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3779,15492", + shape=ellipse, + style=solid, + width=3.5652]; + n321 -> n322 [pos="e,3781.9,15540 3784.1,15576 3783.6,15568 3783.1,15559 3782.5,15551"]; + n323 [color=darkseagreen, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 218\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3764,15360", + shape=ellipse, + style=solid, + width=2.6788]; + n322 -> n323 [pos="e,3769.4,15408 3773.5,15444 3772.6,15435 3771.6,15427 3770.6,15418"]; + n324 [color=burlywood3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 219\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3756,15228", + shape=ellipse, + style=solid, + width=7.3732]; + n323 -> n324 [pos="e,3758.9,15276 3761.1,15312 3760.6,15304 3760.1,15295 3759.5,15287"]; + n325 [color=goldenrod, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 220\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3756,15096", + shape=ellipse, + style=solid, + width=3.5652]; + n324 -> n325 [pos="e,3756,15144 3756,15180 3756,15172 3756,15163 3756,15155"]; + n326 [color=goldenrod, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 220\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3756,14964", + shape=ellipse, + style=solid, + width=2.6788]; + n325 -> n326 [pos="e,3756,15012 3756,15048 3756,15040 3756,15031 3756,15023"]; + n327 [color=darkgoldenrod, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 221\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3756,14832", + shape=ellipse, + style=solid, + width=7.3732]; + n326 -> n327 [pos="e,3756,14880 3756,14916 3756,14908 3756,14899 3756,14891"]; + n328 [color=floralwhite, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 222\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3756,14700", + shape=ellipse, + style=solid, + width=3.5652]; + n327 -> n328 [pos="e,3756,14748 3756,14784 3756,14776 3756,14767 3756,14759"]; + n329 [color=floralwhite, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 222\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3756,14568", + shape=ellipse, + style=solid, + width=2.6788]; + n328 -> n329 [pos="e,3756,14616 3756,14652 3756,14644 3756,14635 3756,14627"]; + n329 -> n330 [pos="e,3756,14484 3756,14520 3756,14512 3756,14503 3756,14495"]; + n331 [color=coral4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 224\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3772,14304", + shape=ellipse, + style=solid, + width=6.5343]; + n330 -> n331 [pos="e,3766.2,14352 3761.8,14388 3762.8,14380 3763.9,14371 3764.9,14363"]; + n332 [color=antiquewhite4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 225\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3833,14172", + shape=ellipse, + style=solid, + width=3.5652]; + n331 -> n332 [pos="e,3811.2,14219 3794,14256 3798.2,14247 3802.7,14238 3807,14228"]; + n333 [color=brown, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 226\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3877,14040", + shape=ellipse, + style=solid, + width=2.817]; + n332 -> n333 [pos="e,3861.1,14088 3848.9,14124 3851.8,14115 3854.9,14106 3857.9,14097"]; + n333 -> n334 [pos="e,3933.2,13954 3906.8,13994 3913.5,13984 3920.6,13973 3927.5,13963"]; + n335 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3963,13776", + shape=ellipse, + style=solid, + width=2.817]; + n334 -> n335 [pos="e,3963,13824 3963,13860 3963,13852 3963,13843 3963,13835"]; + n336 [color=bisque1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 227\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3474,13644", + shape=ellipse, + style=solid, + width=6.5343]; + n335 -> n336 [pos="e,3615.1,13683 3875.3,13752 3805.8,13733 3707,13707 3624.8,13685"]; + n339 [color=burlywood2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 229\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3963,13644", + shape=ellipse, + style=solid, + width=6.5343]; + n335 -> n339 [pos="e,3963,13692 3963,13728 3963,13720 3963,13711 3963,13703"]; + n352 [color=coral, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="4139,11928", + shape=ellipse, + style=solid, + width=2.817]; + n335 -> n352 [pos="e,4164.2,11975 4062.5,13766 4113.6,13756 4172.2,13736 4207,13692 4257,13630 4226,13593 4226,13513 4226,13513 4226,13513 4226,12191 \ +4226,12116 4193.8,12036 4168.6,11984"]; + n337 [color=cornsilk, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 228\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3484,13512", + shape=ellipse, + style=solid, + width=3.5652]; + n336 -> n337 [pos="e,3480.3,13560 3477.6,13596 3478.3,13588 3478.9,13579 3479.6,13571"]; + n338 [color=cornsilk, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 228\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3541,13248", + shape=ellipse, + style=solid, + width=2.6788]; + n337 -> n338 [pos="e,3530.7,13296 3494.3,13464 3503.9,13420 3518.3,13353 3528.6,13306"]; + n348 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 235\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3932,12456", + shape=ellipse, + style=solid, + width=2.6788]; + n338 -> n348 [pos="e,3848.8,12481 3555.2,13200 3569.6,13148 3590,13061 3590,12985 3590,12985 3590,12985 3590,12719 3590,12593 3737.8,12519 3839.1,12484"]; + n340 [color=cyan3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 230\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3955,13512", + shape=ellipse, + style=solid, + width=3.5652]; + n339 -> n340 [pos="e,3957.9,13560 3960.1,13596 3959.6,13588 3959.1,13579 3958.5,13571"]; + n341 [color=cyan3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 230\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3940,13380", + shape=ellipse, + style=solid, + width=2.6788]; + n340 -> n341 [pos="e,3945.4,13428 3949.5,13464 3948.6,13455 3947.6,13447 3946.6,13438"]; + n342 [color=dodgerblue2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 231\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3932,13248", + shape=ellipse, + style=solid, + width=7.3732]; + n341 -> n342 [pos="e,3934.9,13296 3937.1,13332 3936.6,13324 3936.1,13315 3935.5,13307"]; + n343 [color=crimson, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 232\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3932,13116", + shape=ellipse, + style=solid, + width=3.5652]; + n342 -> n343 [pos="e,3932,13164 3932,13200 3932,13192 3932,13183 3932,13175"]; + n344 [color=crimson, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 232\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3932,12984", + shape=ellipse, + style=solid, + width=2.6788]; + n343 -> n344 [pos="e,3932,13032 3932,13068 3932,13060 3932,13051 3932,13043"]; + n345 [color=darkolivegreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 233\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3932,12852", + shape=ellipse, + style=solid, + width=7.3732]; + n344 -> n345 [pos="e,3932,12900 3932,12936 3932,12928 3932,12919 3932,12911"]; + n346 [color=beige, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 234\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3932,12720", + shape=ellipse, + style=solid, + width=3.5652]; + n345 -> n346 [pos="e,3932,12768 3932,12804 3932,12796 3932,12787 3932,12779"]; + n347 [color=beige, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 234\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3932,12588", + shape=ellipse, + style=solid, + width=2.6788]; + n346 -> n347 [pos="e,3932,12636 3932,12672 3932,12664 3932,12655 3932,12647"]; + n347 -> n348 [pos="e,3932,12504 3932,12540 3932,12532 3932,12523 3932,12515"]; + n349 [color=deeppink4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 236\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3948,12324", + shape=ellipse, + style=solid, + width=6.5343]; + n348 -> n349 [pos="e,3942.2,12372 3937.8,12408 3938.8,12400 3939.9,12391 3940.9,12383"]; + n350 [color=brown2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 237\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="4008,12192", + shape=ellipse, + style=solid, + width=3.5652]; + n349 -> n350 [pos="e,3986.6,12239 3969.7,12276 3973.7,12267 3978.1,12258 3982.3,12249"]; + n351 [color=aliceblue, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 238\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="4052,12060", + shape=ellipse, + style=solid, + width=2.817]; + n350 -> n351 [pos="e,4036.1,12108 4023.9,12144 4026.8,12135 4029.9,12126 4032.9,12117"]; + n351 -> n352 [pos="e,4108.9,11974 4082.2,12014 4088.9,12004 4096.2,11993 4103.1,11983"]; + n353 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="4139,11796", + shape=ellipse, + style=solid, + width=2.817]; + n352 -> n353 [pos="e,4139,11844 4139,11880 4139,11872 4139,11863 4139,11855"]; + n354 [color=brown1, + fontsize=14, + height=1.3356, + label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 239\nSize: {1, 1152, 8, 8}\nMem size: 73728", + pos="3817,10872", + shape=ellipse, + style=solid, + width=3.7843]; + n353 -> n354 [pos="e,3784.9,10919 4043.1,11780 3916.1,11755 3708,11689 3708,11533 3708,11533 3708,11533 3708,11135 3708,11059 3748,10979 3779.5,10927"]; + n355 [color=firebrick3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 240\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4005,11532", + shape=ellipse, + style=solid, + width=6.5343]; + n353 -> n355 [pos="e,4001.3,11580 4073.3,11759 4055.9,11747 4038.9,11731 4028,11712 4006.6,11675 4001.4,11627 4001.3,11590"]; + n361 [color=antiquewhite, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 244\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4272,11664", + shape=ellipse, + style=solid, + width=6.5343]; + n353 -> n361 [pos="e,4224.7,11711 4182.5,11752 4193.7,11742 4205.9,11730 4217.5,11718"]; + n367 [color=bisque4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 248\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4762,11664", + shape=ellipse, + style=solid, + width=6.5343]; + n353 -> n367 [pos="e,4599.7,11699 4231.3,11776 4325.4,11756 4473.7,11725 4589.6,11701"]; + n376 [color=brown4, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4246,10476", + shape=ellipse, + style=solid, + width=2.5643]; + n354 -> n376 [pos="e,4201.3,10518 3865,10827 3946.1,10752 4110,10602 4193.9,10525"]; + n356 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 241\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4005,11400", + shape=ellipse, + style=solid, + width=3.5652]; + n355 -> n356 [pos="e,4005,11448 4005,11484 4005,11476 4005,11467 4005,11459"]; + n357 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 241\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="3988,11268", + shape=ellipse, + style=solid, + width=2.6788]; + n356 -> n357 [pos="e,3994.2,11316 3998.8,11352 3997.7,11343 3996.6,11335 3995.5,11326"]; + n358 [color=azure, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 242\nSize: {1, 384, 8, 8}\nMem size: 24576", + pos="3984,11136", + shape=ellipse, + style=solid, + width=6.8916]; + n357 -> n358 [pos="e,3985.5,11184 3986.5,11220 3986.3,11212 3986,11203 3985.8,11195"]; + n359 [color=coral1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 243\nSize: {1, 384, 8, 8}\nMem size: 24576", + pos="4044,11004", + shape=ellipse, + style=solid, + width=3.5652]; + n358 -> n359 [pos="e,4022.6,11051 4005.7,11088 4009.7,11079 4014.1,11070 4018.3,11061"]; + n360 [color=coral1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 243\nSize: {1, 384, 8, 8}\nMem size: 24576", + pos="4173,10740", + shape=ellipse, + style=solid, + width=2.4261]; + n359 -> n360 [pos="e,4150.5,10787 4066.9,10956 4089,10912 4122.6,10843 4146,10796"]; + n360 -> n376 [pos="e,4229.7,10523 4183.7,10692 4192.3,10656 4204.9,10604 4218,10560 4220.6,10551 4223.5,10542 4226.4,10533"]; + n362 [color=blue2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 245\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4484,11400", + shape=ellipse, + style=solid, + width=3.5652]; + n361 -> n362 [pos="e,4447.4,11446 4309.7,11616 4346.4,11571 4402.4,11502 4440.9,11454"]; + n363 [color=blue2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 245\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4492,11268", + shape=ellipse, + style=solid, + width=2.6788]; + n362 -> n363 [pos="e,4489.1,11316 4486.9,11352 4487.4,11344 4487.9,11335 4488.5,11327"]; + n364 [color=dodgerblue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 246\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4498,11136", + shape=ellipse, + style=solid, + width=6.8916]; + n363 -> n364 [pos="e,4495.8,11184 4494.2,11220 4494.6,11212 4495,11203 4495.3,11195"]; + n365 [color=gainsboro, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 247\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4438,11004", + shape=ellipse, + style=solid, + width=3.5652]; + n364 -> n365 [pos="e,4459.4,11051 4476.3,11088 4472.3,11079 4467.9,11070 4463.7,11061"]; + n366 [color=gainsboro, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 247\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4315,10608", + shape=ellipse, + style=solid, + width=2.4261]; + n365 -> n366 [pos="e,4329.5,10656 4423.3,10956 4400.5,10883 4356.9,10743 4332.6,10665"]; + n366 -> n376 [pos="e,4270.2,10523 4290.9,10562 4285.7,10552 4280.3,10542 4275,10532"]; + n368 [color=cyan, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 249\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4762,11532", + shape=ellipse, + style=solid, + width=3.5652]; + n367 -> n368 [pos="e,4762,11580 4762,11616 4762,11608 4762,11599 4762,11591"]; + n369 [color=cyan, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 249\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4796,11400", + shape=ellipse, + style=solid, + width=2.6788]; + n368 -> n369 [pos="e,4783.7,11448 4774.3,11484 4776.5,11476 4778.9,11466 4781.2,11458"]; + n370 [color=brown2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 250\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4889,11268", + shape=ellipse, + style=solid, + width=7.3732]; + n369 -> n370 [pos="e,4855.4,11316 4828,11354 4834.9,11345 4842.3,11334 4849.4,11324"]; + n371 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 251\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4893,11136", + shape=ellipse, + style=solid, + width=3.5652]; + n370 -> n371 [pos="e,4891.5,11184 4890.5,11220 4890.7,11212 4891,11203 4891.2,11195"]; + n372 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 251\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4817,11004", + shape=ellipse, + style=solid, + width=2.6788]; + n371 -> n372 [pos="e,4843.7,11051 4866,11089 4860.4,11079 4854.5,11069 4848.8,11059"]; + n373 [color=darkseagreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 252\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4811,10872", + shape=ellipse, + style=solid, + width=6.8916]; + n372 -> n373 [pos="e,4813.2,10920 4814.8,10956 4814.4,10948 4814,10939 4813.7,10931"]; + n374 [color=chartreuse, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 253\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4560,10740", + shape=ellipse, + style=solid, + width=3.5652]; + n373 -> n374 [pos="e,4634.2,10779 4725.7,10827 4699.1,10813 4669.8,10798 4643.4,10784"]; + n375 [color=chartreuse, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 253\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4518,10608", + shape=ellipse, + style=solid, + width=2.4261]; + n374 -> n375 [pos="e,4533,10655 4544.8,10692 4542,10683 4539,10674 4536.1,10665"]; + n375 -> n376 [pos="e,4313.5,10509 4452.8,10576 4413.7,10557 4363.7,10533 4322.6,10514"]; + n377 [color=coral4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 255\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3757,10344", + shape=ellipse, + style=solid, + width=6.5343]; + n376 -> n377 [pos="e,3898.2,10383 4164.4,10453 4094.7,10435 3992.5,10408 3907.9,10385"]; + n380 [color=cornsilk, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 257\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4246,10344", + shape=ellipse, + style=solid, + width=6.5343]; + n376 -> n380 [pos="e,4246,10392 4246,10428 4246,10420 4246,10411 4246,10403"]; + n393 [color=brown4, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4424,8628", + shape=ellipse, + style=solid, + width=2.5643]; + n376 -> n393 [pos="e,4448.4,8674.4 4337.2,10468 4390.1,10459 4453.3,10438 4490,10392 4540,10330 4509,10293 4509,10213 4509,10213 4509,10213 4509,8891 \ +4509,8816.3 4477.4,8735.3 4452.7,8683.4"]; + n378 [color=darkorange, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 256\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3768,10212", + shape=ellipse, + style=solid, + width=3.5652]; + n377 -> n378 [pos="e,3764,10260 3761,10296 3761.7,10288 3762.4,10279 3763.1,10271"]; + n379 [color=darkorange, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 256\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3832,9948", + shape=ellipse, + style=solid, + width=2.4261]; + n378 -> n379 [pos="e,3820.6,9995.8 3779.5,10164 3790.4,10119 3806.6,10053 3818.2,10006"]; + n389 [color=darkgreen, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 263\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4215,9156", + shape=ellipse, + style=solid, + width=2.4261]; + n379 -> n389 [pos="e,4138.1,9178.8 3844.7,9900.1 3857.7,9847.9 3876,9761.1 3876,9685 3876,9685 3876,9685 3876,9419 3876,9291.5 4028.1,9217.2 4128.5,\ +9182.1"]; + n381 [color=firebrick2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 258\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4231,10212", + shape=ellipse, + style=solid, + width=3.5652]; + n380 -> n381 [pos="e,4236.4,10260 4240.5,10296 4239.6,10287 4238.6,10279 4237.6,10270"]; + n382 [color=firebrick2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 258\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4223,10080", + shape=ellipse, + style=solid, + width=2.4261]; + n381 -> n382 [pos="e,4225.9,10128 4228.1,10164 4227.6,10156 4227.1,10147 4226.5,10139"]; + n383 [color=aliceblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 259\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4215,9948", + shape=ellipse, + style=solid, + width=7.3732]; + n382 -> n383 [pos="e,4217.9,9996.5 4220.1,10032 4219.6,10024 4219.1,10015 4218.5,10007"]; + n384 [color=blanchedalmond, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 260\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4215,9816", + shape=ellipse, + style=solid, + width=3.5652]; + n383 -> n384 [pos="e,4215,9864.5 4215,9899.7 4215,9891.5 4215,9883 4215,9874.6"]; + n385 [color=blanchedalmond, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 260\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4215,9684", + shape=ellipse, + style=solid, + width=2.4261]; + n384 -> n385 [pos="e,4215,9732.5 4215,9767.7 4215,9759.5 4215,9751 4215,9742.6"]; + n386 [color=beige, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 261\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4215,9552", + shape=ellipse, + style=solid, + width=7.3732]; + n385 -> n386 [pos="e,4215,9600.5 4215,9635.7 4215,9627.5 4215,9619 4215,9610.6"]; + n387 [color=coral, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 262\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4215,9420", + shape=ellipse, + style=solid, + width=3.5652]; + n386 -> n387 [pos="e,4215,9468.5 4215,9503.7 4215,9495.5 4215,9487 4215,9478.6"]; + n388 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 262\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4215,9288", + shape=ellipse, + style=solid, + width=2.4261]; + n387 -> n388 [pos="e,4215,9336.5 4215,9371.7 4215,9363.5 4215,9355 4215,9346.6"]; + n388 -> n389 [pos="e,4215,9204.5 4215,9239.7 4215,9231.5 4215,9223 4215,9214.6"]; + n390 [color=blueviolet, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 264\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4231,9024", + shape=ellipse, + style=solid, + width=6.5343]; + n389 -> n390 [pos="e,4225.2,9072.5 4220.8,9107.7 4221.8,9099.5 4222.9,9091 4223.9,9082.6"]; + n391 [color=bisque4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 265\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4292,8892", + shape=ellipse, + style=solid, + width=3.5652]; + n390 -> n391 [pos="e,4270.2,8939.4 4253,8976.1 4257.2,8967.1 4261.7,8957.7 4266,8948.5"]; + n392 [color=cornsilk3, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 266\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4340,8760", + shape=ellipse, + style=solid, + width=2.5643]; + n391 -> n392 [pos="e,4322.9,8807.4 4309.3,8844.1 4312.6,8835.3 4316,8826.1 4319.3,8817.1"]; + n392 -> n393 [pos="e,4395,8673.8 4368.9,8714.3 4375.6,8703.9 4382.7,8692.9 4389.6,8682.2"]; + n394 [color=brown4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4424,8496", + shape=ellipse, + style=solid, + width=2.5643]; + n393 -> n394 [pos="e,4424,8544.5 4424,8579.7 4424,8571.5 4424,8563 4424,8554.6"]; + n395 [color=cornsilk4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 267\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3935,8364", + shape=ellipse, + style=solid, + width=6.5343]; + n394 -> n395 [pos="e,4076.2,8402.5 4342.4,8473.3 4272.7,8454.8 4170.5,8427.6 4085.9,8405.1"]; + n398 [color=azure1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 269\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4424,8364", + shape=ellipse, + style=solid, + width=6.5343]; + n394 -> n398 [pos="e,4424,8412.5 4424,8447.7 4424,8439.5 4424,8431 4424,8422.6"]; + n411 [color=brown4, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4602,6648", + shape=ellipse, + style=solid, + width=2.5643]; + n394 -> n411 [pos="e,4626.4,6694.4 4515.2,8487.8 4568.1,8478.5 4631.3,8457.9 4668,8412 4718,8349.5 4687,8313 4687,8233 4687,8233 4687,8233 4687,6911 \ +4687,6836.3 4655.4,6755.3 4630.7,6703.4"]; + n396 [color=darkorchid1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 268\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3946,8232", + shape=ellipse, + style=solid, + width=3.5652]; + n395 -> n396 [pos="e,3942,8280.5 3939,8315.7 3939.7,8307.5 3940.4,8299 3941.1,8290.6"]; + n397 [color=darkorchid1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 268\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4010,7968", + shape=ellipse, + style=solid, + width=2.4261]; + n396 -> n397 [pos="e,3998.6,8015.8 3957.5,8183.9 3968.4,8139.4 3984.6,8072.8 3996.2,8025.5"]; + n407 [color=forestgreen, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 275\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4393,7176", + shape=ellipse, + style=solid, + width=2.4261]; + n397 -> n407 [pos="e,4315.9,7199.1 4023,7920.1 4036.3,7868 4055,7781.2 4055,7705 4055,7705 4055,7705 4055,7439 4055,7311.9 4206.2,7237.6 4306.4,7202.3"]; + n399 [color=darkslategrey, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 270\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4416,8232", + shape=ellipse, + style=solid, + width=3.5652]; + n398 -> n399 [pos="e,4418.9,8280.5 4421.1,8315.7 4420.6,8307.5 4420.1,8299 4419.5,8290.6"]; + n400 [color=darkslategrey, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 270\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4401,8100", + shape=ellipse, + style=solid, + width=2.4261]; + n399 -> n400 [pos="e,4406.4,8148 4410.5,8183.7 4409.6,8175.4 4408.6,8166.6 4407.6,8158"]; + n401 [color=azure3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 271\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4393,7968", + shape=ellipse, + style=solid, + width=7.3732]; + n400 -> n401 [pos="e,4395.9,8016.5 4398.1,8051.7 4397.6,8043.5 4397.1,8035 4396.5,8026.6"]; + n402 [color=burlywood3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 272\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4393,7836", + shape=ellipse, + style=solid, + width=3.5652]; + n401 -> n402 [pos="e,4393,7884.5 4393,7919.7 4393,7911.5 4393,7903 4393,7894.6"]; + n403 [color=burlywood3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 272\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4393,7704", + shape=ellipse, + style=solid, + width=2.4261]; + n402 -> n403 [pos="e,4393,7752.5 4393,7787.7 4393,7779.5 4393,7771 4393,7762.6"]; + n404 [color=dodgerblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 273\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4393,7572", + shape=ellipse, + style=solid, + width=7.3732]; + n403 -> n404 [pos="e,4393,7620.5 4393,7655.7 4393,7647.5 4393,7639 4393,7630.6"]; + n405 [color=burlywood, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 274\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4393,7440", + shape=ellipse, + style=solid, + width=3.5652]; + n404 -> n405 [pos="e,4393,7488.5 4393,7523.7 4393,7515.5 4393,7507 4393,7498.6"]; + n406 [color=burlywood, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 274\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4393,7308", + shape=ellipse, + style=solid, + width=2.4261]; + n405 -> n406 [pos="e,4393,7356.5 4393,7391.7 4393,7383.5 4393,7375 4393,7366.6"]; + n406 -> n407 [pos="e,4393,7224.5 4393,7259.7 4393,7251.5 4393,7243 4393,7234.6"]; + n408 [color=bisque1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 276\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4409,7044", + shape=ellipse, + style=solid, + width=6.5343]; + n407 -> n408 [pos="e,4403.2,7092.5 4398.8,7127.7 4399.8,7119.5 4400.9,7111 4401.9,7102.6"]; + n409 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 277\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4470,6912", + shape=ellipse, + style=solid, + width=3.5652]; + n408 -> n409 [pos="e,4448.2,6959.4 4431,6996.1 4435.2,6987.1 4439.7,6977.7 4444,6968.5"]; + n410 [color=dodgerblue4, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 278\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4518,6780", + shape=ellipse, + style=solid, + width=2.5643]; + n409 -> n410 [pos="e,4500.9,6827.4 4487.3,6864.1 4490.6,6855.3 4494,6846.1 4497.3,6837.1"]; + n410 -> n411 [pos="e,4573,6693.8 4546.9,6734.3 4553.6,6723.9 4560.7,6712.9 4567.6,6702.2"]; + n412 [color=brown4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4602,6516", + shape=ellipse, + style=solid, + width=2.5643]; + n411 -> n412 [pos="e,4602,6564.5 4602,6599.7 4602,6591.5 4602,6583 4602,6574.6"]; + n413 [color=bisque4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 279\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4113,6384", + shape=ellipse, + style=solid, + width=6.5343]; + n412 -> n413 [pos="e,4254.2,6422.5 4520.4,6493.3 4450.7,6474.8 4348.5,6447.6 4263.9,6425.1"]; + n416 [color=deepskyblue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 281\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4602,6384", + shape=ellipse, + style=solid, + width=6.5343]; + n412 -> n416 [pos="e,4602,6432.5 4602,6467.7 4602,6459.5 4602,6451 4602,6442.6"]; + n429 [color=brown4, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4780,4668", + shape=ellipse, + style=solid, + width=2.5643]; + n412 -> n429 [pos="e,4804.4,4714.4 4693.2,6507.8 4746.1,6498.5 4809.3,6477.9 4846,6432 4896,6369.5 4865,6333 4865,6253 4865,6253 4865,6253 4865,4931 \ +4865,4856.3 4833.4,4775.3 4808.7,4723.4"]; + n414 [color=bisque, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 280\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4124,6252", + shape=ellipse, + style=solid, + width=3.5652]; + n413 -> n414 [pos="e,4120,6300.5 4117,6335.7 4117.7,6327.5 4118.4,6319 4119.1,6310.6"]; + n415 [color=bisque, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 280\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4188,5988", + shape=ellipse, + style=solid, + width=2.4261]; + n414 -> n415 [pos="e,4176.6,6035.8 4135.5,6203.9 4146.4,6159.4 4162.6,6092.8 4174.2,6045.5"]; + n425 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 287\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4571,5196", + shape=ellipse, + style=solid, + width=2.4261]; + n415 -> n425 [pos="e,4493.9,5219.1 4201,5940.1 4214.3,5888 4233,5801.2 4233,5725 4233,5725 4233,5725 4233,5459 4233,5331.9 4384.2,5257.6 4484.4,5222.3"]; + n417 [color=deeppink4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 282\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4594,6252", + shape=ellipse, + style=solid, + width=3.5652]; + n416 -> n417 [pos="e,4596.9,6300.5 4599.1,6335.7 4598.6,6327.5 4598.1,6319 4597.5,6310.6"]; + n418 [color=deeppink4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 282\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4579,6120", + shape=ellipse, + style=solid, + width=2.4261]; + n417 -> n418 [pos="e,4584.4,6168 4588.5,6203.7 4587.6,6195.4 4586.6,6186.6 4585.6,6178"]; + n419 [color=darkolivegreen4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 283\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4571,5988", + shape=ellipse, + style=solid, + width=7.3732]; + n418 -> n419 [pos="e,4573.9,6036.5 4576.1,6071.7 4575.6,6063.5 4575.1,6055 4574.5,6046.6"]; + n420 [color=blueviolet, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 284\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4571,5856", + shape=ellipse, + style=solid, + width=3.5652]; + n419 -> n420 [pos="e,4571,5904.5 4571,5939.7 4571,5931.5 4571,5923 4571,5914.6"]; + n421 [color=blueviolet, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 284\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4571,5724", + shape=ellipse, + style=solid, + width=2.4261]; + n420 -> n421 [pos="e,4571,5772.5 4571,5807.7 4571,5799.5 4571,5791 4571,5782.6"]; + n422 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 285\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4571,5592", + shape=ellipse, + style=solid, + width=7.3732]; + n421 -> n422 [pos="e,4571,5640.5 4571,5675.7 4571,5667.5 4571,5659 4571,5650.6"]; + n423 [color=firebrick1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 286\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4571,5460", + shape=ellipse, + style=solid, + width=3.5652]; + n422 -> n423 [pos="e,4571,5508.5 4571,5543.7 4571,5535.5 4571,5527 4571,5518.6"]; + n424 [color=firebrick1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 286\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4571,5328", + shape=ellipse, + style=solid, + width=2.4261]; + n423 -> n424 [pos="e,4571,5376.5 4571,5411.7 4571,5403.5 4571,5395 4571,5386.6"]; + n424 -> n425 [pos="e,4571,5244.5 4571,5279.7 4571,5271.5 4571,5263 4571,5254.6"]; + n426 [color=brown, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 288\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4587,5064", + shape=ellipse, + style=solid, + width=6.5343]; + n425 -> n426 [pos="e,4581.2,5112.5 4576.8,5147.7 4577.8,5139.5 4578.9,5131 4579.9,5122.6"]; + n427 [color=brown1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 289\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4648,4932", + shape=ellipse, + style=solid, + width=3.5652]; + n426 -> n427 [pos="e,4626.2,4979.4 4609,5016.1 4613.2,5007.1 4617.7,4997.7 4622,4988.5"]; + n428 [color=goldenrod1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 290\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4696,4800", + shape=ellipse, + style=solid, + width=2.5643]; + n427 -> n428 [pos="e,4678.9,4847.4 4665.3,4884.1 4668.6,4875.3 4672,4866.1 4675.3,4857.1"]; + n428 -> n429 [pos="e,4751,4713.8 4724.9,4754.3 4731.6,4743.9 4738.7,4732.9 4745.6,4722.2"]; + n430 [color=brown4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4780,4536", + shape=ellipse, + style=solid, + width=2.5643]; + n429 -> n430 [pos="e,4780,4584.5 4780,4619.7 4780,4611.5 4780,4603 4780,4594.6"]; + n431 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 291\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4291,4404", + shape=ellipse, + style=solid, + width=6.5343]; + n430 -> n431 [pos="e,4432.2,4442.5 4698.4,4513.3 4628.7,4494.8 4526.5,4467.6 4441.9,4445.1"]; + n434 [color=dimgrey, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 293\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4780,4404", + shape=ellipse, + style=solid, + width=6.5343]; + n430 -> n434 [pos="e,4780,4452.5 4780,4487.7 4780,4479.5 4780,4471 4780,4462.6"]; + n447 [color=brown4, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4958,2688", + shape=ellipse, + style=solid, + width=2.5643]; + n430 -> n447 [pos="e,4982.4,2734.4 4871.2,4527.8 4924.1,4518.5 4987.3,4497.9 5024,4452 5074,4389.5 5043,4353 5043,4273 5043,4273 5043,4273 5043,2951 \ +5043,2876.3 5011.4,2795.3 4986.7,2743.4"]; + n432 [color=darkorchid2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 292\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4302,4272", + shape=ellipse, + style=solid, + width=3.5652]; + n431 -> n432 [pos="e,4298,4320.5 4295,4355.7 4295.7,4347.5 4296.4,4339 4297.1,4330.6"]; + n433 [color=darkorchid2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 292\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4366,4008", + shape=ellipse, + style=solid, + width=2.4261]; + n432 -> n433 [pos="e,4354.6,4055.8 4313.5,4223.9 4324.4,4179.4 4340.6,4112.8 4352.2,4065.5"]; + n443 [color=chartreuse, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 299\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4749,3216", + shape=ellipse, + style=solid, + width=2.4261]; + n433 -> n443 [pos="e,4671.9,3239.1 4379,3960.1 4392.3,3908 4411,3821.2 4411,3745 4411,3745 4411,3745 4411,3479 4411,3351.9 4562.2,3277.6 4662.4,3242.3"]; + n435 [color=darkgoldenrod3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 294\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4772,4272", + shape=ellipse, + style=solid, + width=3.5652]; + n434 -> n435 [pos="e,4774.9,4320.5 4777.1,4355.7 4776.6,4347.5 4776.1,4339 4775.5,4330.6"]; + n436 [color=darkgoldenrod3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 294\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4757,4140", + shape=ellipse, + style=solid, + width=2.4261]; + n435 -> n436 [pos="e,4762.4,4188 4766.5,4223.7 4765.6,4215.4 4764.6,4206.6 4763.6,4198"]; + n437 [color=chartreuse, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 295\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4749,4008", + shape=ellipse, + style=solid, + width=7.3732]; + n436 -> n437 [pos="e,4751.9,4056.5 4754.1,4091.7 4753.6,4083.5 4753.1,4075 4752.5,4066.6"]; + n438 [color=cornsilk, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 296\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4749,3876", + shape=ellipse, + style=solid, + width=3.5652]; + n437 -> n438 [pos="e,4749,3924.5 4749,3959.7 4749,3951.5 4749,3943 4749,3934.6"]; + n439 [color=cornsilk, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 296\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4749,3744", + shape=ellipse, + style=solid, + width=2.4261]; + n438 -> n439 [pos="e,4749,3792.5 4749,3827.7 4749,3819.5 4749,3811 4749,3802.6"]; + n440 [color=darkslategrey, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 297\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4749,3612", + shape=ellipse, + style=solid, + width=7.3732]; + n439 -> n440 [pos="e,4749,3660.5 4749,3695.7 4749,3687.5 4749,3679 4749,3670.6"]; + n441 [color=chocolate1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 298\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4749,3480", + shape=ellipse, + style=solid, + width=3.5652]; + n440 -> n441 [pos="e,4749,3528.5 4749,3563.7 4749,3555.5 4749,3547 4749,3538.6"]; + n442 [color=chocolate1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 298\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4749,3348", + shape=ellipse, + style=solid, + width=2.4261]; + n441 -> n442 [pos="e,4749,3396.5 4749,3431.7 4749,3423.5 4749,3415 4749,3406.6"]; + n442 -> n443 [pos="e,4749,3264.5 4749,3299.7 4749,3291.5 4749,3283 4749,3274.6"]; + n444 [color=cornsilk, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 300\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4765,3084", + shape=ellipse, + style=solid, + width=6.5343]; + n443 -> n444 [pos="e,4759.2,3132.5 4754.8,3167.7 4755.8,3159.5 4756.9,3151 4757.9,3142.6"]; + n445 [color=blue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 301\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4826,2952", + shape=ellipse, + style=solid, + width=3.5652]; + n444 -> n445 [pos="e,4804.2,2999.4 4787,3036.1 4791.2,3027.1 4795.7,3017.7 4800,3008.5"]; + n446 [color=cyan, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 302\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4874,2820", + shape=ellipse, + style=solid, + width=2.5643]; + n445 -> n446 [pos="e,4856.9,2867.4 4843.3,2904.1 4846.6,2895.3 4850,2886.1 4853.3,2877.1"]; + n446 -> n447 [pos="e,4929,2733.8 4902.9,2774.3 4909.6,2763.9 4916.7,2752.9 4923.6,2742.2"]; + n448 [color=brown4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4958,2556", + shape=ellipse, + style=solid, + width=2.5643]; + n447 -> n448 [pos="e,4958,2604.5 4958,2639.7 4958,2631.5 4958,2623 4958,2614.6"]; + n449 [color=chocolate4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 303\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4469,2424", + shape=ellipse, + style=solid, + width=6.5343]; + n448 -> n449 [pos="e,4610.2,2462.5 4876.4,2533.3 4806.7,2514.8 4704.5,2487.6 4619.9,2465.1"]; + n452 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 305\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4958,2424", + shape=ellipse, + style=solid, + width=6.5343]; + n448 -> n452 [pos="e,4958,2472.5 4958,2507.7 4958,2499.5 4958,2491 4958,2482.6"]; + n465 [color=brown4, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="5136,708", + shape=ellipse, + style=solid, + width=2.5643]; + n448 -> n465 [pos="e,5160.4,754.41 5049.2,2547.8 5102.1,2538.5 5165.3,2517.9 5202,2472 5252,2409.5 5221,2373 5221,2293 5221,2293 5221,2293 5221,971 \ +5221,896.34 5189.4,815.3 5164.7,763.43"]; + n450 [color=aliceblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 304\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4480,2292", + shape=ellipse, + style=solid, + width=3.5652]; + n449 -> n450 [pos="e,4476,2340.5 4473,2375.7 4473.7,2367.5 4474.4,2359 4475.1,2350.6"]; + n451 [color=aliceblue, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 304\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4544,2028", + shape=ellipse, + style=solid, + width=2.4261]; + n450 -> n451 [pos="e,4532.6,2075.8 4491.5,2243.9 4502.4,2199.4 4518.6,2132.8 4530.2,2085.5"]; + n461 [color=darkorange1, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 311\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4927,1236", + shape=ellipse, + style=solid, + width=2.4261]; + n451 -> n461 [pos="e,4849.9,1259.1 4557,1980.1 4570.3,1928 4589,1841.2 4589,1765 4589,1765 4589,1765 4589,1499 4589,1371.9 4740.2,1297.6 4840.4,1262.3"]; + n453 [color=darkslategray4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 306\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4950,2292", + shape=ellipse, + style=solid, + width=3.5652]; + n452 -> n453 [pos="e,4952.9,2340.5 4955.1,2375.7 4954.6,2367.5 4954.1,2359 4953.5,2350.6"]; + n454 [color=darkslategray4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 306\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4935,2160", + shape=ellipse, + style=solid, + width=2.4261]; + n453 -> n454 [pos="e,4940.4,2208 4944.5,2243.7 4943.6,2235.4 4942.6,2226.6 4941.6,2218"]; + n455 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 307\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4927,2028", + shape=ellipse, + style=solid, + width=7.3732]; + n454 -> n455 [pos="e,4929.9,2076.5 4932.1,2111.7 4931.6,2103.5 4931.1,2095 4930.5,2086.6"]; + n456 [color=coral, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 308\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4927,1896", + shape=ellipse, + style=solid, + width=3.5652]; + n455 -> n456 [pos="e,4927,1944.5 4927,1979.7 4927,1971.5 4927,1963 4927,1954.6"]; + n457 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 308\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4927,1764", + shape=ellipse, + style=solid, + width=2.4261]; + n456 -> n457 [pos="e,4927,1812.5 4927,1847.7 4927,1839.5 4927,1831 4927,1822.6"]; + n458 [color=floralwhite, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 309\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4927,1632", + shape=ellipse, + style=solid, + width=7.3732]; + n457 -> n458 [pos="e,4927,1680.5 4927,1715.7 4927,1707.5 4927,1699 4927,1690.6"]; + n459 [color=blue4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 310\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4927,1500", + shape=ellipse, + style=solid, + width=3.5652]; + n458 -> n459 [pos="e,4927,1548.5 4927,1583.7 4927,1575.5 4927,1567 4927,1558.6"]; + n460 [color=blue4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 310\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4927,1368", + shape=ellipse, + style=solid, + width=2.4261]; + n459 -> n460 [pos="e,4927,1416.5 4927,1451.7 4927,1443.5 4927,1435 4927,1426.6"]; + n460 -> n461 [pos="e,4927,1284.5 4927,1319.7 4927,1311.5 4927,1303 4927,1294.6"]; + n462 [color=cadetblue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 312\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4943,1104", + shape=ellipse, + style=solid, + width=6.5343]; + n461 -> n462 [pos="e,4937.2,1152.5 4932.8,1187.7 4933.8,1179.5 4934.9,1171 4935.9,1162.6"]; + n463 [color=darkgoldenrod4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 313\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="5004,972", + shape=ellipse, + style=solid, + width=3.5652]; + n462 -> n463 [pos="e,4982.2,1019.4 4965,1056.1 4969.2,1047.1 4973.7,1037.7 4978,1028.5"]; + n464 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 314\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="5052,840", + shape=ellipse, + style=solid, + width=2.5643]; + n463 -> n464 [pos="e,5034.9,887.43 5021.3,924.07 5024.6,915.31 5028,906.07 5031.3,897.07"]; + n464 -> n465 [pos="e,5107,753.82 5080.9,794.26 5087.6,783.95 5094.7,772.87 5101.6,762.24"]; + n466 [color=brown4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="5136,576", + shape=ellipse, + style=solid, + width=2.5643]; + n465 -> n466 [pos="e,5136,624.48 5136,659.7 5136,651.54 5136,642.99 5136,634.6"]; + n467 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialAveragePooling(8x8, 1,1)\nStorage id: 315\nSize: {1, 2048, 1, 1}\nMem size: 2048", + pos="5136,444", + shape=ellipse, + style=solid, + width=4.5661]; + n466 -> n467 [pos="e,5136,492.48 5136,527.7 5136,519.54 5136,510.99 5136,502.6"]; + n468 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="nn.View(2048)\nStorage id: 315\nSize: {1, 2048}\nMem size: 2048", + pos="5136,312", + shape=ellipse, + style=solid, + width=2.0688]; + n467 -> n468 [pos="e,5136,360.48 5136,395.7 5136,387.54 5136,378.99 5136,370.6"]; + n469 [color=bisque3, + fontsize=14, + height=1.3356, + label="nn.Dropout(0.200000)\nStorage id: 316\nSize: {1, 2048}\nMem size: 2048", + pos="5136,180", + shape=ellipse, + style=solid, + width=2.7262]; + n468 -> n469 [pos="e,5136,228.48 5136,263.7 5136,255.54 5136,246.99 5136,238.6"]; + n470 [color=aquamarine2, + fontsize=14, + height=1.3356, + label="nn.Linear(2048 -> 1000)\nStorage id: 317\nSize: {1, 1000}\nMem size: 1000", + pos="5136,48", + shape=ellipse, + style=solid, + width=2.9789]; + n469 -> n470 [pos="e,5136,96.483 5136,131.7 5136,123.54 5136,114.99 5136,106.6"]; +} diff --git a/inception-resnet-v2.svg b/inception-resnet-v2.svg new file mode 100644 index 000000000..c429924f7 --- /dev/null +++ b/inception-resnet-v2.svg @@ -0,0 +1,6383 @@ + + + + + + +G + + +n1 + +Input +Storage id: 1 +Size: {1, 3, 299, 299} +Mem size: 268203 + + +n2 + +cudnn.SpatialConvolution(3 -> 32, 3x3, 2,2) without bias +Storage id: 2 +Size: {1, 32, 149, 149} +Mem size: 710432 + + +n1->n2 + + + + +n3 + +nn.SpatialBatchNormalization +Storage id: 3 +Size: {1, 32, 149, 149} +Mem size: 710432 + + +n2->n3 + + + + +n4 + +cudnn.ReLU +Storage id: 3 +Size: {1, 32, 149, 149} +Mem size: 710432 + + +n3->n4 + + + + +n5 + +cudnn.SpatialConvolution(32 -> 32, 3x3) without bias +Storage id: 4 +Size: {1, 32, 147, 147} +Mem size: 691488 + + +n4->n5 + + + + +n6 + +nn.SpatialBatchNormalization +Storage id: 5 +Size: {1, 32, 147, 147} +Mem size: 691488 + + +n5->n6 + + + + +n7 + +cudnn.ReLU +Storage id: 5 +Size: {1, 32, 147, 147} +Mem size: 691488 + + +n6->n7 + + + + +n8 + +cudnn.SpatialConvolution(32 -> 64, 3x3, 1,1, 1,1) without bias +Storage id: 6 +Size: {1, 64, 147, 147} +Mem size: 1382976 + + +n7->n8 + + + + +n9 + +nn.SpatialBatchNormalization +Storage id: 7 +Size: {1, 64, 147, 147} +Mem size: 1382976 + + +n8->n9 + + + + +n10 + +cudnn.ReLU +Storage id: 7 +Size: {1, 64, 147, 147} +Mem size: 1382976 + + +n9->n10 + + + + +n11 + +nn.SpatialMaxPooling(3x3, 2,2) +Storage id: 8 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n10->n11 + + + + +n12 + +cudnn.SpatialConvolution(64 -> 96, 3x3, 2,2) without bias +Storage id: 9 +Size: {1, 96, 73, 73} +Mem size: 511584 + + +n10->n12 + + + + +n15 + +nn.JoinTable +Storage id: 11 +Size: {1, 160, 73, 73} +Mem size: 852640 + + +n11->n15 + + + + +n13 + +nn.SpatialBatchNormalization +Storage id: 10 +Size: {1, 96, 73, 73} +Mem size: 511584 + + +n12->n13 + + + + +n14 + +cudnn.ReLU +Storage id: 10 +Size: {1, 96, 73, 73} +Mem size: 511584 + + +n13->n14 + + + + +n14->n15 + + + + +n16 + +cudnn.SpatialConvolution(160 -> 64, 1x1) without bias +Storage id: 12 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n15->n16 + + + + +n22 + +cudnn.SpatialConvolution(160 -> 64, 1x1) without bias +Storage id: 16 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n15->n22 + + + + +n17 + +nn.SpatialBatchNormalization +Storage id: 13 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n16->n17 + + + + +n18 + +cudnn.ReLU +Storage id: 13 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n17->n18 + + + + +n19 + +cudnn.SpatialConvolution(64 -> 96, 3x3) without bias +Storage id: 14 +Size: {1, 96, 71, 71} +Mem size: 483936 + + +n18->n19 + + + + +n20 + +nn.SpatialBatchNormalization +Storage id: 15 +Size: {1, 96, 71, 71} +Mem size: 483936 + + +n19->n20 + + + + +n21 + +cudnn.ReLU +Storage id: 15 +Size: {1, 96, 71, 71} +Mem size: 483936 + + +n20->n21 + + + + +n34 + +nn.JoinTable +Storage id: 24 +Size: {1, 192, 71, 71} +Mem size: 967872 + + +n21->n34 + + + + +n23 + +nn.SpatialBatchNormalization +Storage id: 17 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n22->n23 + + + + +n24 + +cudnn.ReLU +Storage id: 17 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n23->n24 + + + + +n25 + +cudnn.SpatialConvolution(64 -> 64, 7x1, 1,1, 3,0) without bias +Storage id: 18 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n24->n25 + + + + +n26 + +nn.SpatialBatchNormalization +Storage id: 19 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n25->n26 + + + + +n27 + +cudnn.ReLU +Storage id: 19 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n26->n27 + + + + +n28 + +cudnn.SpatialConvolution(64 -> 64, 1x7, 1,1, 0,3) without bias +Storage id: 20 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n27->n28 + + + + +n29 + +nn.SpatialBatchNormalization +Storage id: 21 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n28->n29 + + + + +n30 + +cudnn.ReLU +Storage id: 21 +Size: {1, 64, 73, 73} +Mem size: 341056 + + +n29->n30 + + + + +n31 + +cudnn.SpatialConvolution(64 -> 96, 3x3) without bias +Storage id: 22 +Size: {1, 96, 71, 71} +Mem size: 483936 + + +n30->n31 + + + + +n32 + +nn.SpatialBatchNormalization +Storage id: 23 +Size: {1, 96, 71, 71} +Mem size: 483936 + + +n31->n32 + + + + +n33 + +cudnn.ReLU +Storage id: 23 +Size: {1, 96, 71, 71} +Mem size: 483936 + + +n32->n33 + + + + +n33->n34 + + + + +n35 + +cudnn.SpatialConvolution(192 -> 192, 3x3, 2,2) without bias +Storage id: 25 +Size: {1, 192, 35, 35} +Mem size: 235200 + + +n34->n35 + + + + +n38 + +nn.SpatialMaxPooling(3x3, 2,2) +Storage id: 27 +Size: {1, 192, 35, 35} +Mem size: 235200 + + +n34->n38 + + + + +n36 + +nn.SpatialBatchNormalization +Storage id: 26 +Size: {1, 192, 35, 35} +Mem size: 235200 + + +n35->n36 + + + + +n37 + +cudnn.ReLU +Storage id: 26 +Size: {1, 192, 35, 35} +Mem size: 235200 + + +n36->n37 + + + + +n39 + +nn.JoinTable +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n37->n39 + + + + +n38->n39 + + + + +n40 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 29 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n39->n40 + + + + +n43 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 31 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n39->n43 + + + + +n49 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 35 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n39->n49 + + + + +n62 + +nn.CAddTable +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n39->n62 + + + + +n41 + +nn.SpatialBatchNormalization +Storage id: 30 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n40->n41 + + + + +n42 + +cudnn.ReLU +Storage id: 30 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n41->n42 + + + + +n58 + +nn.JoinTable +Storage id: 41 +Size: {1, 128, 35, 35} +Mem size: 156800 + + +n42->n58 + + + + +n44 + +nn.SpatialBatchNormalization +Storage id: 32 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n43->n44 + + + + +n45 + +cudnn.ReLU +Storage id: 32 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n44->n45 + + + + +n46 + +cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias +Storage id: 33 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n45->n46 + + + + +n47 + +nn.SpatialBatchNormalization +Storage id: 34 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n46->n47 + + + + +n48 + +cudnn.ReLU +Storage id: 34 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n47->n48 + + + + +n48->n58 + + + + +n50 + +nn.SpatialBatchNormalization +Storage id: 36 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n49->n50 + + + + +n51 + +cudnn.ReLU +Storage id: 36 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n50->n51 + + + + +n52 + +cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias +Storage id: 37 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n51->n52 + + + + +n53 + +nn.SpatialBatchNormalization +Storage id: 38 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n52->n53 + + + + +n54 + +cudnn.ReLU +Storage id: 38 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n53->n54 + + + + +n55 + +cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias +Storage id: 39 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n54->n55 + + + + +n56 + +nn.SpatialBatchNormalization +Storage id: 40 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n55->n56 + + + + +n57 + +cudnn.ReLU +Storage id: 40 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n56->n57 + + + + +n57->n58 + + + + +n59 + +cudnn.SpatialConvolution(128 -> 384, 1x1) without bias +Storage id: 42 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n58->n59 + + + + +n60 + +nn.SpatialBatchNormalization +Storage id: 43 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n59->n60 + + + + +n61 + +nn.MulConstant +Storage id: 44 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n60->n61 + + + + +n61->n62 + + + + +n63 + +cudnn.ReLU +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n62->n63 + + + + +n64 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 45 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n63->n64 + + + + +n67 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 47 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n63->n67 + + + + +n73 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 51 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n63->n73 + + + + +n86 + +nn.CAddTable +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n63->n86 + + + + +n65 + +nn.SpatialBatchNormalization +Storage id: 46 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n64->n65 + + + + +n66 + +cudnn.ReLU +Storage id: 46 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n65->n66 + + + + +n82 + +nn.JoinTable +Storage id: 57 +Size: {1, 128, 35, 35} +Mem size: 156800 + + +n66->n82 + + + + +n68 + +nn.SpatialBatchNormalization +Storage id: 48 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n67->n68 + + + + +n69 + +cudnn.ReLU +Storage id: 48 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n68->n69 + + + + +n70 + +cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias +Storage id: 49 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n69->n70 + + + + +n71 + +nn.SpatialBatchNormalization +Storage id: 50 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n70->n71 + + + + +n72 + +cudnn.ReLU +Storage id: 50 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n71->n72 + + + + +n72->n82 + + + + +n74 + +nn.SpatialBatchNormalization +Storage id: 52 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n73->n74 + + + + +n75 + +cudnn.ReLU +Storage id: 52 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n74->n75 + + + + +n76 + +cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias +Storage id: 53 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n75->n76 + + + + +n77 + +nn.SpatialBatchNormalization +Storage id: 54 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n76->n77 + + + + +n78 + +cudnn.ReLU +Storage id: 54 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n77->n78 + + + + +n79 + +cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias +Storage id: 55 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n78->n79 + + + + +n80 + +nn.SpatialBatchNormalization +Storage id: 56 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n79->n80 + + + + +n81 + +cudnn.ReLU +Storage id: 56 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n80->n81 + + + + +n81->n82 + + + + +n83 + +cudnn.SpatialConvolution(128 -> 384, 1x1) without bias +Storage id: 58 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n82->n83 + + + + +n84 + +nn.SpatialBatchNormalization +Storage id: 59 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n83->n84 + + + + +n85 + +nn.MulConstant +Storage id: 60 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n84->n85 + + + + +n85->n86 + + + + +n87 + +cudnn.ReLU +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n86->n87 + + + + +n88 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 61 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n87->n88 + + + + +n91 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 63 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n87->n91 + + + + +n97 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 67 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n87->n97 + + + + +n110 + +nn.CAddTable +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n87->n110 + + + + +n89 + +nn.SpatialBatchNormalization +Storage id: 62 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n88->n89 + + + + +n90 + +cudnn.ReLU +Storage id: 62 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n89->n90 + + + + +n106 + +nn.JoinTable +Storage id: 73 +Size: {1, 128, 35, 35} +Mem size: 156800 + + +n90->n106 + + + + +n92 + +nn.SpatialBatchNormalization +Storage id: 64 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n91->n92 + + + + +n93 + +cudnn.ReLU +Storage id: 64 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n92->n93 + + + + +n94 + +cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias +Storage id: 65 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n93->n94 + + + + +n95 + +nn.SpatialBatchNormalization +Storage id: 66 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n94->n95 + + + + +n96 + +cudnn.ReLU +Storage id: 66 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n95->n96 + + + + +n96->n106 + + + + +n98 + +nn.SpatialBatchNormalization +Storage id: 68 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n97->n98 + + + + +n99 + +cudnn.ReLU +Storage id: 68 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n98->n99 + + + + +n100 + +cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias +Storage id: 69 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n99->n100 + + + + +n101 + +nn.SpatialBatchNormalization +Storage id: 70 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n100->n101 + + + + +n102 + +cudnn.ReLU +Storage id: 70 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n101->n102 + + + + +n103 + +cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias +Storage id: 71 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n102->n103 + + + + +n104 + +nn.SpatialBatchNormalization +Storage id: 72 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n103->n104 + + + + +n105 + +cudnn.ReLU +Storage id: 72 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n104->n105 + + + + +n105->n106 + + + + +n107 + +cudnn.SpatialConvolution(128 -> 384, 1x1) without bias +Storage id: 74 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n106->n107 + + + + +n108 + +nn.SpatialBatchNormalization +Storage id: 75 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n107->n108 + + + + +n109 + +nn.MulConstant +Storage id: 76 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n108->n109 + + + + +n109->n110 + + + + +n111 + +cudnn.ReLU +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n110->n111 + + + + +n112 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 77 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n111->n112 + + + + +n115 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 79 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n111->n115 + + + + +n121 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 83 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n111->n121 + + + + +n134 + +nn.CAddTable +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n111->n134 + + + + +n113 + +nn.SpatialBatchNormalization +Storage id: 78 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n112->n113 + + + + +n114 + +cudnn.ReLU +Storage id: 78 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n113->n114 + + + + +n130 + +nn.JoinTable +Storage id: 89 +Size: {1, 128, 35, 35} +Mem size: 156800 + + +n114->n130 + + + + +n116 + +nn.SpatialBatchNormalization +Storage id: 80 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n115->n116 + + + + +n117 + +cudnn.ReLU +Storage id: 80 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n116->n117 + + + + +n118 + +cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias +Storage id: 81 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n117->n118 + + + + +n119 + +nn.SpatialBatchNormalization +Storage id: 82 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n118->n119 + + + + +n120 + +cudnn.ReLU +Storage id: 82 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n119->n120 + + + + +n120->n130 + + + + +n122 + +nn.SpatialBatchNormalization +Storage id: 84 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n121->n122 + + + + +n123 + +cudnn.ReLU +Storage id: 84 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n122->n123 + + + + +n124 + +cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias +Storage id: 85 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n123->n124 + + + + +n125 + +nn.SpatialBatchNormalization +Storage id: 86 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n124->n125 + + + + +n126 + +cudnn.ReLU +Storage id: 86 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n125->n126 + + + + +n127 + +cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias +Storage id: 87 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n126->n127 + + + + +n128 + +nn.SpatialBatchNormalization +Storage id: 88 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n127->n128 + + + + +n129 + +cudnn.ReLU +Storage id: 88 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n128->n129 + + + + +n129->n130 + + + + +n131 + +cudnn.SpatialConvolution(128 -> 384, 1x1) without bias +Storage id: 90 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n130->n131 + + + + +n132 + +nn.SpatialBatchNormalization +Storage id: 91 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n131->n132 + + + + +n133 + +nn.MulConstant +Storage id: 92 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n132->n133 + + + + +n133->n134 + + + + +n135 + +cudnn.ReLU +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n134->n135 + + + + +n136 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 93 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n135->n136 + + + + +n139 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 95 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n135->n139 + + + + +n145 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 99 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n135->n145 + + + + +n158 + +nn.CAddTable +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n135->n158 + + + + +n137 + +nn.SpatialBatchNormalization +Storage id: 94 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n136->n137 + + + + +n138 + +cudnn.ReLU +Storage id: 94 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n137->n138 + + + + +n154 + +nn.JoinTable +Storage id: 105 +Size: {1, 128, 35, 35} +Mem size: 156800 + + +n138->n154 + + + + +n140 + +nn.SpatialBatchNormalization +Storage id: 96 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n139->n140 + + + + +n141 + +cudnn.ReLU +Storage id: 96 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n140->n141 + + + + +n142 + +cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias +Storage id: 97 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n141->n142 + + + + +n143 + +nn.SpatialBatchNormalization +Storage id: 98 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n142->n143 + + + + +n144 + +cudnn.ReLU +Storage id: 98 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n143->n144 + + + + +n144->n154 + + + + +n146 + +nn.SpatialBatchNormalization +Storage id: 100 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n145->n146 + + + + +n147 + +cudnn.ReLU +Storage id: 100 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n146->n147 + + + + +n148 + +cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias +Storage id: 101 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n147->n148 + + + + +n149 + +nn.SpatialBatchNormalization +Storage id: 102 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n148->n149 + + + + +n150 + +cudnn.ReLU +Storage id: 102 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n149->n150 + + + + +n151 + +cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias +Storage id: 103 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n150->n151 + + + + +n152 + +nn.SpatialBatchNormalization +Storage id: 104 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n151->n152 + + + + +n153 + +cudnn.ReLU +Storage id: 104 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n152->n153 + + + + +n153->n154 + + + + +n155 + +cudnn.SpatialConvolution(128 -> 384, 1x1) without bias +Storage id: 106 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n154->n155 + + + + +n156 + +nn.SpatialBatchNormalization +Storage id: 107 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n155->n156 + + + + +n157 + +nn.MulConstant +Storage id: 108 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n156->n157 + + + + +n157->n158 + + + + +n159 + +cudnn.ReLU +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n158->n159 + + + + +n160 + +nn.SpatialMaxPooling(3x3, 2,2) +Storage id: 109 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n159->n160 + + + + +n161 + +cudnn.SpatialConvolution(384 -> 384, 3x3, 2,2) without bias +Storage id: 110 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n159->n161 + + + + +n164 + +cudnn.SpatialConvolution(384 -> 256, 1x1) without bias +Storage id: 112 +Size: {1, 256, 35, 35} +Mem size: 313600 + + +n159->n164 + + + + +n173 + +nn.JoinTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n160->n173 + + + + +n162 + +nn.SpatialBatchNormalization +Storage id: 111 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n161->n162 + + + + +n163 + +cudnn.ReLU +Storage id: 111 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n162->n163 + + + + +n163->n173 + + + + +n165 + +nn.SpatialBatchNormalization +Storage id: 113 +Size: {1, 256, 35, 35} +Mem size: 313600 + + +n164->n165 + + + + +n166 + +cudnn.ReLU +Storage id: 113 +Size: {1, 256, 35, 35} +Mem size: 313600 + + +n165->n166 + + + + +n167 + +cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias +Storage id: 114 +Size: {1, 256, 35, 35} +Mem size: 313600 + + +n166->n167 + + + + +n168 + +nn.SpatialBatchNormalization +Storage id: 115 +Size: {1, 256, 35, 35} +Mem size: 313600 + + +n167->n168 + + + + +n169 + +cudnn.ReLU +Storage id: 115 +Size: {1, 256, 35, 35} +Mem size: 313600 + + +n168->n169 + + + + +n170 + +cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias +Storage id: 116 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n169->n170 + + + + +n171 + +nn.SpatialBatchNormalization +Storage id: 117 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n170->n171 + + + + +n172 + +cudnn.ReLU +Storage id: 117 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n171->n172 + + + + +n172->n173 + + + + +n174 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 119 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n173->n174 + + + + +n177 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 121 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n173->n177 + + + + +n190 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n173->n190 + + + + +n175 + +nn.SpatialBatchNormalization +Storage id: 120 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n174->n175 + + + + +n176 + +cudnn.ReLU +Storage id: 120 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n175->n176 + + + + +n186 + +nn.JoinTable +Storage id: 127 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n176->n186 + + + + +n178 + +nn.SpatialBatchNormalization +Storage id: 122 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n177->n178 + + + + +n179 + +cudnn.ReLU +Storage id: 122 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n178->n179 + + + + +n180 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 123 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n179->n180 + + + + +n181 + +nn.SpatialBatchNormalization +Storage id: 124 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n180->n181 + + + + +n182 + +cudnn.ReLU +Storage id: 124 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n181->n182 + + + + +n183 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 125 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n182->n183 + + + + +n184 + +nn.SpatialBatchNormalization +Storage id: 126 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n183->n184 + + + + +n185 + +cudnn.ReLU +Storage id: 126 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n184->n185 + + + + +n185->n186 + + + + +n187 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 128 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n186->n187 + + + + +n188 + +nn.SpatialBatchNormalization +Storage id: 129 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n187->n188 + + + + +n189 + +nn.MulConstant +Storage id: 130 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n188->n189 + + + + +n189->n190 + + + + +n191 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n190->n191 + + + + +n192 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 131 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n191->n192 + + + + +n195 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 133 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n191->n195 + + + + +n208 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n191->n208 + + + + +n193 + +nn.SpatialBatchNormalization +Storage id: 132 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n192->n193 + + + + +n194 + +cudnn.ReLU +Storage id: 132 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n193->n194 + + + + +n204 + +nn.JoinTable +Storage id: 139 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n194->n204 + + + + +n196 + +nn.SpatialBatchNormalization +Storage id: 134 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n195->n196 + + + + +n197 + +cudnn.ReLU +Storage id: 134 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n196->n197 + + + + +n198 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 135 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n197->n198 + + + + +n199 + +nn.SpatialBatchNormalization +Storage id: 136 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n198->n199 + + + + +n200 + +cudnn.ReLU +Storage id: 136 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n199->n200 + + + + +n201 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 137 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n200->n201 + + + + +n202 + +nn.SpatialBatchNormalization +Storage id: 138 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n201->n202 + + + + +n203 + +cudnn.ReLU +Storage id: 138 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n202->n203 + + + + +n203->n204 + + + + +n205 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 140 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n204->n205 + + + + +n206 + +nn.SpatialBatchNormalization +Storage id: 141 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n205->n206 + + + + +n207 + +nn.MulConstant +Storage id: 142 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n206->n207 + + + + +n207->n208 + + + + +n209 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n208->n209 + + + + +n210 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 143 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n209->n210 + + + + +n213 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 145 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n209->n213 + + + + +n226 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n209->n226 + + + + +n211 + +nn.SpatialBatchNormalization +Storage id: 144 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n210->n211 + + + + +n212 + +cudnn.ReLU +Storage id: 144 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n211->n212 + + + + +n222 + +nn.JoinTable +Storage id: 151 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n212->n222 + + + + +n214 + +nn.SpatialBatchNormalization +Storage id: 146 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n213->n214 + + + + +n215 + +cudnn.ReLU +Storage id: 146 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n214->n215 + + + + +n216 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 147 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n215->n216 + + + + +n217 + +nn.SpatialBatchNormalization +Storage id: 148 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n216->n217 + + + + +n218 + +cudnn.ReLU +Storage id: 148 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n217->n218 + + + + +n219 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 149 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n218->n219 + + + + +n220 + +nn.SpatialBatchNormalization +Storage id: 150 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n219->n220 + + + + +n221 + +cudnn.ReLU +Storage id: 150 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n220->n221 + + + + +n221->n222 + + + + +n223 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 152 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n222->n223 + + + + +n224 + +nn.SpatialBatchNormalization +Storage id: 153 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n223->n224 + + + + +n225 + +nn.MulConstant +Storage id: 154 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n224->n225 + + + + +n225->n226 + + + + +n227 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n226->n227 + + + + +n228 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 155 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n227->n228 + + + + +n231 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 157 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n227->n231 + + + + +n244 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n227->n244 + + + + +n229 + +nn.SpatialBatchNormalization +Storage id: 156 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n228->n229 + + + + +n230 + +cudnn.ReLU +Storage id: 156 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n229->n230 + + + + +n240 + +nn.JoinTable +Storage id: 163 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n230->n240 + + + + +n232 + +nn.SpatialBatchNormalization +Storage id: 158 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n231->n232 + + + + +n233 + +cudnn.ReLU +Storage id: 158 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n232->n233 + + + + +n234 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 159 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n233->n234 + + + + +n235 + +nn.SpatialBatchNormalization +Storage id: 160 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n234->n235 + + + + +n236 + +cudnn.ReLU +Storage id: 160 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n235->n236 + + + + +n237 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 161 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n236->n237 + + + + +n238 + +nn.SpatialBatchNormalization +Storage id: 162 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n237->n238 + + + + +n239 + +cudnn.ReLU +Storage id: 162 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n238->n239 + + + + +n239->n240 + + + + +n241 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 164 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n240->n241 + + + + +n242 + +nn.SpatialBatchNormalization +Storage id: 165 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n241->n242 + + + + +n243 + +nn.MulConstant +Storage id: 166 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n242->n243 + + + + +n243->n244 + + + + +n245 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n244->n245 + + + + +n246 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 167 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n245->n246 + + + + +n249 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 169 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n245->n249 + + + + +n262 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n245->n262 + + + + +n247 + +nn.SpatialBatchNormalization +Storage id: 168 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n246->n247 + + + + +n248 + +cudnn.ReLU +Storage id: 168 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n247->n248 + + + + +n258 + +nn.JoinTable +Storage id: 175 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n248->n258 + + + + +n250 + +nn.SpatialBatchNormalization +Storage id: 170 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n249->n250 + + + + +n251 + +cudnn.ReLU +Storage id: 170 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n250->n251 + + + + +n252 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 171 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n251->n252 + + + + +n253 + +nn.SpatialBatchNormalization +Storage id: 172 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n252->n253 + + + + +n254 + +cudnn.ReLU +Storage id: 172 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n253->n254 + + + + +n255 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 173 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n254->n255 + + + + +n256 + +nn.SpatialBatchNormalization +Storage id: 174 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n255->n256 + + + + +n257 + +cudnn.ReLU +Storage id: 174 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n256->n257 + + + + +n257->n258 + + + + +n259 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 176 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n258->n259 + + + + +n260 + +nn.SpatialBatchNormalization +Storage id: 177 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n259->n260 + + + + +n261 + +nn.MulConstant +Storage id: 178 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n260->n261 + + + + +n261->n262 + + + + +n263 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n262->n263 + + + + +n264 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 179 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n263->n264 + + + + +n267 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 181 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n263->n267 + + + + +n280 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n263->n280 + + + + +n265 + +nn.SpatialBatchNormalization +Storage id: 180 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n264->n265 + + + + +n266 + +cudnn.ReLU +Storage id: 180 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n265->n266 + + + + +n276 + +nn.JoinTable +Storage id: 187 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n266->n276 + + + + +n268 + +nn.SpatialBatchNormalization +Storage id: 182 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n267->n268 + + + + +n269 + +cudnn.ReLU +Storage id: 182 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n268->n269 + + + + +n270 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 183 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n269->n270 + + + + +n271 + +nn.SpatialBatchNormalization +Storage id: 184 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n270->n271 + + + + +n272 + +cudnn.ReLU +Storage id: 184 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n271->n272 + + + + +n273 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 185 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n272->n273 + + + + +n274 + +nn.SpatialBatchNormalization +Storage id: 186 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n273->n274 + + + + +n275 + +cudnn.ReLU +Storage id: 186 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n274->n275 + + + + +n275->n276 + + + + +n277 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 188 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n276->n277 + + + + +n278 + +nn.SpatialBatchNormalization +Storage id: 189 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n277->n278 + + + + +n279 + +nn.MulConstant +Storage id: 190 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n278->n279 + + + + +n279->n280 + + + + +n281 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n280->n281 + + + + +n282 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 191 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n281->n282 + + + + +n285 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 193 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n281->n285 + + + + +n298 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n281->n298 + + + + +n283 + +nn.SpatialBatchNormalization +Storage id: 192 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n282->n283 + + + + +n284 + +cudnn.ReLU +Storage id: 192 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n283->n284 + + + + +n294 + +nn.JoinTable +Storage id: 199 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n284->n294 + + + + +n286 + +nn.SpatialBatchNormalization +Storage id: 194 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n285->n286 + + + + +n287 + +cudnn.ReLU +Storage id: 194 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n286->n287 + + + + +n288 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 195 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n287->n288 + + + + +n289 + +nn.SpatialBatchNormalization +Storage id: 196 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n288->n289 + + + + +n290 + +cudnn.ReLU +Storage id: 196 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n289->n290 + + + + +n291 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 197 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n290->n291 + + + + +n292 + +nn.SpatialBatchNormalization +Storage id: 198 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n291->n292 + + + + +n293 + +cudnn.ReLU +Storage id: 198 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n292->n293 + + + + +n293->n294 + + + + +n295 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 200 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n294->n295 + + + + +n296 + +nn.SpatialBatchNormalization +Storage id: 201 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n295->n296 + + + + +n297 + +nn.MulConstant +Storage id: 202 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n296->n297 + + + + +n297->n298 + + + + +n299 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n298->n299 + + + + +n300 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 203 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n299->n300 + + + + +n303 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 205 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n299->n303 + + + + +n316 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n299->n316 + + + + +n301 + +nn.SpatialBatchNormalization +Storage id: 204 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n300->n301 + + + + +n302 + +cudnn.ReLU +Storage id: 204 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n301->n302 + + + + +n312 + +nn.JoinTable +Storage id: 211 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n302->n312 + + + + +n304 + +nn.SpatialBatchNormalization +Storage id: 206 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n303->n304 + + + + +n305 + +cudnn.ReLU +Storage id: 206 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n304->n305 + + + + +n306 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 207 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n305->n306 + + + + +n307 + +nn.SpatialBatchNormalization +Storage id: 208 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n306->n307 + + + + +n308 + +cudnn.ReLU +Storage id: 208 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n307->n308 + + + + +n309 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 209 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n308->n309 + + + + +n310 + +nn.SpatialBatchNormalization +Storage id: 210 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n309->n310 + + + + +n311 + +cudnn.ReLU +Storage id: 210 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n310->n311 + + + + +n311->n312 + + + + +n313 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 212 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n312->n313 + + + + +n314 + +nn.SpatialBatchNormalization +Storage id: 213 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n313->n314 + + + + +n315 + +nn.MulConstant +Storage id: 214 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n314->n315 + + + + +n315->n316 + + + + +n317 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n316->n317 + + + + +n318 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 215 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n317->n318 + + + + +n321 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 217 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n317->n321 + + + + +n334 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n317->n334 + + + + +n319 + +nn.SpatialBatchNormalization +Storage id: 216 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n318->n319 + + + + +n320 + +cudnn.ReLU +Storage id: 216 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n319->n320 + + + + +n330 + +nn.JoinTable +Storage id: 223 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n320->n330 + + + + +n322 + +nn.SpatialBatchNormalization +Storage id: 218 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n321->n322 + + + + +n323 + +cudnn.ReLU +Storage id: 218 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n322->n323 + + + + +n324 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 219 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n323->n324 + + + + +n325 + +nn.SpatialBatchNormalization +Storage id: 220 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n324->n325 + + + + +n326 + +cudnn.ReLU +Storage id: 220 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n325->n326 + + + + +n327 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 221 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n326->n327 + + + + +n328 + +nn.SpatialBatchNormalization +Storage id: 222 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n327->n328 + + + + +n329 + +cudnn.ReLU +Storage id: 222 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n328->n329 + + + + +n329->n330 + + + + +n331 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 224 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n330->n331 + + + + +n332 + +nn.SpatialBatchNormalization +Storage id: 225 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n331->n332 + + + + +n333 + +nn.MulConstant +Storage id: 226 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n332->n333 + + + + +n333->n334 + + + + +n335 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n334->n335 + + + + +n336 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 227 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n335->n336 + + + + +n339 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 229 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n335->n339 + + + + +n352 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n335->n352 + + + + +n337 + +nn.SpatialBatchNormalization +Storage id: 228 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n336->n337 + + + + +n338 + +cudnn.ReLU +Storage id: 228 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n337->n338 + + + + +n348 + +nn.JoinTable +Storage id: 235 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n338->n348 + + + + +n340 + +nn.SpatialBatchNormalization +Storage id: 230 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n339->n340 + + + + +n341 + +cudnn.ReLU +Storage id: 230 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n340->n341 + + + + +n342 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 231 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n341->n342 + + + + +n343 + +nn.SpatialBatchNormalization +Storage id: 232 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n342->n343 + + + + +n344 + +cudnn.ReLU +Storage id: 232 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n343->n344 + + + + +n345 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 233 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n344->n345 + + + + +n346 + +nn.SpatialBatchNormalization +Storage id: 234 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n345->n346 + + + + +n347 + +cudnn.ReLU +Storage id: 234 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n346->n347 + + + + +n347->n348 + + + + +n349 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 236 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n348->n349 + + + + +n350 + +nn.SpatialBatchNormalization +Storage id: 237 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n349->n350 + + + + +n351 + +nn.MulConstant +Storage id: 238 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n350->n351 + + + + +n351->n352 + + + + +n353 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n352->n353 + + + + +n354 + +nn.SpatialMaxPooling(3x3, 2,2) +Storage id: 239 +Size: {1, 1152, 8, 8} +Mem size: 73728 + + +n353->n354 + + + + +n355 + +cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias +Storage id: 240 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n353->n355 + + + + +n361 + +cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias +Storage id: 244 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n353->n361 + + + + +n367 + +cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias +Storage id: 248 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n353->n367 + + + + +n376 + +nn.JoinTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n354->n376 + + + + +n356 + +nn.SpatialBatchNormalization +Storage id: 241 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n355->n356 + + + + +n357 + +cudnn.ReLU +Storage id: 241 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n356->n357 + + + + +n358 + +cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias +Storage id: 242 +Size: {1, 384, 8, 8} +Mem size: 24576 + + +n357->n358 + + + + +n359 + +nn.SpatialBatchNormalization +Storage id: 243 +Size: {1, 384, 8, 8} +Mem size: 24576 + + +n358->n359 + + + + +n360 + +cudnn.ReLU +Storage id: 243 +Size: {1, 384, 8, 8} +Mem size: 24576 + + +n359->n360 + + + + +n360->n376 + + + + +n362 + +nn.SpatialBatchNormalization +Storage id: 245 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n361->n362 + + + + +n363 + +cudnn.ReLU +Storage id: 245 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n362->n363 + + + + +n364 + +cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias +Storage id: 246 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n363->n364 + + + + +n365 + +nn.SpatialBatchNormalization +Storage id: 247 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n364->n365 + + + + +n366 + +cudnn.ReLU +Storage id: 247 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n365->n366 + + + + +n366->n376 + + + + +n368 + +nn.SpatialBatchNormalization +Storage id: 249 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n367->n368 + + + + +n369 + +cudnn.ReLU +Storage id: 249 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n368->n369 + + + + +n370 + +cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias +Storage id: 250 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n369->n370 + + + + +n371 + +nn.SpatialBatchNormalization +Storage id: 251 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n370->n371 + + + + +n372 + +cudnn.ReLU +Storage id: 251 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n371->n372 + + + + +n373 + +cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias +Storage id: 252 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n372->n373 + + + + +n374 + +nn.SpatialBatchNormalization +Storage id: 253 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n373->n374 + + + + +n375 + +cudnn.ReLU +Storage id: 253 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n374->n375 + + + + +n375->n376 + + + + +n377 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 255 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n376->n377 + + + + +n380 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 257 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n376->n380 + + + + +n393 + +nn.CAddTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n376->n393 + + + + +n378 + +nn.SpatialBatchNormalization +Storage id: 256 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n377->n378 + + + + +n379 + +cudnn.ReLU +Storage id: 256 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n378->n379 + + + + +n389 + +nn.JoinTable +Storage id: 263 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n379->n389 + + + + +n381 + +nn.SpatialBatchNormalization +Storage id: 258 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n380->n381 + + + + +n382 + +cudnn.ReLU +Storage id: 258 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n381->n382 + + + + +n383 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 259 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n382->n383 + + + + +n384 + +nn.SpatialBatchNormalization +Storage id: 260 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n383->n384 + + + + +n385 + +cudnn.ReLU +Storage id: 260 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n384->n385 + + + + +n386 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 261 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n385->n386 + + + + +n387 + +nn.SpatialBatchNormalization +Storage id: 262 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n386->n387 + + + + +n388 + +cudnn.ReLU +Storage id: 262 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n387->n388 + + + + +n388->n389 + + + + +n390 + +cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias +Storage id: 264 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n389->n390 + + + + +n391 + +nn.SpatialBatchNormalization +Storage id: 265 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n390->n391 + + + + +n392 + +nn.MulConstant +Storage id: 266 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n391->n392 + + + + +n392->n393 + + + + +n394 + +cudnn.ReLU +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n393->n394 + + + + +n395 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 267 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n394->n395 + + + + +n398 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 269 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n394->n398 + + + + +n411 + +nn.CAddTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n394->n411 + + + + +n396 + +nn.SpatialBatchNormalization +Storage id: 268 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n395->n396 + + + + +n397 + +cudnn.ReLU +Storage id: 268 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n396->n397 + + + + +n407 + +nn.JoinTable +Storage id: 275 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n397->n407 + + + + +n399 + +nn.SpatialBatchNormalization +Storage id: 270 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n398->n399 + + + + +n400 + +cudnn.ReLU +Storage id: 270 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n399->n400 + + + + +n401 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 271 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n400->n401 + + + + +n402 + +nn.SpatialBatchNormalization +Storage id: 272 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n401->n402 + + + + +n403 + +cudnn.ReLU +Storage id: 272 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n402->n403 + + + + +n404 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 273 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n403->n404 + + + + +n405 + +nn.SpatialBatchNormalization +Storage id: 274 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n404->n405 + + + + +n406 + +cudnn.ReLU +Storage id: 274 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n405->n406 + + + + +n406->n407 + + + + +n408 + +cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias +Storage id: 276 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n407->n408 + + + + +n409 + +nn.SpatialBatchNormalization +Storage id: 277 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n408->n409 + + + + +n410 + +nn.MulConstant +Storage id: 278 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n409->n410 + + + + +n410->n411 + + + + +n412 + +cudnn.ReLU +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n411->n412 + + + + +n413 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 279 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n412->n413 + + + + +n416 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 281 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n412->n416 + + + + +n429 + +nn.CAddTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n412->n429 + + + + +n414 + +nn.SpatialBatchNormalization +Storage id: 280 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n413->n414 + + + + +n415 + +cudnn.ReLU +Storage id: 280 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n414->n415 + + + + +n425 + +nn.JoinTable +Storage id: 287 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n415->n425 + + + + +n417 + +nn.SpatialBatchNormalization +Storage id: 282 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n416->n417 + + + + +n418 + +cudnn.ReLU +Storage id: 282 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n417->n418 + + + + +n419 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 283 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n418->n419 + + + + +n420 + +nn.SpatialBatchNormalization +Storage id: 284 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n419->n420 + + + + +n421 + +cudnn.ReLU +Storage id: 284 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n420->n421 + + + + +n422 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 285 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n421->n422 + + + + +n423 + +nn.SpatialBatchNormalization +Storage id: 286 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n422->n423 + + + + +n424 + +cudnn.ReLU +Storage id: 286 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n423->n424 + + + + +n424->n425 + + + + +n426 + +cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias +Storage id: 288 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n425->n426 + + + + +n427 + +nn.SpatialBatchNormalization +Storage id: 289 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n426->n427 + + + + +n428 + +nn.MulConstant +Storage id: 290 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n427->n428 + + + + +n428->n429 + + + + +n430 + +cudnn.ReLU +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n429->n430 + + + + +n431 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 291 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n430->n431 + + + + +n434 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 293 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n430->n434 + + + + +n447 + +nn.CAddTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n430->n447 + + + + +n432 + +nn.SpatialBatchNormalization +Storage id: 292 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n431->n432 + + + + +n433 + +cudnn.ReLU +Storage id: 292 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n432->n433 + + + + +n443 + +nn.JoinTable +Storage id: 299 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n433->n443 + + + + +n435 + +nn.SpatialBatchNormalization +Storage id: 294 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n434->n435 + + + + +n436 + +cudnn.ReLU +Storage id: 294 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n435->n436 + + + + +n437 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 295 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n436->n437 + + + + +n438 + +nn.SpatialBatchNormalization +Storage id: 296 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n437->n438 + + + + +n439 + +cudnn.ReLU +Storage id: 296 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n438->n439 + + + + +n440 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 297 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n439->n440 + + + + +n441 + +nn.SpatialBatchNormalization +Storage id: 298 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n440->n441 + + + + +n442 + +cudnn.ReLU +Storage id: 298 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n441->n442 + + + + +n442->n443 + + + + +n444 + +cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias +Storage id: 300 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n443->n444 + + + + +n445 + +nn.SpatialBatchNormalization +Storage id: 301 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n444->n445 + + + + +n446 + +nn.MulConstant +Storage id: 302 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n445->n446 + + + + +n446->n447 + + + + +n448 + +cudnn.ReLU +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n447->n448 + + + + +n449 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 303 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n448->n449 + + + + +n452 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 305 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n448->n452 + + + + +n465 + +nn.CAddTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n448->n465 + + + + +n450 + +nn.SpatialBatchNormalization +Storage id: 304 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n449->n450 + + + + +n451 + +cudnn.ReLU +Storage id: 304 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n450->n451 + + + + +n461 + +nn.JoinTable +Storage id: 311 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n451->n461 + + + + +n453 + +nn.SpatialBatchNormalization +Storage id: 306 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n452->n453 + + + + +n454 + +cudnn.ReLU +Storage id: 306 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n453->n454 + + + + +n455 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 307 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n454->n455 + + + + +n456 + +nn.SpatialBatchNormalization +Storage id: 308 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n455->n456 + + + + +n457 + +cudnn.ReLU +Storage id: 308 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n456->n457 + + + + +n458 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 309 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n457->n458 + + + + +n459 + +nn.SpatialBatchNormalization +Storage id: 310 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n458->n459 + + + + +n460 + +cudnn.ReLU +Storage id: 310 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n459->n460 + + + + +n460->n461 + + + + +n462 + +cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias +Storage id: 312 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n461->n462 + + + + +n463 + +nn.SpatialBatchNormalization +Storage id: 313 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n462->n463 + + + + +n464 + +nn.MulConstant +Storage id: 314 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n463->n464 + + + + +n464->n465 + + + + +n466 + +cudnn.ReLU +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n465->n466 + + + + +n467 + +cudnn.SpatialAveragePooling(8x8, 1,1) +Storage id: 315 +Size: {1, 2048, 1, 1} +Mem size: 2048 + + +n466->n467 + + + + +n468 + +nn.View(2048) +Storage id: 315 +Size: {1, 2048} +Mem size: 2048 + + +n467->n468 + + + + +n469 + +nn.Dropout(0.200000) +Storage id: 316 +Size: {1, 2048} +Mem size: 2048 + + +n468->n469 + + + + +n470 + +nn.Linear(2048 -> 1000) +Storage id: 317 +Size: {1, 1000} +Mem size: 1000 + + +n469->n470 + + + + + diff --git a/main.lua b/main.lua index c1708cd9b..5e827c0f5 100644 --- a/main.lua +++ b/main.lua @@ -59,7 +59,7 @@ for epoch = startEpoch, opt.nEpochs do print(' * Best model ', testTop1, testTop5) end - checkpoints.save(epoch, model, trainer.optimState, bestModel) + checkpoints.save(epoch, model, trainer.optimState, bestModel, opt) end print(string.format(' * Finished top1: %6.3f top5: %6.3f', bestTop1, bestTop5)) diff --git a/main.sh b/main.sh new file mode 100755 index 000000000..b49dca1e3 --- /dev/null +++ b/main.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +filename=$(date +"%Y:%m:%d-%H:%M:%S:%N")".log" + +#th main.lua -depth 50 -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/ + +th main.lua -netType inception-resnet-v2 -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/inception-resnet-v2/ 2>&1 | tee /media/data0/cache/log/$filename + +#th main.lua -netType inception-resnet-v2-aux -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/inception-resnet-v2/ diff --git a/models/inception-resnet-v2.lua b/models/inception-resnet-v2.lua new file mode 100644 index 000000000..6c83402b6 --- /dev/null +++ b/models/inception-resnet-v2.lua @@ -0,0 +1,320 @@ + +local nn = require 'nn' +require 'cunn' +local cudnn = require 'cudnn' + +local Convolution = cudnn.SpatialConvolution +local Avg = cudnn.SpatialAveragePooling +local ReLU = cudnn.ReLU +local Max = nn.SpatialMaxPooling +local SBatchNorm = nn.SpatialBatchNormalization + +local function addConv(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw) + assert(not (nInputPlane == nil)) + assert(not (nOutputPlane == nil)) + assert(not (kh == nil)) + assert(not (kw == nil)) + local sh = sh or 1 + local sw = sw or 1 + local ph = ph or 0 + local pw = pw or 0 + + local layer = nn.Sequential():add(Convolution(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw)) + :add(SBatchNorm(nOutputPlane)) + :add(ReLU(true)) + return layer +end + +local function addConvLinear(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw) -- addConv without ReLU + assert(not (nInputPlane == nil)) + assert(not (nOutputPlane == nil)) + assert(not (kh == nil)) + assert(not (kw == nil)) + local sh = sh or 1 + local sw = sw or 1 + local ph = ph or 0 + local pw = pw or 0 + + local layer = nn.Sequential():add(Convolution(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw)) + :add(SBatchNorm(nOutputPlane)) + return layer +end + +local function Stem(fs_start) -- fig 3 + local fs0 = {32, 32, 64}; fs0[0] = fs_start + local net = nn.Sequential() + ------------------- nInputPlane, nOutputPlane, k, k, s, s, p, p + net:add(addConv(fs0[0], fs0[1], 3, 3, 2, 2, 0, 0)) + net:add(addConv(fs0[1], fs0[2], 3, 3, 1, 1, 0, 0)) + net:add(addConv(fs0[2], fs0[3], 3, 3, 1, 1, 1, 1)) + + local fs1a = {}; fs1a[0] = fs0[#fs0] + local fs1b = {96}; fs1b[0] = fs0[#fs0] + local concat1 = nn.ConcatTable() + concat1:add(Max(3, 3, 2, 2, 0, 0)) + concat1:add(addConv(fs1b[0], fs1b[1], 3, 3, 2, 2, 0, 0)) + + net:add(concat1) + net:add(nn.JoinTable(2, 4)) + + local fs2a = {64, 96}; fs2a[0] = fs1a[#fs1a] + fs1b[#fs1b] + local fs2b = {64, 64, 64, 96}; fs2b[0] = fs1a[#fs1a] + fs1b[#fs1b] + local concat2 = nn.ConcatTable() + concat2:add(nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2a[1], fs2a[2], 3, 3, 1, 1, 0, 0)) + ) + concat2:add(nn.Sequential():add(addConv(fs2b[0], fs2b[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2b[1], fs2b[2], 7, 1, 1, 1, 3, 0)) + :add(addConv(fs2b[2], fs2b[3], 1, 7, 1, 1, 0, 3)) + :add(addConv(fs2b[3], fs2b[4], 3, 3, 1, 1, 0, 0)) + ) + net:add(concat2) + net:add(nn.JoinTable(2, 4)) + + local fs3a = {192}; fs3a[0] = fs2a[#fs2a] + fs2b[#fs2b] + local fs3b = {}; fs3b[0] = fs2a[#fs2a] + fs2b[#fs2b] + local concat3 = nn.ConcatTable() + concat3:add(addConv(fs3a[0], fs3a[1], 3, 3, 2, 2, 0, 0)) + :add(Max(3, 3, 2, 2, 0, 0)) + net:add(concat3) + net:add(nn.JoinTable(2, 4)) + + local fs_final = fs3a[#fs3a] + fs3b[#fs3b] + return net, fs_final +end + +local function InceptionResnetA(fs_start) -- fig 16 + local path1 = nn.Identity() + + local fs2a = {32}; fs2a[0] = fs_start + local fs2b = {32, 32}; fs2b[0] = fs_start + local fs2c = {32, 48, 64}; fs2c[0] = fs_start + local fs2 = {fs_start}; --local fs2 = {384}; + fs2[0] = fs2a[#fs2a] + fs2b[#fs2b] + fs2c[#fs2c] + local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + local path2b = nn.Sequential():add(addConv(fs2b[0], fs2b[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2b[1], fs2b[2], 3, 3, 1, 1, 1, 1)) + local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2c[1], fs2c[2], 3, 3, 1, 1, 1, 1)) + :add(addConv(fs2c[2], fs2c[3], 3, 3, 1, 1, 1, 1)) + local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) + :add(path2b) + :add(path2c) + ) + :add(nn.JoinTable(2, 4)) + :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(nn.MulConstant(0.1)) + + local net = nn.Sequential() + net:add(nn.ConcatTable():add(path1) + :add(path2) + ) + net:add(nn.CAddTable()) + net:add(ReLU(true)) + + local fs_final = fs2[#fs2] + assert(fs_final == fs_start) + + return net, fs_final +end + +local function ReductionA(fs_start, k, l, m, n) -- fig 7 + local net = nn.Sequential() + + local concat = nn.ConcatTable() + concat:add(Max(3, 3, 2, 2, 0, 0)) -- path1 + concat:add(addConv(fs_start, n, 3, 3, 2, 2, 0, 0)) -- path2 + concat:add(nn.Sequential():add(addConv(fs_start, k, 1, 1, 1, 1, 0, 0)) -- path3 + :add(addConv(k, l, 3, 3, 1, 1, 1, 1)) + :add(addConv(l, m, 3, 3, 2, 2, 0, 0))) + net:add(concat) + net:add(nn.JoinTable(2, 4)) + + local fs_final = fs_start + n + m + + return net, fs_final +end + +local function InceptionResnetB(fs_start) -- fig 17 + local path1 = nn.Identity() + + local fs2a = {192}; fs2a[0] = fs_start + local fs2c = {128, 160, 192}; fs2c[0] = fs_start + local fs2 = {fs_start}; --local fs2 = {1152}; + fs2[0] = fs2a[#fs2a] + fs2c[#fs2c] + local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2c[1], fs2c[2], 1, 7, 1, 1, 0, 3)) + :add(addConv(fs2c[2], fs2c[3], 7, 1, 1, 1, 3, 0)) + local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) + :add(path2c) + ) + :add(nn.JoinTable(2, 4)) + :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(nn.MulConstant(0.1)) + + local net = nn.Sequential() + net:add(nn.ConcatTable():add(path1) + :add(path2) + ) + net:add(nn.CAddTable()) + net:add(ReLU(true)) + + local fs_final = fs2[#fs2] + assert(fs_final == fs_start) + + return net, fs_final +end + +local function ReductionB(fs_start) -- fig 18 + local path1 = Max(3, 3, 2, 2, 0, 0) + local fs2 = {256, 384}; fs2[0] = fs_start + local path2 = nn.Sequential():add(addConv(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2[1], fs2[2], 3, 3, 2, 2, 0, 0)) + --local fs3 = {256, 288}; fs3[0] = fs_start -- in the paper, but seems to have typo + local fs3 = {256, 256}; fs3[0] = fs_start + local path3 = nn.Sequential():add(addConv(fs3[0], fs3[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs3[1], fs3[2], 3, 3, 2, 2, 0, 0)) + --local fs4 = {256, 288, 320}; fs4[0] = fs_start -- in the paper, but seems to have typo + local fs4 = {256, 256, 256}; fs4[0] = fs_start + local path4 = nn.Sequential():add(addConv(fs4[0], fs4[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs4[1], fs4[2], 3, 3, 1, 1, 1, 1)) + :add(addConv(fs4[2], fs4[3], 3, 3, 2, 2, 0, 0)) + + local concat = nn.ConcatTable() + concat:add(path1) + concat:add(path2) + concat:add(path3) + concat:add(path4) + + local net = nn.Sequential() + net:add(concat) + net:add(nn.JoinTable(2,4)) + + local fs_final = fs_start + fs2[#fs2] + fs3[#fs3] + fs4[#fs4] + + return net, fs_final +end + +local function InceptionResnetC(fs_start) -- fig 19 + local path1 = nn.Identity() + + local fs2a = {192}; fs2a[0] = fs_start + local fs2c = {192, 224, 256}; fs2c[0] = fs_start + local fs2 = {fs_start}; --local fs2 = {2048}; + fs2[0] = fs2a[#fs2a] + fs2c[#fs2c] + local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2c[1], fs2c[2], 1, 3, 1, 1, 0, 1)) + :add(addConv(fs2c[2], fs2c[3], 3, 1, 1, 1, 1, 0)) + local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) + :add(path2c) + ) + :add(nn.JoinTable(2, 4)) + :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(nn.MulConstant(0.1)) + + local net = nn.Sequential() + net:add(nn.ConcatTable():add(path1) + :add(path2) + ) + net:add(nn.CAddTable()) + net:add(ReLU(true)) + + local fs_final = fs2[#fs2] + assert(fs_final == fs_start) + + return net, fs_final +end + + + +local function createModel(opt) + local model = nn.Sequential() + + -- Add Stem module + local stem, fs = Stem(3) + model:add(stem) + + -- Add Inception-resnet-A modules (x5) + for i = 1, 5 do + local irA, fs = InceptionResnetA(fs) + model:add(irA) + end + + -- Add Reduction-A module + local rA, fs = ReductionA(fs, 256, 256, 384, 384) + model:add(rA) + + -- Add Inception-resnet-B modules (x10) + for i = 1, 10 do + local irB, fs = InceptionResnetB(fs) + model:add(irB) + end + + -- Add Reduction-B module + local rB, fs = ReductionB(fs) + model:add(rB) + + -- Add Inception-resnet-C modules (x5) + for i = 1, 5 do + local irC, fs = InceptionResnetC(fs) + model:add(irC) + end + + local nFeatures = fs -- set final channels + + -- Add Average Pooling + model:add(Avg(8, 8, 1, 1)) + model:add(nn.View(nFeatures):setNumInputDims(3)) + + -- Add Dropout (keep 0.8) + model:add(nn.Dropout(0.2)) + + -- Add Classifier + model:add(nn.Linear(nFeatures, 1000)) + + -- Init + local function ConvInit(name) + for k,v in pairs(model:findModules(name)) do + local n = v.kW*v.kH*v.nOutputPlane + v.weight:normal(0,math.sqrt(2/n)) + if cudnn.version >= 4000 then + v.bias = nil + v.gradBias = nil + else + v.bias:zero() + end + end + end + local function BNInit(name) + for k,v in pairs(model:findModules(name)) do + v.weight:fill(1) + v.bias:zero() + end + end + + ConvInit('cudnn.SpatialConvolution') + ConvInit('nn.SpatialConvolution') + BNInit('fbnn.SpatialBatchNormalization') + BNInit('cudnn.SpatialBatchNormalization') + BNInit('nn.SpatialBatchNormalization') + for k,v in pairs(model:findModules('nn.Linear')) do + v.bias:zero() + end + + -- Convert to cuda + model:cuda() + + if opt.cudnn == 'deterministic' then + model:apply(function(m) + if m.setMode then m:setMode(1,1,1) end + end) + end + + model:get(1).gradInput = nil + + return model +end + +return createModel diff --git a/models/init.lua b/models/init.lua index 553453902..3171679ac 100644 --- a/models/init.lua +++ b/models/init.lua @@ -110,7 +110,13 @@ function M.setup(opt, checkpoint) model = dpt:cuda() end - local criterion = nn.CrossEntropyCriterion():cuda() + local criterion + if opt.netType == 'inception-resnet-v2-aux' then + local CE = nn.CrossEntropyCriterion() + criterion = nn.ParallelCriterion(true):add(CE):add(CE,0.3):add(CE,0.3):cuda() + else + criterion = nn.CrossEntropyCriterion():cuda() + end return model, criterion end diff --git a/opts.lua b/opts.lua index 4c5112d24..2e9e2df47 100644 --- a/opts.lua +++ b/opts.lua @@ -36,8 +36,10 @@ function M.parse(arg) cmd:option('-LR', 0.1, 'initial learning rate') cmd:option('-momentum', 0.9, 'momentum') cmd:option('-weightDecay', 1e-4, 'weight decay') + cmd:option('-gamma', 0.96, 'gamma for learning rate policy (step)') + cmd:option('-step', 6400, 'step for learning rate policy (step)') ---------- Model options ---------------------------------- - cmd:option('-netType', 'resnet', 'Options: resnet | preresnet') + cmd:option('-netType', 'resnet', 'Options: resnet | preresnet | inception-resnet-v2 | inception-resnet-v2-aux') cmd:option('-depth', 34, 'ResNet depth: 18 | 34 | 50 | 101 | ...', 'number') cmd:option('-shortcutType', '', 'Options: A | B | C') cmd:option('-retrain', 'none', 'Path to model to retrain with') @@ -55,6 +57,12 @@ function M.parse(arg) opt.shareGradInput = opt.shareGradInput ~= 'false' opt.resetClassifier = opt.resetClassifier ~= 'false' + if opt.netType == 'inception-resnet-v2' or opt.netType == 'inception-resnet-v2-aux' then + --opt.momentum = 0.4737 + opt.LR = 0.045 --45 + opt.step = 12800 + end + if opt.dataset == 'imagenet' then -- Handle the most common case of missing -data flag local trainDir = paths.concat(opt.data, 'train') diff --git a/train.lua b/train.lua index 7843a1f8f..1e394ec44 100644 --- a/train.lua +++ b/train.lua @@ -31,7 +31,7 @@ end function Trainer:train(epoch, dataloader) -- Trains the model for a single epoch - self.optimState.learningRate = self:learningRate(epoch) + --self.optimState.learningRate = self:learningRate(epoch) local timer = torch.Timer() local dataTimer = torch.Timer() @@ -50,10 +50,21 @@ function Trainer:train(epoch, dataloader) for n, sample in dataloader:run() do local dataTime = dataTimer:time().real + -- Update learningRate + self.optimState.learningRate = self:learningRateStep(epoch, n, trainSize, self.opt.gamma, self.opt.step) + -- Copy input and target to the GPU self:copyInputs(sample) local output = self.model:forward(self.input):float() + --local output = self.model:forward(self.input) + --if type(output) == 'table' then + -- for i = 1, #output do + -- output[i]:float() + -- end + --else + -- output:float() + --end local loss = self.criterion:forward(self.model.output, self.target) self.model:zeroGradParameters() @@ -63,6 +74,12 @@ function Trainer:train(epoch, dataloader) optim.sgd(feval, self.params, self.optimState) local top1, top5 = self:computeScore(output, sample.target, 1) + --local top1, top5 + --if type(output) == 'table' then + -- top1, top5 = self:computeScore(output[1], sample.target, 1) + --else + -- top1, top5 = self:computeScore(output, sample.target, 1) + --end top1Sum = top1Sum + top1 top5Sum = top5Sum + top5 lossSum = lossSum + loss @@ -100,9 +117,23 @@ function Trainer:test(epoch, dataloader) self:copyInputs(sample) local output = self.model:forward(self.input):float() + --local output = self.model:forward(self.input) + --if type(output) == 'table' then + -- for i = 1, #output do + -- output[i]:float() + -- end + --else + -- output:float() + --end local loss = self.criterion:forward(self.model.output, self.target) local top1, top5 = self:computeScore(output, sample.target, nCrops) + --local top1, top5 + --if type(output) == 'table' then + -- top1, top5 = self:computeScore(output[1], sample.target, nCrops) + --else + -- top1, top5 = self:computeScore(output, sample.target, nCrops) + --end top1Sum = top1Sum + top1 top5Sum = top5Sum + top5 N = N + 1 @@ -171,4 +202,14 @@ function Trainer:learningRate(epoch) return self.opt.LR * math.pow(0.1, decay) end +function Trainer:learningRateStep(epoch, iter, size, gamma, step) + -- epoch = current epoch + -- iter = current iter number + -- size = num of data / batchsize + local iter_ = (epoch-1) * size + iter + + -- Training schedule + return self.opt.LR * math.pow(gamma, torch.floor(iter_ / step)) +end + return M.Trainer