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How to use ops method in pytest-benchmark
Best Python code snippet using pytest-benchmark
inception_model.py
Source:inception_model.py 
1# Copyright 2016 Google Inc. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Inception-v3 expressed in TensorFlow-Slim.16 Usage:17 # Parameters for BatchNorm.18 batch_norm_params = {19 # Decay for the batch_norm moving averages.20 'decay': BATCHNORM_MOVING_AVERAGE_DECAY,21 # epsilon to prevent 0s in variance.22 'epsilon': 0.001,23 }24 # Set weight_decay for weights in Conv and FC layers.25 with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):26 with slim.arg_scope([slim.ops.conv2d],27 stddev=0.1,28 activation=tf.nn.relu,29 batch_norm_params=batch_norm_params):30 # Force all Variables to reside on the CPU.31 with slim.arg_scope([slim.variables.variable], device='/cpu:0'):32 logits, endpoints = slim.inception.inception_v3(33 images,34 dropout_keep_prob=0.8,35 num_classes=num_classes,36 is_training=for_training,37 restore_logits=restore_logits,38 scope=scope)39"""40from __future__ import absolute_import41from __future__ import division42from __future__ import print_function43import tensorflow as tf44from inception.slim import ops45from inception.slim import scopes46def inception_v3(inputs,47 dropout_keep_prob=0.8,48 num_classes=1000,49 is_training=True,50 restore_logits=True,51 scope=''):52 """Latest Inception from http://arxiv.org/abs/1512.00567.53 "Rethinking the Inception Architecture for Computer Vision"54 Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,55 Zbigniew Wojna56 Args:57 inputs: a tensor of size [batch_size, height, width, channels].58 dropout_keep_prob: dropout keep_prob.59 num_classes: number of predicted classes.60 is_training: whether is training or not.61 restore_logits: whether or not the logits layers should be restored.62 Useful for fine-tuning a model with different num_classes.63 scope: Optional scope for name_scope.64 Returns:65 a list containing 'logits', 'aux_logits' Tensors.66 """67 # end_points will collect relevant activations for external use, for example68 # summaries or losses.69 end_points = {}70 with tf.name_scope(scope, 'inception_v3', [inputs]):71 with scopes.arg_scope([ops.conv2d, ops.fc, ops.batch_norm, ops.dropout],72 is_training=is_training):73 with scopes.arg_scope([ops.conv2d, ops.max_pool, ops.avg_pool],74 stride=1, padding='VALID'):75 # 299 x 299 x 376 end_points['conv0'] = ops.conv2d(inputs, 32, [3, 3], stride=2,77 scope='conv0')78 # 149 x 149 x 3279 end_points['conv1'] = ops.conv2d(end_points['conv0'], 32, [3, 3],80 scope='conv1')81 # 147 x 147 x 3282 end_points['conv2'] = ops.conv2d(end_points['conv1'], 64, [3, 3],83 padding='SAME', scope='conv2')84 # 147 x 147 x 6485 end_points['pool1'] = ops.max_pool(end_points['conv2'], [3, 3],86 stride=2, scope='pool1')87 # 73 x 73 x 6488 end_points['conv3'] = ops.conv2d(end_points['pool1'], 80, [1, 1],89 scope='conv3')90 # 73 x 73 x 80.91 end_points['conv4'] = ops.conv2d(end_points['conv3'], 192, [3, 3],92 scope='conv4')93 # 71 x 71 x 192.94 end_points['pool2'] = ops.max_pool(end_points['conv4'], [3, 3],95 stride=2, scope='pool2')96 # 35 x 35 x 192.97 net = end_points['pool2']98 # Inception blocks99 with scopes.arg_scope([ops.conv2d, ops.max_pool, ops.avg_pool],100 stride=1, padding='SAME'):101 # mixed: 35 x 35 x 256.102 with tf.variable_scope('mixed_35x35x256a'):103 with tf.variable_scope('branch1x1'):104 branch1x1 = ops.conv2d(net, 64, [1, 1])105 with tf.variable_scope('branch5x5'):106 branch5x5 = ops.conv2d(net, 48, [1, 1])107 branch5x5 = ops.conv2d(branch5x5, 64, [5, 5])108 with tf.variable_scope('branch3x3dbl'):109 branch3x3dbl = ops.conv2d(net, 64, [1, 1])110 branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3])111 branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3])112 with tf.variable_scope('branch_pool'):113 branch_pool = ops.avg_pool(net, [3, 3])114 branch_pool = ops.conv2d(branch_pool, 32, [1, 1])115 net = tf.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], 3)116 end_points['mixed_35x35x256a'] = net117 # mixed_1: 35 x 35 x 288.118 with tf.variable_scope('mixed_35x35x288a'):119 with tf.variable_scope('branch1x1'):120 branch1x1 = ops.conv2d(net, 64, [1, 1])121 with tf.variable_scope('branch5x5'):122 branch5x5 = ops.conv2d(net, 48, [1, 1])123 branch5x5 = ops.conv2d(branch5x5, 64, [5, 5])124 with tf.variable_scope('branch3x3dbl'):125 branch3x3dbl = ops.conv2d(net, 64, [1, 1])126 branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3])127 branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3])128 with tf.variable_scope('branch_pool'):129 branch_pool = ops.avg_pool(net, [3, 3])130 branch_pool = ops.conv2d(branch_pool, 64, [1, 1])131 net = tf.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], 3)132 end_points['mixed_35x35x288a'] = net133 # mixed_2: 35 x 35 x 288.134 with tf.variable_scope('mixed_35x35x288b'):135 with tf.variable_scope('branch1x1'):136 branch1x1 = ops.conv2d(net, 64, [1, 1])137 with tf.variable_scope('branch5x5'):138 branch5x5 = ops.conv2d(net, 48, [1, 1])139 branch5x5 = ops.conv2d(branch5x5, 64, [5, 5])140 with tf.variable_scope('branch3x3dbl'):141 branch3x3dbl = ops.conv2d(net, 64, [1, 1])142 branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3])143 branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3])144 with tf.variable_scope('branch_pool'):145 branch_pool = ops.avg_pool(net, [3, 3])146 branch_pool = ops.conv2d(branch_pool, 64, [1, 1])147 net = tf.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], 3)148 end_points['mixed_35x35x288b'] = net149 # mixed_3: 17 x 17 x 768.150 with tf.variable_scope('mixed_17x17x768a'):151 with tf.variable_scope('branch3x3'):152 branch3x3 = ops.conv2d(net, 384, [3, 3], stride=2, padding='VALID')153 with tf.variable_scope('branch3x3dbl'):154 branch3x3dbl = ops.conv2d(net, 64, [1, 1])155 branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3])156 branch3x3dbl = ops.conv2d(branch3x3dbl, 96, [3, 3],157 stride=2, padding='VALID')158 with tf.variable_scope('branch_pool'):159 branch_pool = ops.max_pool(net, [3, 3], stride=2, padding='VALID')160 net = tf.concat([branch3x3, branch3x3dbl, branch_pool], 3)161 end_points['mixed_17x17x768a'] = net162 # mixed4: 17 x 17 x 768.163 with tf.variable_scope('mixed_17x17x768b'):164 with tf.variable_scope('branch1x1'):165 branch1x1 = ops.conv2d(net, 192, [1, 1])166 with tf.variable_scope('branch7x7'):167 branch7x7 = ops.conv2d(net, 128, [1, 1])168 branch7x7 = ops.conv2d(branch7x7, 128, [1, 7])169 branch7x7 = ops.conv2d(branch7x7, 192, [7, 1])170 with tf.variable_scope('branch7x7dbl'):171 branch7x7dbl = ops.conv2d(net, 128, [1, 1])172 branch7x7dbl = ops.conv2d(branch7x7dbl, 128, [7, 1])173 branch7x7dbl = ops.conv2d(branch7x7dbl, 128, [1, 7])174 branch7x7dbl = ops.conv2d(branch7x7dbl, 128, [7, 1])175 branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7])176 with tf.variable_scope('branch_pool'):177 branch_pool = ops.avg_pool(net, [3, 3])178 branch_pool = ops.conv2d(branch_pool, 192, [1, 1])179 net = tf.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], 3)180 end_points['mixed_17x17x768b'] = net181 # mixed_5: 17 x 17 x 768.182 with tf.variable_scope('mixed_17x17x768c'):183 with tf.variable_scope('branch1x1'):184 branch1x1 = ops.conv2d(net, 192, [1, 1])185 with tf.variable_scope('branch7x7'):186 branch7x7 = ops.conv2d(net, 160, [1, 1])187 branch7x7 = ops.conv2d(branch7x7, 160, [1, 7])188 branch7x7 = ops.conv2d(branch7x7, 192, [7, 1])189 with tf.variable_scope('branch7x7dbl'):190 branch7x7dbl = ops.conv2d(net, 160, [1, 1])191 branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1])192 branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [1, 7])193 branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1])194 branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7])195 with tf.variable_scope('branch_pool'):196 branch_pool = ops.avg_pool(net, [3, 3])197 branch_pool = ops.conv2d(branch_pool, 192, [1, 1])198 net = tf.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], 3)199 end_points['mixed_17x17x768c'] = net200 # mixed_6: 17 x 17 x 768.201 with tf.variable_scope('mixed_17x17x768d'):202 with tf.variable_scope('branch1x1'):203 branch1x1 = ops.conv2d(net, 192, [1, 1])204 with tf.variable_scope('branch7x7'):205 branch7x7 = ops.conv2d(net, 160, [1, 1])206 branch7x7 = ops.conv2d(branch7x7, 160, [1, 7])207 branch7x7 = ops.conv2d(branch7x7, 192, [7, 1])208 with tf.variable_scope('branch7x7dbl'):209 branch7x7dbl = ops.conv2d(net, 160, [1, 1])210 branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1])211 branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [1, 7])212 branch7x7dbl = ops.conv2d(branch7x7dbl, 160, [7, 1])213 branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7])214 with tf.variable_scope('branch_pool'):215 branch_pool = ops.avg_pool(net, [3, 3])216 branch_pool = ops.conv2d(branch_pool, 192, [1, 1])217 net = tf.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], 3)218 end_points['mixed_17x17x768d'] = net219 # mixed_7: 17 x 17 x 768.220 with tf.variable_scope('mixed_17x17x768e'):221 with tf.variable_scope('branch1x1'):222 branch1x1 = ops.conv2d(net, 192, [1, 1])223 with tf.variable_scope('branch7x7'):224 branch7x7 = ops.conv2d(net, 192, [1, 1])225 branch7x7 = ops.conv2d(branch7x7, 192, [1, 7])226 branch7x7 = ops.conv2d(branch7x7, 192, [7, 1])227 with tf.variable_scope('branch7x7dbl'):228 branch7x7dbl = ops.conv2d(net, 192, [1, 1])229 branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [7, 1])230 branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7])231 branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [7, 1])232 branch7x7dbl = ops.conv2d(branch7x7dbl, 192, [1, 7])233 with tf.variable_scope('branch_pool'):234 branch_pool = ops.avg_pool(net, [3, 3])235 branch_pool = ops.conv2d(branch_pool, 192, [1, 1])236 net = tf.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], 3)237 end_points['mixed_17x17x768e'] = net238 # Auxiliary Head logits239 aux_logits = tf.identity(end_points['mixed_17x17x768e'])240 with tf.variable_scope('aux_logits'):241 aux_logits = ops.avg_pool(aux_logits, [5, 5], stride=3,242 padding='VALID')243 aux_logits = ops.conv2d(aux_logits, 128, [1, 1], scope='proj')244 # Shape of feature map before the final layer.245 shape = aux_logits.get_shape()246 aux_logits = ops.conv2d(aux_logits, 768, shape[1:3], stddev=0.01,247 padding='VALID')248 aux_logits = ops.flatten(aux_logits)249 aux_logits = ops.fc(aux_logits, num_classes, activation=None,250 stddev=0.001, restore=restore_logits)251 end_points['aux_logits'] = aux_logits252 # mixed_8: 8 x 8 x 1280.253 # Note that the scope below is not changed to not void previous254 # checkpoints.255 # (TODO) Fix the scope when appropriate.256 with tf.variable_scope('mixed_17x17x1280a'):257 with tf.variable_scope('branch3x3'):258 branch3x3 = ops.conv2d(net, 192, [1, 1])259 branch3x3 = ops.conv2d(branch3x3, 320, [3, 3], stride=2,260 padding='VALID')261 with tf.variable_scope('branch7x7x3'):262 branch7x7x3 = ops.conv2d(net, 192, [1, 1])263 branch7x7x3 = ops.conv2d(branch7x7x3, 192, [1, 7])264 branch7x7x3 = ops.conv2d(branch7x7x3, 192, [7, 1])265 branch7x7x3 = ops.conv2d(branch7x7x3, 192, [3, 3],266 stride=2, padding='VALID')267 with tf.variable_scope('branch_pool'):268 branch_pool = ops.max_pool(net, [3, 3], stride=2, padding='VALID')269 net = tf.concat([branch3x3, branch7x7x3, branch_pool], 3)270 end_points['mixed_17x17x1280a'] = net271 # mixed_9: 8 x 8 x 2048.272 with tf.variable_scope('mixed_8x8x2048a'):273 with tf.variable_scope('branch1x1'):274 branch1x1 = ops.conv2d(net, 320, [1, 1])275 with tf.variable_scope('branch3x3'):276 branch3x3 = ops.conv2d(net, 384, [1, 1])277 branch3x3 = tf.concat([ops.conv2d(branch3x3, 384, [1, 3]),278 ops.conv2d(branch3x3, 384, [3, 1])], 3)279 with tf.variable_scope('branch3x3dbl'):280 branch3x3dbl = ops.conv2d(net, 448, [1, 1])281 branch3x3dbl = ops.conv2d(branch3x3dbl, 384, [3, 3])282 branch3x3dbl = tf.concat([ops.conv2d(branch3x3dbl, 384, [1, 3]),283 ops.conv2d(branch3x3dbl, 384, [3, 1])], 3)284 with tf.variable_scope('branch_pool'):285 branch_pool = ops.avg_pool(net, [3, 3])286 branch_pool = ops.conv2d(branch_pool, 192, [1, 1])287 net = tf.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], 3)288 end_points['mixed_8x8x2048a'] = net289 # mixed_10: 8 x 8 x 2048.290 with tf.variable_scope('mixed_8x8x2048b'):291 with tf.variable_scope('branch1x1'):292 branch1x1 = ops.conv2d(net, 320, [1, 1])293 with tf.variable_scope('branch3x3'):294 branch3x3 = ops.conv2d(net, 384, [1, 1])295 branch3x3 = tf.concat([ops.conv2d(branch3x3, 384, [1, 3]),296 ops.conv2d(branch3x3, 384, [3, 1])], 3)297 with tf.variable_scope('branch3x3dbl'):298 branch3x3dbl = ops.conv2d(net, 448, [1, 1])299 branch3x3dbl = ops.conv2d(branch3x3dbl, 384, [3, 3])300 branch3x3dbl = tf.concat([ops.conv2d(branch3x3dbl, 384, [1, 3]),301 ops.conv2d(branch3x3dbl, 384, [3, 1])], 3)302 with tf.variable_scope('branch_pool'):303 branch_pool = ops.avg_pool(net, [3, 3])304 branch_pool = ops.conv2d(branch_pool, 192, [1, 1])305 net = tf.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], 3)306 end_points['mixed_8x8x2048b'] = net307 # Final pooling and prediction308 with tf.variable_scope('logits'):309 shape = net.get_shape()310 net = ops.avg_pool(net, shape[1:3], padding='VALID', scope='pool')311 # 1 x 1 x 2048312 net = ops.dropout(net, dropout_keep_prob, scope='dropout')313 net = ops.flatten(net, scope='flatten')314 # 2048315 logits = ops.fc(net, num_classes, activation=None, scope='logits',316 restore=restore_logits)317 # 1000318 end_points['logits'] = logits319 end_points['predictions'] = tf.nn.softmax(logits, name='predictions')320 return logits, end_points321def inception_v3_parameters(weight_decay=0.00004, stddev=0.1,322 batch_norm_decay=0.9997, batch_norm_epsilon=0.001):323 """Yields the scope with the default parameters for inception_v3.324 Args:325 weight_decay: the weight decay for weights variables.326 stddev: standard deviation of the truncated guassian weight distribution.327 batch_norm_decay: decay for the moving average of batch_norm momentums.328 batch_norm_epsilon: small float added to variance to avoid dividing by zero.329 Yields:330 a arg_scope with the parameters needed for inception_v3.331 """332 # Set weight_decay for weights in Conv and FC layers.333 with scopes.arg_scope([ops.conv2d, ops.fc],334 weight_decay=weight_decay):335 # Set stddev, activation and parameters for batch_norm.336 with scopes.arg_scope([ops.conv2d],337 stddev=stddev,338 activation=tf.nn.relu,339 batch_norm_params={340 'decay': batch_norm_decay,341 'epsilon': batch_norm_epsilon}) as arg_scope:...
gen_stimuli.py
Source:gen_stimuli.py
1#!/usr/bin/env python-2.52import sys3import random4# Copyright 2017 ETH Zurich and University of Bologna.5# Copyright and related rights are licensed under the Solderpad Hardware6# License, Version 0.51 (the License); you may not use this file except in7# compliance with the License. You may obtain a copy of the License at8# http://solderpad.org/licenses/SHL-0.51. Unless required by applicable law9# or agreed to in writing, software, hardware and materials distributed under10# this License is distributed on an AS IS BASIS, WITHOUT WARRANTIES OR11# CONDITIONS OF ANY KIND, either express or implied. See the License for the12# specific language governing permissions and limitations under the License.13def write_hex8_arr(f, name, arr):14 f.write('unsigned int %s[] = {\n' % name)15 for v in arr:16 f.write('0x%02X%02X%02X%02X,\n' % (v[0] & 0xFF, v[1] & 0xFF, v[2] & 0xFF, v[3] & 0xFF))17 f.write('};\n\n')18 return19def write_hex16_arr(f, name, arr):20 f.write('unsigned int %s[] = {\n' % name)21 for v in arr:22 f.write('0x%04X%04X,\n' % (v[0] & 0xFFFF, v[1] & 0xFFFF))23 f.write('};\n\n')24 return25def write_hex32_arr(f, name, arr):26 f.write('unsigned int %s[] = {\n' % name)27 for v in arr:28 f.write('0x%08X,\n' % (v & 0xFFFFFFFF))29 f.write('};\n\n')30 return31f = open('testALU_stimuli.h', 'w')32# number of test cases to generate33N = 1534################################################################################35################################################################################36#37# ADDITION38#39################################################################################40################################################################################41################################################################################42# generate testdata for l.add43################################################################################44ops_a = []45ops_b = []46exp_res = []47for i in range(N):48 a = random.randint(-2**31, 2**31-1)49 b = random.randint(-2**31, 2**31-1)50 r = a + b51 ops_a.append(a)52 ops_b.append(b)53 exp_res.append(r)54write_hex32_arr(f, 'g_add_a', ops_a)55write_hex32_arr(f, 'g_add_b', ops_b)56write_hex32_arr(f, 'g_add_act', ops_a)57write_hex32_arr(f, 'g_add_exp', exp_res)58################################################################################59# generate testdata for l.addi60################################################################################61ops_a = []62exp_res = []63for i in range(N):64 a = random.randint(-2**31, 2**31-1)65 r = a + 10066 ops_a.append(a)67 exp_res.append(r)68write_hex32_arr(f, 'g_addi_a', ops_a)69write_hex32_arr(f, 'g_addi_act', ops_a)70write_hex32_arr(f, 'g_addi_exp', exp_res)71################################################################################72################################################################################73#74# SUBTRACTION75#76################################################################################77################################################################################78################################################################################79# generate testdata for l.sub80################################################################################81ops_a = []82ops_b = []83exp_res = []84for i in range(N):85 a = random.randint(-2**31, 2**31-1)86 b = random.randint(-2**31, 2**31-1)87 r = a - b88 ops_a.append(a)89 ops_b.append(b)90 exp_res.append(r)91write_hex32_arr(f, 'g_sub_a', ops_a)92write_hex32_arr(f, 'g_sub_b', ops_b)93write_hex32_arr(f, 'g_sub_act', ops_a)94write_hex32_arr(f, 'g_sub_exp', exp_res)95################################################################################96################################################################################97#98# LOGIC STUFF99#100################################################################################101################################################################################102################################################################################103# generate testdata for l.and104################################################################################105ops_a = []106ops_b = []107exp_res = []108for i in range(N):109 a = random.randint(-2**31, 2**31-1)110 b = random.randint(-2**31, 2**31-1)111 r = a & b112 ops_a.append(a)113 ops_b.append(b)114 exp_res.append(r)115write_hex32_arr(f, 'g_and_a', ops_a)116write_hex32_arr(f, 'g_and_b', ops_b)117write_hex32_arr(f, 'g_and_act', ops_a)118write_hex32_arr(f, 'g_and_exp', exp_res)119################################################################################120# generate testdata for l.or121################################################################################122ops_a = []123ops_b = []124exp_res = []125for i in range(N):126 a = random.randint(-2**31, 2**31-1)127 b = random.randint(-2**31, 2**31-1)128 r = a | b129 ops_a.append(a)130 ops_b.append(b)131 exp_res.append(r)132write_hex32_arr(f, 'g_or_a', ops_a)133write_hex32_arr(f, 'g_or_b', ops_b)134write_hex32_arr(f, 'g_or_act', ops_a)135write_hex32_arr(f, 'g_or_exp', exp_res)136################################################################################137# generate testdata for l.xor138################################################################################139ops_a = []140ops_b = []141exp_res = []142for i in range(N):143 a = random.randint(-2**31, 2**31-1)144 b = random.randint(-2**31, 2**31-1)145 r = a ^ b146 ops_a.append(a)147 ops_b.append(b)148 exp_res.append(r)149write_hex32_arr(f, 'g_xor_a', ops_a)150write_hex32_arr(f, 'g_xor_b', ops_b)151write_hex32_arr(f, 'g_xor_act', ops_a)152write_hex32_arr(f, 'g_xor_exp', exp_res)153################################################################################154################################################################################155#156# Shifting157#158################################################################################159################################################################################160################################################################################161# generate testdata for l.sll162################################################################################163ops_a = []164ops_b = []165exp_res = []166for i in range(N):167 a = random.randint(-2**31, 2**31-1)168 b = random.randint(0, 31)169 r = a << b170 ops_a.append(a)171 ops_b.append(b)172 exp_res.append(r)173write_hex32_arr(f, 'g_sll_a', ops_a)174write_hex32_arr(f, 'g_sll_b', ops_b)175write_hex32_arr(f, 'g_sll_act', ops_a)176write_hex32_arr(f, 'g_sll_exp', exp_res)177################################################################################178# generate testdata for l.srl179################################################################################180ops_a = []181ops_b = []182exp_res = []183for i in range(N):184 a = random.randint(-2**31, 2**31-1)185 b = random.randint(0, 31)186 # python does not provide logical right shifts, so we have to hack187 # something together188 r = (a % 0x100000000) >> b189 ops_a.append(a)190 ops_b.append(b)191 exp_res.append(r)192write_hex32_arr(f, 'g_srl_a', ops_a)193write_hex32_arr(f, 'g_srl_b', ops_b)194write_hex32_arr(f, 'g_srl_act', ops_a)195write_hex32_arr(f, 'g_srl_exp', exp_res)196################################################################################197# generate testdata for l.sra198################################################################################199ops_a = []200ops_b = []201exp_res = []202for i in range(N):203 a = random.randint(-2**31, 2**31-1)204 b = random.randint(0, 31)205 r = a >> b206 ops_a.append(a)207 ops_b.append(b)208 exp_res.append(r)209write_hex32_arr(f, 'g_sra_a', ops_a)210write_hex32_arr(f, 'g_sra_b', ops_b)211write_hex32_arr(f, 'g_sra_act', ops_a)212write_hex32_arr(f, 'g_sra_exp', exp_res)213################################################################################214# generate testdata for l.slli215################################################################################216ops_a = []217exp_res = []218for i in range(N):219 a = random.randint(-2**31, 2**31-1)220 r = a << 10221 ops_a.append(a)222 exp_res.append(r)223write_hex32_arr(f, 'g_slli_a', ops_a)224write_hex32_arr(f, 'g_slli_act', ops_a)225write_hex32_arr(f, 'g_slli_exp', exp_res)226################################################################################227# generate testdata for l.srli228################################################################################229ops_a = []230exp_res = []231for i in range(N):232 a = random.randint(-2**31, 2**31-1)233 # python does not provide logical right shifts, so we have to hack234 # something together235 r = (a % 0x100000000) >> 9236 ops_a.append(a)237 exp_res.append(r)238write_hex32_arr(f, 'g_srli_a', ops_a)239write_hex32_arr(f, 'g_srli_act', ops_a)240write_hex32_arr(f, 'g_srli_exp', exp_res)241################################################################################242# generate testdata for l.srai243################################################################################244ops_a = []245exp_res = []246for i in range(N):247 a = random.randint(-2**31, 2**31-1)248 r = a >> 21249 ops_a.append(a)250 exp_res.append(r)251write_hex32_arr(f, 'g_srai_a', ops_a)252write_hex32_arr(f, 'g_srai_act', ops_a)253write_hex32_arr(f, 'g_srai_exp', exp_res)254# ################################################################################255# # generate testdata for l.cmov256# ################################################################################257# ops_a = []258# ops_b = []259# exp_res = []260#261# for i in range(N):262# a = random.randint(0, 2**32-1)263# b = random.randint(0, 2**32-1)264#265# if(a > b):266# r = a267# else:268# r = b269#270# ops_a.append(a)271# ops_b.append(b)272# exp_res.append(r)273#274# write_hex32_arr(f, 'g_cmov_a', ops_a)275# write_hex32_arr(f, 'g_cmov_b', ops_b)276# write_hex32_arr(f, 'g_cmov_act', ops_a)...
count_hooks.py
Source:count_hooks.py
1import argparse2import torch3import torch.nn as nn4multiply_adds = 15def count_convNd(m, x, y):6 x = x[0]7 cin = m.in_channels8 batch_size = x.size(0)9 kernel_ops = m.weight.size()[2:].numel()10 bias_ops = 1 if m.bias is not None else 011 ops_per_element = kernel_ops + bias_ops12 output_elements = y.nelement()13 # cout x oW x oH14 total_ops = batch_size * cin * output_elements * ops_per_element // m.groups15 # total_ops = batch_size * output_elements * (cin * kernel_ops // m.groups + bias_ops)16 m.total_ops = torch.Tensor([int(total_ops)])17def count_conv2d(m, x, y):18 x = x[0]19 cin = m.in_channels20 cout = m.out_channels21 kh, kw = m.kernel_size22 batch_size = x.size()[0]23 out_h = y.size(2)24 out_w = y.size(3)25 # ops per output element26 # kernel_mul = kh * kw * cin27 # kernel_add = kh * kw * cin - 128 kernel_ops = multiply_adds * kh * kw29 bias_ops = 1 if m.bias is not None else 030 ops_per_element = kernel_ops + bias_ops31 # total ops32 # num_out_elements = y.numel()33 output_elements = batch_size * out_w * out_h * cout34 total_ops = output_elements * ops_per_element * cin // m.groups35 m.total_ops = torch.Tensor([int(total_ops)])36def count_convtranspose2d(m, x, y):37 x = x[0]38 cin = m.in_channels39 cout = m.out_channels40 kh, kw = m.kernel_size41 batch_size = x.size()[0]42 out_h = y.size(2)43 out_w = y.size(3)44 # ops per output element45 # kernel_mul = kh * kw * cin46 # kernel_add = kh * kw * cin - 147 kernel_ops = multiply_adds * kh * kw * cin // m.groups48 bias_ops = 1 if m.bias is not None else 049 ops_per_element = kernel_ops + bias_ops50 # total ops51 # num_out_elements = y.numel()52 # output_elements = batch_size * out_w * out_h * cout53 ops_per_element = m.weight.nelement()54 output_elements = y.nelement()55 total_ops = output_elements * ops_per_element56 m.total_ops = torch.Tensor([int(total_ops)])57def count_bn(m, x, y):58 x = x[0]59 nelements = x.numel()60 # subtract, divide, gamma, beta61 total_ops = 4 * nelements62 m.total_ops = torch.Tensor([int(total_ops)])63def count_relu(m, x, y):64 x = x[0]65 nelements = x.numel()66 total_ops = nelements67 m.total_ops = torch.Tensor([int(total_ops)])68def count_sigmoid(m, x, y):69 x = x[0]70 nelements = x.numel()71 total_exp = nelements72 total_add = nelements73 total_div = nelements74 total_ops = total_exp + total_add + total_div75 m.total_ops = torch.Tensor([int(total_ops)])76def count_pixelshuffle(m, x, y):77 x = x[0]78 nelements = x.numel()79 total_ops = nelements80 m.total_ops = torch.Tensor([int(total_ops)])81def count_softmax(m, x, y):82 x = x[0]83 batch_size, nfeatures = x.size()84 total_exp = nfeatures85 total_add = nfeatures - 186 total_div = nfeatures87 total_ops = batch_size * (total_exp + total_add + total_div)88 m.total_ops = torch.Tensor([int(total_ops)])89def count_maxpool(m, x, y):90 kernel_ops = torch.prod(torch.Tensor([m.kernel_size]))91 num_elements = y.numel()92 total_ops = kernel_ops * num_elements93 m.total_ops = torch.Tensor([int(total_ops)])94def count_adap_maxpool(m, x, y):95 kernel = torch.Tensor([*(x[0].shape[2:])]) // torch.Tensor(list((m.output_size,))).squeeze()96 kernel_ops = torch.prod(kernel)97 num_elements = y.numel()98 total_ops = kernel_ops * num_elements99 m.total_ops = torch.Tensor([int(total_ops)])100def count_avgpool(m, x, y):101 total_add = torch.prod(torch.Tensor([m.kernel_size]))102 total_div = 1103 kernel_ops = total_add + total_div104 num_elements = y.numel()105 total_ops = kernel_ops * num_elements106 m.total_ops = torch.Tensor([int(total_ops)])107def count_adap_avgpool(m, x, y):108 kernel = torch.Tensor([*(x[0].shape[2:])]) // torch.Tensor(list((m.output_size,))).squeeze()109 total_add = torch.prod(kernel)110 total_div = 1111 kernel_ops = total_add + total_div112 num_elements = y.numel()113 total_ops = kernel_ops * num_elements114 m.total_ops = torch.Tensor([int(total_ops)])115def count_linear(m, x, y):116 # per output element117 total_mul = m.in_features118 total_add = m.in_features - 1119 num_elements = y.numel()120 total_ops = (total_mul + total_add) * num_elements...
config.js
Source:config.js
1/**2 * Build widget configuration3 */4(function () {5 'use strict';6 angular.module('devops-dashboard').controller('ChatOpsConfigController',7 ChatOpsConfigController);8 ChatOpsConfigController.$inject = ['modalData', '$modalInstance',9 'collectorData'];10 function ChatOpsConfigController(modalData, $modalInstance, collectorData) {11 var ctrl = this;12 var widgetConfig = modalData.widgetConfig;13 ctrl.chatOpsOptions = [{14 name: 'HipChat',15 value: 'HipChat'16 }, {17 name: 'Slack',18 value: 'Slack (Not implemented)'19 }, {20 name: 'Gitter',21 value: 'Gitter (Not implemented)'22 }];23 if (!widgetConfig.options.chatOpsOption) {24 ctrl.chatOpsOption = "";25 }26 else {27 var myindex;28 for (var v = 0; v < ctrl.chatOpsOptions.length; v++) {29 if (ctrl.chatOpsOptions[v].name == widgetConfig.options.chatOpsOption.name) {30 myindex = v;31 break;32 }33 }34 ctrl.chatOpsOption = ctrl.chatOpsOptions[myindex];35 }36 ctrl.chatOpsRoomName=widgetConfig.options.chatOpsRoomName;37 ctrl.chatOpsRoomAuthToken=widgetConfig.options.chatOpsRoomAuthToken;38 ctrl.chatOpsServerUrl=widgetConfig.options.chatOpsServerUrl;39 // public variables40 ctrl.submitted = false;41 ctrl.collectors = [];42 // public methods43 ctrl.submit = submitForm;44 // Request collecters45 collectorData.collectorsByType('ChatOps').then(processCollectorsResponse);46 function processCollectorsResponse(data) {47 console.log(data);48 ctrl.collectors = data;49 }50 /*51 * function submitForm(valid, url) { ctrl.submitted = true; if (valid &&52 * ctrl.collectors.length) {53 * createCollectorItem(url).then(processCollectorItemResponse); } }54 */55 function submitForm(valid, chatOpsOption, chatOpsRoomAuthToken, chatOpsServerUrl, chatOpsRoomName) {56 ctrl.submitted = true;57 if (valid && ctrl.collectors.length) {58 createCollectorItem(chatOpsOption, chatOpsRoomAuthToken, chatOpsServerUrl, chatOpsRoomName).then(59 processCollectorItemResponse);60 }61 }62 function createCollectorItem(chatOpsOption, chatOpsRoomAuthToken, chatOpsServerUrl, chatOpsRoomName) {63 var item = {64 collectorId: _.findWhere(ctrl.collectors, {name: 'ChatOps'}).id,65 options: {66 chatOpsOption: chatOpsOption,67 chatOpsRoomAuthToken: chatOpsRoomAuthToken,68 chatOpsServerUrl: chatOpsServerUrl,69 chatOpsRoomName: chatOpsRoomName70 }71 };72 return collectorData.createCollectorItem(item);73 }74 function processCollectorItemResponse(response) {75 var postObj = {76 name: "ChatOps",77 options: {78 id: widgetConfig.options.id,79 chatOpsOption: ctrl.chatOpsOption,80 chatOpsRoomName:ctrl.chatOpsRoomName,81 chatOpsRoomAuthToken: ctrl.chatOpsRoomAuthToken,82 chatOpsServerUrl: ctrl.chatOpsServerUrl83 },84 componentId: modalData.dashboard.application.components[0].id,85 collectorItemId: response.data.id86 };87 console.log("PostObject is:"+JSON.stringify(postObj));88 // pass this new config to the modal closing so it's saved89 $modalInstance.close(postObj);90 }91 }...
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