add layer1 inner print
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colin
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6309c2f066
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@ -265,34 +265,74 @@ int RN50_layer4__modules_2_bn3_weight[] = { 94228224,94236415 };
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int RN50_layer4__modules_2_bn3_bias[] = { 94236416,94244607 };
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int RN50_fc_weight[] = { 94244608,102436607 };
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int RN50_fc_bias[] = { 102436608,102440607 };
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int verify_input[] = { 102440608,103042719 };
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int verify_conv1[] = { 103042720,106253983 };
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int verify_bn1[] = { 106253984,109465247 };
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int verify_relu[] = { 109465248,112676511 };
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int verify_maxpool[] = { 112676512,113479327 };
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int verify_layer1[] = { 113479328,116690591 };
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int verify_layer2[] = { 116690592,118296223 };
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int verify_layer3[] = { 118296224,119099039 };
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int verify_layer4[] = { 119099040,119500447 };
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int verify_avgpool[] = { 119500448,119508639 };
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int verify_fc[] = { 119508640,119512639 };
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int input_0[] = { 119512640,120114751 };
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int output_0[] = { 120114752,120118751 };
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int input_1[] = { 120118752,120720863 };
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int output_1[] = { 120720864,120724863 };
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int input_2[] = { 120724864,121326975 };
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int output_2[] = { 121326976,121330975 };
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int input_3[] = { 121330976,121933087 };
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int output_3[] = { 121933088,121937087 };
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int input_4[] = { 121937088,122539199 };
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int output_4[] = { 122539200,122543199 };
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int input_5[] = { 122543200,123145311 };
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int output_5[] = { 123145312,123149311 };
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int input_6[] = { 123149312,123751423 };
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int output_6[] = { 123751424,123755423 };
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int input_7[] = { 123755424,124357535 };
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int output_7[] = { 124357536,124361535 };
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int input_8[] = { 124361536,124963647 };
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int output_8[] = { 124963648,124967647 };
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int input_9[] = { 124967648,125569759 };
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int output_9[] = { 125569760,125573759 };
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int input_0[] = { 102440608,103042719 };
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int output_0[] = { 103042720,103046719 };
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int input_1[] = { 103046720,103648831 };
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int output_1[] = { 103648832,103652831 };
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int input_2[] = { 103652832,104254943 };
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int output_2[] = { 104254944,104258943 };
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int input_3[] = { 104258944,104861055 };
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int output_3[] = { 104861056,104865055 };
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int input_4[] = { 104865056,105467167 };
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int output_4[] = { 105467168,105471167 };
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int input_5[] = { 105471168,106073279 };
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int output_5[] = { 106073280,106077279 };
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int input_6[] = { 106077280,106679391 };
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int output_6[] = { 106679392,106683391 };
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int input_7[] = { 106683392,107285503 };
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int output_7[] = { 107285504,107289503 };
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int input_8[] = { 107289504,107891615 };
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int output_8[] = { 107891616,107895615 };
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int input_9[] = { 107895616,108497727 };
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int output_9[] = { 108497728,108501727 };
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int verify_input[] = { 108501728,109103839 };
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int verify_conv1[] = { 109103840,112315103 };
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int verify_bn1[] = { 112315104,115526367 };
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int verify_relu[] = { 115526368,118737631 };
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int verify_maxpool[] = { 118737632,119540447 };
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int verify_layer1[] = { 119540448,122751711 };
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int verify_layer2[] = { 122751712,124357343 };
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int verify_layer3[] = { 124357344,125160159 };
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int verify_layer4[] = { 125160160,125561567 };
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int verify_avgpool[] = { 125561568,125569759 };
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int verify_fc[] = { 125569760,125573759 };
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int layer1_block0_conv1_input[] = { 125573760,126376575 };
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int layer1_block0_conv1_output[] = { 126376576,127179391 };
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int layer1_block0_bn1_input[] = { 127179392,127982207 };
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int layer1_block0_bn1_output[] = { 127982208,128785023 };
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int layer1_block0_conv2_input[] = { 128785024,129587839 };
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int layer1_block0_conv2_output[] = { 129587840,130390655 };
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int layer1_block0_bn2_input[] = { 130390656,131193471 };
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int layer1_block0_bn2_output[] = { 131193472,131996287 };
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int layer1_block0_conv3_input[] = { 131996288,132799103 };
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int layer1_block0_conv3_output[] = { 132799104,136010367 };
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int layer1_block0_bn3_input[] = { 136010368,139221631 };
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int layer1_block0_bn3_output[] = { 139221632,142432895 };
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int layer1_block0_downsample_conv_input[] = { 142432896,143235711 };
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int layer1_block0_downsample_conv_output[] = { 143235712,146446975 };
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int layer1_block0_downsample_bn_input[] = { 146446976,149658239 };
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int layer1_block0_downsample_bn_output[] = { 149658240,152869503 };
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int layer1_block1_conv1_input[] = { 152869504,156080767 };
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int layer1_block1_conv1_output[] = { 156080768,156883583 };
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int layer1_block1_bn1_input[] = { 156883584,157686399 };
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int layer1_block1_bn1_output[] = { 157686400,158489215 };
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int layer1_block1_conv2_input[] = { 158489216,159292031 };
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int layer1_block1_conv2_output[] = { 159292032,160094847 };
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int layer1_block1_bn2_input[] = { 160094848,160897663 };
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int layer1_block1_bn2_output[] = { 160897664,161700479 };
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int layer1_block1_conv3_input[] = { 161700480,162503295 };
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int layer1_block1_conv3_output[] = { 162503296,165714559 };
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int layer1_block1_bn3_input[] = { 165714560,168925823 };
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int layer1_block1_bn3_output[] = { 168925824,172137087 };
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int layer1_block2_conv1_input[] = { 172137088,175348351 };
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int layer1_block2_conv1_output[] = { 175348352,176151167 };
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int layer1_block2_bn1_input[] = { 176151168,176953983 };
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int layer1_block2_bn1_output[] = { 176953984,177756799 };
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int layer1_block2_conv2_input[] = { 177756800,178559615 };
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int layer1_block2_conv2_output[] = { 178559616,179362431 };
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int layer1_block2_bn2_input[] = { 179362432,180165247 };
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int layer1_block2_bn2_output[] = { 180165248,180968063 };
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int layer1_block2_conv3_input[] = { 180968064,181770879 };
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int layer1_block2_conv3_output[] = { 181770880,184982143 };
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int layer1_block2_bn3_input[] = { 184982144,188193407 };
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int layer1_block2_bn3_output[] = { 188193408,191404671 };
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@ -31,21 +31,19 @@ from struct import Struct
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CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
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resnet50 = models.resnet50(pretrained=True)
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# resnet50 = models.resnet50(pretrained=True)
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# torch.save(resnet50, CurrentPath+'params.pth')
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resnet50 = torch.load(CurrentPath+'params.pth')
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resnet50.eval()
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print("===========================")
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print("===========================")
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print("===========================")
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print(resnet50)
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print("===========================")
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print("===========================")
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print("===========================")
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# print("===========================")
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# print("===========================")
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# print("===========================")
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# print(resnet50)
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# print("===========================")
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# print("===========================")
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# print("===========================")
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ResNet50 = {
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@ -245,7 +243,7 @@ ResNet50 = {
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weightfile = open(CurrentPath+'ResNet50Weight.cc', 'w')
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binaryfile = open(CurrentPath+'ResNet50Weight.bin', 'wb')
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currentbyte = 0
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strg = ''
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def genData(name, data, currentbyte, binaryfile, strg):
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strg = strg + "int "+name+"[] = { "
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@ -260,6 +258,37 @@ def genData(name, data, currentbyte, binaryfile, strg):
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return (currentbyte,binaryfile,strg)
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def hook_fn(m, i, o):
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print(m)
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print("------------Input Grad------------")
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for grad in i:
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try:
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print(grad.shape)
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except AttributeError:
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print ("None found for Gradient")
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print("------------Output Grad------------")
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for grad in o:
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try:
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print(grad.shape)
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except AttributeError:
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print ("None found for Gradient")
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print("\n")
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def hook_print(name, m, i, o):
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global currentbyte
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global binaryfile
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global strg
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currentbyte, binaryfile, strg = genData(
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name+"_input", i[0], currentbyte, binaryfile, strg)
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currentbyte, binaryfile, strg = genData(
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name+"_output", o[0], currentbyte, binaryfile, strg)
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def printDick(d, head, obj):
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global currentbyte
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global binaryfile
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@ -296,8 +325,6 @@ def printDick(d, head, obj):
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return strg
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strg = ''
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strg = printDick(ResNet50, "RN50", resnet50)
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normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
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@ -314,6 +341,16 @@ val_loader = torch.utils.data.DataLoader(
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num_workers=1, pin_memory=True)
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for batch_idx, (data, target) in enumerate(val_loader):
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currentbyte, binaryfile,strg = genData("input_"+str(batch_idx), data, currentbyte, binaryfile, strg)
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out = resnet50(data)
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currentbyte, binaryfile, strg = genData(
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"output_"+str(batch_idx), out, currentbyte, binaryfile, strg)
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for batch_idx, (data, target) in enumerate(val_loader):
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currentbyte,binaryfile,strg = genData("verify_input", data, currentbyte, binaryfile, strg)
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x = resnet50.conv1(data)
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@ -340,11 +377,35 @@ for batch_idx, (data, target) in enumerate(val_loader):
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break
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resnet50.layer1._modules['0'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_bn1", m, i, o))
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resnet50.layer1._modules['0'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_bn2", m, i, o))
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resnet50.layer1._modules['0'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_bn3", m, i, o))
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resnet50.layer1._modules['0'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_conv1", m, i, o))
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resnet50.layer1._modules['0'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_conv2", m, i, o))
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resnet50.layer1._modules['0'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_conv3", m, i, o))
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resnet50.layer1._modules['0'].downsample._modules['0'].register_forward_hook(lambda m, i, o: hook_print("layer1_block0_downsample_conv", m, i, o))
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resnet50.layer1._modules['0'].downsample._modules['1'].register_forward_hook(lambda m, i, o: hook_print("layer1_block0_downsample_bn", m, i, o))
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resnet50.layer1._modules['1'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_bn1", m, i, o))
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resnet50.layer1._modules['1'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_bn2", m, i, o))
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resnet50.layer1._modules['1'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_bn3", m, i, o))
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resnet50.layer1._modules['1'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_conv1", m, i, o))
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resnet50.layer1._modules['1'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_conv2", m, i, o))
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resnet50.layer1._modules['1'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_conv3", m, i, o))
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resnet50.layer1._modules['2'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_bn1", m, i, o))
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resnet50.layer1._modules['2'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_bn2", m, i, o))
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resnet50.layer1._modules['2'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_bn3", m, i, o))
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resnet50.layer1._modules['2'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_conv1", m, i, o))
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resnet50.layer1._modules['2'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_conv2", m, i, o))
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resnet50.layer1._modules['2'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_conv3", m, i, o))
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for batch_idx, (data, target) in enumerate(val_loader):
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currentbyte, binaryfile,strg = genData("input_"+str(batch_idx), data, currentbyte, binaryfile, strg)
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out = resnet50(data)
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currentbyte, binaryfile, strg = genData(
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"output_"+str(batch_idx), out, currentbyte, binaryfile, strg)
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break
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weightfile.write(strg)
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@ -352,7 +413,7 @@ binaryfile.close()
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weightfile.close()
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print(strg)
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# print(strg)
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print("===========================")
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print("===========================")
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