add input and output in resnet50 demo

CNNDemo/ResNet50Weight.cc

@@ -235,3 +235,23 @@ int RN50_layer4__modules_2_bn3_weight[]={23414720,23416767,};
 int RN50_layer4__modules_2_bn3_bias[]={23416768,23418815,};
 int RN50_fc_weight[]={23418816,25466815,};
 int RN50_fc_bias[]={25466816,25467815,};
+int input_0[]={25467816,25618343,};
+int output_0[]={25618344,25619343,};
+int input_1[]={25619344,25769871,};
+int output_1[]={25769872,25770871,};
+int input_2[]={25770872,25921399,};
+int output_2[]={25921400,25922399,};
+int input_3[]={25922400,26072927,};
+int output_3[]={26072928,26073927,};
+int input_4[]={26073928,26224455,};
+int output_4[]={26224456,26225455,};
+int input_5[]={26225456,26375983,};
+int output_5[]={26375984,26376983,};
+int input_6[]={26376984,26527511,};
+int output_6[]={26527512,26528511,};
+int input_7[]={26528512,26679039,};
+int output_7[]={26679040,26680039,};
+int input_8[]={26680040,26830567,};
+int output_8[]={26830568,26831567,};
+int input_9[]={26831568,26982095,};
+int output_9[]={26982096,26983095,};

CNNDemo/Resnet50.py

@@ -14,11 +14,14 @@ import numpy as np
 import struct
 from struct import Struct
 
+
 CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
 
 
+
+
 resnet50 = models.resnet50(pretrained=True)
-torch.save(resnet50, CurrentPath+'params.pth')
+# torch.save(resnet50, CurrentPath+'params.pth')
 resnet50 = torch.load(CurrentPath+'params.pth')
 print("===========================")
 print("===========================")
@@ -29,20 +32,6 @@ print("===========================")
 print("===========================")
 
 
-# ss = resnet50.conv1.weight.cpu().detach().numpy().reshape(-1)
-# ss = ss.tolist()
-# strs = ''
-# # for s in ss:
-# #     strs += str(s) + ","
-
-# bs = struct.pack("f",1.0)
-# f = open('data.hex', 'wb')
-# f.write(bs)
-# f.close()
-# print(strs)
-# ssa = ss.array()
-
-
 ResNet50 = {
     "conv1": "Conv2d",
     "bn1": "BatchNorm2d",
@@ -313,21 +302,62 @@ def printDick(d, head, obj):
 
   return strg
 
+strg = ''
+strg = printDick(ResNet50, "RN50", resnet50)
 
-ss = printDick(ResNet50, "RN50", resnet50)
+normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                 std=[0.229, 0.224, 0.225])
 
-weightfile.write(ss)
+val_loader = torch.utils.data.DataLoader(
+    datasets.ImageFolder(CurrentPath+'ImageNet/', transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        normalize,
+    ])),
+    batch_size=1, shuffle=False,
+    num_workers=1, pin_memory=True)
+
+
+for batch_idx, (data, target) in enumerate(val_loader):
+
+    strg = strg + "int input_"+str(batch_idx)+"[]={"
+    array = data.cpu().detach().numpy().reshape(-1)
+    strg += str(currentbyte) + ","
+    for a in array:
+        bs = struct.pack("f", a)
+        binaryfile.write(bs)
+        currentbyte = currentbyte+1
+    strg += str(currentbyte-1) + ","
+    strg = strg + "};\n"
+
+    out = resnet50(data)
+
+    strg = strg + "int output_"+str(batch_idx)+"[]={"
+    array = out.cpu().detach().numpy().reshape(-1)
+    strg += str(currentbyte) + ","
+    for a in array:
+        bs = struct.pack("f", a)
+        binaryfile.write(bs)
+        currentbyte = currentbyte+1
+    strg += str(currentbyte-1) + ","
+    strg = strg + "};\n"
+
+weightfile.write(strg)
 
 binaryfile.close()
 weightfile.close()
 
-print(ss)
+
+print(strg)
 
 print("===========================")
 print("===========================")
 print("===========================")
 
 
+
+
 # ResNet(
 #   (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
 #   (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)