gistfile1.txt

# print before JIT'ing
print(per_channel_quantized_model)

VGG(                                                                                                              
  (features): Sequential(                                                                                         
    (0): QuantizedConv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.0873441994190216, zero_point=119, paddi
ng=(1, 1))                                                                                                        
    (1): QuantizedReLU(inplace=True)                                                                              
    (2): QuantizedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.19133853912353516, zero_point=148, pad
ding=(1, 1))                                                                                                      
    (3): QuantizedReLU(inplace=True)                                                                              
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)                               
    (5): QuantizedConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), scale=0.30967867374420166, zero_point=167, pa
dding=(1, 1))                                                                                                     
    (6): QuantizedReLU(inplace=True)                                                                              
    (7): QuantizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), scale=0.3568974435329437, zero_point=140, pa
dding=(1, 1))                                                                                                     
    (8): QuantizedReLU(inplace=True)                                                                              
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)                               
    (10): QuantizedConv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), scale=0.46770742535591125, zero_point=142, 
padding=(1, 1))                                                                                                   
    (11): QuantizedReLU(inplace=True)                                                                             
    (12): QuantizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), scale=0.5260586738586426, zero_point=139, p
adding=(1, 1))                                                                                                    
    (13): QuantizedReLU(inplace=True)                                                                             
    (14): QuantizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), scale=0.6772246956825256, zero_point=132, p
adding=(1, 1))                                                                                                    
    (15): QuantizedReLU(inplace=True)                                                                             
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)                              
    (17): QuantizedConv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), scale=0.7331007719039917, zero_point=140, p
adding=(1, 1))                                                                                                    
    (18): QuantizedReLU(inplace=True)                                                                             
    (19): QuantizedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), scale=0.5345668196678162, zero_point=156, p
adding=(1, 1))                                                                                                    
    (20): QuantizedReLU(inplace=True)                                                                             
    (21): QuantizedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), scale=0.41019582748413086, zero_point=155, 
padding=(1, 1))                                                                                                   
    (22): QuantizedReLU(inplace=True)                                                                             
    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)                              
    (24): QuantizedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), scale=0.33723706007003784, zero_point=144, 
padding=(1, 1))                                                                                                   
    (25): QuantizedReLU(inplace=True)                                                                             
    (26): QuantizedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), scale=0.2690058946609497, zero_point=152, p
adding=(1, 1))                                                                                                    
    (27): QuantizedReLU(inplace=True)                                                                             
    (28): QuantizedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), scale=0.20182594656944275, zero_point=158, 
padding=(1, 1))                                                                                                   
    (29): QuantizedReLU(inplace=True)                                                                             
    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)                              
  )                                                                                                               
  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))                                                                
  (classifier): Sequential(                                                                                       
    (0): QuantizedLinear(in_features=25088, out_features=4096, scale=0.10471174865961075, zero_point=164, qscheme=
torch.per_tensor_affine)                                                                                          
    (1): QuantizedReLU(inplace=True)                                                                              
    (2): Dropout(p=0.5, inplace=False)                                                                            
    (3): QuantizedLinear(in_features=4096, out_features=4096, scale=0.06953950971364975, zero_point=138, qscheme=t
orch.per_tensor_affine)                                                                                           
    (4): QuantizedReLU(inplace=True)                                                                              
    (5): Dropout(p=0.5, inplace=False)                                                                            
    (6): QuantizedLinear(in_features=4096, out_features=1000, scale=0.1511237919330597, zero_point=60, qscheme=tor
ch.per_tensor_affine)                                                                                             
  )                                                                                                               
  (quant): Quantize(scale=tensor([0.0186]), zero_point=tensor([114]), dtype=torch.quint8)                         
  (dequant): DeQuantize()                                                                                         
)