| # 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() | |
| ) |
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