PTQ with ResNet conv2x layers #1433
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programming_examples/ml/resnet/ptq_conv2x/test.py|390|
programming_examples/ml/resnet/ptq_conv2x/test.py|399|
programming_examples/ml/resnet/ptq_conv2x/test.py|403|
programming_examples/ml/resnet/ptq_conv2x/test.py|405|
programming_examples/ml/resnet/ptq_conv2x/test.py|446|
programming_examples/ml/resnet/ptq_conv2x/test.py|449|
programming_examples/ml/resnet/ptq_conv2x/test.py|459|
programming_examples/ml/resnet/ptq_conv2x/test.py|465 col 1|
programming_examples/ml/resnet/ptq_conv2x/utils.py|5 col 1|
programming_examples/ml/resnet/ptq_conv2x/utils.py|40|
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| @core(cores[i][1], "conv2dk3.o") | ||
| def core_body(): | ||
| if(i==2): |
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| if(i==2): | |
| if i == 2: |
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| @core(cores[i][3], "conv2dk3.o") | ||
| def core_body(): | ||
| if(i==2): |
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| if(i==2): | |
| if i == 2: |
| NpuWriteRTPOp("rtpComputeTile12", col=1, row=2, index=0, value=9) | ||
| NpuWriteRTPOp("rtpComputeTile13", col=1, row=3, index=0, value=12) | ||
| NpuWriteRTPOp("rtpComputeTile13", col=1, row=3, index=1, value=0) | ||
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| NpuWriteRTPOp("rtpComputeTile22", col=2, row=2, index=0, value=9) | ||
| NpuWriteRTPOp("rtpComputeTile23", col=2, row=3, index=0, value=9) | ||
| NpuWriteRTPOp("rtpComputeTile25", col=2, row=5, index=0, value=9) |
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| NpuWriteRTPOp("rtpComputeTile25", col=2, row=5, index=0, value=9) | |
| NpuWriteRTPOp("rtpComputeTile25", col=2, row=5, index=0, value=9) |
| rtp_1=[7,10,13,-2,10] | ||
| rtp_2=[8,10,12] | ||
| rtp_3=[9,9,12] |
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| rtp_1=[7,10,13,-2,10] | |
| rtp_2=[8,10,12] | |
| rtp_3=[9,9,12] | |
| rtp_1 = [7, 10, 13, -2, 10] | |
| rtp_2 = [8, 10, 12] | |
| rtp_3 = [9, 9, 12] |
| block_0_relu_2 * block_0_weight_scale_3 / block_0_add_scale, 2 | ||
| ) # after conv1x1 | ||
| block_0_combined_scale4 = -math.log( | ||
| block_0_add_scale / block_0_relu_3, 2 |
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| block_0_add_scale / block_0_relu_3, 2 | |
| block_0_add_scale / block_0_relu_3, 2 |
| ) # after skip addition using init scale | ||
| # combined_scale4=-math.log(inp_scale1/inp_scale4) | ||
| block_0_combined_scale_skip = -math.log( | ||
| init_scale * block_0_weight_scale_skip / block_0_add_scale, 2 |
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| init_scale * block_0_weight_scale_skip / block_0_add_scale, 2 | |
| init_scale * block_0_weight_scale_skip / block_0_add_scale, 2 |
| print("Block0 combined_scale after first conv1x1:", block_0_combined_scale1) | ||
| print("Block0 combined_scale after second conv3x3:", block_0_combined_scale2) | ||
| print("Block0 combined_scale after third conv1x1:", block_0_combined_scale3) | ||
| print("Block0 combined_scale after adding skip connection:", (block_0_combined_scale4)) |
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| print("Block0 combined_scale after adding skip connection:", (block_0_combined_scale4)) | |
| print( | |
| "Block0 combined_scale after adding skip connection:", (block_0_combined_scale4) | |
| ) |
| print("Block1 combined_scale after first conv1x1:", block_1_combined_scale1) | ||
| print("Block1 combined_scale after second conv3x3:", block_1_combined_scale2) | ||
| print("Block1 combined_scale after third conv1x1:", block_1_combined_scale3) | ||
| print("Block1 combined_scale after adding skip connection:", (block_1_combined_scale4)) |
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| print("Block1 combined_scale after adding skip connection:", (block_1_combined_scale4)) | |
| print( | |
| "Block1 combined_scale after adding skip connection:", (block_1_combined_scale4) | |
| ) |
| (block_2_combined_scale4), | ||
| ) | ||
| print("------------------------------------------------------------------") | ||
| # ------------------------------------------------------ |
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| # ------------------------------------------------------ | |
| # ------------------------------------------------------ |
singagan
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| im_name = f"./cifar_images/image_{i}.png" | ||
| cv2.imwrite(im_name, im) | ||
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| ) | ||
| quant_id_1.eval() | ||
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| # ------------------------------------------------------ | ||
| # Main run loop | ||
| # ------------------------------------------------------ | ||
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| # ------------------------------------------------------ | ||
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| for i in range(0, 64): | ||
| print("____________________________________IMAGE {}____________________________________________".format(i)) |
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| print("____________________________________IMAGE {}____________________________________________".format(i)) | |
| print( | |
| "____________________________________IMAGE {}____________________________________________".format( | |
| i | |
| ) | |
| ) |
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| # Calculate the five categories with the highest classification probability | ||
| prediction_class_index = ( | ||
| torch.topk(final_output_aie, k=5, sorted=True).indices.squeeze(0).tolist() |
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| torch.topk(final_output_aie, k=5, sorted=True).indices.squeeze(0).tolist() | |
| torch.topk(final_output_aie, k=5, sorted=True) | |
| .indices.squeeze(0) | |
| .tolist() |
| torch.topk(final_output_aie, k=5, sorted=True).indices.squeeze(0).tolist() | ||
| ) | ||
| golden_prediction_class_index = ( | ||
| torch.topk(final_output_base, k=5, sorted=True).indices.squeeze(0).tolist() |
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| torch.topk(final_output_base, k=5, sorted=True).indices.squeeze(0).tolist() | |
| torch.topk(final_output_base, k=5, sorted=True) | |
| .indices.squeeze(0) | |
| .tolist() |
| if x != y: | ||
| print("\nFailed.\n") | ||
| exit(-1) |
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| if x != y: | |
| print("\nFailed.\n") | |
| exit(-1) | |
| if x != y: | |
| print("\nFailed.\n") | |
| exit(-1) |
| print("\nPASS!\n") | ||
| exit(0) | ||
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| if __name__ == "__main__": |
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| if __name__ == "__main__": | |
| if __name__ == "__main__": |
| import cv2 | ||
| import numpy as np | ||
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| def unpickle(file): |
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| def unpickle(file): | |
| def unpickle(file): |
| im = im.transpose(1, 2, 0) | ||
| im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) | ||
| im_name = f"./cifar_images/image_{i}.png" | ||
| cv2.imwrite(im_name, im) |
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| cv2.imwrite(im_name, im) | |
| cv2.imwrite(im_name, im) |
| @@ -0,0 +1,89 @@ | |||
| # This file is licensed under the Apache License v2.0 with LLVM Exceptions. | |||
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Is this CMakeList.txt needed? Testing is only supported through a Python test.py file.
| clean: | ||
| rm -rf build/*.elf* build/*.lst build/*.bif log* build/${mlirFileName}.mlir.prj build/*.xclbin sim \ | ||
| build/chess* build/insts.txt \ | ||
| build/*.log build/aie_partition.json build/*.bin build/BOOT.BIN _x test.exe |
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there is no test.exe being build
| quant_model = quantize_model( | ||
| model_aie, | ||
| backend="flexml", | ||
| scale_factor_type="po2_scale", # Ensuring scale factors are powers of two |
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| scale_factor_type="po2_scale", # Ensuring scale factors are powers of two | |
| scale_factor_type="po2_scale", # Ensuring scale factors are powers of two |
| if name.endswith(".bias"): | ||
| param.data.fill_(0) | ||
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| # Calculate combined scales |
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| # Calculate combined scales | |
| # Calculate combined scales |
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@denolf, added a README and made the requested changes. |
No description provided.