From 6d4e0f04b39bad87d99bcdb1557e90ed23d6d0b7 Mon Sep 17 00:00:00 2001
From: Hendrik Borras <hendrikborras@web.de>
Date: Thu, 8 Sep 2022 12:26:21 +0200
Subject: [PATCH] Remove cell execution
---
...revitas_network_import_via_FINN-ONNX.ipynb | 623 +-----------------
1 file changed, 31 insertions(+), 592 deletions(-)
diff --git a/notebooks/basics/1a_brevitas_network_import_via_FINN-ONNX.ipynb b/notebooks/basics/1a_brevitas_network_import_via_FINN-ONNX.ipynb
index 9f28459f7..ed5375fd7 100644
--- a/notebooks/basics/1a_brevitas_network_import_via_FINN-ONNX.ipynb
+++ b/notebooks/basics/1a_brevitas_network_import_via_FINN-ONNX.ipynb
@@ -6,6 +6,8 @@
"source": [
"# Importing Brevitas networks into FINN with the FINN-ONNX interchange format\n",
"\n",
+ "**Note: This notebook is very similar to the 1b notebook, in that it shows the same concepts for the FINN-ONNX ingestion as 1b does for QONNX.**\n",
+ "\n",
"In this notebook we'll go through an example of how to import a Brevitas-trained QNN into FINN. The steps will be as follows:\n",
"\n",
"1. Load up the trained PyTorch model\n",
@@ -17,7 +19,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -36,121 +38,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "# MIT License\n",
- "#\n",
- "# Copyright (c) 2019 Xilinx\n",
- "#\n",
- "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
- "# of this software and associated documentation files (the \"Software\"), to deal\n",
- "# in the Software without restriction, including without limitation the rights\n",
- "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
- "# copies of the Software, and to permit persons to whom the Software is\n",
- "# furnished to do so, subject to the following conditions:\n",
- "#\n",
- "# The above copyright notice and this permission notice shall be included in all\n",
- "# copies or substantial portions of the Software.\n",
- "#\n",
- "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
- "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
- "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
- "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
- "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
- "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
- "# SOFTWARE.\n",
- "\n",
- "import ast\n",
- "from functools import reduce\n",
- "from operator import mul\n",
- "\n",
- "from torch.nn import Module, ModuleList, BatchNorm1d, Dropout\n",
- "import torch\n",
- "\n",
- "from brevitas.nn import QuantIdentity, QuantLinear\n",
- "from .common import CommonWeightQuant, CommonActQuant\n",
- "from .tensor_norm import TensorNorm\n",
- "\n",
- "DROPOUT = 0.2\n",
- "\n",
- "\n",
- "class FC(Module):\n",
- "\n",
- " def __init__(\n",
- " self,\n",
- " num_classes,\n",
- " weight_bit_width,\n",
- " act_bit_width,\n",
- " in_bit_width,\n",
- " in_channels,\n",
- " out_features,\n",
- " in_features=(28, 28)):\n",
- " super(FC, self).__init__()\n",
- "\n",
- " self.features = ModuleList()\n",
- " self.features.append(QuantIdentity(act_quant=CommonActQuant, bit_width=in_bit_width))\n",
- " self.features.append(Dropout(p=DROPOUT))\n",
- " in_features = reduce(mul, in_features)\n",
- " for out_features in out_features:\n",
- " self.features.append(QuantLinear(\n",
- " in_features=in_features,\n",
- " out_features=out_features,\n",
- " bias=False,\n",
- " weight_bit_width=weight_bit_width,\n",
- " weight_quant=CommonWeightQuant))\n",
- " in_features = out_features\n",
- " self.features.append(BatchNorm1d(num_features=in_features))\n",
- " self.features.append(QuantIdentity(act_quant=CommonActQuant, bit_width=act_bit_width))\n",
- " self.features.append(Dropout(p=DROPOUT))\n",
- " self.features.append(QuantLinear(\n",
- " in_features=in_features,\n",
- " out_features=num_classes,\n",
- " bias=False,\n",
- " weight_bit_width=weight_bit_width,\n",
- " weight_quant=CommonWeightQuant))\n",
- " self.features.append(TensorNorm())\n",
- "\n",
- " for m in self.modules():\n",
- " if isinstance(m, QuantLinear):\n",
- " torch.nn.init.uniform_(m.weight.data, -1, 1)\n",
- "\n",
- " def clip_weights(self, min_val, max_val):\n",
- " for mod in self.features:\n",
- " if isinstance(mod, QuantLinear):\n",
- " mod.weight.data.clamp_(min_val, max_val)\n",
- " \n",
- " def forward(self, x):\n",
- " x = x.view(x.shape[0], -1)\n",
- " x = 2.0 * x - torch.tensor([1.0], device=x.device)\n",
- " for mod in self.features:\n",
- " x = mod(x)\n",
- " return x\n",
- "\n",
- "\n",
- "def fc(cfg):\n",
- " weight_bit_width = cfg.getint('QUANT', 'WEIGHT_BIT_WIDTH')\n",
- " act_bit_width = cfg.getint('QUANT', 'ACT_BIT_WIDTH')\n",
- " in_bit_width = cfg.getint('QUANT', 'IN_BIT_WIDTH')\n",
- " num_classes = cfg.getint('MODEL', 'NUM_CLASSES')\n",
- " in_channels = cfg.getint('MODEL', 'IN_CHANNELS')\n",
- " out_features = ast.literal_eval(cfg.get('MODEL', 'OUT_FEATURES'))\n",
- " net = FC(\n",
- " weight_bit_width=weight_bit_width,\n",
- " act_bit_width=act_bit_width,\n",
- " in_bit_width=in_bit_width,\n",
- " in_channels=in_channels,\n",
- " out_features=out_features,\n",
- " num_classes=num_classes)\n",
- " return net\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from brevitas_examples import bnn_pynq\n",
"showSrc(bnn_pynq.models.FC)"
@@ -165,267 +55,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "FC(\n",
- " (features): ModuleList(\n",
- " (0): QuantIdentity(\n",
- " (input_quant): ActQuantProxyFromInjector(\n",
- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " )\n",
- " (act_quant): ActQuantProxyFromInjector(\n",
- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
- " (activation_impl): Identity()\n",
- " (tensor_quant): ClampedBinaryQuant(\n",
- " (scaling_impl): ConstScaling(\n",
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- " (1): Dropout(p=0.2, inplace=False)\n",
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- " )\n",
- " (bias_quant): BiasQuantProxyFromInjector(\n",
- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " )\n",
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- " (3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (4): QuantIdentity(\n",
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- " )\n",
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- " (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
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- " (tensor_quant): ClampedBinaryQuant(\n",
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- " (clamp_min_ste): Identity()\n",
- " (restrict_value_impl): FloatRestrictValue()\n",
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- " (value): StatelessBuffer()\n",
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- " (bit_width): BitWidthConst(\n",
- " (bit_width): StatelessBuffer()\n",
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- " (zero_point): StatelessBuffer()\n",
- " (delay_wrapper): DelayWrapper(\n",
- " (delay_impl): _NoDelay()\n",
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- " )\n",
- " (bias_quant): BiasQuantProxyFromInjector(\n",
- " (_zero_hw_sentinel): StatelessBuffer()\n",
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- " )\n",
- " (7): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (8): QuantIdentity(\n",
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- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " )\n",
- " (act_quant): ActQuantProxyFromInjector(\n",
- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
- " (activation_impl): Identity()\n",
- " (tensor_quant): ClampedBinaryQuant(\n",
- " (scaling_impl): ConstScaling(\n",
- " (restrict_clamp_scaling): _RestrictClampValue(\n",
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- " (tensor_clamp_impl): TensorClamp()\n",
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- " )\n",
- " )\n",
- " )\n",
- " (9): Dropout(p=0.2, inplace=False)\n",
- " (10): QuantLinear(\n",
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- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " )\n",
- " )\n",
- " (11): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " )\n",
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- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " (fused_activation_quant_proxy): FusedActivationQuantProxy(\n",
- " (activation_impl): Identity()\n",
- " (tensor_quant): ClampedBinaryQuant(\n",
- " (scaling_impl): ConstScaling(\n",
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- " (zero_point): StatelessBuffer()\n",
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- " (delay_impl): _NoDelay()\n",
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- " (tensor_clamp_impl): TensorClamp()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (13): Dropout(p=0.2, inplace=False)\n",
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- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " )\n",
- " (output_quant): ActQuantProxyFromInjector(\n",
- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " )\n",
- " (weight_quant): WeightQuantProxyFromInjector(\n",
- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " (tensor_quant): BinaryQuant(\n",
- " (scaling_impl): ConstScaling(\n",
- " (restrict_clamp_scaling): _RestrictClampValue(\n",
- " (clamp_min_ste): Identity()\n",
- " (restrict_value_impl): FloatRestrictValue()\n",
- " )\n",
- " (value): StatelessBuffer()\n",
- " )\n",
- " (bit_width): BitWidthConst(\n",
- " (bit_width): StatelessBuffer()\n",
- " )\n",
- " (zero_point): StatelessBuffer()\n",
- " (delay_wrapper): DelayWrapper(\n",
- " (delay_impl): _NoDelay()\n",
- " )\n",
- " )\n",
- " )\n",
- " (bias_quant): BiasQuantProxyFromInjector(\n",
- " (_zero_hw_sentinel): StatelessBuffer()\n",
- " )\n",
- " )\n",
- " (15): TensorNorm()\n",
- " )\n",
- ")"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from finn.util.test import get_test_model\n",
"lfc = get_test_model(netname = \"LFC\", wbits = 1, abits = 1, pretrained = True)\n",
@@ -441,30 +73,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<ipython-input-4-e3749ce0f482>:9: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1607370172916/work/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
- " input_tensor_pyt = torch.from_numpy(input_tensor_npy).float()\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import torch\n",
"import matplotlib.pyplot as plt\n",
@@ -480,21 +91,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([0.1020, 0.0113, 0.4806, 0.0571, 0.0482, 0.0079, 0.0450, 0.0076, 0.1851,\n",
- " 0.0552])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from torch.nn.functional import softmax\n",
"# do forward pass in PyTorch/Brevitas\n",
@@ -505,22 +104,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"objects = [str(x) for x in range(10)]\n",
@@ -549,7 +135,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -568,38 +154,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Serving '/tmp/LFCW1A1_finn-onnx.onnx' at http://0.0.0.0:8081\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- " <iframe\n",
- " width=\"100%\"\n",
- " height=\"400\"\n",
- " src=\"http://localhost:8081/\"\n",
- " frameborder=\"0\"\n",
- " allowfullscreen\n",
- " ></iframe>\n",
- " "
- ],
- "text/plain": [
- "<IPython.lib.display.IFrame at 0x7f52a6c48940>"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"showInNetron(export_onnx_path)"
]
@@ -622,34 +179,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/hendrik/Dropbox/a_local/Uni/fpga_synth_system_data/finn_deving/finn/deps/qonnx/src/qonnx/core/modelwrapper.py:93: UserWarning: Some old-style domain attributes were automatically converted to new-style,\n",
- " i.e. domain=finn to domain=qonnx.custom_op.<general|fpgadataflow|...>\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "input: \"37\"\n",
- "input: \"38\"\n",
- "output: \"39\"\n",
- "name: \"MatMul_13\"\n",
- "op_type: \"MatMul\"\n",
- "domain: \"\""
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from qonnx.core.modelwrapper import ModelWrapper\n",
"model = ModelWrapper(export_onnx_path)\n",
@@ -665,26 +197,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[-1., -1., 1., ..., -1., 1., -1.],\n",
- " [ 1., 1., -1., ..., 1., -1., 1.],\n",
- " [-1., -1., -1., ..., 1., -1., 1.],\n",
- " ...,\n",
- " [ 1., -1., -1., ..., -1., -1., 1.],\n",
- " [ 1., -1., -1., ..., 1., 1., 1.],\n",
- " [ 1., -1., 1., ..., 1., -1., 1.]], dtype=float32)"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"model.get_initializer(model.graph.node[8].input[1])"
]
@@ -698,40 +213,18 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'BIPOLAR'"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"model.get_tensor_datatype(model.graph.node[8].input[1]).name"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[784, 1024]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"model.get_tensor_shape(model.graph.node[8].input[1])"
]
@@ -745,7 +238,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -759,39 +252,9 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Stopping http://0.0.0.0:8081\n",
- "Serving '/tmp/LFCW1A1-finn-onnx-clean.onnx' at http://0.0.0.0:8081\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- " <iframe\n",
- " width=\"100%\"\n",
- " height=\"400\"\n",
- " src=\"http://localhost:8081/\"\n",
- " frameborder=\"0\"\n",
- " allowfullscreen\n",
- " ></iframe>\n",
- " "
- ],
- "text/plain": [
- "<IPython.lib.display.IFrame at 0x7f52a6e69790>"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"showInNetron(export_onnx_path_transformed)"
]
@@ -805,22 +268,9 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[-1.3736125, -3.5715756, 0.1768887, -1.9529207, -2.1233053,\n",
- " -3.9293835, -2.1914592, -3.9634604, -0.7772659, -1.9869976]],\n",
- " dtype=float32)"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"import finn.core.onnx_exec as oxe\n",
"input_dict = {\"0\": nph.to_array(input_tensor)}\n",
@@ -832,20 +282,9 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"np.isclose(produced, produced_finn).all()"
]
--
GitLab