(tvm) branch main updated: [DOCS] Few fixes for broken Adreno docs (#17518)

[tqchen]

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The following commit(s) were added to refs/heads/main by this push:
     new 4cfd1817f7 [DOCS] Few fixes for broken Adreno docs (#17518)
4cfd1817f7 is described below

commit 4cfd1817f724066030676d3830ff4cc6649030c4
Author: Siva <quic_s...@quicinc.com>
AuthorDate: Thu Nov 14 19:43:08 2024 +0530

    [DOCS] Few fixes for broken Adreno docs (#17518)
    
    * fix: keras model changed to pytorch format (#67)
    
    * doc fix
---
 .../deploy_models/deploy_model_on_adreno_tvmc.py   | 34 +++++++++++++++-------
 1 file changed, 24 insertions(+), 10 deletions(-)

diff --git a/gallery/how_to/deploy_models/deploy_model_on_adreno_tvmc.py 
b/gallery/how_to/deploy_models/deploy_model_on_adreno_tvmc.py
index b54ac1b2c6..0e037e9f91 100644
--- a/gallery/how_to/deploy_models/deploy_model_on_adreno_tvmc.py
+++ b/gallery/how_to/deploy_models/deploy_model_on_adreno_tvmc.py
@@ -22,7 +22,7 @@ Deploy the Pretrained Model on Adreno™ with tvmc Interface
 ==========================================================
 **Author**: Siva Rama Krishna
 
-This article is a step-by-step tutorial to deploy pretrained Keras resnet50 
model on Adreno™.
+This article is a step-by-step tutorial to deploy pretrained PyTorch resnet50 
model on Adreno™.
 
 Besides that, you should have TVM built for Android.
 See the following instructions on how to build it and setup RPC environment.
@@ -71,16 +71,27 @@ cross_compiler = (
 )
 
 #######################################################################
-# Make a Keras Resnet50 Model
-# ---------------------------
+# Make a PyTorch Resnet50 Model
+# -----------------------------
 
-from tensorflow.keras.applications.resnet50 import ResNet50
+import torch
+import torchvision.models as models
 
-tmp_path = utils.tempdir()
-model_file_name = tmp_path.relpath("resnet50.h5")
+# Load the ResNet50 model pre-trained on ImageNet
+model = models.resnet50(pretrained=True)
 
-model = ResNet50(include_top=True, weights="imagenet", input_shape=(224, 224, 
3), classes=1000)
-model.save(model_file_name)
+# Set the model to evaluation mode
+model.eval()
+
+# Define the input shape
+dummy_input = torch.randn(1, 3, 224, 224)
+
+# Trace the model
+traced_model = torch.jit.trace(model, dummy_input)
+
+# Save the traced model
+model_file_name = "resnet50_traced.pt"
+traced_model.save(model_file_name)
 
 
 #######################################################################
@@ -89,7 +100,10 @@ model.save(model_file_name)
 # Convert a model from any framework to a tvm relay module.
 # tvmc.load supports models from any framework (like tensorflow saves_model, 
onnx, tflite ..etc) and auto detects the filetype.
 
-tvmc_model = tvmc.load(model_file_name)
+input_shape = (1, 3, 224, 224)  # Batch size, channels, height, width
+
+# Load the TorchScript model with TVMC
+tvmc_model = tvmc.load(model_file_name, shape_dict={"input": input_shape}, 
model_format="pytorch")
 
 print(tvmc_model.mod)
 
@@ -158,7 +172,7 @@ else:
     # Altrernatively, we can save the compilation output and save it as a 
TVMCPackage.
     # This way avoids loading of compiled module without compiling again.
     target = target + ", clml"
-    pkg_path = tmp_path.relpath("keras-resnet50.tar")
+    pkg_path = tmp_path.relpath("torch-resnet50.tar")
     tvmc.compile(
         tvmc_model,
         target=target,