Demo version of ResNet 50 (#1133)

### Ticket
Fix #1132

### Problem description
Confirm that ResNet 50 works with valid demo (image classification). 

### What's changed
Created demo version of ResNet 50 that runs on a few images and outputs
a table for CPU comparison (predicted class and confidence score).

Additionally, unified existing ResNet tests into a single file.

### Checklist
- [x] New/Existing tests provide coverage for changes

forge/test/models/pytorch/vision/resnet/test_resnet.py

@@ -1,18 +1,19 @@
 # SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
 
 # SPDX-License-Identifier: Apache-2.0
+import random
+
 import pytest
-import requests
 import timm
 import torch
 from datasets import load_dataset
-from loguru import logger
-from PIL import Image
-from timm.data import resolve_data_config
-from timm.data.transforms_factory import create_transform
+from tabulate import tabulate
+from torchvision.models.resnet import resnet50
 from transformers import AutoImageProcessor, ResNetForImageClassification
 
 import forge
+from forge.verify.config import VerifyConfig
+from forge.verify.value_checkers import AutomaticValueChecker
 from forge.verify.verify import verify
 
 from test.models.utils import Framework, Source, Task, build_module_name
@@ -27,7 +28,9 @@
 @pytest.mark.nightly
 @pytest.mark.parametrize("variant", variants, ids=variants)
 def test_resnet_hf(variant, record_forge_property):
-    # Record model properties
+    random.seed(0)
+
+    # Record model details
     module_name = build_module_name(
         framework=Framework.PYTORCH,
         model="resnet",
@@ -37,69 +40,97 @@ def test_resnet_hf(variant, record_forge_property):
     )
     record_forge_property("model_name", module_name)
 
-    # Load dataset
-    dataset = load_dataset("huggingface/cats-image")
-    image = dataset["test"]["image"][0]
+    # Load tiny dataset
+    dataset = load_dataset("zh-plus/tiny-imagenet")
+    images = random.sample(dataset["valid"]["image"], 10)
 
-    # Load Torch model, preprocess image, and label dictionary
-    processor = download_model(AutoImageProcessor.from_pretrained, variant)
+    # Load framework model
     framework_model = download_model(ResNetForImageClassification.from_pretrained, variant, return_dict=False)
-    label_dict = framework_model.config.id2label
 
-    inputs = processor(image, return_tensors="pt")
-    inputs = inputs["pixel_values"]
+    # Compile model
+    input_sample = [torch.rand(1, 3, 224, 224)]
+    compiled_model = forge.compile(framework_model, input_sample)
 
-    compiled_model = forge.compile(framework_model, inputs)
+    # Verify data on sample input
+    verify(input_sample, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=0.95)))
 
-    cpu_logits = framework_model(inputs)[0]
-    cpu_pred = label_dict[cpu_logits.argmax(-1).item()]
+    # Run model on sample data and print results
+    run_and_print_results(framework_model, compiled_model, images)
 
-    tt_logits = compiled_model(inputs)[0]
-    tt_pred = label_dict[tt_logits.argmax(-1).item()]
 
-    assert cpu_pred == tt_pred, f"Inference mismatch: CPU prediction: {cpu_pred}, TT prediction: {tt_pred}"
+def run_and_print_results(framework_model, compiled_model, inputs):
+    """
+    Runs inference using both a framework model and a compiled model on a list of input images,
+    then prints the results in a formatted table.
 
-    verify([inputs], framework_model, compiled_model)
+    Args:
+        framework_model: The original framework-based model.
+        compiled_model: The compiled version of the model.
+        inputs: A list of images to process and classify.
+    """
+    label_dict = framework_model.config.id2label
+    processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
 
+    results = []
+    for i, image in enumerate(inputs):
+        processed_inputs = processor(image, return_tensors="pt")["pixel_values"]
 
-def generate_model_resnet_imgcls_timm_pytorch(variant):
-    # Load ResNet50 feature extractor and model from TIMM
-    model = download_model(timm.create_model, variant, pretrained=True)
-    config = resolve_data_config({}, model=model)
-    transform = create_transform(**config)
+        cpu_logits = framework_model(processed_inputs)[0]
+        cpu_conf, cpu_idx = cpu_logits.softmax(-1).max(-1)
+        cpu_pred = label_dict.get(cpu_idx.item(), "Unknown")
 
-    # Load data sample
-    try:
-        url = "https://images.rawpixel.com/image_1300/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L3BkMTA2LTA0Ny1jaGltXzEuanBn.jpg"
-        image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
-    except:
-        logger.warning(
-            "Failed to download the image file, replacing input with random tensor. Please check if the URL is up to date"
-        )
-        image = torch.rand(1, 3, 256, 256)
+        tt_logits = compiled_model(processed_inputs)[0]
+        tt_conf, tt_idx = tt_logits.softmax(-1).max(-1)
+        tt_pred = label_dict.get(tt_idx.item(), "Unknown")
 
-    # Data preprocessing
-    pixel_values = transform(image).unsqueeze(0)
+        results.append([i + 1, cpu_pred, cpu_conf.item(), tt_pred, tt_conf.item()])
 
-    return model, [pixel_values], {}
+    print(
+        tabulate(
+            results,
+            headers=["Example", "CPU Prediction", "CPU Confidence", "Compiled Prediction", "Compiled Confidence"],
+            tablefmt="grid",
+        )
+    )
 
 
 @pytest.mark.nightly
 def test_resnet_timm(record_forge_property):
-    pytest.skip("Skipping due to the current CI/CD pipeline limitations")
-
-    # Build Module Name
+    # Record model details
     module_name = build_module_name(
         framework=Framework.PYTORCH, model="resnet", source=Source.TIMM, variant="50", task=Task.IMAGE_CLASSIFICATION
     )
+    record_forge_property("model_name", module_name)
+
+    # Load framework model
+    framework_model = download_model(timm.create_model, "resnet50", pretrained=True)
 
-    # Record Forge Property
+    # Compile model
+    input_sample = [torch.rand(1, 3, 224, 224)]
+    compiled_model = forge.compile(framework_model, sample_inputs=input_sample, module_name=module_name)
+
+    # Verify data on sample input
+    verify(input_sample, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=0.95)))
+
+
+@pytest.mark.nightly
+def test_resnet_torchvision(record_forge_property):
+    # Record model details
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="resnet",
+        source=Source.TORCHVISION,
+        variant="50",
+        task=Task.IMAGE_CLASSIFICATION,
+    )
     record_forge_property("model_name", module_name)
 
-    framework_model, inputs, _ = generate_model_resnet_imgcls_timm_pytorch("resnet50")
+    # Load framework model
+    framework_model = resnet50()
 
-    # Forge compile framework model
-    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+    # Compile model
+    input_sample = [torch.rand(1, 3, 224, 224)]
+    compiled_model = forge.compile(framework_model, input_sample)
 
-    # Model Verification
-    verify(inputs, framework_model, compiled_model)
+    # Verify data on sample input
+    verify(input_sample, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=0.95)))

pytest.ini

@@ -41,10 +41,6 @@ testpaths =
     forge/test/mlir/llama/test_llama_inference.py::test_llama_inference
     forge/test/mlir/llama/tests
 
-    # Resnet
-    forge/test/mlir/resnet/test_resnet_inference.py::test_resnet_inference
-    forge/test/mlir/resnet/test_resnet_unique_ops.py
-
     # Benchmark
     # MNIST Linear
     forge/test/benchmark/benchmark/models/mnist_linear.py::test_mnist_linear