Update ResNet50 E2E test to represent a valid demo version with actua…

…l predictions (#1126)

### Ticket

### Problem description
Update ResNet50 E2E test to represent a valid demo version with actual
predictions

### What's changed
- Loading dataset (currently single sample) & labels dictionary
- Comparing CPU vs TT label predictions

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

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

@@ -5,65 +5,61 @@
 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 transformers import AutoFeatureExtractor, ResNetForImageClassification
+from transformers import AutoImageProcessor, ResNetForImageClassification
 
 import forge
 from forge.verify.verify import verify
 
 from test.models.utils import Framework, Source, Task, build_module_name
 from test.utils import download_model
 
-
-def generate_model_resnet_imgcls_hf_pytorch(variant):
-    # Load ResNet feature extractor and model checkpoint from HuggingFace
-    model_ckpt = variant
-    feature_extractor = download_model(AutoFeatureExtractor.from_pretrained, model_ckpt)
-    model = download_model(ResNetForImageClassification.from_pretrained, model_ckpt)
-
-    # Load data sample
-    try:
-        url = "https://images.rawpixel.com/image_1300/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L3BkMTA2LTA0Ny1jaGltXzEuanBn.jpg"
-        image = Image.open(requests.get(url, stream=True).raw)
-    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)
-
-    # Data preprocessing
-    inputs = feature_extractor(image, return_tensors="pt")
-    pixel_values = inputs["pixel_values"]
-
-    return model, [pixel_values], {}
+variants = [
+    "microsoft/resnet-50",
+]
 
 
+@pytest.mark.push
 @pytest.mark.nightly
-def test_resnet(record_forge_property):
-    # Build Module Name
+@pytest.mark.parametrize("variant", variants, ids=variants)
+def test_resnet_hf(variant, record_forge_property):
+    # Record model properties
     module_name = build_module_name(
         framework=Framework.PYTORCH,
         model="resnet",
         variant="50",
         source=Source.HUGGINGFACE,
         task=Task.IMAGE_CLASSIFICATION,
     )
-
-    # Record Forge Property
     record_forge_property("model_name", module_name)
 
-    framework_model, inputs, _ = generate_model_resnet_imgcls_hf_pytorch(
-        "microsoft/resnet-50",
-    )
+    # Load dataset
+    dataset = load_dataset("huggingface/cats-image")
+    image = dataset["test"]["image"][0]
 
-    # Forge compile framework model
-    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+    # Load Torch model, preprocess image, and label dictionary
+    processor = download_model(AutoImageProcessor.from_pretrained, variant)
+    framework_model = download_model(ResNetForImageClassification.from_pretrained, variant, return_dict=False)
+    label_dict = framework_model.config.id2label
 
-    # Model Verification
-    verify(inputs, framework_model, compiled_model)
+    inputs = processor(image, return_tensors="pt")
+    inputs = inputs["pixel_values"]
+
+    compiled_model = forge.compile(framework_model, inputs)
+
+    cpu_logits = framework_model(inputs)[0]
+    cpu_pred = label_dict[cpu_logits.argmax(-1).item()]
+
+    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}"
+
+    verify([inputs], framework_model, compiled_model)
 
 
 def generate_model_resnet_imgcls_timm_pytorch(variant):