intitial commit

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth filter=lfs diff=lfs merge=lfs -text
+examples/pizza_dad.jpeg filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:96fd40816922c8eb0ea201642420db9989f63dc2f970d15bb74f79258441b261
+size 31857210

app.py

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+
+### 1. Import and class names setup ###
+import gradio as gr
+import os
+from pathlib import Path
+import torch
+
+from model import create_effnetb2_model
+from time import perf_counter
+from typing import Tuple, Dict
+
+from PIL import Image
+import torchvision
+
+# Setup class names (hardcoded, these shall reside in a json file or sth like that...)
+# Open Food101 class names fromt file and import it to list
+
+with open("demos/foodvision_big/class_names.txt","r") as f:
+  class_names = f.read().split("\n")
+
+### 2. Model and transforms preparation ###
+effnetb2_model, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
+
+# Load save weights
+effnetb2_model.load_state_dict(torch.load(f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
+                                          map_location=torch.device("cpu"))) # map location to cpu is a must, as we have trained our model in the GPU
+
+### 3. Predict function
+
+def predict(img) -> Tuple[Dict,float]:
+  # Start a timer
+  start_time = perf_counter()
+
+  # Transform the input image for use with EffNetB2
+  effnetb2_transforms = torchvision.models.EfficientNet_B2_Weights.DEFAULT.transforms()
+  img_tensor = effnetb2_transforms(img)
+
+  # Put model in eval and inference
+  effnetb2_model.eval()
+  with torch.inference_mode():
+    y_logits = effnetb2_model(img_tensor.unsqueeze(dim=0))
+    y_pred_probs = torch.softmax(y_logits,dim=1)
+    y_pred_probs_list = y_pred_probs.squeeze().tolist()
+
+  # Creatae a prediction probability dictionary
+  pred_prob_dict = {class_names[i]:float(prob) for i,prob in enumerate(y_pred_probs_list)}
+
+  # End timer
+  end_time = perf_counter()
+
+  return pred_prob_dict, round(end_time-start_time,4)
+
+
+### 4. Launch app
+
+import gradio as gr
+
+foodvision_big_examples_path = "examples"
+
+example_list = [str(path) for path in Path(foodvision_big_examples_path).rglob("*.jpg")]
+
+# Create title, description and article
+title = "FoodVisionBig V0 🥘 🧗"
+description = "An <a href='https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2'>EfficientNetB2</a> feature extractor computer vision model to classify 101 food images from Food101 dataset<br>I have yet to improve it to label non-food images. Paciencia muchachos"
+article = "Created at <a href='#'>09_pytorch_model_deploy.ipynb</a> Google Colab notebook"
+
+# Create the Gradio app
+demo = gr.Interface(fn=predict,
+                    inputs=gr.Image(type="pil"),
+                    outputs=[gr.Label(num_top_classes=5, label="predictions"),
+                             gr.Number(label="Prediction time (s)")],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo
+demo.launch()
+
+
+# *** IMPORTANTE: The Flag button of the interface will create a folder named "flagged" that will contain the images and predictions of those images that someone has Flagged***

class_names.txt

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+apple_pie
+baby_back_ribs
+baklava
+beef_carpaccio
+beef_tartare
+beet_salad
+beignets
+bibimbap
+bread_pudding
+breakfast_burrito
+bruschetta
+caesar_salad
+cannoli
+caprese_salad
+carrot_cake
+ceviche
+cheese_plate
+cheesecake
+chicken_curry
+chicken_quesadilla
+chicken_wings
+chocolate_cake
+chocolate_mousse
+churros
+clam_chowder
+club_sandwich
+crab_cakes
+creme_brulee
+croque_madame
+cup_cakes
+deviled_eggs
+donuts
+dumplings
+edamame
+eggs_benedict
+escargots
+falafel
+filet_mignon
+fish_and_chips
+foie_gras
+french_fries
+french_onion_soup
+french_toast
+fried_calamari
+fried_rice
+frozen_yogurt
+garlic_bread
+gnocchi
+greek_salad
+grilled_cheese_sandwich
+grilled_salmon
+guacamole
+gyoza
+hamburger
+hot_and_sour_soup
+hot_dog
+huevos_rancheros
+hummus
+ice_cream
+lasagna
+lobster_bisque
+lobster_roll_sandwich
+macaroni_and_cheese
+macarons
+miso_soup
+mussels
+nachos
+omelette
+onion_rings
+oysters
+pad_thai
+paella
+pancakes
+panna_cotta
+peking_duck
+pho
+pizza
+pork_chop
+poutine
+prime_rib
+pulled_pork_sandwich
+ramen
+ravioli
+red_velvet_cake
+risotto
+samosa
+sashimi
+scallops
+seaweed_salad
+shrimp_and_grits
+spaghetti_bolognese
+spaghetti_carbonara
+spring_rolls
+steak
+strawberry_shortcake
+sushi
+tacos
+takoyaki
+tiramisu
+tuna_tartare
+waffles

model.py

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+
+import torch
+import torchvision
+from torch import nn
+from torchvision.models._api import WeightsEnum
+from torch.hub import load_state_dict_from_url
+
+def create_effnetb2_model(num_classes:int=101, seed:int=42):
+  # https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2
+
+  def get_state_dict(self, *args, **kwargs):
+      kwargs.pop("check_hash")
+      return load_state_dict_from_url(self.url, *args, **kwargs)
+  WeightsEnum.get_state_dict = get_state_dict
+
+
+  # 1. Setup pretrained EffNetB2 weights
+  effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT # DEFAULT = BEST
+
+  # 2. Get EffNetB2 transforms
+  effnetb2_transforms = effnetb2_weights.transforms()
+
+  # 3. Setup pretrained model instance
+  effnetb2_model = torchvision.models.efficientnet_b2(weights=effnetb2_weights)
+
+  # 4. Freeze the base layers in the model
+  for param in effnetb2_model.features.parameters():
+    param.requires_grad = False
+
+  # 5. Modify the classifier
+  torch.manual_seed(seed)
+  effnetb2_model.classifier = nn.Sequential(
+      nn.Dropout(p=0.3, inplace=True),
+      nn.Linear(in_features=1408,out_features=num_classes,bias=True)
+  )
+
+  return effnetb2_model, effnetb2_transforms

requirements.txt

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+torch
+torchvision
+gradio