Hugging Face's logo

Spaces:
suyash94
/
acne_grading
Runtime error

App Files Community
acne_grading
File size: 6,223 Bytes 
418196b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
 
import numpy as np
import os
import sys
import cv2
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
import torchvision
from torchvision import transforms
import torch.nn as nn
import torch.optim as optim
from torch.utils.data.sampler import SubsetRandomSampler


sys.path.append('/Users/suyashharlalka/Documents/workspace/gabit/acne_classification/code')

from dataset import AcneDataset
from utils import save_model, get_transforms_to_apply,get_model_architecture, get_training_params, get_criterion
import config
from base import TrainingObjective, ModelBackbone
import json



data_dir = config.DATASET_PATH
image_names = os.listdir(data_dir)
model_training = config.MODEL_TRAINING
isLimited = config.IS_LIMITED
batch_size = config.BATCH_SIZE
shuffle = config.SHUFFLE
num_workers = config.NUM_WORKERS

dataset = AcneDataset(data_dir, limit=isLimited)

validation_split = 0.2
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle:
    np.random.seed(42)
    np.random.shuffle(indices)

train_indices, test_indices = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_indices)
test_sampler = SubsetRandomSampler(test_indices)

transform = get_transforms_to_apply()
dataset.transform = transform

train_dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, sampler=train_sampler)
test_dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, sampler=test_sampler)

num_classes = config.NUM_CLASSES
model = get_model_architecture()
training_params = get_training_params(model)
criterion = get_criterion()

optimizer = optim.Adam(training_params, lr=config.BASE_LR)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=config.LR_DECAY_STEP_SIZE, gamma=config.LR_DECAY_GAMMA)

device = torch.device('mps') if torch.backends.mps.is_available() else torch.device('cpu')
model.to(device)

config_json = {}
config_json['DATASET_PATH'] = config.DATASET_PATH
config_json['MODEL_DIR'] = config.MODEL_DIR
config_json['MODEL_OBJECTIVE'] = config.MODEL_OBJECTIVE.name
config_json['MODEL_BACKBONE'] = config.MODEL_BACKBONE.name
config_json['MODEL_INPUT_SIZE'] = config.MODEL_INPUT_SIZE
config_json['TRANSFORMS_TO_APPLY'] = config.TRANSFORMS_TO_APPLY
config_json['NUM_CLASSES'] = config.NUM_CLASSES
config_json['LAST_N_LAYERS_TO_TRAIN'] = config.LAST_N_LAYERS_TO_TRAIN
config_json['EPOCHS'] = config.EPOCHS
config_json['MODEL_TRAINING'] = config.MODEL_TRAINING
config_json['IS_LIMITED'] = config.IS_LIMITED
config_json['BATCH_SIZE'] = config.BATCH_SIZE
config_json['SHUFFLE'] = config.SHUFFLE
config_json['NUM_WORKERS'] = config.NUM_WORKERS
config_json['BASE_LR'] = config.BASE_LR
config_json['LR_DECAY_STEP_SIZE'] = config.LR_DECAY_STEP_SIZE
config_json['LR_DECAY_GAMMA'] = config.LR_DECAY_GAMMA


if model_training:
    num_epochs = config.EPOCHS
    for epoch in range(num_epochs):
        model.train()
        runningLoss = 0.0
        for i, (images, labels) in enumerate(tqdm(train_dataloader, desc="Processing", unit="batch")):
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            loss = criterion(outputs.squeeze(), labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            runningLoss += loss.item()
        
        # scheduler.step()
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {runningLoss/len(train_dataloader):.4f}')
        
        if 'LOSS' not in config_json:
            config_json['LOSS'] = []
        config_json['LOSS'].append(runningLoss/len(train_dataloader))

        if 'EPOCHS_DONE' not in config_json:
            config_json['EPOCHS_DONE'] = 0
        config_json['EPOCHS_DONE'] = epoch + 1

        if epoch == 0 :
            model_dir = save_model(model, config_json)
        else:
            model_dir = save_model(model, config_json, model_dir)
        
        if 'TRAINED_MODEL_DIR' not in config_json:
            config_json['TRAINED_MODEL_DIR'] = model_dir
        
        config_save_path = os.path.join(model_dir, 'config.json')
        with open(config_save_path, 'w') as f:
            json.dump(config_json, f)


# config_path = os.path.join(model_dir, 'config.json')   
config_path = '/Users/suyash.harlalka/Desktop/personal/acne_classification/model/model_1/config.json' 
with open(config_path, 'r') as f:
    config_loaded = json.load(f)

from sklearn.metrics import confusion_matrix

model_trained_path = os.path.join(config_loaded['TRAINED_MODEL_DIR'], 'model.pth')
model.load_state_dict(torch.load(model_trained_path))
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    mainLabel = []
    predictedLabel = []
    for images, labels in tqdm(test_dataloader, desc="Processing", unit="batch"):
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        predicted = torch.round(outputs.data)
        predicted = predicted.squeeze(1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

        mainLabel.extend(labels.cpu().numpy())
        predictedLabel.extend(predicted.cpu().numpy())
        
    print(f'Accuracy of the network on the {total} test images: {100 * correct / total}%')
    
    cft = confusion_matrix(mainLabel, predictedLabel, labels=[0, 1, 2, 3], normalize='true')
    print(cft)

    correct = 0
    total = 0
    mainLabel = []
    predictedLabel = []
    for images, labels in tqdm(train_dataloader, desc="Processing", unit="batch"):
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        predicted = torch.round(outputs.data)
        predicted = predicted.squeeze(1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

        mainLabel.extend(labels.cpu().numpy())
        predictedLabel.extend(predicted.cpu().numpy())
    
    print(f'Accuracy of the network on the {total} train images: {100 * correct / total}%')
    cft = confusion_matrix(mainLabel, predictedLabel, labels=[0, 1, 2, 3], normalize='true')
    print(cft)